Multi-Physics Bi-directional Evolutionary Topology Optimization on GPU-architecture

Topology optimization has proven to be viable for use in the preliminary phases of real world design problems. Ultimately, the restricting factor is the computational expense since a multitude of designs need to be considered. This is especially imperative in such fields as aerospace, automotive and biomedical, where the problems involve multiple physical models, typically fluids and structures, requiring excessive computational calculations. One possible solution to this is to implement codes on massively parallel computer architectures, such as graphics processing units (GPUs). The present work investigates the feasibility of a GPU-implemented lattice Boltzmann method for multi-physics topology optimization for the first time. Noticeable differences between the GPU implementation and a central processing unit (CPU) version of the code are observed and the challenges associated with finding feasible solutions in a computational efficient manner are discussed and solved here, for the first time on a multi-physics topology optimization problem. The main goal of this paper is to speed up the topology optimization process for multi-physics problems without restricting the design domain, or sacrificing considerable performance in the objectives. Examples are compared with both standard CPU and various levels of numerical precision GPU codes to better illustrate the advantages and disadvantages of this implementation. A structural and fluid objective topology optimization problem is solved to vary the dependence of the algorithm on the GPU, extending on the previous literature that has only considered structural objectives of non-design dependent load problems. The results of this work indicate some discrepancies between GPU and CPU implementations that have not been seen before in the literature and are imperative to the speed-up of multi-physics topology optimization algorithms using GPUs.D. J. Munk thanks the Australian government for their financial support through the Endeavour Fellowship scheme. The authors would like to acknowledge the UK Consortium on Mesoscale Engineering Sciences (UKCOMES) EPSRC grant No EP/L00030X/1 for providing the HPC capabilities used in this article

Enhanced interactive parallel coordinates using machine learning and uncertainty propagation for engineering design

© 2019 IEEE. The design process of an engineering system requires thorough consideration of varied specifications, each with potentially large number of dimensions. The sheer volume of data, as well as its complexity, can overwhelm the designer and obscure vital information. Visualisation of big data can mitigate the issue of information overload but static display can suffer from overplotting. To tackle the issue of overplotting and cluttered data, we present an interactive and touch-screen capable visualisation toolkit that combines Parallel Coordinates and Scatter Plot approaches for managing multidimensional engineering design data. As engineering projects require a multitude of varied software to handle the various aspects of the design process, the combined datasets often do not have an underlying mathematical model. We address this issue by enhancing our visualisation software with Machine Learning methods which also facilitate further insights into the data. Furthermore, various software within the engineering design cycle produce information of different level of fidelity (accuracy and trustworthiness), as well as with different speed. The induced uncertainty is also considered and modelled in the synthetic dataset and is also presented in an interactive way. This paper describes a new visualisation software package and demonstrates its functionality on a complex aircraft systems design dataset

Loading and planform shape influence of the wing structural layout through topology optimization

© 2018, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved. Topology optimization is a technique used to identify the optimal layout of a structure for a given objective and assigned boundary conditions. The progress it has experienced over the last three decades made it ready for industrial applications. In this paper topology optimization is employed to investigate the influence of sweep angle, aspect ratio and loading condition on the wing internal structure. The planform of the Common Research Model wing is used as a baseline. The geometry is modified parametrically to alter sweep angle and aspect ratio. Regarding the baseline planform, the optimization is performed considering the aerodynamic loading induced by the pull-up manoeuvre. Results are provided for AR = 7 and AR = 11, as well as sweep angle of 20 and 30 degrees. The results of topology optimization for all cases are compared. Common patterns are identified and exported to provide guidelines for the preliminary design of the wing primary structure

An investigation of higher-order multi-objective optimisation for 3D aerodynamic shape design

