Parameters In Models Research Articles

Development and Validation of a Predictive Model for Growth of Salmonella Infantis in Ground Turkey

Most retail samples (25 g) of ground turkey contain no or low levels of Salmonella. However, temperature abuse after retail can lead to spread and growth of Salmonella in the package. In addition, it can lead to levels that pose a significant risk of salmonellosis. This is especially true when the serotype is a top human clinical isolate, like Infantis. Therefore, the current study was undertaken to develop and validate a predictive model for growth of Salmonella Infantis in ground turkey subjected to temperature abuse. The purpose was to fill a gap for serotype-specific data and models in risk assessments for this pathogen and food combination. Storage trials with a low initial inoculum (0.85 log10) of Salmonella Infantis in commercial ground turkey samples (0.2 g) with native microflora were conducted at 16 to 40°C for 0 to 28 h. Salmonella was enumerated in ground turkey samples using an automated, whole sample enrichment, miniature, most probable number (MPN) assay. The MPN data were fitted to a three-phase linear primary model. Secondary models for primary model parameters were developed and used in the primary model to create a tertiary model that predicted growth of Salmonella Infantis in ground turkey as a function of time and temperature. Data and tertiary model predictions were evaluated using the test data, model performance, and model validation criteria of the Acceptable Prediction Zones method in the Validation Software Tool. The tertiary model predictions were considered to have acceptable bias and accuracy when the proportion of residuals (observed – predicted) in the partly and fully acceptable prediction zones (pAPZ) was ≥ 0.7. The overall pAPZ of the tertiary model was 0.866 for dependent data (n = 406) and 0.853 for independent data for interpolation (n = 177). However, there were local prediction problems that limited the validated prediction range to a region from 0 to 8 h at 16 to 40°C. Nonetheless, this validation range was sufficient to simulate temperature abuse of ground turkey during meal preparation in the consumers’ home. Thus, the model fills an important data and modeling gap in risk assessments for Salmonella and ground turkey. Additional data is needed to repair and fully validate the model.

Automobile Intelligent Vehicle-machine and Human-computer Interaction System based on Big Data

This paper measures the accelerator, brake pedal, clutch, transmission device and steering wheel under different driving conditions in real-time and accurately on the simulation experiment platform. The goal is to conduct human-machine-road system interaction in a virtual simulation of human-vehicle-road systems. Fitting test data establishes the mathematical model of traffic control parameters. In terms of hardware, the distributed architecture of upper and lower computers is utilized. The system communicates point-to-point with the host computer through the RS-232 serial port. The system adopts multi-thread technology and serial communication technology. The simulation system of driving operation is designed with the visual central controller. The system takes 89S52 as the core. The slave program is written in C language. Then, the system establishes a multi-target coordinated obstacle avoidance method based on a multi-sensor information vehicle cooperation collision avoidance method. The multi-vehicle cooperation obstacle avoidance problem is transformed into an optimal control problem under multiple constraints. Simulation analysis shows that the velocity and displacement obtained by the multi-robot collaborative collision avoidance method are in good agreement with the measured values. Compared with the time series algorithm, the output accuracy of the proposed collaborative collision avoidance algorithm is significantly reduced, and the changes in velocity and displacement in the time domain are more stable.

Improved probabilistic design method to quantify the fracture properties of roller compacted concrete

In this paper, the material parameters pertaining to fracture properties of Roller Compacted Concrete (RCC) were evaluated using the boundary element method (BEM). The fictitious crack growth length Δafic is determined by the relative size (T-a0)/di, while the individualized values of material parameters (KIC&ft) of RCC for different water-to-cement ratios were obtained. In this study, the three-parameter Weibull fracture statistical model was proposed for the first time to analyze the discreteness of these individualized values of material parameters (KIC&ft) of RCC. The statistical analysis minimum value of material parameters (KIC&ft) of each group is obtained. The failure curves of each group of RCC were predicted from the three-parameter Weibull fracture statistical model, and the simple calculation model for material parameters (KIC&ft) was validated. Further, a two-point design method was proposed for RCC material parameters (KIC&ft) with varying strengths and water-to-cement ratios.

