Parameters In Models Research Articles

Use of Computational Fluid Dynamics (CFD) Methods to Analyze Combustion Chamber Processes at HVOF Spraying and Their Comparison with Experimental Data

This paper discusses the process of high-velocity oxygen fuel (HVOF) spraying with modeling of the gas flow parameters and behavior of WC-Co-Cr powder particles of different fractions (up to 20 µm, 21–35 μm and 36–45 μm). It was found that the temperature of the gas stream reaches a maximum of about 2700 °C, after which it gradually decreases, and the pressure in the combustion chamber (before the exit of gases through the nozzle) reaches maximum values, exceeding 400,000 Pa, and the pressure at the exit of the nozzle stabilizes at about 100,000 Pa, which corresponds to the standard atmospheric pressure. The gas velocity increases to 1300–1400 m/s and then decreases to 400 m/s at a distance of about 150 mm. It was determined that powder particles of the 21–35 µm fraction provide more stable parameters of velocity and temperature. Small particles (up to 20 µm) lose velocity and temperature faster as they advance, which deteriorates the coating quality, which was also experimentally confirmed. All results obtained from the HVOF process modeling fully align with the data from experimental studies.

Optimization of Fused Deposition Modeling Parameters for Mechanical Properties of Polylactic Acid Parts Based on Kriging and Cuckoo Search

Optimization of Fused Deposition Modeling Parameters for Mechanical Properties of Polylactic Acid Parts Based on Kriging and Cuckoo Search

A study on the effects of dispersion coefficient on groundwater pollutant transport simulation.

A comprehensive scientific analysis of temporal and spatial fluctuations of pollutants during the migration of groundwater is essential for precisely predicting their dispersion patterns and promoting rational regional development planning. In this research paper, a field radial dispersion test was conducted in decentralized drinking water sources downstream of the Fu Tuan River basin in Rizhao City, Shandong Province, China (FRSC). Chloride ion (Cl-) solution was utilized as a tracer for the experiment. The longitudinal dispersion coefficient ( ) was determined by standard curve matching and using straight-line graphical methods. Additionally, the impact of on pollutant transport was investigated by creating the MT3DMS solute transport model with the help of GMS 10.4 software. The results indicate that the of the submerged aquifer varies depending on the distance from the source well in the main runoff direction. At distances of 10 and 15m, the values of were observed to be 0.56m and 0.88m, respectively. Moreover, when these observed values of are used as simulation parameters in solute transport modeling on the same space-time scale, the hydrodynamic dispersion effect exhibited by the well is found to be weaker than the actual hydrodynamic dispersion effect.

Modal Split and Cost-Sensitivity Analysis for Various Travel Modes Using Calibrated Parameters in NL Modeling

Modal Split and Cost-Sensitivity Analysis for Various Travel Modes Using Calibrated Parameters in NL Modeling

IRSnet: An Implicit Residual Solver and Its unfolding Neural Network with 0.003M Parameters for Total Variation Models

IRSnet: An Implicit Residual Solver and Its unfolding Neural Network with 0.003M Parameters for Total Variation Models

A constrained optimisation framework for parameter identification of the SIRD model.

We consider a numerical framework tailored to identifying optimal parameters in the context of modelling disease propagation. Our focus is on understanding the behaviour of optimisation algorithms for such problems, where the dynamics are described by a system of ordinary differential equations associated with the epidemiological SIRD model. Applying an optimise-then-discretise approach, we examine properties of the solution operator and determine existence of optimal parameters for the problem considered. Further, first-order optimality conditions are derived, the solution of which provides a certificate of goodness of fit, which is not always guaranteed with parameter tuning techniques. We then propose strategies for the numerical solution of such problems, based on projected gradient descent, Fast Iterative Shrinkage-Thresholding Algorithm (FISTA), nonmonotone Accelerated Proximal Gradient (nmAPG), and limited memory BFGS trust region approaches. We carry out a thorough computational study for a range of problems of interest, determining the relative performance of these numerical methods. Our results provide insights into the effectiveness of these strategies, contributing to ongoing research into optimising parameters for accurate and reliable disease spread modelling. Moreover, our approach paves the way for calibration of more intricate compartmental models.

