Competing Design Objectives Research Articles

Optimization of Lift-Plus-Cruise Vertical Take-Off and Landing Aircraft with Electrified Propulsion

Conventional aircraft sizing methods face challenges in analyzing all-electric or hybrid-electric novel aircraft configurations, such as those for urban air mobility applications. The vast design space containing both continuous and discrete design variables and competing design objectives necessitates searching for not necessarily a unique optimal design but rather an array of Pareto-optimal designs. This paper uses the Parametric Energy-Based Aircraft Configuration Evaluator, an aircraft sizing framework for novel aircraft and propulsion system architectures, to pursue multi-objective optimization of a lift-plus-cruise urban air mobility aircraft with all-electric, hybrid-electric, and turbo-electric propulsion system architectures using Nondominated Sorting Genetic Algorithm II. The optimization cases considered include multiple objective functions, such as maximum takeoff mass, mission time, energy and propulsion mass fraction, and energy used per unit distance per unit payload. Optimization was performed for trip distances of 80, 120, and 150 km and battery specific energy levels of 350 and 400 Wh/kg. Except when mission time was an objective function, Pareto-optimal designs occurred near the lower bound of the velocity range. Raising that bound had an impact on the architectural composition of the final generation. All-electric architectures appeared exclusively for lower trip distances, hybrid-electric designs appeared for longer trip distances, and turbo-electric designs only appeared for combinations of longer trip distances, a higher minimum cruise speed, and a lower battery specific energy level. The sizing results were sensitive to assumptions regarding the overload capacity of lift propulsor motors in postfailure conditions.

FRAMED: An AutoML Approach for Structural Performance Prediction of Bicycle Frames

This paper presents a data-driven analysis of the structural performance of 4500 community-designed bicycle frames. We introduce FRAMED – a parametric dataset of bicycle frames based on bicycles designed by bicycle practitioners from across the world. To support our data-driven approach, we also provide a dataset of structural performance values such as weight, displacements under load, and safety factors for all the bicycle frame designs. Our structural simulations are validated against results from physical experiments on real bicycle frames. By exploring a diverse design space of frame design parameters and a set of ten competing design objectives, we present a data-driven approach to analyze the structural performance of bicycle frames. Through our analysis, we highlight overall trends in bicycle frame designs created by community members and study several bicycle frames under different loading conditions. We then undertake a systematic search for optimal performance and feasibility-predictive Machine Learning models, applying a state-of-the-art Automated Machine Learning framework. We demonstrate that the proposed AutoML models outperform commonly used models such as Neural Networks and XGBoost, which we tune using Bayesian hyperparameter optimization. This work aims to simultaneously serve researchers focusing on bicycle design as well as researchers focusing on the development of data-driven design algorithms, such as surrogate models and Deep Generative Models. The dataset and code are provided at http://decode.mit.edu/projects/framed/ . • We introduce a dataset of 4500 bicycle frames inspired by bicycles designed by community members using the BikeCAD software. For each frame, we provide ten structural performance indicators evaluating the frame's performance under three load cases (in-plane, transverse, and eccentric loading). Indicators consist of seven deflections, two safety factors, and a weight value and are calculated through Finite Element Analysis. • We validate our Finite Element Analysis framework through a mesh convergence study and verify the accuracy of our simulation results against physical testing of bicycle frames. • We identify optimal surrogate models which predict the performance and feasibility of frames. Surrogates are selected using an AutoML framework which automates the selection of algorithms, architectures, hyperparameters, and instantiations for optimal model performance. AutoML models achieved a coefficient of determination of 0.605 in structural performance prediction and an F1 score of 0.915 in feasibility classification. • We validate our proposed AutoML framework against several common models (Neural Networks, XGBoost, etc.) which we optimize using Bayesian hyperparameter tuning. The proposed AutoML models attain the best coefficient of determination and mean absolute error among all methods tested in regression and the best F1 score, precision, recall, accuracy, and ROC AUC in classification.