Affective motivations for substance misuse differentially relate to consideration of multiple costs during effortful decision making.

Heightened sensitivity to costs during decision making consistently has been related to substance use. However, no work in this area has manipulated cost information to examine how people evaluate and compare multiple costs. Furthermore, limited work has examined how affective motivations for substance use modulate the evaluation of cost information. We administered a loss-frame variant of the Effort Expenditure for Rewards Task in a diverse community sample (N = 126). Individuals who use substances to avoid negative affect allocated comparable effort across varying likelihoods of loss and computational modeling parameters indicated that they did not systematically consider cost information, which ultimately led these individuals to exert effort when it was disadvantageous to do so. Individuals who use substances to enhance positive affect allocated effort when loss magnitudes were small, suggesting that they effectively compared costs and worked to minimize those costs. Motivations for substance use differentially relate to the comparison of costly information, ultimately influencing effective decision making. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Is the IROC H&N credentialing phantom an effective surrogate for different anatomical sites?: Using IROC's H&N phantom for various sites

PURPOSEThe IROC head and neck phantom is used to credential institutions for IMRT delivery for all anatomical sites where delivery of modulated therapy is a primary challenge. This study evaluated how appropriate the use of this phantom is for varied clinical anatomy by evaluating how closely the IROC head and neck phantom described clinical dose errors from beam modeling compared to various anatomical sites. METHODSThe MLC offset, transmission, PDD and seven additional beam modeling parameters for a Varian accelerator were modified in RayStation to match community data at the 2.5, 25, 50, 75 and 97.5 percentile levels. Modifications were evaluated on 25 H&N phantom cases and 25 clinical cases (H&N, prostate, lung, mesothelioma, and brain), generating 2,000 plan perturbations. Differences in mean dose delivered to clinical target volumes (CTV) and organs at risk (OAR) were compared between phantom and clinical plans to assess the relationship between dose deviations in phantom versus clinical CTVs, and as a function of 18 different complexity metrics. RESULTSPerturbations to MLC offset and transmission parameters demonstrated the greatest impact on dose accuracy for phantom and clinical plans (for all anatomic sites). The phantom demonstrated equivalent or greater sensitivity to these parameter perturbations when compared to clinical sites, largely aligning with treatment complexity. The mean MLC Gap best described the impact of errors in TPS beam modeling parameters in phantoms plan and clinical plans from various anatomical sites. CONCLUSIONWhen compared across various anatomical sites, the IROC H&N credentialing phantom exhibited similar or greater sensitivity to errors in the treatment planning system. As such, it is a suitable surrogate device for assessing institutional performance across various anatomical sites. If an institution successfully irradiates the phantom, that result confers confidence that IMRT to a wide range of anatomical sites can be successfully delivered by the institution.

Parameter identification of PV solar cells and modules using bio dynamics grasshopper optimization algorithm

AbstractThe escalating global population and energy demands underscore the critical role of renewable energy sources, particularly solar power, in mitigating environmental degradation caused by traditional fossil fuels. This paper emphasizes the advantages of solar energy, especially photovoltaic (PV) systems, which have become pivotal in hybrid energy systems. However, accurate modelling and identification of PV cell parameters pose challenges, prompting the adoption of meta‐heuristic optimization algorithms. This work explores the limitations of existing algorithms and introduces a novel approach, the bio‐dynamics grasshopper optimization algorithm (BDGOA). The BDGOA addresses deficiencies in both exploration and exploitation phases, exhibiting exceptional convergence speed and efficiency. The algorithm's simplicity, achieved through the implementation of an elimination phase and controlled search space, enhances its performance without intricate calculations. The study evaluates the BDGOA by applying it to identify unknown parameters of five solar modules. The algorithm's effectiveness is demonstrated through the extraction of parameters for RTC France, PWP201, SM55, KC200GT, and SW255 models, validated against experimental data under diverse conditions. The paper concludes with insights into the impact of radiation and temperature on module parameters. The subsequent sections of the paper delve into the intricacies of the PV cell and module model, articulate the formulation of the proposed algorithm, present simulations, and analyse the obtained results. The BDGOA emerges as a promising solution, overcoming the limitations of existing algorithms and contributing significantly to the advancement of accurate and efficient PV cell parameter identification, thereby propelling progress towards a sustainable energy future.

