Modeling Validation Research Articles

Investigating the advantages of internal impact in high-performance lightweight ultra-low-frequency rotational energy harvesters

Piezoelectric energy harvesters are promising for collecting energy from ultra-low-frequency rotational machines due to their small-scale and lightweight characteristics. However, the power output for the reported rotational piezoelectric energy harvesters can hardly reach the milliwatt level, limiting their applications in sensor systems with high power consumption. To overcome this challenge, this Letter proposes an approach of using the internal impact mechanism to achieve high-performance lightweight ultra-low-frequency rotational energy harvesters. The internal impact is achieved by utilizing the velocity difference between a sliding mass and a tube on a piezoelectric beam. Through mathematical modeling and experimental validation, it is demonstrated that the velocity difference exists at ultra-low-rotational frequencies without a defined frequency lower limit, thus increasing the vibration amplitude of beam and enhancing the power output. The results show that the impact system achieves up to 136 times increase in power output compared to the non-impact system. With a maximum power output of 2.97 mW and a power density of 169.19 μW/g, the proposed energy harvester significantly outperforms the previously reported lightweight ultra-low-frequency rotational energy harvesters and shows great potential in self-powered sensing and monitoring of ultra-low-frequency rotational machines.

Estimasi Sumberdaya Bauksit Menggunakan Metode Ordinary Kriging Pada PT. Wahana Petra Sejahtera Kabupaten Bengkayang Provinsi Kalimantan Barat

PT. Wahana Petra Sejahtera, which operates in the bauxite mining sector, requires resource calculations as a basis for carrying out its mining activities. This research aims to determine the levels and distribution of Al2O3, SiO2, and Fe2O3 and estimate bauxite resources using the ordinary kriging method. This research uses secondary data in coordinates, elevation, test pit depth, and slope data. Data processing uses software in database creation, statistical analysis, block and composite modeling, variogram analysis, resource estimation, and model validation. The calculation results for the Jirak block show that the Al2O3 waste content is 1,488,000 tons, low-grade 4,532,000 tons, medium-grade 1,824,000 tons, and high-grade 32,000 tons. For SiO2 the average content is 13.54% and the average Fe2O3 content is 13.50%. Meanwhile, in the Sindu block the Al2O3 waste content is 64,000 tons, medium-grade 1,188,000 tons, and high-grade 3,056,000 tons. Meanwhile, the average SiO2 content is 9.13% and the average Fe2O3 content is 12.06%. The total resources in the Jirak block are 7,696,000 tons with the amount meeting the COG of 800 tons. Meanwhile, in the Sindu block, the total resources are 5,008,000 tons with an amount that meets the COG of 1,280,000 tons.

Engineering inhibitory repeat domains of Pin-II type proteinase inhibitors indicate their high structural-functional tolerance to mutagenesis

Plant proteinase inhibitors (PIs) are critical in defending against biotic stress. Most PIs contain an inhibitory repeat domain (IRD), which serves as the functional component, displaying a high degree of sequence and structural conservation. In this study, we examined the structural and functional resilience of IRDs using a combination of computational modeling and experimental validation. We have taken an evolution-based approach to enhance the PIs effectiveness of two previously identified Capsicum annuum IRDs, IRD4 and IRD10. Through in silico site-saturation mutagenesis of IRD4 and IRD10, we identified key sites associated with enhanced PI activity for targeted mutagenesis. Binding energy predictions for a mutant IRD library, tested against target proteases, suggested that positions R11 and N32 in IRD4 and N32 and H33 in IRD10 were promising candidates for further modification to improve inhibitory potential. Subsequent experimental validation revealed that the mutant proteins IRD4_R11K and IRD4_N32S exhibited stronger chymotrypsin inhibition than the wild-type (WT) IRD4. Similarly, the mutants IRD10_N32S and IRD10_H33 N demonstrated improved trypsin inhibition relative to the WT IRD10. These findings indicate that engineered IRD variants can tolerate structural changes while maintaining or enhancing their inhibitory activity against target proteases. Overall, this study demonstrates the potential of engineering PIs to increase their structural and functional resilience, offering new opportunities for biotechnological applications.

