Turbine Performance Prediction Research Articles

Digital twinning as an act of governance in the wind energy sector

Digital twins have emerged as novel technology in the wind energy sector that enables the design, monitoring and prediction of wind turbine performance. Despite growing attention on their potential, little is known about how digital twins are designed, by whom and how their design choices affect multiple aspects of decision making in the development of wind energy. Using a framework of co-production, this paper examines digital twins as boundary objects and the role of twinning as boundary work that involves an active process of design and affects multiple aspects of decision making in the development of wind energy. Our results demonstrate how the design of digital twins evolves throughout the twinning process, affected by regulation, choices of expert twinners on data and models, and what constitutes a matter of concern. We shed light on the role of these twinners in influencing which actors and their matters of concern are included and excluded during the twinning process. Our understanding of twinning as an active process of governance by design more clearly reveals how digital twins are not objective representations of reality, but a function of boundary work. We conclude that more transparency is needed over how digital twins are designed to enhance their role as technologies that foster a transition towards more sustainable energy systems and decision-making over wind energy technologies and their integration in landscapes.

Case studies on performance prediction of radial inflow turbine under multiple rotor structures and organic fluids

Due to the lack of selection criteria for the ORC radial inflow turbine's loss models, and information about the models' limitations. This paper presents a comprehensive analysis for performance prediction of the ORC radial inflow turbine, and a novel turbine performance prediction program is proposed. This study can be divided into two parts, during the analysis part, 125 cases of CFD simulation were conducted under 5 rotor structures and R245fa as the working fluid. The performance of 6 loss models and their 18 combinations were compared. And in the examination part, another three organic fluids from different categories were chosen to exam the reliability of the proposed prediction program. The results show that loss model combinations E, O, and Q have a good performance in rotor loss prediction. Besides, Δβ4 has an important influence on prediction reliability and turbine performance, it should be lower than a certain value (about 40° in this paper) during the analysis process to get relatively reliable results under different rotor structures. The proposed program can realize an acceptable accuracy for turbine performance prediction under multiple organic fluids and rotor structures, with an efficiency deviation within ±5% and an output power relative error within ±10%.

Equation-oriented methods for design optimization and performance analysis of radial inflow turbines

This paper presents methods for design optimization and performance analysis of radial inflow turbines. Both methods are formulated in an equation-oriented manner and involve a single mathematical problem that is solved by an efficient, gradient-based optimization algorithm. In addition, the comparison of the model output with experimental data showed that the underlying mean-line flow model accurately predicts the variation of mass flow rate and isentropic efficiency as a function of the pressure ratio, rotational speed, and nozzle throat area. Moreover, the capabilities of the proposed methods were demonstrated by carrying out the preliminary design and performance prediction of the radial inflow turbine of an organic Rankine cycle. The results indicate that the design optimization method converges to the global optimum solution, regardless of the start values for the independent variables. In addition, the performance maps generated by the performance analysis method are physically consistent and agree with general findings from experimental data reported in the open literature. Considering the accuracy, robustness and low computational cost of the proposed methods, they can be regarded as a powerful tool for the preliminary design and performance prediction of radial inflow turbines, either as a standalone component or as part of a larger system.

Aerodynamic Characteristics of a Single Airfoil for Vertical Axis Wind Turbine Blades and Performance Prediction of Wind Turbines

The design of wind turbines requires a deep insight into their complex aerodynamics, such as dynamic stall of a single airfoil and flow vortices. The calculation of the aerodynamic forces on the wind turbine blade at different angles of attack (AOAs) is a fundamental task in the design of the blades. The accurate and efficient calculation of aerodynamic forces (lift and drag) and the prediction of stall of an airfoil are challenging tasks. Computational fluid dynamics (CFD) is able to provide a better understanding of complex flows induced by the rotation of wind turbine blades. A numerical simulation is carried out to determine the aerodynamic characteristics of a single airfoil in a wide range of conditions. Reynolds-averaged Navier–Stokes (RANS) equations and large-eddy simulation (LES) results of flow over a single NACA0012 airfoil are presented in a wide range of AOAs from low lift through stall. Due to the symmetrical nature of airfoils, and also to reduce computational cost, the RANS simulation is performed in the 2D domain. However, the 3D domain is used for the LES calculations with periodical boundary conditions in the spanwise direction. The results obtained are verified and validated against experimental and computational data from previous works. The comparisons of LES and RANS results demonstrate that the RANS model considerably overpredicts the lift and drag of the airfoil at post-stall AOAs because the RANS model is not able to reproduce vorticity diffusion and the formation of the vortex. LES calculations offer good agreement with the experimental measurements.

