Precise Calculation Research Articles

Optimal aggregation and disaggregation for coordinated operation of virtual power plant with distribution network operator

Virtual power plants (VPPs) offer an effective approach for managing distributed energy resources (DERs), including microturbines, distributed generators, demand response aggregators, and energy storage systems. This technology significantly enhances the economic efficiency and flexibility of distribution network systems. This study aims to facilitate a flexible power exchange between the distribution network system and the upper-level grid by aggregating the power flexibility of heterogeneous DERs via a VPP. Additionally, it introduces a methodology for optimal aggregation and disaggregation within a coordinated operation framework between the VPP and distribution network operator (DNO). On one hand, the VPP can determine its day-ahead feasible region and real-time flexible regulation power based on operational constraints of DERs and representative data provided by the DNO, circumventing the need for detailed network information. On the other hand, day-ahead and real-time correction procedures for the DER cost functions are proposed, effectively neutralizing the impact of network operational constraints on these cost functions. Consequently, precise cost functions for both the active power and the flexible regulation power aggregated by the VPP are derived. Employing this aggregated cost function enables the determination of a cost-minimized optimal scheduling solution in real-time by solving a fundamental economic dispatch problem, significantly alleviating computational demands. Finally, case studies demonstrate that the proposed method achieves an error in aggregated power of only 0.77%, compared to the precise computation method that requires comprehensive system information. The proposed optimal VPP disaggregation scheme exhibits power discrepancies of 0.63% and cost discrepancies of 0.91% relative to the precise method. Additionally, when implementing the most cost-effective demand response plan based on the proposed cost function, the average costs for aggregators are reduced by 19.7%.

A Deep Learning Estimation for Probing Depth of Transient Electromagnetic Observation

The probing depth of the transient electromagnetic method (TEM) refers to the depth range at which the underground conductivity changes can be effectively detected. It typically ranges from tens of meters to several kilometers and is influenced by factors such as instrument parameters and the conductivity of the subsurface structure. Rapid and accurate probing depth is useful for the selection of appropriate inversion parameters and improving survey accuracy. However, mainstream methods suffer from issues such as low computational precision, large uncertainties, or high computational requirements, making them unsuitable for processing massive airborne electromagnetic data. In this study, we propose a prediction model based on deep learning that can directly compute the probing depth from the TEM responses, and its effectiveness and accuracy are validated through synthetic models and field measurements. We compared the performance of classic deep learning models, including CNN, RESNET, and RNN, and found that RNN performed the best overall on both synthetic and field data. Furthermore, we apply this algorithm to deep learning-based ATEM inversion by constraining the one-dimensional resistivity model depths in the training set, to reduce the non-uniqueness of the inversion, accelerate the convergence, and improve its prediction accuracy.

Efficient Mixed-Precision Matrix Factorization of the Inverse Overlap Matrix in Electronic Structure Calculations with AI-Hardware and GPUs.

In recent years, a new kind of accelerated hardware has gained popularity in the artificial intelligence (AI) community which enables extremely high-performance tensor contractions in reduced precision for deep neural network calculations. In this article, we exploit Nvidia Tensor cores, a prototypical example of such AI-hardware, to develop a mixed precision approach for computing a dense matrix factorization of the inverse overlap matrix in electronic structure theory, S-1. This factorization of S-1, written as ZZT = S-1, is used to transform the general matrix eigenvalue problem into a standard matrix eigenvalue problem. Here we present a mixed precision iterative refinement algorithm where Z is given recursively using matrix-matrix multiplications and can be computed with high performance on Tensor cores. To understand the performance and accuracy of Tensor cores, comparisons are made to GPU-only implementations in single and double precision. Additionally, we propose a nonparametric stopping criteria which is robust in the face of lower precision floating point operations. The algorithm is particularly useful when we have a good initial guess to Z, for example, from previous time steps in quantum-mechanical molecular dynamics simulations or from a previous iteration in a geometry optimization.

Dosimetric implications of kidney anatomical volume changes in 177Lu-DOTATATE therapy

IntroductionThis study aims to evaluate the use of CT-based whole kidney parenchyma (WKP) segmentation in 177Lu-DOTATATE dosimetry. Specifically, it investigates whether WKP volumes change during treatment and evaluates the accuracy of applying a single delineated WKP volume for dosimetry. Furthermore, it aims to determine the cause of WKP volume changes—whether caused by radiation or amino acid infusion—by comparing them with spleen volume changes as a marker for radiation-induced alterations.MethodsSPECT/CT images of 18 patients were acquired over the abdomen approximately 4 h (h) (D0), 24 h (D1), 48 h (D2) and 168 h (D7) post-administration of 177Lu-DOTATATE. CT guided WKP volumes were measured before (baseline) and during treatment. Kidney activity concentrations at each time point were derived from CT-segmented WKP overlaid on SPECT scans. The accuracy of using WKP segmentation from a single CT for all time points was assessed against the gold standard of segmenting each WKP individually. Time-integrated activity calculations were based on a tri-exponential curve fit of the kidney activity concentration over time. Kidney absorbed doses were estimated under the assumption of local energy deposition. Additionally, the impact of various partial volume correction methods on dosimetry was evaluated.ResultsWhole-kidney parenchyma (WKP) volumes, ranging from 31 to 243 mL, showed a gradual increase from baseline (mean ± SD = 130.6 ± 46.1 mL) at the initial time points D0 (138.5 ± 44.7 mL) and D1 (139.4 ± 41.6 mL), followed by a slight decrease at D2 (132.8 ± 44.5 mL) and a further decrease at D7 (129.2 ± 42.7 mL). The volume increase at D0 and D1 was statistically significant. Spleen volume did not change during treatment, suggesting that amino acid infusion rather than irradiation effects caused WKP volume changes. Bland-Altman analysis revealed WKP volume biases of 8.77% (D0 vs. BL), 10.77% (D1 vs. BL), 1.10% (D2 vs. BL), and 1.10% (D7 vs. BL), with corresponding uncertainties of 24.4%, 23.6%, 25.4%, and 25.4%, respectively. When WKP segmentation from a single CT is applied across all SPECTs, these WKP volume changes could overestimate the activity concentration and mean absorbed doses up to 4.3% and 2.5%, respectively. The absorbed dose uncertainties using a recovery coefficient (RC) of 0.85 for single-time-point WKP delineation increase the absorbed dose uncertainty by 4% compared to the use of patient-specific RCs and time specific segmentation of WKP volumes.ConclusionsKidney volume exhibited significant variation form D0 to D7, affecting the precision of dosimetry calculation, primarily due to errors in whole-kidney parenchyma (WKP) delineation. Notably, using WKP segmentation from a single CT scan applied to sequential SPECT images introduce further uncertainty and may lead to an overestimation of the absorbed dose. The fluctuations in kidney volume are most likely attributable to amino acid infusion.

An integrated optimization method for measurement points layout and error modeling for digital twin of CNC machine tools

Thermal error significantly influences the accuracy of precision computer numerical control (CNC) machine tools. The key to compensating thermal error lies in selecting appropriate temperature measurement points and establishing an accurate error prediction model. Traditional methods separate measurement points selection and prediction modeling, that is, selecting temperature measurement points first and then establishing the prediction model using these points. These methods are difficult to achieve optimal matching between measurement points and the prediction model, resulting in shortcomings in modeling accuracy. To address these challenges, an integrated optimization method for measurement points layout and error modeling for digital twin of CNC machine tools is proposed. A dual-stage attention-based long short-term memory combined with convolutional neural network (DA-CLSTM) error modeling method is proposed to accurately predict the thermal error for different numbers of temperature measurement points. Then, an integrated method of virtual-real temperature measurement points layout and error modeling is proposed, which ensures the temperature measurement points and the error prediction model are closely matched, offering rational sensors layout and high-accuracy prediction. Finally, experiments are conducted on the spindle system test bench to validate the method proposed in this research. Through integrated optimization of measurement points layout and the prediction model, the proposed method achieves high-accuracy thermal error prediction across various sensor counts and maintains reliable prediction ability when the number of sensors is reduced. This method is applied to a digital twin system of MKL7150 grinding machine, and effectively improves the accuracy of actual machining.