Multi-objective Bayesian topology optimization of a lattice-structured heat sink in natural convection

Additive manufacturing (AM) has an affinity with topology optimization to think of various designs with complex structures. Hence, this paper aims to optimize the design of a lattice-structured heat sink, which can be manufactured by AM. The design objectives are to maximize the thermal performance of convective heat transfer in natural convection simulated by computational fluid dynamics (CFD) and to minimize the material cost required for AM process at the same time. The lattice structure is represented as a node/edge system via graph theory with a moderate number of design variables. Bayesian optimization, which employs the non-dominated sorting genetic algorithm II and the Kriging surrogate model, is conducted to search for better designs with the minimum CFD cost. The present topology optimization successfully finds better lattice-structured heat sink designs than a reference fin-structured design regarding thermal performance and material cost. Also, several optimized lattice-structured designs outperform reference pin-fin-structured designs regarding thermal performance though the pin-fin structure is still advantageous for a material cost-oriented design. This paper also discusses the flow mechanism observed in the heat sink to explain how the optimized heat sink structure satisfies the competing design objectives simultaneously.

Optimal placement of fixation system for scaffold-based mandibular reconstruction

A current challenge in bone tissue engineering is to create favourable biomechanical conditions conducive to tissue regeneration for a scaffold implanted in a segmental defect. This is particularly the case immediately following surgical implantation when a firm mechanical union between the scaffold and host bone is yet to be established via osseointegration. For mandibular reconstruction of a large segmental defect, the position of the fixation system is shown here to have a profound effect on the mechanical stimulus (for tissue regeneration within the scaffold), structural strength, and structural stiffness of the tissue scaffold-host bone construct under physiological load. This research combines computer tomography (CT)-based finite element (FE) modelling with multiobjective optimisation to determine the optimal height and angle to place a titanium fixation plate on a reconstructed mandible so as to enhance tissue ingrowth, structural strength and structural stiffness of the scaffold-host bone construct. To this end, the respective design criteria for fixation plate placement are to: (i) maximise the volume of the tissue scaffold experiencing levels of mechanical stimulus sufficient to initiate bone apposition, (ii) minimise peak stress in the scaffold so that it remains intact with a diminished risk of failure and, (iii) minimise scaffold ridge displacement so that the reconstructed jawbone resists deformation under physiological load. First, a CT-based FE model of a reconstructed human mandible implanted with a bioceramic tissue scaffold is developed to visualise and quantify changes in the biomechanical responses as the fixation plate's height and/or angle are varied. The volume of the scaffold experiencing appositional mechanical stimulus is observed to increase with the height of the fixation plate. Also, as the principal load-transfer mechanism to the scaffold is via the fixation system, there is a significant ingress of appositional stimulus from the buccal side towards the centre of the scaffold, notably in the region bounded by the screws. Next, surrogate modelling is implemented to generate bivariate cubic polynomial functions of the three biomechanical responses with respect to the two design variables (height and angle). Finally, as the three design objectives are found to be competing, bi- and tri-objective particle swarm optimisation algorithms are invoked to determine the most optimal Pareto solution, which represents the best possible trade-off between the competing design objectives. It is recommended that consideration be given to placing the fixation system along the upper boundary of the mandible with a small clockwise rotation about its posterior end. The methodology developed here forms a useful decision aid for optimal surgical planning.