Formulation, Validation, and Application of a Novel 3D BEM Tool for Vertical Axis Wind Turbines of General Shape and Size

Low order models based on the Blade Element Momentum (BEM) theory exhibit modeling issues in the performance prediction of Vertical Axis Wind Turbines (VAWT) compared to Computational Fluid Dynamics, despite the widespread engineering practice of such methods. The present study shows that the capability of BEM codes applied to VAWTs can be greatly improved by implementing a novel three-dimensional set of high-order corrections and demonstrates this by comparing the BEM predictions against wind-tunnel experiments conducted on three small-scale VAWT models featuring different rotor design (H-shaped and Troposkein), blade profile (NACA0021 and DU-06-W200), and Reynolds number (from 0.8×105 to 2.5×105). Though based on the conventional Double Multiple Stream Tube (DMST) model, the here-presented in-house BEM code incorporates several two-dimensional and three-dimensional corrections including: accurate extended polar data, flow curvature, dynamic stall, a spanwise-distributed formulation of the tip losses, a fully 3D approach in the modeling of rotors featuring general shape (such as but not only, the Troposkein one), and accounting for the passive effects of supporting struts and pole. The detailed comparison with experimental data of the same models, tested in the large-scale wind tunnel of the Politecnico di Milano, suggests the very good predictive capability of the code in terms of power exchange, torque coefficient, and loads, on both time-mean and time-resolved basis. The peculiar formulation of the code allows including in a straightforward way the usual spanwise non-uniformity of the incoming wind and the effects of skew, thus allowing predicting the turbine operation in a realistic open-field in presence of the environmental boundary layer. A systematic study on the operation of VAWTs in multiple environments, such as in coastal regions or off-shore, and highlighting the sensitivity of VAWT performance to blade profile selection, rotor shape and size, wind shear, and rotor tilt concludes the paper.

Part-load performance prediction model for supercritical CO2 radial inflow turbines

There is recent focus on supercritical CO2 (s-CO2) Brayton power cycles as the next-generation thermal energy conversion choice. They are efficient, compact, economic, and environmentally friendly. They are also scalable without efficiency penalties and suitable for small and large sizes. The most critical component is reported to be the turbine. A turbine design should achieve good performance at its design operating conditions and maintain acceptable performance at off-design conditions. Off-design operating conditions are relevant because thermal power plants are increasingly required to meet varying electricity demand in continuously changing environments. This paper presents a novel one-dimensional part-load performance prediction model for s-CO2 turbines. The novelty of the present model comes from the accurate prediction of the part-load s-CO2 turbine performance with less than 10% deviation, validated against reliable three-dimensional computational fluid dynamics simulation. Furthermore, the proposed model uses fundamental fluid dynamic equations and a real gas property library and does not require calibration. A Python code based on the proposed model can generate a full list of essential performance variables and internal flow information.

WITHDRAWN: Review on gas turbine condition based diagnosis method

Abstract For a long period, computerized models for the diagnosis and performance prediction of the gas turbine is developed. Usage of a strong, productive, and adaptable upkeep method improves essentially the unwavering quality and accessibility of a gas turbine and thus minimizes the unpredicted failures, downtime, and working costs. Early discovery of advancing deficiencies, whereas the plant is still working in a controllable locale, can offer assistance maintain a strategic distance from unusual occasion movement and diminish efficiency loss. Actualizing a legitimate predictive or condition-based maintenance (CBM) methodology makes difference maintenance engineers screen the gas turbine condition and spot up and coming faults. This will empower them to proactively adopt the appropriate maintenance choice which subsequently leads to improved system reliability and availability, decreased maintenance costs, and decreased the number of maintenance operations. This paper focuses to survey the published papers on gas turbine diagnosis strategy and to make summarize the reviewed papers. This paper describes different diagnostics methods and their advantages and disadvantages and finally, the future work is also included.

