Parameters In Models Research Articles

Review of Modelling the Compaction of High Aspect Ratio AgriculturalFeedstock Materials

Agriculture feedstock materials have low bulk density and are difficult to handle and transport. Compaction can enhance the agricultural feedstock density and could be an effective solution for handling problems. To develop a better compaction process, mathematical modelling can help understand the compaction mechanism and can help predict the mechanical behaviour of feedstock materials. This paper presents a review on the energy consumption, applied pressure, as well as rheological models to predict their compaction behaviour and thus help improve compacts’ mechanical strength and reduce densification costs. In summary, energy requirement for densification of biomass depends primarily upon the pressure applied, holding on time and properties of the material. From the published literature, it could be found that the Cooper-Eaton and the Kawakita-Ludde models that considers particle rearrangement and deformation mechanism can fit pressure and density of feedstock materials during compaction. The parameters in the rheological models were correlated to the properties of the feedstock materials and can be used for optimization of machine and operational parameters.

Design and validation of finite element models for the assessment of post-fixation distal femur fracture motion.

Fracture site motion is thought to play an important role in the healing of complex fractures of the distal femur via mechanotransduction. Measuring this motion in vivo is challenging, and this has led researchers to turn to finite element modeling approaches to gain insights into the mechanical environment at the fracture site. Developing a systematic understanding of the effect of different model choices for distal femur fractures may allow more accurate prediction of fracture site motion from these types of simulations. In this study, we aim to assess the effect of four different modeling choices and parameters. We looked at the effect of using bone specific density distributions vs generic values, employing landmark-based geometry generation, varying fracture alignment within clinically relevant ranges, and determining whether direct apposition of the fracture to the plate was achieved. For validation, five cadaveric femurs had fractures created and repaired with plated constructs, and these were then loaded and fracture site motion was directly measured. We found that using landmark based bone geometry and patient-specific bone density distributions had a minimal effect on the overall model predictions. Changing the alignment, particularly into varus and procurvatum could have a large (>50%) effect on predicted shear motion, as could direct apposition of the bone to the plate. These findings demonstrate that modeling choices can play an important role in simulating distal femur fracture mechanics, and it is particularly critical that patient customized models attempt to accurately represent alignment of the bone fragments and lateral plate apposition.

Optimizing Railway Tribology: A Systematic Review and Predictive Modeling of Twin-Disc Testing Parameters

Twin-disc testing is crucial for understanding wheel–rail interactions in railway systems, but the vast array of testing parameters and conditions makes data interpretation challenging. This review presents a comprehensive analysis of the twin-disc literature experimental data, focusing on how various parameters influence friction and wear characteristics under stationary contaminant conditions. We systematically collected and analyzed data from numerous studies, considering factors such as contact pressure, speed, material hardness, sliding speeds, adhesion, and a range of contaminants. This research showed inconsistent data reporting across different studies and statistical analyses revealed significant correlations between testing parameters and wear rates. For sand-contaminated tests, a correlation between particle size and flow rate was also highlighted. Based on these findings, we developed a simple predictive model for forecasting wear rates under varying conditions. This model achieved an adjusted R2 of 0.650, demonstrating its potential for optimizing railway component design and maintenance strategies. Our study provides a valuable resource for researchers and practitioners in railway engineering, offering insights into the complex tribological interactions in wheel–rail systems and a tool for predicting wear behavior.

Study on the influence of pipe effect on the vibration characteristics of electro-hydraulic exciter

In this paper, an electro-hydraulic exciter controlled by an alternating current distribution valve is proposed, and a systematic analysis of the influence of pipe effect on the vibration characteristics of the electro-hydraulic exciter is investigated by simulation and experiments. The vibration characteristics curves for different pipe parameters are obtained, and the relative errors between simulation and experiment are evaluated. Moreover, a significant regression model of pipe parameters and vibration characteristics of the electro-hydraulic exciter is established by using the quadratic regression analysis method and the Box-Behnken experiment design method. Significant differences in the influence of pipe parameters on vibration characteristics are obtained by ANOVA (Analysis of Variance), and the influence of pipe parameter interaction on vibration characteristics is discussed. The results show that the relative importance of pipe parameters on the vibration characteristics of electro-hydraulic exciter, from high to low, is as follows: pipe diameter, elastic modulus, and pipe length. Notably, there is also an interaction between the pipe parameters, and the significance of these interactions is ranked from high to low as the interaction between the layers of steel wire and diameter, the interaction between diameter and length, and the interaction between the layers of steel wire and length.

