A Bayesian Approach for the Identification of Cascade Loss Model Strategy

2020 ◽  
Author(s):  
Daniele Simoni ◽  
Davide Lengani ◽  
Daniele Petronio ◽  
Francesco Bertini
Keyword(s):  
Experimental Data ◽  
Model Selection ◽  
Large Scale ◽  
Information Criterion ◽  
Reynolds Numbers ◽  
Maximum Probability ◽  
Low Pressure Turbine ◽  
Functional Relationships ◽  
Profile Losses ◽  
Polynomial Formulation

Abstract A Bayesian method has been used to identify the best model strategy to describe the profile losses of low pressure turbine (LPT) cascades operating under unsteady inflow. The model has been tuned with experimental data measured in a large scale cascade facility, equipped with a moving bar system. Tests have been carried out on two different cascades, investigating three different reduced frequencies, three mass flow coefficients and several Reynolds numbers (up to eight) per condition, accounting for an overall amount of 51 different combinations of these parameters for each cascade. The predictor functions included into the model have been varied starting from a classic polynomial formulation for each influencing parameter, and then with functional relationships mimicking physical constrains and loss tendencies. Different combinations of the predictors, also including different types and orders of the cross-terms, have been evaluated by means of a Bayesian model selection method searching for the maximum probability of the model in fitting the cloud of experimental data. In particular, the evaluation of the Model Evidence (ME) using the Bayesian Information Criterion approximation (BIC) has allowed obtaining sufficient accuracy and avoiding overfitting at the same time. The best model here identified will be shown to be able to well reproduce the loss surface of a third different cascade that does not participate to the model selection. Realistic profile loss evolutions outside of the design space tested are provided, thus also allowing for a generalization of the structure of the model for other applications and future works.

A Comparative Analysis and Evaluation of Model Selection Criteria

2017 ◽  
Author(s):  
Ahmed H. Kamel ◽  
Ali S. Shaqlaih ◽  
Arslan Rozyyev
Keyword(s):  
Experimental Data ◽  
Model Selection ◽  
Selection Criteria ◽  
Oil And Gas ◽  
Information Criterion ◽  
Oil And Gas Industry ◽  
Absolute Error ◽  
Ongoing Research ◽  
Model Selection Criteria ◽  
Innovative Strategy

The ongoing research for model choice and selection has generated a plethora of approaches. With such a wealth of methods, it can be difficult for a researcher to know what model selection approach is the proper way to proceed to select the appropriate model for prediction. The authors present an evaluation of various model selection criteria from decision-theoretic perspective using experimental data to define and recommend a criterion to select the best model. In this analysis, six of the most common selection criteria, nineteen friction factor correlations, and eight sets of experimental data are employed. The results show that while the use of the traditional correlation coefficient, R2 is inappropriate, root mean square error, RMSE can be used to rank models, but does not give much insight on their accuracy. Other criteria such as correlation ratio, mean absolute error, and standard deviation are also evaluated. The Akaike information criterion, AIC has shown its superiority to other selection criteria. The authors propose AIC as an alternative to use when fitting experimental data or evaluating existing correlations. Indeed, the AIC method is an information theory based, theoretically sound and stable. The paper presents a detailed discussion of the model selection criteria, their pros and cons, and how they can be utilized to allow proper comparison of different models for the best model to be inferred based on sound mathematical theory. In conclusion, model selection is an interesting problem and an innovative strategy to help alleviate similar challenges faced by the professionals in the oil and gas industry is introduced.

Interaction of Rim Seal and Main Annulus Flow in a Low-Speed Turbine Rig

2013 ◽  
Author(s):  
Jesus Pueblas ◽  
Roque Corral ◽  
Sebastian Schrewe
Keyword(s):  
Experimental Data ◽  
Numerical Simulations ◽  
Large Scale ◽  
Secondary Flows ◽  
Operating Conditions ◽  
Main Stream ◽  
Unsteady State ◽  
Cooling Flow ◽  
Low Pressure Turbine ◽  
Annulus Flow

The influence of the sealing flows on the secondary flows of a low-pressure turbine has been assessed numerically using multi-row steady and unsteady simulations. The experimental data obtained at the Large Scale Turbine Rig (LSTR) at Technische Universität Darmstadt have been used to validate the numerical method and complement the simulations. Steady and unsteady state solutions and experiments are compared to understand the importance of the unsteadiness in the accuracy of numerical simulations. It is concluded that unsteady rotor/stator simulations enhance the prediction of the stator secondary flows, especially in the tip region. The effect of the sealing air is analysed, varying the cooling mass flow for two operating conditions. The penetration of the sealing flow in the main stream increases withthe cooling flow, displacing the horseshoe and passage vortices towards the mid-span.

