Simulation Analyses Research Articles

Photoionization analysis of chemodynamical dwarf galaxies simulations. II. Detailed calculation of diffuse ionizing radiation

ABSTRACT Active star formation in dwarf galaxies shapes the morphology of the surrounding nebular environment and ensures the non-uniformity of the chemical elements spatial distribution in it due to the superwind region expansion. Ionizing radiation within the nebular gas produces observed emission lines used for modelling and diagnostics. We introduce a multicomponent photoionization modelling (MPhM) approach that incorporates detailed calculation of diffuse ionizing radiation (DCDIR) based on chemodynamical simulations (ChDSs). Our models aim to replicate crucial emission line intensity ratios within the observed range, employing a thin dense shell between the superwind region and the outer nebular environment to address ChDSs resolution limitations, which render them insensitive to the presence of a superwind shock. MPhM-generated emission line spectra within a small central synthetic aperture and a thin long-slit exhibit excellent agreement with observations, confirming the accuracy of the ionization structure of the nebular environment obtained using the MPhM + DCDIR approach. However, the outward-only approximation fails to reproduce the dwarf galaxies ionization structure. We determined the oxygen abundance using the $T_e$- and $R_{23}$-methods based on emission lines from MPhM + DCDIR. The resulting abundances align well with values obtained by averaging over the ‘observed’ volume within synthetic apertures, weighted by mass. The escape fraction of ionizing photons from the dwarf galaxy was found to be larger than that obtained using the outward-only approximation. Employing Kennicutt’s calibration corrected for near-UV data, the star formation rate (SFR) was calculated using the ${\rm H}\,\alpha$ luminosity from MPhM + DCDIR. The resulting SFR value is nearly 33 per cent higher than the true one.

The Electrostatic Induction Characteristics of SiC/SiC Particles in Aero-Engine Exhaust Gases: A Simulated Experiment and Analysis

This study investigates the electrostatic induction characteristics of silicon carbide-fiber-reinforced silicon carbide (SiC/SiC) particles within aero-engine exhaust gases using a dedicated J20 turbojet engine experimental platform. Our comprehensive experiments explored the electrostatic properties of SiC/SiC particles under varying engine operational states—specifically focusing on different thermal conditions, particle mass concentrations, particle sizes, and exhaust gas velocities compared to those of common engine exhaust constituents like carbon (C) and iron (Fe) particles. The results demonstrate that SiC/SiC particles consistently maintain a stable positive charge across varied temperatures, significantly diverging from the behaviors of carbon (C) and iron (Fe) particles. Additionally, our findings reveal that higher mass concentrations of SiC/SiC particles, smaller particle sizes within a certain range, and greater exhaust gas velocities of the aero-engine all lead to increased particle charge and more pronounced electrostatic induction characteristics. This study highlights the potential of electrostatic sensors for the early detection and diagnosis of failures in aero-engines, offering crucial insights into the development of more resilient real-time aero-engine health monitoring systems.

Comparative study of novel packet loss analysis and recovery capability between hybrid TLI-µTESLA and other variant TESLA protocols

Analyzing packet loss, whether resulting from communication challenges or malicious attacks, is vital for broadcast authentication protocols. It ensures legitimate and continuous authentication across networks. While previous studies have mainly focused on countering Denial of Service (DoS) attacks' impact on packet loss, our research introduces an innovative investigation into packet loss and develops data recovery within variant TESLA protocols. We highlight the efficacy of our proposed hybrid TLI-µTESLA protocol in maintaining continuous and robust connections among network members, while maximizing data recovery in adverse communication conditions. The study examines the unique packet structures associated with each TESLA protocol variant, emphasizing the implications of losing each type on the network performance. We also introduce modifications to variant TESLA protocols to improve data recovery and alleviate the effects of packet loss. Using Java programming language, we conducted simulation analyses that illustrate the adaptability of variant TESLA protocols in recovering lost packet keys and authenticating previously buffered packets, all while maintaining continuous and robust authentication between network members. Our findings also underscore the superiority of the hybrid TLI-µTESLA protocol in terms of packet loss performance and data recovery, alongside its robust cybersecurity features, including confidentiality, integrity, availability, and accessibility. Additionally, we demonstrated the efficiency of our proposed protocol in terms of low computational and communication requirements compared to earlier TESLA protocol variants, as outlined in previous publications.

A robust [formula omitted] fault-tolerant control approach for time-delay LPV systems with uncertain parameters and unknown disturbances

The article proposes a H∞ fault-tolerant control approach for a series of uncertain linear parameter-varying (LPV) time-delay models to obtain disturbance suppressions. Specifically, by applying the Lyapunov functions, a series of feedback controllers are provided to ensure the robust performance of LPV models with actuator faults. Meanwhile, a convex optimization strategy is developed for resolving optimization problems in the presence of bilinear matrix inequalities (BMIs), where the robustness conditions are improved to guarantee the stability of LPV model under uncertain factors. By resolving a class of linear matrix inequalities (LMIs), the gain matrices for LPV systems can be obtained. Furthermore, the less conservative conditions are developed and supported by strict theoretical derivation. Ultimately, the validity of proposed approach is confirmed by simulation analyses of truck–trailer systems.

The role of arabinoxylan purified from wheat bran in bread and biscuits: Impacts on digestibility and colonic fermentability in vitro

In this study, arabinoxylan (AX) isolated from wheat bran was used to prepare AX-fortified bread and biscuits, and the effects of the amount and method of AX addition on the nutritional properties of baked products were investigated through simulated digestibility and colonic simulated fermentability analysis in vitro. The purified AX demonstrated a high total sugar content (92.75%), high molecular weight (Mw) (669.8 kDa), low polymer dispersity index (PDI) and low arabinose to xylose (A/X) ratio (1.21). In vitro digestibility test indicated that AX addition could dose-dependently inhibit the starch digestibility in both bread and biscuits. Most importantly, it could also dose-dependently transfer more starch and protein into the colon through the gastrointestinal tract, leading to the changes in total amount and chemical composition of food residues. Results from the in vitro fermentability test showed that alterations in the composition of food residue affected its fermentability: a high relative ratio of carbohydrate to protein could enhance its prebiotic effect. This study provides valuable insights into the role of dietary fibres in baked foods, aiming to achieve precise nutrition for high-fibre food products.

Enhancing Wave Barrier Design for Train-Induced Vibrations: A Comparative Analysis of 2D and 3D Finite Element Simulations

Enhancing Wave Barrier Design for Train-Induced Vibrations: A Comparative Analysis of 2D and 3D Finite Element Simulations

Maximum Gpq–mean deconvolution for the impulsive fault feature enhancement of rolling bearing

Abstract The bearing fault signal is easily obscured by background noise and random shocks in the initial stage. The maximum Gpq–mean deconvolution (MGD) method is proposed to address the challenge of extracting fault feature signals in the presence of impact interference. The use of a nonlinear activation function in MGD enhances the distribution characteristics of the filtered signal. The proposed method adopts a new sparse measurement method, which enhances the sparse measurement capability and solves the problem of the difficulty in extracting periodic fault signals under impact. The superiority of the method in rolling bearing diagnosis is demonstrated through simulation and experimental analyses. In comparison with traditional methods, such as minimum entropy deconvolution (MED), optimal minimum entropy deconvolution adjustment, and maximum correlated kurtosis deconvolution, the proposed method in this paper significantly improves the ability of extracting bearing fault signals.

Effects of VOC emission reduction on atmospheric photochemistry and ozone concentrations during high-ozone episodes

We studied atmospheric volatile organic compounds (VOCs) characteristics and sources during fifteen high ozone (O3) episodes (daily 1-h maximum >100 ppbv and maximum 8-h average > 80 ppbv) in Nanjing, China, and used a photochemical box model to study the O3 formation sensitivity of VOCs, O3 photochemistry, radical budget, and reactivity for different emission reduction scenarios together with the base case ones. Overall, emission reduction from biogenic sources, followed by combustion sources, multiple sources, and solvent usage, had the major impacts on the O3 concentrations. According to photochemical box model simulations, isoprene was the most sensitive or influential VOC in the formation of O3, followed by ethylene, o-xylene, and cis-2-pentene. A 20.6 ± 11.7% concentration reduction of the sixteen most sensitive VOCs (SV16) for the production of O3 could bring the O3 concentration below the national standard level (daily 1-h maximum <100 ppbv). Besides, a 30% emission reduction from all the anthropogenic sources was more than enough to bring the O3 concentration below the national standard level. The total emission reduction of alkenes was always enough to bring the O3 concentration below the national standard level, which was not the case for aromatics under severe pollution conditions. The simulated OH reactivity analysis illustrates the importance of alkenes and aromatics, which account for about half of the total OH reactivity. The highest OH reactivity decreased due to the emission reduction for biogenic sources, followed by combustion and multiple sources. Alkenes played a major role in the OH, HO2, and RO2 radical's formation in the study area. The biggest drop in OH, HO2, and RO2 radical concentrations was noticed for biogenic emission reduction, followed by combustion and multiple sources. Due to the emission reduction from VOC sources, the highest O3 formation rates decreased for biogenic sources, followed by combustion and multiple sources. The acquired information is valuable to the scientific community and policymakers for formulating and implementing O3 pollution control strategies.

