Modelica^® Language Specification version 3.7-dev 2 Lexical Structure 4 Classes, Predefined Types, and Declarations

Chapter 3 Operators and Expressions

The lexical units are combined to form even larger building blocks such as expressions according to the rules given by the expression part of the Modelica grammar in appendix A. For example, they can be built from operators, function references, components, or component references (referring to components) and literals. Each expression has a type and a variability.

This chapter describes the evaluation rules for expressions, the concept of expression variability, built-in mathematical operators and functions, and the built-in special Modelica operators with function syntax.

Expressions can contain variables and constants, which have types, predefined or user defined. The predefined built-in types of Modelica are Real, Integer, Boolean, String, and enumeration types which are presented in more detail in section 4.9.

3.1 Expressions

Modelica equations, assignments and declaration equations contain expressions.

Expressions can contain basic operations, +, -, *, /, ^, etc. with normal precedence as defined in table 3.1 in section 3.2 and the grammar in appendix A. The semantics of the operations is defined for both scalar and array arguments in section 10.6.

It is also possible to define functions and call them in a normal fashion. The function call syntax for both positional and named arguments is described in section 12.4.1 and for vectorized calls in section 12.4.4. The built-in array functions are given in section 10.1.1 and other built-in operators in section 3.7.

3.2 Operator Precedence and Associativity

Operator precedence determines the implicit subexpression structure of expressions with operators. (Explicit subexpression structure can be expressed by wrapping the subexpression in parentheses.) An operator with higher precedence ties harder to its operands than an operator with lower precedence. For example, ‘*’ having higher precedence than ‘+’ means that 1 + 2 * 3 is implicitly structured as 1 + (2 * 3).

Precedence group associativity is used to determine the implicit subexpression structure when operators belong to the same group of equal precedence. Left associativity means that subexpressions are formed from left to right. For example, left associativity of binary additive operators means that 1 - 2 - 3 is implicitly structured as (1 - 2) - 3. A precedence group may also be non-associative, meaning that there is no implicit subexpression structure defined based on associativity. For example, non-associativity of relational operators means that 1 < 2 < 3 is an invalid expression. Note that the operators don’t need to be identical for associativity to matter; also 1 == 2 < 3 is invalid, and 1 - 2 + 3 is implicitly structured as (1 - 2) + 3. Also note that the non-associative array range in Modelica can be used with either two or three operands separated by ‘:’, meaning that 1 : 2 : 5 is one valid ternary use of the operator rather than two invalid binary uses of the operator.

At the parsing stage – which is where the here defined operator precedence and associativity matters – the subexpression structure is fixed. Since Modelica tools have the freedom to symbolically manipulate expressions, this subexpression structure cannot be expected to reflect order of evaluation, compare section 3.3.

The following table presents the precedence and associativity of all the expression operators, consistent with and complementing information that can be derived from the Modelica grammar in appendix A.

Table 3.1: Operators in order of precedence from highest to lowest. Operators with different precedence are separated by horizontal lines. All operators are binary except array index, member access, function call, those shown as unary together with expr, the conditional operator, the array construction operator { } and concatenation operator [ ], and the array range constructor which is either binary or ternary.

{}^{\dagger}

The associativity of array construction and concatenation refers to the separator (‘,’ or ‘;’), not the enclosing delimiters.

Operator group	Assoc.	Operator syntax	Examples
Array index	left	[]	arr[index]
Member access	left	.	a.b
Function call	none	$\mathit{funcName}$ ( $\mathit{args}$ )	sin(4.36)
Array construction	left ${}^{\dagger}$	{ $\mathit{expr}$ , $\mathit{expr}$ , $\ldots$ }	{2, 3}
Horizontal concatenation	left ${}^{\dagger}$	[ $\mathit{expr}$ , $\mathit{expr}$ , $\ldots$ ]	[5, 6]
Vertical concatenation	left ${}^{\dagger}$	[ $\mathit{expr}$ ; $\mathit{expr}$ ; $\ldots$ ]	[2, 3; 7, 8]
Exponentiation	none	^	2 ^ 3
Multiplicative	left	* /	2 * 3, 2 / 3
Elementwise multiplicative	left	.* ./	{2, 3} .* {4, 5}
Additive unary	none	+ $\mathit{expr}$ - $\mathit{expr}$	-0.5
Additive	left	+ -	1 + 2
Elementwise additive	left	.+ .-	{2, 3} .+ {4, 5}
Relational	none	< <= > >= == <>	a < b, a <= b, a > b
Unary negation	none	not $\mathit{expr}$	not b1
Logical and	left	and	b1 and b2
Logical or	left	or	b1 or b2
Array range	none	$\mathit{expr}$ : $\mathit{expr}$	1 : 5
Array range	none	$\mathit{expr}$ : $\mathit{expr}$ : $\mathit{expr}$	start : step : stop
Conditional	none	if $\mathit{expr}$ then $\mathit{expr}$ else $\mathit{expr}$	if b then 3 else x
Named argument	none	$\mathit{ident}$ = $\mathit{expr}$	x = 2.26

The array index and member access operators can both be part of a component-reference (one of the alternative productions for primary in the grammar) and be applied to general expressions when the left operand is parenthesized. Directly using both member access and array index in a component-reference has a special intuitive meaning. See section 10.5 and section 3.6.6.

[Example: Relative precedence of array index and member access. Consider the following definition of the array variable a:

⬇

record R

Real[2] x;

end R;

R[3] a;

These are some valid as well as invalid ways to using array index and member access:

⬇

a[3].x[2] // OK: Component reference of type Real

a[3].x // OK: Component reference of type Real[2]

a.x[2] // OK: Component reference of type Real[3]

a.x[2, :] // Error.

a.x // OK: Component reference of type Real[3, 2]

(a.x)[2] // OK: Component reference of type Real[2] - same as a[2].x[:]

(a.x)[2, :] // OK: Component reference of type Real[2] - same as a[2].x[:]

a[3] // OK: Component reference of type R

(a[3]).x // OK: Like a[3].x, but not a component reference

(a[3]).x[1] // Error.

((a[3]).x)[1] // OK: Like a[3].x[1], but not a component reference

The relation between a.x, a.x[2], and (a.x)[2] illustrates the effect of giving higher precedence to array index than member access. Had the precedence been equal, this would have changed the meaning of a.x[2] to the same thing that (a.x)[2] expresses, being a component reference of type Real[2].]

[Example: Non-associative exponentiation and array range operator (note that the array range operator only takes scalar operands):

⬇

x ^ y ^ z // Not legal, use parentheses to make it clear.

a : b : c : d // Not legal, and parentheses cannot make it legal.

]

The additive unary expressions are only allowed in the first term of a sum, that is, not immediately to the right of any of the additive or elementwise additive operators. For example, 1 + -1 + 1 is an invalid expression (not parseable according to appendix A), whereas both 1 + (-1) + 1 and -1 + 1 + 1 are fine.

[Example: The unary minus and plus in Modelica is slightly different than in Mathematica¹¹ 1 Mathematica is a registered trademark of Wolfram Research Inc. and in MATLAB²² 2 MATLAB is a registered trademark of MathWorks Inc., since the following expressions are illegal (whereas in Mathematica and in MATLAB these are valid expressions):

⬇

2*-2 // = -4 in Mathematica/MATLAB; is illegal in Modelica

--2 // = 2 in Mathematica/MATLAB; is illegal in Modelica

++2 // = 2 in Mathematica/MATLAB; is illegal in Modelica

2--2 // = 4 in Mathematica/MATLAB; is illegal in Modelica

]

The conditional operator may also include elseif-branches.

Equality = and assignment := are not expression operators since they are allowed only in equations and in assignment statements respectively.

[The operator precedence table is useful when generating textual representations of Modelica expression trees. When doing this, attention must be paid to the rule that the unary additive operators are only allowed for the first term in a sum. A naive implementation might not produce all the required parentheses for an expression tree such as 1 + (-1), as it might think that the higher precedence of the unary operator makes the parentheses redundant. A trick that solves this problem is to instead treat the additive unary operators as left associative with the same precedence as the binary additive operators.]

3.3 Evaluation Order

A tool is free to solve equations, reorder expressions and to not evaluate expressions if their values do not influence the result (e.g., short-circuit evaluation of Boolean expressions). if-statements and if-expressions guarantee that their branches are only evaluated if the appropriate condition is true, but relational operators generating state or time events will during continuous integration have the value from the most recent event.

If a numeric operation overflows the result is undefined. For literals it is recommended to automatically convert the number to another type with greater precision.

[Example: If one wants to guard an expression against incorrect evaluation, it should be guarded by an if:

⬇

Boolean v[n];

Boolean b;

Integer I;

equation

b = (I >= 1 and I <= n) and v[I]; // Unsafe, may result in error

b = if (I >= 1 and I <= n) then v[I] else false; // Safe

To guard square against square root of negative number use noEvent:

⬇

der(h) = if h > 0 then -c * sqrt(h) else 0; // Incorrect

der(h) = if noEvent(h > 0) then -c * sqrt(h) else 0; // Correct

]

3.4 Arithmetic Operators

Modelica supports five binary arithmetic operators that operate on any numerical type:

Operator	Description
^	Exponentiation
*	Multiplication
/	Division
+	Addition
-	Subtraction

Some of these operators can also be applied to a combination of a scalar type and an array type, see section 10.6.

The syntax of these operators is defined by the following rules from the Modelica grammar:

⬇

arithmetic-expression :

[ add-operator ] term { add-operator term }

add-operator :

"+" | "-"

term :

factor { mul-operator factor }

mul-operator :

"*" | "/"

factor :

primary [ "^" primary ]

3.5 Equality, Relational, and Logical Operators

Modelica supports the standard set of relational and logical operators, all of which produce the standard boolean values true or false:

Operator	Description
>	Greater than
>=	Greater than or equal
<	Less than
<=	Less than or equal to
==	Equality within expressions
<>	Inequality

A single equals sign = is never used in relational expressions, only in equations (chapter 8, section 10.6.1) and in function calls using named parameter passing (section 12.4.1).

The following logical operators are defined:

Operator	Description
not	Logical negation (unary operator)
and	Logical and (conjunction)
or	Logical or (disjunction)

The grammar rules define the syntax of the relational and logical operators.

⬇

logical-expression :

logical-term { or logical-term }

logical-term :

logical-factor { and logical-factor }

logical-factor :

[ not ] relation

relation :

arithmetic-expression [ relational-operator arithmetic-expression ]

relational-operator :

"<" | "<=" | ">" | ">=" | "==" | "<>"

The following holds for relational operators:

•

Relational operators <, <=,>, >=, ==, <>, are only defined for scalar operands of simple types. The result is Boolean and is true or false if the relation is fulfilled or not, respectively.
•

For operands of type String, str1 $\mathit{op}$ str2 is for each relational operator, $\mathit{op}$ , defined in terms of the C function strcmp as strcmp(str1, str2) $\mathit{op}$ 0.
•

For operands of type Boolean, false < true.
•

For operands of enumeration types, the order is given by the order of declaration of the enumeration literals.
•

In relations of the form v1 == v2 or v1 <> v2, v1 or v2 shall, unless used in a function, not be a subtype of Real.

[The reason for this rule is that relations with Real arguments are transformed to state events (see section 8.5) and this transformation becomes unnecessarily complicated for the == and <> relational operators (e.g., two crossing functions instead of one crossing function needed, epsilon strategy needed even at event instants). Furthermore, testing on equality of Real variables is questionable on machines where the number length in registers is different to number length in main memory.]
•

Relational operators can generate events, see section 3.8.5.

3.6 Miscellaneous Operators and Variables

Modelica also contains a few built-in operators which are not standard arithmetic, relational, or logical operators. These are described below, including time, which is a built-in variable, not an operator.

3.6.1 String Concatenation

Concatenation of strings (see the Modelica grammar) is denoted by the + operator in Modelica.

[Example: "a" + "b" becomes "ab".]

3.6.2 Array Constructor Operator

The array constructor operator { $\ldots$ } is described in section 10.4.

3.6.3 Array Concatenation Operator

The array concatenation operator [ $\ldots$ ] is described in section 10.4.2.

3.6.4 Array Range Operator

The array range constructor operator : is described in section 10.4.3.

3.6.5 If-Expressions

An expression

⬇

if expression1 then expression2 else expression3

is one example of if-expression. First expression1, which must be Boolean expression, is evaluated. If expression1 is true expression2 is evaluated and is the value of the if-expression, else expression3 is evaluated and is the value of the if-expression. The two expressions, expression2 and expression3, must be type compatible expressions (section 6.7) giving the type of the if-expression. The if-expressions with elseif are defined by replacing elseif by else if. For short-circuit evaluation see section 3.3.

[elseif in expressions has been added to the Modelica language for symmetry with if-equations.]

[Example:

⬇

Integer i;

Integer sign_of_i1 = if i < 0 then -1 elseif i == 0 then 0 else 1;

Integer sign_of_i2 = if i < 0 then -1 else if i == 0 then 0 else 1;

]

3.6.6 Member Access Operator

It is possible to access members of a class instance using dot notation, i.e., the . operator. It is also possible to select a record member of a general expression by enclosing it in parentheses. Note that while the selection is applied to an output-expression-list in the grammar, it is only semantically valid when the output-expression-list represents an expression.

In case the first operand is an array it is seen as a slicing operation, see section 10.6.9.

[Example: The component reference R1.R accesses the resistance component R of resistor R1.

The qualified class name A.B is a reference to the local class B which is a member of the class A. Note that the left operand in this case is a class, not an instance of the class.

(Complex(2, 3)).re constructs the record Complex(2, 3) and then selects the re component in it. Complex(2, 3).re is not valid syntax.]

3.6.7 Built-in Variable time

All declared variables are functions of the independent variable time. The variable time is a built-in variable available in all models and blocks, which is treated as an input variable. It is implicitly defined as:

⬇

input Real time (final quantity = "Time",

final unit = "s");

The value of the start-attribute of time is set to the time instant at which the simulation is started.

[Example:

⬇

encapsulated model SineSource

import Modelica.Math.sin;

connector OutPort = output Real;

OutPort y = sin(time); // Uses the built-in variable time.

end SineSource;

]

3.7 Built-in Operators and Functions

Certain built-in operators of Modelica are called using the same syntax as a function call. However, they do not behave as a mathematical function, because the result depends not only on the input arguments but also on the status of the simulation.

There are also built-in functions that depend only on the input argument, but also may trigger events in addition to returning a value. The built-in functions may also be overloaded such that a single Modelica function cannot be compatible with all calls of the function. Here, built-in means that they are defined at the Modelica language level, not through a Modelica function definition. The following built-in operators/functions are available:

•

Mathematical functions and conversion operators, see section 3.7.1 below.
•

Derivative and special purpose operators with function syntax, see section 3.7.4 below.
•

Event-related operators, see section 3.7.5 below.
•

Array operators/functions, see section 10.3.
•

Synchronous operators, see chapter 16.
•

State machine operators, see section 17.1.

Except where shadowing problems are being discussed, references to built-in functions and operators within this document always assume that the built-in definitions are not shadowed by user-defined definitions, see also section 12.5. With the exception where inputs are named (e.g., String), all operators and functions in this section can only be called with positional arguments.

3.7.1 Numeric Functions and Conversion Operators

The mathematical functions and conversion operators listed below do not generate events.

Expression	Description	Details
abs( $v$ )	Absolute value (event-free)	Function 3.1
sign( $v$ )	Sign of argument (event-free)	Function 3.2
sqrt( $v$ )	Square root	Function 3.3
nthRoot( $v$ , $n$ )	$n$ th root	Function 3.4
Integer( $e$ )	Conversion from enumeration to Integer	Operator 3.1
EnumTypeName( $i$ )	Conversion from Integer to enumeration	Operator 3.2
String( $\ldots$ )	Conversion to String	Operator 3.3

All of these except for the String conversion operator are vectorizable according to section 12.4.6.