Predictive modeling of reservoir geomechanical parameters through computational intelligence approach, integrating core and well logging data

Predictive modeling of reservoir geomechanical parameters through computational intelligence approach, integrating core and well logging data

Influence of energetic heavy ion sputtering on lifetime of alloy target

When energetic heavy ions are incident on negatively charged structure that collects and deposits ions, ion sputtering will occur. Metal wire is a structure commonly used for accelerating ions, the incidence of continuous high-throughput ions can cause surface loss of metal wire, affecting the service performance and lifespan of the metal wire. The SRIM software commonly used for calculating sputtering yield cannot consider the multi-body interaction problem contained in the alloy crystal structure. so, there is a significant error in calculating the sputtering yield of high-energy ions incident on alloy target. Based on the molecular dynamics method and Langevin temperature control model, the calculation model of ion sputtering parameters of energetic metal ions incident on alloy target is established in this work. The model is used to calculate the sputtering yield under the conditions of intact surface lattice of the target material and long-term incident surface lattice damage. The damages to the cathode metal wire under different incident ion fluences are further calculated, and the cross-sectional characterization of the metal wire is carried under typical working condition. The results show that the discrepancy between the experimental value and the theoretical value is less than 10%, which verifies the accuracy and applicability of the theoretical model. Based on this model, the search direction for sputtering resistant materials is proposed, meanwhile, a theoretical optimization is carried out to improve the service life of metal wire, and a method of using Ni-Ti alloy to improve the service life of metal wires is proposed, which is of great significance for predicting the service life of the metal wire under different conditions.

COMPARISON OF THERMOPHYSICAL PROPERTY MODELS OF NANOFLUIDS FOR THERMAL PERFORMANCE IN A MICROCHANNEL

Different thermophysical property models for nanofluids and several dimensionless parameters have been compared in the case of heat transfer for laminar flow in a microchannel. Addition of nanoparticles, including Al<sub>2</sub>O<sub>3</sub>, CuO, and TiO<sub>2</sub>, to water has been considered for volumetric concentrations up to φ = 7%. The numerical analyses have been conducted via the finite difference method for a transient regime. The heat transfer results have been obtained in terms of the interfacial heat flux, bulk temperature, and the Nusselt number. The parametrical effects of Péclet number values, wall thickness ratios, thermal diffusivity ratios, thermal conductivity ratios, and Brinkman number values have been evaluated. Based on the results, the influence of axial conduction has been clearly observed for lower Péclet numbers. Furthermore, the increment of the thermal conductivity ratio and the decrement of the wall thickness ratio have enhanced thermal performance. Nevertheless, the thermal characteristics have not been significantly influenced by the thermal diffusivity ratio. The viscous dissipation has altered the heat transfer direction due to the change in the Brinkman number. The required time to reach the steady state for numerical solution increased because of decreasing Brinkman number values. Heat transfer augmentation has been obviously obtained due to ascending nanofluid volumetric concentrations. The CuO nanoparticles have been recommended owing to their higher thermal performance for the fixed volumetric concentration. According to the thermophysical models compared for each type of nanoparticles, the models for Al<sub>2</sub>O<sub>3</sub> nanoparticles presented the closest results to each other. Nonetheless, it has been observed that one model for CuO nanoparticles and one model for TiO<sub>2</sub> nanoparticles are inadequate.

Sensitivity Analysis on the Impact of Input Parameters on Seismic Hazard Results: A Case Study of Central America

We present a sensitivity analysis on the impact of input parameters and methods used on the results of a probabilistic seismic hazard assessment (PSHA). The accurate estimation of the parameters in recurrence models (declustering and fitting methods), along with the selection of scaling relationships for determining maximum magnitude and the selection of ground motion models (GMMs), enhance control over epistemic uncertainties when constructing the logic tree, minimizing final calculation errors and producing credible results for the study region. This study focuses on Central America, utilizing recent data from seismic, geological, and geophysical studies to improve uncertainty analyses through classic statistical methods. The results demonstrate that proper fitting of the recurrence model can stabilize acceleration variations regardless of the declustering method or b-value fitting method used. Regarding scaling relationships, their low impact on the final results is noted, provided the models are tailored to the tectonic regime under study. Finally, it is shown that the GMM contributes the most variability to seismic hazard results; therefore, their selection should be conditioned on calibration with observed data through residual analysis where region-specific models are not available.