Performance Prediction of a SANDIA 17-m Vertical Axis Wind Turbine Using Improved Double Multiple Streamtube

Performance Prediction of a SANDIA 17-m Vertical Axis Wind Turbine Using Improved Double Multiple Streamtube

Comparison of the Computational Fluid Dynamics Predictions of Vertical Axis Wind Turbine Performance Against Detailed Pressure Measurements

Computational Fluid Dynamics (CFD) simulations are currently one of the most popular methods for the modelling of a Vertical Axis Wind Turbine (VAWT) that gives good insight on the turbine aerodynamics. The current study provides an assessment of the quality of the 2D and 3D CFD predictions of two highly recommended models in the literature, namely the SST K-omega model and the SST K-omega with the Intermittency transition model. The novelty of the study is in the kind of data that is used in the assessment. The CFD predictions of the pressure around the blade at several azimuthal angles are compared to the published experimental data measured by a high-frequency multiport pressure scanner. In addition, the predictions of the pressure contribution to the instantaneous power coefficient are compared to the experimental data. This paper sheds much new light on how the behaviour of the predictions of the SST K-omega with the intermittency transition model changes between the 2D and 3D cases and how the trends of the 2D results based on this transition model deviate from the detailed experimental data. This behaviour has not been previously investigated.

Evaluation of the accuracy of soft computing learning algorithms in performance prediction of tidal turbine

AbstractMarine renewable energy has made significant progress in the last few decades. Even after making substantial progress, the cost of electricity produced by tidal turbines is high. Therefore, the current paper concentrated on reducing the cost of transportation and installation of the turbine by performing a model. Extreme Learning Machine and Support Vector Machines as well as Genetic Programming were applied to predict the performance of the turbine model by creating short‐term, multistep‐ahead prediction models to compute the performance of the H‐rotor vertical axis Folding Tidal turbine. The performance of the turbine was verified by a numerical study using the three‐dimensional approach for the viscous model with the unsteady flow. Statistical evaluation of the outcomes pointed out that advanced Extreme Learning Machine simulation made the assurance in formulating an innovative forecasting strategy for investigating the performances of the tidal turbine. This study shows that the application of the new procedure resulted in confident generality performance and learns faster than orthodox learning algorithms. In conclusion, the assessment indicated that the advanced Extreme Learning Machine simulation was capable as a promising alternative to existing numerical methods for computing the coefficient of performance for turbines.

A reduced-order model of twin-entry nozzleless radial turbine based on flow characteristics

Twin-entry radial turbine has evident advantages on energy recovery of internal combustion engine because of better utilization of exhaust pulse energy. One-dimensional performance prediction plays an important role in automobile industry because of the cost-effective feature. However, as twin-entry turbine is confronted by out-of-phase pulses, to capture the flow distortion by twin-entry turbine model is challenging, especially under partial admission conditions. This paper establishes a reduced-order parallel-rotor model for performance prediction of twin-entry nozzleless radial turbine based on internal flow characteristics. Firstly, twin-entry turbine experiment and 3D simulation are applied to analyze the different flow features under partial admission conditions. The flow distortions in the spanwise and circumferential directions are both demonstrated. Based on these flow characteristics, a reduced-order twin-entry turbine model with parallel rotor passages is established for predicting the rotor at distorted flow field. Finally, the model is carefully validated against the results of CFD. The results show that satisfied consistency can be obtained and the maximum discrepancy of turbine performance is 3.8% and the performance difference and flow distortion under partial admissions can be captured, thus prove the credibility of the model. This investigation provides a reliable methodology for performance prediction and behavior analysis of twin-entry turbine.

Design and verification of a hermetic high-speed turbogenerator concept for biomass and waste heat recovery applications

Many waste heat recovery and biomass applications offer opportunities for small-scale steam turbines to produce electricity and to improve systems’ energy efficiency. However, the currently used axial turbine-based technology is generally characterized by a relatively large physical size and poor design and off-design performances. To overcome these challenges, a new compact water-cooled high-speed radial outflow turbine concept is proposed. While the previous understanding of the chosen novel rotor water cooling approach indicates general system feasibility, improved turbine performance, and good potential, the scientific literature lacks relevant information. Since the design of high-speed machines is always case-dependent, every concept must be verified. Hence, to provide verification and new scientific information, this study combines analytical, numerical, and experimental analyses. The results predict turbine performance levels comparable with the previous radial outflow design, and its efficiency was found to exceed those of conventional turbines. During the experiments, the turbine also produced electricity from poor-quality steam, and its rotor dynamic behavior and magnetic bearing performance were close to the predicted results. These findings are considered a verification of the proposed concept. Furthermore, the rotor water cooling approach improved the stability of the system operation and although its physical behavior was not fully resolved, the study was able to verify its feasibility. In addition, at below 200 €/kW, the turbogenerator costs are competitive against competing technologies.

