TransAT-SArP is a genetic algorithm controller (GA) encapsulating TransAT CFD and aiming at accelerating convergence of the numerical solver. The GA is based on evolutionary and genetic approaches that optimize the search of best adapted and fitted solutions to the current resolution context. The genetic process goes through real time adjustment of the free numerical parameters of the iterative solver within the constraints of physical convergence and objective achievement of the simulation. In order to enhance the process of selection and evaluation of the fitness of solutions, approximation functions inspired from data mining techniques (decision trees) are constructed from the perspective of historical data.
In many industrial applications, neither 1D system codes nor full 3D CFD are capable alone to shed light on the critical issues. The solution is in integrating the two strategies using efficient and robust algorithms capable to retain high-order accuracy, and conservation properties. This is particularly true for sectors as oil & gas, automotive and nuclear energy. TransAT has been coupled with OLGA for oil & gas application, in particular in the flow assurance branch. The coupling can be achieved under both off- and co-simulation. OLGA needs for the purpose to be installed on the same computer.
TransAT Parametric Analyzer is a must-have companion toolbox to TransAT CFD dedicated for parametric simulation studies. The studies are made possible for various problem set-up variations, including changing physics model selection, initial conditions, Boundary Conditions (BC’s), User Defined Functions (UDF’s), fluid properties, etc. The tool can serve at the end to perform a sort of Uncertainty Quantification. The results of the analysis can be compared directly in the application UI.
TransAT Engineering Data Analytics is brand new data archiving and treatment tool made to assemble and archive digitized data bases collected from sensors, measurements, online monitoring sites, and synthetic flow solutions (CFD). The data can be retrieved online for predictive modelling. The aim is to help industrial plants (selective) enable data analysis & treatment, operation optimization, and production prediction. The tool can be used for all sorts of Industrial Plants, e.g. Chemicals, Pulp & Paper, etc. Plant operators can access the data basis online and infer the critical measures using as an input their in-situ/in-time (e.g. sensors) information.