Showing papers on "Engineering design process published in 2008"
18 Jul 2008
TL;DR: In this article, the authors propose a sampling approach to estimate the distribution of elementary effects and then use this information to construct a kriging model of the data set, which is then used for regression.
Abstract: Preface. About the Authors. Foreword. Prologue. Part I: Fundamentals. 1. Sampling Plans. 1.1 The 'Curse of Dimensionality' and How to Avoid It. 1.2 Physical versus Computational Experiments. 1.3 Designing Preliminary Experiments (Screening). 1.3.1 Estimating the Distribution of Elementary Effects. 1.4 Designing a Sampling Plan. 1.4.1 Stratification. 1.4.2 Latin Squares and Random Latin Hypercubes. 1.4.3 Space-filling Latin Hypercubes. 1.4.4 Space-filling Subsets. 1.5 A Note on Harmonic Responses. 1.6 Some Pointers for Further Reading. References. 2. Constructing a Surrogate. 2.1 The Modelling Process. 2.1.1 Stage One: Preparing the Data and Choosing a Modelling Approach. 2.1.2 Stage Two: Parameter Estimation and Training. 2.1.3 Stage Three: Model Testing. 2.2 Polynomial Models. 2.2.1 Example One: Aerofoil Drag. 2.2.2 Example Two: a Multimodal Testcase. 2.2.3 What About the k -variable Case? 2.3 Radial Basis Function Models. 2.3.1 Fitting Noise-Free Data. 2.3.2 Radial Basis Function Models of Noisy Data. 2.4 Kriging. 2.4.1 Building the Kriging Model. 2.4.2 Kriging Prediction. 2.5 Support Vector Regression. 2.5.1 The Support Vector Predictor. 2.5.2 The Kernel Trick. 2.5.3 Finding the Support Vectors. 2.5.4 Finding . 2.5.5 Choosing C and epsilon. 2.5.6 Computing epsilon : v -SVR 71. 2.6 The Big(ger) Picture. References. 3. Exploring and Exploiting a Surrogate. 3.1 Searching the Surrogate. 3.2 Infill Criteria. 3.2.1 Prediction Based Exploitation. 3.2.2 Error Based Exploration. 3.2.3 Balanced Exploitation and Exploration. 3.2.4 Conditional Likelihood Approaches. 3.2.5 Other Methods. 3.3 Managing a Surrogate Based Optimization Process. 3.3.1 Which Surrogate for What Use? 3.3.2 How Many Sample Plan and Infill Points? 3.3.3 Convergence Criteria. 3.3.4 Search of the Vibration Isolator Geometry Feasibility Using Kriging Goal Seeking. References. Part II: Advanced Concepts. 4. Visualization. 4.1 Matrices of Contour Plots. 4.2 Nested Dimensions. Reference. 5. Constraints. 5.1 Satisfaction of Constraints by Construction. 5.2 Penalty Functions. 5.3 Example Constrained Problem. 5.3.1 Using a Kriging Model of the Constraint Function. 5.3.2 Using a Kriging Model of the Objective Function. 5.4 Expected Improvement Based Approaches. 5.4.1 Expected Improvement With Simple Penalty Function. 5.4.2 Constrained Expected Improvement. 5.5 Missing Data. 5.5.1 Imputing Data for Infeasible Designs. 5.6 Design of a Helical Compression Spring Using Constrained Expected Improvement. 5.7 Summary. References. 6. Infill Criteria With Noisy Data. 6.1 Regressing Kriging. 6.2 Searching the Regression Model. 6.2.1 Re-Interpolation. 6.2.2 Re-Interpolation With Conditional Likelihood Approaches. 6.3 A Note on Matrix Ill-Conditioning. 6.4 Summary. References. 7. Exploiting Gradient Information. 7.1 Obtaining Gradients. 7.1.1 Finite Differencing. 7.1.2 Complex Step Approximation. 7.1.3 Adjoint Methods and Algorithmic Differentiation. 7.2 Gradient-enhanced Modelling. 7.3 Hessian-enhanced Modelling. 7.4 Summary. References. 8. Multi-fidelity Analysis. 8.1 Co-Kriging. 8.2 One-variable Demonstration. 8.3 Choosing X c and X e . 8.4 Summary. References. 9. Multiple Design Objectives. 9.1 Pareto Optimization. 9.2 Multi-objective Expected Improvement. 9.3 Design of the Nowacki Cantilever Beam Using Multi-objective, Constrained Expected Improvement. 9.4 Design of a Helical Compression Spring Using Multi-objective, Constrained Expected Improvement. 9.5 Summary. References. Appendix: Example Problems. A.1 One-Variable Test Function. A.2 Branin Test Function. A.3 Aerofoil Design. A.4 The Nowacki Beam. A.5 Multi-objective, Constrained Optimal Design of a Helical Compression Spring. A.6 Novel Passive Vibration Isolator Feasibility. References. Index.
