The Monte Carlo based virtual entropy generation analysis

被引:11
|
作者
Zhang, Zhifeng [1 ]
机构
[1] Penn State Univ, Engn Sci & Mech, State Coll, PA 16802 USA
关键词
Virtual entropy generation analysis; Monto Carlo simulation; Experiment reliability; Critical heat balance error; HEAT-EXCHANGER; UNCERTAINTY ANALYSIS; FLOW; PERFORMANCE; DESIGNS; BALANCE; TUBES;
D O I
10.1016/j.applthermaleng.2017.07.208
中图分类号
O414.1 [热力学];
学科分类号
摘要
Due to a lack of any persuasive principle in defining a reasonable error criterion, regulations of experiment measurement are mostly experience-based. Measurement criteria developed by virtual entropy generation (VEG) analysis provide a new perspective on experiment control and data reliability. However, it is expensive to validate these criteria through experiments. Therefore, developing affordable numerical tools are necessary and important in VEG analysis. In the present research, we developed a Monto Carlo based model for a counter-flow heat exchanger virtual entropy generation analysis. In the present research, the uncertainty boundary of virtual entropy generation analysis was implanted to a counter-flow heat exchanger through Monte Carlo method. By a comparison study with existing analytical and experiment results, capabilities of the Monte Carlo model were demonstrated in providing quantitative and comprehensive data at a low cost. (C) 2017 Elsevier Ltd. All rights reserved.
引用
收藏
页码:915 / 919
页数:5
相关论文
共 50 条
  • [21] Research on RFID Virtual Tag Location Algorithm Based on Monte Carlo
    Guan, Tingliang
    Wang, Ding
    Su, Yixin
    2021 IEEE 13TH INTERNATIONAL CONFERENCE ON COMPUTER RESEARCH AND DEVELOPMENT (ICCRD 2021), 2021, : 68 - 72
  • [22] Comparative Monte Carlo efficiency by Monte Carlo analysis
    Rubenstein, B. M.
    Gubernatis, J. E.
    Doll, J. D.
    PHYSICAL REVIEW E, 2010, 82 (03):
  • [23] Generation of Optimal Weight Values Based on the Recursive Monte Carlo Method for Use in Monte Carlo Deep Penetration Calculations
    Yadav, Pratibha
    Rachamin, Reuven
    Konheiser, Joerg
    Baier, Silvio
    NUCLEAR SCIENCE AND ENGINEERING, 2024, 198 (02) : 497 - 507
  • [24] Monte Carlo generation of Bohmian trajectories
    Coffey, T. M.
    Wyatt, R. E.
    Schieve, W. C.
    JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL, 2008, 41 (33)
  • [25] The effects of wind generation capacity on electricity prices and generation costs: a Monte Carlo analysis
    Lynch, Muireann A.
    Curtis, John
    APPLIED ECONOMICS, 2016, 48 (02) : 133 - 151
  • [26] KLOE Calorimeter Simulation with Virtual Monte Carlo
    Roukoutakis, Filimon
    2010 IEEE NUCLEAR SCIENCE SYMPOSIUM CONFERENCE RECORD (NSS/MIC), 2010, : 1146 - 1148
  • [27] The Geant4 Virtual Monte Carlo
    Hrivnacova, I.
    INTERNATIONAL CONFERENCE ON COMPUTING IN HIGH ENERGY AND NUCLEAR PHYSICS 2012 (CHEP2012), PTS 1-6, 2012, 396
  • [28] Group constants generation by Monte Carlo code MCS for LWR analysis
    Nguyen, Tung Dong Cao
    Lee, Deokjung
    COMPUTER PHYSICS COMMUNICATIONS, 2023, 285
  • [29] Skin image reconstruction using Monte Carlo based color generation
    Aizu, Yoshihisa
    Maeda, Takaaki
    Kuwahara, Tomohiro
    Hirao, Tetsuji
    INFORMATION OPTICS AND OPTICAL DATA STORAGE, 2010, 7851
  • [30] Monte Carlo based Test Pattern Generation for Hardware Trojan Detection
    Xue Mingfu
    Hu Aiqun
    Huang Yi
    Li Guyue
    2013 IEEE 11TH INTERNATIONAL CONFERENCE ON DEPENDABLE, AUTONOMIC AND SECURE COMPUTING (DASC), 2013, : 131 - 136