Quantification of the Dynamic-stall Model Uncertainty in the Performance Prediction of Vertical Axis Wind Turbines

Low-fidelity predictions for vertical-axis wind turbines are affected by uncertainty due to the complexity of the rotor aerodynamics. In particular, in the most common operating conditions the blades undergo periodic excursions beyond the static stall limit, activating dynamic-stall effects. In this study we show how advanced dynamic-stall models, implemented in the frame of the Blade-Element-Momentum theory, are able to upgrade significantly the prediction of low-fidelity tools, both in deterministic and probabilistic terms. In particular, an uncertainty quantification is performed to investigate the epistemic uncertainty of the Strickland dynamic-stall model, introducing a large variability on the empirical parameters appearing in the formulation. The resulting variability in the power coefficient and torque exchange, compared to corresponding wind-tunnel and high-fidelity CFD values, remains relatively limited and, in the conditions around peak efficiency, it is comparable with the measurement uncertainty of the experiment. As a further relevant conclusion, the model uncertainty does not alter the general outcome of the deterministic model, thus demonstrating the robustness of the DMST predictions obtained in the present study.

The effect of porosity on the drag of cylinders

Abstract

Analysis and prediction of gas turbine performance with evaporative cooling processes by developing a stage stacking algorithm

Water injection in stationary gas turbines is effective strategy for power augmentation and gas turbine efficiency improvement, in particular, at high ambient temperatures. In this study, a developed method for simulation of wet compression processes is introduced. This method is followed by the droplet evaporation analysis, aero-thermodynamic stage-stacking model, and a map zooming technique for evaluation the unknown parameters in the generalized performance curves by using grey wolf optimization (GWO) algorithm. The validity of the proposed algorithm is assessed through the comparison of the results with two experimental studies. The operating results are calculated for five different cities and 18 gas turbines organized in three classes. Moreover, a sensitivity analysis on the main input parameters is investigated with the use of variable importance (VI) analysis by constructing and training an artificial neural network. The results show that the variation of the output parameters is highly sensitive to the ambient temperature, relative humidity, and turbine inlet temperature (TIT). Results demonstrate that saturated fogging plus 1% overspray leads to a relative increase of 24.84% and 6.70% in net power output and thermal efficiency, respectively, at the corresponding ambient condition. It is observed that 23.94% of the increase in the inlet mass flow rate in this cooling approach is due to the injected water directly, where the compressor operating point is matched at a point with a higher inlet mass flow rate comparing to dry condition.

Off-Design Performance Prediction of a S-CO2 Turbine Based on Field Reconstruction Using Deep-Learning Approach

The reliable design of the supercritical carbon dioxide (S-CO2) turbine is the core of the advanced S-CO2 power generation technology. However, the traditional computational fluid dynamics (CFD) method is usually applied in the S-CO2 turbine design-optimization, which is a high computational cost, high memory requirement, and long time-consuming solver. In this research, a flexible end-to-end deep learning approach is presented for the off-design performance prediction of the S-CO2 turbine based on physical fields reconstruction. Our approach consists of three steps: firstly, an optimal design of a 60,000 rpm S-CO2 turbine is established. Secondly, five design variables for off-design analysis are selected to reconstruct the temperature and pressure fields on the blade surface through a deconvolutional neural network. Finally, the power and efficiency of the turbine is predicted by a convolutional neural network according to reconstruction fields. The results show that the prediction approach not only outperforms five classical machine learning models but also focused on the physical mechanism of turbine design. In addition, once the deep model is well-trained, the calculation with graphics processing unit (GPU)-accelerated can quickly predict the physical fields and performance. This prediction approach requires less human intervention and has the advantages of being universal, flexible, and easy to implement.