Constitutive modeling of functional fatigue with tension–compression asymmetry for superelastic NiTi shape memory alloy

Under cyclic loads, superelastic shape memory alloys (SMAs) exhibit stress–strain responses featured by functional fatigue, i.e., degradation of superelasticity and accumulation of irrecoverable deformation as cycling number increases, together with an asymmetry between tensile and compressive responses. Comprehensive understanding and modeling of these material complexities are crucial for the design and analysis of various superelastic SMA structures in practical applications. This work has developed a novel constitutive model based on irreversible thermodynamics to account for functional fatigue with tension–compression asymmetry. A potential function, defined as a weighted sum of two potentials that are calibrated against the tensile and compressive responses respectively, is employed to generate the asymmetric responses, and functional fatigue is represented by degradation of superelastic properties and growth of plastic strain as martensitic transformation accumulates. The model is adopted in numerical simulations for superelastic SMA tubes under cyclic lateral compression, which is experimentally investigated as a model problem. The agreement between simulations and experiments shows the validity and effectiveness of this constitutive modeling. Through additional finite element simulations incorporating this model, the effects of tension–compression asymmetry under cycling and diameter-to-thickness ratio of the tubular geometry upon mechanical responses of laterally compressed SMA tubes are also unveiled.

Validation of a three-dimensional finite element constitutive modeling approach for a thermoplastic polyurethane calibrated with uniaxial tests

This paper presents the three-dimensional finite element constitutive modeling validation for a Thermoplastic Poly-Urethane (TPU) material. The material parameters are those given in a previous study obtained by calibrating the constitutive model with uniaxial material-level test results (1-D tensile and compression tests). Confined compression tests were performed to estimate the bulk modulus for more accurate material characterization. The parameters were validated by comparing the results of experimental tests on TPU components with those obtained by its equivalent finite element simulations. The TPU component tests comprised cyclic compression tests of (i) a TPU cylinder, (ii) a solid 100 mm diameter TPU ball, and (iii) a 100 mm diameter TPU ball with an 80 mm steel core. In all tests, the constitutive model parameters showed an excellent performance in representing the mechanical behavior of the material observed in the tests, including the nonlinear stiffness and hysteresis. Finally, a brief case study is presented to illustrate the applicability of the validated constitutive model parameters in modeling a novel TPU damper for structural control before its manufacturing and experimental testing. The validated constitutive modeling approach and available material parameters will allow the performance of reliable and early-stage finite element simulations to prototype the mechanical behavior of TPU components of different shapes and boundary conditions and thus gain relevant insight into the component level response before manufacturing and testing.

A regulatory module mediating temperature control of cell-cell communication facilitates tree bud dormancy release.

The control of cell-cell communication via plasmodesmata (PD) plays a key role in plant development. In tree buds, low-temperature conditions (LT) induce a switch in plasmodesmata from a closed to an open state, which restores cell-to-cell communication in the shoot apex and releases dormancy. Using genetic and cell-biological approaches, we have identified a previously uncharacterized transcription factor, Low-temperature-Induced MADS-box 1 (LIM1), as an LT-induced, direct upstream activator of the gibberellic acid (GA) pathway. The LIM1-GA module mediates low temperature-induced plasmodesmata opening, by negatively regulating callose accumulation to promote dormancy release. LIM1 also activates expression of FT1 (FLOWERING LOCUS T), another LT-induced factor, with LIM1-FT1 forming a coherent feedforward loop converging on low-temperature regulation of gibberellin signaling in dormancy release. Mathematical modeling and experimental validation suggest that negative feedback regulation of LIM1 by gibberellin could play a crucial role in maintaining the robust temporal regulation of bud responses to low temperature. These results reveal genetic factors linking temperature control of cell-cell communication with regulation of seasonally-aligned growth crucial for adaptation of trees.

Exploration of Pharmacological Mechanism of Cinnamomum tamala Essential Oil in Treating Inflammation based on Network Pharmacology, Molecular Modelling, and Experimental Validation.

