Set Of Pareto-optimal Designs Research Articles

Multi-objective Design Optimization and Control Strategy for Digital Hydraulically Driven Knee Exoskeleton

This article presents a multi-objective design optimization strategy to determine an optimal design of digital hydraulically driven knee exoskeleton. To satisfy the overall goal of compact and lightweight design, four key design objectives are defined. Via genetic algorithm based multi-objective optimization technique, the pareto-optimal set of designs is determined and the trade-offs between the design objectives are analysed. Via decisions based on component availability and user-comfort, the dimensionality of the pareto-front is reduced to two and an exoskeleton design is selected that offers a good compromise between the design objectives. For the actuation of the exoskeleton, an energy efficient control strategy is proposed which consists of using passive control during the stance phase and simplified model predictive control during the swing phase. The operation of the chosen knee exoskeleton design and the control strategy is investigated via numerical simulations. The results indicate that the exoskeleton successfully tracks the desired knee motion and delivers the required knee torque.

Aerodynamic Shape Optimization of an Aircraft Propulsor Air Intake with Boundary Layer Ingestion

The growth of the airline industry has highlighted the need for more environmentally conscious aviation, leading to the conceptualization of more fuel-efficient aircraft. One concept that has received significant attention and has been associated with improved fuel efficiency is the boundary layer ingesting (BLI) propulsion system, which refers to the ingesting of the aircraft wake by the propulsors. Although BLI has theoretically been proven to reduce fuel burn, this can potentially be offset by the reduced efficiency and stability experienced by the propulsor in the presence of distorted inflow. Therefore, engine intakes must be optimized in order to mitigate the effects of BLI on the propulsion system. In this work, the shape optimization of a BLI intake is investigated using a free-form deformation technique in combination with a multi-objective genetic algorithm, in order to minimize pressure losses and distortion at the engine inlet. The optimization is performed on an S-duct intake at a cruise altitude of approximately 37,000 feet and a free stream Mach number of 0.7. An optimization strategy was developed for the task which was able to produce a Pareto optimal set of designs with improved pressure recovery and distortion. The general trend of the optimal designs shows that to reduce distortion the optimizer accelerates the flow to reduce the size of the low total pressure region and increase the dynamic pressure at the engine inlet. In contrast, the pressure recovery was increased by reducing velocity as well as shifting the maximum velocity region to the outlet, which reduces the viscous dissipation losses within the intake. The final result is a fully autonomous optimization strategy resulting in reduced pressure losses and reduced distortion leading to higher efficiency BLI S-duct intake designs.

Multi-objective free-form shape optimization of a synchronous reluctance machine

PurposeThis paper aims to deal with the design optimization of a synchronous reluctance machine to be used in an X-ray tube, where the goal is to maximize the torque while keeping low the amount of material used, by means of gradient-based free-form shape optimization.Design/methodology/approachThe presented approach is based on the mathematical concept of shape derivatives and allows to obtain new motor designs without the need to introduce a geometric parametrization. This paper presents an extension of a standard gradient-based free-form shape optimization algorithm to the case of multiple objective functions by determining updates, which represent a descent of all involved criteria. Moreover, this paper illustrates a way to obtain an approximate Pareto front.FindingsThe presented method allows to obtain optimal designs of arbitrary, non-parametric shape with very low computational cost. This paper validates the results by comparing them to a parametric geometry optimization in JMAG by means of a stochastic optimization algorithm. While the obtained designs are of similar shape, the computational time used by the gradient-based algorithm is in the order of minutes, compared to several hours taken by the stochastic optimization algorithm.Originality/valueThis paper applies the presented gradient-based multi-objective optimization algorithm in the context of free-form shape optimization using the mathematical concept of shape derivatives. The authors obtain a set of Pareto-optimal designs, each of which is a shape that is not represented by a fixed set of parameters. To the best of the authors’ knowledge, this approach to multi-objective free-form shape optimization is novel in the context of electric machines.

