Simple Design Problem Research Articles

A multifidelity Bayesian optimization method for inertial confinement fusion design

Due to their cost, experiments for inertial confinement fusion (ICF) heavily rely on numerical simulations to guide design. As simulation technology progresses, so too can the fidelity of models used to plan for new experiments. However, these high-fidelity models are by themselves insufficient for optimal experimental design, because their computational cost remains too high to efficiently and effectively explore the numerous parameters required to describe a typical experiment. Therefore, traditionally, ICF design has relied on low-fidelity modeling to initially identify potentially interesting design regions, which are then subsequently explored via selected high-fidelity modeling. In this paper, we demonstrate that this two-step approach can be insufficient: even for simple design problems, a two-step optimization strategy can lead high-fidelity searching toward incorrect regions and consequently waste computational resources on parameter regimes far away from the true optimal solution. We reveal that a primary cause of this behavior in ICF design problems is the presence of low-fidelity optima in different regions of the parameter space far away from high-fidelity optima. To address this issue, we propose an iterative multifidelity Bayesian optimization method based on Gaussian Process Regression that leverages both low- and high-fidelity models simultaneously. We demonstrate, using both two- and eight-dimensional ICF test problems, that our algorithm can effectively utilize both low-fidelity and high-fidelity models to refine the designs. This approach proves to be more efficient than relying solely on high-fidelity modeling for optimization.

Development of an agent-based model incorporating Function–Behavior–Structure framework to enable systems engineering design process evaluation

An important step in the development of any process is evaluation. Evaluation ensures that the process affects effectiveness, efficiency, or other metrics as designed. Without evaluation, processes must stand solely on the theories, axioms, and/or heuristics that formed them. Outcome or objectives-based evaluations are especially useful in seeing if the expected impact is realized. Systems and design engineering present problems for outcome evaluations due to system design efforts being long in duration, expensive, organization/team specific, and environment/context specific. These characteristics make repeating the same effort under the same conditions using different processes difficult, if not impossible. While a comparison of processes for evaluation in a real application may not be feasible, a simulation of the processes with agents that capture system design behaviors may produce findings to support hypotheses. This paper examines the use of agent-based modeling and simulation to compare pseudo-waterfall and pseudo-agile engineering processes for a simple design problem. The Function–Behavior–Structure (FBS) model of design is used along with empirical data from FBS studies to examine the performance of a two-person design team using pseudo-waterfall and pseudo-agile engineering processes. The results of this exercise show possible advantages of agile-like processes in total time to complete the design.

Optimum design of nonlinear semi-rigid steel frame based on performance-price ratio via genetic algorithm

In this study, a two-stage structural optimization method is presented for multistory steel frames with semi-rigid connections. Based on the traditional optimization result in the first stage, the performance-price ratio (PPR) is introduced in the second stage to provide an idea to modify the unreasonable structural design that might occur in the price (or weight) only oriented optimization of first stage. The efficient advanced analysis (EAA) method accounting for the flexibility of beam-to-column connections, second-order effect, initial imperfection and plasticity development in members is applied in structural calculation, which is also used to acquire the ultimate capacity of structures. All schemes satisfying traditional optimization constraints in a simple design problem were obtained by exhaustive search. The price-performance relation of schemes showed that the structural ultimate capacity depends only on the rationality of members’ combination and has low correlation with the total cost. Design examples showed that the two-stage optimization considering PPR enables much more robust structural design at a slightly higher cost than the traditional optimum one.

A study on multi-objective design exploration based on deep clustering and logistic regression

Multi-objective design exploration is a framework to aim to extract knowledge to make rational decisions in multi-objective design problems. Since a set of Pareto solutions obtained by multi-objective topology optimization contains various types implicitly, it is difficult to extract the useful design knowledge by conventional data mining methods based on the objective function values. This study focuses on a framework for multi-objective design exploration that reveals the structure of the solution space by deep clustering and logistic regression for multi-objective topology optimization. In the framework, the deep clustering technique classifies the Pareto solution set in design variables space based on structural similarity to reveal representative types, then the logistic regression technique identifies the classification in evaluation criteria space that explains the differences in the types, and then designers arrange these results into design knowledge. This paper discusses its basic validity and possibilities through an application to a simple design problem of the conceptual design of a bridge structure.

