東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁 到查詢結果 [ subject:"Computational physics." ]

切換: 標籤 | MARC模式 | ISBD

CFD Simulations of Cardiovascular De...

Nassau, Christopher J.

FindBook

Google Book

Amazon

博客來

CFD Simulations of Cardiovascular Devices with Shape Optimization to Reduce Blood Damage Using Genetic Algorithm.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	CFD Simulations of Cardiovascular Devices with Shape Optimization to Reduce Blood Damage Using Genetic Algorithm./
作者:	Nassau, Christopher J.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	140 p.
附註:	Source: Dissertations Abstracts International, Volume: 81-10, Section: B.
Contained By:	Dissertations Abstracts International81-10B.
標題:	Mechanical engineering. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27736657
ISBN:	9781658453141

CFD Simulations of Cardiovascular Devices with Shape Optimization to Reduce Blood Damage Using Genetic Algorithm.
Nassau, Christopher J.

CFD Simulations of Cardiovascular Devices with Shape Optimization to Reduce Blood Damage Using Genetic Algorithm. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 140 p.

Source: Dissertations Abstracts International, Volume: 81-10, Section: B.

Thesis (D.Sc.)--Washington University in St. Louis, 2020.

This item is not available from ProQuest Dissertations & Theses.

Rapid development of computer power and computational fluid dynamics (CFD) technology has made CFD a critical component of analysis in many engineering disciplines; however its use in standardization of cardiovascular assist devices through analysis and design is still in its infancy. U.S. Food and Drug Administration (FDA) created a "Critical Path Initiative" and reached out to industry professionals and academics with knowledge in CFD to participate in simulation of flow in a blood contacting nozzle seen in many medical devices and in a blood pump. These two "Critical Path Initiative" projects were aimed at determining the best practices for CFD simulations such as the grid quality, numerical algorithms, turbulence models and boundary conditions, etc. A large number of people participated in these projects including the author in the CFD group at WUSTL under the direction of Dr. Agarwal. The results for the nozzle case have been compiled in a report and released by the FDA. However, due to the complexity of the blood pump project, since 2014 only a portion of the results have been released by the FDA with the full data analysis to be released in the future. Some highlights using data from this author include acknowledgement from the FDA for being one of a small group of participants in the pump benchmark CFD simulation and his data was used in a paper in the ASAIO's annual conference which was the winner of the Willem Kolff Top Abstract prize and was also published in an ASME conference paper.Simulations of the nozzle, blood pump and a left ventricular assist device (LVAD) cannula are conducted in this dissertation and the machine learning technique of a genetic algorithm (GA) is applied to optimize the shape of these devices in order to minimize the blood damage which characterizes the hemolysis or rupture potential of red blood cells. These "shape optimizations" are conducted by automation with no user interaction after the initial setup for definition of boundary conditions, mesh generation and evaluation of intermediate results. "Optimizations" differ from "design improvements" where the physics and the resulting shape of the profile are not easily determined by intuition or "guess and improve." Here, the results are loft surfaces of various orders that best describe the fluid physics as opposed to conventional parametric studies or design of experiments approaches. Results for the original and shape optimized devices are presented to demonstrate the effectiveness of optimized devices in reducing the blood damage. For the original configurations, CFD computations are validated using the experimental data or other computations depending upon their availability.

ISBN: 9781658453141Subjects--Topical Terms:

649730
Mechanical engineering.
Subjects--Index Terms:

Biofluid dynamics

CFD Simulations of Cardiovascular Devices with Shape Optimization to Reduce Blood Damage Using Genetic Algorithm.
LDR:04125nmm a2200397 4500 001 2278339
005 20210628075002.5
008 220723s2020 ||||||||||||||||| ||eng d
020 $a 9781658453141
035 $a (MiAaPQ)AAI27736657
035 $a AAI27736657
040 $a MiAaPQ $c MiAaPQ
100 1 $a Nassau, Christopher J. $0 (orcid)0000-0002-1560-999X $3 3556718
245 1 0 $a CFD Simulations of Cardiovascular Devices with Shape Optimization to Reduce Blood Damage Using Genetic Algorithm.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 140 p.
500 $a Source: Dissertations Abstracts International, Volume: 81-10, Section: B.
500 $a Advisor: Agarwal, Ramesh K.
502 $a Thesis (D.Sc.)--Washington University in St. Louis, 2020.
506 $a This item is not available from ProQuest Dissertations & Theses.
506 $a This item must not be sold to any third party vendors.
506 $a This item must not be added to any third party search indexes.
520 $a Rapid development of computer power and computational fluid dynamics (CFD) technology has made CFD a critical component of analysis in many engineering disciplines; however its use in standardization of cardiovascular assist devices through analysis and design is still in its infancy. U.S. Food and Drug Administration (FDA) created a "Critical Path Initiative" and reached out to industry professionals and academics with knowledge in CFD to participate in simulation of flow in a blood contacting nozzle seen in many medical devices and in a blood pump. These two "Critical Path Initiative" projects were aimed at determining the best practices for CFD simulations such as the grid quality, numerical algorithms, turbulence models and boundary conditions, etc. A large number of people participated in these projects including the author in the CFD group at WUSTL under the direction of Dr. Agarwal. The results for the nozzle case have been compiled in a report and released by the FDA. However, due to the complexity of the blood pump project, since 2014 only a portion of the results have been released by the FDA with the full data analysis to be released in the future. Some highlights using data from this author include acknowledgement from the FDA for being one of a small group of participants in the pump benchmark CFD simulation and his data was used in a paper in the ASAIO's annual conference which was the winner of the Willem Kolff Top Abstract prize and was also published in an ASME conference paper.Simulations of the nozzle, blood pump and a left ventricular assist device (LVAD) cannula are conducted in this dissertation and the machine learning technique of a genetic algorithm (GA) is applied to optimize the shape of these devices in order to minimize the blood damage which characterizes the hemolysis or rupture potential of red blood cells. These "shape optimizations" are conducted by automation with no user interaction after the initial setup for definition of boundary conditions, mesh generation and evaluation of intermediate results. "Optimizations" differ from "design improvements" where the physics and the resulting shape of the profile are not easily determined by intuition or "guess and improve." Here, the results are loft surfaces of various orders that best describe the fluid physics as opposed to conventional parametric studies or design of experiments approaches. Results for the original and shape optimized devices are presented to demonstrate the effectiveness of optimized devices in reducing the blood damage. For the original configurations, CFD computations are validated using the experimental data or other computations depending upon their availability.
590 $a School code: 0252.
650 4 $a Mechanical engineering. $3 649730
650 4 $a Computational physics. $3 3343998
653 $a Biofluid dynamics
653 $a Blood damage
653 $a Blood pump
653 $a Cardiovascular medical devices
653 $a Computational fluid dynamics
653 $a Optimization algorithms
690 $a 0548
690 $a 0216
710 2 $a Washington University in St. Louis. $b Mechanical Engineering. $3 1684966
773 0 $t Dissertations Abstracts International $g 81-10B.
790 $a 0252
791 $a D.Sc.
792 $a 2020
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27736657