Browsing by Author "Deshpande, Shubhangi"

Now showing 1 - 6 of 6
  • Thumbnail Image
    ADML: Aircraft Design Markup Language for Multidisciplinary Aircraft Design and Analysis
    Deshpande, Shubhangi; Watson, Layne T.; Love, Nathan J.; Canfield, Robert A.; Kolonay, Raymond M. (Department of Computer Science, Virginia Polytechnic Institute & State University, 2013-12-31)
    The process of conceptual aircraft design has advanced tremendously in the past few decades due to rapidly developing computer technology. Today’s modern aerospace systems exhibit strong, interdisciplinary coupling and require a multidisciplinary, collaborative approach. Efficient transfer, sharing, and manipulation of aircraft design and analysis data in such a collaborative environment demands a formal structured representation of data. XML, a W3C recommendation,is one such standard concomitant with a number of powerful capabilities that alleviate interoperability issues in a collaborative environment. A compact, generic, and comprehensive XML schema for an aircraft design markup language (ADML) is proposed here to represent aircraft conceptual design and analysis data. The purpose of this unified data format is to provide a common language for data communication, and to improve efficiency and productivity within a multidisciplinary, collaborative aricraft design environment. An important feature of the proposed schema is the very expressive and efficient low level schemata (raw data, mathematical objects, and basic geometry). As a proof of concept the schema is used to encode an entire Convair B58. As the complexity of models and number of disciplines increases, the reduction in effort to exchange data models and analysis results in ADML also increases.
  • Thumbnail Image
    Algorithm 1028: VTMOP: Solver for Blackbox Multiobjective Optimization Problems
    Chang, Tyler; Watson, Layne T.; Larson, Jeffrey; Neveu, Nicole; Thacker, William; Deshpande, Shubhangi; Lux, Thomas (ACM, 2022-09-10)
    VTMOP is a Fortran 2008 software package containing two Fortran modules for solving computationally expensive bound-constrained blackbox multiobjective optimization problems. VTMOP implements the algorithm of Deshpande et al. [2016], which handles two or more objectives, does not require any derivatives, and produces well-distributed points over the Pareto front. The first module contains a general framework for solving multiobjective optimization problems by combining response surface methodology, trust region methodology, and an adaptive weighting scheme. The second module features a driver subroutine that implements this framework when the objective functions can be wrapped as a Fortran subroutine. Support is provided for both serial and parallel execution paradigms, and VTMOP is demonstrated on several test problems as well as one real-world problem in the area of particle accelerator optimization.
  • Thumbnail Image
    Computational Steering in the Problem Solving Environment WBCSim
    Shu, Jiang; Watson, Layne T.; Ramakrishnan, Naren; Kamke, Frederick A.; Deshpande, Shubhangi (Department of Computer Science, Virginia Polytechnic Institute & State University, 2009)
    Computational steering allows scientists to interactively control a numerical experiment and adjust parameters of the computation on-the-fly and explore “what if ” analysis. Computational steering effectively reduces computational time, makes research more efficient, and opens up new product design opportunities. There are several problem solving environments (PSEs) featuring computational steering. However, there is hardly any work explaining how to enable computational steering for PSEs embedded with legacy simulation codes. This paper describes a practical approach to implement computational steering for such PSEs by using WBCSim as an example. WBCSim is a Web based simulation system designed to increase the productivity of wood scientists conducting research on wood-based composites manufacturing processes. WBCSim serves as a prototypical example for the design, construction, and evaluation of small-scale PSEs. Various changes have been made to support computational steering across the three layers—client, server, developer—comprising the WBCSim system. A detailed description of the WBCSim system architecture is presented, along with a typical scenario of computational steering usage.