Size-dependent vibration analysis of porous 3D-FG microshells in complex thermal environments using a neural network enhanced finite element model

This work aims to investigate the vibration behavior of porous three-directional functionally graded (3D-FG) microshells in thermal environments, where both mechanical and thermal material properties vary along three spatial directions following a power-law distribution, based on the modified couple stress theory (MCST). An isoparametric thermal element is employed for uncoupled heat conduction analysis, followed by dynamic analysis using a penalty solid element with three translation degrees of freedom (DOFs) and three rotation DOFs per node. However, due to the strong nonlinearity of the temperature field in 3D-FG microshells, obtaining exact temperature values at integration points in dynamic analysis requires a pretty dense mesh of the solid element. To overcome this issue, a back propagation (BP) neural network is utilized to predict the temperature field for various gradient indexes, enhancing the efficiency and precision of temperature distribution calculation. Numerical results demonstrate the effectiveness of the neural network enhanced finite element model. Furthermore, the effects of the material length scale parameter (MLSP), temperature difference, and gradient indexes on the natural frequencies are investigated. It is shown that higher structural stiffness weakens the impact of temperature difference on the frequency.

Robotical Automation in CNC Machine Tools: A Review

Abstract Robotics and automation have significantly transformed Computer Numerical Control (CNC) machining operations, enhancing productivity, precision, and efficiency. Robots are employed to load and unload raw materials, workpieces, and finished parts onto CNC machines. They can efficiently handle heavy and bulky components, reducing the demand of manual labour and minimizing the risk of injuries. Robots can also be used in CNC machine tools to perform tasks such as automatic tool changing system, part inspection, and workpiece positioning. Automation technologies, including in-line inspection systems and Non-Destructive Testing (NDT) methods, can be integrated into CNC machining cells to enhance accuracy and reduce scrap and rework in machining operations. These systems collect real-time data on process parameters and machine tool performance to predict maintenance, optimize machining parameters, and improve overall efficiency. In the current study, applications of robotics and automation in the modification of CNC machine tools are reviewed and discussed. Different applications of robotics and automation in CNC machine tools, such as automated material handling, automatic tool changing, robotic work cells, adaptive machining, machine tending, quality inspection, data monitoring and analysis, and production line integration, are discussed. Thus, by analysing recent achievements in published papers, new ideas and concepts of future research works are suggested. As a result, accuracy as well as productivity in the process of part production can be enhanced by applying robotics and automation in CNC machining operations.

Transient electromagnetic inversion to image the shallow subsurface based on convolutional bidirectional long short-term memory neural networks

SUMMARY The conventional transient electromagnetic inversion method has a low calculation speed and precision and is susceptible to falling into local minima, which does not meet the fine detection requirements of urban underground space. In this study, we proposed a novel inversion method based on convolutional bidirectional long short-term memory neural networks for shallow subsurface transient electromagnetic inversion. This network structure possessed strong spatial feature extraction capabilities and a proficient understanding of sequential data, thereby addressing the issues of slow conventional inversion computations and inadequate inversion accuracy. Utilizing the apparent resistivity from a three-layer model as the sample input and the real model as the target, the network was trained using batch normalization and dropout techniques to accelerate the convergence rate. The resulting model achieved real-time inversion speeds and high accuracy, with robust generalization capabilities and adaptability to new data. To assess the inversion performance, we used a novel 1-D inversion error calculation index, the correlation area loss error, for a more accurate measurement. Numerical simulation experiments showed that the proposed method required only 2.121 s to invert data from 100 observation points. The inversion efficiency was significantly superior to the conventional methods, maintaining excellent accuracy while effectively discerning subsurface electrical stratification in geophysics. Applying convolutional bidirectional long short-term memory neural networks to multidimensional and field data yielded results superior to those of conventional inversion, demonstrating the promising applicability and generalization of this approach. This study offers an efficient solution for shallow subsurface transient electromagnetic exploration and holds potential for application in other areas.