Thermoeconomic analysis and optimization of a hybrid solar-thermal power plant using a genetic algorithm

The hybridization of solar-thermal and combustion technologies for power generation is an emerging concept that brings the possibility to mitigate carbon dioxide emissions while maintaining a firm supply. This work presents a thermoeconomic model for a distributed-scale (<100 kWe) hybrid solar-thermal power plant, developed to study its performance under different operating conditions in a region with a yearly direct normal irradiance of around 1600 kWh/m2 yr. The proposed system consists of a combined Rankine-Brayton cycle with a solar receiver and natural gas combustor working in series as heat sources to the topping cycle. A genetic algorithm was employed to perform a multi-objective optimization of such system, and the result was a set of Pareto-optimal designs, which were compared to a pre-defined reference design. Resulting optimized solutions yield levelized electricity costs as low as 0.179 USD/kWh, as opposed to the 0.237 USD/kWh associated with the reference design. Average 1st and 2nd law efficiencies of up to 27.97 % and 33.53 % were achieved, respectively, which represent increases of up to 7.71 % and 7.31 %. Finally, average solar shares of up to 65 % are possible for optimized designs versus the 58.4 % yielded by the base design.

Performance‐based optimum tuning of tuned mass dampers on steel moment frames for seismic applications using the endurance time method

AbstractA performance‐based framework is presented to investigate the optimum tuning of tuned mass dampers (TMDs) for seismic applications in several steel moment‐resisting frames by single and multi‐objective genetic algorithm. The single objective problems have been developed for a set of given mass ratios so as to meet the best performance, while the multi‐objective problems try to attain a set of Pareto optimal designs so as to find the relation between TMD efficiency and TMD mass. In the proposed approach, nonlinear time history analysis via endurance time (ET) method is utilized to estimate the structural responses at two seismic hazard levels in order to assess the performance of structures, while a new criterion for scaling period of TMD‐equipped structures is proposed to be used in scaling process of seismic excitations. A criterion of interstory drift ratios at BSE‐1N seismic hazard level, as the performance index of the studied frames, is considered to be minimized and finally, detuning effects of the optimum TMD designs are checked at BSE‐2N seismic hazard level. In order to illustrate the superiority of the proposed approach, the robustness of optimal TMD parameters is evaluated under near‐ and far‐field ground motion record sets recommended in FEMA‐P695. Also, the optimum results are compared to those from several analytical methods. The proposed framework, taking the advantage of the ET method, is found to be an efficient and feasible approach for practical and cost‐effective optimum seismic design of TMDs on nonlinear structures with a reasonable accuracy and significantly reduced computational effort.

MULTIDISCIPLINARY DESIGN OPTIMIZATION OF A MOBILE MINER USING THE OPENMDAO PLATFORM

AbstractThis paper proposes an optimization framework based on the OpenMDAO software library intended for engineer-to-order products and applies it to the conceptual design of a Mobile Miner. A Mobile Miner is a complex machine and a flexible alternative to Tunnel Boring Machines for small-scale tunneling and mining applications. The proposed framework is intended for use in early design and quotation stages with the objective to get fast estimates of important product characteristics, such as excavation rate and cutter lifetime. The ability to respond fast to customer requests is vital when offering customized products for specific applications and thereby to stay competitive on the global market. This is true for most engineer-to-order products and especially for mining equipment where each construction project is unique with different tunnel geometries and rock properties. The presented framework is applied to a specific use-case where the design of the miner's cutter wheel is in focus and a set of Pareto optimal designs are obtained. Furthermore, the framework extends the capabilities of OpenMDAO by including support for mixed-variable formulations and it supports an exploratory approach to design optimization.

Performance-based Bi-objective optimization of structural systems subject to stochastic wind excitation

This paper outlines the development of a stochastic simulation-based design optimization approach for dynamic wind excited structures in which correlations between component damages and losses are explicitly treated. The proposed approach integrates a bi-objective design optimization scheme with a probabilistic performance-based wind engineering methodology which systematically accounts for the various sources of uncertainties involved in system loss estimation. Through the ∊-constraint technique, the bi-objective optimization problem is transformed into a series of single-objective stochastic optimization problems. To solve each ∊-constraint optimization problem, a pseudo-simulation scheme is proposed that allows for the formulation of an approximate sub-problem that can be solved sequentially to identify solutions that define a set of Pareto optimal designs. In the proposed scheme, samples of engineering demands are approximated in terms of auxiliary variable vectors, which are by-products of an augmented simulation carried out in a fixed design point. Analytical expressions are derived that relate the engineering demand samples to the second-order statistics of wind-induced losses based on the concept of fragility. Potential correlations between the component capacities and component losses are explicitly treated. The effectiveness of the proposed approach and its scalability to high-dimensional problems are illustrated through optimal designs of moment-resisting frames subject to stochastic wind loads.