Against a workplace contagion: a digital approach to support hygiene-conscious office space planning

PurposeThis research presents a multi-objective optimization approach to integrate spatial planning measures in open-plan office environments in order to lower the risk of a workplace contagion. These measures were gathered, formalized, parameterized, and coded and integrated into a digital tool.Design/methodology/approachTo demonstrate the research's approach, a simple design problem was designed, explored, and the results were evaluated. The researchers assumed an empty open office space, with the windows and doors (as exits and/or as access to amenities) already in place (Figure 1). The aim is to optimize the space planning, with the following objectives in mind: maximize the number of employees in a floor while maintaining physical distancing recommendations for avoiding infections; no face-to-face or back-to-back seating positions are allowed; maximize physical access to windows for natural ventilation; minimizing areas with potential “congestions” in the space, i.e. areas susceptible to overlapping foot traffic from numerous employees, which increases the potential for close encounters and minimizing the travel distance from the employee's desk to all neighbouring desks, hence reducing the foot traffic in the space. In the experiment, the following was assumed: the workspace layout is rectangular, the workstation desks are rectangular, the seating area, windows, and access to exits and amenities are well-defined.FindingsIt was found that configurations with desks parallel to the longer side of the space provided more employee capacity; however, they usually performed poorer in terms of the buzz score. On the other hand, configurations with desks perpendicular to the longer side of the space had, on average, better buzz scores, usually at the cost of the reduction of the number of potential employees. There was however one alternative in the latter set of configurations, which achieved above-average buzz and adjacency scores, and the potential to accommodate 56 employees, one of the highest capacities for employees in the solution space (the highest being 60). Designers could explore the design space further to make sure it complies with these basic spatial rules for mitigating the spread of infections, while experimenting with the workspace layout.Research limitations/implicationsIt is important to note that in order for a designer to handle any given design problem even with the aid of a computer system, it is important to provide a set of initial conditions and assumptions and a set of variables. In the universe of all possible variables, the designer can pick a number of variations of the initial conditions and run parallel experiments to compare their outcomes. In the experiment demonstrated here the following was assumed. The workspace layout is rectangular with predefined entrances/exits. Free flow of employees is allowed. No pre-set one-way paths. The workstation desks are rectangular. The seating area windows and access to amenities are well-defined.Originality/valueThis research presented a digital optimization approach to enhance the spatial planning process in open-plan office spaces, with the aim of mitigating the risks of infectious diseases' transmission. Spatial design considerations were gathered from literature and formalized as design objectives and constraints, then further parameterized and represented as numerical values and scores for objective evaluation. The design parameters, constraints and calculations to derive the scores for the designated design objectives were coded into a digital tool that can receive a building information model (BIM) model of an office space and provide preliminary furniture plans using a multi-objective optimization (MOO) approach. It is obvious that the furniture layouts that can be considered “acceptable”, based on this approach, are not considered “ready-to-implement” solutions, because designers need to integrate a multitude of other design factors in their design. This approach can still, however, be useful to help the designer integrate spatial considerations for slowing down a contagion.

Identification and Mapping of Manufacturability Constraints for Extrusion-Based Additive Manufacturing

This article develops and demonstrates a set of design-focused manufacturability constraints for the fused deposition modeling/fused filament fabrication (FDM/FFF) process. These can be mapped from the basic behavior and process characteristics and formulated in terms of implicit or explicit design constraints. When the FDM/FFF process is explored and examined for its natural limitations and behavior, it can provide a set of manufacturing considerations (advantages, limitations, and best practices). These can be converted into manufacturing constraints, which are practical limits on the ability of the process. Finally, these can be formulated in terms of design–useful manufacturability constraints. Many of the constants and parameters must be determined experimentally for specific materials. The final list of 54 major manufacturability constraints presented in this work will better inform designers considering using FDM/FFF as a manufacturing process, and help guide design decisions. After derivation and presentation of the constraint set, extensive discussion about practical implementation is provided at the end of the paper, including advice about experimentally determining constants and appropriate printing parameters. Finally, three case studies are presented which implement the constraints for simple design problems.

