I am working with Dymola, and try to use the functions provided by Modelica standard library in the command window, but it seems that I can't use them, and I couldn't claim a variable of a specific type either. I am wondering if there is some kind of limit of the command I could use in the command window of Dymola. Where should I find all the allowable commands?
I try to use some functions from Modelica.Media, it seems the input variables are out of range, but I tried a lot of times and different units system. I find that I can't declare a variable of pressure type in the command window, but Modelica.Media.Water.IF97_Utilities.h_pT() requires that I need to provide the variable as pressure and enthalpy type, is this the reason I can't use this function in the command window?
Modelica.Media.Water.IF97_Utilities.h_pT(1e6,800,1)
Failed to expand Modelica.Media.Water.IF97_Utilities.h_props_pT(
1000000.0,
800,
Modelica.Media.Common.IF97BaseTwoPhase(
phase = 1,
region = 1,
p = 1000000.0,
T = 800.0,
h = 9.577648835649013E+20,
R = 461.526,
cp = 1.8074392528071426E+20,
cv = -3.7247229288028774E+18,
rho = 5.195917767496603E-13,
s = 1.2052984524009106E+18,
pt = 645518.9415389205,
pd = 6.693617079374418E+18,
vt = 357209983199.2206,
vp = -553368.7088215105,
x = 0.0,
dpT = 645518.9415389205
)).
Failed to expand Modelica.Media.Water.IF97_Utilities.h_pT(1000000.0, 800, 1).
Assuming the inputs are valid there seems to be an issue specifically related to evaluating some media-functions interactively in Dymola (since they shouldn't be evaluated in models). It will be corrected in Dymola 2022x.
A temporary work-around is to first set the flag Advanced.SemiLinear = false; and then:
Modelica.Media.Water.IF97_Utilities.h_pT(1e6,800,1)
= 9.577648835649013E+20
(I'm not sure how valid the formulation is in that region.)
But please remember to set Advanced.SemiLinear = true; before translating and simulating any models - in particular models using media-functions.
The problem is that you are giving the function an invalid input. It seems Dymola does not give you the error-message for this based on the screenshot and logs you provided. I tried it in OpenModelica and got:
Modelica.Media.Water.IF97_Utilities.h_pT(100e5, 500e3)
[Modelica 4.0.0/Media/Water/IF97_Utilities.mo:2245:9-2246:77] Error: assert triggered: IF97 medium function g5: input temperature (= 500000 K) is higher than limit of 2273.15K in region 5
By using a value within the limits, it returns a value:
Modelica.Media.Water.IF97_Utilities.h_pT(100e5, 1e3)
Related
I am new to Julia and trying to use the Julia package DifferentialEquations to simultaneously solve for several conditions of the same set of coupled ODEs. My system is a model of an experiment and in one of the conditions, I increase the amount of one of the dependent variables at mid-way through the process.
I would like to be able to adjust the condition of this single trajectory, however so far I am only able to adjust all the trajectories at once. Is it possible to access a single one using callbacks? If not, is there a better way to do this?
Here is a simplified example using the lorentz equations for what I want to be doing:
#Differential Equations setup
function lorentz!(du,u,p,t)
a,r,b=p
du[1]= a*(u[2]-u[1])
du[2]=u[1]*(r-u[3])-u[2]
du[3]=u[1]*u[2]-b*u[3];
end
#function to cycle through inital conditions
function prob_func(prob,i,repeat)
remake(prob; u0 = u0_arr[i]);
end
#inputs
t_span=[(0.0,100.0),(0.0,100.0)];
u01=[0.0;1.0;0.0];
u02=[0.0;1.0;0.0];
u0_arr = [u01,u02];
p=[10.,28.,8/3];
#initialising the Ensemble Problem
prob = ODEProblem(lorentz!,u0_arr[1],t_span[1],p);
CombinedProblem = EnsembleProblem(prob,
prob_func = prob_func, #-> (prob),#repeat is a count for how many times the trajectories had been repeated
safetycopy = true # determines whether a safetly deepcopy is called on the prob before the prob_func (sounds best to leave as true for user-given prob_func)
);
#introducing callback
function condition(u,t,repeat)
return 50 .-t
end
function affect!(repeat)
repeat.u[1]=repeat.u[1] +50
end
callback = DifferentialEquations.ContinuousCallback(condition, affect!)