  • Thumbnail Image
    Data Driven Surrogate Based Optimization in the Problem Solving Environment WBCSim
    Deshpande, Shubhangi; Watson, Layne T.; Shu, Jiang; Kamke, Frederick A.; Ramakrishnan, Naren (Department of Computer Science, Virginia Polytechnic Institute & State University, 2009)
    Large scale, multidisciplinary, engineering designs are always difficult due to the complexity and dimensionality of these problems. Direct coupling between the analysis codes and the optimization routines can be prohibitively time consuming due to the complexity of the underlying simulation codes. One way of tackling this problem is by constructing computationally cheap(er) approximations of the expensive simulations, that mimic the behavior of the simulation model as closely as possible. This paper presents a data driven, surrogate based optimization algorithm that uses a trust region based sequential approximate optimization (SAO) framework and a statistical sampling approach based on design of experiment (DOE) arrays. The algorithm is implemented using techniques from two packages—SURFPACK and SHEPPACK that provide a collection of approximation algorithms to build the surrogates and three different DOE techniques—full factorial (FF), Latin hypercube sampling (LHS), and central composite design (CCD)—are used to train the surrogates. The results are compared with the optimization results obtained by directly coupling an optimizer with the simulation code. The biggest concern in using the SAO framework based on statistical sampling is the generation of the required database. As the number of design variables grows, the computational cost of generating the required database grows rapidly. A data driven approach is proposed to tackle this situation, where the trick is to run the expensive simulation if and only if a nearby data point does not exist in the cumulatively growing database. Over time the database matures and is enriched as more and more optimizations are performed. Results show that the proposed methodology dramatically reduces the total number of calls to the expensive simulation runs during the optimization process.
  • Thumbnail Image
    Data Driven Surrogate Based Optimization in the Problem Solving Environment WBCSim
    Deshpande, Shubhangi (Virginia Tech, 2009-11-11)
    Large scale, multidisciplinary, engineering designs are always difficult due to the complexity and dimensionality of these problems. Direct coupling between the analysis codes and the optimization routines can be prohibitively time consuming. One way of tackling this problem is by constructing computationally cheap(er) approximations of the expensive simulations, that mimic the behavior of the simulation model as closely as possible. This paper presents a data driven, surrogate based optimization algorithm that uses a trust region based sequential approximate optimization (SAO) framework and a statistical sampling approach based on design of experiment (DOE) arrays. The algorithm is implemented using techniques from the two packages SURFPACK and SHEPPACK that provide a collection of approximation algorithms to build the surrogates and three different DOE techniques: full factorial (FF), Latin hypercube sampling (LHS), and central composite design (CCD) are used to train the surrogates. The biggest concern in using the proposed methodology is the generation of the required database. This thesis proposes a data driven approach where an expensive simulation run is required if and only if a nearby data point does not exist in the cumulatively growing database. Over time the database matures and is enriched as more and more optimizations are performed. Results show that the response surface approximations constructed using design of experiments can be effectively managed by a SAO framework based on a trust region strategy. An interesting result is the significant reduction in the number of simulations for the subsequent runs of the optimization algorithm with a cumulatively growing simulation database.
  • Thumbnail Image
    Multiobjective Optimization Using an Adaptive Weighting Scheme
    Deshpande, Shubhangi; Watson, Layne T.; Canfield, Robert A. (Department of Computer Science, Virginia Polytechnic Institute & State University, 2013-12-31)
    A new Pareto front approximation method is proposed for multiobjective optimization problems with bound constraints. The method employs a hybrid optimization approach using two derivative free direct search techniques, and intends to solve blackbox simulation based multiobjective optimization problems where the analytical form of the objectives is not known and/or the evaluation of the objective function(s) is very expensive. A new adaptive weighting scheme is proposed to convert a multiobjective optimization problem to a single objective optimization problem. Another contribution of this paper is the generalization of the star discrepancy based performance measure for problems with more than two objectives. The method is evaluated using five test problems from the literature. Results show that the method achieves an arbitrarily close approximation to the Pareto front with a good collection of well-distributed nondominated points for all five test problems.