Traditional Analytical Models Research Articles

Prediction of offshore wind turbine wake and output power using large eddy simulation and convolutional neural network

Predicting offshore wind turbine wake and output power is crucial for optimizing wind farm layouts and maximizing wind energy production. In recent years, several Computational Fluid Dynamics methods have been developed to predict wind turbine wake and output power and demonstrated good performance compared with traditional analytical models. However, Computational Fluid Dynamics often involve high computational costs in offshore wind farm design because a wide range of offshore wind conditions need to be considered for turbines with different inter-turbine spacings. To ensure both the fidelity and efficiency for predicting offshore wind turbine wake and output power, Large Eddy Simulation and Convolutional Neural Network are utilized in this study. The Large Eddy Simulation effectively integrates the Actuator Line Method and Discretizing and Synthesizing Random Flow Generation to generate wake velocity, wake turbulence intensity, and output power for a stand-alone turbine under different incoming wind speeds and turbulence intensities. Using the generated dataset, Convolutional Neural Network effectively captures the relationship between inputs and outputs for the stand-alone turbine. The predicted wake data for the turbine can then act as input to estimate the output power density and wake characteristics of a downstream turbine. This process can be iteratively applied to predict the wake and output power of each subsequent turbine in a wind farm, supporting the identification of optimal inter-turbine spacing. The proposed method is illustrated using a utility-scale 5 MW wind turbine. The results show that the errors of predicted output power for a stand-alone wind turbine and multiple wind turbines are blew 3 %.

Real-time deep learning-based model predictive control of a 3-DOF biped robot leg

Our research utilized deep learning to enhance the control of a 3 Degrees of Freedom biped robot leg. We created a dynamic model based on a detailed joint angles and actuator torques dataset. This model was then integrated into a Model Predictive Control (MPC) framework, allowing for precise trajectory tracking without the need for traditional analytical dynamic models. By incorporating specific constraints within the MPC, we met operational and safety standards. The experimental results demonstrate the effectiveness of deep learning models in improving robotic control, leading to precise trajectory tracking and suggesting potential for further integration of deep learning into robotic system control. This approach not only outperforms traditional control methods in accuracy and efficiency but also opens the way for new research in robotics, highlighting the potential of utilizing deep learning models in predictive control techniques.

Prediction of heat exchanger efficiency using laminar heat transfer in swirling flow of radiated graphene oxide with nano fluid additives using machine learning technique

Nanotechnology has recently led to new possibilities for enhancing heat transfer in heat exchangers. The remarkable thermal characteristics of graphene oxide (GO)-based nanofluids with nanoscale additions have garnered significant attention in particular. When nanofluids and intricate flow patterns are involved, traditional analytical models frequently fail to appropriately forecast the efficiency of heat exchangers. Analyzed the phenomenon of laminar heat transfer in a heat exchanger that has a swirling flow of fluid dynamics, and pressure drop, and predicted the condensation heat transfer coefficient (HTC) of LHT. The functionalized radiated GO was chosen as nanomaterials in the present for the Pre-processing of Data Methods for managing outliers by machine learning (ML) models, like the CLAHE algorithm. The logarithmic mean temperature difference (LMTD) can be used to determine the temperature driving power for heat transfer within a heat exchanger. The Dynamic Smagorinsky Model (DSLM) and Wall-Adapting Local Eddy-viscosity (WALE) Model are turbulence models primarily designed for capturing turbulent behavior in fluid flows. The Kern technique and Hagen–Poiseuille equation are used for the pressure drop and pumping power needed in a shell and tube in laminar flow through a microtube based on Levenberg–Marquardt and Momentum Algorithm to predict Nusselt number and pressure drop in heat exchangers. The prediction sensitivity of the HTC by an LHT-trained ML model is used to evaluate the performance. The ML methods may be effectively used to forecast and maximize heat exchanger performance in the setting of laminar heat transfer in radiated GO nanofluid swirling flows. The accuracy score of 99%, demonstrating its exceptional predictive capabilities and results has great potential to improve energy efficiency, save operating costs, and advance sustainable practices in various industrial applications.

