Unique Set Of Parameters Research Articles

ACMamba: A State Space Model-Based Approach for Multi-Weather Degraded Image Restoration

In computer vision, eliminating the effects of adverse weather conditions such as rain, snow, and fog on images is a key research challenge. Existing studies primarily focus on image restoration for single weather types, while methods addressing image restoration under multiple combined weather conditions remain relatively scarce. Furthermore, current mainstream restoration networks, mostly based on Transformer and CNN architectures, struggle to achieve an effective balance between global receptive field and computational efficiency, limiting their performance in practical applications. This study proposes ACMamba, an end-to-end lightweight network based on selective state space models, aimed at achieving image restoration under multiple weather conditions using a unified set of parameters. Specifically, we design a novel Visual State Space Module (VSSM) and a Spatially Aware Feed-Forward Network (SAFN), which organically combine the local feature extraction capabilities of convolutions with the long-range dependency modeling capabilities of selective state space models (SSMs). This combination significantly improves computational efficiency while maintaining a global receptive field, enabling effective application of the Mamba architecture to multi-weather image restoration tasks. Comprehensive experiments demonstrate that our proposed approach significantly outperforms existing methods for both specific and multi-weather tasks across multiple benchmark datasets, showcasing its efficient long-range modeling potential in multi-weather image restoration tasks.

Plastic strain effect on cleavage microcracks propagation in probabalistic statement. Part 2. Research results

The second part of the paper presents the results of uniaxial tension testing of smooth round bars of 2Cr–Ni–Mo–V steel in the thermally-embrittled state and St3 steel in the initial state. SEM examination of the fracture surfaces is carried out. The brittle fracture probability is calculated using the Prometey model presented in the first part of this article. It has been found that the plastic strain effect on the microcrack propagation probability is caused by two reasons: (1) the critical brittle fracture stress increase due to formation of new barriers for microcrack under plastic deformation, and (2) the working volume decrease due to the neck formation in tensile round bar. A unified set of parameters has been proposed to take into account the plastic strain effect on cleavage microcracks propagation probability for WWER-1000 RPV steel and low-alloyed low-strength steel.

A new high-order RKDG method based on the TENO-THINC scheme for shock-capturing

In recent years, Runge-Kutta Discontinuous Galerkin (RKDG) methods have gained substantial attention in solving hyperbolic conservation laws, attributed to their high-order accuracy and adaptability to unstructured meshes. However, standard RKDG methods cannot capture discontinuities without oscillation unless they are supplemented with troubled cell indicators and limiters. Existing indicators, such as the total variation bounded (TVB) minmod indicator and the KXRCF indicator, typically depend on a critical parameter tied to the equation's solution, necessitating tuning for different cases. In terms of limiters, the popular limiters even fail to guarantee the high-order property in the smooth regions. The advent of Weighted Essentially Non-Oscillatory (WENO) schemes prompts the implementation of WENO limiters for RKDG methods, preserving high-order properties. However, WENO-family schemes exhibit significant numerical dissipation, potentially smearing small-scale flow structures. In this work, the Targeted Essentially Non-Oscillatory (TENO) indicator [1] is utilized, which leverages the nonlinear weighting strategy of the TENO scheme to separate high-wavenumber physical fluctuations and genuine discontinuities from smooth regions with a unified set of parameters. For troubled cells, a novel limiter is proposed for structured meshes, which combines a TENO scheme for resolving high-wavenumber physical fluctuations and a novel non-polynomial Tangent of Hyperbola for the INterface Capturing (THINC) scheme for resolving genuine discontinuities with extremely low numerical dissipation. Furthermore, the shifting between the TENO and THINC schemes is based on a new boundary variation diminishing (BVD) strategy, which only relies on compact neighborhoods and is significantly simpler than its predecessors. Meanwhile, a new strategy is proposed to ensure the consistency of the new limiter applied for 1D and 2D cases. A set of 1D and 2D benchmark cases including strong shockwaves and a broad range of flow length scales is simulated with uniform meshes for 1D cases and structured quadrilateral meshes for 2D cases to demonstrate the performance of the new numerical scheme. The indicator does not activate any limiters in the accuracy test cases to ensure the high-order property of the whole numerical scheme. Other cases with discontinuities demonstrate the low-dissipation properties of the new limiter in comparison to the simple WENO limiter.

