scholarly journals Machine-Learnt Turbulence Closures for Low-Pressure Turbines With Unsteady Inflow Conditions

2019 ◽  
Vol 141 (10) ◽  
Author(s):  
H. D. Akolekar ◽  
R. D. Sandberg ◽  
N. Hutchins ◽  
V. Michelassi ◽  
G. Laskowski
Keyword(s):  
Learning Algorithm ◽  
A Priori ◽  
Model Development ◽  
Boussinesq Approximation ◽  
Low Pressure ◽  
Accurate Prediction ◽  
Phase Lock ◽  
Turbulence Closures ◽  
Unsteady Inflow ◽  
Inflow Conditions

Abstract The design of low-pressure turbines (LPTs) must account for the losses generated by the unsteady interaction with the upstream blade row. The estimation of such unsteady wake-induced losses requires the accurate prediction of the incoming wake dynamics and decay. Existing linear turbulence closures (stress–strain relationships), however, do not offer an accurate prediction of the wake mixing. Therefore, machine-learnt, nonlinear turbulence closures (models) have been developed for LPT flows with unsteady inflow conditions using a zonal-based model development approach, with an aim to enhance the wake mixing prediction for unsteady Reynolds-averaged Navier–Stokes calculations. High-fidelity time-averaged and phase-lock averaged data at a realistic isentropic Reynolds number and two reduced frequencies, i.e., with discrete incoming wakes and with wake “fogging,” have been used as reference data for a machine learning algorithm based on gene expression programing to develop models. Models developed via phase-lock averaged data were able to capture the effect of certain prominent physical phenomena in LPTs such as wake–wake interactions, whereas models based on the time-averaged data could not. Correlations with the flow physics lead to a set of models that can effectively enhance the wake mixing prediction across the entire LPT domain for both cases. Based on a newly developed error metric, the developed models have reduced the a priori error over the Boussinesq approximation on average by 45%. This study thus aids blade designers in selecting the appropriate nonlinear closures capable of mimicking the physical mechanisms responsible for loss generation.

scholarly journals Development and Use of Machine-Learnt Algebraic Reynolds Stress Models for Enhanced Prediction of Wake Mixing in Low-Pressure Turbines

2019 ◽  
Vol 141 (4) ◽  
Author(s):  
H. D. Akolekar ◽  
J. Weatheritt ◽  
N. Hutchins ◽  
R. D. Sandberg ◽  
G. Laskowski ◽  
...  
Keyword(s):  
Reynolds Stress ◽  
Gene Expression Programming ◽  
A Priori ◽  
Low Pressure ◽  
Navier Stokes ◽  
Flow Features ◽  
Turbulence Closures ◽  
Rans Models ◽  
Development Analysis ◽  
Stress Models

Nonlinear turbulence closures were developed that improve the prediction accuracy of wake mixing in low-pressure turbine (LPT) flows. First, Reynolds-averaged Navier–Stokes (RANS) calculations using five linear turbulence closures were performed for the T106A LPT profile at isentropic exit Reynolds numbers 60,000 and 100,000. None of these RANS models were able to accurately reproduce wake loss profiles, a crucial parameter in LPT design, from direct numerical simulation (DNS) reference data. However, the recently proposed kv2¯ω transition model was found to produce the best agreement with DNS data in terms of blade loading and boundary layer behavior and thus was selected as baseline model for turbulence closure development. Analysis of the DNS data revealed that the linear stress–strain coupling constitutes one of the main model form errors. Hence, a gene-expression programming (GEP) based machine-learning technique was applied to the high-fidelity DNS data to train nonlinear explicit algebraic Reynolds stress models (EARSM), using different training regions. The trained models were first assessed in an a priori sense (without running any RANS calculations) and showed much improved alignment of the trained models in the region of training. Additional RANS calculations were then performed using the trained models. Importantly, to assess their robustness, the trained models were tested both on the cases they were trained for and on testing, i.e., previously not seen, cases with different flow features. The developed models improved prediction of the Reynolds stress, turbulent kinetic energy (TKE) production, wake-loss profiles, and wake maturity, across all cases.

scholarly journals Machine Learning for Turbulence Model Development Using a High-Fidelity HPT Cascade Simulation

