901,279 research outputs found
Edward C. M. Chen, oral history interview and transcript
No full textThis recording and transcript form part of a collection of oral history interviews conducted by the Chao Center for Asian Studies at Rice University. This collection includes audio recordings and transcripts of interviews with Asian Americans native to Houston.Edward C. M. Chen, interviewed by Juean Chen and Clarissa Cox, June 4, 2010, in Houston, Texas
Jingyi-Chen/cloud_parcel_mode: cloud parcel model
No full textThis is the first release of the cloud parcel model developed by Jingyi Chen. The development of this model was supported by Stony Brook University and Brookhaven National Laboratory in 2012-2014 (adiabatic version) and 2015-2017 (entrainment-mixing with entrained aerosols).
The publications associated with this code is below:
Chen, J., Y. Liu, M. Zhang, and Y. Peng (2016), New understanding and quantification of the regime dependence of aerosol-cloud interaction for studying aerosol indirect effects, Geophysical Research Letter, 43, 1780–1787, doi:10.1002/2016GL067683.
Chen, J., Y. Liu, M. Zhang, and Y. Peng (2018), Height dependency of aerosol-cloud interaction regimes, Journal of Geophysical Research: Atmospheres, doi: 10.1002/2017JD027431.
Chen, J., Y. Liu and M. Zhang (2020), Effects of Lateral Entrainment-Mixing with Entrained Aerosols on Cloud Microphysics, Geophysical Research Letter, doi: 10.1029/2020GL087667
Conférence de M. Chen Zhichao
No full textZhichao Chen. Conférence de M. Chen Zhichao . In: École pratique des hautes études, Section des sciences religieuses. Annuaire. Tome 96, 1987-1988. 1987. pp. 88-89
Continuum free-energy formulation for a class of lattice Boltzmann multiphase models
No full textWe characterize theoretically the free energy of the so called 'Shan-Chen' model for non ideal fluid flows
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M-estimator, and D-optimality model construction using orthogonal forward regression
No full textThis correspondence introduces a new orthogonal forward regression (OFR) model identification algorithm using D-optimality for model structure selection and is based on an M-estimators of parameter estimates. M-estimator is a classical robust parameter estimation technique to tackle bad data conditions such as outliers. Computationally, The M-estimator can be derived using an iterative reweighted least squares (IRLS) algorithm. D-optimality is a model structure robustness criterion in experimental design to tackle ill-conditioning in model Structure. The orthogonal forward regression (OFR), often based on the modified Gram-Schmidt procedure, is an efficient method incorporating structure selection and parameter estimation simultaneously. The basic idea of the proposed approach is to incorporate an IRLS inner loop into the modified Gram-Schmidt procedure. In this manner, the OFR algorithm for parsimonious model structure determination is extended to bad data conditions with improved performance via the derivation of parameter M-estimators with inherent robustness to outliers. Numerical examples are included to demonstrate the effectiveness of the proposed algorithm
Conférence de M. Chen Yung-Shen
No full textYung-Shen Chen. Conférence de M. Chen Yung-Shen. In: École pratique des hautes études, Section des sciences religieuses. Annuaire. Tome 85, 1976-1977. 1976. pp. 105-107
Original immunoblots Ouyang and Chen et al Developmental Cell
No full textUncropped immunoblotting images Ouyang and Chen Developmental Cell
Simulation of thermal plant optimization and hydraulic aspects of thermal distribution loops for large campuses
Get PDFFollowing an introduction, the author describes Texas A&M University and its utilities system. After that, the author presents how to construct simulation models for chilled water and heating hot water distribution systems. The simulation model was used in a $2.3 million Ross Street chilled water pipe replacement project at Texas A&M University. A second project conducted at the University of Texas at San Antonio was used as an example to demonstrate how to identify and design an optimal distribution system by using a simulation model. The author found that the minor losses of these closed loop thermal distribution systems are significantly higher than potable water distribution systems. In the second part of the report, the author presents the latest development of software called the Plant Optimization Program, which can simulate cogeneration plant operation, estimate its operation cost and provide optimized operation suggestions. The author also developed detailed simulation models for a gas turbine and heat recovery steam generator and identified significant potential savings. Finally, the author also used a steam turbine as an example to present a multi-regression method on constructing simulation models by using basic statistics and optimization algorithms. This report presents a survey of the author??s working experience at the Energy Systems Laboratory (ESL) at Texas A&M University during the period of January 2002 through March 2004. The purpose of the above work was to allow the author to become familiar with the practice of engineering. The result is that the author knows how to complete a project from start to finish and understands how both technical and nontechnical aspects of a project need to be considered in order to ensure a quality deliverable and bring a project to successful completion. This report concludes that the objectives of the internship were successfully accomplished and that the requirements for the degree of Degree of Engineering have been satisfied
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