Design of Experiments for Effects and Interactions during Brake Emissions Testing Using High-Fidelity Computational Fluid Dynamics

2019 ◽  
Author(s):  
Carlos Agudelo ◽  
Ravi Teja Vedula ◽  
Jesse Capecelatro ◽  
Qingquan Wang
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Design Of Experiments ◽  
High Fidelity ◽  
Emissions Testing

A Fully Integrated Approach to Component Zooming Using Computational Fluid Dynamics

2004 ◽  
Vol 128 (3) ◽  
pp. 579-584 ◽  
Author(s):  
Vassilios Pachidis ◽  
Pericles Pilidis ◽  
Fabien Talhouarn ◽  
Anestis Kalfas ◽  
Ioannis Templalexis
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Gas Turbine ◽  
Engine Performance ◽  
Integrated Approach ◽  
High Fidelity ◽  
Performance Simulation ◽  
Fully Integrated ◽  
Engine Component ◽  
Simulation Strategy

Background . This study focuses on a simulation strategy that will allow the performance characteristics of an isolated gas turbine engine component, resolved from a detailed, high-fidelity analysis, to be transferred to an engine system analysis carried out at a lower level of resolution. This work will enable component-level, complex physical processes to be captured and analyzed in the context of the whole engine performance, at an affordable computing resource and time. Approach. The technique described in this paper utilizes an object-oriented, zero-dimensional (0D) gas turbine modeling and performance simulation system and a high-fidelity, three-dimensional (3D) computational fluid dynamics (CFD) component model. The work investigates relative changes in the simulated engine performance after coupling the 3D CFD component to the 0D engine analysis system. For the purposes of this preliminary investigation, the high-fidelity component communicates with the lower fidelity cycle via an iterative, semi-manual process for the determination of the correct operating point. This technique has the potential to become fully automated, can be applied to all engine components, and does not involve the generation of a component characteristic map. Results. This paper demonstrates the potentials of the “fully integrated” approach to component zooming by using a 3D CFD intake model of a high bypass ratio turbofan as a case study. The CFD model is based on the geometry of the intake of the CFM56-5B2 engine. The high-fidelity model can fully define the characteristic of the intake at several operating condition and is subsequently used in the 0D cycle analysis to provide a more accurate, physics-based estimate of intake performance (i.e., pressure recovery) and hence, engine performance, replacing the default, empirical values. A detailed comparison between the baseline engine performance (empirical pressure recovery) and the engine performance obtained after using the coupled, high-fidelity component is presented in this paper. The analysis carried out by this study demonstrates relative changes in the simulated engine performance larger than 1%. Conclusions. This investigation proves the value of the simulation strategy followed in this paper and completely justifies (i) the extra computational effort required for a more automatic link between the high-fidelity component and the 0D cycle, and (ii) the extra time and effort that is usually required to create and run a 3D CFD engine component, especially in those cases where more accurate, high-fidelity engine performance simulation is required.

scholarly journals Assessing the Sensitivity of Stall-Regulated Wind Turbine Power to Blade Design Using High-Fidelity Computational Fluid Dynamics

2019 ◽  
Vol 141 (10) ◽  
Author(s):  
Andrea G. Sanvito ◽  
Giacomo Persico ◽  
M. Sergio Campobasso
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Field Testing ◽  
Turbine Rotor ◽  
Operating Conditions ◽  
High Fidelity ◽  
Blade Design ◽  
Computationally Efficient ◽  
Wind Speeds ◽  
Wide Range

Abstract This study provides a novel contribution toward the establishment of a new high-fidelity simulation-based design methodology for stall-regulated horizontal axis wind turbines. The aerodynamic design of these machines is complex, due to the difficulty of reliably predicting stall onset and poststall characteristics. Low-fidelity design methods, widely used in industry, are computationally efficient, but are often affected by significant uncertainty. Conversely, Navier–Stokes computational fluid dynamics (CFD) can reduce such uncertainty, resulting in lower development costs by reducing the need of field testing of designs not fit for purpose. Here, the compressible CFD research code COSA is used to assess the performance of two alternative designs of a 13-m stall-regulated rotor over a wide range of operating conditions. Validation of the numerical methodology is based on thorough comparisons of novel simulations and measured data of the National Renewable Energy Laboratory (NREL) phase VI turbine rotor, and one of the two industrial rotor designs. An excellent agreement is found in all cases. All simulations of the two industrial rotors are time-dependent, to capture the unsteadiness associated with stall which occurs at most wind speeds. The two designs are cross-compared, with emphasis on the different stall patterns resulting from particular design choices. The key novelty of this work is the CFD-based assessment of the correlation among turbine power, blade aerodynamics, and blade design variables (airfoil geometry, blade planform, and twist) over most operational wind speeds.

