Creating Polytope Representation of Design Spaces for Visual Exploration Using Consistency Technique

2009 ◽  
Author(s):  
Srikanth Devanathan ◽  
Karthik Ramani
Keyword(s):  
Design Space ◽  
Convex Polytopes ◽  
Visual Exploration ◽  
Feasible Point ◽  
Performance Parameters ◽  
3 Dimensional ◽  
Design Variables ◽  
Finite Set ◽  
And Performance ◽  
Feasible Space

A polytope-based representation is presented to approximate the feasible space of a design concept that is described mathematically using constraints. A method for constructing such design spaces is also introduced. Constraints include equality and inequality relationships between design variables and performance parameters. The design space is represented as a finite set of (at most) 3-dimensional (possibly non-convex) polytopes, i.e., points, intervals, polygons (both open and closed) and polyhedra (both open and closed). These polytopes approximate the locally connected design space around an initial feasible point. The algorithm for constructing the design space is developed by adapting consistency algorithm for polytope representations.

scholarly journals Creating Polytope Representations of Design Spaces for Visual Exploration Using Consistency Techniques

2010 ◽  
Vol 132 (8) ◽  
Author(s):  
Srikanth Devanathan ◽  
Karthik Ramani
Keyword(s):  
Design Process ◽  
Design Space ◽  
Parametric Design ◽  
Three Dimensional ◽  
Visual Exploration ◽  
Support Design ◽  
Design Exploration ◽  
Design Variables ◽  
Finite Set ◽  
Geometry Problem

Understanding the limits of a design is an important aspect of the design process. When mathematical models are constructed to describe a design concept, the limits are typically expressed as constraints involving the variables of that concept. The set of values for the design variables that do not violate constraints constitute the design space of that concept. In this work, we transform a parametric design problem into a geometry problem thereby enabling computational geometry algorithms to support design exploration. A polytope-based representation is presented to geometrically approximate the design space. The design space is represented as a finite set of (at most) three-dimensional (possibly nonconvex) polytopes, i.e., points, intervals, polygons, and polyhedra. The algorithm for constructing the design space is developed by interpreting constraint-consistency algorithms as computational-geometric operations and consequently extending (3,2)-consistency algorithm for polytope representations. A simple example of a fingernail clipper design is used to illustrate the approach.

Possibility-Based Multidisciplinary Optimisation For Electric-Powered Unmanned Aerial Vehicle Design

2015 ◽  
Vol 119 (1221) ◽  
pp. 1397-1414 ◽  
Author(s):  
N. V. Nguyen ◽  
J.-W. Lee ◽  
M. Tyan ◽  
D. Lee
Keyword(s):  
Design Space ◽  
Point Of View ◽  
Vehicle Design ◽  
High Fidelity ◽  
Performance Parameters ◽  
Piston Engine ◽  
Design Variables ◽  
Aerial Vehicle ◽  
And Performance ◽  
System Point

AbstractThis paper describes a possibility-based multidisciplinary optimisation for electric-powered unmanned aerial vehicles (UAVs) design. An in-house integrated UAV (iUAV) analysis program that uses an electric-powered motor was developed and validated by a Predator A configuration for aerodynamics, weight, and performance parameters. An electric-powered propulsion system was proposed to replace a piston engine and fuel with an electric motor, power controllers, and battery from an eco-system point of view. Moreover, an in-house Possibility-Based Design Optimisation (iPBDO) solver was researched and developed to effectively handle uncertainty variables and parameters and to further shift constraints into a feasible design space. A sensitivity analysis was performed to reduce the dimensions of design variables and the computational load during the iPBDO process. Maximising the electric-powered UAV endurance while solving the iPBDO yields more conservative, but more reliable, optimal UAV configuration results than the traditional deterministic optimisation approach. A high fidelity analysis was used to demonstrate the effectiveness of the process by verifying the accuracy of the optimal electric-powered UAV configuration at two possibility index values and a baseline.