Cinnamomum tamala (Buch.-Ham.) T.Nees & Eberm., also known as Indian Bay leaf, holds a distinctive position in complementary and alternative medicinal systems due to its anti-inflammatory properties. However, the active constituents and key molecular targets by which C. tamala essential oil (CTEO) exerts its anti-inflammatory action remain unclear. The present study used network pharmacology and experimental validation to investigate the mechanism of CTEO in the treatment of inflammation. GC-MS analysis was used to identify the constituents of CTEO. The key constituents and core targets of CTEO against inflammation were obtained by network pharmacology. The binding mechanism between the active compounds and inflammatory genes was ascertained by molecular docking and molecular dynamics simulation analysis. The pharmacological mechanism predicted by network pharmacology was verified in lipopolysaccharide-stimulated murine macrophage (RAW 264.7) cell lines. Forty-nine constituents were identified by GC-MS analysis, with 44 constituents being drug-like candidates. A total of 549 compounds and 213 inflammation-related genes were obtained, revealing 68 overlapping genes between them. Compound target network analysis revealed cinnamaldehyde as the core bioactive compound with the highest degree score. PPI network analysis demonstrated Il-1β, TNF-α, IL8, IL6 and TLR4 as key hub anti-inflammatory targets. KEGG enrichment analysis revealed a Toll-like receptor signalling pathway as the principally regulated pathway associated with inflammation. A molecular docking study showed that cinnamaldehyde strongly interacted with the Il-1β, TNF-α and TLR-4 proteins. Molecular dynamics simulations and MMPBSA analysis revealed that these complexes are stable without much deviation and have better free energy values. In cellular experiments, CTEO showed no cytotoxic effects on RAW 264.7 murine macrophages. The cells treated with LPS exhibited significant reductions in NO, PGE2, IL-6, TNF-α, and IL-1β levels following treatment with CTEO. Additionally, CTEO treatment reduced the ROS levels and increased the antioxidant enzymes such as SOD, GSH, GPx and CAT. Immunofluorescence analysis revealed that CTEO inhibited LPS-stimulated NF-κB nuclear translocation. The mRNA expression of TLR4, MyD88 and TRAF6 in the CTEO group decreased significantly compared to the LPS-treated group. The current findings suggest that CTEO attenuates inflammation by regulating TLR4/MyD88/NF- κB signalling pathway.

Modeling, Simulation, and Full-Scale Validation of an On-Board Hybrid AC/DC Power System

Modeling, Simulation, and Full-Scale Validation of an On-Board Hybrid AC/DC Power System

Ion solvation modulating via Imidazolidinyl urea additive for stable Zn2+ deposition

The development of energy conversion and storage equipment is a necessary guarantee for social development, and aqueous Zn-ion batteries have become a promising alternative to Li-ion batteries due to its high safety, environmental friendliness, and low cost. However, aqueous Zn-ion batteries suffer from the dendrite growth and parasitic reactions, which hinder the wide application of the batteries. In this paper, imidazolidinyl urea (IDU) is added to aqueous electrolytes because of the special structural properties of IDU. The effects of IDU as an additive on battery stability are elucidated through theoretical modelling, calculations, and experimental validation. Firstly, the effect of concentration on the ion transport properties of the electrolyte was obtained by modelling and calculating the Zn2+ diffusion coefficient and ionic conductivity of electrolytes with different concentrations of IDU added. In addition, the optimal IDU addition concentration was achieved by verification of cycling experiments in Zn symmetric cells. Moreover, it was confirmed using in situ deposition experiments that the addition of IDU could participate in the coordination of Zn2+ into the solvated layer of Zn2+, thereby accelerating the desolvation process of Zn2+, and further improving the uneven deposition of Zn on the negative electrode surface and the growth of dendrites. Finally, it was verified that the IDU addition could enhance the cycle life and stability of the battery, maintaining an efficiency of about 95% and a specific capacity of discharge of 90 mAh·g-1 after more than 1000 cycles in a full cell assembled from Zn flakes and improved iodine electrodes.