Development of a Bayesian calibration framework for archetype-based housing stock models of summer indoor temperature

Archetype-based housing stock models of summer indoor temperature can support the development of policies to manage the climate change-driven increase in cooling demand and heat-related health impacts. Calibration can reduce the performance gap of such models, however, work on this topic is limited. Motivated by the growing importance of this underexplored research area, this paper introduces a framework for the Bayesian calibration of archetype-based housing stock models of summer indoor temperature. The framework includes data-driven procedures to classify dwellings into homogeneous groups and specify prior probability distributions. To demonstrate its application, an established bottom-up model based on EnergyPlus was calibrated using data collected from 193 dwellings monitored during the 2009 4M survey in Leicester, England. Post-calibration, the root-mean-square error reduced from 2.5°C to 0.6°C and remaining uncertainties were quantified. The application of this modular framework may be extended to models of energy use and other indoor environmental parameters.

A comprehensive evaluation of biases in convective-storm parameters in CMIP6 models over North America

A comprehensive evaluation of biases in convective-storm parameters in CMIP6 models over North America

Prediction of the R3 Test-Based Reactivity of Supplementary Cementitious Materials: A Machine Learning Approach Utilizing Physical and Chemical Properties

This study utilized machine learning (ML) models to investigate the effect of physical and chemical properties on the reactivity of various supplementary cementitious materials (SCMs). Six SCMs, including ground granulated blast furnace slag (GGBFS), pulverized coal fly ash (FA), and ground bottom ash (BA), underwent thorough material characterization and reactivity tests, incorporating the modified strength activity index (ASTM C311) and the R3 (ASTM C1897) tests. A data set comprising 46 entries, derived from both experimental results and literature sources, was employed to train ML models, specifically artificial neural network (ANN), support vector machine (SVM), and random forest (RF). The results demonstrated the robustness of the ANN model, achieving superior prediction accuracy with a testing mean absolute error (MAE) of 9.6%, outperforming SVM and RF models. The study classified SCMs into reactivity classes based on correlation analysis, establishes a comprehensive database linking material properties to reactivity, and identifies key input parameters for predictive modeling. While most SCMs exhibited consistent predictions across types, GGBFS displayed significant variations, prompting a recommendation for the inclusion of additional input parameters, such as fineness, to enhance predictive accuracy. This research provided valuable insights into predicting SCM reactivity, emphasizing the potential of ML models for informed material selection and optimization in concrete applications.

Research on Data Feature Selection of Sharing Platform Based on Particle Swarm Optimization Algorithm

Abstract. This paper is based on the optimization of machine learning models such as SVM (Support Vector Machine) and LightGBM through the application of PSO (Particle Swarm Optimization) and combines shared platform recovery data to provide training material for machine models. The goal is to accelerate the parameter configuration of machine learning models and enhance the rigor of feature selection through the application of algorithmic models. The experimental results show that PSO performs well in optimizing parameters for machine learning models like SVM and LightGBM, achieving high levels in evaluation metrics such as accuracy, recall, precision, and F1 score. Based on this, a framework is proposed for embedding the PSO algorithm into a shared platform architecture, including layers for data collection and preprocessing, algorithm integration and optimization, decision support and service, and feedback and optimization. This framework allows for precise predictions of user behavior, market demand, and other factors, achieving automated scheduling of machine model parameters.