Model order reduction in aerodynamics: Review and applications

2019 ◽  
Vol 233 (15) ◽  
pp. 5816-5836
Author(s):  
Gonçalo Mendonça ◽  
Frederico Afonso ◽  
Fernando Lau
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Model Reduction ◽  
Adaptive Sampling ◽  
Stokes Equations ◽  
Order Reduction ◽  
High Fidelity ◽  
Least Mean Squares ◽  
Speed Up ◽  
Dynamics Models

The need of the aerospace industry, at national or European level, of faster yet reliable computational fluid dynamics models is the main drive for the application of model reduction techniques. This need is linked to the time cost of high-fidelity models, rendering them inefficient for applications like multi-disciplinary optimization. With the goal of testing and applying model reduction to computational fluid dynamics models applicable to lifting surfaces, a bibliographical research covering reduction of nonlinear, dynamic, or steady models was conducted. This established the prevalence of projection and least mean squares methods, which rely on solutions of the original high-fidelity model and their proper orthogonal decomposition to work. Other complementary techniques such as adaptive sampling, greedy sampling, and hybrid models are also presented and discussed. These projection and least mean squares methods are then tested on simple and documented benchmarks to estimate the error and speed-up of the reduced order models thus generated. Dynamic, steady, nonlinear, and multiparametric problems were reduced, with the simplest version of these methods showing the most promise. These methods were later applied to single parameter problems, namely the lid-driven cavity with incompressible Navier–Stokes equations and varying Reynolds number, and the elliptic airfoil at varying angles of attack for compressible Euler flow. An analysis of the performance of these methods is given at the end of this article, highlighting the computational speed-up obtained with these techniques, and the challenges presented by multiparametric problems and problems showing point singularities in their domain.

scholarly journals Propagation of Input Uncertainty in Presence of Model-Form Uncertainty: A Multifidelity Approach for Computational Fluid Dynamics Applications

2017 ◽  
Vol 4 (1) ◽  
Author(s):  
Jian-Xun Wang ◽  
Christopher J. Roy ◽  
Heng Xiao
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Parameter Space ◽  
Uncertainty Propagation ◽  
High Fidelity ◽  
Sampling Errors ◽  
Uncertainty Distribution ◽  
Cfd Models ◽  
Computational Fluid Dynamics Cfd ◽  
Cfd Applications

Proper quantification and propagation of uncertainties in computational simulations are of critical importance. This issue is especially challenging for computational fluid dynamics (CFD) applications. A particular obstacle for uncertainty quantifications in CFD problems is the large model discrepancies associated with the CFD models used for uncertainty propagation. Neglecting or improperly representing the model discrepancies leads to inaccurate and distorted uncertainty distribution for the quantities of interest (QoI). High-fidelity models, being accurate yet expensive, can accommodate only a small ensemble of simulations and thus lead to large interpolation errors and/or sampling errors; low-fidelity models can propagate a large ensemble, but can introduce large modeling errors. In this work, we propose a multimodel strategy to account for the influences of model discrepancies in uncertainty propagation and to reduce their impact on the predictions. Specifically, we take advantage of CFD models of multiple fidelities to estimate the model discrepancies associated with the lower-fidelity model in the parameter space. A Gaussian process (GP) is adopted to construct the model discrepancy function, and a Bayesian approach is used to infer the discrepancies and corresponding uncertainties in the regions of the parameter space where the high-fidelity simulations are not performed. Several examples of relevance to CFD applications are performed to demonstrate the merits of the proposed strategy. Simulation results suggest that, by combining low- and high-fidelity models, the proposed approach produces better results than what either model can achieve individually.

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