Paving the way ahead: protocol optimization of mouse models in crush syndrome related acute kidney injury research.

Crush syndrome (CS) is the leading cause of death after earthquakes, second only to direct trauma. Acute kidney injury (AKI) is the most severe complication of CS. Research based on the CS-AKI mouse model and kidney function assessment by glomerular filtration rate (GFR) helps to elucidate the pathogenesis of CS-AKI, which contributes to effective treatment measures. Mice were modeled by the multi-channel small animal crushing platform. We set up different CS-AKI modeling parameters by applying different crushing weights (0.5 kg, 1.0 kg, 1.5kg), crushing durations (6 h, 12 h, 16h), and decompression durations (6 h, 12 h, 24h). The GFR, serum creatinine (SCr), blood urea nitrogen (BUN), kidney tissue Kim-1 mRNA and Ngal mRNA expression levels, and HE staining were examined to evaluate the results of different protocols. The results showed that with the crushing weight increased, the kidney function assessment's gold standard GFR significantly decreased, and the levels of SCr and BUN increased. Meanwhile, the longer crushing durations found a higher extension of inflammatory cell infiltration in the kidney. The degree of kidney injury continued to worsen with the duration of decompression, indicating severe damage after reperfusion, which was associated with tubular injury and a sustained elevation of the inflammatory state. We successfully constructed CS-AKI mouse models with different severities under the above parameters. Applying 1.5kg for 16h and then decompressing for 24h induced severe AKI. These findings provide clues for further exploration of the mechanism and treatment of traumatic AKI.

Annual emissions of N2O, NO, HONO, and NH3 from maize-wheat fields in the North China Plain

Fertilized agricultural soil is a significant source of gaseous nitrogen compounds (GNCs), including N2O, NO, HONO, and NH3. The North China Plain (NCP) is the “hot region” for the release of these GNCs due to intensive fertilization practices. However, existing research has primarily focused on N2O emissions from fertilized farmland in the NCP, lacking comprehensive observational studies on other GNCs. Therefore, a continuous cumulative sampling technique (open-top dynamic chamber system) was utilized in this study to simultaneously measure the exchange fluxes of N2O, NO, HONO, and NH3 over summer maize-winter wheat rotation fields in the NCP. Results showed that GNC emissions from the soil displayed distinct diurnal variations, with higher emissions during the day attributed to elevated soil temperature. However, N2O emissions remained consistent between day and night, potentially influenced not only by soil temperature but also by soil humidity. Annual cumulative emissions and emission factors (EFs) for four GNCs were determined, indicating that N2O, NO, and NH3 emissions during the maize season were 1.38–2.37 times higher than those during the wheat season, with 98 % of HONO emissions occurring in the maize season. Additionally, the study first presented the annual HONO EFs of 0.36 ± 0.03 % in fertilized farmland. Furthermore, a comparison revealed that the fluxes of N2O, NO, NH3, and HONO using the conventional single-point sampling method were 26.4 %, 13.9 %, and 8.10 % lower, and 7.86 % higher compared to the continuous cumulative sampling method recommended in this study. In general, this study provided precise measurements of GNC emissions from farmland, offering essential foundational data for modeling parameters and contributing to the formulation of regional air pollution prevention and control policies.

Transformation trajectory of wetland and suitability of migratory water bird habitat in the moribund Ganges delta.

