Semi-empirical Model Research Articles

Modeling, assessment and characterization of soiling on PV Technologies. Toward a Better understanding of the Relation between dust deposition and performance losses

Soiling is a critical, site-specific challenge that affects the performance and economic viability of photovoltaic power plants. This study evaluates the effectiveness of an outdoor microscopy method under the specific climatic conditions and dust composition of the Mid-south region of Morocco. Semi-empirical models were developed to correlate the Soiling Coverage Index with losses in optical and electrical performance. Additionally, a comprehensive analysis of local dust, encompassing its chemical composition, morphology, and size distribution, was conducted. Results indicate quartz as the predominant mineral in Benguerir city’s dust particles, with diameters ranging from 0 μm to 26 μm. Additionally, following a 2-week exposure with no rainfall, a dust density of 2.5 g/m2 accumulated on the deployed glass coupons, resulting in a soiling ratio of 6.9 % and a relative transmittance loss of 15.19 %. The surface coverage index, as calculated by the outdoor microscope, was 9.16 %. Furthermore, the evaluation of this metric revealed strong positive correlations (r2 = 0.95 to 0.99) with key soiling indicators such as dust density, soiling ratio, and transmittance loss. These findings underscore the efficacy of the outdoor microscope as a fast, low-cost, and reliable soiling monitoring sensor, offering valuable insights for future monitoring and mitigation efforts.

Constitutive behaviour of a granular matrix containing coal mine waste intermixed with rubber crumbs

AbstractTraditional railway substructure materials (i.e., natural crushed rock aggregates used for ballast and capping layers) degrade under service loads, incurring higher periodic maintenance costs compared to recycled materials. Using recycled waste materials such as coal wash and rubber crumbs for infrastructure upgrades not only reduces construction and maintenance costs but also supports environmental sustainability. By exploring unconventional avenues, earlier studies have delved into the viability of blending rubber crumbs (RC) and coal wash (CW) as an innovative substitute for traditional railway substructure materials, with a specific focus on the capping layer. This study introduces a semi-empirical constitutive model to simulate the response of mixtures of coal wash and rubber crumbs (CWRC) using the bounding surface plasticity framework. The novelty of this study is that a modified volumetric strain expression is introduced to capture the compressibility of rubber, thus enabling a more accurate representation of the internal deformation of rubber within the granular matrix. The variation of rubber content in the mixture is captured by the corresponding critical state void ratio surface and the hardening modulus. The theoretical model is then calibrated and validated using static drained triaxial test data for CWRC mixtures as well as mixtures of steel furnace slag, coal wash, and rubber crumbs (SFS + CW + RC).

Semi-Empirical Model for Calculating Birefringence and Confinement Loss in Bi-Thickness Fourfold Anti-Resonant Hollow-Core Fiber

Semi-Empirical Model for Calculating Birefringence and Confinement Loss in Bi-Thickness Fourfold Anti-Resonant Hollow-Core Fiber

Advancing mechanistic understanding of cohesive sediment transport: Integrating flume experiments, field measurements, and modelling approaches in a gravel-bed river.

Gravel-bed rivers draining mountainous forested headwater regions are critically important for drinking water supply and ecological integrity. These rivers, however, have been increasingly impacted by intensifying anthropogenic and natural (especially climate change exacerbated) landscape disturbances that commonly increase hillslope/channel connectivity and the delivery of cohesive sediment (<63 μm) and associated pollutants. Despite the known deleterious threats of excess cohesive sediments, there is still limited understanding of their transport and intra-gravel storage due to the complexities of such processes. Accordingly, the objectives of this study were to: i) calibrate and validate a semi-empirical cohesive sediment transport model (RIVFLOC) using the observations from flume experiments; ii) estimate the intra-gravel storage capacity for cohesive sediment with the calibrated model based on the field dataset (collected in two field campaigns between 2019 and 2021), and; iii) investigate mechanisms of cohesive sediment transport dynamics in this gravel-bed river, identifying knowledge gaps and areas for future research. Our results showed that despite the increased floc settling velocity, deposition was hindered by turbulent flow fields. The model predicted that ~60 % of upstream cohesive sediment would ingress within the 10 km study reach due to the flow interaction with the gravel-bed. Despite the agreement between flume and field observations on ingress rates and preferential ingress of coarser (~100 μm) flocs, notable differences were observed between modelled and field datasets, highlighting unknowns regarding cohesive sediment exfiltration without framework mobilization. This study uniquely integrates field measurements, flume experiments, and modelling strategies to evaluate the transport and fate of cohesive sediment in a gravel-bed river. Accordingly, our findings advance current knowledge on the mechanistic understanding of cohesive sediment transport and highlight future research directions.

