Im creating a model of a rather complex electric circuit in modelica.
I want to find out how to include "s" equations (I mean frequency domain equations) in the model.
Of course I may transform the expresion to time and use modelicas internal "time" variable, but I not sure this is optimal way to complete my model.
thanks in advance.
In modelica you can implement a block to switch from one domain to the other and then develop the equation in the proper domain. To do this I suggest you to look at this article written by Michael Tiller.
In general it depends on the type of equations you have to implement, whether it is more convenient to write the equations in time or frequency domain, although you have to take into account that going from time domain to frequency domain you are always introducing some semplifications because of the sampling. Therefore if the equations rewritten in time are not too stiff or if you are not introducing too non-linearities, and if you have a big range of frequencies in your systems, I think it is better to rewrite the equations in the time domain.
I hope this helps,
Marco
It is the transfer function you are describing, right? If so, check out Modelica_LinearSystems2
Related
Has anyone tried to implement the Navier Stokes Partial Differential Equations (PDE) in Modelica?
I found the method of the spatial basis functions (SBF) which by means of numerical modifications gets Ordinary Differential Equations (ODE) that could be handled by Dymola.
Regards,
Victor
The aim of the method I was saying before is to convert PDEs in ODEs, so the issues with the CFL coefficient would disappear, the problem is that the Modelica.Fluids elements just define the equations in function of the variables in both ends of each component.
i.e dp=port_a.p-port_b.p
but with that sort of methodology, the variables such as pressure, density, mass flow... would be function also of the surrounding components... it would be a kind of massive interaction between all the components,
I would like to see an example in Modelica, because I hardly haven't found information about that topic linked to Modelica.
Modelica is a language for modeling behavior described by DAEs. As such, as long as you can create a system of ODEs, you should be able to express your problem in Modelica.
However, if your PDEs are hyperbolic, the wave dynamics in the equations might cause some issues with simulation. This is because the CFL condition imposes limits on time steps that an ordinary differential equation solver will be unaware of. If the solver includes error control, it will probably manage to get a solutions but may run quite slow because it won't know how to explicitly limit the simulation step size. If it doesn't include error control and it violates the CFL condition, the system will go unstable. Note, this only applies to systems where the CFL condition applies.
I am currently running a multiple linear regression using MATLAB's LinearModel.fit function, and I am bit confused in regards to how to properly add interaction terms to the model by hand. As I am aware, LinearModel.fit does not standardize variables on its own, so I have been doing so manually.
So far, the way I have done it has been to
Standardize the observations for each variables
Multiply corresponding standardized values from specific variables to create the interaction terms and then add these new variables to the set of regression data
Run the regression
Is this the correct way to go about doing this? Should I standardize the interaction term variables also after calculating the 'raw' terms? Any help would be greatly appreciated!
Whether or not to standardize interaction terms probably depends on what you intend to do with the model. Standardization typically does not affect model performance as much as it allows for more straightforward model interpretation as your learned coefficients will be on similar scales. I suspect whether to do this or not is largely a matter of opinion. Here is a relevant stats.stackexchange post that may help.
My intuition would be the same as how you have described your process so far.
I am looking for a way to fit my experimental data by a theoretical model which is described by a non-linear differential equation.
Unfortunately this latter can only be solved numerically (by solving this second degree, non-linear differential equation).
I manage to solve the differential equation for a set of parameters using the ode45 Matlab solver but now I want to find the proper fit parameters of the model. Also, I may have to mention that my ode45 is initiated at z=zmax (max being large so I can assume it is infinity) by y(zmax)=y0 and yprime(zmax)=yprime0 and I solve backward (from zmax to z=0).
I am quite new to this kind of numerical problems, are there classical ways to solve such problems?
Does anyone knows if there is a Matlab procedure which would help me solve this? On which principles is it based/constructed? (if possible I'd like to know the theoretical trick to solve this problem in a smart way, not by trying all the possible sets of parameters which would be very time consuming (I have 5 fit parameters!).
Thank you for your precious help!
You have facy methods in the Optimization Toolbox. In case you don't have access to it, you could do it manually by:
Selecting a cost function between the experimental and model data. For example, mean-squared-error.
