A Plug-and-Contract Mechanism for a Robust Assessment of Design Concepts

2005 ◽  
Author(s):  
Bernard Yannou ◽  
Roy Awedikian
Keyword(s):  
Design Space ◽  
Truss Structures ◽  
Space Representation ◽  
Design Decision ◽  
Functional Requirements ◽  
Design Concepts ◽  
Design Variables ◽  
Variable Domains ◽  
Parameterized Model ◽  
Relative Potential

Starting from a need and a set of functional requirements (FRs), a designer is often perplexed to assess the potential of a given concept to fit these requirements. He is even more perplexed when several concepts are candidates. This paper proposes a definition of a concept in a practical way as a parameterized model linking a set of design variables (DVs) to a set of performance variables (PVs). This set of PVs is supposed to be the same for any concept candidate to fulfill a need. This is why our model propose to “plug” a card of FRs into candidate concepts in order to lead concurrent reasonings on competing concepts until one or several of them appear to be of poor interest. The plugging mechanism is implemented by constraint programming techniques (evolved interval arithmetics) that immediately contract the performance and design variable domains to provide an outer approximation of the solution (or design) space. Two sets of comparison operators between solution spaces are proposed: operators for comparing the relative potential of two concepts submitted to the same FRs, and operators for comparing two successive stages of solution spaces of a given concept. These last operators provide the way to tackle the robustness of design decision making under uncertainty. All the mentioned features: plugging mechanism, contraction of domains and design space representation, comparison operators and robustness considerations have been experimented on an example of a pair of candidate concepts of truss structures.

scholarly journals Exploring the Application of Network Analytics in Characterizing a Conceptual Design Space

2019 ◽  
Vol 1 (1) ◽  
pp. 1953-1962 ◽  
Author(s):  
Joshua T. Gyory ◽  
Kosa Goucher-Lambert ◽  
Kenneth Kotovsky ◽  
Jonathan Cagan
Keyword(s):  
Conceptual Design ◽  
Network Theory ◽  
Design Space ◽  
Early Stage ◽  
Assessment Method ◽  
Design Decision ◽  
Support Tool ◽  
Design Concepts ◽  
Network Analytics ◽  
Early Stage Design

AbstractThe ability to effectively analyse design concepts is essential for making early stage design decisions. Human evaluations, the most common assessment method, describe individual design concepts on a variety of ideation metrics. However, this approach falls short in creating a holistic representation of the design space as a whole that informs the underlying relations between concepts. Motivated by this shortcoming, this work leverages network theory to visualize and characterize features of a conceptual design space. To illustrate the utility of network theory for these purposes, a network composed of a corpus of solutions to a design problem and their semantic similarity is derived, and its design properties (e.g., uniqueness and innovation potential) are studied. This network-based approach not only characterizes features of individual designs themselves, but also uncovers more nuanced properties of the design space through studying emerging clusters of concepts. Overall, this work expands on developing research in design, demonstrating the value in applying network analytics to a conceptual design space as an engineering support tool to aid design decision-making.

Multi-Objective Optimization of an Autonomous Underwater Vehicle

2009 ◽  
Vol 43 (2) ◽  
pp. 48-60 ◽  
Author(s):  
M. Martz ◽  
W. L. Neu
Keyword(s):  
Design Space ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Design Problems ◽  
Single Measure ◽  
Design Variables ◽  
Complex Design ◽  
Final Design ◽  
Undersea Vehicle ◽  
Hierarchy Process

AbstractThe design of complex systems involves a number of choices, the implications of which are interrelated. If these choices are made sequentially, each choice may limit the options available in subsequent choices. Early choices may unknowingly limit the effectiveness of a final design in this way. Only a formal process that considers all possible choices (and combinations of choices) can insure that the best option has been selected. Complex design problems may easily present a number of choices to evaluate that is prohibitive. Modern optimization algorithms attempt to navigate a multidimensional design space in search of an optimal combination of design variables. A design optimization process for an autonomous underwater vehicle is developed using a multiple objective genetic optimization algorithm that searches the design space, evaluating designs based on three measures of performance: cost, effectiveness, and risk. A synthesis model evaluates the characteristics of a design having any chosen combination of design variable values. The effectiveness determined by the synthesis model is based on nine attributes identified in the U.S. Navy’s Unmanned Undersea Vehicle Master Plan and four performance-based attributes calculated by the synthesis model. The analytical hierarchy process is used to synthesize these attributes into a single measure of effectiveness. The genetic algorithm generates a set of Pareto optimal, feasible designs from which a decision maker(s) can choose designs for further analysis.