On a Study of Photoionization of Atoms and Ions from Endohedral Anions

We study the relationship between the results of two qualitatively different semi-empirical models for photoionization cross sections, σnℓ, of neutral atoms (A) and their cations (A+) centrally encapsulated inside a fullerene anion, CNq, where q represents the negative excess charge on the shell. One of the semi-empirical models, broadly employed in previous studies, assumes a uniform excess negative charge distribution over the entire fullerene cage, by analogy with a charged metallic sphere. The other model, presented here, considers the quantum states of the excess electrons on the shell, determined by specific n and ℓ values of their quantum numbers. Remarkably, both models yield similar photoionization cross sections for the encapsulated species. Consequently, we find that the photoionization of the encapsulated atoms or cations inside the CNq anion is influenced only slightly by the quantum states of the excess electrons on the fullerene cage. Furthermore, we demonstrate that the influence decreases even further as the size of the fullerene cage increases. All this holds true at least under the assumption that the encapsulated atom or cation is compact, i.e., its electron density remains primarily within itself rather than being drawn into the fullerene shell. This remarkable finding results from Hartree–Fock calculations combined with a popular modeling of the fullerene shell which is simulated by an attractive spherical annular potential.

Analysis on density of states and ION/IOFF ratio of GaN/GaN-graphene nanoribbon tunnel FET for enhanced bio-sensing applications

Abstract This research introduces a new analytical model that studies the effect of ferro-dielectric on the operational performance of TFETs doped with halogens. The effect of source and drain depletions, voltage-drain &amp; gate, thickness and capacitance of gate insulator are all investigated in this study. Accurate measurements of the surface potential are required to ascertain the transconductance, gate-to-drain capacitance, and lateral electric field of the device. Our model, which employs Ferroelectric Halo-Doped double gate (FHDD)-gated device designs, has been demonstrated to produce results that nearly match those produced from TCAD simulations. This was accomplished by doing a comparative analysis of the outcomes derived from both sets of simulations. Furthermore, the performance of the suggested structure of TFET, which integrates a dielectric of Fe and GaN heterostructure, surpasses that of other similar devices (fT) in terms of ON current, ON/OFF ratio, transconductance, and cut-off frequency. A ferroelectric dielectric was used to create a ferroelectric heterostructure. This study also centers on the creation and application of a graphene nanoribbon field effect transistor (GNR-TFET) to detect sugar molecules, specifically fructose, xylose, and glucose. The detecting signal is generated by utilizing the fluctuation in the electrical current of the GNR-TFET caused by the presence of individual sugar molecules. The GNR-TFET exhibits noticeable variations in the density of states, transmission spectrum, and current when exposed to individual sugar molecules. The sensor under investigation is being developed and examined using a combination of semi-empirical modeling and non-equilibrium Green's functional theory (SE + NEGF). According to the research, the modified GNR TFET has the ability to quickly and accurately detect individual sugar molecules in real-time.&amp;#xD;

Development of empirical models for calculating global and diffuse erythemal weighted solar ultraviolet radiation under clear sky conditions in Thailand

This study introduced semi-empirical models for calculating hourly global and diffuse erythemal weighted ultraviolet (EUV) solar radiation under clear sky conditions in Thailand. To develop these models, global and diffuse erythemal ultraviolet data collected over a decade (2011–2020) from four solar monitoring stations situated in the main regions of Thailand were used. The data was classified into two groups. The first group (2011–2018) was used for modeling, while the second group (2019–2020) was reserved for model validation. The global and diffuse EUV radiation models revolve around semi-empirical functions. These functions express both types of EUV radiation in terms of normalized variables, specifically the total ozone column, aerosol optical depth, and air mass. To assess the accuracy of the proposed models, their outputs for hourly global and diffuse EUV radiation were calculated at the four monitoring stations. These outputs were then compared against actual measurements to validate the effectiveness of the models. The evaluation revealed a root mean square difference of 15.8% for global EUV radiation and 14.9% for diffuse EUV radiation when compared to the mean measured values.