1,961 citations
TL;DR: In this paper, a "creative design process" is proposed, based on an integration between a modernised consensus view of both the design process from engineering design and the creative process from cognitive psychology.
791 citations
TL;DR: In this article, the authors present a brief survey on some of the most relevant developments in the field of optimization under uncertainty, including reliability-based optimization, robust design optimization and model updating.
522 citations
TL;DR: In this article, the authors introduced some of the basic principles, techniques, and key design issues common to ISPs used in many diverse scientific, military, and commercial applications, and touched on some of less intuitive effects that must be dealt with.
Abstract: We introduced some of the basic principles, techniques, and key design issues common to ISPs used in many diverse scientific, military, and commercial applications, and we touched on some of the less intuitive effects that must be dealt with. Many of these effects can influence the initial configuration of the system as well as design details later in the design process. The successful design of an ISP usually requires a multidisciplinary design team. The design of an ISP must often be closely coordinated with that of other major subsystems such as the primary sensor and the optics. The role of the systems engineer in the design process is perhaps the most critical because other members of the team may not be aware of the consequences of many of the effects discussed above. Inertially stabilized platforms (ISPs) are used to stabilize and point a broad array of sensors, cameras, telescopes, and weapon systems.
500 citations
TL;DR: A nondominated sorting genetic algorithm is employed to search for Pareto solution to a full-scale vehicle design problem that undergoes both the full frontal and 40% offset-frontal crashes, demonstrating the capability and potential of this procedure in solving the crashworthiness design of vehicles.
Abstract: In automotive industry, structural optimization for crashworthiness criteria is of special importance. Due to the high nonlinearities, however, there exists substantial difficulty to obtain accurate continuum or discrete sensitivities. For this reason, metamodel or surrogate model methods have been extensively employed in vehicle design with industry interest. This paper presents a multiobjective optimization procedure for the vehicle design, where the weight, acceleration characteristics and toe-board intrusion are considered as the design objectives. The response surface method with linear and quadratic basis functions is employed to formulate these objectives, in which optimal Latin hypercube sampling and stepwise regression techniques are implemented. In this study, a nondominated sorting genetic algorithm is employed to search for Pareto solution to a full-scale vehicle design problem that undergoes both the full frontal and 40% offset-frontal crashes. The results demonstrate the capability and potential of this procedure in solving the crashworthiness design of vehicles.
338 citations
TL;DR: In this article, the authors describe an 8-week high school curriculum unit, the Heating/Cooling System, in which engineering design is used to teach students central and difficult chemistry concepts such as atomic interactions, reactions, and energy changes in reactions.
Abstract: Infusing engineering design projects in K-12 settings can promote interest and attract a wide range of students to engineering careers. However, the current climate of high-stakes testing and accountability to standards leaves little room to incorporate engineering design into K-12 classrooms. We argue that design-based learning, the combination of scientific inquiry and engineering design, is an approach that can be used to meet both K-12 educators’ and engineering advocates’ goals. This paper describes an 8-week high school curriculum unit, the Heating/Cooling System, in which engineering design is used to teach students central and difficult chemistry concepts such as atomic interactions, reactions, and energy changes in reactions. The goals of the paper are to (1) describe this successful design-based unit, (2) provide guidelines for incorporating design-based learning into other science topics, and (3) provide some evidence of its value for teaching difficult chemistry concepts and increasing interest in engineering careers.