Predicting urinary stone recurrence: a joint model analysis of repeated 24-hour urine collections from the MSTONE database

To address the limitations in existing urinary stone recurrence (USR) models, including failure to account for changes in 24-hour urine (24U) parameters over time and ignoring multiplicity of stone recurrences, we presented a novel statistical method to jointly model temporal trends in 24U parameters and multiple recurrent stone events. The MSTONE database spanning May 2001 to April 2015 was analyzed. A joint recurrent model was employed, combining a linear mixed-effects model for longitudinal 24U parameters and a recurrent event model with a dynamic first-order Autoregressive (AR(1)) structure. A mixture cure component was included to handle patient heterogeneity. Comparisons were made with existing methods, multivariable Cox regression and conditional Prentice-Williams-Peterson regression, both applied to established nomograms. Among 396 patients (median follow-up of 2.93 years; IQR, 1.53–4.36 years), 34.6% remained free of stone recurrence throughout the study period, 30.0% experienced a single recurrence, and 35.4% had multiple recurrences. The joint recurrent model with a mixture cure component identified significant associations between 24U parameters - including urine pH (adjusted HR = 1.991; 95% CI 1.490–2.660; p < 0.001), total volume (adjusted HR = 0.700; 95% CI 0.501–0.977; p = 0.036), potassium (adjusted HR = 0.983; 95% CI 0.974–0.991; p < 0.001), uric acid (adjusted HR = 1.528; 95% CI 1.105–2.113, p = 0.010), calcium (adjusted HR = 1.164; 95% CI 1.052–1.289; p = 0.003), and citrate (adjusted HR = 0.796; 95% CI 0.706–0.897; p < 0.001), and USR, achieving better predictive performance compared to existing methods. 24U parameters play an important role in prevention of USR, and therefore, patients with a history of stones are recommended to closely monitor for future recurrence by regularly conducting 24U tests.

Исследование влияния отклонений параметров моделей синхронных генераторов на результаты расчета динамической устойчивости электроэнергетической системы

One of the important tasks solved in planning the electric power system (EPS) operation is the assessment of its transient stability. Use of incorrect parameters for EPS element models, in particular synchronous generators (SG), contributes to the occurrence of errors when determining the operating conditions of power systems, and, therefore, can lead to a decrease in the reliability of EPS operation. This article is devoted to the study of the impact of errors in SG model parameters on the transient stability assessment results. The analysis presented in this article allows us to substantiate the relevance of the SG parameter estimation task using a “passive experiment”, such as synchronized phasor measurements. This research is based on the theory of electrical circuits, mathematical description of electrical machines and transients in EPS, as well as statistical analysis. Computational experiments have been carried out in the software package MATLAB using the simulation modeling environment “Simulink”. During the research, two different multi-machine EPS models have been used, including the well-known “IEEE 9-bus” system. To quantitatively assess the influence of SG parameter uncertainties on transient stability analysis, the values of the maximum permissible fault clearance time are determined, as well as the values of the maximum power output of generating equipment in prefault conditions. The authors have carried out a comprehensive analysis of the influence of deviations in SG model parameters on the maximum fault clearance time, as well as on the maximum power output of generating equipment in the prefault conditions for a given fault duration. During the study, two approaches have been considered; in each case, the EPS transient stability assessment has been performed almost automatically, and quantitative indicators have been computed for the influence of SG model parameter deviations on the outcome of EPS transient stability analysis. Analysis of the computational experiment results has demonstrated a significant influence of the SG model errors on the errors in calculating certain transient stability-related parameters. For the considered scenarios, the spread of errors in calculating the maximum fault clearance time reached 100 % relative to a base-case (errorless) result, and the error in calculating the maximum power output of generating equipment in prefault conditions for a given fault duration may reach 20 % relative to the errorless result.

On the accuracy of Eulerian-Lagrangian CFD simulations for spray evaporation in turbulent flow

CFD simulation of droplet evaporation in turbulent flows is challenging as the accuracy and reliability of the results strongly depend on the available sub-models and their modeling parameters. This study presents a systematic sensitivity analysis focused on the impact of the most widely used discrete random walk (DRW) model, the constant time scale coefficient (CL), the turbulence model, and the drag coefficient model. CFD simulations with the Eulerian-Lagrangian approach are employed. The analysis is based on grid-sensitivity analysis and validation with measurements of spray evaporation in a heated turbulent airflow. The results show that using the DRW model leads to a good agreement between the CFD results and the experimental data of droplet size and droplet mean velocity, attributed to the turbulent fluctuations inducing droplet dispersion. The best performance is observed for the standard k-ε turbulence model with CL = 0.30 and 0.45. This is mainly attributed to the reasonable interaction time between droplets and turbulent eddies at these CL values. The three drag coefficient models (i.e., Spherical, Ischii-Zuber, and Grace) lead to similar results due to the low droplet Reynolds number.