Vectorcardiography Predicts Heart Failure in Patients Following ST Elevation Myocardial Infarction.

Modeling outcomes, such as onset of heart failure (HF) or mortality, in patients following ST elevation myocardial infarction (STEMI) is challenging but clinically very useful. The acute insult following a myocardial infarction and chronic degeneration seen in HF involve a similar process where a loss of cardiomyocytes and abnormal remodeling lead to pump failure. This process may alter the strength and direction of the heart's net depolarization signal. We hypothesize that changes over time in unique parameters extracted using vectorcardiography (VCG) have the potential to predict outcomes in patients post-STEMI and could eventually be used as a noninvasive and cost-effective surveillance tool for characterizing the severity and progression of HF to guide evidence-based therapies. We identified 162 patients discharged from Michigan Medicine between 2016 and 2021 with a diagnosis of acute STEMI. For each patient, a single 12-lead ECG > 1 week pre-STEMI and > 1 week post-STEMI were collected. A set of unique VCG parameters were derived by analyzing features of the QRS complex. We used LASSO regression analysis incorporating clinical variables and VCG parameters to create a predictive model for HF, mortality, or the composite at 90, 180, and 365 days post-STEMI. The VCG model is most predictive for HF onset at 90 days with a robust AUC. Variables from the HF model mitigating or driving risk, at a p < 0.05, were primarily parameters that assess the area swept by the depolarization vector including the 3D integral and convex hull in select spatial octants and quadrants.

Computational assessment of Amazon forest plots regrowth capacity under strong spatial variability for simulating logging scenarios

In this paper, we assess the regrowth capacity of tropical forest plots by developing an original computational procedure based on statistical model checking methods. We calibrate a new mathematical model of forest dynamics with respect to post-logging data, produced in the Amazon basin. Our mathematical model is determined by a mechanistic system of ordinary differential equations and integrates a new nonlinear aging term, which is necessary to reproduce the complex post-logging dynamics of the tropical forest plots. Our method is based on an efficient algorithmic procedure that explores the parameter space of the model and computes a score for each parameter value, depending on the distance between the trajectory of the model and the data. We distinguish a group of four reference plots, for which the logging was the most intense, from another group of five non-reference plots, which are fitted a posteriori. Our results provide a set trajectories of the new model, which successfully fit the non-monotone post-logging data on each forest plot, in spite of the high level of biological variability identified in the study site. Rather than a unique set of parameters, we return a small cell of parameters, extracted from the parameter space, which contains in its close vicinity several relevant sets of parameters that are equally able to reproduce the regrowth dynamics of distinct tropical forest plots. The size of the cells that should be extracted from the parameter space increases with the level of biological variability. Finally, we show how to use the calibrated mathematical model for simulating logging scenarios, so as to better understand the temporal dynamics of forest regrowth.

Evaluation of a decomposition-based interpolation method for fourth-order fiber-orientation tensors: An eigensystem approach

We propose and assess a new decomposition-based interpolation method on fourth-order fiber-orientation tensors. This method can be used to change the resolution of discretized fields of fiber-orientation tensors, e.g., obtained from flow simulations or computer tomography, which are common in the context of short- and long-fiber–reinforced composites. The proposed interpolation method separates information on structure and orientation using a parametrization which is based on tensor components and a unique eigensystem. To identify this unique eigensystem of a given fourth-order fiber-orientation tensor in the absence of material symmetry, we propose a sign convention on tensor coefficients. We explicitly discuss challenges associated with material symmetries, e.g., non-distinct eigenvalues of the second-order fiber-orientation tensor and propose algorithms to obtain a unique set of parameters combined with a minimal number of eigensystems of a given fourth-order fiber-orientation tensor. As a side product, we specify for the first time, parametrizations and admissible parameter ranges of cubic, tetragonal, and trigonal fiber-orientation tensors. Visualizations in terms of truncated Fourier series, quartic plots, and tensor glyphs are compared.