Dynamics analysis and verification for slotted limited-angle torque motor in electromagnetic valve applications

Abstract Limited-angle torque motor (LATM) as a key component of the exhaust gas recirculation (EGR) valve, which can precisely control the position of the EGR valve, thus achieving dynamic adjust the amount of exhaust gas recirculation. This paper introduces the dynamic analysis, modelling and experimental validation of a concentrated winding axial-flux slotted LTAM with 4-poles/4-slots. Aiming at the prominent issues such as cogging torque and nonlinear torque characteristics in the operating region of slotted LATMs, analytical equations are derived based on the magnetic circuit to predict the torque-angle characteristics of LATM, covering both constant torque and linear torque regions. These equations help establish the relationship between performance and key parameters, enabling rapid design of LATM. The test results indicate that the proposed model can accurately describe the electromagnetic torque and cogging torque of the LTAM, with an error range of 5%. Utilizing the effective angle range proposed in this paper, an approximate linear relationship between the electromagnetic torque and current can be achieved, with a linear error of 6%.

Application Value of a Machine Learning Model in Predicting Mild Depression Associated with Migraine without Aura.

Aims/Background To investigate the application value of a machine learning model in predicting mild depression associated with migraine without aura (MwoA). Methods 178 patients with MwoA admitted to the Department of Neurology of the First Affiliated Hospital of Anhui University of Traditional Chinese Medicine from March 2022 to March 2024 were selected as subjects. According to their inpatient medical records, 38 patients were selected as the validation group by random number method, and the remaining 140 patients were included in the modelling group. According to the diagnosis results, the patients in the modelling group and validation group were further divided into a MwoA with mild depression group and a MwoA without mild depression group. Results The results of univariate analysis and Multivariate logistic regression analysis showed that gender, course of disease, attack frequency, headache duration, Migraine Disability Assessment Questionnaire (MIDAS), and Headache Impact Test-6 (HIT-6) score were independent influencing factors for mild depression in MwoA patients (p < 0.05). The receiver operating characteristic (ROC) analysis results showed that the area under the curve of the established prediction model for MwoA patients with mild depression in the modelling group and the validation group was 0.982 and 0.901, respectively, the sensitivity was 0.978 and 0.857, respectively, and the specificity was 0.892 and 0.929, respectively. Conclusion Gender, course of disease, seizure frequency, headache duration, MIDAS score, and HIT-6 score are independent influencing factors for mild depression in patients with MwoA. The model displays good performance for the prediction of mild depression in patients with MwoA.

Study on robot trajectory planning and coating thickness prediction for plasma spraying on complex surface

Plasma spraying techniques are commonly employed for the deposition of thermal barrier coatings (TBCs) due to their efficiency and cost-effectiveness. However, ensuring uniform coating thickness and quality on complex free-form surfaces poses significant challenges. This paper investigates the influence of spraying trajectory and related parameters (spraying distance, angle, velocity) on coating thickness distribution, addressing the need for simplified analysis among numerous variables affecting coating quality. Different from the predominantly existing research focusing on flat or rotationally symmetric substrates, this study delves into the planning of spray trajectories for free-form surfaces, which is crucial for industries dealing with complex components, such as turbine blades. Innovative optimization approaches are employed to refine spray trajectories and improve coating consistency. Through theoretical modeling, simulation and experimental validation, the impact of spray parameters on coating thickness was demonstrated. Both the mean and disperssion coefficient errors of the coating thickness, obtained by the theoretical prediction model and the spray experiments, are lower than 10 %. The normal spray trajectory makes the coatings more evenly distributed, and the coating uniformity is at least 50 % higher than that of the codirectional spraying. This research contributes to the optimization of plasma spraying processes, particularly on irregular surfaces, thereby facilitating the development of high-performance TBCs for industrial applications.

3D CFD modelling and experimental validation of conjugate heat transfer in wheel hub motors in micro-mobility vehicles

