I am trying to solve DDE using ode45 in Matlab. My question is about the way that I am solving this equation. I don't know if I am right or I am wrong and I should use dde23 instead.
I have a following equation:
xdot(t)=Ax+BU(t-td)+E(t) & U(t-td)=Kx(t-td) & K=constant
Normally, when I don’t have delay on my equation, I solve this using ode45. Now with delay on my equation, again I am using ode45 to get the result. I have the exact amount of U(t-td) at each step and I replace its amount and solve the equation.
Is my solution correct or should I use dde23?
You have two problems here:
ode45 is a solver with adaptive step size. This means that your sampling steps are not necessarily equivalent to the actual integration steps. Instead, the integrator splits a sampling step into several integration steps as needed to achieve the desired accuracy (see this question on Scientific Computing for more information).
As a consequence, you may not provide correct delayed value of U at each step of the integration, even if you believe to do so.
However, if your sampling steps are sufficiently small, you will indeed have one time step per sampling step. The reason for this is that you effectively disable the adaptive integration by making your time step smaller than needed (and thus waste computation time).
Higher-order Runge–Kutta methods such as ode45 do not only make use of the value of the derivative at each integration step, but also evaluate it in-between (and no, they cannot provide a usable solution for this in-between time step).
For example, suppose that your delay and integration step are td=16. To make the integration step from t=32 to t=48, you need to evaluate U not only at t = 32−16 = 16 and t = 48−16 = 32, but also at t = 40−16 = 24. Now, you might say: Okay, let’s integrate such that we have an integration step at all those time points. But for these integration steps, you again need steps in the middle, e.g., if you want to integrate from t=16 to t=24, you need to evaluate U at t=0, t=4, and t=8. You get a never-ending cascades of smaller and smaller time steps.
Due to problem 2, it is impossible to provide the exact states from the past with any but a one-step integrator – using which is probably not a good idea in your case. For this reason, it is inevitable to use some sort of interpolation to obtain past values if you want to integrate DDEs with a multi-step integrator. dde23 does this in a sophisticated way using a good interpolation.
If you only provide U at the integration steps, you are essentially performing a piecewise-constant interpolation, which is the worst possible interpolation and therefore requires you to use very small integration steps. While you can do this if you really want to, dde23 with its more sophisticated piecewise cubic Hermite interpolation can work with much larger time steps and integrate adaptively, and therefore will be much faster. Also, it’s less likely that you somehow make a mistake. Finally, dde23 can deal with very small delays (smaller than the integration step), if you’re into that sort of thing.
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I am using Matlab for this project. I have introduced some modifications to the ode45 solver.
I am using sometimes up to 64 components, all in the [0,1] interval and the components sum up to 1.
At some intervals I halt the integration process in order to run a quick check to see whether further integration is needed and I am looking for some clever way to efficiently figure this one.
I have found four cases and I should be able to detect each of them during a check:
1: The system has settled into an equilibrium and all components are unchanged.
2: Three or more components are wildly fluctuating in a periodic manner.
3: One or two components are changing very rapidly with low amplitude and short frequency.
4: None of the above is true and the integration must be continued.
To give an idea: I have found it to be a good practice to use the last ~5k states generated by the ode45 solver to a function for this purpose.
In short: how does one detect equilibrium or a nonchanging periodic pattern during ODE integration?
Steady-state only occurs when the time derivatives your model function computes are all 0. A periodic solution like you described corresponds rather to a limit cycle, i.e. oscillations around an unstable equilibrium. I don't know if there are methods to detect these cycles. I might update my answers to give more info on that. Maybe an idea would be to see if the last part of the signal correlates with itself (with a delay corresponding to the cycle period).
Note that if you are only interested in the steady state, an implicit method like ode15s may be more efficient, as it can "dissipate" all the transient fluctuations and use much larger time steps than explicit methods, which must resolve the transient accurately to avoid exploding. However, they may also dissipate small-amplitude limit cycles. A pragmatic solution is then to slightly perturb the steady-state values and see if an explicit integration converges towards the unperturbed steady-state.
