Dr. Richard Dwight

Aerodynamics, TU Delft

research

Active research lines:


The overall theme of my research is combining high-fidelity numerical computer models and experimental data in fluid dynamics. These two methods of studying fluids are often highly complementary, and unifying them promises broad benefits. The basic framework I use comes from mathematical statistics; the challenge is to develop numerical-statistical approaches and tools that exploit the capabilities of CFD fully and efficiently. Goals are to:

(If you're a TU Delft MSc student looking for a thesis subject, projects can be defined in all of the following areas.)

Predictive RANS simulation with stochastic error estimates

with Paola Cinnella (ENSAM, ParisTech) and Wouter Edeling

The turbulence closure model is the dominant source of error in most Reynolds Averaged Navier-Stokes (RANS) simulations, yet no reliable estimators of this error currently exist. This leads to a lack of confidence in the predictions of CFD. The introduction of better turbulence models does not help much, given the continued lack of a measure of accuracy.

We aim to develop stochastic, a posteriori error estimates, calibrated for a specific class of flow. Our estimates are based on variability in model closure coefficients across multiple flow scenarios (within the class), for multiple closure models. The variability is estimated using Bayesian calibration against experimental data for each scenario, and Bayesian Model-Scenario Averaging (BMSA) is used to collate the resulting posteriors, to obtain a stochastic estimate of a Quantity of Interest (QoI) in an unmeasured (prediction) scenario.

So far we've demonstrated the effectiveness of the methodology for the class of flows consisting of turbulent boundary layers at a variety of pressure-gradients. We're now aiming for more complex flows with the goal of industrial relevance.

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Left: Experimental velocity profiles for flat-plate flows at a variety of pressure gradients. Centre: Stochastic prediction of a velocity profile, mean and two-standard deviations, with experimental data in red as a reference. Right: Improved stochastic prediction exploiting multiple turbulence models.

Stochastic processes for experimental data reconstruction

with Jouke de Baar, Iliass Azijli

TODO.

Deterministic flow reconstruction with Navier-Stokes

with Fulvio Scarano and Jan Schneiders

TODO.

Bayesian structural model updating in aeroelasticity

with Rakesh Sarma and Jouke de Baar

TODO.

Surrogate modelling for uncertainty quantification/optimization

with Andrea Resmini and Jacques Peter (ONERA)

TODO.

Past research lines: