Optimization of design parameters of glazed hybrid photovoltaic thermal module using genetic algorithm

This work aims at developing a closed-form correlation between key building design variables and its energy use. The results can be utilized during the initial design stages to assess the different building shapes and designs according to their expected energy use. Prototypical, 20-floor office buildings were used. The relative compactness, footprint area, projection factor, and window-to-wall ratio were changed and the resulting buildings performances were simulated. In total, 729 different office buildings were developed and simulated in order to provide the training cases for optimizing the correlation’s coefficients. Simulations were done using the VisualDOE TM software with a Typical Meteorological Year data file, Kuwait City, Kuwait. A real-coded genetic algorithm (GA) was used to optimize the coefficients of a proposed function that relates the energy use of a building to its four key parameters. The figure of merit was the difference in the ratio of the annual energy use of a building normalized by that of a reference building. The objective was to minimize the difference between the simulated results and the four-variable function trying to predict them. Results show that the real-coded GA was able to come up with a function that estimates the thermal performance of a proposed design with an accuracy of around 96%, based on the number of buildings tested. The goodness of fit, roughly represented by R2, ranged from 0.950 to 0.994. In terms of the effects of the various parameters, the area was found to have the smallest role among the design parameters. It was also found that the accuracy of the function suffers the most when high window-to-wall ratios are combined with low projection factors. In such cases, the energy use develops a potential optimum compactness. The proposed function (and methodology) will be a great tool for designers to inexpensively explore a wide range of alternatives and assess them in terms of their energy use efficiency. It will also be of great use to municipality officials and building codes authors.

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Optimization of Turbofan Propulsion Cycle Using a Genetic Algorithm

Volume 3: Controls, Diagnostics and Instrumentation; Cycle Innovations; Marine ◽

10.1115/gt2010-22420 ◽

2010 ◽

Cited By ~ 1

Author(s):

Adel Ghenaiet

Keyword(s):

Genetic Algorithm ◽

Fuel Consumption ◽

Pressure Ratio ◽

Engine Performance ◽

Optimization Procedure ◽

Specific Fuel Consumption ◽

High Mass ◽

Design Parameters ◽

Inlet Temperature ◽

Simplifying Assumptions

This paper presents an evolutionary approach as the optimization framework to design for the optimal performance of a high-bypass unmixed turbofan to match with the power requirements of a commercial aircraft. The parametric analysis had the objective to highlight the effects of the principal design parameters on the propulsive performance in terms of specific fuel consumption and specific thrust. The design optimization procedure based on the genetic algorithm PIKAIA coupled to the developed engine performance analyzer (on-design and off-design) aimed at finding the propulsion cycle parameters minimizing the specific fuel consumption, while meeting the required thrusts in cruise and takeoff and the restrictions of temperatures limits, engine size and weight as well as pollutants emissions. This methodology does not use engine components’ maps and operates on simplifying assumptions which are satisfying the conceptual or early design stages. The predefined requirements and design constraints have resulted in an engine with high mass flow rate, bypass ratio and overall pressure ratio and a moderate turbine inlet temperature. In general, the optimized engine is fairly comparable with available engines of equivalent power range.

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The Influence and Optimization of Design Parameters on Integrated Circuits Package Warpage

10.1109/icept52650.2021.9568069 ◽

2021 ◽

Author(s):

Qiang Wei ◽

Cao Ting ◽

Weidong Liu ◽

Ning Sun ◽

Qu Fang ◽

...

Keyword(s):

Integrated Circuits ◽

Design Parameters ◽

Optimization Of Design Parameters

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