Grant: Idea to Innovation
University of Waterloo
$125,000.00
Design Automation of Heat exchangers using Deep reinforcement learning
FY 2022-23
Summary
The objective of this project is to further develop a software with an AI engine for design automation of thermal devices. This phase of the project is essential for the product to become commercially viable and competitive. Heat exchangers play a vital role for the removal of unwanted heat generated in a device and, as such, have wide variety of applications in cooling electronics, HVAC, data centers, and electric vehicles. Heat exchanger design automation software is capable of automatic optimization of heat exchanger geometry based on parameters such as available space and operating conditions. Design Automation of Heat exchangers using Deep reinforcement learning provides significant improvement in design performance of up to 60 percent and enhances productivity of engineering design teams through automation. This project aims to implement new features into the existing design automation software to include high volume manufacturing processes and compressible flow conditions. Another goal of this project is the case-study and manufacturing of prototypes with industrial applications to demonstrate the manufacturability of the designed heat exchangers. Successful implementation of these features along with cost per design analysis will greatly improve the commercial viability of heat exchanger design automation software. It also provides industry leaders with proof of concept of the capabilities of our software.
Program
Idea to Innovation
Recipient
Location
Ontario, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
580748-2023
Name nom
Hamdullahpur, Feridun X
Department département
Mechanical Engineering and Mechatronics
Organization id
33
Institution établissement
University of Waterloo
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2022
Competition year année de concours
2023
Award amount
125000
Program id
I2IPJ
Program name en
Idea to Innovation
Program name fr
De l'idée à l'innovation
Group en
RESEARCH PARTNERSHIPS
Group fr
PARTENARIATS DE RECHERCHE
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
1206
Area of application group en
Advancement of knowledge
Area of application group fr
Avancement des connaissances
Area of application en
Engineering
Area of application fr
Génie
Research subject code
1700
Research subject group en
Design and manufacturing
Research subject group fr
Conception et fabrication
Research subject en
Design and manufacturing
Research subject fr
Conception et fabrication
Installment
inst:1
Partie
1
Nb partie
1
Application title
Design Automation of Heat exchangers using Deep reinforcement learning
Application summary
The objective of this project is to further develop a software with an AI engine for design automation of thermal devices. This phase of the project is essential for the product to become commercially viable and competitive. Heat exchangers play a vital role for the removal of unwanted heat generated in a device and, as such, have wide variety of applications in cooling electronics, HVAC, data centers, and electric vehicles. Heat exchanger design automation software is capable of automatic optimization of heat exchanger geometry based on parameters such as available space and operating conditions. Design Automation of Heat exchangers using Deep reinforcement learning provides significant improvement in design performance of up to 60 percent and enhances productivity of engineering design teams through automation. This project aims to implement new features into the existing design automation software to include high volume manufacturing processes and compressible flow conditions. Another goal of this project is the case-study and manufacturing of prototypes with industrial applications to demonstrate the manufacturability of the designed heat exchangers. Successful implementation of these features along with cost per design analysis will greatly improve the commercial viability of heat exchanger design automation software. It also provides industry leaders with proof of concept of the capabilities of our software.