#solving
sim=solve(CombinedProblem,Rosenbrock23(),EnsembleSerial(),trajectories=2,callback=callback);
# Plotting for ease of understanding example
plot(sim[1].t,sim[1][1,:])
plot!(sim[2].t,sim[2][1,:])
I want to produce something like this:
Example_desired_outcome
But this code produces:
Example_current_outcome
Thank you for your help!
You can make that callback dependent on a parameter and make the parameter different between problems. For example:
function f(du,u,p,t)
if p == 0
du[1] = 2u[1]
else
du[1] = -2u[1]
end
du[2] = -u[2]
end
condition(t,u,integrator) = u[2] - 0.5
affect!(integrator) = integrator.prob.p = 1
For more information, check out the FAQ on this topic: https://diffeq.sciml.ai/stable/basics/faq/#Switching-ODE-functions-in-the-middle-of-integration
When I simulate the model below, I get additional variables labelled $STATESET1, which are obviously auto-generated.
What is the purpose of these variables from the perspective of the user? Generally I am only interested in the solution, not in the specific strategies a specific solver achieved it with, right? So isn't this more like something that should be output only if one turns on model debugging of some kind rather than being something the average OpenModelica user can take advantage of? What if there is more than one "state set" (say $STATESET1 and $STATESET2): how am I supposed to know how these variables relate to my model, given their generic names? More specifically, what is $STATESET1.x[:]? Nothing in the original or flattened model gives a hint on this...
model StateSetTest
import SI = Modelica.SIunits;
Real[3] q(start = zeros(3), each fixed = true);
Real q4(start = 1);
Real[3] w(start = zeros(3), each fixed = true);
SI.Torque[3] TResult;
equation
q * q + q4 * q4 = 1;
w = 2.0 * (q4 * der(q) - der(q4) * q - cross(der(q), q));
der(w) = TResult;
TResult = zeros(3);
end StateSetTest;
They are used for dynamic state selection, i.e. changing the state during the simulation. And yes, they are not really needed for the user. I guess we could filter them out from OMEdit. I'll open a ticket about this.
In the following code I am trying to generate a NumericVector of values from a normal distribution, where every time rnorm() is called each time with a different mean and variance.
Here is the code:
// [[Rcpp::export]]
NumericVector generate_ai(NumericVector log_var) {
int log_var_length = log_var.size();
NumericVector temp(log_var_length);
for(int i = 0; i < log_var_length; i++) {
temp[i] = rnorm(1, -0.5 * log_var[i], sqrt(log_var[i]));
}
return(temp);
}
The line that is giving me trouble is this one:
temp[i] = rnorm(1, -0.5 * log_var[i], sqrt(log_var[i]));
It is causing the error:
assigning to 'typename storage_type<14>::type' (aka 'double') from
incompatible type 'NumericVector' (aka 'Vector<14>')
Since I'm returning one number from rnorm, is there a way to convert this NumericVector return type to a double?
Rcpp provides two methods to access RNG sampling schemes. The first option is a single draw and the second enables n draws using some sweet sweet Rcpp sugar. Under your current setup, you are opting for the later setup.
Option 1. Use just the scalar sampling scheme instead of sugar by accessing the RNG function through R::, e.g.
temp[i] = R::rnorm(-0.5 * log_var[i], sqrt(log_var[i]));
Option 2. Use the subset operator on the NumericVector to obtain the only element.
// C++ indices start at 0 instead of 1
temp[i] = Rcpp::rnorm(1, -0.5 * log_var[i], sqrt(log_var[i]))[0];
The prior option will be faster and better. Why you might ask?
Well, Option 2 creates a new NumericVector, fills it with a call to Option 1, then requires a subset operation to retrieve the value before assigning it to the desired scalar.
In any case, RNG can be a bit confusing. Just make sure to always prefix the function call with the correct namespace (e.g. R:: or Rcpp::) so that you and perhaps future programmers avoid any ambiguity as to what kind of sampling scheme you've opted for.
(This is one of the downside of using namespace Rcpp;)
For a numerical simulation in MATLAB I have parameters defined in an .m file.