2017 ◽  
Author(s):  
Jack Weatheritt ◽  
Richard Pichler ◽  
Richard D. Sandberg ◽  
Gregory Laskowski ◽  
Vittorio Michelassi
Keyword(s):  
High Pressure ◽  
Evolutionary Algorithm ◽  
Turbine Blade ◽  
A Priori ◽  
Model Development ◽  
Boussinesq Approximation ◽  
High Pressure Turbine ◽  
Stress Strain ◽  
Non Linear ◽  
Anisotropy Tensor

The validity of the Boussinesq approximation in the wake behind a high-pressure turbine blade is explored. We probe the mathematical assumptions of such a relationship by employing a least-squares technique. Next, we use an evolutionary algorithm to modify the anisotropy tensor a priori using highly resolved LES data. In the latter case we build a non-linear stress-strain relationship. Results show that the standard eddy-viscosity assumption underpredicts turbulent diffusion and is theoretically invalid. By increasing the coefficient of the linear term, the farwake prediction shows minor improvement. By using additional non-linear terms in the stress-strain coupling relationship, created by the evolutionary algorithm, the near-wake can also be improved upon. Terms created by the algorithm are scrutinized and the discussion is closed by suggesting a tentative non-linear expression for the Reynolds stress, suitable for the wake behind a high-pressure turbine blade.

The Effects of a Trip Wire and Unsteadiness on a High-Speed Highly Loaded Low-Pressure Turbine Blade

2004 ◽  
Vol 127 (4) ◽  
pp. 747-754 ◽  
Author(s):  
M. Vera ◽  
H. P. Hodson ◽  
R. Vazquez
Keyword(s):  
Boundary Layer ◽  
High Speed ◽  
Low Pressure ◽  
Boundary Layer Transition ◽  
Layer Transition ◽  
Low Pressure Turbine ◽  
Roughness Elements ◽  
Unsteady Inflow ◽  
Mach Numbers ◽  
Inflow Conditions

This paper presents the effect of a single spanwise two-dimensional wire upon the downstream position of boundary layer transition under steady and unsteady inflow conditions. The study is carried out on a high turning, high-speed, low pressure turbine (LPT) profile designed to take account of the unsteady flow conditions. The experiments were carried out in a transonic cascade wind tunnel to which a rotating bar system had been added. The range of Reynolds and Mach numbers studied includes realistic LPT engine conditions and extends up to the transonic regime. Losses are measured to quantify the influence of the roughness with and without wake passing. Time resolved measurements such as hot wire boundary layer surveys and surface unsteady pressure are used to explain the state of the boundary layer. The results suggest that the effect of roughness on boundary layer transition is a stability governed phenomena, even at high Mach numbers. The combination of the effect of the roughness elements with the inviscid Kelvin–Helmholtz instability responsible for the rolling up of the separated shear layer (Stieger, R. D., 2002, Ph.D. thesis, Cambridge University) is also examined. Wake traverses using pneumatic probes downstream of the cascade reveal that the use of roughness elements reduces the profile losses up to exit Mach numbers of 0.8. This occurs with both steady and unsteady inflow conditions.

Boundary Layer Control on a Low Pressure Turbine Blade by Means of Pulsed Blowing

2013 ◽  
Vol 135 (5) ◽  
Author(s):  
Marion Mack ◽  
Reinhard Niehuis ◽  
Andreas Fiala ◽  
Yavuz Guendogdu
Keyword(s):  
Boundary Layer ◽  
Pressure Ratio ◽  
Reynolds Numbers ◽  
Low Pressure ◽  
Moderate Amount ◽  
Low Pressure Turbine ◽  
Wide Range ◽  
Unsteady Inflow ◽  
Inflow Conditions ◽  
Highly Loaded

The current work investigates the performance benefits of pulsed blowing with frequencies up to 10 kHz on a highly loaded low pressure turbine (LPT) blade. The influence of blowing position and frequency on the boundary layer and losses are investigated. Pressure profile distribution measurements and midspan wake traverses are used to assess the effects on the boundary layer under a wide range of Reynolds numbers from 50,000 to 200,000 at a cascade exit Mach number of 0.6 under steady as well as periodically unsteady inflow conditions. High-frequency blowing at sufficient amplitudes is achieved with the use of fluidic oscillators. The integral loss coefficient calculated from wake traverses is used to assess the optimum pressure ratio driving the fluidic oscillators. The results show that pulsed blowing with fluidic oscillators can significantly reduce the profile losses of the highly loaded LPT blade T161 with a moderate amount of air used in a wide range of Reynolds numbers under both steady and unsteady inflow conditions.

Low-Pressure Turbine Cascade Performance Calculations With Incidence Variation and Periodic Unsteady Inflow Conditions

2015 ◽  
Author(s):  
Michele Marconcini ◽  
Roberto Pacciani ◽  
Andrea Arnone ◽  
Francesco Bertini
Keyword(s):  
Boundary Conditions ◽  
Incidence Angle ◽  
Low Pressure ◽  
Navier Stokes ◽  
Generation Process ◽  
Angle Variation ◽  
Flow Angle ◽  
Low Pressure Turbine ◽  
Turbulence Closures ◽  
Unsteady Inflow

The performance of a Low-Pressure Turbine (LPT) cascade were investigated under both steady and periodic unsteady inflow boundary conditions with different Reynolds numbers and a reduced frequency representative of real LPT working conditions. A sensitivity analysis to the variation of the inlet flow angle was performed to assess the performance during off-design operation. The numerical framework is based on a steady/unsteady Reynolds Averaged Navier-Stokes (RANS/URANS) flow solver which includes some state-of-the-art transition-sensitive turbulence closures. Boundary conditions for the time-accurate computations upstream of the cascade were derived from the experimental characterization of a moving bar system used to generate the wake periodic perturbations. The computed performance of the cascade, as a function of Reynolds number and incidence angle variation, is discussed in comparison with experimental data. Steady and unsteady boundary layer quantities are also compared with measurements in order to gain more insights into the loss generation process and to better support the numerical results. Such an assessment proves that the proposed procedure is adequate for the analysis of the off-design behavior of cascades operating in low pressure turbine conditions.

The Effects of a Trip Wire and Unsteadiness on a High Speed Highly Loaded Low-Pressure Turbine Blade

2004 ◽  
Author(s):  
M. Vera ◽  
H. P. Hodson ◽  
R. Vazquez
Keyword(s):  
Boundary Layer ◽  
High Speed ◽  
Low Pressure ◽  
Boundary Layer Transition ◽  
Layer Transition ◽  
Low Pressure Turbine ◽  
Roughness Elements ◽  
Unsteady Inflow ◽  
Mach Numbers ◽  
Inflow Conditions

This paper presents the effect of a single spanwise 2D wire upon the downstream position of boundary layer transition under steady and unsteady inflow conditions. The study is carried out on a high turning, high-speed, low pressure turbine (LPT) profile designed to take account of the unsteady flow conditions. The experiments were carried out in a transonic cascade wind tunnel to which a rotating bar system had been added. The range of Reynolds and Mach numbers studied includes realistic LPT engine conditions and extends up to the transonic regime. Losses are measured to quantify the influence of the roughness with and without wake passing. Time resolved measurements such as hot wire boundary layer surveys and surface unsteady pressure are used to explain the state of the boundary layer. The results suggest that the effect of roughness on boundary layer transition is a stability governed phenomena, even at high Mach numbers. The combination of the effect of the roughness elements with the inviscid Kelvin-Helmholtz instability responsible for the rolling up of the separated shear layer (Stieger [1]) is also examined. Wake traverses using pneumatic probes downstream of the cascade reveal that the use of roughness elements reduces the profile losses up to exit Mach numbers of 0.8. This occurs with both steady and unsteady inflow conditions.

Development of a machine learning algorithm for fault detection in a cantilever beam

2021 ◽  
pp. 095745652110004
Author(s):  
Amit Kumar Gorai ◽  
Tarapada Roy ◽  
Sumeet Mishra
Keyword(s):  
Mechanical Properties ◽  
Fault Detection ◽  
Cantilever Beam ◽  
Learning Algorithm ◽  
Model Development ◽  
Vibration Signal ◽  
Beam Specimen ◽  
Discrete Wavelet ◽  
Feed Forward Network ◽  
Model Training

The mechanical properties of a component change with any type of damage such as crack development, generation of holes, bend, excessive wear, and tear. The change in mechanical properties causes the material to behave differently in terms of noise and vibration under different loading conditions. Thus, the present study aims to develop an artificial neural network model using vibration signal data for early fault detection in a cantilever beam. The discrete wavelet transform coefficients of de-noised vibration signals were used for model development. The vibration signal was recorded using the OROS OR35 module for different fault conditions (no fault, notch fault, and hole fault) of a cantilever beam. A feed-forward network was trained using backpropagation to map the input features to output. A total of 603 training datasets (201 datasets for three types of cantilever beam—no fault, notch fault, and hole fault) were used for training, and 201 datasets were used for testing of the model. The testing dataset was recorded for a hole fault cantilever beam specimen. The results indicated that the proposed model predicted the test samples with 78.6% accuracy. To increase the accuracy of prediction, more data need to be used in the model training.

scholarly journals On the Unfounded Enthusiasm for Soft Selective Sweeps III: The Supervised Machine Learning Algorithm That Isn’t

Genes ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 527
Author(s):  
Eran Elhaik ◽  
Dan Graur
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
A Priori ◽  
Neutral Theory ◽  
Dominant Mode ◽  
Supervised Machine Learning ◽  
Training Dataset ◽  
Selective Sweeps ◽  
Two Factors ◽  
Negative Controls

In the last 15 years or so, soft selective sweep mechanisms have been catapulted from a curiosity of little evolutionary importance to a ubiquitous mechanism claimed to explain most adaptive evolution and, in some cases, most evolution. This transformation was aided by a series of articles by Daniel Schrider and Andrew Kern. Within this series, a paper entitled “Soft sweeps are the dominant mode of adaptation in the human genome” (Schrider and Kern, Mol. Biol. Evolut. 2017, 34(8), 1863–1877) attracted a great deal of attention, in particular in conjunction with another paper (Kern and Hahn, Mol. Biol. Evolut. 2018, 35(6), 1366–1371), for purporting to discredit the Neutral Theory of Molecular Evolution (Kimura 1968). Here, we address an alleged novelty in Schrider and Kern’s paper, i.e., the claim that their study involved an artificial intelligence technique called supervised machine learning (SML). SML is predicated upon the existence of a training dataset in which the correspondence between the input and output is known empirically to be true. Curiously, Schrider and Kern did not possess a training dataset of genomic segments known a priori to have evolved either neutrally or through soft or hard selective sweeps. Thus, their claim of using SML is thoroughly and utterly misleading. In the absence of legitimate training datasets, Schrider and Kern used: (1) simulations that employ many manipulatable variables and (2) a system of data cherry-picking rivaling the worst excesses in the literature. These two factors, in addition to the lack of negative controls and the irreproducibility of their results due to incomplete methodological detail, lead us to conclude that all evolutionary inferences derived from so-called SML algorithms (e.g., S/HIC) should be taken with a huge shovel of salt.

Stochastic Bayesian optimization for predicting borderline knock

2021 ◽  
pp. 146808742110652
Author(s):  
Jian Tang ◽  
Anuj Pal ◽  
Wen Dai ◽  
Chad Archer ◽  
James Yi ◽  
...  
Keyword(s):  
Fuel Economy ◽  
Learning Algorithm ◽  
Feedforward Control ◽  
Model Development ◽  
Kriging Model ◽  
Optimization Method ◽  
Surrogate Models ◽  
Bayesian Optimization ◽  
Control Parameters ◽  
Operation Conditions

Engine knock is an undesirable combustion that could damage the engine mechanically. On the other hand, it is often desired to operate the engine close to its borderline knock limit to optimize combustion efficiency. Traditionally, borderline knock limit is detected by sweeping tests of related control parameters for the worst knock, which is expensive and time consuming, and also, the detected borderline knock limit is often used as a feedforward control without considering its stochastic characteristics without compensating current engine operational condition and type of fuel used. In this paper, stochastic Bayesian optimization method is used to obtain a tradeoff between stochastic knock intensity and fuel economy. The log-nominal distribution of knock intensity signal is converted to Gaussian one using a proposed map to satisfy the assumption for Kriging model development. Both deterministic and stochastic Kriging surrogate models are developed based on test data using the Bayesian iterative optimization process. This study focuses on optimizing two competing objectives, knock intensity and indicated specific fuel consumption using two control parameters: spark and intake valve timings. Test results at two different operation conditions show that the proposed learning algorithm not only reduces required time and cost for predicting knock borderline but also provides control parameters, based on trained surrogate models and the corresponding Pareto front, with the best fuel economy possible.

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