Additional non-event generating mathematical functions are described in section 3.7.3, whereas the event-triggering mathematical functions are described in section 3.7.2.

Function 3.1 abs

⬇

abs(

v

)

Expands into noEvent(if $v$ >= 0 then $v$ else - $v$ ). Argument $v$ needs to be an Integer or Real expression.

[By not generating events the property abs( $x$ ) $\geq 0$ for all $x$ is ensured at the cost of sometimes having a derivative that changes discontinuously between events.

A typical case requiring the event-free semantics is a flow equation of the form abs(x) * x = y. With event generation, the equation would switch between the two forms x^2 = y and -x^2 = y at the events, where the events would not be coinciding exactly with the sign changes of y. When y passes through zero, neither form of the equation would have a solution in an open neighborhood of y $=0$ , and hence solving the equation would have to fail at some point sufficiently close to y $=0$ . Without event generation, on the other hand, the equation can be solved easily for x, also as y passes through zero. Note that without event generation the derivative of abs(x) * x never changes discontinuously, despite abs(x) having a discontinuous derivative.

In inverted form this equation is x = sign(y) * sqrt(abs(y)). With event generation, the call to sqrt would fail when applied to a negative number during root finding of the zero crossing for abs(y), compare section 8.5. Without event generation, on the other hand, evaluating sqrt(abs(y)) will never fail.]

Function 3.2 sign

⬇

sign(

v

)

Expands into noEvent(if $v$ > 0 then 1 else if $v$ < 0 then -1 else 0). Argument $v$ needs to be an Integer or Real expression.

Function 3.3 sqrt

⬇

sqrt(

v

)

Square root of $v$ , equivalent to nthRoot(v, 2).

Function 3.4 nthRoot

⬇

nthRoot(

v

n

)

$n$ th root of $v$ , where $v$ shall be a Real expression, and $n>0$ shall be an Integer expression. The result $y$ is a real root of the equation $y^{n}=v$ . If $n$ is even, $v$ must be non-negative and $y$ shall be the non-negative root. (If $n$ is odd, there is no constraint on $v$ and $y$ will have the same sign as $v$ .)

Operator 3.1 Integer

⬇

Integer(

e

)

Ordinal number of the expression $e$ of enumeration type that evaluates to the enumeration value E.enumvalue, where Integer(E.e1) = 1, Integer(E.en) = n, for an enumeration type E = enumeration(e1, $\ldots$ , en). See also section 4.9.5.2.

Operator 3.2 <EnumTypeName>

⬇

EnumTypeName(

i

)

For any enumeration type EnumTypeName, returns the enumeration value EnumTypeName.e such that ${\text{\lstinline{{\lst@@@set@language\lst@@@set@numbers\lst@@@set@frame% \lst@@@set@rulecolor\lst@@@set@language\lst@@@set@language\lst@@@set@frame% \normalsize{\@listingGroup{ltx_lst_keywords3}{\color[rgb]{0.33,0.24,0.03}Integ% er}}({\@listingGroup{ltx_lst_identifier}{EnumTypeName}}.{\@listingGroup{% ltx_lst_identifier}{e}})}}}}=i$ . Refer to the definition of Integer above.

It is an error to attempt to convert values of $i$ that do not correspond to values of the enumeration type. See also section 4.9.5.3.

Operator 3.3 String

⬇

String(

b

\langle

options

\rangle

)

String(

i

\langle

options

\rangle

)

String(

i

, format =

s

)

String(

r

\langle

options

\rangle

)

String(

r

, format =

s

)

String(

e

\langle

options

\rangle

)

Convert a scalar non-String expression to a String representation. The first argument may be a Boolean $b$ , an Integer $i$ , a Real $r$ , or an enumeration value $e$ (section 4.9.5.2). The $\langle\mathit{options}\rangle$ represent zero or more of the following named arguments (that cannot be passed as positional arguments):
- –
  
  Integer minimumLength = 0: Minimum length of the resulting string. If necessary, the blank character is used to fill up unused space.
- –
  
  Boolean leftJustified = true: If true, the converted result is left justified in the string; if false it is right justified in the string.
- –
  
  Integer significantDigits = 6: Number of significant digits in the result string. Only allowed when formatting a Real value.
The standard type coercion described in section 10.6.13 shall not be applied for the first argument of String. Hence, specifying significantDigits is an error when the first argument of String is an Integer expression.

For Real expressions the output shall be according to the Modelica grammar.

[Examples of Real values formatted with 6 significant digits: 12.3456, 0.0123456, 12345600, 1.23456E-10.]
The format string corresponding to $\langle\mathit{options}\rangle$ is:
- –
  
  For Real:
  (if leftJustified then "-" else "") + String(minimumLength)
  + "." + String(signficantDigits) + "g"
- –
  
  For Integer:
  (if leftJustified then "-" else "") + String(minimumLength) + "d"
The ANSI-C style format string (which cannot be combined with any of the other named arguments) consists of a single conversion specification without the leading %. It shall not contain a length modifier, and shall not use ‘*’ for width and/or precision. For both Real and Integer values, the conversion specifiers ‘f’, ‘e’, ‘E’, ‘g’, ‘G’ are allowed. For Integer values it is also allowed to use the ‘d’, ‘i’, ‘o’, ‘x’, ‘X’, ‘u’, and ‘c’ conversion specifiers. Using the Integer conversion specifiers for a Real value is a deprecated feature, where tools are expected to produce a result by either rounding the value, truncating the value, or picking one of the Real conversion specifiers instead.

The ‘x’/‘X’ formats (hexa-decimal) and c (character) for Integer values give results that do not agree with the Modelica grammar.
[Example: Some situations worth a remark:
- –
  
  String(4.0, format = "g") produces 4 which is not a valid Real literal. However, it is an Integer literal that can be used almost anywhere in Modelica code instead of the Real literal 4.0 (with the first argument to String being a notable exception here).
- –
  
  String(4, format = ".3f") uses the Integer case of String since no automatic type coerction takes place for the first argument. An implementation may internally convert the value to floating point and then fall back on the Real case implementation of format = ".3f".
- –
  
  String(4611686018427387648, format = ".0f") (a valid Integer value in an implementation with 64 bit IntegerType) may produce 4611686018427387904 (not equal to input value), in case internal conversion to a 64 bit double is applied.
]

3.7.2 Event Triggering Mathematical Functions

The operators listed below trigger events if used outside of a when-clause and outside of a clocked discrete-time partition (see section 16.8.1).

Expression	Description	Details
div( $x$ , $y$ )	Division with truncation toward zero	Operator 3.4
mod( $x$ , $y$ )	Integer modulus	Operator 3.5
rem( $x$ , $y$ )	Integer remainder	Operator 3.6
ceil( $x$ )	Smallest integer Real not less than $x$	Operator 3.7
floor( $x$ )	Largest integer Real not greater than $x$	Operator 3.8
integer( $x$ )	Largest Integer not greater than $x$	Operator 3.9

These expression for div, ceil, floor, and integer are event generating expression. The event generating expression for mod(x,y) is floor(x/y), and for rem(x,y) it is div(x,y) – i.e., events are not generated when mod or rem changes continuously in an interval, but when they change discontinuously from one interval to the next.

[If this is not desired, the noEvent operator can be applied to them. E.g., noEvent(integer(v)).]

Operator 3.4 div

⬇

div(

x

y

)

Algebraic quotient $x/y$ with any fractional part discarded (also known as truncation toward zero).

[This is defined for / in C99; in C89 the result for negative numbers is implementation-defined, so the standard function div must be used.]

Result and arguments shall have type Real or Integer. If either of the arguments is Real the result is Real otherwise Integer.

Operator 3.5 mod

⬇

mod(

x

y

)

Integer modulus of $x/y$ , i.e., mod( $x$ , $y$ ) = $x$ - floor( $x$ / $y$ ) * $y$ . Result and arguments shall have type Real or Integer. If either of the arguments is Real the result is Real otherwise Integer.

[Note, outside of a when-clause state events are triggered when the return value changes discontinuously. Examples: mod(3, 1.4) = 0.2, mod(-3, 1.4) = 1.2, mod(3, -1.4) = -1.2.]