Landing cushioning analysis of new generation heavy airborne armored vehicles

PurposeThe purpose of this study is to ensure the successful implementation of a landing cushion for the new generation armored vehicles with significantly enhanced quality. Furthermore, to introduce a high-precision landing cushioning analysis model.Design/methodology/approachTo accurately analyze the cushioning performance of the new generation armored vehicles, a nonlinear finite element dynamics model considering the complex travel system was established. The model considered the influence of various nonlinear factors to measure the dynamic response difference between the proposed and traditional models. The cushioning performance of airbags under different landing conditions and their various influence factors were analyzed.FindingsThe travel system has a large influence on the key points of the vehicle, whose rear end of the upper deck has a larger acceleration fluctuation compared with the traditional model. The increase in the body material stiffness is helpful to reduce this fluctuation. The established nonlinear finite element model can effectively analyze the landing cushioning performance of airborne armored vehicles. The area of the external airbag vent has a large influence on the cushioning performance, and the cushioning system has excellent cushioning performance under various operating conditions.Practical implicationsThis study introduces the travel system, which is ignored by traditional analytical models. The interactions between various types of complex structures are included in the analysis process in its entirety, leading to valuable new conclusions. Quantitatively reveals the analytical errors of traditional simulation models in multiple dimensions and the reasons for their formation. Based on a high-precision simulation model, it is verified that the designed airbag cushioning system has an excellent cushioning effect for the new generation of heavy airborne armored vehicles.Originality/valueThe novelty of this work comes from the need for smooth landing with low overload for a new type of large-load airborne armored vehicle and provides a high-precision model that quantifies the traditional analytical modeling errors and error principle.

Numerical study of PPVC modular steel constructions (MSCs) with selected connection strategies under varied earthquake scenarios

Modular steel construction (MSC) represents an innovative approach in civil engineering. Yet, the complexities of its structural response to seismic forces are not fully understood, nor is the practicality concerning its connection systems. Traditional analytical models, often linear and elastic, fail to capture the MSC connection's nuanced, non-linear behaviour under seismic stress. This research embarks on a detailed numerical and parametric analysis of three MSC systems, introducing a novel stiffness coupling spring matrix approach to reflect the connection's behaviour more accurately in seismic conditions. The study analysis identifies MSC1 as demonstrating resilience, with intra and internal inter-module connections experiencing displacement up to 0.719 m and 0.685 m, respectively, under a 7.7Mw Chi-Chi earthquake. Conversely, MSC2, under a 6.7Mw Denali earthquake, highlights an urgent need for design optimisation, with connections displacement up to 0.714 m. MSC3 exhibits superior structural integrity and seismic resilience during a 5.3Mw Kocaeli earthquake, achieving a stiffness peak of 5499.1 N/m and registering minimal displacement of 0.184 m and 0.177 m in intra and internal inter-module connections. Further, a sensitivity analysis incorporating ±10% stiffness variations is crucial in seismic resilience. MSC1 and MSC2 display pronounced sensitivity, particularly MSC1's intra-connection with displacement fluctuations up to 0.789 m and a decrease to −8.273 m. This analysis underscores the imperative for enhanced stiffness in MSC3's external inter-module connections to withstand less severe seismic impact while necessitating increased stiffness for MSC1 and MSC's corner inter-module connections. Maintaining consistent stiffness in module-to-foundation connections is essential for models, notably on MSC1 and MSC2, highlighting the intricate challenge of engineering MSC systems for peak seismic performance.

Single-Well Simulation for Horizontal Wells in Fractured Gas Condensate Reservoirs.