Comparison of the performances of Statistical and Artificial Neural Network models in the prediction of geometry and density of PLA/wood biocomposite cubes manufactured by FDM

The present study investigates the impact of scanning speed, printing temperature, and layer height on the density, dimensions, errors of parallelism, and surface finish of cubical specimens made of PLA/wood biocomposite and manufactured by Fused Deposition Modelling (FDM). The study examined 64 specimens, each produced with a unique set of process parameters. The Response Surface Methodology (RSM) was employed to evaluate the effects of process parameters on the examined properties of the manufactured cubes. RSM analysis revealed the statistical significance of direct proportion between the layer height, printing temperature, and x-and y-dimensions of the manufactured specimens (with P-values of 0, 0, 0.002, and 0, respectively). Also, the scanning speed and error of parallelism in z-oriented faces were statistically correlated (with a P-value of 0.035). For layer height and cube density, an indirect proportion was observed (with a P-value of 0). Compared to the regression model, ANN exhibited better performance at process parameters effect evaluation. The worse performance of regression models can be attributed to their limited capacity to represent non-linear relationships, while ANN models can capture the complex non-linear nature of the process, leading to better performances (R2 close to 100%). An evaluation of the defects in the specimens was carried out using the go/no-go diagram.

Class Symbolic Regression: Gotta Fit ’Em All

We introduce “Class Symbolic Regression” (Class SR), the first framework for automatically finding a single analytical functional form that accurately fits multiple data sets—each realization being governed by its own (possibly) unique set of fitting parameters. This hierarchical framework leverages the common constraint that all the members of a single class of physical phenomena follow a common governing law. Our approach extends the capabilities of our earlier Physical Symbolic Optimization (Φ-SO) framework for symbolic regression, which integrates dimensional analysis constraints and deep reinforcement learning for unsupervised symbolic analytical function discovery from data. Additionally, we introduce the first Class SR benchmark, comprising a series of synthetic physical challenges specifically designed to evaluate such algorithms. We demonstrate the efficacy of our novel approach by applying it to these benchmark challenges and showcase its practical utility for astrophysics by successfully extracting an analytic galaxy potential from a set of simulated orbits approximating stellar streams.

Histological tissue classification with a novel statistical filter-based convolutional neural network.

Deep networks have been of considerable interest in literature and have enabled the solution of recent real-world applications. Due to filters that offer feature extraction, Convolutional Neural Network (CNN) is recognized as an accurate, efficient and trustworthy deep learning technique for the solution of image-based challenges. The high-performing CNNs are computationally demanding even if they produce good results in a variety of applications. This is because a large number of parameters limit their ability to be reused on central processing units with low performance. To address these limitations, we suggest a novel statistical filter-based CNN (HistStatCNN) for image classification. The convolution kernels of the designed CNN model were initialized by continuous statistical methods. The performance of the proposed filter initialization approach was evaluated on a novel histological dataset and various histopathological benchmark datasets. To prove the efficiency of statistical filters, three unique parameter sets and a mixed parameter set of statistical filters were applied to the designed CNN model for the classification task. According to the results, the accuracy of GoogleNet, ResNet18, ResNet50 and ResNet101 models were 85.56%, 85.24%, 83.59% and 83.79%, respectively. The accuracy was improved by 87.13% by HistStatCNN for the histological data classification task. Moreover, the performance of the proposed filter generation approach was proved by testing on various histopathological benchmark datasets, increasing average accuracy rates. Experimental results validate that the proposed statistical filters enhance the performance of the network with more simple CNN models.