296 citations
Book•
15 Jan 2008
TL;DR: This book serves as an introduction to the state of the art on data warehouse design, with many references to more detailed sources, and may help experienced data warehouse designers to enlarge their analysis possibilities by incorporating spatial and temporal information.
Abstract: A data warehouse stores large volumes of historical data required for analytical purposes. This data is extracted from operational databases; transformed into a coherent whole using a multidimensional model that includes measures, dimensions, and hierarchies; and loaded into a data warehouse during the extraction-transformation-loading (ETL) process. Malinowski and Zimnyi explain in detail conventional data warehouse design, covering in particular complex hierarchy modeling. Additionally, they address two innovative domains recently introduced to extend the capabilities of data warehouse systems, namely the management of spatial and temporal information. Their presentation covers different phases of the design process, such as requirements specification, conceptual, logical, and physical design. They include three different approaches for requirements specification depending on whether users, operational data sources, or both are the driving force in the requirements gathering process, and they show how each approach leads to the creation of a conceptual multidimensional model. Throughout the book the concepts are illustrated using many real-world examples and completed by sample implementations for Microsoft's Analysis Services 2005 and Oracle 10g with the OLAP and the Spatial extensions. For researchers this book serves as an introduction to the state of the art on data warehouse design, with many references to more detailed sources. Providing a clear and a concise presentation of the major concepts and results of data warehouse design, it can also be used as the basis of a graduate or advanced undergraduate course. The book may help experienced data warehouse designers to enlarge their analysis possibilities by incorporating spatial and temporal information. Finally, experts in spatial databases or in geographical information systems could benefit from the data warehouse vision for building innovative spatial analytical applications.
246 citations
TL;DR: It was found that students taking a course in engineering design and/or studying engineering for four years acquired engineering design language that is common to a larger community of practice as well as common to their own programs and institutions of higher learning.
Abstract: Using multiple quantitative and qualitative methods to examine engineering design learning, we found that students taking a course in engineering design and/or studying engineering for four years acquired engineering design language that is common to a larger community of practice as well as common to their own programs and institutions of higher learning. The study also suggests that engineering design language shapes the knowledge that students have about engineering design. Finally, students did not always put their design knowledge into practice, suggesting the need for educational improvements and research to bridge this gap.
208 citations
TL;DR: Multidisciplinary design optimization (MDO) is an emerging optimization method that considers a design environment with multiple disciplines and seven methods have been proposed for MDO.
Abstract: Recently, engineering systems are quite large and complicated. The design requirements are fairly complex and it is not easy to satisfy them by considering only one discipline. Therefore, a design methodology that can consider various disciplines is needed. Multidisciplinary design optimization (MDO) is an emerging optimization method that considers a design environment with multiple disciplines. Seven methods have been proposed for MDO. They are Multiple-discipline-feasible (MDF), Individual-discipline-feasible (IDF), All-at-once (AAO), Concurrent subspace optimization (CSSO), Collaborative optimization (CO), Bi-level integrated system synthesis (BLISS), and Multidisciplinary design optimization based on independent subspaces (MDOIS). Through several mathematical examples, the performances of the methods are evaluated and compared. Specific requirements are defined for comparison and new types of mathematical problems are defined based on the requirements. All the methods are coded and the performances of the methods are compared qualitatively and quantitatively.
163 citations
TL;DR: An efficient framework, consisting of two stages, is presented here for the optimization of the reliability of a base-isolated structure considering future near-fault ground motions.
137 citations
TL;DR: A comparative study of both top-down and bottom-up design approaches with particular emphasis on applications to multi-agent system engineering and robotics is presented, and the generic characteristics of both approaches from the MAS perspective are outlined.
