Selection-Integrated Optimization (SIO) Methodology for Optimal Design of Adaptive Systems

2008 ◽  
Vol 130 (10) ◽  
Author(s):  
Ritesh A. Khire ◽  
Achille Messac
Keyword(s):  
Adaptive Systems ◽  
Adaptive System ◽  
System Optimization ◽  
Building Envelope ◽  
Operating Conditions ◽  
Significant Source ◽  
Integrated Optimization ◽  
Design Variables ◽  
Fixed Design ◽  
Selection Of

Many engineering systems are required to operate under changing operating conditions. A special class of systems called adaptive systems has been proposed in the literature to achieve high performance under changing environments. Adaptive systems acquire this powerful feature by allowing their design configurations to change with operating conditions. In the optimization of the adaptive systems, designers are often required to select (i) adaptive and (ii) nonadaptive (or fixed) design variables of the design configuration. Generally, the selection of these variables and the optimization of adaptive systems are performed sequentially, thus being a source of suboptimality. In this paper, we propose the Selection-Integrated Optimization (SIO) methodology, which integrates the two key processes: (1) the selection of the adaptive and fixed design variables and (2) the optimization of the adaptive system, thereby eliminating a significant source of suboptimality from adaptive system optimization problems. A major challenge to integrating these two key processes is the selection of appropriate fixed and adaptive design variables, which is discrete in nature. We propose the Variable-Segregating Mapping-Function (VSMF), which overcomes this challenge by progressively approximating the discreteness in the design variable selection process. This simple yet effective approach allows the SIO methodology to integrate the selection and optimization processes and helps avoid one significant source of suboptimality from the optimization procedure. The SIO methodology finds its applications in a variety of other engineering fields, such as product family optimization. However, in this paper, we limit the scope of our discussion to adaptive system optimization. The effectiveness of the SIO methodology is demonstrated by designing a new air-conditioning system called Active Building Envelope (ABE) system.

Selection-Integrated Optimization (SIO) Methodology for Optimal Design of Adaptive Systems

2006 ◽  
Author(s):  
Ritesh A. Khire ◽  
Achille Messac
Keyword(s):  
Optimal Design ◽  
Adaptive Systems ◽  
Adaptive System ◽  
System Optimization ◽  
Building Envelope ◽  
Operating Conditions ◽  
Integrated Optimization ◽  
Design Variables ◽  
Fixed Design ◽  
Selection Of

Many engineering systems are required to operate under changing operating conditions. A special class of systems called adaptive systems have been proposed in the literature to achieve high performance under changing environments. Adaptive systems acquire this powerful feature by allowing their design configuration to change with operating conditions. In the optimization of the adaptive systems, designers are often required to select (i) adaptive and (ii) non-adaptive (or fixed) design variables of the design configuration. Generally, the selection of these variables, and the optimization of adaptive systems are performed sequentially, thus leaving a likelihood of a sub-optimal design. In this paper, we propose the Selection-Integrated Optimization (SIO) methodology that integrates the two key processes: (1) the selection of the adaptive and fixed design variables, and (2) the optimization of the adaptive system, thereby leading to an optimum design. A major challenge to integrating these two key processes is the selection of the number of fixed and adaptive design variables, which is discrete in nature. We propose the Variable-Segregating Mapping-Function (VSMF) that overcomes this roadblock by progressively approximating the discreteness in the design variable selection process. This simple yet effective approach allows the SIO methodology to integrate the selection and optimization processes, and help avoid one significant source of sub-optimality from typical optimization formulations. The SIO methodology finds its applications in a variety of other engineering fields as well, such as product family optimization. However, in this paper, we limit the scope of our discussion to adaptive system optimization. The effectiveness of the SIO methodology is demonstrated by optimally designing a new air-conditioning system called Active Building Envelope (ABE) System.

Color Trend Analysis using Machine Learning with Fashion Collection Images

2021 ◽  
pp. 0887302X2199594
Author(s):  
Ahyoung Han ◽  
Jihoon Kim ◽  
Jaehong Ahn
Keyword(s):  
Machine Learning ◽  
Trend Analysis ◽  
Image Data ◽  
Fashion Industry ◽  
Color Palette ◽  
Web Scraping ◽  
Design Variables ◽  
Sales Organizations ◽  
Fashion Designers ◽  
Selection Of

Fashion color trends are an essential marketing element that directly affect brand sales. Organizations such as Pantone have global authority over professional color standards by annually forecasting color palettes. However, the question remains whether fashion designers apply these colors in fashion shows that guide seasonal fashion trends. This study analyzed image data from fashion collections through machine learning to obtain measurable results by web-scraping catwalk images, separating body and clothing elements via machine learning, defining a selection of color chips using k-means algorithms, and analyzing the similarity between the Pantone color palette (16 colors) and the analysis color chips. The gap between the Pantone trends and the colors used in fashion collections were quantitatively analyzed and found to be significant. This study indicates the potential of machine learning within the fashion industry to guide production and suggests further research expand on other design variables.