Powertrain Design Optimization for a Range-Extended Electric Pickup and Delivery Truck

&lt;div&gt;The ongoing electrification and data-intelligence trends in logistics industries enable efficient powertrain design and operation. In this work, the commercial package delivery vehicle powertrain design space is revisited with a specific combination of optimization and control techniques that promise accurate results with relatively fast computational time. The specific application that is explored here is a Class 6 pickup and delivery truck. A statistical learning approach is used to refine the search for the most optimal designs. Five hybrid powertrain architectures, namely, two-speed e-axle, three-speed and four-speed automatic transmission (AT) with electric motor (EM), direct-drive, and dual-motor options are explored, and a set of Pareto-optimal designs are found for a specific driving mission that represents the variations in a hypothetical operational scenario. The modeling and optimization processes are performed on the MATLAB&lt;sup&gt;™&lt;/sup&gt;-Simulink platform. A cross-architecture performance and cost comparison is performed, which shows that two-speed e-axle is the optimal architecture for the selected application.&lt;/div&gt;

Fast Multi-Objective Aerodynamic Optimization Using Sequential Domain Patching and Multifidelity Models

Exploration of design tradeoffs for aerodynamic surfaces requires solving of multi-objective optimization (MOO) problems. The major bottleneck here is the time-consuming evaluations of the computational fluid dynamics (CFD) model used to capture the nonlinear physics involved in designing aerodynamic surfaces. This, in conjunction with a large number of simulations necessary to yield a set of designs representing the best possible tradeoffs between conflicting objectives (referred to as a Pareto front), makes CFD-driven MOO very challenging. This paper presents a computationally efficient methodology aimed at expediting the MOO process for aerodynamic design problems. The extreme points of the Pareto front are obtained quickly using single-objective optimizations. Starting from these extreme points, identification of an initial set of Pareto-optimal designs is carried out using a sequential domain patching algorithm. Refinement of the Pareto front, originally obtained at the level of the low-fidelity CFD model, is carried out using local response surface approximations and adaptive corrections. The proposed algorithm is validated using a few multi-objective analytical problems and an aerodynamic problem involving MOO of two-dimensional transonic airfoil shapes where the figures of interest are the drag and pitching moment coefficients. A multifidelity model is constructed using CFD model and control points parameterizing the shape of the airfoil. The results demonstrate that an entire or a part of the Pareto front can be obtained at a low cost when considering up to eight design variables.

DP Pareto optimal balanced orthogonal subplot designs

ABSTRACT This paper considers the problem of identifying optimal Split-Plot Designs (SPDs) using traditional design criteria along with pure error degrees of freedom. An optimal SPD is of little use if it does not allow for suitable variance estimation of the whole plot and subplot variance components. The proposed approach incorporates a multi-criterion version of DP-optimality to represent the conflicting criteria involving the D-criterion and the replication at each design level. A set of Pareto optimal designs can then be obtained that consists of the optimal design with respect to each criterion and which cannot be dominated with respect to all the criteria. Key to this development is the notion of Orthogonal Subplot Designs (OSUBD) since this class of SPDs leads to a clear representation of DP-optimality. A number of common SPD design scenarios are provided to demonstrate the approach and to highlight the benefits of using the proposed multi-objective criterion.

A comparison of helical and spur external gear machines for fluid power applications: Design and optimization

This paper addresses the problem of determining the relative performance of spur and helical external gear pumps (EGPs) for fluid power applications. Helical EGPs are commonly considered as quieter units than spur ones; however, there is no specific study reported in literature that proves such claim. Similarly, it is hard to find data indicating which design is better in terms of compactness or energy performance. To tackle this problem, a procedure is developed for virtual design optimization of both helical and spur gear EGPs. This includes addressing the two fundamental challenges of the work, parameterizing a generic EGP design and quantifying its performance. By applying this procedure, a genetic algorithm can continue improving from generation to generation, until the pareto optimal set of designs is identified for both helical and spur units. In the comparison of these, the differences between the two configurations will be identified and the fundamental question of this work can be answered; are helical gears ‘better’ than spur gears?

Multi-objective optimization with Kriging surrogates using “moko”, an open source package

Abstract Many modern real-world designs rely on the optimization of multiple competing goals. For example, most components designed for the aerospace industry must meet some conflicting expectations. In such applications, low weight, low cost, high reliability, and easy manufacturability are desirable. In some cases, bounds for these requirements are not clear, and performing mono-objective optimizations might not provide a good landscape of the required optimal design choices. For these cases, finding a set of Pareto optimal designs might give the designer a comprehensive set of options from which to choose the best design. This article shows the main features and functionalities of an open source package, developed by the present authors, to solve constrained multi-objective problems. The package, named moko (acronym for Multi-Objective Kriging Optimization), was built under the open source programming language R. Popular Kriging based multi-objective optimization strategies, as the expected volume improvement and the weighted expected improvement, are available in the package. In addition, an approach proposed by the authors, based on the exploration using a predicted Pareto front is implemented. The latter approach showed to be more efficient than the two other techniques in some case studies performed by the authors with moko.