Operator 3.6 rem

⬇

rem(

x

y

)

Integer remainder of $x/y$ , such that div( $x$ , $y$ ) * $y$ + rem( $x$ , $y$ ) = $x$ . Result and arguments shall have type Real or Integer. If either of the arguments is Real the result is Real otherwise Integer.

[Note, outside of a when-clause state events are triggered when the return value changes discontinuously. Examples: rem(3, 1.4) = 0.2, rem(-3, 1.4) = -0.2.]

Operator 3.7 ceil

⬇

ceil(

x

)

Smallest integer not less than $x$ . Result and argument shall have type Real.

[Note, outside of a when-clause state events are triggered when the return value changes discontinuously.]

Operator 3.8 floor

⬇

floor(

x

)

Largest integer not greater than $x$ . Result and argument shall have type Real.

[Note, outside of a when-clause state events are triggered when the return value changes discontinuously.]

Operator 3.9 integer

⬇

integer(

x

)

Largest integer not greater than $x$ . The argument shall have type Real. The result has type Integer.

[Note, outside of a when-clause state events are triggered when the return value changes discontinuously.]

3.7.3 Elementary Mathematical Functions

The functions listed below are elementary mathematical functions. Tools are expected to utilize well known properties of these functions (derivatives, inverses, etc) for symbolic processing of expressions and equations.

Expression	Description	Details
sin( $x$ )	Sine
cos( $x$ )	Cosine
tan( $x$ )	Tangent ( $x$ shall not be: $\ldots$ , - $\pi/2$ , $\pi/2$ , $3\pi/2$ , $\ldots$ )
asin( $x$ )	Inverse sine ( $-1\leq x\leq 1$ )
acos( $x$ )	Inverse cosine ( $-1\leq x\leq 1$ )
atan( $x$ )	Inverse tangent
atan2( $y$ , $x$ )	Principal value of the arc tangent of $y/x$	Function 3.5
sinh( $x$ )	Hyperbolic sine
cosh( $x$ )	Hyperbolic cosine
tanh( $x$ )	Hyperbolic tangent
exp( $x$ )	Exponential, base $\mathrm{e}$
log( $x$ )	Natural (base $\mathrm{e}$ ) logarithm ( $x>0$ )
log10( $x$ )	Base 10 logarithm ( $x>0$ )

These functions are the only ones that can also be called using the deprecated "builtin" external language, see section 12.9.

[End user oriented information about the elementary mathematical functions can be found for the corresponding functions in the Modelica.Math package.]

Function 3.5 atan2

⬇

atan2(

y

x

)

Principal value of the arc tangent of $y/x$ , using the signs of the two arguments to determine the quadrant of the result. The result $\varphi$ is in the interval $\left[-\pi,\,\pi\right]$ and satisfies:

$\displaystyle\left\lvert(x,\,y){}\right\rvert\,\cos(\varphi)$ $\displaystyle=x$

$\displaystyle\left\lvert(x,\,y){}\right\rvert\,\sin(\varphi)$ $\displaystyle=y$

3.7.4 Derivative and Special Purpose Operators with Function Syntax

The operators listed below include the derivative operator and special purpose operators with function syntax.

Expression	Description	Details
der( $\mathit{expr}$ )	Time derivative	Operator 3.10
delay( $\mathit{expr}$ , $\ldots$ )	Time delay	Operator 3.11
cardinality( $c$ )	Number of occurrences in connect-equations	Operator 3.12
homotopy( $\mathit{actual}$ , $\mathit{simplified}$ )	Homotopy initialization	Operator 3.13
semiLinear( $x$ , $k^{+}$ , $k^{-}$ )	Sign-dependent slope	Operator 3.14
inStream( $v$ )	Stream variable flow into component	Operator 3.15
actualStream( $v$ )	Actual value of stream variable	Operator 3.16
spatialDistribution( $\ldots$ )	Variable-speed transport	Operator 3.17
getInstanceName()	Name of instance at call site	Operator 3.18

The special purpose operators with function syntax where the call below uses named arguments can be called with named arguments (with the specified names), or with positional arguments (the inputs of the functions are in the order given in the calls below).

Operator 3.10 der

⬇

der(

\mathit{expr}

)

The time derivative of $\mathit{expr}$ . If the expression $\mathit{expr}$ is a scalar it needs to be a subtype of Real. The expression and all its time-varying subexpressions must be continuous and semi-differentiable. An exception is when the operator reinit (Operator 3.27) is activated, as not even states are continuous. If $\mathit{expr}$ is an array, the operator is applied to all elements of the array. For non-scalar arguments the function is vectorized according to section 10.6.12.

[For Real parameters and constants the result is a zero scalar or array of the same size as the variable.]

[Example: For continuous expression we have several cases.

⬇

  Real when1, x1, x2, x3, w1, y1, y2, y3;

equation

  when sample(1, 1) then

    when1 = $\ldots$ ;

  end when;

  x1 = if time>=0 then 0 else time; // Continuous

  x2 = if time<1 then 0 else time;  // Not continuous

  x3 = x1 * x2;    // The product is continuous, despite x2

  w1 = der(when1); // Explicitly illegal

  y1 = der(x1);    // Ok

  y2 = der(x2);    // Not ok at time=1

  y3 = der(x3);    // Ok

Since it is difficult to prove whether expressions are continuous, tools can dynamically check the continuity such that x2 only generates an error when the integration reaches the discontinuity.]

[Example: Exception for reinit:

⬇

  Real x4, x5, x6;

equation

  when sample(1, 1) then

    reinit(x4, 0);

  end when;

  der(x4) = time;

  x5 = x4^2;

  x6 = der(x5); // Allowed even if x5 is not continuous, since that is due to reinit

]

Operator 3.11 delay

⬇

delay(

\mathit{expr}

\mathit{delayTime}

\mathit{delayMax}

)

delay(

\mathit{expr}

\mathit{delayTime}

)

Evaluates to $\mathit{expr}$ (time - $\mathit{delayTime}$ ) for ${{\text{\lstinline{{\lst@@@set@language\lst@@@set@numbers\lst@@@set@frame% \lst@@@set@rulecolor\lst@@@set@language\lst@@@set@language\lst@@@set@frame% \normalsize{\@listingGroup{ltx_lst_identifier}{time}}}}}}>\text{\lstinline{{% \lst@@@set@language\lst@@@set@numbers\lst@@@set@frame\lst@@@set@rulecolor% \lst@@@set@language\lst@@@set@language\lst@@@set@frame\normalsize{% \@listingGroup{ltx_lst_identifier}{time}}.{\@listingGroup{ltx_lst_identifier}{% start}}}}}}+\mathit{delayTime}$ and $\mathit{expr}$ (time.start) for ${{\text{\lstinline{{\lst@@@set@language\lst@@@set@numbers\lst@@@set@frame% \lst@@@set@rulecolor\lst@@@set@language\lst@@@set@language\lst@@@set@frame% \normalsize{\@listingGroup{ltx_lst_identifier}{time}}}}}}\leq\text{\lstinline{% {\lst@@@set@language\lst@@@set@numbers\lst@@@set@frame\lst@@@set@rulecolor% \lst@@@set@language\lst@@@set@language\lst@@@set@frame\normalsize{% \@listingGroup{ltx_lst_identifier}{time}}.{\@listingGroup{ltx_lst_identifier}{% start}}}}}}+\mathit{delayTime}$ . The arguments, i.e., $\mathit{expr}$ , $\mathit{delayTime}$ and $\mathit{delayMax}$ , need to be subtypes of Real. $\mathit{delayMax}$ needs to be additionally a parameter expression. The following relation shall hold: $0\leq\mathit{delayTime}\leq\mathit{delayMax}$ , otherwise an error occurs. If $\mathit{delayMax}$ is not supplied in the argument list, $\mathit{delayTime}$ needs to be a parameter expression. For non-scalar arguments the function is vectorized according to section 10.6.12. For further details, see section 3.7.4.1.

Operator 3.12 cardinality

⬇

cardinality(

c

)

[This is a deprecated operator. It should no longer be used, since it will be removed in one of the next Modelica releases.]