Development of Semi-Empirical and Machine Learning Models for Photoelectrochemical Cells

We introduce a theoretical model for the photocurrent-voltage (I-V) characteristics designed to elucidate the interfacial phenomena in photoelectrochemical cells (PECs). This model investigates the sources of voltage losses and the distribution of photocurrent across the semiconductor–electrolyte interface (SEI). It calculates the whole exchange current parameter to derive cell polarization data at the SEI and visualizes the potential drop across n-type cells. The I-V model’s simulation outcomes are utilized to distinguish between the impacts of bulk recombination and space charge region (SCR) recombination within semiconductor cells. Furthermore, we develop an advanced deep neural network model to analyze the electron–hole transfer dynamics using the I-V characteristic curve. The model’s robustness is evaluated and validated with real-time experimental data, demonstrating a high degree of concordance with observed results.

Prediction of TBM cutter wear in heterogeneous ground under high ambient pressure

To prevent tunnel face collapse in heterogeneous ground, the applied face pressure in the excavation chamber can be increased to stabilise the tunnel face, which may hinder cutter rotation during tunnel boring machine (TBM) tunnelling. This study proposes a TBM cutter wear prediction model that considers the impact of the variation in the cutter normal force on the cutterhead owing to the high applied face pressure and low penetration rate. First, the cutter motion mode in heterogeneous ground is investigated. The cutter is found to slide when cutting soft rock based on an analysis of the field data. The evolution process of the cutter wear morphology under heterogeneous ground conditions is summarised. A method for calculating the cutter normal force when the cutter slides on the tunnel face is proposed by referring to the cutting mechanism of the drag bits. The cutter hardness is measured on-site using a portable hardness tester. A semi-empirical model considering the variation in the cutter normal force on the cutterhead is proposed to predict the cutter wear in heterogeneous ground. Wear prediction is compared with other predictive models and field data for validation, and the discrepancies between the different models are discussed. The results show that the proposed model is effective for predicting TBM cutter wear under complex geological conditions.

Validation of a new road and contact model for vehicle–soft soil terrain interaction through an elaborate modelling of the FED-Alpha vehicle

Employing multibody dynamic simulations with semi-empirical tyre models is widely recognised as a cost-effective approach. A recent development introduces a novel road and tyre–soil contact model that is not only swift and memory-efficient but also addresses limitations in classical semi-empirical models. This study conducts a thorough validation of the new road and contact model by creating a detailed multibody model of the four-wheeled vehicle, Fuel Efficiency Demonstrator – Alpha, integral to NATO’s Next-Generation reference mobility model. The comprehensive model encompasses the chassis, suspension, tyres, engine, transmission and various other components. Through simulations of various driving scenarios, accounting for complex terrain geometries, spatially varying soil properties and multi-pass phenomena, the model’s performance is evaluated. The simulation results are compared with physical measurements, providing a detailed assessment of the tyre–soil model’s predictive accuracy for wheeled vehicle mobility on deformable terrains.

Review of Cell Level Battery (Calendar and Cycling) Aging Models: Electric Vehicles

Electrochemical battery cells have been a focus of attention due to their numerous advantages in distinct applications recently, such as electric vehicles. A limiting factor for adaptation by the industry is related to the aging of batteries over time. Characteristics of battery aging vary depending on many factors such as battery type, electrochemical reactions, and operation conditions. Aging could be considered in two sections according to its type: calendar and cycling. We examine the stress factors affecting these two types of aging in detail under subheadings and review the battery aging literature with a comprehensive approach. This article presents a review of empirical and semi-empirical modeling techniques and aging studies, focusing on the trends observed between different studies and highlighting the limitations and challenges of the various models.

Efficient and Robust Ab Initio Self-Consistent Field Acceleration Algorithm Based on a Semiempirical Model Hamiltonian.

A novel doubly iterative self-consistent field (SCF) approach using a semiempirical model Hamiltonian (denoted as the SMH algorithm) is proposed to accelerate the Hartree-Fock (HF) and density functional theory (DFT) calculations. This method first constructs the Fock matrix exactly in each SCF macroiteration, followed by a few SCF microiterations, where the Fock matrix is incrementally updated using an inexpensive semiempirical approximation. This leads to an improved wave function in each SCF macroiteration with minimal additional cost, and therefore a reduced number of exact Fock builds is required for SCF convergence. The SMH algorithm can be combined with conventional SCF convergence techniques such as level shifting, direct inversion in the iterative subspace (DIIS), and energy-DIIS (EDIIS). When integrated with DIIS, SMH enhances the convergence of simple organic molecules by approximately 10% compared to plain DIIS, with speedups of up to 60% for the more challenging transition metal systems compared to EDIIS + DIIS. Our results show that SMH is a reliable SCF accelerator that seldom deteriorates convergence and is highly robust.