Numerical prediction of aerodynamic loads acting on a blade section-model oscillating in stall

For blades operating close to stall angle of attack, one of the potential instabilities is known as stall flutter. It is a dynamic instability in torsion for which the mechanism of energy transfer relies on a dynamic stall process where the flow separates partially or completely during each cycle of oscillation. It leads to dynamic loops in lift, drag and moment that affect the aerodynamic performance of the blade, create unsteady stresses in the blades and can lead to fatigue damage. In the present study, dynamic loops of a NACA 634421 airfoil in forced pitch oscillations have been simulated with the FastS CFD solver based on a URANS modelling approach and compared to benchmark experiments as well as results obtained with the ONERA semi-empirical dynamic stall model using a unique set of dynamic parameters that best fit the experimental dynamic loops. The dynamic tests have been conducted for a mean angle of attack of 13.5°, an amplitude of oscillation of 8° and three reduced frequencies k 1 = 0.0183, k 2 = 0.0785 and k 3 = 0.183. This study has shown that the CFD FastS solver can satisfactorily reproduce the unsteady lift and moment coefficients experienced by a section model oscillating in stall.

Soft tissue material properties based on human abdominal in vivo macro-indenter measurements

Simulations of human-technology interaction in the context of product development require comprehensive knowledge of biomechanical in vivo behavior. To obtain this knowledge for the abdomen, we measured the continuous mechanical responses of the abdominal soft tissue of ten healthy participants in different lying positions anteriorly, laterally, and posteriorly under local compression depths of up to 30 mm. An experimental setup consisting of a mechatronic indenter with hemispherical tip and two time-of-flight (ToF) sensors for optical 3D displacement measurement of the surface was developed for this purpose. To account for the impact of muscle tone, experiments were conducted with both controlled activation and relaxation of the trunk muscles. Surface electromyography (sEMG) was used to monitor muscle activation levels. The obtained data sets comprise the continuous force-displacement data of six abdominal measurement regions, each synchronized with the local surface displacements resulting from the macro-indentation, and the bipolar sEMG signals at three key trunk muscles. We used inverse finite element analysis (FEA), to derive sets of nonlinear material parameters that numerically approximate the experimentally determined soft tissue behaviors. The physiological standard values obtained for all participants after data processing served as reference data. The mean stiffness of the abdomen was significantly different when the trunk muscles were activated or relaxed. No significant differences were found between the anterior-lateral measurement regions, with exception of those centered on the linea alba and centered on the muscle belly of the rectus abdominis below the intertubercular plane. The shapes and areas of deformation of the skin depended on the region and muscle activity. Using the hyperelastic Ogden model, we identified unique material parameter sets for all regions. Our findings confirmed that, in addition to the indenter force-displacement data, knowledge about tissue deformation is necessary to reliably determine unique material parameter sets using inverse FEA. The presented results can be used for finite element (FE) models of the abdomen, for example, in the context of orthopedic or biomedical product developments.

Variational autoencoder-based techniques for a streamlined cross-topology modeling and optimization workflow in electrical drives

The generation and optimization of simulation data for electrical machines remain challenging, largely due to the complexities of magneto-static finite element analysis. Traditional methodologies are not only resource-intensive, but also time-consuming. Deep learning models can be used to shortcut these calculations. However, challenges arise when considering the unique parameter sets specific to each machine topology. Building on two recent studies (Parekh et al. in IEEE Trans. Magn. 58(9):1–4, 2022; Parekh et al., Deep learning based meta-modeling for multi-objective technology optimization of electrical machines, 2023, arXiv:2306.09087), that utilized a variational autoencoder to cohesively map diverse topologies into a singular latent space for subsequent optimization, this paper proposes a refined architecture and optimization workflow. Our modifications aim to streamline and enhance the robustness of both the training and optimization processes, and compare the results with the variational autoencoder architecture proposed recently.

