Numeric  simulations  allow  the  study  of  physical  systems  and  engineering  concepts  at  conditions  and  scales  beyond  the  reach  of  traditional  experiments.  One  approach  to achieving  higher  fidelity  is  to  build  and  calibrate  computational  models  by  solving  an  inverse  problem  to  match  available  measurements  and  data.  MIT  is  currently  developing optimization-­based computational tools for solving such data-­intensive inverse problems. The  optimization-­based  computational  tools  are  designed  to  work  together  with  Large  Eddy Simulation (LES), a computational method for solving turbulent flows that depends on accurate modeling of energy transfer between turbulent length scales.  

Turbulent flows appear in engineering applications ranging from wind energy to internal combustion engines. To demonstrate the applicability of these tools to engineering design, MIT has partnered with GE Aviation. In this project, MIT and GE will partner to use these tools to simulate flow over jet engine turbine components, using a fundamental vane/blade geometry  described  in  the  literature  by  Kopriva  and  Laskowski.  Applying  these  high-­fidelity  simulation  and  optimization  methods  to  jet  engine  design  has  the  potential  to significantly  reduce  fuel  consumption,  leading  to  billions  of  dollars  in  fuel  savings  and  a  commensurate decrease in carbon emissions.