Wetland is a suitable habitat for water birds, and it enhances cultural ecosystem services. But the rapid transformation of such habitat, especially in floodplain environments, is an emerging crisis. Wetland reclamation and fragmentation are two major issues leading to poor habitat and landscape. The present paper aimed to explore the spatio-temporal changes in the suitability of wetland bird habitat, wetland landscape pattern, and the connection between them. Two wetlands, including a wetland of national importance, were taken as cases for this study. Time series Landsat and Sentinel images were taken for developing modeling parameters and Land Use Land Cover (LULC) for the years 2016 and 2020. The first transformation of wetland was accounted from the LULC maps of both years. Machine learning algorithm-based spatial models were developed for mapping the poor landscape condition of the existing wetland parts. Finally, semi-subjective analytic hierarchy approach (AHP)-based models were developed for assessing waterbird habitat suitability. Results demarcated more than 48% area belonging primarily to edges and tiny patches of wetlands under a poor state in 2020. Although the total wetland area was reduced between 2016 and 2020, the wetland area found to be highly suitable habitat increased from 25.5 to 59.44% of the total area during that period. The suitability of edge-preferring bird habitat showed a 10% increase. The increasing poverty of the landscape was caused by declining edge-preferring bird habitat suitability. From 1990 to 2020, 27% of wetlands were converted to single-cropped lands, and 5% were converted to multi-cropped agricultural land. Since the study spatially identified the potential suitable area and trend of wetland habitat transformation, this could help policymakers define suitable planning for the restoration and conservation of such promising bird habitat.

Modeling of thermo-aerodynamic and environmental parameters on the example of a two-pipe water heating boiler that has worked out the park resource

Topics related to fuel combustion and its impact on the environment will never lose their relevance, as the issues of efficient combustion and emission reduction are key in power generation and environmental protection. The countries of the European Union are massively abandoning the use of natural gas as a fuel for thermal power station. However, in Asian countries, the ease of using natural gas in industry as the main fuel, its environmental friendliness compared to coal, made it possible to widely use natural gas in industry and energy. Comparing natural gas with alternative combustible gases (generator, blast furnace, mine, biogas), the main conclusion can be drawn that it has the most attractive characteristics for its use in industry, including energy. Therefore, it is impossible to replace it with alternative fuels in the chemical, heavy industry and energy industry in the near future. The presented work is devoted to CFD modeling of stabilized combustion without premixing in a burner with low swirl for two operating modes of the boiler unit - nominal and at 60% capacity. The study was carried out using numerical methods using the ANSYS-Fluent application program package. The object of the study is a burner built according to the technology based on the use of jet-niche systems with gas distribution of fuel by circular jets fed perpendicularly into the flow of the oxidizer through a single- row system of holes. Hydrodynamics and heat exchange processes were chosen as the subject of research, based on the analysis of which a model of NO x generation in SNS was obtained. In this work, two types of burners are considered. In one of the burners, fuel is supplied through rectangular slits, in the other – through round holes arranged in a row. Air is supplied to both burners through rectangular slits. It was determined that gas distribution through round holes increases the spraying of the mixture and increases the area of spraying of combustion products. Visualization of the distribution of pressure, temperature, kinetic energy profiles of turbulent pulsations and vorticity was carried out. The obtained results indicate that there are no changes in the flow regime, flame displacement or its instability. It was determined that both the axial velocity and the tangential velocity of the flow affect the distribution of combustion products and harmful impurities such as NO x . Gas distribution in circular jets stabilizes combustion and reduces flame expansion.

Characterization of the active fault deformation zone of the Chegualin Fault in the alluvial plain of southwestern Taiwan

The activity of a creeping active fault poses significant challenges to engineering structures due to surface deformation. Therefore, quantifying the strain concentration caused by an active fault, delineating the extent and location of the Active Fault Deformation Zone (AFDZ), estimating long-term deformation trends, and predicting future deformations are crucial in the field of engineering geology.This study comprehensively integrates multi-timescale analytical methods by incorporating detailed geodetic data, rupture surveys, morphotectonic analysis, geological borehole data, biochronological data, radiocarbon dating, and Holocene uplift rate analysis to identify or confirm the locations of active faults and the long-term evolution trends of active deformation zones. Based on our findings, three active fault planes are identified, with one possibly being the main fault with the highest activity. Furthermore, by comparing long-term deformation rates derived from isochrone lines with short-term deformation rates obtained from leveling, we observe a risk of slip rate deficit. These findings have significant implications for engineering geology. As a general contribution, our study can serve as a site screening strategy for similar locations. Regarding the infrastructure we targeted, we provide critical input parameters for further numerical models (such as the trishear model) to simulate surface deformation, and offer essential design parameters for structures. Considering potential coseismic deformation on the investigated fault, these results are fundamental for future investigations or mitigation plans.