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.

Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency

Machine learning and optimization algorithms have been widely applied in the design and optimization for photonics devices. We briefly review recent progress of this field of research and show data-driven applications, including spectrum prediction, inverse design and performance optimization, for novel graphene metamaterials (GMs). The structure of the GMs is well-designed to achieve the wideband plasmon induced transparency (PIT) effect, which can be theoretically demonstrated by using the transfer matrix method. Some traditional machine learning algorithms, including k nearest neighbour, decision tree, random forest and artificial neural networks, are utilized to equivalently substitute the numerical simulation in the forward spectrum prediction and complete the inverse design for the GMs. The calculated results demonstrate that all algorithms are effective and the random forest has advantages in terms of accuracy and training speed. Moreover, evolutionary algorithms, including single-objective (genetic algorithm) and multi-objective optimization (NSGA-II), are used to achieve the steep transmission characteristics of PIT effect by synthetically taking many different performance metrics into consideration. The maximum difference between the transmission peaks and dips in the optimized transmission spectrum reaches 0.97. In comparison to previous works, we provide a guidance for intelligent design of photonics devices based on machine learning and evolutionary algorithms and a reference for the selection of machine learning algorithms for simple inverse design problems.

Terrorism, Counterterrorism and Optimal Striking Rules

Nous étudions un mécanisme d’incitation simple qui décrit le comportement optimal d’un pays ciblé par un groupe terroriste étranger. Le pays est incertain quant à la force des terroristes et peut décider d’acquérir de telles informations auprès de la communauté accueillant les terroristes. Nous soulignons un nouveau compromis entre le durcissement de la cible – i.e. , atténuer l’incidence d’une attaque en renforçant les contrôles internes et améliorer la protection des citoyens – et les mesures militaires préventives visant à éradiquer le problème à sa racine – i.e. , une attaque dans le pays d’accueil des terroristes. Nous montrons que, sous réserve d’être informé de la force des terroristes, le pays ne s’engage dans une attaque préventive que lorsqu’il est confronté à une menace suffisamment sérieuse et lorsque les normes de la communauté favorables aux terroristes sont faibles. Pourtant, contrairement à la littérature existante, nous montrons qu’il est optimal que le pays n’acquiert des informations que lorsque ces normes sont suffisamment fortes et lorsque ces informations préalables sur la force des terroristes sont insuffisantes. JEL Codes: D02, D74, D73, D78.

Joint digital-optical design of complex lenses using a surrogate image quality criterion adapted to commercial optical design software.

Like classical optical design, joint digital-optical design of complex lenses requires a skilled optical designer helped by powerful optical design software. Consequently, if optimization criteria have to be modified to take into account digital post-processing, the convenient optimization environment provided by commercial optical design software needs to be preserved. For that purpose, we define a joint-design criterion based on a merit function that contains terms classically implemented in optical design software but used in a non-standard way. After validation on a simple design problem, the proposed method is applied to the design of a very fast (f/0.75) complex lens. The obtained joint-designed lens is shown to be superior to a classically designed one in terms of weight and image quality in the field.

Synthesis of large‐scale heat exchanger networks using a T‐Q diagram method

For design of a heat exchanger network (HEN) by pinch technology, it is not uncommon to determine stream splitting and stream matching by empirical experience, in which numerous attempts are usually required to reach the final configuration of the HEN. In this work, a novel T‐Q diagram method is proposed to integrate large‐scale HENs on the basis of partitioning and merging heat recovery intervals. It aims at reducing the difficulties of the stream matching process for large‐scale HENs. The implementation procedure is illustrated via a simple HEN design problem. Furthermore, the HEN of a vacuum distillation unit in a refinery is used to further demonstrate the advantages and versatility of the proposed method. The results indicate that the proposed method can reduce the computational effort required and obtain a cost‐effective design of large‐scale HENs. Therefore, it provides an effective graphical analysis tool for the integration of large‐scale HENs in practice.