GPU implementation of the Frenet Path Planner for embedded autonomous systems: A case study in the F1tenth scenario

Autonomous vehicles are increasingly utilized in safety-critical and time-sensitive settings like urban environments and competitive racing. Planning maneuvers ahead is pivotal in these scenarios, where the onboard compute platform determines the vehicle’s future actions. This paper introduces an optimized implementation of the Frenet Path Planner, a renowned path planning algorithm, accelerated through GPU processing. Unlike existing methods, our approach expedites the entire algorithm, encompassing path generation and collision avoidance. We gauge the execution time of our implementation, showcasing significant enhancements over the CPU baseline (up to 22x of speedup). Furthermore, we assess the influence of different precision types (double, float, half) on trajectory accuracy, probing the balance between completion speed and computational precision. Moreover, we analyzed the impact on the execution time caused by the use of Nvidia Unified Memory and by the interference caused by other processes running on the same system. We also evaluate our implementation using the F1tenth simulator and in a real race scenario. The results position our implementation as a strong candidate for the new state-of-the-art implementation for the Frenet Path Planner algorithm.

Quantum phase transitions in one-dimensional nanostructures: a comparison between DFT and DMRG methodologies.

In the realm of quantum chemistry, the accurate prediction of electronic structure and properties of nanostructures remains a formidable challenge. Density functional theory (DFT) and density matrix renormalization group (DMRG) have emerged as two powerful computational methods for addressing electronic correlation effects in diverse molecular systems. We compare ground-state energies ( ), density profiles ( ), and average entanglement entropies ( ) in metals, insulators and at the transition from metal to insulator, in homogeneous, superlattices, and harmonically confined chains described by the fermionic one-dimensional Hubbard model. While for the homogeneous systems, there is a clear hierarchy between the deviations, , and all the deviations decrease with the chain size; for superlattices and harmonic confinement, the relation among the deviations is less trivial and strongly dependent on the superlattice structure and the confinement strength considered. For the superlattices, in general, increasing the number of impurities in the unit cell represents lower precision in the DFT calculations. For the confined chains, DFT performs better for metallic phases, while the highest deviations appear for the Mott and band-insulator phases. This work provides a comprehensive comparative analysis of these methodologies, shedding light on their respective strengths, limitations, and applications. The DFT calculations were performed using the standard Kohn-Sham scheme within the BALDA approach. It integrated the numerical Bethe-Ansatz (BA) solution of the Hubbard model as the homogeneous density functional within a local-density approximation (LDA) for the exchange-correlation energy. The DMRG algorithms were implemented using the ITensor library, which is based on the matrix product states (MPS) ansatz. The calculations were performed until the energy reaches convergence of at least .

Synergy between noisy quantum computers and scalable classical deep learning for quantum error mitigation

We investigate the potential of combining the computational power of noisy quantum computers and of classical scalable convolutional neural networks (CNNs). The goal is to accurately predict exact expectation values of parameterized quantum circuits representing the Trotter-decomposed dynamics of quantum Ising models. By incorporating (simulated) noisy expectation values alongside circuit structure information, our CNNs effectively capture the underlying relationships between circuit architecture and output behaviour, enabling, via transfer learning, also predictions for circuits with more qubits than those included in the training set. Notably, thanks to the quantum information, our CNNs succeed even when supervised learning based only on classical descriptors fails. Furthermore, they outperform a popular error mitigation scheme, namely, zero-noise extrapolation, demonstrating that the synergy between quantum and classical computational tools leads to higher accuracy compared with quantum-only or classical-only approaches. By tuning the noise strength, we explore the crossover from a computationally powerful classical CNN assisted by quantum noisy data, towards rather precise quantum computations, further error-mitigated via classical deep learning.