Quantitative impact damage of apple based on hyperspectral imaging combined with mechanical parameters and size correction.

In order to solve the problem of decreasing the accuracy of quantitative prediction of damage of fruits resulting in the size difference of fruits, the spectral correction method based on the size difference of fruits was adopted. To provide richer theoretical knowledge for the quality detection of fruits and the design of damage reduction programs in reality. First, the undamaged spectra of the group of apples with better performance of the model were selected as the reference spectra by analyzing and comparing the modeling results of the prediction models of mechanical parameters with the single fruit diameter groups. The spectral correction coefficient was calculated with the formulas, and the damage spectra of three groups of apples were size-corrected by this coefficient to build the mechanical parameter models. Finally, the corrected spectra were screened for characteristic wavelengths by competitive adaptive reweighting and uninformative variable elimination algorithms. The results of study showed that the correlation coefficients of the prediction set of the models were improved by 2.1%-13% and the root mean square errors were reduced by 16%-51% with the spectrally corrected models compared with the precorrection models. Therefore, the size correction method can be used to eliminate the effect of size difference on the mechanical parameter models to improve the applicability of the quantitative damage prediction models, and it can provide the theoretical guidance to design the loss-reducing protective measures and the agricultural mechanized operation process.

An Enhanced Symmetric Sand Cat Swarm Optimization with Multiple Strategies for Adaptive Infinite Impulse Response System Identification

An infinite impulse response (IIR) system might comprise a multimodal error surface and accurately discovering the appropriate filter parameters for system modeling remains complicated. The swarm intelligence algorithms facilitate the IIR filter’s parameters by exploring parameter domains and exploiting acceptable filter sets. This paper presents an enhanced symmetric sand cat swarm optimization with multiple strategies (MSSCSO) to achieve adaptive IIR system identification. The principal objective is to recognize the most appropriate regulating coefficients and to minimize the mean square error (MSE) between an unidentified system’s input and the IIR filter’s output. The MSSCSO with symmetric cooperative swarms integrates the ranking-based mutation operator, elite opposition-based learning strategy, and simplex method to capture supplementary advantages, disrupt regional extreme solutions, and identify the finest potential solutions. The MSSCSO not only receives extensive exploration and exploitation to refrain from precocious convergence and foster computational efficiency; it also endures robustness and reliability to facilitate demographic variability and elevate estimation precision. The experimental results manifest that the practicality and feasibility of the MSSCSO are superior to those of other methods in terms of convergence speed, calculation precision, detection efficiency, regulating coefficients, and MSE fitness value.

A combined DFT and finite difference simulation study on hybrid halide perovskite-based solar cells

This work reports the modelling and numerical estimation of the photovoltaic parameters of perovskite solar cells (PSCs) containing formamidinium lead iodide (FAPbI3) and formamidinium tin iodide (FASnI3) in the form of light active materials using SCAPS 1D software. The analysis is done by introducing diverse hole and electron transport materials and back metal contacts to identify the most appropriate device configuration that can deliver optimum photovoltaic output. The optoelectronic properties and the absorption spectra of the two compounds are estimated by DFT calculation using WIEN2k, which incorporates density functional theory (DFT) principles and are provided as input to SCAPS 1D to improve the accuracy of modelling study. For FAPbI3 as absorber material, the configuration FTO/IGZO/FAPbI3/NiO/Au shows the highest photovoltaic parameter exhibiting a power conversion efficiency (PCE) and fill factor (FF) of 19.75 % and 74.29 % respectively. With FASnI3 as the absorber material, FTO/PCBM/FASnI3/P3HT/Au gives a PCE and FF of 21.6 % and 67.74 % respectively. The work also includes the analysis of influence of defect density at the interface layers and series and shunt resistance on performance of above-mentioned device architectures to identify their limitations under real experimental conditions. The influence of various attributes of perovskite on the performance of the device is examined to determine their optimum values.