A tool to support the integration of noise control criteria in building design

This study describes an acoustic tool for architects and architecture students. The tool is intended to assist them in the determination of airborne sound insulation requirements and in the evaluation of building elements and their acoustic performance. The Building Elements Selection Tool has two parts: a catalogue and a calculator. The catalogue serves as a practical database of the sound insulation properties of various building elements, whereas the calculator is used for acoustic calculations. In this study, the usability of the tool has been tested by means of a validation task, in which a simple design problem was given to a group of architects, and the outcomes were then discussed.

Integrating External Simulations Within the Model-Based Systems Engineering Approach Using Statistical Metamodels

The Model-Based Systems Engineering (MBSE) approach uses the Systems Modeling Language (SysML) to express the architectural information of a system throughout different stages of its lifecycle. Within the approach, the system's structure, behavior, requirements, and parametrics are defined as elements within an integrated model. System Engineers use nine SysML diagrams to display unique views of these system elements. A significant limitation of SysML is that it is only descriptive in nature and cannot produce analytical results to inform system effectiveness. In order to achieve the full benefit of the MBSE approach the systems engineering community must also rely on external models that capture more sophisticated analysis across a wide variety of domains. These domain models include simulations that measure operational effectiveness, life cycle costing models, physics-based computational simulations, manufacturing models, and many more. The purpose of this paper is to propose an approach that integrates these external simulation models with the MBSE integrated model using statistical metamodels that act as surrogates to the simulations. We demonstrate the approach using a simple system design problem and an agent-based simulation.

Optimization Algorithms for Kinematically Optimal Design of Parallel Manipulators

Optimal design is an inevitable step for parallel manipulators. The formulated optimal design problems are generally constrained, nonlinear, multimodal, and even without closed-form analytical expressions. Numerical optimization algorithms are thus applied to solve the problems. However, the optimization algorithms are usually chosen ad arbitrium. This paper aims to provide a guideline to choose algorithms for optimal design problems. Typical algorithms, the sequential quadratic programming (SQP) with multiple initial points, the controlled random search (CRS), the genetic algorithm (GA), the differential evolution (DE), and the particle swarm optimization (PSO), are investigated in detail for their convergence performances by using two canonical design examples, the Delta robot and the Gough-Stewart platform. It is shown that SQP with multiple initial points can be efficient for simple design problems, while DE and PSO perform effectively and steadily for all design problems. CRS can be used to generate good initial points since it exhibits excellent convergence evolution in the starting period.

Precision Die Design by The Die Expansion Method

A new method for the design of precision dies used in cold forging, extrusion and drawing processes is presented. It is based on die expansion. It attempts to provide a clear-cut theoretical basis for the selection of critical die dimensions for this group of precision dies when the tolerance on product diameter (or thickness) is specified. It also presents a procedure for selecting the minimum-production-cost die from a set of design alternatives. The results show that as external die diameter increases, die expansion decreases while die strength increases. Also, as the degree of die compounding increases, die expansion decreases and die strength increases. Comparison of theoretical and experimental results shows good agreement especially at full die cavity filling stage. Die selection from a set of multi-layer dies is based on the die that has minimum Design Effectiveness Index (DEI) which gives the minimum-production-cost die. It is hoped that the design method, apart from providing solutions to simple design problems, can be applied as a first approximation to the solution of more complex problems, based on a clear-cut theory rather than the usual rule-of-thumb method. Thereafter, more sophisticated computer-based methods may be applied for refinement.

Efficient algorithms for a simple network design problem

AbstractWe consider the following simple network design problem. The input consists of n weighted nodes, and the output is an edge‐weighted connected network such that the total weight of the edges incident to a node is at least the given weight of the node. We aim to design the cheapest connected network; that is, the reachability of the network should be guaranteed, and the network is better if its total weight is less. In this article, we first show an efficient algorithm that produces an optimal network with minimum weight. The algorithm runs in linear time, and the resulting network contains at most n edges, where n is the number of nodes. To construct a connected network, at least n ‐ 1 edges are required. However, the algorithm sometimes outputs n edges. Next, we aim to minimize not only the weight but also the number of edges. That is, for given n weighted nodes, we aim to design a cheapest tree. Then, the problem becomes \documentclass{article}\usepackage{mathrsfs, amsmath, amssymb}\pagestyle{empty}\begin{document}\begin{align*}\mathcal{N}\mathcal{P}\end{align*} \end{document} ‐complete. We also propose efficient approximation algorithms for constructing a cheapest tree. © 2013 Wiley Periodicals, Inc. NETWORKS, 2013

