Surrogates for Numerical Simulations; Optimization of Eddy-Promoter Heat Exchangers

Abstract

Although the advent of fast and inexpensive parallel computers has rendered numerous previously intractable calculations feasible, many numerical simulations remain too resource-intensive to be directly inserted in engineering optimization efforts. An attractive alternative to direct insertion considers models for computational systems: the expensive simulation is evoked only to construct and validate a simplified input-output model; this simplified input- output model then serves as a simulation surrogate in subsequent engineering optimization studies. We present here a simple 'Bayesian-validated' statistical framework for the construction, validation, and purposive application of static computer-simulation surrogates. As an example, we consider dissipation-transport optimization of laminar-flow eddy-promoter heat exchangers: parallel spectral element Navier-Stokes calculations serve to construct and validate surrogates for the flowrate and Nusselt number; these surrogates then represent the originating Navier-Stokes equations in the ensuing design process. Numerical analysis, Fluid dynamics, Optimization, Surrogates, Monte Carlo Method, Modelling.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1993
Accession Number
ADA270204

Entities

People

  • Anthony Patera
  • Serhat Yesilyurt

Tags

Communities of Interest

  • Autonomy
  • C4I

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Computational Fluid Dynamics
  • Computational Science
  • Computer Programming
  • Computers
  • Differential Equations
  • Fluid Dynamics
  • Fluid Flow
  • Fluid Mechanics
  • Heat Transfer
  • Information Science
  • Mathematical Models
  • Mechanics
  • Monte Carlo Method
  • Numerical Analysis
  • Partial Differential Equations
  • Reliability

Readers

  • Combustion and Flow Dynamics.
  • Computational Fluid Dynamics (CFD)
  • Theoretical Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms