Towards topology-free optimization - An application to turbine internal cooling geometries

The application of automated design optimization to real-world, complex geometry problems is a significant challenge - especially if the topology is not known a priori like in turbine internal cooling. The long term goal of our work is to focus on an end-to-end integration of the whole CFD Process, from solid model through meshing, solving and post-processing to enable this type of design optimization to become viable & practical. In recent papers we have reported the integration of a Level Set based geometry kernel with an octree-based cut- Cartesian mesh generator, RANS flow solver, post-processing & geometry editing all within a single piece of software - and all implemented in parallel with commodity PC clusters as the target. The cut-cells which characterize the approach are eliminated by exporting a body-conformal mesh guided by the underpinning Level Set. This paper extends this work still further with a simple scoping study showing how the basic functionality can be scripted & automated and then used as the basis for automated optimization of a generic gas turbine cooling geometry. Copyright © 2008 by W.N.Dawes.

Optimization of burnable poison design for Pu incineration in fully fertile free PWR core

The design challenges of the fertile-free based fuel (FFF) can be addressed by careful and elaborate use of burnable poisons (BP). Practical fully FFF core design for PWR reactor has been reported in the past [1]. However, the burnable poison option used in the design resulted in significant end of cycle reactivity penalty due to incomplete BP depletion. Consequently, excessive Pu loading were required to maintain the target fuel cycle length, which in turn decreased the Pu burning efficiency. A systematic evaluation of commercially available BP materials in all configurations currently used in PWRs is the main objective of this work. The BP materials considered are Boron, Gd, Er, and Hf. The BP geometries were based on Wet Annular Burnable Absorber (WABA), Integral Fuel Burnable Absorber (IFBA), and Homogeneous poison/fuel mixtures. Several most promising combinations of BP designs were selected for the full core 3D simulation. All major core performance parameters for the analyzed cases are very close to those of a standard PWR with conventional UO2 fuel including possibility of reactivity control, power peaking factors, and cycle length. The MTC of all FFF cores was found at the full power conditions at all times and very close to that of the UO2 core. The Doppler coefficient of the FFF cores is also negative but somewhat lower in magnitude compared to UO2 core. The soluble boron worth of the FFF cores was calculated to be lower than that of the UO2 core by about a factor of two, which still allows the core reactivity control with acceptable soluble boron concentrations. The main conclusion of this work is that judicial application of burnable poisons for fertile free fuel has a potential to produce a core design with performance characteristics close to those of the reference PWR core with conventional UO2 fuel.

Uniform stability of a particle approximation of the optimal filter derivative

Sequential Monte Carlo methods, also known as particle methods, are a widely used set of computational tools for inference in non-linear non-Gaussian state-space models. In many applications it may be necessary to compute the sensitivity, or derivative, of the optimal filter with respect to the static parameters of the state-space model; for instance, in order to obtain maximum likelihood model parameters of interest, or to compute the optimal controller in an optimal control problem. In Poyiadjis et al. [2011] an original particle algorithm to compute the filter derivative was proposed and it was shown using numerical examples that the particle estimate was numerically stable in the sense that it did not deteriorate over time. In this paper we substantiate this claim with a detailed theoretical study. Lp bounds and a central limit theorem for this particle approximation of the filter derivative are presented. It is further shown that under mixing conditions these Lp bounds and the asymptotic variance characterized by the central limit theorem are uniformly bounded with respect to the time index. We demon- strate the performance predicted by theory with several numerical examples. We also use the particle approximation of the filter derivative to perform online maximum likelihood parameter estimation for a stochastic volatility model.

Neutronic optimization in high conversion Th-233U fuel assembly with simulated annealing

This paper reports on fuel design optimization of a PWR operating in a self sustainable Th-233U fuel cycle. Monte Carlo simulated annealing method was used in order to identify the fuel assembly configuration with the most attractive breeding performance. In previous studies, it was shown that breeding may be achieved by employing heterogeneous Seed-Blanket fuel geometry. The arrangement of seed and blanket pins within the assemblies may be determined by varying the designed parameters based on basic reactor physics phenomena which affect breeding. However, the amount of free parameters may still prove to be prohibitively large in order to systematically explore the design space for optimal solution. Therefore, the Monte Carlo annealing algorithm for neutronic optimization is applied in order to identify the most favorable design. The objective of simulated annealing optimization is to find a set of design parameters, which maximizes some given performance function (such as relative period of net breeding) under specified constraints (such as fuel cycle length). The first objective of the study was to demonstrate that the simulated annealing optimization algorithm will lead to the same fuel pins arrangement as was obtained in the previous studies which used only basic physics phenomena as guidance for optimization. In the second part of this work, the simulated annealing method was used to optimize fuel pins arrangement in much larger fuel assembly, where the basic physics intuition does not yield clearly optimal configuration. The simulated annealing method was found to be very efficient in selecting the optimal design in both cases. In the future, this method will be used for optimization of fuel assembly design with larger number of free parameters in order to determine the most favorable trade-off between the breeding performance and core average power density.

Application of the multi-objective Alliance algorithm to a benchmark aerodynamic optimization problem

This paper introduces a new version of the multiobjective Alliance Algorithm (MOAA) applied to the optimization of the NACA 0012 airfoil section, for minimization of drag and maximization of lift coefficients, based on eight section shape parameters. Two software packages are used: XFoil which evaluates each new candidate airfoil section in terms of its aerodynamic efficiency, and a Free-Form Deformation tool to manage the section geometry modifications. Two versions of the problem are formulated with different design variable bounds. The performance of this approach is compared, using two indicators and a statistical test, with that obtained using NSGA-II and multi-objective Tabu Search (MOTS) to guide the optimization. The results show that the MOAA outperforms MOTS and obtains comparable results with NSGA-II on the first problem, while in the other case NSGA-II is not able to find feasible solutions and the MOAA is able to outperform MOTS. © 2013 IEEE.

A self-adaptive genetic algorithm applied to multi-objective optimization of an airfoil

Genetic algorithms (GAs) have been used to tackle non-linear multi-objective optimization (MOO) problems successfully, but their success is governed by key parameters which have been shown to be sensitive to the nature of the particular problem, incorporating concerns such as the numbers of objectives and variables, and the size and topology of the search space, making it hard to determine the best settings in advance. This work describes a real-encoded multi-objective optimizing GA (MOGA) that uses self-adaptive mutation and crossover, and which is applied to optimization of an airfoil, for minimization of drag and maximization of lift coefficients. The MOGA is integrated with a Free-Form Deformation tool to manage the section geometry, and XFoil which evaluates each airfoil in terms of its aerodynamic efficiency. The performance is compared with those of the heuristic MOO algorithms, the Multi-Objective Tabu Search (MOTS) and NSGA-II, showing that this GA achieves better convergence.

Optimization of efficiency-loss figure of merit in carrier-depletion silicon Mach-Zehnder optical modulator.

In this paper we study the optimization of interleaved Mach-Zehnder silicon carrier depletion electro-optic modulator. Following the simulation results we demonstrate a phase shifter with the lowest figure of merit (modulation efficiency multiplied by the loss per unit length) 6.7 V-dB. This result was achieved by reducing the junction width to 200 nm along the phase-shifter and optimizing the doping levels of the PN junction for operation in nearly fully depleted mode. The demonstrated low FOM is the result of both low V(π)L of ~0.78 Vcm (at reverse bias of 1V), and low free carrier loss (~6.6 dB/cm for zero bias). Our simulation results indicate that additional improvement in performance may be achieved by further reducing the junction width followed by increasing the doping levels.