A comprehensive investigation of the bending and vibration behavior of size-dependent functionally graded nanoplates via an enhanced nonlocal finite element shear model

This research paper conducts a comprehensive investigation into the linear bending and free vibration of size-dependent functionally graded (FG) nanoplates using an improved first-order shear deformation theory (IFSDT). The present IFSDT offers an enhanced representation and precise computation of normal and shear stresses across the thickness of the nanoplates. Notably, it not only ensures compliance with free conditions on both the upper and lower surfaces but also eliminates the need for a conventional correction factor commonly employed in the first-order shear deformation theory (FSDT). To transcend the assumptions of classical continuum mechanics and address the impacts of small-scale effects in discrete nanoplates, Eringen’s nonlocal elasticity theory is applied. The formulation of the governing equation for the bending and free vibration analyses of the FG nanoplates is achieved through the application of Hamilton’s principle. The proposed IFSDT is implemented with an efficient C0-continuous quadrilateral element which is suitable for analysis of structures with arbitrary shapes and complex forces. The model’s performance is showcased through a comparative evaluation against literature predictions, highlighting its high accuracy and rapid convergence. Additionally, the research scrutinizes the effects of various parameters, such as plate thickness, boundary conditions, aspect ratio, nonlocal parameter, different material compositions, and power-law index on the deflections, stresses, and naturel frequencies of the FG nanoplates. The results underscore the significant impact of size-dependent effects on the linear bending and vibration behaviors of the nanoplates.

Spatial accessibility and equity of community healthcare: unraveling the impact of varying time and transport mode.

Achieving a higher level of accessibility and equity to community healthcare services has become a major concern for health service delivery from the perspectives of health planners and policy makers in China. In this study, we introduced a comprehensive door-to-door (D2D) model, integrating it with the open OD API results for precise computation of accessibility to community hospitals over different transport modes. For the D2D public transit mode, we computed the temporal variation and standard deviation of accessibility at different times of the day. Additionally, accessibility values for D2D riding mode, D2D driving mode, and simple driving mode were also computed for comparison. Moreover, we introduced Lorenz curve and Gini index to assess the differences in equity of community healthcare across different times and transport modes. The D2D public transit mode exhibits noticeable fluctuations in accessibility and equity based on the time of day. Accessibility and equity were notably influenced by traffic flow between 8 AM and 11 AM, while during the period from 12 PM to 10 PM, the open hours of community hospitals became a more significant determinant in Nanjing. The moments with the most equitable and inequitable overall spatial layouts were 10 AM and 10 PM, respectively. Among the four transport modes, the traditional simple driving mode exhibited the smallest equity index, with a Gini value of only 0.243. In contrast, the D2D riding mode, while widely preferred for accessing community healthcare services, had the highest Gini value, reaching 0.472. The proposed method combined the D2D model with the open OD API results is effective for accessibility computation of real transport modes. Spatial accessibility and equity of community healthcare experience significant fluctuations influenced by time variations. The transportation mode is also a significant factor affecting accessibility and equity level. These results are helpful to both planners and scholars that aim to build comprehensive spatial accessibility and equity models and optimize the location of public service facilities from the perspective of different temporal scales and a multi-mode transport system.

Cutout effects on the vibration of sandwich auxetic cylindrical shells with an experimental validation

This study investigates the influence of auxetic core layers on the vibrational characteristics of sandwich cylindrical shells with cut-outs. The analysis utilizes the First-order Shear Deformation Theory (FSDT) and analytical techniques. The irregular behavior of auxetic re-entrante honeycomb cells necessitates the exploration of their effects on various systems. Additionally, the proposed method offers advantages over Finite Element Method (FEM). FEM modeling suffers from increased computational time and reduced accuracy with a large number of elements or high aspect ratio elements. These challenges are particularly significant for shells with geometric features of disparate scales, such as large cylinders with tiny cutouts or cutouts with low aspect ratios. However, the present research addresses these challenges by employing a method that integrates five panels with varying dimensions. Consequently, each section can utilize specific Two-Dimensional Generalized Differential Quadrature (2D-GDQ) grid points, tailored to the size of each side, enabling rapid and accurate prediction of displacement and stress parameters, particularly for shorter sides. The proposed method excels at precisely modeling cutouts while preserving their realistic properties. This is achieved by avoiding geometric and material simplifications and by assigning distinct boundary conditions to each region, including critical areas like corners, internal, and external sides. Moreover, Frequency Response Functions (FRFs) obtained from accelerometer and laser signals, alongside vibration-induced sound wave signals captured by electroacoustic microphones, validate the precision of the numerical calculations.