Something I often do is to look at the norm of the difference between the solution at each step and the solution at the last step. If this difference is small for a sufficiently high number of steps, then steady-state is reached. You can also observe how the norm $||frac{dy}{dt}||$ converges to zero.
This question is actually better suited for the computational science forum I think.
I tried to use ode45 to solve an equation, and get output like the following. I get the idea it is trying to estimate using nearby points (as explained here https://www.mathworks.com/videos/solving-odes-in-matlab-6-ode45-117537.html). By my understanding, it should solve the equation in one round of computation? but the output looks like ode45 is an iterative algorithm (so that it generates output that repeat the '... steps ... failed attempt ... function evaluations' over and over again)? If it is iterative, could you help give some detail or references? Thanks!
ode45 is an iterative adaptive ODE solver. That is, it uses a 5th order (FSAL) method to propose the an update using some stepsize h. Then it does the same again, but now with a 4th order method, then it compared those two updates to one another, if the difference is less than some local tolerance, it accepts the proposed update. If the difference is larger than some local tolerance, the update is rejected and the stepsize is lowered (in some smart way).
To reduce the cost of using both a 4th and 5th order method, those two methods uses (roughly) the same function evaluations.
As for your output, it is, as also noted by #LutzL, not the standard output, which might point to an error in your code.
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.
I wrote a code to solve a system using ode45 and ode15s in matlab. I am wondering if I can improve the speed of the code using multiple core (or parallel code) in my script.
Anyone have tried this ??
Thanks
No, you can't.
All numerical integrators, ode45 and friends included, use some form of iterative scheme to solve the user-implemented (coupled) non-linear (partial) differential equations.
Each new step in the iterative schemes of ode45/15s/.. (to compute the new state of the system) depends on the previous step (the old state of the system), therefore, these numerical integrators cannot be parallelized effectively.
The only speedup you can do that's likely to have a big impact is to optimize your implementation of the differential equation.
From my experience, the only way to use multiple cores for ODE suite solvers in MATLAB is to use "parfor loop" to start multiple computations together at the same time, your single computation not be any faster, but you can start many with different parameters and have multiple solutions after that long wait. So if you need to start ODE many times that might speed up your work.
To speed up one ODE function it also a good idea to play with RelTol and AbsTol settings (changes time form seconds to hours), using Jpattern option can also be very helpful (my almost tridiagonal pattern made it run twice as fast). If your differential equation is simple maybe try to compile it first, or at least vectorize (I used to write some part of code in Java and then point MATLAB to use compiled .class file). Obviously the length of your solution vector plays important role, so don't make it more than a few hounded.
I have a program in SimMechanics that uses 6 derivative blocks (du/dt). It takes about 24 hours to do 10 secs of simulation. Is there any way to reduce the calculation time of the Simulink derivative blocks?
You don't say what your integration time step is. If it's on the order of milliseconds, and you're simulating a 10 sec total transient time, that means 10,000 time steps.
The stability limit of the time step is determined by the characteristics of the dynamic system you're simulating.
It's also affected by the integration scheme you're using. Explicit integration is well-known to have stability problems for larger time steps, so if you're using an Euler method of integration you'll be forced to use a small time step.
Maybe you can switch your integration scheme to an implicit method, 5th order Runge Kutta with error correction, or Burlich-Storer. See your documentation for details.
You've given no useful information about the physics of the system of interest, the size of the model, or your simulation choices, so all this is an educated guess on my part.
Runge-Kutta methods (called ODE45 or ODE23 in Matlab dialect) are not always useful with mechanical problems, due to best performance with variable time slice setup. Move to fixed time setup and select the solver by evaluating the error order you can admit. Refer to both Matlab documentation (and some Numerical Analysis texts too, :-) ) for deeper detail.
Consider also if your problem needs some "stiff-enabled" technique of resolution. Huge constant terms could drive to instability your solver if not properly handled.