turbo-RANS: Straightforward and Efficient Bayesian Optimization of Turbulence Model Coefficients

Industrial simulations of turbulent flows often rely on Reynolds-averaged Navier-Stokes (RANS) turbulence models, which contain numerous closure coefficients that need to be calibrated. Although tuning these coefficients can produce significantly improved predictive accuracy, their default values are often used. We believe users do not calibrate RANS models for several reasons: there is no clearly recommended framework to optimize these coefficients; the average user does not have the expertise to implement such a framework; and, the optimization of the values of these coefficients can be a computationally expensive process. In this work, we address these issues by proposing a semi-automated calibration of these coefficients using a new framework based on Bayesian optimization. We introduce the generalized error and default coefficient preference (GEDCP) objective function, which can be used with integral, sparse, or dense reference data. We demonstrate the computationally efficient performance of turbo-RANS for three example cases: predicting the lift coefficient of an airfoil; predicting the velocity and turbulent kinetic energy fields for a separated flow; and, predicting the wall pressure coefficient distribution for flow through a converging-diverging channel. In the first two examples, we calibrate the $k$-$\omega$ shear stress transport (SST) turbulence model and, in the last example, we calibrate user-specified coefficients for the Generalized $k$-$\omega$ (GEKO) model in Ansys Fluent. An in-depth hyperparameter tuning study is conducted to recommend efficient settings for the turbo-RANS optimization procedure. Towards the goal of facilitating RANS turbulence closure model calibration, we provide an open-source implementation of the turbo-RANS framework that includes OpenFOAM, Ansys Fluent, and solver-agnostic templates for user application.Comment: 30 pages, 18 figures. Submitted to Journal of Computational Physic

Short-term forecasting and uncertainty analysis of wind turbine power based on long short-term memory network and Gaussian mixture model

Wind power plays a leading role in the development of renewable energy. However, the random nature of wind turbine power and its associated uncertainty create challenges in dispatching this power effectively in the power system, which can result in unnecessary curtailment of the wind turbine power. Improving the accuracy of wind turbine power forecasting is an effective measure for resolving such problems. This study uses a deep learning network to forecast the wind turbine power based on a long short-term memory network (LSTM) algorithm and uses the Gaussian mixture model (GMM) to analyze the error distribution characteristics of short-term wind turbine power forecasting. The LSTM algorithm is used to forecast the power and uncertainties for three wind turbines within a wind farm. According to numerical weather prediction (NWP) data and historical power data for three turbines, the forecasting accuracy of the turbine with the largest number of training samples is the best of the three. For one of the turbines, the LSTM, radial basis function (RBF), wavelet, deep belief network (DBN), back propagation neural networks (BPNN), and Elman neural network (ELMAN) have been used to forecast the wind turbine power. This study compares the results and demonstrates that LSTM can greatly improve the forecasting accuracy. Moreover, this study obtains different confidence intervals for the three units according to the GMM, mixture density neural network (MDN), and relevance vector machine (RVM) model results. The LSTM method is shown to have higher accuracy and faster convergence than the other methods. However, the GMM method has better performance and evaluation than other methods and thus has practical application value for wind turbine power dispatching

Simulations for three-dimensional landmine detonation using the SPH method

The final publication is available at Elsevier via https://doi.org/10.1016/j.ijimpeng.2018.12.004 © 2018. Thisvmanuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/The simulation of the soil fragmentation under buried explosive detonation is an extremely difficult task. In this paper, the modified smoothed particle hydrodynamics method (SPH) in combination with the elastoplastic and hypoplastic constitutive models is introduced to simulate the three-dimensional (3D) landmine detonation for the first time. The modified continuity equation is incorporated to tackle the multiphase interfacial problems with high density ratios. The elastoplastic and hypoplastic constitutive models are employed to describe the soil mechanical behavior. Furthermore, the in-house SPH code is parallelized using the Open-MP program interface to solve problems with large number of particles efficiently. At the end, the simulation results are compared with the experimental data, which shows that the SPH method in conjunction with these two constitutive models can tackle landmine detonation problems involving large deformation very well.China Scholarship Council [201506030072]Natural Sciences and Engineering Research Counci