A physics-informed neural network method for identifying parameters and predicting remaining life of fatigue crack growth

Predicting the remaining life of fatigue cracks is crucial for planning maintenance and repair strategies to prevent untoward incidents. This paper proposes a novel physics-informed neural network (PINN) method for identifying parameters and predicting remaining fatigue crack growth life (FCGL). Initially, the relationship between crack length and fatigue cycles is established through a neural network, and the gradient of fatigue cycles with respect to crack length is obtained by automatic differentiation. Subsequently, a composite loss function is designed to incorporate this gradient within the confines of physical knowledge, ensuring that the established relationship not only aligns with observed data but also adheres to physical knowledge. Furthermore, during the network training, the parameters in physical models are simultaneously updated to better conform to the individuality of the monitored subject. All predicted remaining FCGLs fall within the 1.5 times error band. Compared to purely data-driven or physics-based methods, the proposed method offers more robust and accurate predictions of remaining FCGLs.

Enhanced cationic/anionic dyes removal in wastewater by green nanocomposites synthesized from acid-modified biomass and CuFe2O4 nanoparticles: Mechanism, Taguchi optimization and toxicity evaluation

This article addresses the nanoadsorption mechanisms of rhodamine B (RB), crystal violet (CV), and Congo red (CR) using acid-treated C.edulis (ATCE)/CuFe2O4 (ATCE@CuFe2O4) from an aqueous solution. The physical and chemical characterizations of nanobiomass were studied using different techniques. The specific surface areas of the ATCE and ATCE@CuFe2O4 composites were 15.88 and 337.81 m2/g, respectively, indicating a significant specific surface area of ​​the ATCE@CuFe2O4 nanocomposite. A number of functional groups were determined, which promote the binding of the dye to the adsorbent. The SEM also shows that the adsorbent has a homogeneous texture with deep voids and significant porosity, which likely explains the retention and binding of dye ions on the surface of the adsorbent. In fact, the Langmuir isotherm with a correlation coefficient of 99 % for CV, RB and CR, respectively, represents the most suitable model to explain the adsorption mechanism. The maximum adsorption amount is 666.6 mg/g for CV, 645.16 mg/g for RB and 434.71 mg/g for CR at 308 °K. The adsorption kinetic processes were predicted by the pseudo-second order kinetic model. The thermodynamic properties showed that the adsorption on ATCE@CuFe2O4 was possible and spontaneous. The ATCE@CuFe2O4 recycling and elimination CV, RB, and CR were 74.23 %, 72.75 %, and 67.84 %, respectively, after seven cycles. The design, modeling and optimization of the adsorption parameters were carried out using the Taguchi experimental design. The maximum removal efficiency of CV, RB and CR dyes in optimal operating conditions were 99.96, 98.29 and 97.76 %, respectively. Which at the optimal conditions of 1 g/L, 90 min, 20 mg/L, 298 °K, pH 10 for CV and RB dyes and 1 g/L, 90 min, 20 mg/L, 308 °K, pH 4 for CR. This research demonstrated the performance of ATCE@CuFe2O4 in bean seed germination test and its effectiveness in removing dyes from wastewater.

Assessing the Tribological Impact of 3D Printed Carbon-Reinforced ABS Composite Cylindrical Gears

The tribological performance of carbon-reinforced acrylonitrile butadiene styrene (ABS) composites is very important in determining their suitability for advanced engineering applications. This study employs response surface methodology (RSM) to evaluate the effects of printing temperature and post-processing annealing on the wear resistance and frictional properties of these composites. A central composite design is used to systematically explore the interaction between these two factors, enabling the development of predictive models for key tribological parameters. The results reveal that both the coefficient of friction (COF) and wear are affected by printing and annealing temperatures, although in a non-linear manner. Moderate printing temperatures and lower annealing temperatures were found to reduce friction and wear, with annealing temperature having a more pronounced effect on wear. To further optimize these responses, the desirability approach was applied for predicting the optimal conditions. The optimal combination of input parameters for minimizing both COF and wear was found to be a printing temperature of 256 °C and an annealing temperature of 126 °C. This research provides valuable insights for optimizing additive manufacturing processes of carbon-reinforced ABS composites, contributing to enhanced material durability in practical applications.