scholarly journals An Analytical Approach for the Selection of Technologies to be Integrated in a Plug&play Façade Unit: The RenoZEB Case Study

Proceedings ◽  
2021 ◽  
Vol 65 (1) ◽  
pp. 29
Author(s):  
Alessandro Pracucci ◽  
Sara Magnani ◽  
Laura Vandi ◽  
Oscar Casadei ◽  
Amaia Uriarte ◽  
...  
Keyword(s):  
Building Envelope ◽  
Design System ◽  
Decision Matrix ◽  
Design Guideline ◽  
Existing Building ◽  
Building Conditions ◽  
Zero Energy Building ◽  
System A ◽  
Selection Of

The nearly Zero Energy building (nZEB) renovation market is currently the key feature in the construction sector. RenoZEB aims to develop a systematic approach for retrofitting by assembling different technologies in a plug and play building envelope. This paper presents the methodology used to transform the RenoZEB concept in the design system. A multi-criteria decision matrix is used for the selection of the best façade technologies within the market while the analysis of the existing building conditions allows to develop a replicable approach for designing deep retrofitting intervention through a plug&play façade. The methodology appears to be a valuable support for the selection of technologies and allows to define a design guideline for the envelope.

Fully Parametric High-Fidelity CFD Model for the Design Optimisation of the Cyclic Stagger Pattern of a Set of Fan Outlet Guide Vanes

2009 ◽  
Author(s):  
Andrea Milli ◽  
Olivier Bron
Keyword(s):  
Complex Geometry ◽  
Single Point ◽  
Sensitivity Study ◽  
Operating Conditions ◽  
High Fidelity ◽  
Cfd Model ◽  
Design Optimisation ◽  
Guide Vanes ◽  
High Loss ◽  
Design Variables

The present paper deals with the redesign of cyclic variation of a set of fan outlet guide vanes by means of high-fidelity full-annulus CFD. The necessity for the aerodynamic redesign originated from a change to the original project requirement, when the customer requested an increase in specific thrust above the original engine specification. The main objectives of this paper are: 1) make use of 3D CFD simulations to accurately model the flow field and identify high-loss regions; 2) elaborate an effective optimisation strategy using engineering judgement in order to define realistic objectives, constraints and design variables; 3) emphasise the importance of parametric geometry modelling and meshing for automatic design optimisation of complex turbomachinery configurations; 4) illustrate that the combination of advanced optimisation algorithms and aerodynamic expertise can lead to successful optimisations of complex turbomachinery components within practical time and costs constrains. The current design optimisation exercise was carried out using an in-house set of software tools to mesh, resolve, analyse and optimise turbomachinery components by means of Reynolds-averaged Navier-Stokes simulations. The original configuration was analysed using the 3D CFD model and thereafter assessed against experimental data and flow visualisations. The main objective of this phase was to acquire a deep insight of the aerodynamics and the loss mechanisms. This was important to appropriately limit the design scope and to drive the optimisation in the desirable direction with a limited number of design variables. A mesh sensitivity study was performed in order to minimise computational costs. Partially converged CFD solutions with restart and response surface models were used to speed up the optimisation loop. Finally, the single-point optimised circumferential stagger pattern was manually adjusted to increase the robustness of the design at other flight operating conditions. Overall, the optimisation resulted in a major loss reduction and increased operating range. Most important, it provided the project with an alternative and improved design within the time schedule requested and demonstrated that CFD tools can be used effectively not only for the analysis but also to provide new design solutions as a matter of routine even for very complex geometry configurations.

Improvement of the Static and Dynamic Characteristics of Magnetic Head Sliders by Optimum Design

1999 ◽  
Vol 122 (1) ◽  
pp. 280-287 ◽  
Author(s):  
Hiromu Hashimoto ◽  
Yasuhisa Hattori
Keyword(s):  
Objective Function ◽  
Optimum Design ◽  
Amplitude Ratio ◽  
Optimization Technique ◽  
Hard Disk Drives ◽  
Maximum Amplitude ◽  
Disk Surface ◽  
Operating Conditions ◽  
Magnetic Head ◽  
Design Variables

