Complex Design Problems Research Articles

Predicting Sequential Design Decisions Using the Function-Behavior-Structure Design Process Model and Recurrent Neural Networks

Abstract In engineering systems design, designers iteratively go back and forth between different design stages to explore the design space and search for the best design solution that satisfies all design constraints. For complex design problems, human has shown surprising capability in effectively reducing the dimensionality of design space and quickly converging it to a reasonable range for algorithms to step in and continue the search process. Therefore, modeling how human designers make decisions in such a sequential design process can help discover beneficial design patterns, strategies, and heuristics, which are essential to the development of new algorithms embedded with human intelligence to augment the computational design. In this paper, we develop a deep learning-based approach to model and predict designers’ sequential decisions in the systems design context. The core of this approach is an integration of the function-behavior-structure (FBS) model for design process characterization and the long short-term memory unit (LSTM) model for deep leaning. This approach is demonstrated in two case studies on solar energy system design, and its prediction accuracy is evaluated benchmarking on several commonly used models for sequential design decisions, such as the Markov Chain model, the Hidden Markov Chain model, and the random sequence generation model. The results indicate that the proposed approach outperforms the other traditional models. This implies that during a system design task, designers are very likely to rely on both short-term and long-term memory of past design decisions in guiding their future decision-making in the design process. Our approach can support human–computer interactions in design and is general to be applied in other design contexts as long as the sequential data of design actions are available.

Adaptive Multi-Level Search for Global Optimization: An Integrated Swarm Intelligence-Metamodelling Technique

Over the last decade, metaheuristic algorithms have emerged as a powerful paradigm for global optimization of multimodal functions formulated by nonlinear problems arising from various engineering subjects. However, numerical analyses of many complex engineering design problems may be performed using finite element method (FEM) or computational fluid dynamics (CFD), by which function evaluations of population-based algorithms are repetitively computed to seek a global optimum. It is noted that these simulations become computationally prohibitive for design optimization of complex structures. To efficiently and effectively address this class of problems, an adaptively integrated swarm intelligence-metamodelling (ASIM) technique enabling multi-level search and model management for the optimal solution is proposed in this paper. The developed technique comprises two steps: in the first step, a global-level exploration for near optimal solution is performed by adaptive swarm-intelligence algorithm, and in the second step, a local-level exploitation for the fine optimal solution is studied on adaptive metamodels, which are constructed by the multipoint approximation method (MAM). To demonstrate the superiority of the proposed technique over other methods, such as conventional MAM, particle swarm optimization, hybrid cuckoo search, and water cycle algorithm in terms of computational expense associated with solving complex optimization problems, one benchmark mathematical example and two real-world complex design problems are examined. In particular, the key factors responsible for the balance between exploration and exploitation are discussed as well.

Advanced hybrid optimization methods for the design of complex separation processes

While the need for more efficient and integrated processes for the separation of non-ideal and azeotropic mixtures mandates model-based design methods, the resulting mixed-integer problems are highly nonlinear and particularly hard to solve without proper initialization. In order to handle such complex design problems, two hybrid optimization methods are presented in the current contribution. Both methods rely on a nested approach, which integrates a polylithic modeling and solution strategy, based on the solution of successively refined nonlinear programming problems, with an upper level metaheuristic for the initialization and optimization of discrete design decisions. Both methods are evaluated for two case studies. The optimization of a simple column design problem allows for the direct comparison with an available global deterministic optimization solver for identifying the global optimal solution. The optimization of an extractive distillation process with simultaneous solvent selection and energy integration illustrates the potential complexity that can be handled effectively.