Fractured gas condensate reservoirs (FGCR) are a complex, special, and highly valuable type of gas reservoir, accounting for a significant proportion of gas reservoir development. In recent years, with the continuous advancement of horizontal well technology, it has become the main approach for the development of FGCR. The current model is unable to accurately represent the fluid distribution in the near-well area of horizontal wells due to the unique retrograde condensation phenomenon in GCR. Additionally, the presence of fractures complicates the solution of traditional analytical models. In response to this issue, this paper proposes a novel semianalytical model for horizontal wells in FGCR, which incorporates natural fractures, multiphase flow, and the influence of stress sensitivity on pressure response. A dual-porosity model is employed to simulate fractured reservoirs, and a four-region radial composite model is developed to characterize multiphase flow resulting from retrograde condensation in GCR. The pseudopressure transform, Pedrosa transform, Laplace transform, and Finite Cosine transform are utilized to address the nonlinear partial differential equation. A systematic verification of the semianalytical solution is confirmed through a comparison with the numerical solution from computer modeling group (CMG). We thoroughly explain the physical significance of the various features by identifying the 12 flow regimes of the typical curve. Furthermore, we offer a method for assessing the extent of retrograde condensation and the size of the retrograde condensate region based on the curve's characteristics. Finally, the pressure measurements recorded from the Bohai field are carried out to validate the accuracy of the proposed model. The results show that the predictions of the new model are in good agreement with the actual production data, demonstrating the proposed solution's applicability.

Predicting the indentation load of FRP facesheet / foam core sandwiches

Due to concentrated bending loads, indentation is a common failure mode for sandwich structures. Indentation restricts the efficient use of sandwiches and is seriously affected by the sandwich design. Currently-used Traditional Analytical Models (TAM) are insufficient to predict the indentation load of the sandwiches with fiber-reinforced polymer (FRP) composite facesheets and foam core. Because of anisotropic features of FRP composites, experimental stiffness data is required to implement TAM. Their prediction accuracy is also disputable for different fiber orientations of facesheets. In this study, a modified analytical model (MAM) was developed to predict core yield and indentation failure loads without needing the experimental data of FRP composite facesheets. The modification is applied to currently-used two different traditional indentation models, and it is based on the analytically-obtained stiffness terms. The results obtained by MAM were then compared with the results determined by TAM and with the flexural test results of various sandwich structures. The modified analytical model, presented herein, yielded more accurate results than the TAM even for angle-ply configurations. An analytical solution to predict indentation is ultimately more attractive to structural sandwich designers because it provides rapid predictions that eliminate trial-and-error experimental efforts.

Influence of Hydraulic Distribution Pattern on the Rock Slope Stability under Block Toppling Failure

In this paper, an analytical model for the stability analysis of rock slope subjected to block toppling pertaining to different hydraulic forms has been developed. In the traditional analytical model, ground water pressure is considered to be varied hydrostatically. To better reflect the physical situation, three different hydraulics forms have been considered in developing a stability model for a rock slope susceptible to block toppling. It is well known fact that presence of ground water causes the instability in a rock slope. The present study observes that hydraulic distribution forms also significantly influence the stability of the rock slope. Ground water pressure markedly increases the toppling forces on the blocks and reduces the normal and shear force at the base of block along failure plane, thereby causing instability. The increase in toppling force and reduction in the factor of safety on the blocks are more prominent when the flow slit is blocked, indicating a condition of permanent or seasonal frozen strata. The study highlights that adopting the traditional hydraulic form to analyse block toppling stability, considering presence of ground water would not be suitable for all field conditions. This necessitates the selection of an appropriate hydraulic distribution form based on the encountered field conditions.

Dynamic Pressure Analysis of Shale Gas Wells Considering Three-Dimensional Distribution and Properties of the Hydraulic Fracture Network