Returns the number of (inside and outside) occurrences of connector instance $c$ in a connect-equation as an Integer number. For further details, see section 3.7.4.3.

Operator 3.13 homotopy

⬇

homotopy(actual =

\mathit{actual}

, simplified =

\mathit{simplified}

)

The scalar expressions $\mathit{actual}$ and $\mathit{simplified}$ are subtypes of Real. A Modelica translator should map this operator into either of the two forms:
1. 1.
  
  Returns $\mathit{actual}$ (trivial implementation).
2. 2.
  
  In order to solve algebraic systems of equations, the operator might during the solution process return a combination of the two arguments, ending at actual.
  
  [Example: $\mathit{actual}\cdot\lambda+\mathit{simplified}\cdot(1-\lambda)$ , where $\lambda$ is a homotopy parameter going from 0 to 1.]
  
  The solution must fulfill the equations for homotopy returning $\mathit{actual}$ .
For non-scalar arguments the function is vectorized according to section 12.4.6. For further details, see section 3.7.4.4.

Operator 3.14 semiLinear

⬇

semiLinear(

x

k^{+}

k^{-}

)

Returns: smooth(0, if $x$ >= 0 then $k^{+}$ * $x$ else $k^{-}$ * $x$ ). The result is of type Real. For non-scalar arguments the function is vectorized according to section 10.6.12. For further details, see section 3.7.4.5 (especially in the case when $x=0$ ).

Operator 3.15 inStream

⬇

inStream(

v

)

inStream( $v$ ) is only allowed for stream variables $v$ defined in stream connectors, and is the value of the stream variable $v$ close to the connection point assuming that the flow is from the connection point into the component. This value is computed from the stream connection equations of the flow variables and of the stream variables. The operator is vectorizable. For further details, see section 15.2.

Operator 3.16 actualStream

⬇

actualStream(

v

)

actualStream( $v$ ) returns the actual value of the stream variable $v$ for any flow direction. The operator is vectorizable. For further details, see section 15.3.

Operator 3.17 spatialDistribution

⬇

spatialDistribution(

in0 =

\mathit{in0}

, in1 =

\mathit{in1}

, x =

x

positiveVelocity =

\ldots

initialPoints =

\ldots

initialValues =

\ldots

)

spatialDistribution allows approximation of variable-speed transport of properties. For further details, see section 3.7.4.2.

Operator 3.18 getInstanceName

⬇

getInstanceName()

Returns a string with the name of the model/block that is simulated, appended with the fully qualified name of the instance in which this function is called. For further details, see section 3.7.4.6.

A few of these operators are described in more detail in the following.

3.7.4.1 delay

[delay allows a numerical sound implementation by interpolating in the (internal) integrator polynomials, as well as a more simple realization by interpolating linearly in a buffer containing past values of expression $\mathit{expr}$ . Without further information, the complete time history of the delayed signals needs to be stored, because the delay time may change during simulation. To avoid excessive storage requirements and to enhance efficiency, the maximum allowed delay time has to be given via $\mathit{delayMax}$ . This gives an upper bound on the values of the delayed signals which have to be stored. For real-time simulation where fixed step size integrators are used, this information is sufficient to allocate the necessary storage for the internal buffer before the simulation starts. For variable step size integrators, the buffer size is dynamic during integration.

In principle, delay could break algebraic loops. For simplicity, this is not supported because the minimum delay time has to be given as additional argument to be fixed at compile time. Furthermore, the maximum step size of the integrator is limited by this minimum delay time in order to avoid extrapolation in the delay buffer.]

3.7.4.2 spatialDistribution

[Many applications involve the modelling of variable-speed transport of properties. One option to model this infinite-dimensional system is to approximate it by an ODE, but this requires a large number of state variables and might introduce either numerical diffusion or numerical oscillations. Another option is to use a built-in operator that keeps track of the spatial distribution of $z(x,t)$ , by suitable sampling, interpolation, and shifting of the stored distribution. In this case, the internal state of the operator is hidden from the ODE solver.]

spatialDistribution allows the infinite-dimensional problem below to be solved efficiently with good accuracy

	$\displaystyle\frac{\partial z(x,t)}{\partial t}+v(t)\frac{\partial z(x,t)}{% \partial x}$	$\displaystyle=0.0$
	$\displaystyle z(0.0,t)$	$\displaystyle=\mathrm{in}_{0}(t)\text{ if $v\geq 0$}$
	$\displaystyle z(1.0,t)$	$\displaystyle=\mathrm{in}_{1}(t)\text{ if $v<0$}$

where $z(x,t)$ is the transported quantity, $x$ is the normalized spatial coordinate ( $0.0\leq x\leq 1.0$ ), $t$ is the time, $v(t)=\mathrm{der}(x)$ is the normalized transport velocity and the boundary conditions are set at either $x=0.0$ or $x=1.0$ , depending on the sign of the velocity. The calling syntax is:

⬇

(out0, out1) = spatialDistribution(in0, in1, x, positiveVelocity,

initialPoints = {0.0, 1.0},

initialValues = {0.0, 0.0});

where in0, in1, out0, out1, and x are all subtypes of Real, positiveVelocity is a Boolean, initialPoints and initialValues are arrays of subtypes of Real of equal size, containing the x coordinates and the $z$ values of a finite set of points describing the initial distribution of $z(x,\mathit{t0})$ . The out0 and out1 are given by the solutions at $z(0.0,t)$ and $z(1.0,t)$ ; and in0 and in1 are the boundary conditions at $z(0.0,t)$ and $z(1.0,t)$ (at each point in time only one of in0 and in1 is used). Elements in the initialPoints array must be sorted in non-descending order. The operator can not be vectorized according to the vectorization rules described in section 12.4.6. The operator can be vectorized only with respect to the arguments in0 and in1 (which must have the same size), returning vectorized outputs out0 and out1 of the same size; the arguments initialPoints and initialValues are vectorized accordingly.

The solution, $z$ , can be described in terms of characteristics:

z(x+\int_{t}^{t+\beta}v(\alpha)\mathrm{d}\alpha,t+\beta)=z(x,t),\quad\text{for% all $\beta$ as long as staying inside the domain}

This allows the direct computation of the solution based on interpolating the boundary conditions.

spatialDistribution can be described in terms of the pseudo-code given as a block:

⬇

block spatialDistribution

input Real in0;

input Real in1;

input Real x;

input Boolean positiveVelocity;

parameter Real initialPoints(each min=0, each max=1)[:] = {0.0, 1.0};

parameter Real initialValues[:] = {0.0, 0.0};

output Real out0;

output Real out1;

protected

Real points[:];

Real values[:];

Real x0;

Integer m;

algorithm

/* The notation

* x <and then> y

* is used below as a shorthand for

* if x then y else false

* also known as ”short-circuit evaluation of x and y”.

if positiveVelocity then

out1 := interpolate(points, values, 1 - (x - x0));

out0 := values[1]; // Similar to in0 but avoiding algebraic loop.

else

out0 := interpolate(points, values, 0 - (x - x0));

out1 := values[end]; // Similar to in1 but avoiding algebraic loop.

end if;

when <acceptedStep> then

if x > x0 then

m := size(points, 1);

while m > 0 <and then> points[m] + (x - x0) >= 1 loop

m := m - 1;

end while;

values := cat(1,

{in0},

values[1:m],

{interpolate(points, values, 1 - (x - x0))});

points := cat(1, {0}, points[1:m] .+ (x-x0), {1});

elseif x < x0 then

m := 1;

while m < size(points, 1) <and then> points[m] + (x - x0) <= 0 loop

m := m + 1;

end while;

values := cat(1,

{interpolate(points, values, 0 - (x - x0))},

values[m:end],

{in1});

points := cat(1, {0}, points[m:end] .+ (x - x0), {1});

end if;

x0 := x;

end when;

initial algorithm

x0 := x;

points := initialPoints;

values := initialValues;

end spatialDistribution;

[Note that the implementation has an internal state and thus cannot be described as a function in Modelica; initialPoints and initialValues are declared as parameters to indicate that they are only used during initialization.