Optimal tuned mass dampers for wind turbines using a Sigmoid satisfaction function‐based multiobjective optimization during earthquakes

AbstractRecent installation of many wind turbines in earthquake‐prone areas worldwide has increased concerns regarding their seismic performance. This study introduces a multiobjective optimization design method using a modified Sigmoid satisfaction function to efficiently minimize the seismic response by installing tuned mass dampers (TMDs) on wind turbines. Inspired by the human decision‐making process, the modified Sigmoid function was utilized to improve the satisfaction function and balance the optimization objectives. Incorporating the particle swarm optimization algorithm resulted in lower sensitivity to the combination weighting factors and significantly reduced the effort required to calibrate the weighting factors for any practical multiobjective optimization problem. To verify the efficacy of the proposed methodology, a TMD was designed on a National Renewable Energy Laboratory (NREL) 5‐MW benchmark wind turbine, with constraints on both the top displacement and TMD stroke as design objectives. The results were compared to those obtained using the conventional linear weighted sum method, thereby highlighting the advantages of the proposed method. The scope of application of the proposed method is further demonstrated by a more complex TMD design problem involving 13 design objectives with typical seismic excitations.

Design of aperiodic spherical antenna arrays for wideband performance

This paper illustrates a highly nonlinear problem related to the design of aperiodic antenna arrays using spherical geometry for maximum performance. This synthesis of aperiodic spherical antenna arrays considers two design cases: (1) aperiodic arrays with a nonuniform spacing between rings assuming equal element spacings on the same ring (ASANRUE case) and (2) aperiodic arrays with a nonuniform spacing between rings and between antenna elements of the same ring (ASANRNE case). This process is carried out by using the differential evolution (DE) optimization technique with the goal of finding the optimum angular position of each element on the elevation and azimuthal planes to achieve the maximum performance in terms of the side lobe level, directivity, and number of antenna elements used in the array for three design objectives: natural response of the radiation pattern, beam scanning, and the restriction of a minimum distance among elements. The innovative contribution of this paper is the application of the differential evolution algorithm to a nontrivial and highly complex design problem: the aperiodic element distribution over a spherical antenna arrangement. The advantage of this approach with respect to the state of the art on the topic of spherical array design is that a lower side lobe level and maximum directivity for a spherical antenna array can be obtained for aperiodic element distributions with a reduced number of antenna elements and hence a smaller radius, which means less complexity and lower fabrication costs. Simulation results based on CST Microwave Studio are provided to take mutual coupling into account.

Building a Foundation for Data-Driven, Interpretable, and Robust Policy Design using the AI Economist

Optimizing economic and public policy is critical to address socioeconomic issues and trade-offs, e.g., improving equality, productivity, or wellness, and poses a complex mechanism design problem.A policy designer needs to consider multiple objectives, policy levers, and behavioral responses from strategic actors who optimize for their individual objectives.Moreover, real-world policies should be explainable and robust to simulation-to-reality gaps, e.g., due to calibration issues.Existing approaches are often limited to a narrow set of policy levers or objectives that are hard to measure, do not yield explicit optimal policies, or do not consider strategic behavior, for example.Hence, it remains challenging to optimize policy in real-world scenarios.Here we show that the AI Economist framework enables effective, flexible, and interpretable policy design using two-level reinforcement learning (RL) and data-driven simulations.We validate our framework on optimizing the stringency of \USState{} policies and Federal subsidies during a pandemic, e.g., COVID-19, using a simulation fitted to real data.We find that log-linear policies trained using RL significantly improve social welfare, based on both public health and economic outcomes, compared to past outcomes.Their behavior can be explained, e.g., well-performing policies respond strongly to changes in recovery and vaccination rates.They are also robust to calibration errors, e.g., infection rates that are over or underestimated.As of yet, real-world policymaking has not seen adoption of machine learning methods at large, including RL and AI-driven simulations.Our results show the potential of AI to guide policy design and improve social welfare amidst the complexity of the real world.