Model-predictive kinetic control with data-driven models on EAST

Abstract In this work, model-predictive control (MPC) was combined for the first time with singular perturbation theory, and an original plasma kinetic control method based on extremely simple data-driven models and a two-time-scale MPC algorithm has been developed. A comprehensive review is presented in this paper. Slow and fast semi-empirical models are identified from data, by considering the fast kinetic plasma dynamics as a singular perturbation of a quasi-static equilibrium, which itself is governed, on the slow time scale, by the flux diffusion equation. This control technique takes advantage of the large ratio between the time scales involved in magnetic and kinetic plasma transport. It is applied here to the simultaneous control of the safety factor profile, q(x), and of several kinetic variables, such as the poloidal beta parameter, βp, and the internal inductance parameter, li, on the EAST tokamak. In the experiments, the available control actuators were lower hybrid current drive (LHCD) and co-current neutral beam injection (NBI) from different sources. Ion cyclotron resonant heating (ICRH) and electron cyclotron resonant heating (ECRH) are used as additional actuators in control simulations. In the controller design, an observer provides, in real time, an estimate of the system states and of the mismatch between measured and predicted outputs, which ensures robustness to model errors and offset-free control. A number of control applications are described in the paper, either in nonlinear simulations with EAST-like parameters or in real experiments on EAST. Various controller configurations were tested, with up to three controlled variables chosen among q0 = q(x=0), q1 = q(x=0.5), βp and li. Both the extensive nonlinear simulations and the experiments described in this paper have demonstrated the relevance of combining model-predictive control, data-driven models and singular perturbation methods for plasma kinetic control.

Experimental investigation on shear fatigue behavior of perfobond strip connectors made of high strength steel and ultra-high performance concrete

This paper explores the fatigue performance of perfobond strip connectors (PBL) made from high-strength steel (HSS) and ultra-high performance concrete (UHPC) by fabricating and testing eight push-out specimens. The failure modes, load-slip curves, stiffness degradation, and residual strength of the PBLs are presented and discussed. The experimental results show that fatigue loads caused connector’s crack formation and stiffness degradation, leading to concrete dowel shear fracture and transeverse rebar bending-shear rupture damage. Compared to the static performance, the fatigue residual strength of the PBL subjected to 0.5 × 106, 1.25 × 106, and 2.0 × 106 loading cycles decreases by 1.2 %, 3.6 %, and 14.5 %, respectively, with corresponding reductions in ductility of 14.7 %, 19.4 %, and 4.7 %. Under the same loading cycles, the fatigue residual strength and ductility of the PBL with a maximum repeated load of 0.65Pu are 13.0 % and 3.9 %, respectively, smaller than those of 0.55Pu. A semi-empirical model based on the elastic foundation beam theory and linear cumulative damage model is developed to predict the residual strength of the PBLs using HSS and UHPC. The accuracy and applicability of the model are calibrated using the experimental results. The findings of this study can serve as an experimental foundation and theoretical reference for the fatigue design of PBLs made with high-strength materials.

Prediction of shear capacity of RC columns and discussion on shear contribution via the explainable machine learning

The research goal of this paper is to establish a machine learning (ML) model of shear capacity of reinforced concrete (RC) columns that can reflect the shear mechanism. The main work includes building database, training ML model, and interpretability analysis of optimal model. Aiming at the core problem of interpretability of ML methods, this paper proposes an interpretability method for the shear mechanism of RC columns. An experimental database including 427 sets of results of tests on RC columns is established, and eight salient input features are discerned through both mechanical mechanism assessment and Pearson correlation analysis. Twelve models, consisting of six basic ML models and six ensemble ML models, are verified against the database. The assessment reviews that the Light Gradient Boosting Machine (LightGBM) model was demonstrated to be superior to other models, achieving R2 values of 0.999 and 0.987 in training and test sets respectively. Additionally, compared to five semi-empirical models, the results from the LightGBM model also provides more accurate predictions and adeptly considered the influence of parameters. The Shapley additive explanation (SHAP) method and the explainable method based on the RC column shear mechanism proposed by this paper are used to explain the working mechanism of the LightGBM model. The proposed explanatory method of shear contribution rate is combine the additivity of SHAP value with the additivity of shear component contribution, the proposed explanatory method of shear contribution rate. An evaluation of SHAP feature Importance and SHAP feature dependence revealed that LightGBM aligns closely with the inherent shearing mechanism of the RC column. The shear contribution ratios of the concrete, stirrup, and axial loading in RC columns for the LightGBM model are similar to those predicted based on the semi-empirical models.