The aim of this paper is to develop a general methodology for the optimum design of magnetic head sliders in improving the spacing characteristics between a slider and disk surface under static and dynamic operating conditions of hard disk drives and to present an application of the methodology to the IBM 3380-type slider design. To generate the optimal design variables, the objective function is defined as the weighted sum of the minimum spacing, the maximum difference in the spacing due to variation of the radial location of the head, and the maximum amplitude ratio of the slider motion. Slider rail width, taper length, taper angle, suspension position, and preload are selected as the design variables. Before the optimization of the head, the effects of these five design variables on the objective function are examined by a parametric study, and then the optimum design variables are determined by applying the hybrid optimization technique, combining the direct search method and successive quadratic programming. From the obtained results, the effectiveness of optimum design on the spacing characteristics of magnetic heads is clarified. [S0742-4787(00)03701-2]

scholarly journals Evaluation of a High-Performance Solar Home in Loveland, Colorado

2006 ◽  
Vol 129 (2) ◽  
pp. 226-234
Author(s):  
Robert Hendron ◽  
Mark Eastment ◽  
Ed Hancock ◽  
Greg Barker ◽  
Paul Reeves
Keyword(s):  
High Performance ◽  
Energy Savings ◽  
High Efficiency ◽  
Building Envelope ◽  
Operating Conditions ◽  
Hot Water ◽  
Space Heating ◽  
Solar Water Heater ◽  
Water Heater ◽  
Solar Water

Building America (BA) partner McStain Neighborhoods built the Discovery House in Loveland, CO, with an extensive package of energy-efficient features, including a high-performance envelope, efficient mechanical systems, a solar water heater integrated with the space-heating system, a heat-recovery ventilator (HRV), and ENERGY STAR appliances. The National Renewable Energy Laboratory (NREL) and Building Science Consortium conducted short-term field-testing and building energy simulations to evaluate the performance of the house. These evaluations are utilized by BA to improve future prototype designs and to identify critical research needs. The Discovery House building envelope and ducts were very tight under normal operating conditions. The HRV provided fresh air at a rate of about 35L∕s(75cfm), consistent with the recommendations of ASHRAE Standard 62.2. The solar hot water system is expected to meet the bulk of the domestic hot water (DHW) load (>83%), but only about 12% of the space-heating load. DOE-2.2 simulations predict whole-house source energy savings of 54% compared to the BA Benchmark (Hendron, R., 2005 NREL Report No. 37529, NREL, Golden, CO). The largest contributors to energy savings beyond McStain’s standard practice are the solar water heater, HRV, improved air distribution, high-efficiency boiler, and compact fluorescent lighting package.

Optimum Selection of Hydraulic Devices for Water Hammer Control in the Pipeline Systems Using Genetic Algorithm

2003 ◽  
Author(s):  
Bong Seong Jung ◽  
Bryan W. Karney
Keyword(s):  
Genetic Algorithm ◽  
Steady State ◽  
Optimization Problems ◽  
System Optimization ◽  
Water Hammer ◽  
Pipeline System ◽  
Optimal Size ◽  
Protection Strategy ◽  
Hydraulic Devices ◽  
Selection Of

Genetic algorithms have been used to solve many water distribution system optimization problems, but have generally been limited to steady state or quasi-steady state optimization. However, transient events within pipe system are inevitable and the effect of water hammer should not be overlooked. The purpose of this paper is to optimize the selection, sizing and placement of hydraulic devices in a pipeline system considering its transient response. A global optimal solution using genetic algorithm suggests optimal size, location and number of hydraulic devices to cope with water hammer. This study shows that the integration of a genetic algorithm code with a transient simulator can improve both the design and the response of a pipe network. This study also shows that the selection of optimum protection strategy is an integrated problem, involving consideration of loading condition, device and system characteristics, and protection strategy. Simpler transient control systems are often found to outperform more complex ones.

Adaptation Application to Engine Modelling

2007 ◽  
Author(s):  
Jude Iyinbor
Keyword(s):  
Measurement Data ◽  
Engine Performance ◽  
Performance Model ◽  
Operating Conditions ◽  
Adaptation Process ◽  
Design Point ◽  
Component Map ◽  
Important Measurement ◽  
Unknown Component ◽  
Selection Of

The optimisation of engine performance by predictive means can help save cost and reduce environmental pollution. This can be achieved by developing a performance model which depicts the operating conditions of a given engine. Such models can also be used for diagnostic and prognostic purposes. Creating such models requires a method that can cope with the lack of component parameters and some important measurement data. This kind of method is said to be adaptive since it predicts unknown component parameters that match available target measurement data. In this paper an industrial aeroderivative gas turbine has been modelled at design and off-design points using an adaptation approach. At design point, a sensitivity analysis has been used to evaluate the relationships between the available target performance parameters and the unknown component parameters. This ensured the proper selection of parameters for the adaptation process which led to a minimisation of the adaptation error and a comprehensive prediction of the unknown component and available target parameters. At off-design point, the adaptation process predicted component map scaling factors necessary to match available off-design point performance data.

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.

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