Acceleration of Primitive Variable Hydrocodes by the Non-   Linear Generalized Minimum Residual Method
{'Code': '07020000', 'Directorate': {'Abbreviation': 'ENG', 'LongName': 'Directorate For Engineering'}, 'Division': {'Abbreviation': 'CBET', 'LongName': 'Div Of Chem, Bioeng, Env, & Transp Sys'}}
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Computational modeling has become an essential tool in the                      analysis and design of engineering systems.  Often such models                  involve the reduction of continuous system of governing partial                 differential equations to a set of discrete nonlinear algebraic                 equations.  The accurate and efficient solution of the large set                of nonlinear algebraic system poses a great challenge to the                    numerical analysts.  Existing algorithms for the discrete                       nonlinear system convergence thus slowly limits the size of the                 problem that can be effectively handled.  The dollar value of                   increasing the execution.time efficiency of these codes over                    their life.cycle is staggering.  Yet many innovations from                      computational mathematics which can yield comparable results in                 less time are only slowly being incorporated into production                    codes.  This is due to the high cost of retrofitting existing                   applications codes with new algorithmic kernels and of developing               new codes which inherit all of the application.specific features                of existing codes.                                                                                                                                              An adaptive nonlinear generalized minimum residual (NLGMR)                      algorithm which would accelerate convergence where possible and                 pass the iterates through unmodified, otherwise, is the goal of                 this Engineering Initiation project.  The NLGMR lgorithm treats                 an existing iteration scheme as a "black box" and computes                      improved iterates on the basis of iteration history alone.  Thus,               it can be used to retrofit existing codes.  However, the memory                 requirements of an NLGMR.enhanced algorithm are more substantial                than those of the original code, but with the availability of                   supercomputer resources limitations on memory storage, this                     should not be a problem.                                                                                                                                        The institutional support is adequate and the P.I. is well                      qualified to carry out the research.  I strongly recommend                      support.
{'FirstName': 'David', 'LastName': 'Keyes', 'PI_MID_INIT': 'E', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'David E Keyes', 'EmailAddress': 'david.keyes@columbia.edu', 'NSF_ID': '000098296', 'StartDate': '07/01/1987', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'Yale University', 'CityName': 'NEW HAVEN', 'ZipCode': '065113572', 'PhoneNumber': '2037854689', 'StreetAddress': '150 MUNSON ST', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Connecticut', 'StateCode': 'CT', 'CONGRESSDISTRICT': '03', 'CONGRESS_DISTRICT_ORG': 'CT03', 'ORG_UEI_NUM': 'FL6GV84CKN57', 'ORG_LGL_BUS_NAME': 'YALE UNIV', 'ORG_PRNT_UEI_NUM': 'FL6GV84CKN57'}
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{'Code': '1495', 'Text': 'COMPUTATIONAL ENGINEERING'}
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