Abstract: Traditionally, two alternative design approaches have been available to engineers: top-down and bottom-up. In the top-down approach, the design process starts with specifying the global system state and assuming that each component has global knowledge of the system, as in a centralized approach. The solution is then decentralized by replacing global knowledge with communication. In the bottom-up approach, on the other hand, the design starts with specifying requirements and capabilities of individual components, and the global behavior is said to emerge out of interactions among constituent components and between components and the environment. In this paper we present a comparative study of both approaches with particular emphasis on applications to multi-agent system engineering and robotics. We outline the generic characteristics of both approaches from the MAS perspective, and identify three elements that we believe should serve as criteria for how and when to apply either of the approaches. We demonstrate our analysis on a specific example of load balancing problem in robotics. We also show that under certain assumptions on the communication and the external environment, both bottom-up and top-down methodologies produce very similar solutions.
TL;DR: The design process for applications intended for direct use by the patients themselves, often referred to as self-help tools, is focused on, and a framework for the user involvement in the design process is presented.
Abstract: Involving end users in the design process can be challenging and in many cases fails to become a priority for system developers. This is also the case with numerous applications in the health care domain. This article focuses on the design process for applications intended for direct use by the patients themselves, often referred to as self-help tools. A framework for the user involvement in the design process is presented. This framework is inspired both from existing methods and standards within the field of human computer interaction, as well as documented experiences from relevant e-health projects. An analysis of three case studies highlights the importance of patient involvement in the design process and informs guidelines for patient-centric system design.
1 Jan 2008
TL;DR: The WordTree Design-by-Analogy Method (WBM) as mentioned in this paper is a method for identifying analogies as part of the ideation process, which does not require specialized computational knowledge bases.
Abstract: Numerous examples of innovation through analogy are found throughout current trade journals, magazines and product offerings. Design-by-analogy is a powerful tool in creative design, but generally relies on unproven, ad-hoc approaches. Although a few notable computational knowledge bases have been created to support analogous design, very few methods provide suitable guidance on how to identify analogies and analogous domains. This paper presents a novel approach, referred to as the WordTree Design-by-Analogy Method, for identifying analogies as part of the ideation process. The WordTree Method derives its effectiveness through a design team’s knowledge and readily available information sources but does not require specialized computational knowledge bases. A controlled experiment and an evaluation of the method with redesign projects illustrate the method’s influence in assisting engineers in design-by-analogy. Unexpected and unique solutions are identified using the method. The experimental results also highlight potential improvements for the method and areas for future research in engineering design theory.
TL;DR: This review paper is written with the purpose of presenting a condensed overview of the history, background, philosophy, methodology, and standardization aspects of compact thermal modeling to non-experts.
Abstract: In order to reduce design-cycle time and physical prototyping, equipment manufacturers need to ascertain the thermal performance of new systems at the earliest possible stage of the design process. In the early 1990s, some European industries began to realize that the accurate prediction of the temperatures of critical electronic parts at the package, board, and system levels was seriously hampered by a lack of reliable, standardized input data that characterize the thermal behavior of these parts. It was the start of a number of European projects concerned with the creation and experimental calibration of thermal models for a range of electronic parts. The ultimate goal of these projects was to get component manufacturers to supply calibrated compact thermal models (CTMs) of their parts to end users by adopting the experimental techniques used to calibrate the detailed thermal conduction models of the parts and the methods to generate compact models. This review paper is written with the purpose of prese...
TL;DR: A new methodology for making easier the design process of interpretable knowledge bases that considers both expert knowledge and knowledge extracted from data, comparable to that achieved by other methodologies is described.
Abstract: This work describes a new methodology for making easier the design process of interpretable knowledge bases. It considers both expert knowledge and knowledge extracted from data. The combination of both kinds of knowledge is likely to yield robust compact systems with a good trade-off between accuracy and interpretability. Fuzzy logic offers an integration framework where both types of knowledge are represented using the same formalism. However, as two knowledge bases may convey contradictions and-or redundancies, the integration process must be made carefully. Results obtained, in four well-known benchmark classification problems, show that our methodology leads to highly interpretable knowledge bases with a good accuracy, comparable to that achieved by other methodologies. © 2008 Wiley Periodicals, Inc.