Aerodynamic Efficiency Optimization of the 1st Stage of Transonic High Pressure Turbine through Lean and Sweep Angles

2019 ◽  
Vol 36 (3) ◽  
pp. 245-256
Author(s):  
Yoonki Kim ◽  
Sanga Lee ◽  
Kwanjung Yee ◽  
Young-Seok Kang
Keyword(s):  
High Pressure ◽  
Surrogate Model ◽  
Design Space ◽  
Latin Hypercube Sampling ◽  
Expected Improvement ◽  
Total Efficiency ◽  
High Pressure Turbine ◽  
Sweep Angle ◽  
Design Variables ◽  
Optimal Latin Hypercube Sampling

Abstract The purpose of this study is to optimize the 1st stage of the transonic high pressure turbine (HPT) for enhancement of aerodynamic performance. Isentropic total-to-total efficiency is designated as the objective function. Since the isentropic efficiency can be improved through modifying the geometry of vane and rotor blade, lean angle and sweep angle are chosen as design variables, which can effectively alter the blade geometry. The sensitivities of each design variable are investigated by applying lean and sweep angles to the base nozzle and rotor, respectively. The design space is also determined based on the results of the parametric study. For the design of experiment (DoE), Optimal Latin Hypercube sampling is adopted, so that 25 evenly distributed samples are selected on the design space. Sequentially, based on the values from the CFD calculation, Kriging surrogate model is constructed and refined using Expected Improvement (EI). With the converged surrogate model, optimum solution is sought by using the Genetic Algorithm. As a result, the efficiency of optimum turbine 1st stage is increased by 1.07 % point compared to that of the base turbine 1st stage. Also, the blade loading, pressure distribution, static entropy, shock structure, and secondary flow are thoroughly discussed.

scholarly journals A new stress-based topology optimization approach for finding flexible structures

2021 ◽  
Author(s):  
Martin Noack ◽  
Arnold Kühhorn ◽  
Markus Kober ◽  
Matthias Firl
Keyword(s):  
Topology Optimization ◽  
Stress State ◽  
Design Space ◽  
Flexible Structures ◽  
Optimization Approach ◽  
Principal Stresses ◽  
Output Displacement ◽  
Design Concepts ◽  
Flexible Designs ◽  
Gauss Points

AbstractThis paper presents a new FE-based stress-related topology optimization approach for finding bending governed flexible designs. Thereby, the knowledge about an output displacement or force as well as the detailed mounting position is not necessary for the application. The newly developed objective function makes use of the varying stress distribution in the cross section of flexible structures. Hence, each element of the design space must be evaluated with respect to its stress state. Therefore, the method prefers elements experiencing a bending or shear load over elements which are mainly subjected to membrane stresses. In order to determine the stress state of the elements, we use the principal stresses at the Gauss points. For demonstrating the feasibility of the new topology optimization approach, three academic examples are presented and discussed. As a result, the developed sensitivity-based algorithm is able to find usable flexible design concepts with a nearly discrete 0 − 1 density distribution for these examples.