Identification of constitutive materials of bi-layer soft tissues from multimodal indentations

The personalisation of finite element models is an important problem in the biomechanical fields where subject-specific analyses are fundamental, particularly in studying soft tissue mechanics. The personalisation includes the choice of the constitutive law of the model's material, as well as the choice of the material parameters. In vivo identification of the material properties of soft tissues is challenging considering the complex behaviour of soft tissues that are, among other things, non-linear hyperelastic and heterogeneous. Hybrid experimental-numerical methods combining in vivo indentations and inverse finite element analyses are common to identify these material parameters. Yet, the uniqueness and the uncertainty of the multi-material hyperelastic model have not been evaluated. This study presents a sensitivity analysis performed on synthetic indentation data to investigate the identification uncertainties of the material parameters in a bi-material thigh phantom. Synthetic numerical data, used to replace experimental measurements, considered several measurement modalities: indenter force and displacement, stereo-camera 3D digital image correlation of the indented surface, and ultrasound B-mode images. A finite element model of the indentation was designed with either Ogden-Moerman or Mooney-Rivlin constitutive laws for both materials. The parameters' identifiability (i.e. the possibility of converging to a unique parameter set within an acceptable margin of error) was assessed with various cost functions formulated using the different synthetic data sets. The results underline the need for multiple experimental modalities to reduce the uncertainty of the identified parameters. Additionally, the experimental error can impede the identification of a unique parameter set, and the cost function depends on the constitutive law. This study highlights the need for sensitivity analyses before the design of the experimental protocol. Such studies can also be used to define the acceptable range of errors in the experimental measurement. Eventually, the impact of the evaluated uncertainty of the identified parameters should be further investigated according to the purpose of the finite element modelling.

ECG-based estimation of respiration-induced autonomic modulation of AV nodal conduction during atrial fibrillation.

Introduction: Information about autonomic nervous system (ANS) activity may offer insights about atrial fibrillation (AF) progression and support personalized AF treatment but is not easily accessible from the ECG. In this study, we propose a new approach for ECG-based assessment of respiratory modulation in atrioventricular (AV) nodal refractory period and conduction delay. Methods: A 1-dimensional convolutional neural network (1D-CNN) was trained to estimate respiratory modulation of AV nodal conduction properties from 1-minute segments of RR series, respiration signals, and atrial fibrillatory rates (AFR) using synthetic data that replicates clinical ECG-derived data. The synthetic data were generated using a network model of the AV node and 4 million unique model parameter sets. The 1D-CNN was then used to analyze respiratory modulation in clinical deep breathing test data of 28 patients in AF, where an ECG-derived respiration signal was extracted using a novel approach based on periodic component analysis. Results: We demonstrated using synthetic data that the 1D-CNN can estimate the respiratory modulation from RR series alone with a Pearson sample correlation of r = 0.805 and that the addition of either respiration signal (r = 0.830), AFR (r = 0.837), or both (r = 0.855) improves the estimation. Discussion: Initial results from analysis of ECG data suggest that our proposed estimate of respiration-induced autonomic modulation, a resp, is reproducible and sufficiently sensitive to monitor changes and detect individual differences. However, further studies are needed to verify the reproducibility, sensitivity, and clinical significance of a resp.

Calibration of a sewage sludge anaerobic digestion model with multiple mineral precipitation for two case studies

Including multiple mineral precipitation in anaerobic digestion models is important to accurately assess the quantity of phosphates (PO4-P) in the liquid phase of digested sludge that can be recovered as a fertilizer in struvite precipitation reactors or by emerging processes such as ion exchange membranes. However, the performance of such processes is highly impacted by the presence of potassium (K+) or calcium (Ca2+) ions, which are most often neglected when validating precipitation models. The novelty of this work was to provide a calibration procedure of an anaerobic digestion model with multiple mineral precipitation that can be used for different case studies and do not focus only on PO4-P but also on K+, Ca2+ Mg2+. To draw this calibration procedure, data from two anaerobic digesters located near Lyon (France) and in the Navarra region (Spain), treating sludge with different biodegradability and concentration ranges of phosphorus (P), calcium (Ca), magnesium (Mg), potassium (K) and soluble inorganic carbon (SIC), have been used. This procedure includes the calibration of the inert COD fraction (fXI,COD) and kinetic precipitation constants (Kr) using Bayesian Monte Carlo techniques. However, a unique parameter set for both anaerobic digesters cannot be identified, leading to site-specific parameters for fXI,COD (0.30 and 0.43), Kr,MgCO3 (274 and 0.4), Kr,CaCO3 (21 and 0.12) and Kr,ACP (270 and 58). The calibrated model was able to estimate PO4-P, Ca2+, Mg2+ and K+ concentration in digested sludge with a relative error below 10 %.