A major challenge in transient pressure analysis for shale gas wells is their complex transient flow behavior and fracturing parameters. While numerical simulations offer high accuracy, analytical models are attractive for transient pressure analysis due to their high computational efficiency and broad applicability. However, traditional analytical models are often oversimplified, making it difficult to capture the complex seepage system, and three-dimensional fracture characteristics are seldom considered. To address these limitations, this study presents a comprehensive hybrid model that characterizes the transient flow behavior and analyzes the pressure response of a fractured shale gas well with a three-dimensional discrete fracture. To achieve this, the hydraulic fracture is discretized into several panels, and the transient flow equation is numerically solved using the finite difference method. Based on the Langmuir adsorption isotherm and the pseudo-steady diffusion in matrix and Darcy flow in the network of micro-fractures, a reservoir model is established, and the Laplace transformation is adopted to solve the model analytically. The transient responses are obtained by dynamically coupling the flow in the reservoir and the discrete fracture. The precision of the proposed model is validated using the commercial numerical simulator, Eclipse. A series of transient pressure dynamic curves are drawn to make a precise observation of different flow regimes, and the effects of several parameters on transient pressure response are also examined. The results show that the shale gas well testing interpretation curves comprise nine flow stages. The pressure drop of shale gas reservoirs is lower than that of conventional gas reservoirs due to the replenishment of desorbed gas. The artificial fracture flow capacity, fracture length, and height are the main engineering factors affecting the pressure responses of shale gas wells. Maximizing the degree and scope of reconstruction can enhance the gas well production capacity during fracturing construction. The research results also indicate that our model is a reliable semi-analytical model for well test interpretations in real case studies.

Modeling the hotspot effect for vegetation canopies based on path length distribution

The hotspot effect, which refers to the increased reflectance in the solar direction, relies on the canopy structure. Numerous bi-directional reflectance distribution function (BRDF) models have been developed with careful attention to the hotspot effect. However, different assumptions may lead to very different shapes of BRDF, especially in the hotspot direction. With the development of high-resolution remote sensing satellites and three-dimensional (3D) mapping technologies, abundant 3D structural vegetation canopy information is available. However, traditional analytical models might not fully use these kinds of information. As a result, most of them are still very limited by their assumptions, such as the shape and spatial distribution of crowns. One BRDF model may do well only for one vegetation type and shows significant errors under other scenarios. We propose a new model named PATH_RT, which can model the hotspot effect at both the leaf and crown levels using the path length distribution (PLD). PLD can be easily obtained with the 3D information of the canopy at given remote sensing geometries. We validated the bi-directional reflectance factor (BRF) predicted by the PATH_RT model with the simulated scenes and field measurements which were produced by a 3D radiative transfer model LESS and UAV observations respectively. Compared to traditional analytical models SAILH, 4SAIL2, GORT, and 5SCALE, the PATH_RT model exhibits the highest accuracy, reducing the average RMSE by 86%, 66%, 52%, and 58% for the hotspot region respectively. PATH_RT accurately depicts the hotspot effects at the canopy and leaf levels and is highly efficient (99%, 75%, 86%, 99%, and 99% less time spent for one repeat run compared to LESS, SAILH, 4SAIL2, GORT, and 5SCALE models, respectively). Considering its high accuracy and efficiency, PATH_RT is expected to improve the accuracy and efficiency of remote sensing inversion.

Research on Pedestrian Crossing Decision Models and Predictions Based on Machine Learning.

Systematically and comprehensively enhancing road traffic safety using artificial intelligence (AI) is of paramount importance, and it is gradually becoming a crucial framework in smart cities. Within this context of heightened attention, we propose to utilize machine learning (ML) to optimize and ameliorate pedestrian crossing predictions in intelligent transportation systems, where the crossing process is vital to pedestrian crossing behavior. Compared with traditional analytical models, the application of OpenCV image recognition and machine learning methods can analyze the mechanisms of pedestrian crossing behaviors with greater accuracy, thereby more precisely judging and simulating pedestrian violations in crossing. Authentic pedestrian crossing behavior data were extracted from signalized intersection scenarios in Chinese cities, and several machine learning models, including decision trees, multilayer perceptrons, Bayesian algorithms, and support vector machines, were trained and tested. In comparing the various models, the results indicate that the support vector machine (SVM) model exhibited optimal accuracy in predicting pedestrian crossing probabilities and speeds, and it can be applied in pedestrian crossing prediction and traffic simulation systems in intelligent transportation.