Unveiling GruPol: Predicting Electric and Electrostatic Properties of Macromolecules via the Building Block Approach.

Understanding electrostatics and electric properties of macromolecules is crucial in uncovering the intricacies of their behavior and functionality. The precise knowledge of these properties enhances our ability to manipulate and engineer macromolecules for diverse applications, spanning from drug design to materials science. Having that in mind, we present here the GruPol database approach to characterize and accurately predict dipole moments, static polarizabilities, and electrostatic potential of proteins and their subunits. The method involves partitioning of the electron density, calculated at the M06-HF/aug-cc-pVDZ level of theory, of small peptides into predefined building blocks that are averaged over the database. By manipulating and positioning these building blocks, GruPol enables the description of proteins assembled from over nearly 100 residual entries, allowing for efficient and precise computation of the above-mentioned properties across a broad range of proteins. The database enables the user to include solvent effects as well as define protonation states on the protein's backbone to account for pH variations. The precision of the proposed scheme is benchmarked against experimental data for myoglobin species.

Automated identification and long-term tracking of modal parameters for a super high-rise building

Structural modal parameter identification plays an important role in wind resistance design, damage identification, and health monitoring. At present, the modal identification process still suffers from low automation and low computational efficiency, which are not conducive to the continuous processing of monitoring data. To address this, an automated modal parameter identification approach is proposed by introducing the fast density and grid based (DGB) clustering, and applied to the Shanghai Tower, which is the tallest building in China. Moreover, in order to determine the optimal model order of the stochastic subspace identification (SSI), the trend of the logarithmic reduction rate for singular values is extracted by empirical mode decomposition (EMD). The numerical simulation of a cantilever beam and the field measurement of Shanghai Tower under the influence of Typhoon In-Fa are conducted to validate the effectiveness of the proposed approach. Results demonstrate that the approach can obtain modal parameters without human intervention and has the advantages of simple process, fast computation and high precision. Furthermore, based on the data collected from the structural health monitoring system of the Shanghai Tower over a period of 5.5 years, the approach is applied to analyze the correlation between natural frequencies and ambient temperature, as well as the long-term evolution law of modes, discovering that multi-order frequencies show a decreasing trend over time. The finite element analysis suggests that this fact might be attributed to the increase in structural mass caused by the rising usage rate and the degradation in material properties.

Coupling molecular density functional theory with converged selected configuration interaction methods to study excited states in aqueous solution.

This paper presents the first implementation of a coupling between advanced wavefunction theories and molecular density functional theory (MDFT). This method enables the modeling of solvent effect into quantum mechanical (QM) calculations by incorporating an electrostatic potential generated by solvent charges into the electronic Hamiltonian. Solvent charges are deduced from the spatially and angularly dependent solvent particle density. Such a density is obtained through the minimization of the functional associated with the molecular mechanics (MM) Hamiltonian describing the interaction between the fluid particles. The introduced QM/MDFT framework belongs to QM/MM family of methods, but its originality lies in the use of MDFT as the MM solver, offering two main advantages. First, its functional formulation makes it competitive with respect to sampling-based molecular mechanics. Second, it preserves a molecular-level description lost in macroscopic continuum approaches. The excited state properties of water and formaldehyde molecules solvated into water have been computed at the selected configuration interaction (SCI) level. The excitation energies and dipole moments have been compared with experimental data and previous theoretical work. A key finding is that using the Hartree-Fock method to describe the solute allows for predicting the solvent charge around the ground state with sufficient precision for the subsequent SCI calculations of excited states. This significantly reduces the computational cost of the described procedure, paving the way for the study of more complex molecules.