Discrete Element Method Simulation of the Idaho Calcine Simulant for Hot Isostatic Pressing Canister-Filling Process: Part 1

In preliminary engineering studies and equipment commissioning, readily available simulants with certain resemblances to the target waste, such as particle size, may be employed. However, for the validation of engineering processes, it is crucial to use simulants that closely replicate the physical and flow characteristics of the waste material, typically excluding radioactivity. The production of such simulants can entail significant costs. To facilitate the development of novel engineering solutions, the U.S. Department of Energy provided a nonradioactive alumina calcine simulant. This simulant was utilized to demonstrate a hot isostatic press (HIP) canister-filling system designed by Gravitas Technologies in Australia. The simulant was synthesized through a fluidized-bed calcination process, which mirrored the chemistry and procedure employed for the actual alumina Idaho calcine waste material. Consequently, the simulant exhibited physical properties akin to the actual calcine, including particle size distribution, bulk density, and flow properties. This first paper, Part 1, presents the powder characterization test results determined by a Freeman FT-4 rheometer and the calibration methods that determined a set of unique contact model parameters for dynamically simulating Idaho calcine simulant in a discrete element method (DEM) model. The second paper, Part 2, will present the dynamic simulations of two bulk material-handling scenarios in a full-scale, three-dimensional integrated HIP canister-filling system. The predicted results are compared with historical experimental results for validating the contact model. The contact model, which represents the particle-particle and particle-boundary interactions, was calibrated according to experimental data obtained from six calibration tests. This work aims to support future research with powder characterization experimental data and a calibrated DEM contact model to assist in developing processes for the safe handling and treatment of Idaho calcine waste.

Processing-Microstructure-Properties of Columns in Thermal Barrier Coatings: A Study of Thermo-Chemico-Mechanical Durability.

Contemporary gas turbine engines rely on thermal barrier coatings (TBCs), which protect the structural components of the engine against degradation at extremely high operating temperatures (1300-1500 °C). The operational efficiencies of aircraft engines have seen significant improvement in recent years, primarily through the increase in operating temperatures; however, the longevity of TBCs can be potentially impacted by several types of degradation mechanisms. In this comprehensive study, a wide range of novel columnar suspension plasma sprayed (SPS) coatings were developed for their erosion, calcium-magnesium-aluminum-silicate (CMAS), and furnace cycling test (FCT) performance. Through a comprehensive investigation, the first of its kind, we achieved a range of SPS microstructures by modifying the spray parameters and measuring their microhardness, fracture toughness, column densities, and residual stresses using Raman spectroscopy. We were able to produce dendritic, lateral, branched, and columnar microstructures with a unique set of processing parameters. Coatings enhanced with a refined columnar microstructure, achieved by modulating the distance from the plasma torch, exhibited superior thermal cycling resilience. Conversely, the development of a columnar microstructure with dendritic branches, obtained by decreasing the robot's traversal speed during deposition, bolstered resistance to erosion and minimized damage from molten CMAS infiltration, thereby notably augmenting the coating's lifespan and robustness. The pursuit of the optimal columnar microstructure led to the conclusion that for each SPS coating, a general framework of optimization needs to be conducted to achieve their desired thermo-chemico-mechanical resistance as the properties required for TBCs are intertwined.