Large-scale model selection in misspecified generalized linear models

Biometrika ◽  
2021 ◽  
Author(s):  
Emre Demirkaya ◽  
Yang Feng ◽  
Pallavi Basu ◽  
Jinchi Lv
Keyword(s):  
Model Selection ◽  
Generalized Linear Models ◽  
Large Scale ◽  
Linear Models ◽  
Information Criterion ◽  
Scale Model ◽  
High Dimensional ◽  
Model Selection Consistency ◽  
New Information ◽  
Large Scale Model

Summary Model selection is crucial both to high-dimensional learning and to inference for contemporary big data applications in pinpointing the best set of covariates among a sequence of candidate interpretable models. Most existing work assumes implicitly that the models are correctly specified or have fixed dimensionality, yet both are prevalent in practice. In this paper, we exploit the framework of model selection principles under the misspecified generalized linear models presented in Lv and Liu (2014) and investigate the asymptotic expansion of the posterior model probability in the setting of high-dimensional misspecified models.With a natural choice of prior probabilities that encourages interpretability and incorporates the Kullback–Leibler divergence, we suggest the high-dimensional generalized Bayesian information criterion with prior probability for large-scale model selection with misspecification. Our new information criterion characterizes the impacts of both model misspecification and high dimensionality on model selection. We further establish the consistency of covariance contrast matrix estimation and the model selection consistency of the new information criterion in ultra-high dimensions under some mild regularity conditions. The numerical studies demonstrate that our new method enjoys improved model selection consistency compared to its main competitors.

scholarly journals Performance comparison of model selection criteria by generated experimental data

2018 ◽  
Vol 16 ◽  
pp. 02006
Author(s):  
Radoslav Mavrevski ◽  
Peter Milanov ◽  
Metodi Traykov ◽  
Nevena Pencheva
Keyword(s):  
Experimental Data ◽  
Model Selection ◽  
Mathematical Models ◽  
Bayesian Information Criterion ◽  
Selection Criteria ◽  
Information Criterion ◽  
Data Fitting ◽  
Performance Comparison ◽  
Artificial Data ◽  
Criteria For Evaluation

In Bioinformatics and other areas the model selection is a process of choosing a model from set of candidate models of different classes which will provide the best balance between goodness of fitting of the data and complexity of the model. There are many criteria for evaluation of mathematical models for data fitting. The main objectives of this study are: (1) to fitting artificial experimental data with different models with increasing complexity; (2) to test whether two known criteria as Akaike’s information criterion (AIC) and Bayesian information criterion (BIC) can correctly identify the model, used to generate the artificial data and (3) to assess and compare empirically the performance of AIC and BIC.

Prediction of Separation-Induced Transition on High Lift Low Pressure Turbine Blade

2011 ◽  
Author(s):  
Abdelkader Benyahia ◽  
Lionel Castillon ◽  
Robert Houdeville
Keyword(s):  
Experimental Data ◽  
Reynolds Numbers ◽  
Low Pressure ◽  
Transition Model ◽  
Freestream Turbulence ◽  
Low Pressure Turbine ◽  
European Program ◽  
Inlet Conditions ◽  
Development And Validation ◽  
Good Agreement

This paper deals with the development and validation of the Menter and Langtry correlation-based transition model in the RANS code elsA. Two types of experimental linear cascades of low pressure turbine (LPT) airfoils having different loading distributions have been considered for the validation: the T106C and T108 blades. Experimental data have been provided by the Von Karman Institute in the framework of the European program TATMo. Different Reynolds numbers varying from 80 000 to 250 000 and different freestream turbulence intensities have been investigated. The results obtained for the T106C blade are in good agreement with the experimental data: the bubble size and the kinetic energy losses are well predicted. Sensitivity to freestream turbulence is also well demonstrated for the considered Reynolds numbers. However the results for the T108 blade show the limitations of the current version. These limitations are explained and discussed in this paper. The second part of this paper deals with the numerical and physical aspects of periodical unsteady inlet conditions which are introduced in order to take into account the incoming wakes. The original Menter and Langtry transition model has required a modification for performing correct unsteady computations of wake induced transition which is discussed in this paper. The unsteady results obtained with elsA are in quite good agreement with the experimental data.

scholarly journals Low-Pressure Turbine Separation Control: Comparison With Experimental Data

2002 ◽  
Author(s):  
Vijay K. Garg
Keyword(s):  
Experimental Data ◽  
Computational Study ◽  
Vortex Generator ◽  
Reynolds Numbers ◽  
Low Pressure ◽  
Separation Control ◽  
Suction Surface ◽  
Low Reynolds Numbers ◽  
Low Pressure Turbine ◽  
Control Comparison

The present work details a computational study, using the Glenn-HT code, that analyzes the use of vortex generator jets (VGJs) to control separation on a low-pressure turbine (LPT) blade at low Reynolds numbers. The computational results are also compared with the experimental data of Bons et al. [1] for steady VGJs. It is found that the code determines the proper location of the separation point on the suction surface of the baseline blade (without any VGJ) for Reynolds numbers of 50,000 or less. Also, the code finds that the separated region on the suction surface of the blade vanishes with the use of VGJs. However, the separated region and the wake characteristics are not well predicted. The wake width is generally over-predicted while the wake depth is under-predicted.