Study on fatigue life of bend-twist coupling wind turbine blade based on anisotropic beam model and stress-based fatigue analysis method

The final publication is available at Elsevier via https://doi.org/10.1016/j.compstruct.2018.10.032 © 2018 This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/Bend-twist coupling (BTC), also called aeroelastic tailoring or passive pitch control method, is often utilized to reduce the fatigue loading on wind turbine blades. With BTC, the blade can twist as it bends, this alleviates the aerodynamic force due to the decrease in the angle of attack when the load is increased suddenly. In this research, the stress-based method is employed to investigate the fatigue load due to the BTC effect under different wake conditions. To begin with, the one-dimensional anisotropic beam model is adopted in aeroelastic simulation. Next the static and modal analyses for the NREL 5 MW wind turbine blade with different fiber orientations are performed to verify the anisotropic beam model. Finally, the stress history of each element on each cross section is reconstructed using DTU BECAS. The fatigue life of different materials in each cross section under different wake conditions has been analysed. The results show that the predicted fatigue life of NREL 5 MW wind turbine blade (26.0187 years) is very close to the design life (20 years). The fatigue effect has an impact on the life of wind turbine blades, which can be affected by the layout of wind turbines and alleviated by BTC effect.SHARCNET [project "Development of a multiscale modeling framework for short-term wind power forecasting"]Natural Sciences and Engineering Research CouncilChina Scholarship Counci

Numerical modelling of interaction between aluminium structure and explosion in soil

In this paper, a graphics processing unit-accelerated smoothed particle hydrodynamics solver is presented to simulate the three-dimensional explosions in soils and their damage to aluminium structures. To achieve this objective, a number of equations of state and constitutive models required to close the governing equations are incorporated into the proposed smoothed particle hydrodynamics framework, including the Jones-Wilkins-Lee equation of state for explosive materials, the Grüneisen equation of state for metals, the elastic-perfectly plastic constitutive model for metals, and the elastoplastic and elasto-viscoplastic constitutive models for soils. The proposed smoothed particle hydrodynamics methodology was implemented using the Compute Unified Device Architecture programming interface on an NVIDIA graphics processing unit in order to improve the computational efficiency. The various components of the proposed methodology were validated using four test cases, namely, a C4 detonation and an aluminium bar expanded by denotation to validate the modelling of explosion, a cylindrical Taylor bar impact test case to validate the modelling of large deformation in metals, a sand collapse test for the modelling of soils. Following the validation, the proposed method was used to simulate the detonation of an explosive material (C4) in soil and the concomitant deformation of an aluminium plate resulting from this explosion. The predicted results of this simulation are shown to be in good conformance with available experimental data. Finally, it is shown that the proposed graphics processing unit-accelerated SPH solver is able to model interaction problems involving millions of particles in a reasonable time. © 2021 The Author

Investigations of Vertical-Axis Wind-Turbine Group Synergy Using an Actuator Line Model

The presence of power augmentation effects, or synergy, in vertical-axis wind turbines (VAWTs) offers unique opportunities for enhancing wind-farm performance. This paper uses an open-source actuator-line-method (ALM) code library for OpenFOAM (turbinesFoam) to conduct an investigation into the synergy patterns within two- and three-turbine VAWT arrays. The application of ALM greatly reduces the computational cost of simulating VAWTs by modelling turbines as momentum source terms in the Navier–Stokes equations. In conjunction with an unsteady Reynolds-Averaged Navier–Stokes (URANS) approach using the k-ω shear stress transport (SST) turbulence model, the ALM has proven capable of predicting VAWT synergy. The synergy of multi-turbine cases is characterized using the power ratio which is defined as the power coefficient of the turbine cluster normalized by that for turbines in isolated operation. The variation of the power ratio is characterized with respect to the array layout parameters, and connections are drawn with previous investigations, showing good agreement. The results from 108 two-turbine and 40 three-turbine configurations obtained using ALM are visualized and analyzed to augment the understanding of the VAWT synergy landscape, demonstrating the effectiveness of various layouts. A novel synergy superposition scheme is proposed for approximating three-turbine synergy using pairwise interactions, and it is shown to be remarkably accurate