TL;DR: In this paper, a lightweight glass fiber-reinforced polymer roof structure was constructed in Basel, Switzerland using foam blocks with a computerized numerical control machine and adhesive bonding.
Abstract: This paper reports on the design and construction of a lightweight glass fiber-reinforced polymer roof structure in Basel, Switzerland The sandwich construction allowed for an integration of static, building physical and architectural functions that enabled the prefabrication of the entire roof in only four lightweight elements that were easily transported to the site and rapidly installed Cutting of foam blocks with a computerized numerical control machine and adhesive bonding proved to be advantageous procedures for the fabrication of the complex roof shape, without the use of expensive molds The factor of safety of the design was adjusted during the design process through experimental verification Although some of the characteristic material properties were overestimated in the preliminary design, the design properties for the final design were higher than for the preliminary design as it was possible to reduce the resistance factors (according to Eurocode format) after testing by a factor of 20 Existing design models and test standards to determine material properties proved to be applicable © 2008 ASCE
18 Oct 2008
TL;DR: In this chapter, a survey of meta-modeling approaches and their suitability to specific problem contexts is given, and the aspects of dimensionality, noise, expensiveness of evaluations and others, are related to choice of methods.
Abstract: In many practical engineering design and other scientific optimization problems, the objective function is not given in closed form in terms of the design variables. Given the value of the design variables, the value of the objective function is obtained by some numerical analysis, such as structural analysis, fluidmechanic analysis, thermodynamic analysis, and so on. It may even be obtained by conducting a real (physical) experiment and taking direct measurements. Usually, these evaluations are considerably more time-consuming than evaluations of closed-form functions. In order to make the number of evaluations as few as possible, we may combine iterative search with meta-modeling . The objective function is modeled during optimization by fitting a function through the evaluated points. This model is then used to help predict the value of future search points, so that high performance regions of design space can be identified more rapidly. In this chapter, a survey of meta-modeling approaches and their suitability to specific problem contexts is given. The aspects of dimensionality, noise, expensiveness of evaluations and others, are related to choice of methods. For the multiobjective version of the meta-modeling problem, further aspects must be considered, such as how to define improvement in a Pareto approximation set, and how to model each objective function. The possibility of interactive methods combining meta-modeling with decision-making is also covered. Two example applications are included. One is a multiobjective biochemistry problem, involving instrument optimization; the other relates to seismic design in the reinforcement of cable-stayed bridges.
TL;DR: It is demonstrated that the Bayesian approach provides a flexible framework for drawing inferences for predictions in the intended, but maybe untested, design domain.
Abstract: In most of the existing work, model validation is viewed as verifying the model accuracy, measured by the agreement between computational and experimental results. Due to the lack of resource, accuracy can only be assessed at very limited test points. However, from the design perspective, a good model should be considered the one that can provide the discrimination (with good resolution) between competing design candidates under uncertainty. In this work, a design-driven validation approach is presented. By combining data from both physical experiments and the computer model, a Bayesian approach is employed to develop a prediction model as the replacement of the original computer model for the purpose of design. Based on the uncertainty quantification with the Bayesian prediction and, subsequently, that of a design objective, some decision validation metrics are further developed to assess the confidence of using the Bayesian prediction model in making a specific design choice. We demonstrate that the Bayesian approach provides a flexible framework for drawing inferences for predictions in the intended, but maybe untested, design domain. The applicability of the proposed decision validation metrics is examined for designs with either a discrete or continuous set of design alternatives. The approach is demonstrated through an illustrative example of a robust engine piston design.