Parameter estimation in vapor-liquid equilibrium modeling of compounds related to biodiesel production using opposite point-based differential evolution algorithm.

Biodiesel stands out as the most favorable alternatives to fossil-derived diesel, offering a multitude of environmental benefits. At various stages of biodiesel production, the separation processes necessitate thermodynamic models with the capability to correlate and predict phase equilibria of mixtures. In this study, application of the classical differential evolution (DE) and its new enhanced version, the OPDE algorithm, for modeling vapor-liquid equilibrium (VLE) associated with components related to biodiesel production is presented. The algorithms were analysed and contrasted in terms of their performance, in estimating parameters for Wilson, NRTL and UNIFAC models. Additionally, classical least-squares (LS) and error-in-variable (EIV) approaches were examined and compared. Also, VLE datasets for a specific system have been merged, and parameters have been estimated. The findings suggest that parameters obtained through LS approach align with those reported in literature, indicating faster convergence in all problems. In contrast, the EIV approach achieved a higher objective function value compared to the LS approach, exhibiting low deviation. OPDE outperformed DE in terms of performance. The enhancement in RMSTD value has been found within range 91%-99% for EIV approach. Further, novel findings derived from some of the studied VLE datasets are presented.

Correlating FDM Printing Parameters with Mechanical Properties and Surface Quality of PLA Printouts

Abstract This study explores the correlation between four critical Fused Deposition Modeling (FDM) parameters—extrusion temperature, layer height, print speed, and infill density—and the mechanical properties and surface quality of PLA (Polylactic Acid) printouts. The mechanical properties, including Young’s modulus, tensile strength, and bending strength, were evaluated alongside surface roughness and dimensional accuracy. The results indicate that extrusion temperatures between 200°C and 220°C optimize mechanical performance, enhancing tensile strength and bending strength, while smaller layer heights (0.1 mm) improve surface smoothness but increase print time. Moderate print speeds (around 60 mm/s) offer the best balance between strength and efficiency, while higher infill densities (above 80%) enhance mechanical properties, increasing both tensile and bending strength by up to 25%. These findings provide key insights into optimizing FDM printing parameters to achieve improved structural integrity and aesthetic quality in PLA prints.

Wind effects on individual male and female Bactrocera jarvisi (Diptera: Tephritidae) tracked using harmonic radar.

Wind affects the movement of most volant insects. While the effects of wind on dispersal are relatively well understood at the population level, how wind influences the movement parameters of individual insects in the wild is less clear. Tephritid fruit flies, such as Bactrocera jarvisi, are major horticultural pests worldwide and while most tephritids are nondispersive when host plants are plentiful, records exist for potentially wind-assisted movements up to 200 km. In this study, harmonic radar (HR) was used to track the movements of both male and female lab-reared B. jarvisi in a papaya field. Overall flight directions were found to be correlated with wind direction, as were the subset of between-tree movements, while within-tree movements were not. Furthermore, the effect of wind direction on fly trajectories varied by step-distance but not strongly with wind speed. Mean path distance, step distance, flight direction, turning angle, and flight propensity did not vary by sex. Both male and female movements are well fit by 2-state hidden Markov models further supporting the observation that B. jarvisi move differently within (short steps with random direction) and between (longer more directional steps) trees. Data on flight directionality and step-distances determined in this study provide parameters for models that may help enhance current surveillance, control, and eradication methods, such as optimizing trap placements and pesticide applications, determining release sites for parasitoids, and setting quarantine boundaries after incursions.

High variability exists in 3D leg alignment analysis, but underlying principles that might lead to agreement on a universal framework could be identified: A systematic review.

