5 research outputs found
RANS Turbulence Model Development using CFD-Driven Machine Learning
This paper presents a novel CFD-driven machine learning framework to develop
Reynolds-averaged Navier-Stokes (RANS) models. The CFD-driven training is an
extension of the gene expression programming method (Weatheritt and Sandberg,
2016), but crucially the fitness of candidate models is now evaluated by
running RANS calculations in an integrated way, rather than using an algebraic
function. Unlike other data-driven methods that fit the Reynolds stresses of
trained models to high-fidelity data, the cost function for the CFD-driven
training can be defined based on any flow feature from the CFD results. This
extends the applicability of the method especially when the training data is
limited. Furthermore, the resulting model, which is the one providing the most
accurate CFD results at the end of the training, inherently shows good
performance in RANS calculations. To demonstrate the potential of this new
method, the CFD-driven machine learning approach is applied to model
development for wake mixing in turbomachines. A new model is trained based on a
high-pressure turbine case and then tested for three additional cases, all
representative of modern turbine nozzles. Despite the geometric configurations
and operating conditions being different among the cases, the predicted wake
mixing profiles are significantly improved in all of these a posteriori tests.
Moreover, the model equation is explicitly given and available for analysis,
thus it could be deduced that the enhanced wake prediction is predominantly due
to the extra diffusion introduced by the CFD-driven model.Comment: Accepted by Journal of Computational Physic
ChatGPT -- a Blessing or a Curse for Undergraduate Computer Science Students and Instructors?
ChatGPT is an AI language model developed by OpenAI that can understand and
generate human-like text. It can be used for a variety of use cases such as
language generation, question answering, text summarization, chatbot
development, language translation, sentiment analysis, content creation,
personalization, text completion, and storytelling. While ChatGPT has garnered
significant positive attention, it has also generated a sense of apprehension
and uncertainty in academic circles. There is concern that students may
leverage ChatGPT to complete take-home assignments and exams and obtain
favorable grades without genuinely acquiring knowledge. This paper adopts a
quantitative approach to demonstrate ChatGPT's high degree of unreliability in
answering a diverse range of questions pertaining to topics in undergraduate
computer science. Our analysis shows that students may risk self-sabotage by
blindly depending on ChatGPT to complete assignments and exams. We build upon
this analysis to provide constructive recommendations to both students and
instructors.Comment: This is a work in progres
Transition Modeling for Low Pressure Turbines Using Computational Fluid Dynamics Driven Machine Learning
Existing Reynolds Averaged Navier–Stokes-based transition models do not accurately predict separation induced transition for low pressure turbines. Therefore, in this paper, a novel framework based on computational fluids dynamics (CFD) driven machine learning coupled with multi-expression and multi-objective optimization is explored to develop models which can improve the transition prediction for the T106A low pressure turbine at an isentropic exit Reynolds number of Re2is=100,000. Model formulations are proposed for the transfer and laminar eddy viscosity terms of the laminar kinetic energy transition model using seven non-dimensional pi groups. The multi-objective optimization approach makes use of cost functions based on the suction-side wall-shear stress and the pressure coefficient. A family of solutions is thus developed, whose performance is assessed using Pareto analysis and in terms of physical characteristics of separated-flow transition. Two models are found which bring the wall-shear stress profile in the separated region at least two times closer to the reference high-fidelity data than the baseline transition model. As these models are able to accurately predict the flow coming off the blade trailing edge, they are also able to significantly enhance the wake-mixing prediction over the baseline model. This is the first known study which makes use of ‘CFD-driven’ machine learning to enhance the transition prediction for a non-canonical flow
Assessment of Machine-Learned Turbulence Models Trained for Improved Wake-Mixing in Low-Pressure Turbine Flows
This paper presents an assessment of machine-learned turbulence closures, trained for improving wake-mixing prediction, in the context of LPT flows. To this end, a three-dimensional cascade of industrial relevance, representative of modern LPT bladings, was analyzed, using a state-of-the-art RANS approach, over a wide range of Reynolds numbers. To ensure that the wake originates from correctly reproduced blade boundary-layers, preliminary analyses were carried out to check for the impact of transition closures, and the best-performing numerical setup was identified. Two different machine-learned closures were considered. They were applied in a prescribed region downstream of the blade trailing edge, excluding the endwall boundary layers. A sensitivity analysis to the distance from the trailing edge at which they are activated is presented in order to assess their applicability to the whole wake affected portion of the computational domain and outside the training region. It is shown how the best-performing closure can provide results in very good agreement with the experimental data in terms of wake loss profiles, with substantial improvements relative to traditional turbulence models. The discussed analysis also provides guidelines for defining an automated zonal application of turbulence closures trained for wake-mixing predictions
Assessment of Machine-Learned Turbulence Models Trained for Improved Wake-Mixing in Low-Pressure Turbine Flows
This paper presents an assessment of machine-learned turbulence closures, trained for improving wake-mixing prediction, in the context of LPT flows. To this end, a three-dimensional cascade of industrial relevance, representative of modern LPT bladings, was analyzed, using a state-of-the-art RANS approach, over a wide range of Reynolds numbers. To ensure that the wake originates from correctly reproduced blade boundary-layers, preliminary analyses were carried out to check for the impact of transition closures, and the best-performing numerical setup was identified. Two different machine-learned closures were considered. They were applied in a prescribed region downstream of the blade trailing edge, excluding the endwall boundary layers. A sensitivity analysis to the distance from the trailing edge at which they are activated is presented in order to assess their applicability to the whole wake affected portion of the computational domain and outside the training region. It is shown how the best-performing closure can provide results in very good agreement with the experimental data in terms of wake loss profiles, with substantial improvements relative to traditional turbulence models. The discussed analysis also provides guidelines for defining an automated zonal application of turbulence closures trained for wake-mixing predictions