A two-stage design optimization framework for multifield origami-inspired structures

Different types of active materials have been used to actuate origami-inspired self-folding structures. To model the highly nonlinear deformation and material responses, as well as the coupled field equations and boundary conditions of such structures, high-fidelity models such as finite element (FE) models are needed but usually computationally expensive, which makes optimization intractable. In this paper, a computationally efficient two-stage optimization framework is developed as a systematic method for the multi-objective designs of such multifield self-folding structures where the deformations are concentrated in crease-like areas, active and passive materials are assumed to behave linearly, and low- and high-fidelity models of the structures can be developed. In Stage 1, low-fidelity models are used to determine the topology of the structure. At the end of Stage 1, a distance measure [Formula: see text] is applied as the metric to determine the best design, which then serves as the baseline design in Stage 2. In Stage 2, designs are further optimized from the baseline design with greatly reduced computing time compared to a full FEA-based topology optimization. The design framework is first described in a general formulation. To demonstrate its efficacy, this framework is implemented in two case studies, namely, a three-finger soft gripper actuated using a PVDF-based terpolymer, and a 3D multifield example actuated using both the terpolymer and a magneto-active elastomer, where the key steps are elaborated in detail, including the variable filter, metrics to select the best design, determination of design domains, and material conversion methods from low- to high-fidelity models. In this paper, analytical models and rigid body dynamic models are developed as the low-fidelity models for the terpolymer- and MAE-based actuations, respectively, and the FE model of the MAE-based actuation is generalized from previous work. Additional generalizable techniques to further reduce the computational cost are elaborated. As a result, designs with better overall performance than the baseline design were achieved at the end of Stage 2 with computing times of 15 days for the gripper and 9 days for the multifield example, which would rather be over 3 and 2 months for full FEA-based optimizations, respectively. Tradeoffs between the competing design objectives were achieved. In both case studies, the efficacy and computational efficiency of the two-stage optimization framework are successfully demonstrated.

Optimization tool based on multi-objective adaptive surrogate modeling for surface texture design of slipper bearing in axial piston pump

A novel multiobjective adaptive surrogate model is employed for the prediction of full-film lubrication performance of surface texture profile for thrust bearings in axial piston pumps. At present, a surface texture optimization design of thrust bearing in axial piston pump has not been provided based on a unique design solutions. In this study, multi-objective adaptive surrogate-based optimization (MO-ASMO) algorithm was used for solving both the mechanical and volumetric losses performance design of surface texture. The non-dominated sorting genetic algorithm (NSGA-II) formed part of the developed multi-objective optimization model, having three competing design objectives which mainly include load capacity, friction torque and leakage rate. A comparative study of two models with accuracy analysis of each case was performed. The results show that the MO-ASMO has the best performance among the different surrogate models. Comparing with the NSGA-II model, the improvement of the leakage rate and the load carrying force made by MO-ASMO are higher about 16.12% and 3.67%, respectively. In other words, multi-objective optimization is capable of enhancing textured slipper bearing performance using MO-ASMO method. The optimal texture radius and texture depth are chosen to minimize the leakage rate and texture bearing capacity.

Integrating human factors early in the design process using digital human modelling and surrogate modelling

Embedding ergonomics in the design process is often delayed as it requires building physical prototypes and human subject data collection. This causes retrofitting or expensive late design changes which increase overall cost and time-to-market. This paper presents a computational design methodology that employs Digital Human Modelling (DHM) research and the surrogate modelling technique to integrate ergonomics early in design. The goal of this methodology is to provide a proactive ergonomics assessment toolset that allows designers to identify the effects of design variables on human performance, computationally. In this research, a design space exploration study was conducted to evaluate the ergonomics of a concept aircraft cockpit model built based on a set of flight deck layout variables. Variability in the pilot's anthropometry was added through accommodating the extremities in population-based on computational human manikins. A genetic algorithm with the surrogate model was used to search the concept variants that meet ergonomics objectives. The results show that the proposed proactive ergonomics approach allows designers to explore the design space and evaluate trade-offs between the competing design objectives early in design. Our computational ergonomics approach, based on the cockpit packaging study, demonstrates the advantages of the DHM based surrogate modelling during product conceptualisation.

Comparison of Multi-Objective Evolutionary Algorithms to Solve the Modular Cell Design Problem for Novel Biocatalysis

A large space of chemicals with broad industrial and consumer applications could be synthesized by engineered microbial biocatalysts. However, the current strain optimization process is prohibitively laborious and costly to produce one target chemical and often requires new engineering efforts to produce new molecules. To tackle this challenge, modular cell design based on a chassis strain that can be combined with different product synthesis pathway modules has recently been proposed. This approach seeks to minimize unexpected failure and avoid task repetition, leading to a more robust and faster strain engineering process. In our previous study, we mathematically formulated the modular cell design problem based on the multi-objective optimization framework. In this study, we evaluated a library of state-of-the-art multi-objective evolutionary algorithms (MOEAs) to identify the most effective method to solve the modular cell design problem. Using the best MOEA, we found better solutions for modular cells compatible with many product synthesis modules. Furthermore, the best performing algorithm could provide better and more diverse design options that might help increase the likelihood of successful experimental implementation. We identified key parameter configurations to overcome the difficulty associated with multi-objective optimization problems with many competing design objectives. Interestingly, we found that MOEA performance with a real application problem, e.g., the modular strain design problem, does not always correlate with artificial benchmarks. Overall, MOEAs provide powerful tools to solve the modular cell design problem for novel biocatalysis.

High-resolution multi-objective optimization of feedstock landscape design for hybrid first and second generation biorefineries

Biofuels have been proposed as a potential solution for climate change mitigation. However, there exist several barriers, such as “food vs fuel” issues and technological constraints, restricting the sustainable commercialization of both first- and second-generation biofuels. Combining arable crops and their residues for hybrid first- and second-generation biofuel production provides opportunities to overcome these barriers. This study presents a high-resolution quantitative tool to support decision-making in feedstock production and sourcing for hybrid biofuel supply chains. We demonstrate this work with a case study on optimizing feedstock landscape design for a hybrid corn grain- and stover-based ethanol production system at Front Range Energy biorefinery, Windsor, Colorado, USA using a coupled simulation modeling and life-cycle assessment approach. The case study considered three competing design objectives including the minimization of feedstock-delivered costs, farm-to-refinery greenhouse gas emissions (GHG), and nitrogen (N) leaching, subject to constraints in land use and biofuel feedstock demand. Social costs of carbon (SC-CO2) and nitrogen leaching (SC-NL) were used as weighting factors for GHG and N leaching in the objective function. Our results showed that marginal decreases of feedstock-delivered costs (below $0.31 L−1), N leaching (below 0.44 g N L−1), and GHG emissions (below 125 g CO2e L−1) resulted in extreme trade-offs among the design objectives. Changes in feedstock landscape design were most sensitive to the variations of the SC-CO2 between $400 and $800 per Mg CO2e, SC-NL between $0 and $50 per kg N leaching, and their ratio between 0 and 350, respectively.

Texture mediated grain boundary network design in three dimensions

Experimental grain boundary engineering studies have demonstrated the potential for materials properties enhancement via the modification of grain boundary network structure. These techniques apply to materials that readily form annealing twins and are amenable to cyclic thermomechanical processing and have resulted in dramatic property enhancement. In this work we present a theoretical framework that enables the design of grain boundary networks in polycrystalline materials through an alternative approach: exploitation of a relationship between crystallographic texture and grain boundary network structure. Because crystallographic texture is a universal characteristic of polycrystalline materials, this work has the potential to significantly expand the class of materials whose grain boundary networks can be controlled. We demonstrate the utility of the approach by application to a concrete design problem involving competing design objectives for yield strength, elastic compliance, and resistance to electromigration. We construct the first materials properties closure to comprise grain boundary network sensitive properties and identify an optimal microstructure that is predicted to outperform an undesigned isotropic material.

Feature Selection Methods for Zero-Shot Learning of Neural Activity.

Dimensionality poses a serious challenge when making predictions from human neuroimaging data. Across imaging modalities, large pools of potential neural features (e.g., responses from particular voxels, electrodes, and temporal windows) have to be related to typically limited sets of stimuli and samples. In recent years, zero-shot prediction models have been introduced for mapping between neural signals and semantic attributes, which allows for classification of stimulus classes not explicitly included in the training set. While choices about feature selection can have a substantial impact when closed-set accuracy, open-set robustness, and runtime are competing design objectives, no systematic study of feature selection for these models has been reported. Instead, a relatively straightforward feature stability approach has been adopted and successfully applied across models and imaging modalities. To characterize the tradeoffs in feature selection for zero-shot learning, we compared correlation-based stability to several other feature selection techniques on comparable data sets from two distinct imaging modalities: functional Magnetic Resonance Imaging and Electrocorticography. While most of the feature selection methods resulted in similar zero-shot prediction accuracies and spatial/spectral patterns of selected features, there was one exception; A novel feature/attribute correlation approach was able to achieve those accuracies with far fewer features, suggesting the potential for simpler prediction models that yield high zero-shot classification accuracy.

Analytical results for the multi-objective design of model-predictive control

In model-predictive control (MPC), achieving the best closed-loop performance under a given computational capacity is the underlying design consideration. This paper analyzes the MPC tuning problem with control performance and required computational capacity as competing design objectives. The proposed multi-objective design of MPC (MOD-MPC) approach extends current methods that treat control performance and the computational capacity separately – often with the latter as a fixed constraint – which requires the implementation hardware to be known a priori. The proposed approach focuses on the tuning of structural MPC parameters, namely sampling time and prediction horizon length, to produce a set of optimal choices available to the practitioner. The posed design problem is then analyzed to reveal key properties, including smoothness of the design objectives and parameter bounds, and establish certain validated guarantees. Founded on these properties, necessary and sufficient conditions for an effective and efficient optimizer are presented, leading to a specialized multi-objective optimizer for the MOD-MPC being proposed. Finally, two real-world control problems are used to illustrate the results of the tuning approach and importance of the developed conditions for an effective optimizer of the MOD-MPC problem.

Computational multiobjective topology optimization of silicon anode structures for lithium-ion batteries

This study utilizes computational topology optimization methods for the systematic design of optimal multifunctional silicon anode structures for lithium-ion batteries. In order to develop next generation high performance lithium-ion batteries, key design challenges relating to the silicon anode structure must be addressed, namely the lithiation-induced mechanical degradation and the low intrinsic electrical conductivity of silicon. As such this work considers two design objectives, the first being minimum compliance under design dependent volume expansion, and the second maximum electrical conduction through the structure, both of which are subject to a constraint on material volume. Density-based topology optimization methods are employed in conjunction with regularization techniques, a continuation scheme, and mathematical programming methods. The objectives are first considered individually, during which the influence of the minimum structural feature size and prescribed volume fraction are investigated. The methodology is subsequently extended to a bi-objective formulation to simultaneously address both the structural and conduction design criteria. The weighted sum method is used to derive the Pareto fronts, which demonstrate a clear trade-off between the competing design objectives. A rigid frame structure was found to be an excellent compromise between the structural and conduction design criteria, providing both the required structural rigidity and direct conduction pathways. The developments and results presented in this work provide a foundation for the informed design and development of silicon anode structures for high performance lithium-ion batteries.

Balancing technical and user objectives in the redesign of a peruvian cookstove

Over the past decade a large amount of research has been dedicated in academic literature to improving the technical capabilities of improved cookstoves; primarily the performance efficiency and reduction of emissions. Unfortunately, as published literature has highlighted, the trade-offs that result from placing such a concentrated emphasis on these technical objectives is that improved cookstoves lack the same level of usability as the traditional cookstoves. Thus, users often return to using their traditional stoves and the potential impact of the improved cookstoves is never fully realized. In order for improved cookstoves to have greater impact, there must be better balance between the two competing design objectives of technical capabilities and usability. This paper explores the challenges and benefits associated with achieving the appropriate balance, and demonstrates its application through the modification of a traditional cookstove used in the Tambogrande region of Peru. This modification includes an inexpensive set of pot skirts that integrate directly with the traditional stove. These pot skirts allow for varying sizes and number of pots, and the use of traditional fuels. Laboratory testing, using the Water Boiling Test (WBT), identified the skirts' technical improvements: 41.7% increased thermal efficiency, 32.7% decreased fuel consumption, 28.8% decreased time to boil. Field testing was performed to determine the pot skirts acceptance and compatibility with the traditional cookstoves, with over 75% of the participants recognizing some type of benefit. Although the technical improvements of these pot skirts are less than other cookstoves on the market, the higher levels of usability are likely to lead to a more positive end-user reaction, which could potentially lead to higher rates of adoption and impact. Though these results are primarily focused on the application of improved cookstoves, the need for more balance between technical and user objectives is applicable to nearly all products being designed for the developing world.

Airfoil Optimization Using Practical Aerodynamic Design Requirements

Practical aerodynamic design problems must balance the goal of performance optimization over a range of on-design operating conditions with the need to meet design constraints at various o-design operating conditions. Such design problems can be cast as multipoint optimization problems where the on-design and o-design operating conditions are represented as design points with corresponding objective/constraint functions. Two methods are presented for obtaining optimal airfoil designs that satisfy all design objectives and constraints. The first method uses an unconstrained optimization algorithm where the optimal design is achieved by minimizing a weighted sum of the objective functions at each of the operating conditions. To address the competing design objectives between on-design and o-design operating conditions, an automated procedure is used to eciently weight the o-design objective functions so as to limit their influence on the overall optimization while satisfying the design constraints. The second method uses the constrained optimization algorithm SNOPT, which allows the aerodynamic constraints imposed at the o-design operating conditions to be treated explicitly. Both methods are applied to the design of an airfoil for a hypothetical aircraft where the problem is formulated as an 18-point multipoint optimization.

PediaFlow™ Maglev Ventricular Assist Device: A Prescriptive Design Approach.

This report describes a multi-disciplinary program to develop a pediatric blood pump, motivated by the critical need to treat infants and young children with congenital and acquired heart diseases. The unique challenges of this patient population require a device with exceptional biocompatibility, miniaturized for implantation up to 6 months. This program implemented a collaborative, prescriptive design process, whereby mathematical models of the governing physics were coupled with numerical optimization to achieve a favorable compromise among several competing design objectives. Computational simulations of fluid dynamics, electromagnetics, and rotordynamics were performed in two stages: first using reduced-order formulations to permit rapid optimization of the key design parameters; followed by rigorous CFD and FEA simulations for calibration, validation, and detailed optimization. Over 20 design configurations were initially considered, leading to three pump topologies, judged on the basis of a multi-component analysis including criteria for anatomic fit, performance, biocompatibility, reliability, and manufacturability. This led to fabrication of a mixed-flow magnetically levitated pump, the PF3, having a displaced volume of 16.6 cc, approximating the size of a AA battery and producing a flow capacity of 0.3-1.5 L/min. Initial in vivo evaluation demonstrated excellent hemocompatibility after 72 days of implantation in an ovine. In summary, combination of prescriptive and heuristic design principles have proven effective in developing a miniature magnetically levitated blood pump with excellent performance and biocompatibility, suitable for integration into chronic circulatory support system for infants and young children; aiming for a clinical trial within 3 years.