Multi-fidelity aerodynamic design trade-off exploration using point-by-point Pareto set identification

Aerodynamic design is inherently a multi-objective optimization (MOO) problem. Determining the best possible trade-offs between conflicting aerodynamic objectives can be computationally challenging when carried out directly at the level of high-fidelity computational fluid dynamics simulations. This paper presents a computationally cheap methodology for exploration of aerodynamic design trade-offs. In particular, point-by-point identification of a set of Pareto-optimal designs is executed starting in the neighborhood of a single-objective optimal design, and using a trust-region-based, multi-fidelity optimization algorithm as well as locally constructed response surface approximations (RSAs). In this work, the RSAs are constructed using second-order polynomials without mixed terms, multi-fidelity models, and adaptive corrections. The application of the point-by-point MOO algorithm is demonstrated through MOO of transonic airfoil shapes using the Reynold–Averaged Navier Stokes equations and the Spalart–Allmaras turbulence model. The results demonstrate that the Pareto front in the neighborhood of an initial design can be obtained at a low cost when considering up to 12 design variables. The results also indicate that the computational cost of the optimization process grows slowly with the number of the design variables, and the repeatability of the algorithm is very good when starting the search from different initial points.

Multi-objective design optimization of antenna structures using sequential domain patching with automated patch size determination

ABSTRACTIn this article, a simple yet efficient and reliable technique for fully automated multi-objective design optimization of antenna structures using sequential domain patching (SDP) is discussed. The optimization procedure according to SDP is a two-step process: (i) obtaining the initial set of Pareto-optimal designs representing the best possible trade-offs between considered conflicting objectives, and (ii) Pareto set refinement for yielding the optimal designs at the high-fidelity electromagnetic (EM) simulation model level. For the sake of computational efficiency, the first step is realized at the level of a low-fidelity (coarse-discretization) EM model by sequential construction and relocation of small design space segments (patches) in order to create a path connecting the extreme Pareto front designs obtained beforehand. The second stage involves response correction techniques and local response surface approximation models constructed by reusing EM simulation data acquired in the first step. A major contribution of this work is an automated procedure for determining the patch dimensions. It allows for appropriate selection of the number of patches for each geometry variable so as to ensure reliability of the optimization process while maintaining its low cost. The importance of this procedure is demonstrated by comparing it with uniform patch dimensions.

Automated Design Framework for Synthetic Biology Exploiting Pareto Optimality.

In this work we consider Pareto optimality for automated design in synthetic biology. We present a generalized framework based on a mixed-integer dynamic optimization formulation that, given design specifications, allows the computation of Pareto optimal sets of designs, that is, the set of best trade-offs for the metrics of interest. We show how this framework can be used for (i) forward design, that is, finding the Pareto optimal set of synthetic designs for implementation, and (ii) reverse design, that is, analyzing and inferring motifs and/or design principles of gene regulatory networks from the Pareto set of optimal circuits. Finally, we illustrate the capabilities and performance of this framework considering four case studies. In the first problem we consider the forward design of an oscillator. In the remaining problems, we illustrate how to apply the reverse design approach to find motifs for stripe formation, rapid adaption, and fold-change detection, respectively.

Exploring multi-objective trade-offs in the design space of a waste heat recovery system

A waste heat recovery system (WHRS) is used to capture waste heat released from an industrial process, and transform the heat into reusable energy. In practice, it can be difficult to identify the optimal form of a WHRS for a particular installation, since this can depend on various design objectives, which are often mutually exclusive. More so when the number of objectives is large. To address this problem, a multiobjective evolutionary algorithm (MOEA) was used to explore and characterise the trade-off surface within the design space of a particular WHRS. A combination of clustering algorithm and parallel coordinates plots was proposed for use in analysing the results. The trade-off surface is first segmented using a clustering algorithm and parallel coordinates plots are then used to both visualise and understand the resulting set of Pareto-optimal designs. As a case study, a simulation of a WHRS commonly found in the food and drinks process industries was developed, comprising of a desuperheater coupled to a hot water reservoir. The system was parameterised, considering typical objectives, and the MOEA used to build a library of alternative Pareto-optimal designs that can be used by installers. The resulting visualisation are used to better understand the sensitivity of the system’s parameters and their trade-offs, providing another source of information for prospective installations.