Pharmacophore modelling based virtual screening and molecular dynamics identified the novel inhibitors and drug targets against Waddlia chondrophila

Waddlia chondrophila is a possible cause of fetal death in humans. This Chlamydia-related bacterium is an emergent pathogen that causes human miscarriages and ruminant abortions, which results in financial losses. Despite the years of efforts, the underlying mechanism behind the pathogenesis of W. chondrophila is little known which hindered the development of novel treatment options. In the framework of current study, computational approaches were used to identify novel inhibitors (phytocompounds) and drug targets against W. chondrophila. At first, RNA polymerase sigma factor SigA and 3-deoxy-d-manno-octulosonic acid transferase were identified through subtractive proteomics pipeline. Afterwards, extensive docking and simulation analyses were conducted to optimize potentially novel phytocompounds by assessing their binding affinity to target proteins. A 100ns molecular dynamics simulation well complimented the compound's binding affinity and indicated strong stability of predicted compounds at the docked site. The calculation of binding free energies with MMGBSA corroborated the significant binding affinity between phytocompounds and target protein binding sites. The proposed phytocompounds may be a viable treatment option for patients infected with W. chondrophila; however, further research is required to ensure their safety.

Information Bottleneck Approach for Markov Model Construction.

Markov state models (MSMs) have proven valuable in studying the dynamics of protein conformational changes via statistical analysis of molecular dynamics simulations. In MSMs, the complex configuration space is coarse-grained into conformational states, with dynamics modeled by a series of Markovian transitions among these states at discrete lag times. Constructing the Markovian model at a specific lag time necessitates defining states that circumvent significant internal energy barriers, enabling internal dynamics relaxation within the lag time. This process effectively coarse-grains time and space, integrating out rapid motions within metastable states. Thus, MSMs possess a multiresolution nature, where the granularity of states can be adjusted according to the time-resolution, offering flexibility in capturing system dynamics. This work introduces a continuous embedding approach for molecular conformations using the state predictive information bottleneck (SPIB), a framework that unifies dimensionality reduction and state space partitioning via a continuous, machine learned basis set. Without explicit optimization of the VAMP-based scores, SPIB demonstrates state-of-the-art performance in identifying slow dynamical processes and constructing predictive multiresolution Markovian models. Through applications to well-validated mini-proteins, SPIB showcases unique advantages compared to competing methods. It autonomously and self-consistently adjusts the number of metastable states based on a specified minimal time resolution, eliminating the need for manual tuning. While maintaining efficacy in dynamical properties, SPIB excels in accurately distinguishing metastable states and capturing numerous well-populated macrostates. This contrasts with existing VAMP-based methods, which often emphasize slow dynamics at the expense of incorporating numerous sparsely populated states. Furthermore, SPIB's ability to learn a low-dimensional continuous embedding of the underlying MSMs enhances the interpretation of dynamic pathways. With these benefits, we propose SPIB as an easy-to-implement methodology for end-to-end MSM construction.

Enhanced parameter estimation in multiparametric arterial spin labeling using artificial neural networks.

Multiparametric arterial spin labeling (MP-ASL) can quantify cerebral blood flow (CBF) and arterial cerebral blood volume (CBVa). However, its accuracy is compromised owing to its intrinsically low SNR, necessitating complex and time-consuming parameter estimation. Deep neural networks (DNNs) offer a solution to these limitations. Therefore, we aimed to develop simulation-based DNNs for MP-ASL and compared the performance of a supervised DNN (DNNSup), physics-informed unsupervised DNN (DNNUns), and the conventional lookup table method (LUT) using simulation and in vivo data. MP-ASL was performed twice during resting state and once during the breath-holding task. First, the accuracy and noise immunity were evaluated in the first resting state. Second, CBF and CBVa values were statistically compared between the first resting state and the breath-holding task using the Wilcoxon signed-rank test and Cliff's delta. Finally, reproducibility of the two resting states was assessed. Simulation and first resting-state analyses demonstrated that DNNSup had higher accuracy, noise immunity, and a six-fold faster computation time than LUT. Furthermore, all methods detected task-induced CBF and CBVa elevations, with the effect size being larger with the DNNSup (CBF, p = 0.055, Δ = 0.286; CBVa, p = 0.008, Δ = 0.964) and DNNUns (CBF, p = 0.039, Δ = 0.286; CBVa, p = 0.008, Δ = 1.000) than that with LUT (CBF, p = 0.109, Δ = 0.214; CBVa, p = 0.008, Δ = 0.929). Moreover, all the methods exhibited comparable and satisfactory reproducibility. DNNSup outperforms DNNUns and LUT with respect to estimation performance and computation time.