Statistical modeling and optimization of process parameters for additive manufacturing of styrene–ethylene–butylene–styrene block copolymer parts using solvent cast 3D printing technique

AbstractAdditive manufacturing of thermoplastic elastomers is challenging using fused deposition modeling due to their high melt viscosity, low column strength, and poor fusion among layers. Solvent‐cast 3D printing (SC‐3DP) is an efficient alternative to successfully 3D print such materials. However, selection of suitable 3D printing parameters is crucial to realize parts with optimum physicomechanical properties. In this work, statistical modeling and SC‐3DP parameter optimization for styrene–ethylene–butylene–styrene (SEBS) block copolymer were performed. The effect of 3D printing process parameters on shrinkage, relative density, and tensile strength was analyzed using response surface methodology. Experiments were planned as per central composite design and analysis of variance was performed to evaluate the significant parameters. SEBS content in the polymer solution significantly affected the shrinkage of SC‐3DP samples. Moreover, relative density and tensile strength were significantly affected by print speed and layer height. A significant interaction between print speed and layer height was also noticed for tensile strength and relative density of printed samples. Multi‐objective optimization using genetic algorithm was also performed to minimize shrinkage and maximize relative density and tensile strength. Finally, a case study was conducted comparing the physicomechanical properties of SC‐3DP samples printed at optimized process parameters and compression molded samples.Highlights Statistical models were developed using response surface methodology. Genetic algorithm based multi‐objective optimization was performed. Optimum solvent‐cast 3D printing (SC‐3DP) process parameters were determined.

Modelling of Some Physical-Chemical Parameters of the Bikoro Peat Bogs in the Congo Basin in the North-West of the Democratic Republic of Congo

This study, carried out in the heart of one of the world's most important wetlands, focuses on the modelling of certain physico-chemical parameters of the Bikoro peat bogs in the Congo Basin in the north-west of the Democratic Republic of Congo. To this end, we have characterized the above-mentioned parameters using digital modeling based on satellite and in situ data from five villages that make up the three sectors of this territory. Some of the equipment used includes three GPS (Garminextrex 30), Cybertacker v3.435 on Android, cameras (Samsung Wifi 12x + GPS), passive sensors (Radar). We also used an infrared spectrophotometer. The main results in relation to the 240 samples taken show that the pH of the peat bogs in the Bikoro territory varies between (2.600±0.001) and (5.000±0.004), the electrical conductivity measured varies between [85.48±3.17] μS/cm and [97.99±5. 47] μS/cm, the experimental carbon rate reported in tonnes per hectare is 135.3021, the forest carbon stock derived from WWF LiDar is 137.1484 and the spatial distribution of the temperature of these peatlands indicates that it ranges between (22.39±1.05)°C and (24.79±1.95)°C. The results of this study show that the peat bogs in the Bikoro area are wetlands that are both significantly acidic and carbon sinks.

Uncertainty-informed identification of tie-rod mechanical properties in monumental buildings

Tie-rods are classic structural elements that have been used for centuries to enhance stability in historic masonry buildings and offer valuable insights into structural performance over time through dynamic vibration measurements. This paper tackles the inverse problem, aiming to quantify uncertainties in mechanical properties, tensile axial force and boundary conditions of tie-rods based on experimental vibration frequencies. The approach assumes known probabilistic models for random parameters, such as mass per unit length, bending stiffness, tensile force and boundary conditions, with certain parameters requiring estimation. Finite dimensional models, i.e., deterministic functions in time and/or space, and a finite number of random variables are employed to discretize both physical and probability spaces. The paper concludes with a Monte Carlo simulation, that provides a comprehensive characterization of uncertainties and the best estimates for unknown features, including mass per unit length, stiffness, boundary conditions and tensile force necessary for achieving target free vibration modal parameters, i.e. natural frequencies.

