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Mr. Alexandros Priftis

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Posts posted by Mr. Alexandros Priftis

  2. Posted · Report reply

    Hello,

     

    I was wondering if any CAESES user has succeeded in connecting ShipX with CAESES. I know that some elements of ShipX can be connected e.g. Veres can be run in batch mode (https://brage.bibsys.no/xmlui/bitstream/handle/11250/2455846/17431_FULLTEXT.pdf?sequence=1&isAllowed=y) but has anyone connected Waveres plug-in with CAESES? The same reference suggests that Waveres cannot be run in batch mode so I am not sure if there is another way of doing that?

     

    Thank you in advance for your reply!

     

    Alex

  4. Posted · Report reply

    Thanks! I will keep it that way for now.

     

    What problem would that be...? As a first step, I was planning to do something similar to what you have already tried (https://www.caeses.com/blog/2017/how-to-create-a-response-surface-in-4-easy-steps/). Is this way affected by that issue? In any case I need some time to run the required simulations related to the design of experiment I am planning to do so the new version will probably be available by then.

     

    Cheers,

     

    Alex

  5. Posted · Report reply

    Hi Joerg,

     

    I suppose one way would be to run Dakota as an external application (not sure how Dakota runs outside CAESES, but if I understand correctly, it should be batch mode or similar?), but then CAESES functionality regarding presentation or results etc. is lost...?

     

    Anyway, I am uploading a sample file where I used an example shown in Dakota manual (Rosenbrock test problem). First I used the "Sensitivity Analysis" template included in CAESES (that means sampling, LHS method used), and ran 10 samples (Dakota_01). Then I used a variant of "Sensitivity Analysis" where I just added a line in the .in file. Tried to upload the .in file here but it doesn't let me do so. Basically, everything is the same, but the method block looks like this (added the refinement_samples option):

     

    method
      sampling
      sample_type lhs
      <samples, unsigned, 10, Samples, number of samples>
      <refinement_samples, unsigned, 10, Refinement Samples, number of samples>
      seed = 12345
     
    Then, I ran Dakota using this updated template, choosing the option to use the result pool of Dakota_01. Samples value remains the same (10) and refinement samples have to be same number as the initial (that is 10) so that in the end 20 samples are created, 10 existing (from Dakota_01) and 10 new; all presented in Dakota_02.
     
    As far as my last question is concerned, I would be interested in getting the same function using different kind of variables, but having read the manual in more detail since my last post here, I see that uniform_uncertain means that each variable is only characterised by an upper and lower value and no special distribution is considered - that means the probability of attaining an value between those bounds is equal...? Which could potentially be used for a design of experiment to use its results as a base to a response surface definition.
     
    In any case I will contact Dakota directly if I have more specific questions on that topic and keep this topic updated.
     
    Cheers,
     
    Alex

    Test.fdb

  6. Posted · Report reply

    Hello Joerg,

     

    thanks for your reply! I had already tried what you proposed but it seems that once the initial samples number changes, the functionality of 'seed' option resets, i.e. it only works between two or more runs when the number of samples remains the same. I thought of adding the 'refinement_samples' option though under the method block and it did work (in conjunction with the use of an existing result pool)!

     

    If I understood correctly, this only works when a sampling method (in this case LHS) is used and the variables are uniform_uncertain. Do you know if it is possible to do that using a DACE method (again LHS), in which case the variables are continuous_design? Or that would be only answered by Dakota? TBH, I am not sure it is even possible to do that (maybe because of the way DACE work...?).

     

    Cheers,

     

    Alex

  7. Posted · Report reply

    Hello,

     

    After reading Dakota manual, I have noticed there is the possibility of running a sampling method (LHS) which can be incremental, i.e. first run can contain e.g. 50 samples and then I could use those samples to expand the results to double the initial amount (that would mean to 100) by only evaluating 50 more samples, but keeping the correct correlation etc. between all 100 results. That can be done by specifying a .rst file in the file which initiates Dakota (e.g. dakota -i input100.in -r dakota.50.rst) (Dakota 6.7 manual p. 78). However, since Dakota is initiated by pressing the 'run' button in CAESES, I was wondering if the above can be done by writing some extra lines in the input file read by Dakota, or in any other way...?

     

    Thanks!

     

    Alex

  9. Posted · Report reply

    Hi Suraj,

     

    Now I understand. Optimizing on an imported data set using only the response surface is not readily available in the current release of CAESES. Sure, you can use the imported data to build up the initial response surface, to make use of it in the dakota design engine. However, the response surface algorithm requires an evaluation for the optimal design (e.g. using the CFD analysis). This evaluated design is then added to the response surface to further improve it. In your setup, there is no CFD etc linked to the objective so it simply returns zero all the time (that's the constant value of the parameter).

     

    If you want to only find the best design on the response surface, we can use some other mechanisms. We have also done this but I have to double-check with colleagues whether we can hand it out.

     

    Cheers

    Joerg

     

    Hi Joerg,

     

    sorry for getting involved in this discussion but I have a question related to what you wrote.

     

    From the above description, I understand that when MOGA w/ RSM is utilised in CAESES/Dakota connection, an initial result pool is used to define the RS at first, but during the optimisation, the selected optimal designs from the Pareto front must be evaluated using the higher-fidelity method (e.g. CFD) also used to create the initial result pool. Does this happen every time a new generation is created in the optimisation process? Because if that's the case, the CAESES project used for the formal optimisation would have to be set up appropriately to be connected to CFD software and/or High Performance Computer so that the external s/w connection is run for the evaluation of the optimal designs?

     

    Cheers,

     

    Alex

  11. Posted · Report reply

    Hello, I installed CAESES a few days ago and automatically got a free licence. Then I requested an academic licence which I understand it was activated, however I cannot get to change the licence state, from free to academic. What should I do?

     

    Thanks in advance!

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