Improvement of the Optimal Design Procedure Using Randomized Algorithm and Process Simulators

Though a global optimization procedure using a randomized algorithm and a commercial process simulator is relatively easy to implement for complex design problems (i.e., intensified design processes), a dominant problem is their heavy computation load. As the process simulation is repeatedly executed to calculate the objective function, it is inevitable to spend long computation time to derive the optimal solution. Also, the randomized algorithms consider the treatment of all variables as continuous. Thus, the reduction of the number of iterations is crucial for such optimization procedures that include integer variables. In this work, an estimation procedure of the objective function having integer design variables is proposed. In the proposed procedure, the values of the objective function at the nodes of hyper-triangle that includes the suggested next search point are used to estimate the objective function, at the same time normalization of the design optimization variables is recommended. The procedure was implemented on the simulated annealing stochastic algorithm with a trivial case of a binary mixture in order to know the optimal solution and compare the traditional optimizations procedures and the proposed one. The proposed procedure show improvement not only for reducing the number iterations, but also for an increase of accuracy of finding the optimal solution.

Adjoint-based topology optimization to reduce the energy consumption of a Hall effect thruster

PurposeThe presented study aims to minimize the energy consumed by a Hall effect thruster (HET) under a constraint which makes it possible to generate a specified magnetic field in a target region of the thruster.Design/methodology/approachHerein topology optimization (TO) is used to reduce the energy consumption of an HET while keeping its performance unchanged. The design variables are the current densities in the coils and the distribution of materials in the polar pieces of the thruster. Intermediate values of material distribution are penalized using the solid isotropic material with penalization method to favor binary solutions. By means of the adjoint method, this paper provides the derivatives of the objective and constraint functions with respect to material distribution and current density variables.FindingsThe TO-based design methodology is developed and validated on a design example involving 2,051 variables. The approach shows its interest and its effectiveness of on a large scale two-criteria problem.Research limitations/implicationsIn this paper, TO is presented as a tool that has allowed to explore new and innovative designs. However, although the design presented is original, its fabrication is not feasible. Despite this, the designs found give a good idea of the starting points for shape and parametric optimization tools.Practical implicationsThrough the HET design problem, TO shows the ability to explore more original design possibilities of a complex magnetostatic design problem and to discover designs that make a HET more efficient with respect to several criteria at the same time.Originality/valueA new way to reduce the energy consumption of a HET is presented. To achieve this, an adjoint-based TO method is developed and then implemented in a simple way. This approach shows that, for efficiency purposes, TO is a key tool for extending the state of the art of HET designs.

A novel chaotic manta-ray foraging optimization algorithm for thermo-economic design optimization of an air-fin cooler

This research study aims to introduce chaos theory into the Manta Ray Foraging Optimization (MRFO) Algorithm and optimize a real-world design problem through the chaos-enhanced versions of this method. Manta Ray Foraging Optimization algorithm is a bio-inspired swarm intelligence-based metaheuristic algorithm simulating the distinctive food search behaviors of the manta rays. However, MRFO suffers from some intrinsic algorithmic inefficiencies such as slow and premature convergence and unexpected entrapment to the local optimum points in the search domain like most of the metaheuristic algorithms in the literature. Recently, random numbers generated by chaos theory have been incorporated into the metaheuristic algorithms to solve these problems. More than twenty chaotic maps are applied to the base algorithm and ten best performing methods are considered for performance evaluation on high-dimensional optimization test problems. Forty test problems comprising unimodal and multimodal functions have been solved by chaotic variants of MRFO and extensive statistical analysis is performed. Furthermore, thermo-economic design optimization of an air-fin cooler is maintained by the chaotic MRFO variants to assess their optimization capabilities over complex engineering design problems. Ten decisive design variables of an air fin cooler are optimized in terms of total annual cost rates and optimum solutions obtained by five best chaotic MRFO algorithms are compared to the preliminary design. A significant improvement is observed in the objective function values when MRFO with chaotic operators is applied to this considered thermal design problem.