Development of Mathematical Support and Software for Ballistic Analysis and Optimization of Active-Missile Projectile Parameters

The paper presents the results of developing mathematical and software support for internal and external ballisticsmodelling, as well as optimizing the parameters of an active-reactive projectile. A complex mathematical model of active-reactive projectile ballistic analysis includes the problem of external ballistics, the problem of internal ballistics inside the gun barrel and the problem of internal ballistics of a solid-fuel jet engine. The problem of active-reactive projectile parameteroptimization is to increase the flight range with respecttomotion stabilitycondition along the entire trajectory. Based on the mathematical model of ballistic analysis, the algorithms for solving a multi-criteria problem of active-reactive projectileparameteroptimization have been developed. The developed software package includes a block of calculation modules implementing mathematical models and computational algorithms, a database of projectile parameters and simulation results, a block for visualization the computational experiment results in the form of diagrams and tables. The software package can be used in the design and optimization of the projectileparameters of artillery systems implementing the active-reactive throwingprinciple. The results of mathematical modeling and optimization of parameters are considered using the example of the designed active-reactive projectile for the 152 mm artillery system. To improve the projectilestability on the trajectory during jet engineoperation, it is proposed to install ribs at an angle to the projectile axis on the inner surface of the nozzle so as to create extra torque. Based on the developed model, the optimal characteristics of firing an active-reactive projectile of caliber 152 mm are determined. The results of internal and external ballisticsproblem solutionfor an active-reactive projectile obtained by means ofthe software package for ballistic process modeling are presented. The main problem of internal ballistics inside the gun barrel, the problem of internal ballistics of a solid-fuel jet engine, the direct problem of external ballistics of an active-reactive projectile and the study of the projectile motion stability along the trajectory are solved. Optimal parameters of internal and external ballistics, providing the maximum firing range of an active-reactive projectile with respect toitsmotionstability along the entire trajectory, are found.

Key Parameters for Performance and Resilience Modeling of 3D Time-of-Flight Cameras Under Consideration of Signal-to-Noise Ratio and Phase Noise Wiggling.

Because of their resilience, Time-of-Flight (ToF) cameras are now essential components in scientific and industrial settings. This paper outlines the essential factors for modeling 3D ToF cameras, with specific emphasis on analyzing the phenomenon known as "wiggling". Through our investigation, we demonstrate that wiggling not only causes systematic errors in distance measurements, but also introduces periodic fluctuations in statistical measurement uncertainty, which compounds the dependence on the signal-to-noise ratio (SNR). Armed with this knowledge, we developed a new 3D camera model, which we then made computationally tractable. To illustrate and evaluate the model, we compared measurement data with simulated data of the same scene. This allowed us to individually demonstrate various effects on the signal-to-noise ratio, reflectivity, and distance.

DRIFT MOBILITY OF ELECTRONS IN INDIUM ANTIMONIDE IN THE WEAK ELECTRIC FIELD REGIME

In scientific publications, theoretical and experimental studies of the Hall mobility of indium antimonide are presented; its dependence on temperature, charge carrier concentration, and electric field strength. Note that a typical form of the weak-field temperature dependence of the electron drift mobility, showing its behavior in a wide range of temperature and impurity atom concentration, is not presented. The processes of charge carrier scattering in indium antimonide are poorly represented. The aim of this work is a detailed study of the main mechanisms of electron scattering in indium antimonide, the temperature dependence of the weak-field electron mobility in a wide range of the semiconductor doping degree. A quantitative assessment of the electron scattering rates in indium antimonide is given for the main types of impurity and phonon mechanisms. A detailed analysis of the scattering processes is carried out. It is emphasized that a difference in the scattering rates in the Г- and L-valleys of the conduction band is observed. Scattering by ionized impurity atoms determines the resulting scattering velocity up to 40 K. At temperatures above 40 K, the role of polar optical scattering in the Г-valley increases significantly, practically determining the behavior of the resulting velocity. In the L-valleys, the greatest contribution is made by 2 types of phonon scattering: acoustic and polar optical. Based on the obtained scattering process modeling results, the temperature dependence of the weak-field drift mobility in the Г- and L-valleys was calculated. From the simulation results it is clear that the mobility of electrons in the Г-valley exceeds the corresponding values in the L-valleys by 2 – 4 orders of magnitude. The resulting mobility is determined taking into account the valley population. From the results of modeling the drift mobility it is clear that the area of its increase is determined by the processes of scattering by impurity ions. The higher the doping degree of the semiconductor, the lower the electron mobility. With a further increase in temperature, the mobility decreases. A characteristic feature of the typical temperature dependence of the weak-field electron mobility in InSb is that at high temperatures its behavior is determined by scattering processes on polar optical phonons. A set of modeling parameters has been determined that ensure compliance with experimental data.