A novel design space concept for design of concrete foundation supporting chemical reactors

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Osama Bedair
Keyword(s):  
Design Space ◽  
Cost Savings ◽  
Space Representation ◽  
Chemical Reactors ◽  
Content Type ◽  
Gas Recovery ◽  
Wide Range ◽  
Numerical Finite Element ◽  
Novel Concept ◽  
Reactor Models

PurposeThis paper presents a novel concept for design of concrete support system for chemical reactors used in refineries and petrochemical plants. Graphical method is described that can be used to size the concrete base and piling system. Recommendations are also provided to optimize the parameters required for the design. The procedure is illustrated for design of two reactor models commonly used in gas recovery units.Design/methodology/approachDesign space representation for the foundation system is described for chemical reactors with variable heights. The key points of the design graph are extracted from the numerical finite element models. The reactor load is idealized at discrete points to transfer the loads to the piles. Bilateral spring system is used to model the soil restrains.FindingsThe graphical approach is economical and provides the design engineer the flexibility to select the foundation parameters from wide range of options.Practical implicationsThe concept presented in the paper can be utilized by engineers in the industry for design of chemical reactors. It must be noted that little guidelines are currently available in practice addressing the structural design aspects.Originality/valueA novel concept is presented in this paper based on significant industrial design experience of reactor supports. Using the described method leads to significant cost savings in material quantity and engineering time.

Function Ordering Within Morphological Charts: An Experimental Study

2018 ◽  
Author(s):  
Anant Chawla ◽  
Joshua D. Summers
Keyword(s):  
Experimental Study ◽  
Design Space Exploration ◽  
Engineering Students ◽  
Design Space ◽  
Space Exploration ◽  
Integrated Design ◽  
Input Function ◽  
Output Function ◽  
Design Concepts ◽  
Morphological Chart

Morphological charts are widely recognized tools in engineering design applications and research. However, a literature gap exists in instructing the representation and exploration of morphological charts. In this paper, an experiment is conducted to understand how morphological charts are explored and what impact functional arrangement has on it. The experiment consisted of two problem statements, each with five different functional arrangements: 1) Most to Least Important Function, 2) Least to Most Important Function, 3) Input to Output Function, 4) Output to Input Function, and 5) Random. Sixty-seven junior mechanical engineering students were provided a prepopulated morphological chart and asked to generate integrated design concepts. The generated concepts were analyzed to determine how frequently a given means is selected, how much of the chart is explored, what is the sequence of exploration, and finally the influence of function ordering on them. Experimental results indicate a tendency to focus more on the initial columns of the chart irrespective of functional order. Moreover, the Most-to-Least-Important functional order results in higher chances and uniformity of design space exploration.

Beyond the Known: Detecting Novel Feasible Domains Over an Unbounded Design Space

2017 ◽  
Vol 139 (11) ◽  
Author(s):  
Wei Chen ◽  
Mark Fuge
Keyword(s):  
Real World ◽  
Adaptive Sampling ◽  
Design Space ◽  
Fundamental Problem ◽  
Domain Boundary ◽  
Sampling Technique ◽  
Standard Test ◽  
Worst Case ◽  
Trade Offs ◽  
Design Variables

To solve a design problem, sometimes it is necessary to identify the feasible design space. For design spaces with implicit constraints, sampling methods are usually used. These methods typically bound the design space; that is, limit the range of design variables. But bounds that are too small will fail to cover all possible designs, while bounds that are too large will waste sampling budget. This paper tries to solve the problem of efficiently discovering (possibly disconnected) feasible domains in an unbounded design space. We propose a data-driven adaptive sampling technique—ε-margin sampling, which learns the domain boundary of feasible designs and also expands our knowledge on the design space as available budget increases. This technique is data-efficient, in that it makes principled probabilistic trade-offs between refining existing domain boundaries versus expanding the design space. We demonstrate that this method can better identify feasible domains on standard test functions compared to both random and active sampling (via uncertainty sampling). However, a fundamental problem when applying adaptive sampling to real world designs is that designs often have high dimensionality and thus require (in the worst case) exponentially more samples per dimension. We show how coupling design manifolds with ε-margin sampling allows us to actively expand high-dimensional design spaces without incurring this exponential penalty. We demonstrate this on real-world examples of glassware and bottle design, where our method discovers designs that have different appearance and functionality from its initial design set.