The infinite-dimensional problem stated above can then be formulated in the following way:

⬇

der(x) = v;

(out0, out1) = spatialDistribution(in0, in1, x, v >= 0,

initialPoints, initialValues);

Events are generated at the exact instants when the velocity changes sign – if this is not needed, noEvent can be used to suppress event generation.

If the velocity is known to be always positive, then out0 can be omitted, e.g.:

⬇

der(x) = v;

(, out1) = spatialDistribution(in0, 0, x, true, initialPoints, initialValues);

Technically relevant use cases for the use of spatialDistribution are modeling of electrical transmission lines, pipelines and pipeline networks for gas, water and district heating, sprinkler systems, impulse propagation in elongated bodies, conveyor belts, and hydraulic systems. Vectorization is needed for pipelines where more than one quantity is transported with velocity v in the example above.]

3.7.4.3 cardinality (deprecated)

[cardinality is deprecated for the following reasons and will be removed in a future release:

•

Reflective operator may make early type checking more difficult.
•

Almost always abused in strange ways
•

Not used for Bond graphs even though it was originally introduced for that purpose.

]

[cardinality allows the definition of connection dependent equations in a model, for example:

⬇

connector Pin

Real v;

flow Real i;

end Pin;

model Resistor

Pin p, n;

equation

assert(cardinality(p) > 0 and cardinality(n) > 0,

"Connectors p and n of Resistor must be connected");

// Equations of resistor

\ldots

end Resistor;

]

The cardinality is counted after removing conditional components, and shall not be applied to expandable connectors, elements in expandable connectors, or to arrays of connectors (but can be applied to the scalar elements of array of connectors). cardinality should only be used in the condition of assert and if-statements that do not contain connect and similar operators, see section 16.8.1).

3.7.4.4 homotopy

[During the initialization phase of a dynamic simulation problem, it often happens that large nonlinear systems of equations must be solved by means of an iterative solver. The convergence of such solvers critically depends on the choice of initial guesses for the unknown variables. The process can be made more robust by providing an alternative, simplified version of the model, such that convergence is possible even without accurate initial guess values, and then by continuously transforming the simplified model into the actual model. This transformation can be formulated using expressions of this kind:

{{\lambda\cdot\text{\lstinline{{\lst@@@set@language\lst@@@set@numbers% \lst@@@set@frame\lst@@@set@rulecolor\lst@@@set@language\lst@@@set@language% \lst@@@set@frame\normalsize{\@listingGroup{ltx_lst_identifier}{actual}}}}}}+(1% -\lambda)\cdot\text{\lstinline{{\lst@@@set@language\lst@@@set@numbers% \lst@@@set@frame\lst@@@set@rulecolor\lst@@@set@language\lst@@@set@language% \lst@@@set@frame\normalsize{\@listingGroup{ltx_lst_identifier}{simplified}}}}}}

in the formulation of the system equations, and is usually called a homotopy transformation. If the simplified expression is chosen carefully, the solution of the problem changes continuously with $\lambda$ , so by taking small enough steps it is possible to eventually obtain the solution of the actual problem.

The operator can be called with ordered arguments or preferably with named arguments for improved readability.

It is recommended to perform (conceptually) one homotopy iteration over the whole model, and not several homotopy iterations over the respective non-linear algebraic equation systems. The reason is that the following structure can be present:

⬇

w =

f_{1}

(x) // has homotopy

0 =

f_{2}

(der(x), x, z, w)

Here, a non-linear equation system $f_{2}$ is present. homotopy is, however used on a variable that is an “input” to the non-linear algebraic equation system, and modifies the characteristics of the non-linear algebraic equation system. The only useful way is to perform the homotopy iteration over $f_{1}$ and $f_{2}$ together.

The suggested approach is “conceptual”, because more efficient implementations are possible, e.g., by determining the smallest iteration loop, that contains the equations of the first BLT block in which homotopy is present and all equations up to the last BLT block that describes a non-linear algebraic equation system.

A trivial implementation of homotopy is obtained by defining the following function in the global scope:

⬇

function homotopy

input Real actual;

input Real simplified;

output Real y;

algorithm

y := actual;

annotation(Inline = true);

end homotopy;

]

[Example 1: In electrical systems it is often difficult to solve non-linear algebraic equations if switches are part of the algebraic loop. An idealized diode model might be implemented in the following way, by starting with a “flat” diode characteristic and then move with homotopy to the desired “steep” characteristic:

⬇

model IdealDiode

\ldots

parameter Real Goff = 1e-5;

protected

Real Goff_flat = max(0.01, Goff);

Real Goff2;

equation

off = s < 0;

Goff2 = homotopy(actual = Goff, simplified = Goff_flat);

u = s * (if off then 1 else Ron2) + Vknee;

i = s * (if off then Goff2 else 1 ) + Goff2*Vknee;

\ldots

end IdealDiode;

]

[Example 2: In electrical systems it is often useful that all voltage sources start with zero voltage and all current sources with zero current, since steady state initialization with zero sources can be easily obtained. A typical voltage source would then be defined as:

⬇

model ConstantVoltageSource

extends Modelica.Electrical.Analog.Interfaces.OnePort;

parameter Modelica.Units.SI.Voltage V;

equation

v = homotopy(actual = V, simplified = 0.0);

end ConstantVoltageSource;

]

[Example 3: In fluid system modelling, the pressure/flowrate relationships are highly nonlinear due to the quadratic terms and due to the dependency on fluid properties. A simplified linear model, tuned on the nominal operating point, can be used to make the overall model less nonlinear and thus easier to solve without accurate start values. Named arguments are used here in order to further improve the readability.

⬇

model PressureLoss

import Modelica.Units.SI;

\ldots

parameter SI.MassFlowRate m_flow_nominal "Nominal mass flow rate";

parameter SI.Pressure dp_nominal "Nominal pressure drop";

SI.Density rho "Upstream density";

SI.DynamicViscosity lambda "Upstream viscosity";

equation

\ldots

m_flow = homotopy(actual = turbulentFlow_dp(dp, rho, lambda),

simplified = dp/dp_nominal*m_flow_nominal);

\ldots

end PressureLoss;

]

[Example 4: Note that homotopy shall not be used to combine unrelated expressions, since this can generate singular systems from combining two well-defined systems.

⬇

model DoNotUse

Real x;

parameter Real x0 = 0;

equation

der(x) = 1-x;

initial equation

0 = homotopy(der(x), x - x0);

end DoNotUse;

The initial equation is expanded into

0=\lambda*\mathrm{der}(x)+(1-\lambda)(x-x_{0})

and you can solve the two equations to give

x=\frac{\lambda+(\lambda-1)x_{0}}{2\lambda-1}

which has the correct value of $x_{0}$ at $\lambda=0$ and of 1 at $\lambda=1$ , but unfortunately has a singularity at $\lambda=0.5$ .]

3.7.4.5 semiLinear

(See definition of semiLinear in section 3.7.4). In some situations, equations with semiLinear become underdetermined if the first argument (x) becomes zero, i.e., there are an infinite number of solutions. It is recommended that the following rules are used to transform the equations during the translation phase in order to select one meaningful solution in such cases:

•

The equations

⬇

y = semiLinear(x, sa, s1);

y = semiLinear(x, s1, s2);

y = semiLinear(x, s2, s3);

$\ldots$

y = semiLinear(x, sN, sb);

$\ldots$

may be replaced by

⬇

s1 = if x >= 0 then sa else sb

s2 = s1;

s3 = s2;

$\ldots$

$s_{N}=s_{N-1}$ ;

y = semiLinear(x, sa, sb);
•

The equations

⬇

x = 0;

y = 0;

y = semiLinear(x, sa, sb);

may be replaced by

⬇

x = 0

y = 0;

sa = sb;

[For symbolic transformations, the following property is useful (this follows from the definition):

⬇

semiLinear(m_flow, port_h, h);

is identical to:

⬇

-semiLinear(-m_flow, h, port_h);

The semiLinear function is designed to handle reversing flow in fluid systems, such as

⬇

H_flow = semiLinear(m_flow, port.h, h);

i.e., the enthalpy flow rate H_flow is computed from the mass flow rate m_flow and the upstream specific enthalpy depending on the flow direction.]