Parameter Extraction for Photovoltaic Models with Flood-Algorithm-Based Optimization

Accurately modeling photovoltaic (PV) cells is crucial for optimizing PV systems. Researchers have proposed numerous mathematical models of PV cells to facilitate the design and simulation of PV systems. Usually, a PV cell is modeled by equivalent electrical circuit models with specific parameters, which are often unknown; this leads to formulating an optimization problem that is addressed through metaheuristic algorithms to identify the PV cell/module parameters accurately. This paper introduces the flood algorithm (FLA), a novel and efficient optimization approach, to extract parameters for various PV models, including single-diode, double-diode, and three-diode models and PV module configurations. The FLA’s performance is systematically evaluated against nine recently developed optimization algorithms through comprehensive comparative and statistical analyses. The results highlight the FLA’s superior convergence speed, global search capability, and robustness. This study explores two distinct objective functions to enhance accuracy: one based on experimental current–voltage data and another integrating the Newton–Raphson method. Applying metaheuristic algorithms with the Newton–Raphson-based objective function reduced the root-mean-square error (RMSE) more effectively than traditional methods. These findings establish the FLA as a computationally efficient and reliable approach to PV parameter extraction, with promising implications for advancing PV system design and simulation.

Основные принципы построения расчетной модели поврежденных участков трубопровода с композитными накладками в программном комплексе ANSYS

In the article, based on a typical reduced model of the linear part of a steel main pipeline between four supports with a crack-like through defect, methods for numerical modeling of the main key parameters of the bandage in the form of circular composite linings are developed: the type of composite material, the length of the lining and the thickness of the lining. The presented calculation method is recommended to be used in the ANSYS software package. The results obtained can be used in modeling traditional and reinforced with composite linings main steel gas pipelines. Also, the described calculation method can be used for numerical studies of full-scale and reduced models of main steel gas pipelines, taking into account operational loads in the issue of increasing strength characteristics.

Evaluation and comparison of methods for neuronal parameter optimization using the Neuroptimus software framework.

Finding optimal parameters for detailed neuronal models is a ubiquitous challenge in neuroscientific research. In recent years, manual model tuning has been gradually replaced by automated parameter search using a variety of different tools and methods. However, using most of these software tools and choosing the most appropriate algorithm for a given optimization task require substantial technical expertise, which prevents the majority of researchers from using these methods effectively. To address these issues, we developed a generic platform (called Neuroptimus) that allows users to set up neural parameter optimization tasks via a graphical interface, and to solve these tasks using a wide selection of state-of-the-art parameter search methods implemented by five different Python packages. Neuroptimus also offers several features to support more advanced usage, including the ability to run most algorithms in parallel, which allows it to take advantage of high-performance computing architectures. We used the common interface provided by Neuroptimus to conduct a detailed comparison of more than twenty different algorithms (and implementations) on six distinct benchmarks that represent typical scenarios in neuronal parameter search. We quantified the performance of the algorithms in terms of the best solutions found and in terms of convergence speed. We identified several algorithms, including covariance matrix adaptation evolution strategy and particle swarm optimization, that consistently, without any fine-tuning, found good solutions in all of our use cases. By contrast, some other algorithms including all local search methods provided good solutions only for the simplest use cases, and failed completely on more complex problems. We also demonstrate the versatility of Neuroptimus by applying it to an additional use case that involves tuning the parameters of a subcellular model of biochemical pathways. Finally, we created an online database that allows uploading, querying and analyzing the results of optimization runs performed by Neuroptimus, which enables all researchers to update and extend the current benchmarking study. The tools and analysis we provide should aid members of the neuroscience community to apply parameter search methods more effectively in their research.

Sensitivity Analysis of Scallop Damper Seal Design Parameters for Leakage and Static Performance

The leakage characteristics and static stiffness of scallop damper seals have a significant impact on rotor vibration and stability. A parameter sensitivity analysis model for geometrical parameters in scallop damper seals was developed using a design of experiments (DOE) approach. The method employed a central composite design, integrating factorial, axial, and center points to assess non-linear effects efficiently. And the effects of radial clearance, cavity depth, and length–diameter ratios on leakage performance and rotor stability were investigated. The leakage rate, flow-induced force, and static stiffness coefficient for 15 different combinations of geometric parameters at eccentricities of 0.2 and 0.4 were numerically calculated. The results show that eccentricity has little effect on leakage and its parameter sensitivity. Larger cavity depths and length–diameter ratios are beneficial for seal leakage performance. The tangential force increases with increasing eccentricity but decreases with increasing radial clearance, while it first decreases and then increases with the increase in the cavity depth and length–diameter ratios. Additionally, the radial force decreases with the increase in the length-to-diameter ratio and increases first and then decreases with the increase in radial clearance. The parameter level in this study is defined as the ratio of the actual parameter value to the maximum parameter value. Static direct stiffness reaches its maximum value at a radial clearance level of 30.2%. It remains positive within a cavity depth range of 92.3~100%, as well as a length–diameter ratio range of 0~20.3%. The static cross-coupled stiffness gradually decreases with the increase in radial clearance but first decreases and then increases with the increase in the cavity depth or length–diameter ratio levels. The research results presented in this paper can serve as a reference for the analysis of the performance and design of scallop damper seals.