Development of IEEE Complaint Software ‘Economical Substation Grounding System Designer’ Using MATLAB GUI Development Environment

Substations as a whole being an important part of the complete power system, grounding system of substations plays a vital role in overall performance of reliable power system. Grounding system of any substation deserves considerable care and attention at design stage, primarily to ensure electrical safety of persons working within or near substations. Looking to such great significance of grounding system design procedure, software which analyzes performance of ground grids of various shapes as per method described in IEEE standard 80 2000 has been developed using MATLAB. Proposed software is able to calculate various performance parameters of grounding system for given input data related to grid geometry, soil and system conditions for all the basic shapes of grounding grid in uniform and two layered soils. Additionally this software can also suggest optimal i.e. most appropriate, safe and costeffective design of the grounding system under given conditions and safety constraints. This software is deployed as windows standalone application using MATLAB run time compiler. Hence it can run on systems where MATLAB is not installed. In order to test the performance of the software, simple design problems have been solved and the results given by it are compared with those obtained form ETAP – GGS module and after verifying its technical performance it is found to be quite reliable tool in the field of grounding system design.

On the accurate analysis of linear elastic meta-material properties for use in design optimization problems

In the field of meta-materials engineering, asymptotic homogenization has proven to be a valuable tool to estimate material properties for many different design needs. Homogenization is used in the design of an object to tie the global material properties to the local material microstructure of the meta-material. The governing equations of the theory are derived from a small-parameter expansion that assumes the scaling length of the unit cell, the smallest repeatable structure found in the material, is much smaller than the entire material length scale. However, for those design problems that do not meet this scaling requirement, the meta-material properties given by homogenization may be inaccurate, leading to error in the material design process.To demonstrate some of these limitations, the more general set of macroscopic analyses derived from the average stress and average strain theorems are applied to several designs in which the assumptions of homogenization theory are no longer valid, those chiefly being materials made of a single or a few layers of unit cells. Then, by applying both materials analyses to simple design problems, the necessity of using a meta-material design method to link global and local properties in a manner consistent with global design methods is demonstrated. In particular, the use of homogenization in designing single layer materials is shown to incur a very large error in the design process, whereas the averaging method is much more accurate.

On the Design of Adaptive Cable-Driven Systems

Several systematic approaches have been developed for the optimal design of cable-based systems. Global indices are usually employed to quantify the effectiveness of a specific design inside a reference region of the workspace. The performances at the moving platform are strictly related to cable configuration, which, in turn, depends on the pose of the moving platform. As a result, traditional designs are characterized by the high variability of performances within the workspace and are often badly tailored to the design goals. The motivation behind this paper is to formalize a new design methodology for cable-driven devices. Based on a total or partial decoupling between cable disposition and end-effector pose, this methodology allows us to achieve well-tailored design solutions for a given design requirement. The resulting systems are here defined as adaptive cable-driven systems. Two simple design problems are presented and solved with both the traditional and the novel approaches, and the advantages of the latter are emphasized by comparing the resulting design solutions.

115 レベルセット法に基づく機械構造物の構造最適設計法における数理計画法の適用

In this paper, a structural optimization based on the level set method using mathematical programming is proposed. Topology optimization has been successfully applied to a variety of structural optimization problems such as the stiffness maximization problem, vibration problems and optimum design for compliant mechanism. However, due to its high flexibility, the utility of topology optimization results is often spoiled by a preponderance of impractical designs such as structures containing grayscale areas. To overcome such problems, a type of structural optimization method using the level set boundary expression has been proposed, in which the boundary of the optimal configuration are implicitly represented using the level set function. In the proposed method, the level set function is updated using a mathematical programming. We apply our method to a simple design problem to verify its capability.