We investigate the performance of different variants of a suitably tailored Tabu Search optimisation algorithm on a higher-order design problem. We consider four objective func- tions to describe the performance of a compressor stator row, subject to a number of equality and inequality constraints. The same design problem has been previously in- vestigated through single-, bi- and three-objective optimisation studies. However, in this study we explore the capabilities of enhanced variants of our Multi-objective Tabu Search (MOTS) optimisation algorithm in the context of detailed 3D aerodynamic shape design. It is shown that with these enhancements to the local search of the MOTS algorithm we can achieve a rapid exploration of complicated design spaces, but there is a trade-off be- tween speed and the quality of the trade-off surface found. Rapidly explored design spaces reveal the extremes of the objective functions, but the compromise optimum areas are not very well explored. However, there are ways to adapt the behaviour of the optimiser and maintain both a very efficient rate of progress towards the global optimum Pareto front and a healthy number of design configurations lying on the trade-off surface and exploring the compromise optimum regions. These compromise solutions almost always represent the best qualitative balance between the objectives under consideration. Such enhancements to the effectiveness of design space exploration make engineering design optimisation with multiple objectives and robustness criteria ever more practicable and attractive for modern advanced engineering design. Finally, new research questions are addressed that highlight the trade-offs between intelligence in optimisation algorithms and acquisition of qualita- tive information through computational engineering design processes that reveal patterns and relations between design parameters and objective functions, but also speed versus optimum quality

On the effect of fluid-structure interactions and choice of algorithm in multi-physics topology optimisation

This article presents an optimisation framework for the compliance minimisation of structures subjected to design-dependent pressure loads. A finite element solver coupled to a Lattice Boltzmann method is employed, such that the effect of the fluid-structure interactions on the optimised design can be considered. It is noted that the main computational expense of the algorithm is the Lattice Boltzmann method. Therefore, to improve the computational efficiency and to assess the effect of the fluid-structure interactions on the fi nal optimised design, the degree of coupling is changed. Several successful topology optimisation algorithms exist with thousands of associated publications in the literature. However, only a small portion of these are applied to real-world problems, with even fewer offering a comparison of methodologies. This is especially important for problems involving fluid-structure interactions, where discrete and continuous methods can provide different advantages. The goal of this research is to couple two key disciplines, fluids and structures, into a topology optimisation framework, which shows fast convergence for multi-physics optimisation problems. This is achieved by offering a comparison of three popular, but competing, optimisation methodologies. The needs for the exploration of larger design spaces and to produce innovative designs make meta-heuristic algorithms less efficient for this task. A coupled analysis, where the fluid and structural mechanics are updated, provides superior results compared with an uncoupled analysis approach, however at some computational expense. The results in this article show that the method is sensitive to whether fluid-structure coupling is included, i.e. if the fluid mechanics are updated with design changes, but not to the degree of the coupling, i.e. how regularly the fluid mechanics are updated, up to a certain limit. Therefore, the computational efficiency of the algorithm can be considerably increased with small penalties in the quality of the objective by relaxing the coupling

Impact of Fluid Substitution on the Performance of an Axial Compressor Blade Cascade Working with Supercritical Carbon Dioxide

Abstract Recent research on turbomachinery design and analysis for supercritical carbon dioxide (sCO2) power cycles has relied on computational fluid dynamics. This has produced a large number of works whose approach is mostly case-specific, rather than of general application to sCO2 turbomachinery design. As opposed to such approach, this work explores the aerodynamic performance of compressor blade cascades operating on air and supercritical CO2 with the main objective to evaluate the usual aerodynamic parameters of the cascade for variable boundary conditions and geometries, enabling “full” or “partial” similarity. The results present both the global performance of the cascades and certain features of the local flow (trailing edge and wake). The discussion also highlights the mechanical limitations of the analysis (forces exerted on the blades), which is the main restriction for applying similarity laws to extrapolate the experience gained through decades of work on air turbomachinery to the new working fluid. This approach is a step toward the understanding and appropriate formulation of a multi-objective optimization problem for the design of such turbomachinery components where sCO2 is used as the operating fluid. With this objective, the paper aims to identify and analyze what would be expected if a common description of such computational design problems similar to those where air is the working fluid were used.</jats:p

