Search Results (3,174)

Construction companies, petrochemical enterprises, and airports are examples of large-scale organizational–technical systems (OTSs) and are characterized by a distributed structure, numerous parallel technological and business processes, and substantial energy consumption. The control of such systems is implemented through hierarchical distributed systems that require the regular collection, synchronization, and analysis of large volumes of heterogeneous data. This paper proposes a methodology for performance analysis and energy consumption optimization in OTSs based on the combined use of hierarchical control, business process modeling in BPMN and DRAKON notations, and the use of snapshots—consistent global states of a distributed system captured at specified time instants. The specifics of snapshot generation algorithms are discussed, including copy-on-write, the Chandy–Lamport algorithm, cloud orchestration, and log-based point-in-time recovery. A snapshot acquisition optimization problem is formulated, which minimizes the deviation of the captured state from the actual state under constraints on frequency, synchronization delay, and cost. The feasibility of the approach is illustrated by a numerical example of energy redistribution between the levels of a hierarchical control system using distributed model predictive control (DMPC). The advantages of the method include obtaining an objective “as is” picture, the applicability of control-theoretic methods for distributed systems based on big data processing, the ability to localize faulty subsystems, and its utility in assessing a company’s condition for stakeholders. Full article

The inherent brittleness of cementitious materials presents a fundamental limitation for advanced structural applications. While bio-inspired Bouligand architectures have demonstrated remarkable damage tolerance in natural composites, their systematic translation to brittle inorganic binders via 3D concrete printing (3DCP)—and the development of high-fidelity meso-scale models to quantitatively map the resulting strength–toughness design space—remains underexplored. This study aims to decouple the intrinsic topological toughening potential of helicoidal Bouligand architectures from the stochastic defects inherent to additive manufacturing, through a meso-scale finite element (FE) framework. To physically validate the model, a nano-clay-assisted rheological strategy was utilized to enable the support-free fabrication of these helicoidal prototypes. Computationally, a meso-scale FE framework integrating the concrete damaged plasticity (CDP) model with three-dimensional cohesive zone elements was developed to explicitly resolve inter- and intra-layer interfacial crack kinematics. Coupled physical compression tests and numerical simulations indicate that the 15° Bouligand architecture achieves a computationally predicted 16.3-fold increase in volumetric energy absorption (experimentally: 13.7-fold) compared to the 0° unidirectional baseline, with a modest ~11% reduction in compressive strength (from ~33.0 MPa to ~29.5 MPa in simulations; ~12% experimentally). Furthermore, numerical parametric studies across the complete pitch-angle design space reveal an optimal topological window at 15–30°, wherein the competing effects of crack deflection and structural integrity are balanced. Imperfection sensitivity analysis demonstrates that the topological toughening mechanism is relatively robust: even with a 30% reduction in inter-filament bonding strength, the work of fracture remains 12.4 times higher than that of the 0° control. These findings suggest that spatial toolpath programming offers a viable, geometry-driven strategy for developing damage-tolerant cementitious composites, complementing conventional material-level reinforcement approaches. Full article

Banks must balance deposit stability, loan expansion, and regulatory compliance while operating under liquidity constraints and financial risks. This study presents a mathematical model to examine the dynamics of bank deposits and loans under the influence of liquidity mechanisms and regulatory policies. The model proceeds in three stages: a deterministic nonlinear model, a dynamic optimal control model, and a stochastic model. Under the deterministic model, deposit withdrawals are liquidity-dependent, leading to a feedback mechanism in which liquidity improves deposit stability while financing loan growth. The theoretical results demonstrate the model’s positive and bounded solutions and show the existence and local stability of equilibria. Several parameters are based on regulatory policies or calibrated from Indonesian banking data, while the unknown parameters are estimated using the particle swarm optimization (PSO) algorithm. The results show that the proposed model is capable of fitting and predicting the data and has slightly lower mean absolute percentage errors for in-sample and out-of-sample compared with the benchmark model, and achieves comparable directional forecasting performance based on the index of directionality. Sensitivity analysis shows that the capital adequacy ratio supports lending, whereas an increased reserve requirement limits lending. An optimal control approach is developed by considering the reserve and capital requirements as time-varying policy variables. By applying Pontryagin’s maximum principle, we establish the necessary conditions for optimality. Numerical experiments demonstrate that the optimal control regulation enhances financial ratios, particularly the loan-to-deposit and liquidity ratios, at a reasonable cost. Finally, the stochastic model accounts for random variations in withdrawals and credit risks. Simulation-based observations reveal that although the system becomes more volatile, the mean dynamics are close to the deterministic case. Our framework offers a data-based and analytically tractable approach for studying the dynamics of banking variables and the effects of regulatory policies. The proposed model provides a mathematical tool for assessing the long-term effects of regulatory policies on banking performance and can assist bank managers and regulators in designing strategies that balance lending activity and liquidity resilience. Full article

