A Computational Aerodynamic Design Optimization Method Using Sensitivity Analysis

A new and efficient procedure for aerodynamic shape optimization is presented. The salient lineaments of this procedure are: (1) using a discrete sensitivity analysis approach to determine analytically the aerodynamic sensitivity coefficients; (2) obtaining the flowfield solution either by a computational fluid dynamics (CFD) analysis or, alternatively, by a flowfield extrapolation method which is based on a truncated Taylor\u27s series; (3) defining the aerodynamic shape in such a way that it is not restricted to any class of surfaces and the optimizer automatically shapes the aerodynamic configuration to any arbitrary geometry; and (4) requiring no expertise other than that needed for formulating the optimization problem in question. This procedure is successfully demonstrated on different aerodynamic optimization problems. In one of the optimization problems, the ramp shape of a scramjet nozzle-afterbody configuration is optimized to yield a maximum thrust force coefficient. However, prior to its design optimization, a CFD capability for the mixing of two-dimensional, viscous, multispecies flows has been developed in order to gain a detailed understanding of the complex flowfield features of the scramjet nozzle-afterbody configuration. It is shown that heavier exhaust mixture (simulated by a Freon-Argon mixture) undergoes gasdynamic expansion at a smaller rate than does lighter air exhaust flow. In the sensitivity analysis approach, both Euler and thin-layer Navier-Stokes equations are used. Their discretized equations are solved using an implicit, upwind-biased, finite-volume scheme. The van Leer flux-vector splitting is used in the discretization of the pressure and convective terms. The direct and iterative solution methods, which are deemed most applicable to the large linear systems of algebraic equations arising in the sensitivity approach, are investigated with regards to their accuracies, computational time, and computer memory requirements. These methods are shown to be feasible only for small two-dimensional problems. Due to the prohibitively high memory requirements, they become impractical for large two-dimensional problems and inapplicable for any of the three-dimensional problems. To alleviate this limitation, a new scheme based on domain decomposition principles has been developed and is called the Sensitivity Analysis Domain-Decomposition (SADD) scheme

Design for Additive Manufacturing of Conformal Cooling Channels Using Thermal-Fluid Topology Optimization and Application in Injection Molds

Additive manufacturing allows the fabrication parts and tools of high complexity. This capability challenges traditional guidelines in the design of conformal cooling systems in heat exchangers, injection molds, and other parts and tools. Innovative design methods, such as network-based approaches, lattice structures, and structural topology optimization have been used to generate complex and highly efficient cooling systems; however, methods that incorporate coupled thermal and fluid analysis remain scarce. This paper introduces a coupled thermal-fluid topology optimization algorithm for the design of conformal cooling channels. With this method, the channel position problem is replaced to a material distribution problem. The material distribution directly depends on the effect of flow resistance, heat conduction, as well as forced and natural convection. The problem is formulated based on a coupling of Navier-Stokes equations and convection-diffusion equation. The problem is solved by gradient-based optimization after analytical sensitivity derived using the adjoint method. The algorithm leads a two -dimensional conceptual design having optimal heat transfer and balanced flow. The conceptual design is converted to three-dimensional channels and mapped to a morphological surface conformal to the injected part. The method is applied to design an optimal conformal cooling for a real three dimensional injection mold. The feasibility of the final designs is verified through simulations. The final designs can be exported as both three-dimensional graphic and surface mesh CAD format, bringing the manufacture department the convenience to run the tool path for final fitting

A "poor man's" approach for high-resolution three-dimensional topology optimization of natural convection problems

This paper treats topology optimization of natural convection problems. A simplified model is suggested to describe the flow of an incompressible fluid in steady state conditions, similar to Darcy's law for fluid flow in porous media. The equations for the fluid flow are coupled to the thermal convection-diffusion equation through the Boussinesq approximation. The coupled non-linear system of equations is discretized with stabilized finite elements and solved in a parallel framework that allows for the optimization of high resolution three-dimensional problems. A density-based topology optimization approach is used, where a two-material interpolation scheme is applied to both the permeability and conductivity of the distributed material. Due to the simplified model, the proposed methodology allows for a significant reduction of the computational effort required in the optimization. At the same time, it is significantly more accurate than even simpler models that rely on convection boundary conditions based on Newton's law of cooling. The methodology discussed herein is applied to the optimization-based design of three-dimensional heat sinks. The final designs are formally compared with results of previous work obtained from solving the full set of Navier-Stokes equations. The results are compared in terms of performance of the optimized designs and computational cost. The computational time is shown to be decreased to around 5-20% in terms of core-hours, allowing for the possibility of generating an optimized design during the workday on a small computational cluster and overnight on a high-end desktop

A "poor man's" approach to topology optimization of natural convection problems

Topology optimization of natural convection problems is computationally expensive, due to the large number of degrees of freedom (DOFs) in the model and its two-way coupled nature. Herein, a method is presented to reduce the computational effort by use of a reduced-order model governed by simplified physics. The proposed method models the fluid flow using a potential flow model, which introduces an additional fluid property. This material property currently requires tuning of the model by comparison to numerical Navier-Stokes based solutions. Topology optimization based on the reduced-order model is shown to provide qualitatively similar designs, as those obtained using a full Navier-Stokes based model. The number of DOFs is reduced by 50% in two dimensions and the computational complexity is evaluated to be approximately 12.5% of the full model. We further compare to optimized designs obtained utilizing Newton's convection law.Comment: Preprint version. Please refer to final version in Structural Multidisciplinary Optimization https://doi.org/10.1007/s00158-019-02215-

Shape optimization of Stokesian peristaltic pumps using boundary integral methods

This article presents a new boundary integral approach for finding optimal shapes of peristaltic pumps that transport a viscous fluid. Formulas for computing the shape derivatives of the standard cost functionals and constraints are derived. They involve evaluating physical variables (traction, pressure, etc.) on the boundary only. By emplyoing these formulas in conjuction with a boundary integral approach for solving forward and adjoint problems, we completely avoid the issue of volume remeshing when updating the pump shape as the optimization proceeds. This leads to significant cost savings and we demonstrate the performance on several numerical examples