3.7.4.6 getInstanceName

Returns a string with the name of the model/block that is simulated, appended with the fully qualified name of the instance in which this function is called.

[Example:

⬇

package MyLib

model Vehicle

Engine engine;

\ldots

end Vehicle;

model Engine

Controller controller;

\ldots

end Engine;

model Controller

equation

Modelica.Utilities.Streams.print("Info from: " + getInstanceName());

end Controller;

end MyLib;

If MyLib.Vehicle is simulated, the call of getInstanceName() returns "Vehicle.engine.controller".]

If this function is not called inside a model or block (e.g., the function is called in a function or in a constant of a package), the return value is not specified.

The simulation result should not depend on the return value of this function.

3.7.5 Event-Related Operators with Function Syntax

The operators listed below are event-related operators with function syntax. The operators noEvent, pre, edge, and change, are vectorizable according to section 12.4.6.

Expression	Description	Details
initial()	Predicate for the initialization phase	Operator 3.19
terminal()	Predicate for the end of a successful analysis	Operator 3.20
noEvent( $\mathit{expr}$ )	Evaluate $\mathit{expr}$ without triggering events	Operator 3.21
smooth( $p$ , $\mathit{expr}$ )	Treat $\mathit{expr}$ as $p$ times continuously differentiable	Operator 3.22
sample( $\mathit{start}$ , $\mathit{interval}$ )	Periodic triggering of events	Operator 3.23
pre( $y$ )	Left limit $y(t^{-})$ of variable $y(t)$	Operator 3.24
edge( $b$ )	Expands into ( $b$ and not pre( $b$ ))	Operator 3.25
change( $v$ )	Expands into ( $v$ <> pre( $v$ ))	Operator 3.26
reinit( $x$ , $\mathit{expr}$ )	Reinitialize $x$ with $\mathit{expr}$	Operator 3.27

Operator 3.19 initial

⬇

initial()

Returns true during the initialization phase and false otherwise.

[Hereby, initial() triggers a time event at the beginning of a simulation.]

Operator 3.20 terminal

⬇

terminal()

Returns true at the end of a successful analysis.

[Hereby, terminal() ensures an event at the end of successful simulation.]

Operator 3.21 noEvent

⬇

noEvent(

\mathit{expr}

)

Real elementary relations within $\mathit{expr}$ are taken literally, i.e., no state or time event is triggered. No zero crossing functions shall be used to monitor any of the normally event-generating subexpressions inside $\mathit{expr}$ . Inside functions, noEvent only makes a difference in combination with the function annotation GenerateEvents = true (see annotation 12.7). See also operator 3.22 and section 8.5.

Operator 3.22 smooth

⬇

smooth(

p

\mathit{expr}

)

If $p\geq 0$ smooth( $p$ , $\mathit{expr}$ ) returns $\mathit{expr}$ and states that $\mathit{expr}$ is $p$ times continuously differentiable, i.e., $\mathit{expr}$ is continuous in all Real variables appearing in the expression and all partial derivatives with respect to all appearing real variables exist and are continuous up to order $p$ . The argument $p$ should be a scalar Integer parameter expression. The only allowed types for $\mathit{expr}$ in smooth are: Real expressions, arrays of allowed expressions, and records containing only components of allowed expressions.

smooth should be used instead of noEvent in order to avoid events for efficiency reasons. A tool is free to not generate events for expressions inside smooth. However, smooth does not guarantee that no events will be generated, and thus it can be necessary to use noEvent inside smooth.

[Note that smooth does not guarantee a smooth output if any of the occurring variables change discontinuously.]

[Example:

⬇

  Real x, y, z;

equation

  x = if time < 1 then 2 else time - 2;

  z = smooth(0, if time < 0 then 0 else time);

  y = smooth(1,

        noEvent(if x < 0 then 0 else sqrt(x) * x)); // Needs noEvent.

]

Operator 3.23 sample

⬇

sample(

\mathit{start}

\mathit{interval}

)

Returns true and triggers time events at time instants $\mathit{start}+i\cdot\mathit{interval}$ for $i=0,\,1\,\ldots$ , and is only true during the first event iteration at those times. At event iterations after the first one at each event and during continuous integration the operator always returns false. The starting time $\mathit{start}$ and the sample interval $\mathit{interval}$ must be parameter expressions and need to be a subtype of Real or Integer. The sample interval $\mathit{interval}$ must be a positive number.

Operator 3.24 pre

⬇

pre(

y

)

Returns the left limit $y(t^{-})$ of variable $y(t)$ at a time instant $t$ . At an event instant, $y(t^{-})$ is the value of $y$ after the last event iteration at time instant $t$ (see comment below). Any subscripts in the component expression $y$ must be parameter expressions. pre can be applied if the following three conditions are fulfilled simultaneously: (a) variable $y$ is either a subtype of a simple type or is a record component, (b) $y$ is a discrete-time expression (c) the operator is not applied in a function class.

[This can be applied to continuous-time variables in when-clauses, see section 3.8.5 for the definition of discrete-time expression.]

The first value of pre( $y$ ) is determined in the initialization phase.

A new event is triggered if there is at least for one variable v such that pre(v) <> v after the active model equations are evaluated at an event instant. In this case the model is at once reevaluated. This evaluation sequence is called event iteration. The integration is restarted once pre(v) == v for all v appearing inside pre( $\ldots$ ).

[If v and pre(v) are only used in when-clauses, the translator might mask event iteration for variable v since v cannot change during event iteration. It is a quality of implementation to find the minimal loops for event iteration, i.e., not all parts of the model need to be reevaluated.

The language allows mixed algebraic systems of equations where the unknown variables are of type Real, Integer, Boolean, or an enumeration. These systems of equations can be solved by a global fix point iteration scheme, similarly to the event iteration, by fixing the Boolean, Integer, and/or enumeration unknowns during one iteration. Again, it is a quality of implementation to solve these systems more efficiently, e.g., by applying the fix point iteration scheme to a subset of the model equations.]

Operator 3.25 edge

⬇

edge(

b

)

Expands into ( $b$ and not pre( $b$ )) for Boolean variable $b$ . The same restrictions as for pre apply (e.g., not to be used in function classes).

Operator 3.26 change

⬇

change(v)

Expands into ( $v$ <> pre( $v$ )). The same restrictions as for pre apply.

Operator 3.27 reinit

⬇

reinit(

x

\mathit{expr}

)

In the body of a when-clause, reinitializes $x$ with $\mathit{expr}$ at an event instant. $x$ is a scalar or array Real variable that is implicitly defined to have StateSelect.always.

[It is an error if the variable cannot be selected as a state.]

$\mathit{expr}$ needs to be type-compatible with $x$ . reinit can only be applied once for the same variable – either as an individual variable or as part of an array of variables. It can only be applied in the body of a when-clause in an equation section. See also section 8.3.6.

3.8 Variability of Expressions

The concept of variability of an expression indicates to what extent the expression can vary over time. See also section 4.5 regarding the concept of variability. There are four levels of variability of expressions, starting from the least variable:

•

constant variability
•

parameter variability
•

discrete-time variability
•

continuous-time variability

While many invalid models can be rejected based on the declared variabilities of variables alone (without the concept of expression variability), the following rules both help enforcing compliance of computed solutions to declared variability, and impose additional restrictions that simplify reasoning and reporting of errors:

•

For an assignment v := expr or binding equation v = expr, v must be declared to be at least as variable as expr.
•

For multiple return assignment (x1, $\ldots$ , xn) := expr (see section 11.2.1.1), all of x1, …, xn must be declared to be at least as variable as expr.
•

When determining whether an equation can contribute to solving for a variable v (for instance, when applying the perfect matching rule, see section 8.4), the equation can only be considered contributing if the resulting solution would be at most as variable as v.

[Example: The (underdetermined) model Test below illustrates two kinds of consequences due to variability constraints. First, it contains variability errors for declaration equations and assignments. Second, it illustrates the impact of variability on the matching of equations to variables, which can lead to violation of the perfect matching rule. Details of how variabilities are determined are given in the sections below. The discrete-valued equation variability rule mentioned in the comments below refer to the rule in section 3.8.5 that requires both sides of the Boolean equation to be discrete-time.

⬇

model Constants

parameter Real p1 = 1;

constant Real c1 = p1 + 2; // Error, not a constant expression.

parameter Real p2 = p1 + 2; // Fine.

end Constants;

model Test

Constants c1(p1 = 3); // Fine.

Constants c2(p2 = 7); // Fine, declaration equation can be modified.

Real x;

Boolean b1 = noEvent(x > 1); // Error, since b1 is a discrete-time variable

// and noEvent(x > 1) is not discrete-time.

Boolean b2;

Integer i1;

Integer i2;

algorithm

i1 := x; // Error, assignment to variable of lesser variability.

equation

/* The equation below can be rejected for two reasons:

* 1. Discrete-valued equation variability rule requires both sides to be

* discrete-time.

* 2. It violates the perfect matching rule, as no variable can be solved

* with correct variability using this equation.

b2 = noEvent(x > 1); // Error, see above.

i2 = x; // No variability error, and can be matched to x.

end Test;

]

3.8.1 Function Variability

The variability of function calls needs to consider both the variability of arguments directly given in the function and the variability of the used default arguments, if any. This is especially a concern for functions given as a short class, see section 12.1.3. This has additional implications for redeclarations, see definition 6.8. The purity of the function, see section 12.3, does not influence the variability of the function call.

[The restrictions for calling functions declared as impure serve a similar purpose as the variability restrictions, see section 12.3, and thus it is not necessary to consider purity in the definition of variability. Consider a function reading an external file and returning some value from that file. Different uses can have the file updated before the simulation (as a parameter-expression), or during the simulation (as a discrete-time expression). Thus it depends on the use case and the specific file, not the function itself, and it would even be possible to update the file in continuous time (as part of an algorithm) and still use the same function.]

3.8.2 Constant Expressions

Constant expressions are:

•

Real, Integer, Boolean, String, and enumeration literals.
•

Constant variables, see section 4.5.
•

Except for the special built-in operators initial, terminal, der, edge, change, sample, and pre, a function or operator with constant subexpressions as argument (and no parameters defined in the function) is a constant expression.
•
Some function calls are constant expressions regardless of the arguments:
- –
  
  ndims(A)

3.8.3 Evaluable Expressions

Evaluable expressions are:

•

Constant expressions.
•

Evaluable parameter variables, see section 4.5.
•

Input variables in functions behave as though they were evaluable expressions.
•

Except for the special built-in operators initial, terminal, der, edge, change, sample, and pre, a function or operator with evaluable subexpressions is an evaluable expression.
•

The sub-expression end used in A[ $\ldots$ end $\ldots$ ] if A is a variable declared in a non-function class.
•
Some function calls are evaluable expressions even if the arguments are not:
- –
  
  cardinality(c), see restrictions for use in section 3.7.4.3.
- –
  
  size(A) (including size(A, j) where j is an evaluable expression) if A is variable declared in a non-function class.
- –
  
  Connections.isRoot(A.R)
- –
  
  Connections.rooted(A.R)

3.8.4 Parameter Expressions

Parameter expressions are:

•

Evaluable expressions.
•

Non-evaluable parameter variables, see section 4.5.
•

Except for the special built-in operators initial, terminal, der, edge, change, sample, and pre, a function or operator with parameter subexpressions is a parameter expression.
•
Some function calls are parameter expressions even if the arguments are not:
- –
  
  size(A, j) where j is a parameter expression, if A is variable declared in a non-function class.

3.8.5 Discrete-Time Expressions

Discrete-time expressions are:

•

Parameter expressions.
•

Discrete-time variables, see section 4.5.
•

Function calls where all input arguments of the function are discrete-time expressions.
•

Expressions where all the subexpressions are discrete-time expressions.
•

Expressions in the body of a when-clause, initial equation, or initial algorithm.
•

Expressions in a clocked discrete-time partition, see section 16.8.1.
•

Unless inside noEvent: Ordered relations (>, <, >=, <=) and the event generating functions ceil, floor, div, and integer, if at least one argument is non-discrete-time expression and subtype of Real.

[These will generate events, see section 8.5. Note that rem and mod generate events but are not discrete-time expressions. In other words, relations inside noEvent, such as noEvent(x>1), are not discrete-time expressions.]
•

The functions pre, edge, and change result in discrete-time expressions.
•

Expressions in functions behave as though they were discrete-time expressions.

Inside an if-expression, if-clause, while-statement or for-clause, that is controlled by a non-discrete-time (that is continuous-time, but not discrete-time) switching expression and not in the body of a when-clause, it is not legal to have assignments to discrete-time variables, equations between discrete-time expressions, or real elementary relations/functions that should generate events.

[The restriction above is necessary in order to guarantee that all equations for discrete-time variable are discrete-time expressions, and to ensure that crossing functions do not become active between events.]

For a scalar or array equation expr1 = expr2 where neither expression is of base type Real, both expressions must be discrete-time expressions. For a record equation, the rule applies recursively to each of the components of the record. This is called the discrete-valued equation variability rule.

[For a scalar equation, the rule follows from the observation that a discrete-valued equation does not provide sufficient information to solve for a continuous-valued variable. Hence, and according to the perfect matching rule (see section 8.4), such an equation must be used to solve for a discrete-valued variable. By the interpretation of (B.1c) in appendix B, it follows that one of expr1 and expr2 must be the variable, and the other expression its solution. Since a discrete-valued variable is a discrete-time expression, it follows that its solution on the other side of the equation must have at most discrete-time variability. That is, both sides of the equation are discrete-time expressions.

For example, this rule shows that (outside of a when-clause) noEvent cannot be applied to either side of a Boolean, Integer, String, or enumeration equation, as this would result in a non-discrete-time expression.

For an array equation, note that each array can have only one element type, so if one element is Real, then all other entries must also be Real, possibly making use of standard type coercion, section 10.6.13. Hence, if the base type is not Real, all elements of the array are discrete-valued, allowing the argument above for a scalar equation to be applied elementwise to the array equation. That is, all array elements on both sides of the array equation will have discrete-time variability, showing that also the entire arrays expr1 and expr2 are discrete-time expressions.

For a record equation, the components of the record have independent types, and the equation is seen as a collection of equations for the individual components of the record. In order to support records with components of mixed variability, a record equation with sides given by either record variables or record constructors is conceptually split before variability is determined.]

[Example: Discrete-valued equation variability rule applied to record equations. In the first of the equations below, having a record constructor on both sides of the equation, the equation is conceptually split, and variabilities of time and true are considered separately. In the second equation, the makeR function call – regardless of inlining – means that the equation cannot be conceptually split into individual components of the record. The variability of the makeR call is continuous-time due to the time argument, which also becomes the variability of the b member of the call.

⬇

record R

Real x;

Boolean b;

end R;

function makeR "Function wrapper around record constructor"

input Real xx;

input Boolean bb;

output R r = R(xx, bb);

annotation(Inline = true); // Inlining doesn't help.

end makeR;

model Test

R r1, r2;

equation

r1 = R(time, true); // OK: Discrete-time Boolean member.

r2 = makeR(time, true); // Error: Continuous-time Boolean member.

end Test;

]

3.8.6 Continuous-Time and Non-Discrete-Time Expressions

All expressions are continuous-time expressions including constant, parameter and discrete-time expressions. The term non-discrete-time expression refers to expressions that are neither constant, parameter nor discrete-time expressions. For example, time is a continuous-time built-in variable (see section 4.5) and time + 1 is a non-discrete-time expression. Note that plain time may, depending on context, refer to the continuous-time variable or the non-discrete-time expression.

	$\displaystyle\left\lvert(x,\,y){}\right\rvert\,\cos(\varphi)$	$\displaystyle=x$
	$\displaystyle\left\lvert(x,\,y){}\right\rvert\,\sin(\varphi)$	$\displaystyle=y$