3 Sep 2008
Abstract: Communication across and integration of disciplines in the urban-water sector seems today more imperative than ever before. Water is a strategic and shrinking resource. It is probably the world's most valuable resource and clean water has even been touted as the 'next oil'. Control of water - from access to management - has always been a highly politicised affair. The complexities that surround it are proving to be major challenges as the world continues to urbanise and human habits of mass consumption and pollution deplete natural resources and destroy natural eco-systems. Water issues are increasingly high on the international agenda a " particularly in desert, tropical and sub-tropical regions. Water and Urban Development Paradigms includes the papers presented at the International Conference on Water and Urban Development Paradigms: Towards an Integration of Engineering, Design and Management Approaches (Leuven, Belgium, 15-19 September 2008), and intends to bridge the gap between the disciplines of water management, ecology and the approaches of engineering, urban design and spatial planning. The volume explores a number of themes, discussing the historical relationship between water systems and human settlements, and related management problems regarding urban floods, water use and water sanitation. The aim of Water and Urban Development Paradigms is to contribute to the better integration of approaches currently considered in the separate disciplines of water management, water engineering, urban planning and design, and aquatic ecology - and lead to the emergence of new, more effective water and urban development paradigms. The book will be of special interest to scientists and professionals in the fields of architecture, urban planning, water resources engineering, water supply and sanitation, flood protection, among related fields; to public and non-governmental organizations active in urban planning and the water sector, and to university teachers and students in architecture, urbanism and planning, water and sanitation engineering.
TL;DR: In this article, the authors present the design process as performed within both projects and with the applied analysis procedures, focusing on the DLR experience in the design and analysis of stringer-stiffened CFRP panels gained within the scope of these two projects.
TL;DR: This paper takes a practical approach to clarifying what ontologies are and how they might be useful in an important and representative phase of the engineering design process, that of design requirement development and capture.
Book•
25 Feb 2008
TL;DR: In this article, the authors present an overview of the state of the art on data warehouse design, including three different approaches for requirements specification depending on whether users, operational data sources or both are the driving force in the requirements gathering process, and how each approach leads to the creation of a conceptual multidimensional model.
Abstract: A data warehouse stores large volumes of historical data required for analytical purposes. This data is extracted from operational databases; transformed into a coherent whole using a multidimensional model that includes measures, dimensions, and hierarchies; and loaded into a data warehouse during the extraction-transformation-loading (ETL) process. Malinowski and Zimnyi explain in detail conventional data warehouse design, covering in particular complex hierarchy modeling. Additionally, they address two innovative domains recently introduced to extend the capabilities of data warehouse systems, namely the management of spatial and temporal information. Their presentation covers different phases of the design process, such as requirements specification, conceptual, logical, and physical design. They include three different approaches for requirements specification depending on whether users, operational data sources, or both are the driving force in the requirements gathering process, and they show how each approach leads to the creation of a conceptual multidimensional model. Throughout the book the concepts are illustrated using many real-world examples and completed by sample implementations for Microsoft's Analysis Services 2005 and Oracle 10g with the OLAP and the Spatial extensions. For researchers this book serves as an introduction to the state of the art on data warehouse design, with many references to more detailed sources. Providing a clear and a concise presentation of the major concepts and results of data warehouse design, it can also be used as the basis of a graduate or advanced undergraduate course. The book may help experienced data warehouse designers to enlarge their analysis possibilities by incorporating spatial and temporal information. Finally, experts in spatial databases or in geographical information systems could benefit from the data warehouse vision for building innovative spatial analytical applications.
TL;DR: In this article, a multidisciplinary design exploration technique with a high-fidelity analysis applied to the winglet design for a commercial jet aircraft was described, where the minimization of the block fuel at a fixed aircraft operating range and a maximum takeoff weight were selected as design objectives.
Abstract: In this paper, we describe a multidisciplinary design exploration technique with a high-fidelity analysis applied to the winglet design for a commercial jet aircraft. The minimization of the block fuel at a fixed aircraft operating range and a maximum takeoff weight were selected as design objectives. Both objective functions were estimated from a computational fluid dynamics based aerodynamic drag and a finite element method based structural weight. Various computational fluid dynamics and optimization techniques, such as the midfield drag decomposition method, the automatic computational fluid dynamics mesh generation, the kriging surrogate model, and multi-objective genetic algorithms, were integrated and applied to the detail design exploration. Computational fluid dynamics with the midfield drag decomposition method showed the effect on wave, induced, and profile drag components due to different winglet defining parameters. Practical design decision was explored based on the Pareto front and some design criteria that were uncovered within the numerical optimization. Finally, the design process was validated through the validation of the kriging approximation and aerodynamic characteristics based on the wind-tunnel test.