Exploring parallel coordinates plots in virtual reality

Parallel Coordinates Plots (PCP) are a widely used approach to interactively visualize and analyze multidimensional scientific data in a 2D environment. In this paper, we explore the use of Parallel Coordinates in an immersive Virtual Reality (VR) 3D visualization environment as a means to support the decision-making process in engineering design processes. We evaluate the potential of VR PCP using a formative qualitative study with seven participants. In a task involving 54 points with 29 dimensions per point, we found that participants were able to detect patterns in the dataset compared with a previously published study with two expert users using traditional 2D PCP, which acts as the gold standard for the dataset. The dataset describes the Pareto front for a three-objective aerodynamic design optimization study in turbomachinery.Cambridge European & Trinity Hall Scholarshi

Congestion management with aggregated delivery of flexibility using distributed energy resources

Increasing penetrations of small scale electricity generation and storage technologies are making an important contribution to the decentralisation and decarbonisation of power system control and operation. Although not currently realised, coordination of local distributed energy resources (DERs) and a greater degree of demand flexibility through digital aggregation, offer the potential to lower the cost of energy at source and to enable remuneration for consumer participation, addressing the rising costs of energy supply, which impacts strongly on all consumers. Methods are required to manage potential distribution network constraints caused by flexible DERs, as well as for determining the risk to delivery of flexibility from these DERs for aggregators. A heuristic network flexibility dispatch methodology is proposed, which can be used to calculate the probability of constraints, and any required adjustments of flexible agent positions to resolve them, at half hourly resolution. The aggregator can use this methodology to manage their portfolio risk, while a distribution system operator can estimate required flexibility to manage constraints down to low voltage level

Multiobjective and multi-physics topology optimization using an updated smart normal constraint bi-directional evolutionary structural optimization method

To date the design of structures using topology optimization methods has mainly focused on single-objective problems. Since real-world design problems typically involve several different objectives, most of which counteract each other, it is desirable to present the designer with a set of Pareto optimal solutions that capture the trade-off between these objectives, known as a smart Pareto set. Thus far only the weighted sums and global criterion methods have been incorporated into topology optimization problems. Such methods are unable to produce evenly distributed smart Pareto sets. However, recently the smart normal constraint method has been shown to be capable of directly generating smart Pareto sets. Therefore, in the present work, an updated smart Normal Constraint Method is combined with a Bi-directional Evolutionary Structural Optimization (SNC-BESO) algorithm to produce smart Pareto sets for multiobjective topology optimization problems. Two examples are presented, showing that the Pareto solutions found by the SNC-BESO method make up a smart Pareto set. The first example, taken from the literature, shows the benefits of the SNC-BESO method. The second example is an industrial design problem for a micro fluidic mixer. Thus, the problem is multi-physics as well as multiobjective, highlighting the applicability of such methods to real-world problems. The results indicate that the method is capable of producing smart Pareto sets to industrial problems in an effective and efficient manner.D.J. Munk thanks the Australian government for their financial support through the Endeavour Fellowship scheme

Topology optimisation of micro fluidic mixers considering fluid-structure interactions with a coupled Lattice Boltzmann algorithm

Recently, the study of micro fluidic devices has gained much interest in various fi elds from biology to engineering. In the constant development cycle, the need to optimise the topology of the interior of these devices, where there are two or more optimality criteria, is always present. In this work, twin physical situations, whereby optimal fluid mixing in the form of vorticity maximisation is accompanied by the requirement that the casing in which the mixing takes place has the best structural performance in terms of the greatest speci c stiffness, are considered. In the steady state of mixing this also means that the stresses in the casing are as uniform as possible, thus giving a desired operating life with minimum weight. The ultimate aim of this research is to couple two key disciplines, fluids and structures, into a topology optimisation framework, which shows fast convergence for multidisciplinary optimisation problems. This is achieved by developing a bi-directional evolutionary structural optimisation algorithm that is directly coupled to the Lattice Boltzmann method, used for simulating the flow in the micro fluidic device, for the objectives of minimum compliance and maximum vorticity. The needs for the exploration of larger design spaces and to produce innovative designs make meta-heuristic algorithms, such as genetic algorithms, particle swarms and Tabu Searches, less efficient for this task. The multidisciplinary topology optimisation framework presented in this article is shown to increase the stiffness of the structure from the datum case and produce physically acceptable designs. Furthermore, the topology optimisation method outperforms a Tabu Search algorithm in designing the baffle to maximise the mixing of the two fluids