Assessment of stress-blended eddy simulation model for accurate performance prediction of vertical axis wind turbine

PurposeThe aim of this paper is to assess the ability of a stress-blended eddy simulation (SBES) turbulence model to predict the performance of a three-straight-bladed vertical axis wind turbine (VAWT). The grid sensitivity study is conducted to evaluate the simulation accuracy.Design/methodology/approachThe unsteady Reynolds-averaged Navier–Stokes equations are solved using the computational fluid dynamics (CFD) technique. Two types of grid topology around the blades, namely, O-grid (OG) and C-grid (CG) types, are considered for grid sensitivity studies.FindingsWith regard to the power coefficient (Cp), simulation results have shown significant improvements of predictions using compared to other turbulence models such as the k-e model. The Cp distributions predicted by applying the CG mesh are in good agreement with the experimental data than that by the OG mesh.Research limitations/implicationsThe current study provides some new insights of the use of SBES turbulence model in VAWT CFD simulations.Practical implicationsThe SBES turbulence model can significantly improve the numerical accuracy on predicting the VAWT performance at a lower tip speed ratio (TSR), which other turbulence models cannot achieve. Furthermore, it has less computational demand for the finer grid resolution used in the RANS-Large Eddy Simulation (LES) “transition” zone compared to other hybrid RANS-LES models.Originality/valueTo authors’ knowledge, this is the first attempt to apply SBES turbulence model to predict VAWT performance resulting for accurate CFD results. The better prediction can increase the credibility of computational evaluation of a new or an improved configuration of VAWT.

구심터빈 성능예측을 위한 상용프로그램의 적용성 평가

Radial-type turbines have been used in small power generation systems as well as aircraft engines since the mid-20th century. It is also a core part of the turbocharger, so much research has been conducted. In addition, commercial software for performance prediction and design have been released. Recently, many research results have been published for the performance prediction and design of radial-type turbines for Organic Rankine Cycle using a working fluid as a refrigerant instead of air using a commercial software. Therefore, in this study, a performance prediction technique was developed based on the loss models with the experimental results, and then it was expanded to estimate the acceptability of a commercial software with the working fluid of refrigerant. In conclusion, it is important to recognize the range of error in performance prediction according to the application of commercial software.

Investigation of parameters affecting horizontal axis tidal current turbines modeling by blade element momentum theory

The Blade Element Momentum Theory (BEMT) is one of the most common and widely used methods in horizontal axis wind and tidal turbine performance prediction. It mainly relies on two-dimensional (2-D) hydrodynamic, lift and drag, coefficients which are used to calculate the hydrodynamic forces within the blade element section of the model. These coefficients are greatly affected by both Reynolds numbers and turbulence transition parameter (Ncrit). The performance sensitivity of scale models tidal current turbines (TCTs) to the change of Reynolds numbers and Ncrit parameter was assessed. Initially, the BEMT was validated with experimental measurements of two scale models of horizontal axis tidal current turbines. A very good agreement was seen, thus, confidence in both the implementation of the theory and the applicability of the method was built up. Lift and drag coefficients were calculated from XFoil through the use of different sets of Reynolds numbers at different Ncrit values. The best BEMT validations with experimental data were seen when the hydrodynamic coefficients were calculated at two sets of Reynolds numbers considered at optimal performance. The first set was calculated at 75% of the blade span while the second set was calculated at every local elemental position of the blade. For facilitation, reduction of time and effort, Reynolds numbers at 75% of the blade span is considered the best choice. Furthermore, the BEMT model results showed better agreements with experimental data when lift and drag coefficients were calculated at low values of Ncrit parameter. The predicted tidal turbine performances of previous experimental data by the present BEM model were compared with those derived from the academic SERG-Tidal BEM code. The present BEM model, based on the proper selection of both Reynolds number and Ncrit parameter, showed an obvious superiority over SERG-Tidal model.

Performance prediction of loop-type wind turbine

An experimental and analytical method to evaluate the performance of a loop-type wind turbine generator is presented. The loop-type wind turbine is a horizontal axis wind turbine with a different shaped blade. A computational fluid dynamics analysis and experimental studies were conducted in this study to validate the performance of the computational fluid dynamics method, when compared with the experimental results obtained for a 1/15 scale model of a 3 kW wind turbine. Furthermore, the performance of a full sized wind turbine is predicted. The computational fluid dynamics analysis revealed a sufficiently large magnitude of external flow field, indicating that no factor influences the flow other than the turbine. However, the experimental results indicated that the wall surface of the wind tunnel significantly affects the flow, due to the limited cross-sectional size of the wind tunnel used in the tunnel test. The turbine power is overestimated when the blockage ratio is high; thus, the results must be corrected by defining the appropriate blockage factor (the factor that corrects the blockage ratio). The turbine performance was corrected using the Bahaj method. The simulation results showed good agreement with the experimental results. The performance of an actual 3 kW wind turbine was also predicted by computational fluid dynamics.