TL;DR: In this article, a coupled shape and topology optimization (CSTO) technique is proposed to study the layout design of the components and their supporting structures in a finite packing space.
Abstract: The purpose of this paper was to study the layout design of the components and their supporting structures in a finite packing space. A coupled shape and topology optimization (CSTO) technique is proposed. On one hand, by defining the location and orientation of each component as geometric design variables, shape optimization is carried out to find the optimal layout of these components and a finite-circle method (FCM) is used to avoid the overlap between the components. On the other hand, the material configuration of the supporting structures that interconnect components is optimized simultaneously based on topology optimization method. As the FE mesh discretizing the packing space, i.e., design domain, has to be updated itertively to accommodate the layout variation of involved components, topology design variables, i.e., density variables assigned to density points that are distributed regularly in the entire design domain will be introduced in this paper instead of using traditional pseudo-density variables associated with finite elements as in standard topology optimization procedures. These points will thus dominate the pseudo-densities of the surrounding elements. Besides, in the CSTO, the technique of embedded mesh is used to save the computing time of the remeshing procedure, and design sensitivities are calculated w.r.t both geometric variables and density variables. In this paper, several design problems maximizing structural stiffness are considered subject to the material volume constraint. Reasonable designs of components layout and supporting structures are obtained numerically.
TL;DR: The service matrix is introduced as a tool to systematically represent the call paths of the more distributed NGN network and the multiple services it supports and the concept of significant point of failure (SgPoF) enables design-for-service reliability early in the design process.
Abstract: Next-generation networks (NGNs) offer several advantages, such as support for many application-rich new services, along with more traditional telephony applications, agnostic access, and improved economics. This paper presents a new design and engineering methodology that addresses areas where traditional design for reliability approaches will not work for NGNs. It introduces the service matrix as a tool to systematically represent the call paths of the more distributed NGN network and the multiple services it supports. The concept of significant point of failure (SgPoF) enables design-for-service reliability early in the design process. The traditional approach of reliability assessment and design after the initial design and economic optimization is much less efficient given the complexity of NGN reliability. The effect of non-traditional failure sources, such as those that may be introduced by new entrant service providers or simply new operating environments (e.g., alternating current (AC) power and non-central office deployment), is considered in the design for network reliability. The paper includes examples from implementation of the methodology to IP Multimedia Systems (IMS) networks.
TL;DR: This study explicitly considers the design space structure and the resulting correlations among design performances, and examines their implications for learning, and derives the optimal dynamic testing policy and analyzes its qualitative properties.
Abstract: Past research in new product development (NPD) has conceptualized prototyping as a “design-build-test-analyze” cycle to emphasize the importance of the analysis of test results in guiding the decisions made during the experimentation process. New product designs often involve complex architectures and incorporate numerous components, and this makes the ex ante assessment of their performance difficult. Still, design teams often learn from test outcomes during iterative test cycles enabling them to infer valuable information about the performances of (as yet) untested designs. We conceptualize the extent of useful learning from analysis of a test outcome as depending on two key structural characteristics of the design space, namely whether the set of designs are “close” to each other (i.e., the designs are similar on an attribute level) and whether the design attributes exhibit nontrivial interactions (i.e., the performance function is complex).
This study explicitly considers the design space structure and the resulting correlations among design performances, and examines their implications for learning. We derive the optimal dynamic testing policy, and we analyze its qualitative properties. Our results suggest optimal continuation only when the previous test outcomes lie between two thresholds. Outcomes below the lower threshold indicate an overall low performing design space and, consequently, continued testing is suboptimal. Test outcomes above the upper threshold, on the other hand, merit termination because they signal to the design team that the likelihood of obtaining a design with a still higher performance (given the experimentation cost) is low. We find that accounting for the design space structure splits the experimentation process into two phases: the initial exploration phase, in which the design team focuses on obtaining information about the design space, and the subsequent exploitation phase in which the design team, given their understanding of the design space, focuses on obtaining a “good enough” configuration. Our analysis also provides useful contingency-based guidelines for managerial action as information gets revealed through the testing cycle. Finally, we extend the optimal policy to account for design spaces that contain distinct design subclasses.