Preferences and Correlated Uncertainties in Engineering Design

2003 ◽  
Author(s):  
Tomonori Honda ◽  
Erik K. Antonsson
Keyword(s):  
Engineering Design ◽  
Design Variable ◽  
Design Space ◽  
Design Method ◽  
Design Problems ◽  
Performance Variables ◽  
Design Variables ◽  
Engineering Design Problems ◽  
Engineering Problems ◽  
And Performance

The Method of Imprecision (MOI) is a multi-objective design method that maximizes the overall degree of both design and performance preferences. Sets of design variables are iteratively selected, and the corresponding performances are approximately computed. The designer’s judgment (expressed as preferences) are combined (aggregated) with the customer’s preferences, to determine the overall preference for sets of points in the design space. In addition to degrees of preference for values of the design and performance variables, engineering design problems also typically include uncertainties caused by uncontrolled variations, for example, measuring and fabrication limitations. This paper illustrates the computation of expected preference for cases where the uncertainties are uncorrelated, and also where the uncertainties are correlated. The result is a “best” set of design variable values for engineering problems, where the overall aggregated preference is maximized. As is illustrated by the examples shown here, where both preferences and uncontrolled variations are present, the presence of uncertainties can have an important effect on the choice of the overall best set of design variable values.

The Environmental Design Space: Modeling and Performance Updates

2021 ◽  
Author(s):  
Luis Salas Nunez ◽  
Jimmy C. Tai ◽  
Dimitri N. Mavris
Keyword(s):  
Design Space ◽  
Environmental Design ◽  
Space Modeling ◽  
And Performance

Effect of Feeding Bt176 Maize on Physiological and Performance Parameters in Broiler Chickens

2004 ◽  
Vol 15 (3) ◽  
pp. 246-246
Author(s):  
M.A. Tony ◽  
A. Butschke ◽  
J. Zagon ◽  
H. Broll ◽  
M. Schauzu ◽  
...  
Keyword(s):  
Broiler Chickens ◽  
Performance Parameters ◽  
And Performance ◽  
Effect Of Feeding

scholarly journals Comparison of a Novel Polymeric Hollow Fiber Heat Exchanger and a Commercially Available Metal Automotive Radiator

Polymers ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1175
Author(s):  
Tereza Kroulíková ◽  
Tereza Kůdelová ◽  
Erik Bartuli ◽  
Jan Vančura ◽  
Ilya Astrouski
Keyword(s):  
Heat Transfer ◽  
Heat Exchanger ◽  
Hollow Fiber ◽  
Pressure Loss ◽  
Air Flow ◽  
Performance Parameters ◽  
University Of Technology ◽  
Flat Tubes ◽  
And Performance ◽  
Louvered Fins

A novel heat exchanger for automotive applications developed by the Heat Transfer and Fluid Flow Laboratory at the Brno University of Technology, Czech Republic, is compared with a conventional commercially available metal radiator. The heat transfer surface of this heat exchanger is composed of polymeric hollow fibers made from polyamide 612 by DuPont (Zytel LC6159). The cross-section of the polymeric radiator is identical to the aluminum radiator (louvered fins on flat tubes) in a Skoda Octavia and measures 720 × 480 mm. The goal of the study is to compare the functionality and performance parameters of both radiators based on the results of tests in a calibrated air wind tunnel. During testing, both heat exchangers were tested in conventional conditions used for car radiators with different air flow and coolant (50% ethylene glycol) rates. The polymeric hollow fiber heat exchanger demonstrated about 20% higher thermal performance for the same air flow. The efficiency of the polymeric radiator was in the range 80–93% and the efficiency of the aluminum radiator was in the range 64–84%. The polymeric radiator is 30% lighter than its conventional metal competitor. Both tested radiators had very similar pressure loss on the liquid side, but the polymeric radiator featured higher air pressure loss.

Performance Distribution Analysis and Robust Design

2000 ◽  
Vol 123 (1) ◽  
pp. 11-17 ◽  
Author(s):  
Jianmin Zhu ◽  
Kwun-Lon Ting
Keyword(s):  
Robust Design ◽  
Robust Parameter Design ◽  
Distribution Analysis ◽  
Performance Space ◽  
Sensitivity Distribution ◽  
Performance Sensitivity ◽  
Design Variables ◽  
Robust Parameter ◽  
Performance Distribution ◽  
Feasible Space

The paper presents the theory of performance sensitivity distribution and a novel robust parameter design technique. In the theory, a Jacobian matrix describes the effect of the component tolerance to the system performance, and the performance distribution is characterized in the variation space by a set of eigenvalues and eigenvectors. Thus, the feasible performance space is depicted as an ellipsoid. The size, shape, and orientation of the ellipsoid describe the quantity as well as quality of the feasible space and, therefore, the performance sensitivity distribution against the tolerance variation. The robustness of a design is evaluated by comparing the fitness between the ellipsoid feasible space and the tolerance space, which is a block, through a set of quantitative and qualitative indexes. The robust design can then be determined. The design approach is demonstrated in a mechanism design problem. Because of the generality of the analysis theory, the method can be used in any design situation as long as the relationship between the performance and design variables can be expressed analytically.

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.

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