%; Parameters as simple definitons
amb.T = 273.15+25; ... ambient temperature [K]
amb.P = 101325; ... ambient pressure [Pa]
combustor.T = 273.15+800; ... [K]
combustor.P = 100000; ... [Pa]
combustor.lambda = 1.1;
fuel.x.CH4 = 0.5; ... [0..1]
fuel.n = 1;
air.x.O2 = 0.21;
%; more complex definitions consisting of other params
air.P = reactor.P;
air.T = amb.T;
air.n = fuel.x.CH4 * 2 * fuel.n * combustor.lambda / air.x.O2;
Consider this set as 'default' definitions. For running one simulation this definitions works fine.
It's getting more complicated if I want to change one of these parameters programmatically for a parameter study (the effect of changing parameters on the results), that is, to perform multiple simulations by using a for loop. In the script performing this I want to change the defintion of several parameters beforehand, i.e. overwrite default definitions. Is there a way to do this without touching the default definitions in-code (comment them/overwrite them literally)? It should be possible to change any parameter in the study-performing script and catch up on default definitions from the listing above (or the other way round).
Let me illustrate the problem with the following example: If I want to vary combustor.lambda (let's say running from 0.9 to 1.3) field air.n has to be evaluated again for the change to take place in the actual simulation. So, I could evaluate the listing again, but this way I would lose the study-defined combustor.lambda for the default one.
I am thinking about these solutions but I cannot get to how to do this:
Use references/handles in a way that the struct fields only hold the definitions, not the actual values. This allows for changing default definitions before 'parsing' the whole struct to get the actual values.
Evaluate the default definition set by a function considering (non-default) definitions defined preliminarily, i.e. skipping these lines of the default definition set during evaluation.
Any OOP approach. Of course, it is not limited to struct data types, but on the other hand, maybe there are useful functions for structs?
Edit:
The purpose of the default set is for the programmer to be as free as possible in choosing the varying parameters with any of the other parameters keeping their default definition which can be independent (= values) as well as dependent (= equations like air.n).
% one default parameter set
S = struct('T', 25, 'P', 101000, 'lambda', .5, 'fuel', .5);
GetNByLambda = #(fuel, lambda) fuel * 2 * lambda;
T = struct('P', S.P, 'n', GetNByLambda(S.fuel, S.lambda));
% add more sets
S(end+1) = struct('T', 200, 'P', 10000, 'lambda', .8, 'fuel', .7);
T(end+1) = struct('P', S.P, 'n', GetNByLambda(S(end+1).fuel, S(end+1).lambda));
% iterate over parameter sets
for ii = 1:length(S)
disp(S(end+1))
disp(T(end+1))
end
I have the following problem:
I have over 20 different models which I want to simulate one after another but I want to change the simulation directory each time.
Right now I'm manually changing directory after each simulation (from ./ModelOne to ./ModelTwo) and I'd like to know if there's a way to change it automatically when I initialize or translate the new model.
Regards
Nev
the best way is to write a script I think:
pathOfSave = {"E:\\work\\modelica\\SimulationResult\\Model1\\","E:\\work\\modelica\\SimulationResult\\Model2\\"};
nbSim = 2;
pathOfMod = { "MyModel.",
"MyModel.};
modelsToSimulate = { ""Model1" ,
"Model2"};
//If equdistant=true: ensure that the same number of data points is written in all result files
//store variables at events is disabled.
experimentSetupOutput(equdistant=false, events=false);
//Keep in the plot memory the last nbSim results
experimentSetupOutput(equdistant=false, events=false);
for i in 1:nbSim loop
//delete the result file if it already exists
Modelica.Utilities.Files.removeFile(pathOfSave + modelsToSimulate[i]);
//translate models
translateModel(pathOfMod[i]+modelsToSimulate[i]);
// simulate
simulateModel(
pathOfMod[i]+modelsToSimulate[i],
method="dassl",
stopTime=186350,
numberOfIntervals=nbOfPoi,
resultFile=pathOfSave + modelsToSimulate[i]);
end for;
You can also put the command cd("mynewpath") in the initial algorithm section, if you want it tobe attached to the model.
model example
Real variable;
protected
parameter String currDir = Modelica.Utilities.System.getWorkDirectory();
initial algorithm
cd("C:\\Users\\xxx\\Documents\\Dymola\\MyModelFolder");
equation
variable = time;
when terminal() then
cd(currDir);
end when;
end example;
In any case you can find all commands of dymola in the manual one under the section "builtin commands".
I hope this helps,
Marco