Analysis of the impact of key strata failure on overlying strata and surface transport patterns

After mining the coal seam under the slope terrain in the southwestern mining area, it is difficult to predict the damage characteristics of the overlying strata and the surface movement and deformation law. To solve this problem, this paper comprehensively analyses the key strata by applying the theory, similarity simulation and the GDEM simulation software, and explores the influence of the change of the key strata’s height and thickness on the damage characteristics of the overlying strata and the surface movement and deformation. By analysing the key strata in the “inclined step” structure, the minimum support force for the key strata not to slip is obtained, and the minimum support force required for the key strata at different levels is deduced. The results of similar tests and numerical simulation analyses show that the vertical displacement of the overburden and surface will be smaller as the key layer is further away from the coal seam. AT the same time, the damage to the overlying strata and the surface is smaller when the key strata fails, and the location of the first subsidence of the slope surface due to mining is usually located in the area close to the bottom of the slope. In the stable structure from the key strata to the ground surface, once the key strata is unstable and broken, the damage and vertical displacement of the ground surface will increase dramatically, thus causing a series of geological disasters such as ground subsidence. Therefore, before the key strata are broken, grouting reinforcement measures are carried out for the rock fissures below the key strata to enhance the tensile strength of the key strata, which can effectively reduce the surface subsidence and deformation.

Experimental and simulative analysis of flexural performance in UHPC-RC hybrid beams

With high strength, excellent durability, and cracking resistance, ultra-high-performance concrete (UHPC) has gradually become popular. In this study, a novel hybrid beam was designed to fully harness the cracking resistance and tensile strength of UHPC. A series of experiments were conducted to investigate the influence of UHPC layer depth and steel bar diameter on the flexural performance of UHPC-RC hybrid beams. The experimental results indicated that the use of UHPC in the tensile zone of UHPC-RC hybrid beams can significantly improve the bearing capacity, crack resistance, and ductility of the structure. Based on the application of cohesive zone model (CZM), a finite-element (FE) model was proposed to simulate the mechanical behavior of the UHPC-RC hybrid beams, and the cohesive elements were used to represent the potential fracture surfaces of UHPC (concrete) and the rebar-concrete interface. A constitutive model for potential fracture of UHPC, taking into account the bridging effect of fibers, has been proposed. This FE model can reflect the fracture behavior of UHPC-RC hybrid beams. The simulation results demonstrate that the proposed model can accurately simulates the mechanical response and fracture behavior during the whole fracture process. Based on the results obtained from the experiment and simulation, a theoretical method for predicting the bearing capacity of UHPC-RC hybrid beams was developed, which can provide the reference for the design of similar structures.

Unraveling microstructure evolution induced mechanical responses in coaxial fiber-diode laser hybrid welded Fe–36Ni Invar alloy

Invar alloys, renowned for its ultra-low coefficient of thermal expansion, are qualified for widespread use in aerospace applications. This study explores the utilization of fiber-diode laser hybrid welding in Fe–36Ni Invar alloy, specifically focusing on addressing the undesirable microstructure and properties arising from unstable molten pool and high-temperature gradient encountered during single fiber laser welding. Experimental and simulation analyses were conducted to systematically explore the influence of diode laser power on the microstructure evolution and mechanical responses of fiber-diode hybrid laser welded Invar alloy joints. Results indicate that the addition of diode laser could notably widen the keyhole opening and improve the molten pool stability. An acceleration of dynamic recrystallization is observed in the weld seam, with a significant increase in high-angle grain boundaries, which account for over 90 % of the total. Moreover, with increasing diode laser power, temperature gradient decreases, leading to the formation of numerous equiaxed grains. Electron backscatter diffraction (EBSD) results demonstrate a considerable enhancement in the intensity of the γ texture, particularly the {111}<110> texture. Comparatively, fiber-diode laser hybrid welding exhibits superior plasticity and tensile strength over single fiber laser welding, with a tensile strength reaching 493.1 MPa. The research findings presented here provide essential theoretical foundations and technical guidance for enhancing the quality of Invar alloy weld joints and facilitating the broader application of fiber-diode laser hybrid welding in the future.

