{"id":1839,"date":"2015-12-03T23:24:49","date_gmt":"2015-12-03T23:24:49","guid":{"rendered":"http:\/\/blogs.uakron.edu\/cmg\/?page_id=1839"},"modified":"2015-12-03T23:26:51","modified_gmt":"2015-12-03T23:26:51","slug":"compare","status":"publish","type":"page","link":"https:\/\/blogs.uakron.edu\/cmg\/compare\/","title":{"rendered":"COMPARE"},"content":{"rendered":"

Commercialization of the COMPARE Software<\/p>\n

\"1\"<\/a>\u00a0&<\/strong>\"2\"<\/a><\/p>\n

A Partnership with Open Channel Software<\/a> to market the COMPARE software, developed at the University of Akron, has been established.<\/h3>\n
\n

\"3\"<\/a><\/p>\n

\n

\"4\"<\/a><\/p>\n

\"5\"<\/a><\/p>\n

\"6\"<\/a><\/p>\n

 <\/p>\n

A key ingredient in the design of engineering components and structures under general thermomechanical loading is the use of sophisticated mathematical constitutive models (e.g., in finite element analysis) capable of accurate representation of stress\/deformation responses. With the ever-increasing complexity of such mathematical constitutive models (e.g., viscoplasticity, viscoelasticity, hyperelasticity), they often also require a large number of material constants to describe a host of physical phenomena and complicated deformation mechanisms. In turn, the experimental characterization of these material parameters constitutes the major factor in the successful and effective utilization of any given constitutive model. But extracting the needed material parameters for the model from the volumes of data generated in the test lab can be a frustrating and often unsuccessful task.<\/p>\n

Traditionally, basic trial-and-error procedures (i.e., graphical\/mechanistic) fitting the predicted response from the constitutive model and that from the experimental test data have been used for simple models. However, these are certainly rather limited in more general situations. This is particularly true when dealing with a very large number of material constants that; (i) are often lacking in their direct physical interpretation, (ii) may have vastly different magnitudes, (iii) are highly interactive with each other. In addition, there may be a large number of experimental tests of various types (i.e., stress-, strain-, or mixed-control) under transient and\/or steady-state conditions. Therefore, an urgent and obvious need exists for a systematic development of a general methodology for constitutive parameter estimation.<\/p>\n

Consequently, the COnstitutive Material PARameter Estimator (COMPARE) has been developed to enable the design engineer to bridge this gap between constitutive theory and experimental test data in which optimum material parameters are determined by minimizing the errors between experimental data and the correlated response. The code COMPARE, which contains both optimization and constitutive model analysis tools, casts the estimation of the material parameters as a minimum-error weighted, multi-objective optimization problem. The code then solves this optimization problem using the Sequential Quadratic Programming Technique implemented in COMPARE.<\/p>\n

 <\/p>\n