TL;DR: In this article, a numerical procedure for designing robust structures under uncertainty is presented, which can be coupled with any arbitrary nonlinear computational model for statical or dynamic structural analysis, and a measure for the global robustness of the design alternatives is introduced based on an analog to Shannon's entropy.
18 Oct 2008
TL;DR: This chapter presents a number of illustrative case studies of a wide range of applications of multiobjective optimization methods, in areas ranging from engineering design to medical treatments, and in one case an integration of the two approaches.
Abstract: This chapter presents a number of illustrative case studies of a wide range of applications of multiobjective optimization methods, in areas ranging from engineering design to medical treatments. The methods used include both conventional mathematical programming and evolutionary optimization, and in one case an integration of the two approaches. Although not a comprehensive review, the case studies provide evidence of the extent of the potential for using classical and modern multiobjective optimization in practice, and opens many opportunities for further research.
TL;DR: In this paper, the authors combine the advanced multidisciplinary design optimization (MDO) methodology to optimize vehicle structure by using uniform Latin hypercube sampling and subset selection regression methods to construct the response surface models for the highly nonlinear impact and seatbelt pull responses.
Abstract: Tailor rolled blank (TRB) is an emerging steel rolling process to produce lightweight vehicle components. It allows continuous metal thickness changes, and as a result, it offers opportunities for automotive design in weight reduction, part complexity reduction, reduced capital investment, yet, maintains equal to or better strength characteristics. The objective of this research is to take advantages of the TRB manufacturing technology and combine with the advanced multidisciplinary design optimization (MDO) methodology to optimize vehicle structure. The process begins with noise vibration and harshness (NVH) optimization. The outputs of the optimal NVH response sensitivities are employed to build the first order response surface models. Uniform Latin Hypercube sampling and subset selection regression methods are used to construct the response surface models for the highly nonlinear impact and seatbelt pull responses. The optimal NVH design is then used as the starting point for MDO to obtain the optimal thickness profiles for the TRB parts. A vehicle application considering multiple impact modes, seatbelt pulls, and NVH, is used to demonstrate the proposed process for vehicle underbody TRB design. Results of this MDO TRB study is presented and discussed.
10 Sep 2008
TL;DR: This paper describes a method to incorporate models of two fidelities and perform a gradient-free global search on expensive functions that are not necessarily smooth everywhere, and demonstrates this improved technique on some academic problems with an artificially constructed ‘low-fidelity’ approximation, and on a simple application problem in supersonic design optimization.
Abstract: The use of expensive simulations in engineering design optimization often rules out conventional techniques for design optimization for a variety of reasons, such as lack of smoothness, unavailability of gradient information, presence of multiple local optima, and most importantly, limits on available computing resources and time. Often, the designer also has access to lower-fidelity simulations that may suer from poor accuracy in some regions of the design space, but are much cheaper to evaluate than the original expensive simulation. We can accelerate the design process by eciently managing these models of various fidelities. There has been previous research in this area: some algorithms in the literature first estimate of the relationships between these models, and then perform optimization on the corrected low-fidelity models. Others adaptively select new high-fidelity designs, but these usually require gradient information; those that relax this requirement use a trust-region-based local search method. In contrast, most global optimization methods in the literature require smoothness, and do not incorporate multifidelity analyses. We would like to combine the advantages of all these techniques, and in this paper, we describe a method to incorporate models of two fidelities and perform a gradient-free global search on expensive functions that are not necessarily smooth everywhere. The main contribution of this paper is an extension of the well-known technique of maximization of expected improvement to the two-fidelity case. We demonstrate this improved technique on some academic problems with an artificially constructed ‘low-fidelity’ approximation, and also on a simple application problem in supersonic design optimization.