Materialist epistemology lends design wings: educational design as an embodied process

Materialist design is presented as an embodied perspective on educational design that can be applied to redesign of classroom-based learning environments. Materialist design is informed by a framework of materialist epistemology, which positions material innovation on equal placement with symbol-based formal theory. Historical examples of Einstein’s conceptual reliance on trains for his Theory of Relativity and the Wright brothers’ use of wind tunnels in aeronautics illustrate how materialist design drives progress on complex design problems. A key aspect is the application of scale-down methodology, where complex systems are reconceptualized as interactions among nearly decomposable subsystems that can be redesigned and integrated back into the entire system. The application of materialist design is illustrated with the redesign of an embodied video game that uses real-time motion capture technology to promote high school geometry reasoning and proof, following its use in an ethnically and linguistically diverse classroom. Our embodied perspective offers particular insights for understanding and implementing designs of complex learning environments, and assessing their influences on educational practices and student outcomes.

Practice of Simulation and Life Cycle Assessment in Tribology-A Review.

To simulate today’s complex tribo-contact scenarios, a methodological breakdown of a complex design problem into simpler sub-problems is essential to achieve acceptable simulation outcomes. This also helps to manage iterative, hierarchical systems within given computational power. In this paper, the authors reviewed recent trends of simulation practices in tribology to model tribo-contact scenario and life cycle assessment (LCA) with the help of simulation. With the advancement of modern computers and computing power, increasing effort has been given towards simulation, which not only saves time and resources but also provides meaningful results. Having said that, like every other technique, simulation has some inherent limitations which need to be considered during practice. Keeping this in mind, the pros and cons of both physical experiments and simulation approaches are reviewed together with their interdependency and how one approach can benefit the other. Various simulation techniques are outlined with a focus on machine learning which will dominate simulation approaches in the future. In addition, simulation of tribo-contacts across different length scales and lubrication conditions is discussed in detail. An extension of the simulation approach, together with experimental data, can lead towards LCA of components which will provide us with a better understanding of the efficient usage of limited resources and conservation of both energy and resources.

Bayesian optimization of variable-size design space problems

Within the framework of complex system design, it is often necessary to solve mixed variable optimization problems, in which the objective and constraint functions can depend simultaneously on continuous and discrete variables. Additionally, complex system design problems occasionally present a variable-size design space. This results in an optimization problem for which the search space varies dynamically (with respect to both number and type of variables) along the optimization process as a function of the values of specific discrete decision variables. Similarly, the number and type of constraints can vary as well. In this paper, two alternative Bayesian optimization-based approaches are proposed in order to solve this type of optimization problems. The first one consists of a budget allocation strategy allowing to focus the computational budget on the most promising design sub-spaces. The second approach, instead, is based on the definition of a kernel function allowing to compute the covariance between samples characterized by partially different sets of variables. The results obtained on analytical and engineering related test-cases show a faster and more consistent convergence of both proposed methods with respect to the standard approaches.

A Review on Laser Assisted Jet Electrochemical Machining Process

Hybrid Machining Processes (HMP) consist of two different types of machining processes which are applied at the same time in the machining zone. These processes are applied for machining of various advanced materials of complex shapes and sizes. One of the important HMP is Laser Assisted Jet Electrochemical Machining (LAJECM) where a laser beam of low power and a jet of electrolyte are coaxially focused through a nozzle on a particular machine zone. Thus it eliminates the stray machining effect of ECM process. Machining zone temperature as well as electrolyte current density is increased by laser. Thus electrochemical dissolution rate and MRR are increased. In this process, jet of electrolyte is used as tool which eliminates the complex tool design problem of stand-alone ECM process. Laser assistance in ECM process improves precision, accuracy, surface quality and also productivity. Various researchers have performed their research work on LAJECM. This article focuses on various aspects and implementations of LAJECM.