CFD Modeling of a Pitching Airfoil for the Estimation of the Performance Curve in an H‐Rotor Vertical Axis Wind Turbine

In this work, the performance curve of a straight‐bladed vertical axis wind turbine (VAWT) is estimated through a CFD two‐dimensional modeling of a single airfoil under unsteady pitching motion. A typical URANS approach, with a k‐ω SST turbulence model and UDF functionalities, has been employed to simulate a fixed airfoil under variable inlet conditions. Induction factors are previously computed using a simple stream tube model (SSTM) in an iterative process. Frequencies and amplitudes of the pitching motion are modified to simulate the operative range of tip‐speed ratios (TSRs) for the wind turbine. The obtained results have been compared with experimental curves of a 0.5 solidity turbine measured in a wind tunnel. The experimental results of the wind‐tunnel turbine provide a maximum Cp value of 0.22 at a TSR = 3.1, while the numerical procedure estimates a value of 0.33, also at TSR = 3.1. Despite of the discrepancy, due to the 2D assumptions in the simulation, the procedure makes reliable and more accurate estimations than traditional analytical models with steady coefficients. This alternative methodology significantly reduces the computational cost. Moreover, these nonrotating simulations allow more robust and practical routines that can be easily implemented in optimization algorithms for the design of VAWTs.

Data-driven research into the inaccuracy of traditional models of thermal comfort in offices

The accurate prediction of thermal sensation among office workers, at design and post-occupancy stages, is crucial for controlling indoor temperature efficiently and correcting deficiencies in workspaces, ensuring healthy and productive working conditions. Traditional analytical comfort models are still the best tool for this purpose given their potential for interpretation. However, their reliability is undermined by their poor accuracy. Based on 304 data series of point-in-time measurements of quantitative and qualitative comfort-related parameters collected in an experimental campaign in three office buildings, one air-conditioned and two in free evolution, in San Luis Potosí (Mexico), this work aims to identify the major error-causing factors of steady and adaptive comfort models. The divergences between predicted and reported thermal sensation were set as a dependant variable of two multiple regressions, one for each model. Eighteen independent demographic, environmental, contextual and subjective variables were considered. No multicollinearity problems were identified. Our findings show that contextual factors and humidity perception were relevant in the adaptive model error. Clothing insulation highly impacted the accuracy of both models while age and body mass were not statistically significant for either of them. Metabolic rate was the factor with the greatest influence in the error of the steady model. Although not covered, other influential factors played a key role in models’ accuracy and further research is needed to integrate these in a new generation of more accurate and flexible analytical models.

Review of the current status and the potential of machine learning tools in boiling heat transfer

Boiling is a liquid-to-vapor phase change process that involves complex and high dimensional processes spanning multiple length- and time-scales. Most correlations apply to a narrow range of conditions and the development of mechanistic models has been limited. Machine learning (ML) provides tools to uncover fundamental principles governing such challenging problems. It uses a variety of techniques to analyze large datasets and identify patterns and relationships that are otherwise challenging to establish using the traditional methods. Attempts have already been made to leverage the power of ML to provide new insights and make more accurate predictions in boiling heat transfer. We here report a comprehensive review of such studies, put in perspective their findings with the existing knowledgebase obtained using traditional approaches, and identify the key advantages and challenges of ML in this domain. The review suggests that it is important to pay attention to data collection, feature selection and extraction, choice of algorithm, and performance metrics to improve the accuracy, reliability, and robustness of ML models. While the traditional analytical and empirical models are more interpretable and easier to visualize, ML models are noted to enable more accurate predictions over a wider range of operating conditions. The issues of interpretability can be addressed by reducing the complexity of the data via dimensionality reduction and clustering techniques. Moreover, physics-inspired techniques can be used to align predictions with physical principles and improve generalizability. The ability of ML to swiftly process alternative forms of data such as optical images, thermal maps, and acoustics in real-time opens new frontiers for investigations. Finally, we close the discussion by emphasizing the need for high-quality data collection and standardization for increasing the impact of ML in phase change heat transfer.

Hybrid Machine-Learning-Based Prediction Model for the Peak Dilation Angle of Rock Discontinuities.