Value-Driven Modeling of Tactical and Operational Decisions in Support of Aerospace Product-Service Systems Design

2015 ◽  
Author(s):  
Cassio D. Goncalves ◽  
Michael Kokkolaras
Keyword(s):  
Quality Function Deployment ◽  
Systems Design ◽  
Service Systems ◽  
Business Strategies ◽  
Functional Requirements ◽  
Operational Decisions ◽  
Design Engineers ◽  
Design Variables ◽  
Product Service ◽  
Product Service Systems

Competitive markets and complex business-to-business environments compel manufacturers to provide innovative service offerings along with their products. This necessitates effective methodologires for developing and implementing sucessful new business strategies. This article presents an approach to model tactical and operational decisions to support the design and development of Product-Service Systems (PSSs). A combination of Quality Function Deployment and Design-to-Cost techniques is proposed as the first step of a PSS design framework that aids design engineers to determine the relations among value to customer, functional requirements, design variables and cost. The objective is to identify PSS design alternatives that deliver value to customer while respecting cost targets. An aerospace software case study is conducted to demonstrate the proposed approach.

Evaluation of Fuel Preparation Systems for Lean Premixing-Prevaporizing Combustors

1986 ◽  
Vol 108 (2) ◽  
pp. 391-395
Author(s):  
W. J. Dodds ◽  
E. E. Ekstedt
Keyword(s):  
System Design ◽  
Large Scale ◽  
Fuel Injection ◽  
Operating Conditions ◽  
Turbine Engine ◽  
Fuel Injectors ◽  
Design Concepts ◽  
Mixture Preparation ◽  
Design Variables ◽  
System Length

A series of tests was conducted to provide data for the design of premixing-prevaporizing fuel-air mixture preparation systems for aircraft gas turbine engine combustors. Fifteen configurations of four different fuel-air mixture preparation system design concepts were evaluated to determine fuel-air mixture uniformity at the system exit over a range of conditions representative of cruise operation for a modern commercial turbofan engine. Operating conditions, including pressure, temperature, fuel-air ratio, and velocity had no clear effect on mixture uniformity in systems which used low-pressure fuel injectors. However, performance of systems using pressure atomizing fuel nozzles and large-scale mixing devices was shown to be sensitive to operating conditions. Variations in system design variables were also evaluated and correlated. Mixture uniformity improved with increased system length, pressure drop, and number of fuel injection points per unit area. A premixing system compatible with the combustor envelope of a typical combustion system and capable of providing mixture nonuniformity (standard deviation/mean) below 15% over a typical range of cruise operating conditions was demonstrated.

Design of a Damper for a Suspension System via Evolutive Optimization

2013 ◽  
Author(s):  
Carlos A. Duchanoy ◽  
Marco A. Moreno-Armendáriz ◽  
Carlos A. Cruz-Villar
Keyword(s):  
Dynamic Optimization ◽  
Optimization Algorithm ◽  
Physical Characteristics ◽  
Suspension System ◽  
Dynamic Interactions ◽  
Multi Objective ◽  
Damping Coefficients ◽  
Design Variables ◽  
Parameterized Model ◽  
Stiffness And Damping

In this paper a dynamic optimization methodology for designing a passive automotive damper is proposed. The methodology proposes to state the design problem as a dynamic optimization one by considering the nonlinear dynamic interactions between the damper and the other elements of the suspension system, emphasizing geometry, dimensional and movement constraints. In order to obtain realistic simulations of the suspension, a link between a Computer-Aided Engineering Model (CAEM) and a multi-objective dynamic optimization algorithm is developed. As design objectives we consider the vehicle safety and the passenger comfort which are represented by the contact area of the tire and the vibrations of the cockpit respectively. The damper is optimized by stating a set of physical variables that determine the stiffness and damping coefficients as independent variables for the dynamic optimization problem, they include the spring helix diameter, the spring wire diameter, the oil physical characteristics and the bleed orifice diameters among others. The optimization algorithm that we use to solve the problem at hand is a multi-objective evolutive optimization algorithm. For this purpose we developed a parameterized model of the damper which is used to link the CAE tools and the optimization software, thus enabling fitness evaluations during the dynamic optimization process. By selecting the physical characteristics of the damper as design variables instead of the typical stiffness and damping coefficients, it is possible to consider important design constrains as the damper size, movement limitations and anchor points. As result of the proposed methodology a set of blueprints of non dominated Pareto configurations of the damper are provided to the decision maker.

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