An experimental and 3D CFD (computational fluid dynamics) based numerical investigation of the in-wheel hub internal permanent magnet synchronous machine (IPMSM) has been carried out to understand the thermal dynamics associated with the power loss within the motor. This study can serve as a clear case baseline for the future development of thermal management systems in micro-powertrain systems. Experimental results show that the maximum winding temperature of 65.9 °C has been observed on the end-windings (EW) for an operating condition of 500 rpm (base speed), 4.5 Nm. Stretching beyond this operating point would increase the winding temperature beyond 65.9 °C, potentially damaging the winding insulation’s capabilities and magnetic properties of the permanent magnets (PMs) and adversely affecting the performance of the wheel motors. The hysteresis and eddy current loss coefficients for the stator core have been identified as 14.5×10-2 and 1.6×10-4 respectively. The experimentally identified losses have been distributed among the motor components for the development of the numerical thermal model in commercial CFD software Star-CCM+™. The CHT (conjugate heat transfer) thermal paths show that 68.4 % and 31.4 % of the total power loss from the stator components undergo convection and conduction, respectively. This indicates that convection heat transfer dominates on the in-wheel hub IPMSM motor topology. Almost 34.5 % of the heat loss is transferred radially through the air gap to the Permanent Magnets. When the air gap size decreased by 80 %, the amount of convection heat transfer through the air gap increased by a factor of 2 (69 %) and reduced the winding temperature by 13 % (57.3 °C).

Modeling and Experimental Validation of Cell Morphology in Microcellular-Foamed Polycaprolactone.

This study investigates the modeling and experimental validation of cell morphology in microcellular-foamed polycaprolactone (PCL) using supercritical carbon dioxide (scCO2) as the blowing agent. The microcellular foaming process (MCP) was conducted using a solid-state batch foaming process, where PCL was saturated with scCO2 at 6 to 9 MPa and 313 K, followed by depressurization at a rate of -0.3 and -1 MPa/s. This study utilized the Sanchez-Lacombe equation of state and the Peng-Robinson-Stryjek-Vera equation of state to model the solubility and density of the PCL-CO2 mixture. Classical nucleation theory was modified and combined with numerical analysis to predict cell density, incorporating factors such as gas absorption kinetics, the role of scCO2 in promoting nucleation, and the impact of depressurization rate and saturation pressure on cell growth. The validity of the model was confirmed by comparing the theoretical predictions with experimental and reference data, with the cell density determined through field-emission scanning electron microscopy analysis of foamed PCL samples. This study proposes a method for predicting cell density that can be applied to various polymers, with the potential for wide-ranging applications in biomaterials and industrial settings. This research also introduces a Python-based numerical analysis tool that allows for easy calculation of solubility and cell density based on the material properties of polymers and penetrant gases, offering a practical solution for optimizing MCP conditions in different contexts.

Comparative modeling reveals the molecular determinants of aneuploidy fitness cost in a wild yeast model

Although implicated as deleterious in many organisms, aneuploidy can underlie rapid phenotypic evolution. However, aneuploidy will be maintained only if the benefit outweighs the cost, which remains incompletely understood. To quantify this cost and the molecular determinants behind it, we generated a panel of chromosome duplications in Saccharomyces cerevisiae and applied comparative modeling and molecular validation to understand aneuploidy toxicity. We show that 74%-94% of the variance in aneuploid strains' growth rates is explained by the cumulative cost of genes on each chromosome, measured for single-gene duplications using a genomic library, along with the deleterious contribution of small nucleolar RNAs (snoRNAs) and beneficial effects of tRNAs. Machine learning to identify properties of detrimental gene duplicates provided no support for the balance hypothesis of aneuploidy toxicity and instead identified gene length as the best predictor of toxicity. Our results present a generalized framework for the cost of aneuploidy with implications for disease biology and evolution.

Temperature ratio RT = TL/TH of fully accelerated complementary fragments (used for TXE partition) obtained independently of prompt emission model calculations

Several refined prompt emission model codes, nowadays employed, use the partition of total excitation energy (TXE) according to the temperature ratio RT = TL/TH of fully accelerated fragments. In such codes RT is given as input, either as an unique value for all fragmentations or as a function of AH; This temperature ratio RT being obtained by fitting the experimental ν(A) data with the respective prompt emission model code. This paper proposes a method for obtaining “experimental RT(AH)” (also based on experimental ν(A)), but without resorting to prompt emission model calculations for the fit of ν(A) data. So that this is an independent method providing RT(AH) which can be employed by any prompt emission model code in which the TXE partition is done at the full acceleration according to RT given as input. A procedure which facilitates the parameterization of RT(AH) is proposed, too. A prediction of the RT(AH) shape at very high excitation energies of the fissioning nucleus is also reported. The comparison of RT(AH) ratios obtained from any TXE partition based on modeling at scission with those provided by the present method can constitute a supplementary validation of the respective modeling at scission.

Analytical Modeling and Experimental Validation of the Coefficient of Friction in AlSi10Mg-SiC Composites

Analytical Modeling and Experimental Validation of the Coefficient of Friction in AlSi10Mg-SiC Composites

Synergistic approach of GCW-ISTR for enhanced remediation of semi-volatile organic contaminants in groundwater: Modeling and experimental validation

The poor remediation performance of groundwater circulation well (GCW) on semi-volatile organic contaminants (such as aniline) has severely limited its practical application. To address the challenges posed by the low volatility and solubility of these contaminants, an innovative integration of GCW with in-situ thermal remediation (ISTR) was proposed to create a thermal enhanced circulation well (GCW-ISTR) in this study. Laboratory experiments and model simulations were performed to evaluate the heat transfer and enhanced remediation effect by GCW-ISTR. Results demonstrate that the heat transfer process is controlled by the water circulation around GCW-ISTR and is influenced by factors such as aeration flow rate, groundwater velocity and aquifer permeability. Heating area is directly proportional to the seepage velocity of groundwater which can be analyzed by multiplying the water head difference between the upper and lower screens with the aquifer permeability. Therefore, the heat transfer model, based on Darcy's seepage theory and the energy conservation equation, effectively simulates the heat transfer with an error margin of less than 10%. Compared to individual GCW, GCW-ISTR exhibits a 25.8% improvement in aniline remediation efficiency, resulting in a decrease in the average concentration from 97.95 mg/L to 0.168 mg/L within 48 h, effectively mitigating the occurrence of tailing phenomena. This study provides a feasible method of enhancing the remediation of GCW on semi-volatile contaminants and is anticipated to broaden the applicability of GCW in site application.

Refined one-dimensional modeling and experimental validation of scroll compressor with vapor injection for electric vehicles

Vapor injection technology represents a highly promising avenue for enhancing the efficiency of heat pump systems within electric vehicles, especially in challenging cold ambient conditions. Although a simplified isentropic process is commonly employed to assess the thermodynamic functioning of scroll compressors with vapor injection (SCVI), it diverges significantly from actual operational dynamics. This study introduces a sophisticated 1D mathematical model that incorporates key factors such as internal leakage and thermal losses, thereby providing a more accurate representation of SCVI's operational realities. The research includes comprehensive performance evaluations of a short wrap profile SCVI, with a specific focus on low-temperature ambient conditions, supported by rigorous experimental validation. Comparative analyses against non-injection scenarios reveal notable enhancements, including a maximum 17.2 % increase in mass flow, a 10.5 % rise in heating capacity, and a 2.15 % improvement in heating COP. Both the simplified isentropic process calculation model and the enhanced 1D mathematical model are utilized to analyze compressor operations. The integration of internal leakage and heat loss considerations significantly narrows the gap between calculated and experimental results for heating capacity and discharge temperature, reducing discrepancies from nearly 20 % to a mere 4 %. This refined mathematical model demonstrates a high level of alignment with experimental data, achieving an accuracy within 5 % when assessing the compressor's real-world operational dynamics.

Evolution and governance of online public opinion during COVID-19: a hybrid approach using communication visualization, SIR modeling, and simulation validation

IntroductionThis study investigates the mechanisms of public opinion dissemination and governance strategies during public health events, using a two-stage SIR model informed by the Information Cascade Theory.MethodsThe research employs Gephi visual analysis to identify principal nodes of public opinion and combines model simulations with dynamic propagation analysis to verify the model's precision and applicability.ResultsThe findings reveal that pivotal information nodes significantly accelerate the spread of public opinion, while ordinary nodes contribute to the natural attenuation of public discourse due to their strong spontaneous recovery capabilities. The simulation analysis further identifies the optimal timing for government intervention, particularly during the initial and peak phases of public opinion dissemination.DiscussionBased on the results, the study recommends strategies to strengthen the management of key opinion nodes, enhance public information literacy, optimize policy implementation, and utilize simulation tools to assist in public opinion management. These recommendations offer valuable theoretical and practical insights for managing public opinion during public health events.