Pareto-Ranking Bisection Algorithm for Expedited Multiobjective Optimization of Antenna Structures

The purpose of this letter is the introduction of a novel methodology for expedited multiobjective design of antenna structures. The key component of the presented approach is fast identification of the initial representation of the Pareto front (i.e., a set of design representing the best possible tradeoffs between conflicting objectives) using a Pareto-ranking bisection algorithm. The algorithm finds a discrete set of Pareto-optimal designs. Its operation principle is sequential partitioning of the line segments connecting the designs found in the previous iterations, and refining the new designs allocated this way by means of poll-type search involving Pareto ranking. Subsequently, the final Pareto set is obtained by means of response correction techniques. Our methodology is demonstrated using an ultrawideband monopole antenna and compared to state-of-the-art multiobjective optimization methods.

ANN-based modeling and reducing dual-fuel engine’s challenging emissions by multi-objective evolutionary algorithm NSGA-II

In this study, the combination of artificial neural network (ANN) and non-dominated sorting genetic algorithm II (NSGA-II) has been implemented for modeling and reducing CO and NOx emissions from a direct injection dual-fuel engine. A multi-layer perceptron (MLP) network is developed to predict the values of the emissions based on experimental data. The controllable variables such as engine speed, output power, intake temperature, mass flow rate of diesel fuel, and mass flow rate of the gaseous fuel are considered as input parameters. In order to identify the uncertainties due to the experiments and the ANN-based model, uncertainty analysis is carried out. Finally, optimum values of intake temperature, mass flow rate of diesel and gaseous fuels are obtained for a desired output power and engine speed via NSGA-II. The use of the developed evolutionary optimization algorithm allows the calculation of the Pareto-optimal set of designs under any combination of engine speed and output power, defined in the range of the experiments.

Scalability of surrogate-assisted multi-objective optimization of antenna structures exploiting variable-fidelity electromagnetic simulation models

Multi-objective optimization of antenna structures is a challenging task owing to the high computational cost of evaluating the design objectives as well as the large number of adjustable parameters. Design speed-up can be achieved by means of surrogate-based optimization techniques. In particular, a combination of variable-fidelity electromagnetic (EM) simulations, design space reduction techniques, response surface approximation models and design refinement methods permits identification of the Pareto-optimal set of designs within a reasonable timeframe. Here, a study concerning the scalability of surrogate-assisted multi-objective antenna design is carried out based on a set of benchmark problems, with the dimensionality of the design space ranging from six to 24 and a CPU cost of the EM antenna model from 10 to 20 min per simulation. Numerical results indicate that the computational overhead of the design process increases more or less quadratically with the number of adjustable geometric parameters of the antenna structure at hand, which is a promising result from the point of view of handling even more complex problems.

FCUDA-HB: Hierarchical and Scalable Bus Architecture Generation on FPGAs With the FCUDA Flow

Recent progress in high-level synthesis (HLS) has helped raise the abstraction level of hardware design. HLS flows reduce designer effort by allowing development in a high-level language, which improves debugging, code reuse and ability to explore different implementation options. However, although the HLS process is fast, implementation and performance analysis still require lengthy logic synthesis and physical design. For design optimization, HLS tools require design space exploration to obtain parallelism at multiple levels of granularity including parallelism within a single HLS-generated core and parallelism between multiple instances of cores. Core interconnect and external bandwidth limitations can significantly impact feasible options in the design space. With many dimensions in a design space exploration, it quickly becomes infeasible to perform full logic synthesis and physical design for each possible design point. However, generation and evaluation of communications infrastructure as part of the exploration is critical to determine the system performance. Thus, in this paper, we extend the prior multilevel granularity parallelism exploration in the FCUDA HLS flow, which takes CUDA code as design input and generates a corresponding field programmable gate array implementation. Our framework performs an initial characterization of the application design space, then analytically explores the design space considering parallelism, core interconnect, and external memory bandwidth, and selects a pareto-optimal set of designs. Our flow is completely automated to perform the exploration to characterize the analytical model, perform the exploration, select a solution, and integrate multiple instantiations of FCUDA cores via an advanced extensible interface bus interconnect. Our results demonstrate that this new FCUDA flow efficiently identifies and generates implementations with up to $5\times $ improved system performance compared to single-level granularity parallelism (core-level optimization).