A periodic-modulation-oriented noise resistant correlation method for industrial fault diagnostics of rotating machinery under the circumstances of limited system signal availability

The periodical impulses caused by localized defects of components are the vital characteristic information for fault detection and diagnosis of rotating machines. In recent years, multitudinous spectrum analysis-based signal processing methods have been developed and authenticated as the powerful tools for excavating fault-related repetitive transients from the measured complex signals. Nonetheless, in practice, their applications can be severely confined by the constraints of limited system signal availability and incomplete information extraction under intricate noise interferences. To tackle the aforementioned issues, this paper proposes a periodic-modulation-oriented noise resistant correlation (PMONRC) method for target period detection and fault diagnosis of rotating machinery. Firstly, the envelope of raw signal is obtained via a novel sequential procedure of signal element-wise squaring, spectral Gini index-guided adaptive low-pass filtering, and signal element-wise square root computation, to highlight the modulated wave component that is more likely to be related to the potential fault-induced periods. Subsequently, a series of sub-signals, which can encode the fault-related repetitive information and enhance noise resistance, are constructed utilizing the envelope signal. Based upon the envelope signal and the obtained sub-signals, a weighted envelope noise resistant correlation function can be derived with the assistance of the L-moment ratio-based indicator and Sigmoid transformation. Finally, the specific fault type of the rotating machinery can be identified and affirmed accordingly. The proposed PMONRC method, which is nonparametric and completely adaptive to the signal being processed itself, overcomes the deficiencies of spectral analysis-based approaches, and is applicable for the engineering circumstances of system signal limitation and low signal-to-noise ratio (SNR), possessing immense practical merit. Both simulation analyses and experimental validations profoundly demonstrate that the proposed method is superior to other existing state-of-the-art time-domain correlation methods. Moreover, as an attempt as well as exemplar to apply this method, the PMONRC-based incipient fault diagnostic results of rolling bearing data from the well-known experimental platform PRONOSTIA are presented and discussed as well, to further elucidate the effectiveness and practical engineering significance of the proposed method.

Fault modulation mechanism and kinematic modeling of planetary gear with local fault

A planetary gear train involves multiple components and has more complex kinematic relationships than a parallel gear train. Consequently, the fault characteristics extraction is more complicated. To ensure safe operation, it is necessary to properly understand the fault behavior of the planetary gear train. In the previous kinematic modeling, the relationship between the faulty tooth meshing location and the fault impact response phase is not fully considered. However, the fault impact response phase variation plays an important role in accurately characterizing the fault characteristics in the planetary gear train. To solve this problem, we analyze the relationship between the faulty tooth location variation and the fault impact response phase variation and then a novel fault response model is established. Based on this model, a revised planetary damage detection scheme is formulated. Through the simulated data analysis, it is revealed that the faulty tooth location variation not only affects the fault impact response phase, but also reallocates the sideband distributions in the response spectrum. Finally, using a laboratory planetary gear set, it is validated that the proposed model is able to reflect the real damage response better. Therefore, using the proposed model, more accurate damage features are able to be extracted from the measured vibration responses.

Maximizing solar energy generation: guidelines for optimizing photovoltaic panel placement on building facades

PurposeThis purpose of this paper is to address the research problem of optimizing photovoltaic (PV) panel placement on building facades to maximize solar energy generation.Design/methodology/approachThe study examines the significance of various design configurations and their implications for PV system performance. The research involves analysis of relevant literature and energy simulations. An exemplary case study is conducted in a hot climate zone to quantify the impacts of PV panel placement on energy generation. Various application scenarios are developed, resulting in 28 scenarios for PV on building facades. Energy simulations using Grasshopper Rhino software and Ladybug plugin components are performed.FindingsThe paper identifies key factors influencing PV panel placement and energy generation through qualitative analysis. It introduces an appropriateness matrix as a decision-making framework to evaluate placement options. The study identifies design configurations and external features impacting PV location selection and performs a qualitative classification to determine their impact on energy generation.Practical implicationsThe results and decision-making framework enable informed choices based on solar radiation levels, shading conditions, and building requirements. Optimizing PV panel placement enhances solar energy harvesting in buildings, benefiting architects and engineers.Originality/valueThe novel contributions of this paper include practical insights and guidance for strategically placing PV panels on building facades.