Chemical-Inspired Material Generation Algorithm (MGA) of Single- and Double-Diode Model Parameter Determination for Multi-Crystalline Silicon Solar Cells

The optimization of solar photovoltaic (PV) cells and modules is crucial for enhancing solar energy conversion efficiency, a significant barrier to the widespread adoption of solar energy. Accurate modeling and estimation of PV parameters are essential for the optimal design, control, and simulation of PV systems. Traditional optimization methods often suffer from limitations such as entrapment in local optima when addressing this complex problem. This study introduces the Material Generation Algorithm (MGA), inspired by the principles of material chemistry, to estimate PV parameters effectively. The MGA simulates the creation and stabilization of chemical compounds to explore and optimize the parameter space. The algorithm mimics the formation of ionic and covalent bonds to generate new candidate solutions and assesses their stability to ensure convergence to optimal parameters. The MGA is applied to estimate parameters for two different PV modules, RTC France and Kyocera KC200GT, considering their manufacturing technologies and solar cell models. The significant nature of the MGA in comparison to other algorithms is further demonstrated by experimental and statistical findings. A comparative analysis of the results indicates that the MGA outperforms the other optimization strategies that previous researchers have examined for parameter estimation of solar PV systems in terms of both effectiveness and robustness. Moreover, simulation results demonstrate that MGA enhances the electrical properties of PV systems by accurately identifying PV parameters under varying operating conditions of temperature and irradiance. In comparison to other reported methods, considering the Kyocera KC200GT module, the MGA consistently performs better in decreasing RMSE across a variety of weather situations; for SD and DD models, the percentage improvements vary from 8.07% to 90.29%.

A 3D convolutional neural network model with multiple outputs for simultaneously estimating the reactive transport parameters of sandstone from its CT images

Porosity, tortuosity, specific surface area (SSA), and permeability are four key parameters of reactive transport modeling in sandstone, which are important for understanding solute transport and geochemical reaction processes in sandstone aquifers. These four parameters reflect the characteristics of pore structure of sandstone from different perspectives, and the traditional empirical formulas cannot make accurate predictions of them due to their complexity and heterogeneity. In this paper, eleven types of sandstone CT images were firstly segmented into numerous subsample images, the porosity, tortuosity, SSA, and permeability of the subsamples were calculated, and the dataset was established. The 3D convolutional neural network (CNN) models were subsequently established and trained to predict the key reactive transport parameters based on subsample CT images of sandstones. The results demonstrated that the 3D CNN model with multiple outputs exhibited excellent prediction ability for the four parameters compared to the traditional empirical formulas. In particular, for the prediction of tortuosity and permeability, the 3D CNN model with multiple outputs even showed slightly better prediction ability than its single-output variant model. Additionally, it demonstrated good generalization performance on sandstone CT images not included in the training dataset. The study showed that the 3D CNN model with multiple outputs has the advantages of simplifying operation and saving computational resources, which has the prospect of popularization and application.

Optimization of Ansys CFX Input Parameters for Numerical Modeling of Pump Performance in Turbine Operation

The paper deals with the issue of determining the optimal setting of input variables in Ansys CFX for modeling pump flow in turbine operation (PAT). The pump model was created in Autodesk Inventor. The mesh for numerical simulations was created using Ansys Fluent Meshing, considering the mesh quality parameters’ skewness and aspect ratio. The Ansys CFX computational model was experimentally verified on an actual pump by measuring the performance parameters on a test circuit and using the PIV (particle image velocimetry) method. The research indicated that the most suitable setting for the model input variables was the inlet pressure and PAT flow rate combination. Another option was to adjust the pressure at the pump inlet and outlet. However, the calculation time in this case was up to 30% longer. The comparison of the model results with the experiment showed that the deviations in the numerical model performance values did not exceed 10% of the values measured on the test circuit. Only the calculated torque was 1.2 ± 0.13 Nm higher on average than the torque measured on the test circuit. This difference is most likely due to the simplification of the geometry of the computational mesh in order to reduce the computation time.