To (1) investigate the hypothesis that there is high variability in the reported methods to derive axes and joint orientations from three-dimensional (3D) bone models to (a) perform 3D knee-related leg alignment analysis and (b) define coordinate systems for the femur, tibia and leg and (2) identify underlying principles that might lead to agreement on a universal 3D leg alignment analysis framework. A systematic review of the literature between January 2006 and June 2024 was performed. Articles explicitly reporting methods to derive axes and joint orientations from CT-based 3D bone models for alignment parameters and/or coordinate systems of the femur, tibia and leg were included. Study characteristics and reported methods were extracted and presented as a qualitative synthesis. A total of 93 studies were included. There was high variability in the reported methods to derive axes and joint orientations from 3D bone models. Nevertheless, the reported methods could be categorized into four groups, and several underlying principles of the four groups could be identified. Furthermore, the definitions of femoral and tibial coordinate systems were most frequently based on the mechanical axis (femoral, 13/19 [68%]; tibial, 13/26 [50%]) and a central medial-lateral axis (femoral, 16/19 [84%]; tibial, 12/26 [46%]); no leg coordinate system was reported. Interestingly, of the included studies that reported on leg alignment parameters (76/93, 82%), only a minority reported expressing these in a complete coordinate system (25/76, 33%). There is high variability in 3D knee-related leg alignment analysis. Therefore, universal 3D reference values for alignment parameters cannot yet be defined, and comparison of alignment parameter values between different studies is impossible. However, several underlying principles to the reported methods were identified, which could serve to reach more agreement on a future universal 3D framework for leg alignment analysis. Level I (1).

Cost-Utility Analysis of Low-Dose Pioglitazone in a Population With Prediabetes and a History of Stroke or Transient Ischemic Attack.

Pioglitazone significantly reduces the risk of stroke in people with diabetes, and in those with prediabetes, it markedly reduces the risk of stroke/myocardial infarction and new-onset diabetes. Low-dose pioglitazone provides most of the clinical benefits of high-dose pioglitazone, with fewer adverse effects. We report an economic evaluation of the cost-effectiveness of low-dose pioglitazone versus placebo from a Canadian public payer perspective in 2023 Canadian dollars. A Markov model was developed at a lifetime horizon with an annual cycle length and 5 health states (event-free, myocardial infarction, stroke, new-onset diabetes, and death). Transition probabilities were extracted from the IRIS (Insulin Resistance Intervention in Stroke) trial. Health state costs and utilities were based on public sources. Annual discount rates of 1.5% were applied in the reference-case analysis. Probabilistic analyses were conducted to deal with parameter uncertainty through 5000 simulations. The costs were estimated as $24 887 (interquartile range [IQR], $14 632-$41507) for low-dose pioglitazone and $57 301 (IQR, $48 730-$67368) for placebo, resulting in a cost saving of -$30 287 (IQR, -$43 374 to -$14 587) in favor of low-dose pioglitazone. Quality-adjusted life years were estimated as 25.99 (IQR, 24.56-26.81) for the low-dose pioglitazone and 19.44 (IQR, 18.68-20.13) for placebo, resulting in a difference of 6.37 (IQR, 5.07-7.36) in favor of low-dose pioglitazone. Consistent findings were observed from scenario analyses and 1-way probability sensitivity analyses. Holding across a wide range of values in modeling parameters, low-dose pioglitazone is found as the dominant strategy versus a placebo.

Wind Farm Power Maximisation via Wake Steering: A Gaussian Process‐Based Yaw‐Dependent Parameter Tuning Approach

ABSTRACTMaximising the power production of wind farms is vital to meet the growing demand for wind energy and reduce its cost. Wake effects, resulting from the aerodynamic interactions between turbines in a wind farm, significantly impact farm efficiency, leading to substantial annual power losses. Wake steering, an influential control strategy, involves mitigating wake effects by strategically yaw misaligning upstream turbines to deflect their wakes. Conventional wake steering approaches typically rely on physics‐based analytical wake models with their parameters often calibrated using higher fidelity data. However, these approaches determine a fixed set of parameters prior to conducting wake steering, neglecting each parameter's dependency on yaw misalignment (i.e. the optimisation variables) exhibited throughout the optimisation process, potentially affecting its accuracy. To address this limitation, this paper introduces a novel data‐driven parameter tuning approach that integrates higher fidelity power measurements using Gaussian processes to continuously adapt parameters in lower fidelity wake models based on the current farm's yaw configuration. The effectiveness of the proposed approach is demonstrated on a wind farm and a layout corresponding to the Horns Rev wind farm, where various wind directions are investigated. The results reveal that the approach can enable a lower fidelity model to capture more complex physics, thereby improving its accuracy in wake steering optimisation, while maintaining robustness and computational efficiency. This method holds promise for real‐time control applications and can be extended to other control strategies and closed‐loop frameworks.