Linear theory of the spatial signatures of critical slowing down

Critical slowing down is considered one of the main signs that a dynamical system is close to a tipping point. For this reason, early-warning indicators have been developed to identify it, mainly based on the temporal evolution of the system under analysis. Yet, it has been shown that critical slowing down also displays spatial signatures, such as an increase in the spatial correlation. However, these signatures are based only on heuristic observations and the analysis of numerical simulations and do not have a sound theoretical foundation. In this work I will derive analytical expressions for typical spatial early-warning indicators of critical transitions in spatially extended systems, such as spatial correlation, spatial variance, and spatial permutation entropy, in the linearized limit. As a result, I will show that the common belief that the spatial correlation increases when a bifurcation is imminent is false. Other indicators, instead, might represent more robust alternatives, such as the spatial permutation entropy or the spatial variance. Published by the American Physical Society 2024

Bioinformatics and Gene Expression Omnibus Analysis of Key Candidate Genes and Pathways Associated with Femoral Head Necrosis

Background: Femoral head necrosis (FHN) is a debilitating bone disease affecting an estimated 8 million people worldwide. Although specific drugs for FHN have limitations, targeted therapies have shown promising results. The significance of this study is underscored by the high prevalence of FHN, the limitations of current treatments, and the potential of targeted drugs and natural compounds for effective therapeutic interventions. Objectives: This study aimed to explore the genetic landscape and associated pathways of FHN through bioinformatics analysis of Gene Expression Omnibus (GEO) data and molecular docking simulations targeting specific enzymes implicated in FHN. Methods: Differentially expressed genes (DEGs) in FHN samples were identified from GEO datasets, specifically accession number GSE123568 (Platform: GPL15207). Functional enrichment analysis was performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) to identify enriched pathways and Gene Ontology (GO) terms. Additionally, a protein-protein interaction (PPI) network was constructed using the STITCH (search tool for interaction of chemicals) database, which helped identify top hub genes and proteins. Molecular docking was conducted against key proteins using compounds from the topical chinese herbal medicine (TCHM) database associated with FHN. Results: The study provided a comprehensive bioinformatics analysis of key candidate genes and pathways associated with FHN, which may serve as potential therapeutic targets. It was found that FHN is associated with mitogen-activated protein kinases (MAP4K4/ MAPK8/ MAPK9) and interleukins (IL1b/ IL19/ IL26). Molecular docking results showed strong interactions of traditional Chinese herbal compounds through hydrogen bonding and electrostatic interactions at the active sites of the top ten target proteins associated with FHN. Conclusions: The study confirmed that FHN is linked with enzymes such as mitogen-activated protein kinases (MAPKs), interleukins, tumor necrosis factors (TNFs), and VEGFA (vascular endothelial growth factor A). Molecular docking simulations demonstrated that hesperidin, naringin, and curcumin exhibit potent inhibition against key proteins involved in FHN. Future research will focus on elucidating the specific roles of genes associated with FHN and exploring potential therapeutic targets using natural compounds.

Ballistic response mechanism and resistance-driven evaluation method of UHMWPE composite

The use of ultra-high molecular weight polyethylene (UHMWPE) composite in the design of lightweight protective equipment, has gained a lot of interest. However, there is an urgent need to understand the ballistic response mechanism and theoretical prediction model of performance. This paper explores the ballistic response mechanism of UHMWPE composite through experimental and simulation analyses. Then, a resistance-driven modeling method was proposed to establish a theoretical model for predicting the bulletproof performance. The ballistic response mechanism of UHMWPE composite encompassed three fundamental modes: local response, structural response, and coupled response. The occurrence ratio of these fundamental response modes during impact was dependent on the projectile velocity and laminate thickness. The bulletproof performance of laminate under different response modes was assessed based on the penetration depth of the projectile, the bulging height on the rear face of the laminate, the thickness of remaining sub-laminate, and residual velocity of the projectile. The absolute deviations of bulletproof performance indicator between theoretical value and experimental value were well within 11.13%, demonstrating that the established evaluation model possessed high degree of prediction accuracy.