Optimal Design of a Drive Shaft with Composite Materials Through Particle Swarm Optimization

This paper presents the use of metaheuristics to support the design of mechanical systems, as a proposal of robust design methodology. The Particle Swarm Optimization algorithm (PSO) approach in the drive shaft made of composite materials, in order to obtain the optimal design parameters to support the loads specific to which this element will be subject. The problem in this type of design, is to find the optimal thickness of the piece, i.e. the number of layers and their respective orientation that will form it, for which there are too many possible combinations when working with composite laminates, the proposed methodology looks for the best possible solution according to the parameters of the problem. The main contribution of this work is the use of metaheuristics to solve complex design problems and difficult to solve by traditional methods. The experimental results lead to the conclusion that this type of implementations is very useful to solve problems of numerical optimization in the design with composite materials for diverse engineering applications.

Multiobjective discrete optimization using the TOPSIS and entropy method for protection of pedestrian lower extremity

Design of vehicle frontal structures signifies an important topic of studies on protection of pedestrian lower extremity. Conventional optimization has been largely focused on continuous variable problems without involving complexity of human model. Nevertheless, design of frontal structures is commonly discrete from a manufacturing perspective and the responses of the lower extremity of pedestrian are highly nonlinear in nature. For this reason, this study aimed to develop a multiobjective discrete optimization (MODO) algorithm for design of frontal structures involving a pedestrian model. In the proposed MODO method, the order preference by similarity to ideal solution (TOPSIS) was coupled with the entropy algorithm to develop a multiple attribute decision making (MADM) model for converting multiple conflicting objectives into a unified single cost function. The presented optimization procedure is iterated using the successive orthogonal experiment to deal with a large number of design variables and design levels. In this study, the proposed method was first verified by two benchmark examples; and then was applied to a more sophisticated real-life design case of vehicle frontal structure to better protect pedestrian lower extremity from impacting injury. The finite element model was validated via experimental tests first and the optimized design was then prototyped for the further physical tests. The results showed that the algorithm is able to achieve an optimal design in a fairly efficient manner. The proposed algorithm exhibits considerable potential to solve other complex engineering design problems.

A Mixed-Integer Linear Programming Formulation for Optimizing Multi-Scale Material and Energy Integration

This research presents a mathematical formulation for optimising integration of complex industrial systems from the level of unit operations to processes, entire plants, and finally to considering industrial symbiosis opportunities between plants. The framework is constructed using mixed-integer linear programming (MILP) which exhibits rapid conversion and a global optimum with well-defined solution methods. The framework builds upon previous efforts in process integration and considers materials and energy with thermodynamic constraints imposed by formulating the heat cascade within the MILP. The model and method which form the fundamentals of process integration problems are presented, considering exchange restrictions and problem formulation across multiple time-scales to provide flexibility in solving complex design, planning and operational problems. The work provides the fundamental problem formulation, which has not been previously presented in a comprehensive way, to provide the basis for future work, where many process integration elements can be appended to the formulation. A case study is included to demonstrate the capabilities and results for a simple, fictional, example though the framework and method are broadly applicable across scale, time and plant complexity.

Deep learning approach based on dimensionality reduction for designing electromagnetic nanostructures

In this paper, we demonstrate a computationally efficient new approach based on deep learning (DL) techniques for analysis, design and optimization of electromagnetic (EM) nanostructures. We use the strong correlation among features of a generic EM problem to considerably reduce the dimensionality of the problem and thus, the computational complexity, without imposing considerable errors. By employing the dimensionality reduction concept using the more recently demonstrated autoencoder technique, we redefine the conventional many-to-one design problem in EM nanostructures into a one-to-one problem plus a much simpler many-to-one problem, which can be simply solved using an analytic formulation. This approach reduces the computational complexity in solving both the forward problem (i.e., analysis) and the inverse problem (i.e., design) by orders of magnitude compared to conventional approaches. In addition, it provides analytic formulations that, despite their complexity, can be used to obtain intuitive understanding of the physics and dynamics of EM wave interaction with nanostructures with minimal computation requirements. As a proof-of-concept, we applied such an efficacious method to design a new class of on-demand reconfigurable optical metasurfaces based on phase-change materials (PCMs). The experimental results of the fabricated devices are in good agreement with those predicted by the proposed approach. We envision that the integration of such a DL-based technique with full-wave commercial software packages offers a powerful toolkit to facilitate the analysis, design, and optimization of the EM nanostructures as well as explaining, understanding, and predicting the observed responses in such structures. It will thus enable to solve complex design problems that could not be solved with existing techniques.