Form factors and volume models for Falcataria moluccana in smallholder plantations, Central Kalimantan, Indonesia

ABSTRACT Falcataria moluccana (sengon) is grown extensively for timber production in smallholder plantations in Indonesia. However, stem form and volume information necessary for its sustainable forest management, yield estimation and carbon assessment are largely unavailable. Therefore, this study developed form factor and stem volume models for sengon trees in Central Kalimantan, Indonesia. Data were collected using a destructive method from 24 trees across six diameter classes. Two types of form factors, artificial and absolute, were determined, and their relationship with tree growth was established. Four form factor functions were fitted, and the accuracy of volume estimation using a default form factor was compared to the functions. Additionally, 11 stem volume models were developed, and their performance was compared with the commonly used published models. The form factor for sengon was estimated at 0.48, which was higher and more accurate than the absolute form factor. A linear relationship was observed between sengon tree form and growth, particularly with the artificial form factor (R 2 = 0.55–0.62). The Pollanschütz function was identified as the best form factor function, accurately predicting form factors for precise tree volume computations. Using the default form factor of 0.5 led to an overestimation of volume by up to 17%. Among the volume models, the power models with diameter as the sole predictor (MD3: V = 0.00018 * DBH2.3817) and with both diameter and height variables (MDH4: V = 0.000095 * DBH1.9974 * H0.6327) were the most accurate. Importantly, the models developed in this study offer improved accuracy compared to existing models from Java, which tend to overestimate stem volume. Therefore, this study provides suitable models and valuable insights for better valuation and management of smallholder sengon plantations in Central Kalimantan and other areas with similar site and stand conditions.

A method for fast and accurate prediction of wind turbine thrust coefficients using classical momentum theory and power curve

Abstract The planning and development of windfarms require accurate prediction of the thrust coefficient (cT ) of wind turbines, which significantly affects the downstream wake. Traditional methods, such as blade element momentum theory (BEMT), often necessitate detailed geometric information of wind turbines for cT computation, information that is not frequently available, especially in the early stages of windfarm planning. This paper aims to address this challenge by presenting a novel and efficient approach to predict cT for horizontal-axis wind turbines (HAWTs). The proposed method integrates classical momentum theory with power curve data to estimate the average axial induction factor (a), thereby enabling the calculation of cT without requiring detailed geometric information of HAWTs. The method was validated against thirty-five existing pitch-controlled HAWTs, with R2 values ranging from 0.9604 to 0.9989. This validation confirms the accuracy of the method, making it a viable alternative to traditional techniques that demand comprehensive wind turbine geometric details. The method has demonstrated both rapidity and precision in cT computation for turbine wake analysis, ensuring high levels of prediction accuracy and potentially lowering the barrier to entry for windfarm development. Unlike existing models predominantly focused on wind turbine power curves, cT modelling has largely been overlooked. This study makes a unique contribution to the field by proposing a novel method for cT prediction, thereby filling a critical gap in windfarm planning and development. However, while the study shows promising results, further research is warranted to explore its applicability in diverse windfarm scenarios and turbine configurations.

Modeling of Li-Ion Battery Electrodes Accounting for Microstructure Properties: The Newman’s Model Revisited

The most established lithium-ion battery (LIB) porous-based model is the Newman's pseudo-two-dimensional (P2D) model used as a good trade-off between numerical computational cost and physics precision. However, this model does not resolve the local fluctuations of physical quantities on the microstructure and can give inaccurate results especially at high C-rate. In this work, we revisit the P2D model by applying the method of volume averaging to mass and charge transport equations of LIB microstructure models. The outcome is a non-classical homogenized model where the effective properties do not only depend on geometry, but are shown to be functions of the local current density. The model reduces to the classical P2D at low C-rate, but extends the validity range of the P2D at high C-rate for complex microstructures. Moreover, the new model allows to reconstruct the local fluctuations from the averaged variables, whereas this information is not available from the classical P2D model.