Scaling artificial heat islands to enhance precipitation in the United Arab Emirates

Abstract. Potential for regional climate engineering is gaining interest as a means of solving regional environmental problems like water scarcity and high temperatures. In the hyper-arid United Arab Emirates (UAE), water scarcity is reaching a crisis point due to high consumption and over-extraction and is being exacerbated by climate change. To counteract this problem, the UAE has conducted cloud-seeding operations and intensive desalination for many years but is now considering other means of increasing water resources. Very large “artificial black surfaces” (ABSs), made of black mesh, black-painted, or solar photovoltaic (PV) panels have been proposed as a means of enhancing convective precipitation via surface heating and amplification of vertical motion. Under the influence of the daily UAE sea breeze, this can lead to convection initiation under the right conditions. Currently it is not known how strong this rainfall enhancement would be or what scale of black surface would need to be employed. This study simulates the impacts at different ABS scales using the WRF-Noah-MP model chain and investigates impacts on precipitation quantities and underlying convective processes. Simulations of five square ABSs of 10, 20, 30, 40, and 50 km sizes were made on four 1 d cases, each for a period of 24 h. These were compared with a Control model run, with no land use change, to quantify impacts. The ABSs themselves were simulated by altering land cover static data and prescribing a unique set of land surface parameters like albedo and roughness length. On all 4 d, rainfall is enhanced by low-albedo surfaces of 20 km or larger, primarily through a reduction of convection inhibition and production of convergence lines and buoyant updrafts. The 10 km square ABS had very little impact. From 20 km upwards there is a strong scale dependency, with ABS size influencing the strength of convective processes and volume of rainfall. In terms of rainfall increases, 20 km produces a mean rainfall increase over the Control simulation of 571 616 m3 d−1, with the other sizes as follows: 30 km (∼ 1 million m3 d−1), 40 km (∼ 1.5 million m3 d−1), and 50 km (∼ 2.3 million m3 d−1). If we assume that such rainfall events happen only on 10 d in a year, this would equate to respective annual water supplies for &gt; 31 000, &gt; 50 000, &gt; 79 000, and &gt; 125 000 extra people yr−1 at UAE per capita consumption rates. Thus, artificial heat islands made from black panels or solar PV offer a means of enhancing rainfall in arid regions like the UAE and should be made a high priority for further research.

Research on the simulation accuracy of static hysteresis loops of electrical steels using an improved simplified LLG equation

For tackling the problem of large errors in simulating the static inner symmetrical minor hysteresis loops through the Simplified Landau-Lifshitz-Gilbert (S-LLG) equation, a modification by improving the grain structure arrangement and introducing additional hysteresis energy function is proposed. With the proposed model, only a unified set of parameters identified by four static hysteresis loops is needed to predict hysteresis loops under arbitrarily different magnetic flux density levels. To verify the global simulation capability of the proposed model, the simulation results of hysteresis loops are compared with the measured data of a grain-oriented (GO) steel. The obtained results show that the model has good simulation ability for hysteresis loops under different magnetic flux densities for both sinusoidal and harmonic excitation conditions.

Refining the physical description of charge trapping and detrapping in a transport model for dielectrics using an optimization algorithm

PurposeThe purpose of this paper is to optimize and improve a bipolar charge transport (BCT) model used to simulate charge dynamics in insulating polymer materials, specifically low-density polyethylene (LDPE).Design/methodology/approachAn optimization algorithm is applied to optimize the BCT model by comparing the model outputs with experimental data obtained using two kinds of measurements: space charge distribution using the pulsed electroacoustic (PEA) method and current measurements in nonstationary conditions.FindingsThe study provides an optimal set of parameters that offers a good correlation between model outputs and several experiments conducted under varying applied fields. The study evaluates the quantity of charges remaining inside the dielectric even after 24 h of short circuit. Moreover, the effects of increasing the electric field on charge trapping and detrapping rates are addressed.Research limitations/implicationsThis study only examined experiments with different applied electric fields, and thus the obtained parameters may not suit the experimental outputs if the experimental temperature varies. Further improvement may be achieved by introducing additional experiments or another source of measurements.Originality/valueThis work provides a unique set of optimal parameters that best match both current and charge density measurements for a BCT model in LDPE and demonstrates the use of trust region reflective algorithm for parameter optimization. The study also attempts to evaluate the equations used to describe charge trapping and detrapping phenomena, providing a deeper understanding of the physics behind the model.