An Examination of the Effect of Reynolds Number on Airfoil Performance

2011 ◽  
Author(s):  
Tim Burdett ◽  
Jason Gregg ◽  
Kenneth Van Treuren
Keyword(s):  
Experimental Data ◽  
Reynolds Number ◽  
Wind Turbines ◽  
Large Scale ◽  
Reynolds Numbers ◽  
Energy Sources ◽  
Small Scale ◽  
Number Range ◽  
Low Reynolds Numbers ◽  
Low Reynolds

The standard of living throughout the world has increased dramatically over the last 30 years and is projected to continue to rise. This growth leads to an increased demand on conventional energy sources, such as fossil fuels. However, these are finite resources. Thus, there is an increasing demand for alternative energy sources, such as wind energy. Much of current wind turbine research focuses on large-scale (>1 MW), technologically-complex wind turbines installed in areas of high average wind speed (>20 mph). An alternative approach is to focus on small-scale (1–10kW), technologically-simple wind turbines built to produce power in low wind regions. While these turbines may not be as efficient as the large-scale systems, they require less industrial support and a less complicated electrical grid since the power can be generated at the consumer’s location. To pursue this approach, a design methodology for small-scale wind turbines must be developed and validated. This paper addresses one element of this methodology, airfoil performance prediction. In the traditional design process, an airfoil is selected and published lift and drag curves are used to optimize the blade twist and predict performance. These published curves are typically generated using either experimental testing or a numeric code, such as PROFIL (the Eppler Airfoil Design and Analysis Code) or XFOIL. However, the published curves often represent performance over a different range of Reynolds numbers than the actual design conditions. Wind turbines are typically designed from 2-D airfoil data, so having accurate airfoil data for the design conditions is critical. This is particularly crucial for small-scale, fixed-pitched wind turbines, which typically operate at low Reynolds numbers (<500,000) where airfoil performance can change significantly with Reynolds number. From a simple 2-D approach, the ideal operating condition for an airfoil to produce torque is the angle of attack at which lift is maximized and drag is minimized, so prediction of this angle will be compared using experimental and simulated data. Theoretical simulations in XFOIL of the E387 airfoil, designed for low Reynolds numbers, suggest that this optimum angle for design is Reynolds number dependent, predicting a difference of 2.25° over a Reynolds number range of 460,000 to 60,000. Published experimental data for the E387 airfoil demonstrate a difference of 2.0° over this same Reynolds number range. Data taken in the Baylor University Subsonic Wind Tunnel for the S823 airfoil shows a similar trend. This paper examines data for the E387 and S823 airfoils at low Reynolds numbers (75,000, 150,000, and 200,000 for the S823) and compares the experimental data with XFOIL predictions and published PROFIL predictions.

Active Flow Control Using Steady Blowing for a Low-Pressure Turbine Cascade

2004 ◽  
Author(s):  
B. R. McAuliffe ◽  
S. A. Sjolander
Keyword(s):  
Reynolds Number ◽  
Flow Control ◽  
Separation Bubble ◽  
Active Flow Control ◽  
Reynolds Numbers ◽  
Wall Jet ◽  
Freestream Turbulence ◽  
Low Pressure Turbine ◽  
Blowing Ratio ◽  
Profile Losses

The paper presents mid-span measurements for a turbine cascade with active flow control. Steady blowing through an inclined plane wall jet has been used to control the separation characteristics of a high-lift low-pressure turbine airfoil at low Reynolds numbers. Measurements were made at design incidence for blowing ratios from approximately 0.25 to 2.0 (ratio of jet-to-local freestream velocity), for Reynolds numbers of 25000 and 50000 (based on axial chord and inlet velocity), and for freestream turbulence intensities of 0.4% and 4%. Detailed flow field measurements were made downstream of the cascade using a three-hole pressure probe, static pressure distributions were measured on the airfoil suction surface, and hot-wire measurements were made to characterize the interaction between the wall jet and boundary layer. The primary focus of the study is on the low-Reynolds number and low-freestream turbulence intensity cases, where the baseline airfoil stalls and high profile losses result. For low freestream turbulence (0.4%), the examined method of flow control was effective at preventing stall and reducing the profile losses. At a Reynolds number of 25000, a blowing ratio greater than 1.0 was required to suppress stall. At a Reynolds number of 50000, a closed separation bubble formed at a very low blowing ratio (0.25) resulting in a significant reduction in the profile loss. For high freestream turbulence intensity (4%), where the baseline airfoil has a closed separation bubble and low profile losses, blowing ratios below 1.0 resulted in a larger separation bubble and higher losses. The mechanism by which the wall jet affects the separation characteristics of the airfoil is examined through hot-wire traverse measurements in the vicinity of the slot.

