Cloud-Based CAD Parametrization for Design Space Exploration and Design Optimization in Numerical Simulations

Abstract

In this manuscript, an automated framework dedicated to design space exploration and design optimization studies is presented. The framework integrates a set of numerical simulation, computer-aided design, numerical optimization, and data analytics tools using scripting capabilities. The tools used are open-source and freeware, and can be deployed on any platform. The main feature of the proposed methodology is the use of a cloud-based parametrical computer-aided design application, which allows the user to change any parametric variable defined in the solid model. We demonstrate the capabilities and flexibility of the framework using computational fluid dynamics applications; however, the same workflow can be used with any numerical simulation tool (e.g., a structural solver or a spread-sheet) that is able to interact via a command-line interface or using scripting languages. We conduct design space exploration and design optimization studies using quantitative and qualitative metrics, and, to reduce the high computing times and computational resources intrinsic to these kinds of studies, concurrent simulations and surrogate-based optimization are used.

Flow-Based Optimization of Products or Devices

“…Another area where more synergy can be explored is to combine Design Space Exploration and Machine Learning [9,10] as is also mentioned by Guerrero et al [8].…”

“…Kumar et al [2] Parametric optimization 2D CFD: Turbulent flow Rogié et al [3] Parametric optimization 3D CFD: Turbulent flow Alexias et al [4] Continuous adjoint 3D CFD: Laminar flow Olivetti et al [5] Automated DoE Optimization tool and 3D CFD: Turbulent flow Parker et al [6] Smooth and corrugated cylinder Measurements of pressure and velocity Grossberg et al [7] Continuous adjoint Derivation of the adjoint drift flux equations Guerrero et al [8] Cloud-based DSE and DO Optimization tool and 3D CFD: Turbulent flow The paper by Kumar et al [2] is on the topic of small-scale decentralized wind power generation; the authors propose an adaptive hybrid Darrieus turbine (AHDT) to overcome issues experienced by Savonius and Darrieus wind turbines. The AHDT has a Savonius rotor nested inside a Darrieus rotor, where the Savonius rotor can change shape.…”

“…Guerrero et al [8] present an engineering design framework with a cloud-based parametrical CAD application which can be used on any platform without the need for a local installation. The optimization loop is fault-tolerant and scalable in the sense that both concurrent and parallel simulations can be deployed.…”

“…Kumar et al [2] Wind turbine Power and torque coefficients Rogié et al [3] Microchannel evaporator Heat transfer and pressure drop Alexias et al [4] Longer static mixing device Mixture uniformity and pressure drop Alexias et al [4] Shorter static mixing device Mixture uniformity and pressure drop Olivetti et al [5] Valve Mass flow rate Parker et al [6] Accelerated wind bodies Pressure coefficient and velocity Grossberg et al [7] Dispersed multiphase flow Mass flow rate Guerrero et al [8] Cylinder Surface area Guerrero et al [8] Static mixer Velocity distribution Guerrero et al [8] Ahmed bodies Normalized drag coefficient…”

Surrogate-Based Optimization of Horizontal Axis Hydrokinetic Turbine Rotor Blades

“…The optimization was performed on the computationally inexpensive surrogate model using the Dakota library [23,39], which contains many gradient-based and derivative-free methods for design optimization studies. Dakota also provides a flexible interface between the analysis tool (i.e., OpenFOAM) and its optimization algorithms, which allows for the setup of an automated workflow [40,41]. Dakota uses OpenFOAM as a black box function that takes some input, specified by Dakota, and transforms it into some output, used by Dakota to determine whether it meets certain performance conditions.…”