A spatial kernel approach for topology optimization

This paper introduces a spatial kernel approach for topology optimization. This kernel influences the material distribution in the final design. The resulting spatial variables not only have physical meaning – such as the mass of the part, a stiffness gradient, or a local strengthening; but the spatial variables can also tailor the structural performances – such as the occupant protection – of the resulting design. This means only a few spatial variables are needed to represent a design otherwise described using millions of topology design variables. This ability to restate the problem in terms of the spatial variables unlocks new design schemes. Here we use a few spatial variables to address (i) constrained topology design optimization, (ii) to compute the derivatives of the constraints using numerical finite differences, and (iii) to build metamodels to the topology design optimization results. A significant benefit is therefore that complex design problems can be considered: the mechanics can include highly nonlinear events, while the use of metamodels allows a diversity of design optimization studies. Examples, including a simplified side impact occupant protection problem, are used to demonstrate.

Sensing for aerospace combustor health monitoring

Purpose This paper proposes new methods of fault detection for fuel systems in order to improve system availability. Novel fault systems are required for environmentally friendly lean burn combustion, but can carry high risk failure modes particularly through their control valves. The purpose of the developed technology is the rapid detection of these failure modes, such as valve sticking or impending sticking, and therefore to reduce this risk. However, sensing valve state is challenging due to hot environmental temperatures, which results in a low reliability for conventional position sensing. Design/methodology/approach Starting with the business needs elicited from stakeholders, a quality functional deployment process is performed to derive sensing system requirements. The process acknowledges the difference between test-bed and in-service aerospace needs through weightings on requirements and maps these customer requirements to systems performance metrics. The design of the system must therefore optimise the sensor suite, on- and off-board signal processing and acquisition strategy. Findings Against this systems engineering process, two sensing strategies are outlined which illustrate the span of solutions, from conventional gas path sensing with advanced signal processing to novel non-invasive sensing concepts. While conventional sensing may be feasible within a test cell, the constraints of aerospace in-service operation may necessitate more novel alternatives. Acoustic emission (detecting very high frequency surface vibration waves) sensing technology is evaluated to provide a non-invasive, remote and high temperature tolerant solution. Through this comparison, the considerations for the end-to-end system design are highlighted to be critical to sensor deployment success in-service. Practical implications The paper provides insight into different means of addressing the important problem of monitoring faults in combustor systems in gas turbines. By casting of the complex design problem within a systems engineering framework, the outline of a toolset for solution evaluation is provided. Originality/value The paper provides three areas of significant contributions: a diversity of methods to diagnosing fuel system malfunctions by measuring changes fuel flow distributions, through novel means, and the combustor exit temperature profiles (cause and effect); the use of analytical methods to support the selection (types and quantities) and placement of sensors to ensure adequate state awareness while minimising their impact on the engine system cost and weight; and an end-to-end data processing approach to provide optimised information for the engine maintainers allowing informed decision-making.

Heuristic Thinking and Teaching Practice of Digital Circuit Online Course

Based on the design and experience of “digital circuit” online course, this paper analyzes the existing problems in teaching and rethinks how to impart knowledge and skills more effectively in the network environment. Apply a heuristic approach to digital Circuits and Systems Design. In practice, the complex design problems are solved step by step through the idea of “question heuristic consideration-solution” by adopting four steps of “raising questions”, “enlightening thinking”, “bifurcating solutions” and “comparative analysis”. The teaching intention, teaching design and teaching behavior should be comprehensively reflected and improved, to improve teaching behavior and improve teaching ability. The teaching practice shows that heuristic teaching has achieved good teaching effect and is worthy of further promotion.