Active Flow Control Using Steady Blowing for a Low-Pressure Turbine Cascade

2004 ◽  
Vol 126 (4) ◽  
pp. 560-569 ◽  
Author(s):  
Brian R. McAuliffe ◽  
Steen A. Sjolander
Keyword(s):  
Reynolds Number ◽  
Flow Control ◽  
Separation Bubble ◽  
Active Flow Control ◽  
Reynolds Numbers ◽  
Wall Jet ◽  
Freestream Turbulence ◽  
Low Pressure Turbine ◽  
Blowing Ratio ◽  
Profile Losses

The paper presents mid-span measurements for a turbine cascade with active flow control. Steady blowing through an inclined plane wall jet has been used to control the separation characteristics of a high-lift low-pressure turbine airfoil at low Reynolds numbers. Measurements were made at design incidence for blowing ratios from approximately 0.25 to 2.0 (ratio of jet-to-local freestream velocity), for Reynolds numbers of 25,000 and 50,000 (based on axial chord and inlet velocity), and for freestream turbulence intensities of 0.4% and 4%. Detailed flow field measurements were made downstream of the cascade using a three-hole pressure probe, static pressure distributions were measured on the airfoil suction surface, and hot-wire measurements were made to characterize the interaction between the wall jet and boundary layer. The primary focus of the study is on the low-Reynolds number and low-freestream turbulence intensity cases, where the baseline airfoil stalls and high profile losses result. For low freestream turbulence (0.4%), the examined method of flow control was effective at preventing stall and reducing the profile losses. At a Reynolds number of 25,000, a blowing ratio greater than 1.0 was required to suppress stall. At a Reynolds number of 50,000, a closed separation bubble formed at a very low blowing ratio (0.25) resulting in a significant reduction in the profile loss. For high freestream turbulence intensity (4%), where the baseline airfoil has a closed separation bubble and low profile losses, blowing ratios below 1.0 resulted in a larger separation bubble and higher losses. The mechanism by which the wall jet affects the separation characteristics of the airfoil is examined through hot-wire traverse measurements in the vicinity of the slot.

scholarly journals Insectivorous songbirds as early indicators of future defoliation by spruce budworm

2021 ◽  
Author(s):  
Marion Germain ◽  
Daniel Kneeshaw ◽  
Louis De Grandpré ◽  
Mélanie Desrochers ◽  
Patrick M. A. James ◽  
...  
Keyword(s):  
Large Scale ◽  
Spatial Scales ◽  
Spruce Budworm ◽  
Detection Methods ◽  
Maximum Probability ◽  
Point Count ◽  
Probability Of Presence ◽  
Time Lagged ◽  
One Year ◽  
Local Sampling

Abstract Context Although the spatiotemporal dynamics of spruce budworm outbreaks have been intensively studied, forecasting outbreaks remains challenging. During outbreaks, budworm-linked warblers (Tennessee, Cape May, and bay-breasted warbler) show a strong positive response to increases in spruce budworm, but little is known about the relative timing of these responses. Objectives We hypothesized that these warblers could be used as sentinels of future defoliation of budworm host trees. We examined the timing and magnitude of the relationships between defoliation by spruce budworm and changes in the probability of presence of warblers to determine whether they responded to budworm infestation before local defoliation being observed by standard detection methods. Methods We modelled this relationship using large-scale point count surveys of songbirds and maps of cumulative time-lagged defoliation over multiple spatial scales (2–30 km radius around sampling points) in Quebec, Canada. Results All three warbler species responded positively to defoliation at each spatial scale considered, but the timing of their response differed. Maximum probability of presence of Tennessee and Cape May warbler coincided with observations of local defoliation, or provided a one year warning, making them of little use to guide early interventions. In contrast, the probability of presence of bay-breasted warbler consistently increased 3–4 years before defoliation was detectable. Conclusions Early detection is a critical step in the management of spruce budworm outbreaks and rapid increases in the probability of presence of bay-breasted warbler could be used to identify future epicenters and target ground-based local sampling of spruce budworm.

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