Investigation of dissolution kinetics of colemanite in propionic acid solutions

ABSTRACT Colemanite is one of the raw materials used to produce boron compounds which have a constituent of the richest composition ratio boron trioxide ( B 2 O 3 ) . The main purpose of this study is to investigate the dissolution kinetics of colemanite in a propionic acid solution. Reaction temperature, solid–liquid ratio, propionic acid concentration, stirring speed, and particle size were chosen as relevant parameters in the dissolution. According to the results of the study, the dissolution rate was directly proportional to the rise in the reaction temperature and inversely proportional to the increase in the solid–liquid ratio, acid concentration, and particle size. Additionally, it was observed that stirring speed did not significantly effect. Experiment results were correlated with the use of the Statistica10 software programme for linear regression. For the dissolution kinetics of colemanite, a model that can control the reaction was obtained by using homogeneous and heterogeneous reaction models. It was determined that the dissolution rate of the process was controlled by diffusion through the product film (ash) and the Activation energy was calculated as 37.51 kJ . mo l − 1 . Experimental data were analyzed with graphical and statistical methods, and a semi-empirical mathematical model was determined, which included the parameters of the study.

Correction to: sMILES SSPs: a library of semi-empirical MILES stellar population models with variable [α/Fe] abundances

Correction to: sMILES SSPs: a library of semi-empirical MILES stellar population models with variable [α/Fe] abundances

Coupled CFD-DEM modeling of surface erosion in granular soils: Simulation of erosion function apparatus experiments

This study introduces a numerical modeling approach that couples the computational fluid dynamics (CFD) with the discrete element method (DEM) to simulate grain-scale soil erosion processes induced by water flows. In this modeling framework, CFD simulates fluid flows by solving the volume-averaged Navier-Stokes equations, and uses the k-ω turbulent model for turbulent flows. Simultaneously, DEM computes the displacement of solid particles by incorporating the fluid-particle interactions driven by fluid flows while adhering to Newton's laws of motion. These interactions encompass drag force, buoyancy force, pressure-gradient force, and viscous force exerted by fluid flows and acting on the particles. The coupled CFD-DEM modeling adeptly replicates soil erosion processes, demonstrating good alignment with results obtained from laboratory erosion function apparatus (EFA) tests. In particular, the DEM facilitates the estimation of shear stress acting on the soil surface based on fluid-particle interaction forces, which has been roughly approximated by empirical or semi-empirical models. This study underscores the capability of coupled CFD-DEM in providing valuable insights into the grain-scale behavior of soil particles subjected to fluid flows, with the potential for extension to address soil erosion and fines migration.

Modeling Kinetics and Transport Mechanism Study of Poorly Soluble Drug Formulation in High Acidic Medium

Some medicinal particles are poorly soluble in highly acidic solutions, particularly those subjected to various production processes. Therefore, the present research investigated the kinetics and mechanisms of the drug release rate of newly formulated solid pills in a low pH medium. Three pills were prepared: one from a non-moisturized powder mixture (PILD) and the other two, PILC and PILM, from the dried powder mixtures, which were dried using hot-air heating and microwave radiation, respectively. These pills were subjected to drug release tests, and the outcomes were considered in the kinetics investigation using various models. Zero-order, Hixson–Crowell, First-order, Higuchi, Hopfenberg, Korsmeyer-Peppas, Logistic, and Peppas-Sahlin were the kinetic models used to inspect the release rate mechanism of these tablets. It was found that the Peppas-Sahlin and zero-order were the most reliable models to represent the drug release profile of all prepared pills with very high accuracy, estimated by R^2&gt;0.99. The Hixon and first-order models were the weakest to characterize this work outcome. This work also applied these models to describe the controlling mechanism of the drug release for each prepared pill. It is detected that the non-Fickian diffusion and polymer chain relaxation control the PILC’s release behavior. However, case II transport and super case II transport with erosions is the dominant mechanism for PILD and PILM pills, respectively. Additionally, new semi-empirical models were modified to describe the kinetics of the solid release of those tablets with greater accuracy.

A Semi-empiric Compensation Model for Defect Segmentation in Ultrasonic Thermographic Inspection of Coatings Based on a Chebyshev Series

A Semi-empiric Compensation Model for Defect Segmentation in Ultrasonic Thermographic Inspection of Coatings Based on a Chebyshev Series