Probabilistic building block identification for the optimal design and rehabilitation of water distribution systems

The multi-objective design and rehabilitation of water distribution systems (WDS) is defined as the search for the set of system designs which offers the best trade-off between competing design objectives. Typically these objectives will consist of the cost of implementing a system design and a measure of the performance of that system. These measures are often in competition since improvements in the performance of a system generally come at a cost. Here three genetic algorithms which use probabilistic methods to identify building blocks—the Univariate Marginal Distribution Algorithm (UMDA) (Mühlenbein 1997), the hierarchical Bayesian Optimisation Algorithm (hBOA) (Pelikan 2002) and the Chi-Square Matrix methodology (Aporntewan & Chongstitvatana 2004)—are compared to the well-known multi-objective evolutionary algorithm NSGAII (Deb et al. 2002) for the multi-objective design and rehabilitation of water distribution systems. For single-objective problems the identification of building blocks has been seen to make evolutionary algorithms more scalable to large problems than simple genetic algorithms. In this paper these algorithms are shown to offer significantly better solutions than NSGA-II for the case of large systems. However, this improvement comes at the expense of diversity of solutions in the fronts identified.

Modeling and analysis of security trade-offs – A goal oriented approach

In designing software systems, security is typically only one design objective among many. It may compete with other objectives such as functionality, usability, and performance. Too often, security mechanisms such as firewalls, access control, or encryption are adopted without explicit recognition of competing design objectives and their origins in stakeholders’ interests. Recently, there is increasing acknowledgement that security is ultimately about trade-offs. One can only aim for “good enough” security, given the competing demands from many parties. This paper investigates the criteria for a conceptual modeling technique for making security trade-offs. We examine how conceptual modeling can provide explicit and systematic support for modeling and analyzing security trade-offs. We examine several existing approaches for dealing with trade-offs and security trade-offs in particular. From analyzing the limitations of existing methods, we propose an extension to the i ∗ Framework for security trade-off analysis, taking advantage of its multi-agent and goal orientation. The method was applied to several case studies used to exemplify existing approaches. The resulting models developed using different approaches are compared.

Pareto optimization of analog circuits considering variability

AbstractThe trade‐off between competing design objectives is often decided at the beginning of the analog sizing process by assigning weights to the design objectives. Architectural design, however, requires the knowledge of all possible optimal trade‐offs of a building block. Methods for Pareto optimization provide the set of all optimal trade‐offs, the so‐called Pareto front. The next generation of analog Pareto optimization tools additionally has to consider the manufacturing variations. This paper describes an approach to this challenging problem. First, analog Pareto optimization will be developed as a set of specific nonlinear programming problems that provide a discretization of the Pareto front of competing performance features. Second, a general problem formulation to consider manufacturing variations and operating ranges of circuit parameters in analog Pareto optimization will be presented. Third, an efficient solution approach to the resulting tolerance Pareto optimization problem will be described. It is based on a combination of nominal Pareto optimization and realistic worst‐case analysis on discrete Pareto points. It provides high efficiency at acceptable loss in accuracy. Copyright © 2008 John Wiley & Sons, Ltd.

Applying axiomatic design theory to the evaluation and optimization of large-scale engineering systems

Large-scale engineering systems provide important functions to the human society. The involvement of multiple, competing functionality requirements and lots of resources has imposed high expectations, and at the same time challenges, for achieving reliable, affordable design. Axiomatic design approach has demonstrated its strength in various types of large-scale system design, including vehicles, aircrafts, manufacturing facilities, and so on. However, several obstacles are yet to be overcome in proper application of the axioms to a real-world design problem, by both researchers and practitioners. In this work, a systematic methodology is presented for applying Suh’s axioms to evaluate and optimize large-scale engineering systems. When using the proposed methodology, design matrices are first obtained to map multiple, competing functionality requirements to their associated physical embodiments at different system hierarchical levels based on Axiom 1. An R/S analysis using surrogate modelling is then conducted on the design matrices to evaluate the existing system design. If the evaluation shows any areas for improvement, a combination of surrogate modelling-based R/S analysis and optimization is used to achieve a less functionally coupled design. An application in a nuclear reactor system design is used to demonstrate the use of the proposed methodology in dealing with real-world design problems. The results show that the proposed methodology provides a promising approach to efficiently evaluate and optimize a large-scale engineering system against multiple, competing design objectives.