On leveraging physical knowledge to augment neural network-based surrogate models for simulations

The computational cost of high-fidelity engineering simulations, for example CFD, is prohibitive if the application requires frequent design iterations or even fully fledged optimization. A popular way to reduce the computational cost and enable fast iteration cycles is to use surrogate models that are trained to predict simulation results from...

Development of a CFD data-driven surrogate model using the neural network approach for prediction of aircraft performance characteristics

Evaluation of aircraft performance for design, certification, and maintenance purposes requires aerodynamic knowledge for the entire flight envelope of an aircraft. Simplified models that relate geometric properties and flight conditions of an aircraft to its aerodynamic properties are simply not sufficient anymore, as non-linear aerodynamic...

Thermal Management of a Data Center White Space: A numerical study using computational fluid dynamics, flow & temperature predictions using neural networks, and white space design optimization using a genetic algorithm.

The total electrical energy consumption by all the operational data centers (located all over the world) is enormous (approx. 1% of the global electricity demand 20000TWh). This electrical energy is required 24x7 to operate and cool all the IT equipment present in the data center. The electrical energy required to cool all...