The peak dilation angle is an important mechanical feature of rock discontinuities, which is significant in assessing the mechanical behaviour of rock masses. Previous studies have shown that the efficiency and accuracy of traditional experimental methods and analytical models in determining the shear dilation angle are not completely satisfactory. Machine learning methods are popular due to their efficient prediction of outcomes for multiple influencing factors. In this paper, a novel hybrid machine learning model is proposed for predicting the peak dilation angle. The model incorporates support vector regression (SVR) techniques as the primary prediction tools, augmented with the grid search optimization algorithm to enhance prediction performance and optimize hyperparameters. The proposed model was employed on eighty-nine datasets with six input variables encompassing morphology and mechanical property parameters. Comparative analysis is conducted between the proposed model, the original SVR model, and existing analytical models. The results show that the proposed model surpasses both the original SVR model and analytical models, with a coefficient of determination (R2) of 0.917 and a mean absolute percentage error (MAPE) of 4.5%. Additionally, the study also reveals that normal stress is the most influential mechanical property parameter affecting the peak dilation angle. Consequently, the proposed model was shown to be effective in predicting the peak dilation angle of rock discontinuities.

Novel calculation method to predict gas–water two-phase production for the fractured tight-gas horizontal well

The prediction of production capacity in tight gas wells is greatly influenced by the characteristics of gas–water two-phase flow and the fracture network permeability parameters. However, traditional analytical models simplify the nonlinear problems of two-phase flow equations to a large extent, resulting in significant errors in dynamic analysis results. To address this issue, this study considers the characteristics of gas–water two-phase flow in the reservoir and fracture network, utilizes a trilinear flow model to characterize the effects of hydraulic fracturing, and takes into account the stress sensitivity of the reservoir and fractures. A predictive model for gas–water two-phase production in tight fractured horizontal wells is established. By combining the mass balance equation with the Newton–Raphson iteration method, the nonlinear parameters of the flow model are updated step by step using the average reservoir pressure. The accuracy of the model is validated through comparisons with results from commercial numerical simulation software and field case applications. The research results demonstrate that the established semi-analytical solution method efficiently handles the nonlinear two-phase flow problems, allowing for the rapid and accurate prediction of production capacity in tight gas wells. Water production significantly affects gas well productivity, and appropriate fracture network parameters are crucial for improving gas well productivity. The findings of this work could provide more clear understanding of the gas production performance from the fractured tight-gas horizontal well.

Characterization and Design of Three-Phase Particulate Composites: Microstructure-Free Finite Element Modeling vs. Analytical Micromechanics Models.

Three-phase particulate composites offer greater design flexibility in the selection of phase materials and have more design variables than their two-phase counterparts, thus providing larger space for tailoring effective properties to meet intricate engineering requirements. Predicting effective elastic properties is essential for composite design. However, experimental methods are both expensive and time intensive, whereas the scope of analytical micromechanics models is limited by their inherent assumptions. The newly developed microstructure-free finite element modeling (MF-FEM) approach has been demonstrated to be accurate and reliable for two-phase particulate composites. In this study, we investigate whether the MF-FEM approach can be applied to three-phase particulate composites and, if applicable, under which conditions. The study commences with a convergence analysis to establish the threshold ratio between the element size and the RVE (representative volume element) dimension. We then validate the MF-FEM approach using experimental data on three-phase composites from the existing literature. Subsequently, the MF-FEM method serves as a benchmark to assess the accuracy of both traditional and novel analytical micromechanics models, in predicting the effective elasticity of two distinct types of three-phase particulate composites, characterized by their small and large phase contrasts, respectively. We found that the threshold element-to-RVE ratio (1/150) for three-phase composites is considerably smaller than the ratio (1/50) for two-phase composites. The validation underscores that MF-FEM predictions align closely with experimental data. The analytical micromechanics models demonstrate varying degrees of accuracy depending on the phase volume fractions and the contrast in phase properties. The study indicates that the analytical micromechanics models may not be dependable for predicting effective properties of three-phase particulate composites, particularly those with a large contrast in phase properties. Even though more time-intensive, the MF-FEM proves to be a more reliable approach than the analytical models.