Doing heuristic optimization of the cost function. For example, Nelder-Mead method.
I'm building a Media-Library in Dymola similar to Helmholtz-Media but for Ammonia+Water, a mixture.
You get a lot of not explicitly solvable equations.
Because of the structure of the Media and Fluid libraries in Modelica I need to be able to get my thermodynamic state from p, h and x. The state-vector consists of d, T, and x.
This is a simple example how to get the state-vector:
model getState_phX
parameter AbsolutePressure p = 500000 "pressure";
parameter SpecificEnthalpy h = 2500000 "enthalpy";
parameter SI.MassFraction x = 0.7 "mole fraction of amonia";
parameter Real[2] start = getStart_Td_phx(p,h,xL);
output ThermodynamicState state(d(start=start[2]),T(start=start[1]),X={(1 - xL),xL});
DerivateFull f = Derivates(state);
equation
p = (1 + f.delta*f.phirdelta)*R*state.T*state.d/molarMass(state);
h = state.T*R*(1 + f.delta*f.phirdelta + f.tau*f.phirtau + f.tau0*f.phi0tau0)/molarMass(state);
end getState_phX;
Please don't mind the parts of the equations. They consist of many parts (sums and log) dependent on the state-vector.
This is solved by the solver in Dymola with good start values.
But I don't really need all of the 'time-dependent' solving capabilities of Dassl.
Are there build in libraries for solving such stationary equation systems without the solver?
Is it possible to make a Function out of this Model using these?
I know I could write a simple solver by hand but for other parts of the Media-Model (VLE) I need highly reliable stationary solver too (but with 4 nonlinear independent equations)
Please tell me if I didn't explain myself clearly. Thank you for the help.
The basis of your fluid properties library is a forward part, that is the actual Helmholtz energy equation of state (EoS). It takes d,T,X as input. That part is more or less straigthforward to implement.
If you want to specify the thermodynamic state using p,h,X or if you want to find the equilibrium between multiple phases, you will usually set up a system of resdiual functions and try to find the root of your system of equations using some iterative procedures. Span (2000) writes
"the formulation of reliable iterative procedures [for root finding]
is often the most crucial problem when setting up program packages for
the evaluation of equations of state".
Re-using existing solvers has advantages and disadvantages, they are usually very well tested, writing them takes a lot of effort, but if you write your own solver, you have more control about what it does. As far as I know, Dassl has various strength, but solving that kind of equations is not its original objective.
Olson, Tummescheit and Elmqvist (2005) tried to use the Dymola solver to find the VLE, see section 3.2 of the linked pdf. Sounds like it works, but not very reliable.
The MSL already includes a non-linear solver, based on the Brent algorithm, which works with one unknown only, see Modelica.Math.Nolinear.solveOneNonlinearEquation. You could, if you want, add additional generic solvers.
Before writing your own solver, you should get in touch with the developers of the Modelica.Media interface (it will be extended in future versions of the MSL to include multi-component, multi-phase mixtures) and also consider re-using existing fluid properties libraries like RefProp, CoolProp, FluidProp or MultiFlash, to name just a few.
I am constructing a finite volume model in Dymola which evolves in time and space. The spatial discretization is hard coded in the equations section, the time evolution is implemented with a term consisting of der(phi).
Is the time integration of Dymola always numerically stable when using a variable step size algorithm? If not, can I do something about that?
Is the Euler integration algorithm from Dymola the explicit or implicit Euler method?
The Dymola Euler solver by default is explicit (if an in-line sovler is not selected).
The stability of time integration is going to depend on your integrator. Generally speaking, implicit methods are going to be much better than explicit ones.
But since you mention spatial and time discretization, I think it is worth pointing out that for certain classes of problems things can get pretty sticky. In general, I think elliptic and parabolic PDEs are pretty safe to solve in this way. But hyperbolic PDEs can get very tricky.
For example, the Courant-Friedrichs-Lewy condition will affect the overall stability of the solution method. But by discretizing in space first, you leave the solver with information only regarding time and it cannot check or conform to the CFL condition. My guess is that a variable time step integrator will detect the error being introduced by not following the CFL condition but that it will struggle to identify the proper time step and probably also end up permitting an unacceptably unstable solution.