Possibilities and challenges in the moral growth of large language models: a philosophical perspective

With the rapid expansion of parameters in large language models (LLMs) and the application of Reinforcement Learning with Human Feedback (RLHF), there has been a noticeable growth in the moral competence of LLMs. However, several questions warrant further exploration: Is it really possible for LLMs to fully align with human values through RLHF? How can the current moral growth be philosophically contextualized? We identify similarities between LLMs’ moral growth and Deweyan ethics in terms of the discourse of human moral development. We then attempt to use Dewey’s theory on an experimental basis to examine and further explain the extent to which the current alignment pathway enables the development of LLMs. A beating experiment serves as the foundational case for analyzing LLMs’ moral competence across various parameters and stages, including basic moral cognition, moral dilemma judgment, and moral behavior. The results demonstrate that the moral competence of the GPT series has seen a significant improvement, and Dewey’s Impulse-Habit-Character theory of moral development can be used to explain this: the moral competence of LLMs has been enhanced through experience-based learning, supported by human feedback. Nevertheless, LLMs’ moral development through RLHF remains constrained and does not reach the character stage described by Dewey, possibly due to their lack of self-consciousness. This fundamental difference between humans and LLMs underscores both the limitations of LLMs’ moral growth and the challenges of applying RLHF for AI alignment. It also emphasizes the need for external societal governance and legal regulation.

Polymeric Microneedles for Transdermal Delivery of Human Placental Tissue for the Treatment of Osteoarthritis.

Biologics targeting matrix-degrading proteases, cartilage repair, and inflammation are emerging as promising approaches for osteoarthritis (OA) treatment. Recent research highlights biologic-human placental tissue (HPT) as a potential OA therapy due to its biocompatibility, abundant protein biofactors, and ability to reduce cartilage degradation by suppressing protease expression. Microneedles (MNs) are receiving growing attention for enhancing transdermal delivery of biologics as an alternative to conventional subcutaneous injections. The lyophilized human placental extract (LHP) loaded polymeric MNs are fabricated using a micromolding technique for transdermal delivery. Ex vivo release studies reveal that MNs exhibit a gradual and consistent release of LHP, indicating a sustained delivery profile. LHP-MNs are nontoxic and anti-inflammatory in nature against human skin cells and interleukin (IL-1β) induced synovial cells. Furthermore, the in vivo study shows that LHP-MNs substantially improve behavioral parameters in OA rat models and lower serum concentrations of tumor necrosis factor- α (TNF-α) and cartilage oligomeric matrix protein (COMP) biomarkers, thereby alleviating knee and ankle joint injuries. Histopathological analysis indicates that LHP-MNs significantly preserve cartilage integrity. The study results suggest that employing polymeric MNs for transdermal delivery of LHP can be a promising treatment approach for OA, with the added benefit of excellent patient compliance.

Research on rock strength prediction model based on machine learning algorithm

The compressive strength of rocks is one of its mechanical characteristics. It has been a difficult problem to predict rock compressive strength conveniently and efficiently, and to solve the limitations of traditional rock compressive strength tests such as high cost, long time consumption, and reliability assurance. In this study, a data set containing 1774 groups of rock compressive strength test data was constructed through file retrieval, including 9 input parameters: rock type, temperature, confining pressure, dimension of specimen, shape of specimen, and experimental method. Eight supervised learning algorithms were used to learn the rock compressive strength test data, and eight rock compressive strength prediction models considering multiple factors were established to obtain a better method of predicting rock compressive strength. By selecting different features, the optimal feature combination for predicting rock compressive strength was obtained, and the optimal parameters for different models were obtained through the Sparrow Search Algorithm (SSA). Finally, four regression evaluation indicators, including mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R2), were used to evaluate the predictive performance of the established regression models. The results showed that the best-trained model had a MAPE as low as 3.61%, MAE as low as 9.19 MPa, and R2 as high as 0.995. It is noteworthy that AdaBoost was found to be the best model for predicting rock compressive strength. This study presents a significant advancement in the field by demonstrating the effectiveness of machine learning algorithms in this context, which have not been extensively applied to rock compressive strength predictions. The findings suggest that these models can offer substantial improvements over traditional methods, not only in accuracy but also in operational efficiency. This research is important for geotechnical engineering, as accurate rock strength predictions are critical for the design and stability assessments of construction projects, ultimately contributing to safer and more cost-effective engineering solutions.