Semianalytical Modeling for Multiphase Flow in a Fractured Low-Permeability Gas Condensate Reservoir.

During the production of fractured low-permeability gas condensate reservoir (FLPGCR), a phase transition takes place in both the formation and wellbore, resulting in multiphase flow when the pressure drops below the dew point pressure. Additionally, the presence of fractures causes the formation of stress-sensitive characteristics. Nevertheless, traditional analytical models, such as the two-region model or three-region model, overlook the coupling impact of the above factors, which could lead to incorrect pressure transient response and erroneous estimation of well and formation parameters. Therefore, this work presents a semianalytical model for an FLPGCR considering the effects of multiphase flow, stress-sensitive, and wellbore phase redistribution. The gas condensate reservoir is divided into N banks, and the radial fluid saturation variation is modeled by multiple annular reservoirs with a constant saturation in each annular reservoir. The behavior of a fractured reservoir is modeled by using the dual-porosity model. The Pedrosa transform was utilized to address the nonlinear differential equation arising from stress-sensitive behavior. To verify the semianalytical solution, it was compared with numerical simulation results from CMG. The results showed that there are 10 flow regimes for the proposed model. The shape of the type curve has the potential to identify the degree of blockage within the FLPGCR. The wellbore phase redistribution only affects the first transitional-flow regime, which slows the rate of pressure drop. The stress sensitivity will lead to the upward characteristic of the curve in a later stage. More attention should be paid to the upward pressure derivative curve at late times, which is conventionally regarded as the effect of a closed boundary when it may not be the case. In addition, the shape factor and composite radius may obscure the radial flow regime. Finally, the proposed model was applied to interpret the pressure measurements recorded from the Bohai field in China, which exhibits a better fitting quality than the traditional models.

Aerodynamic load evaluation of leading edge and trailing edge windward states of large-scale wind turbine blade under parked condition

The safety and reliability of wind turbine blades are increasingly challenged by extreme wind conditions such as typhoons, as wind turbines tend to become larger. Under these conditions, most units will be shut down and the blades will be pitched to around 90° to minimize the loads. This paper aims to compare a new strategy for the parked condition, i.e., the trailing edge windward state, with the traditional leading edge windward state to verify its technical feasibility. The aerodynamic loads of a 30%-thickness airfoil and a commercial wind turbine blade are comprehensively evaluated by wind tunnel experiment, CFD simulation and engineering analytical model. The two-dimensional airfoil cases indicate that the airfoil resultant force is lower in the trailing edge windward state than in the leading edge windward state for a wide range of angles of attack. Consequently, the three-dimensional blade cases shows that the low load region of the trailing edge windward state is relatively wider than that of the leading edge windward state. The averaged blade root load is reduced by 41.4% ~ 57.8% through trailing edge windward state with prescribed error bounds of windward angular. Besides, it is suggested that the traditional engineering analytical model should improve the precision of the extrapolated airfoil data around AOA = 180 deg. to ensure the accurate load evaluation under the trailing edge windward state. This study suggests a new control strategy for wind turbine blades under the parked condition, which offers significant benefits for load reduction and has a good potential for future applications.

A fast and efficient mesh stiffness model for spur gears considering thin-walled or oblique web

Time-varying meshing stiffness has an important contribution to the vibration control in gear systems. However, the design of gears in the electric vehicles and aerospace field is more focused on lightweight. The accurate and efficient evaluation of the time-varying meshing stiffness of lightweight spur gears has become a challenging problem, such as considering the thin-walled or oblique web. Therefore, a new approach is proposed to calculate the mesh stiffness of the lightweight spur gear by combining an analytical model and the dynamic model of the gear pair. The gear model is constructed by adopting the shell element to save computer resources and model preparation time. The validity of the presented model is investigated by comparing it with the traditional analytical and finite element models. The analytical results indicate that the proposed model has the same level of computational efficiency as the analytical model while maintaining the precision of the finite element model. The advantages of the proposed method are the ability to consider the effects of thin-walled web and oblique spoke plate structures on the mesh stiffness.