This study examines the low-speed impact response of Nomex honeycomb-core sandwich structures using an approach combining experimental tests and three-dimensional numerical modeling. A finite element model was developed using Abaqus/Explicit to predict contact force, displacement, damage evolution, and absorbed energy under different impact configurations. The influence of core density, skin thickness, and impactor geometry was analyzed to identify the parameters governing impact resistance and energy dissipation mechanisms. The numerical results show good agreement with experimental measurements, with maximum relative differences between 7.3% and 8.3% for the maximum force and between 1.8% and 4.3% for the absorbed energy. Core density appears to be a determining factor: the D144 configuration reaches a maximum force of approximately 4400 N, compared to 2600 N for the D80 configuration, representing an increase of approximately 69%. However, sensitivity analysis indicates that skin thickness exerts the most dominant overall influence on load-bearing capacity; increasing this thickness from 0.2 mm to 1.2 mm leads to a fivefold increase in maximum force (from 1800 N to over 10,000 N) and a significant rise in absorbed energy (from 20 J to 105 J). The geometry of the impactor strongly controls the damage modes and stress distribution. A 60° conical impactor promotes localized deformation and rapid perforation, while a 70° angle offers a better compromise between local resistance and progressive energy absorption. At 80°, the stresses are distributed over a larger surface area, which delays perforation. The geometry of the impactor strongly controls the spatial distribution of damage modes. A sharper 60° conical impactor induces highly localized core crushing and rapid skin perforation, while a 70° angle offers a better compromise between local resistance and progressive energy absorption. At 80°, the stresses are distributed over a wider area, promoting diffuse damage and delaying perforation. These results show that the combined optimization of core density, skin thickness, and the impactor–structure interaction is an effective way to improve the impact tolerance of lightweight sandwich structures intended for aerospace, automotive, and marine applications. Full article

The Chang 8 Member of the Jinghe Oilfield in the Ordos Basin is a low-porosity, ultra-low-permeability reservoir with many faults and fractures, complex structures, and strong heterogeneity. Conventional logging curves do not clearly distinguish among different structural units, making it difficult to identify the internal structures of fault-fracture reservoirs. Current methods mainly use logging curves and rock mechanical parameters. In these reservoirs, experiments are costly, numerical simulations take a long time, and identification is often inefficient. To improve identification accuracy and efficiency, this study developed a two-layer Stacking ensemble model for the Chang 8 Member. The dataset was derived from conventional well-log data from five wells in the Chang 8 Member and contained 816 labelled depth samples. Among them, 569 original samples from wells A1, A2, and A3 were used for model development, while 247 samples from wells JH55P10 and JH2301H were reserved for independent well-level validation. In the first layer, a support vector machine (SVM), XGBoost, and a random forest (RF) were used as the base learners. The hyperparameters of the base learners were optimized using grid search and K-fold cross-validation. In the second layer, multinomial logistic regression was used as the meta-learner to integrate the class-probability outputs of the base learners and generate the final predictions. Individual models showed limitations in distinguishing the three internal structural units of fault-fracture reservoirs. By integrating the complementary outputs of the base learners, the Stacking model achieved an overall accuracy of 0.89, exceeding the accuracies of the individual models on the internal hold-out test set. The results indicate that the proposed framework can improve the accuracy and class balance of multi-class identification on the present dataset and provide a practical approach for the detailed evaluation of internal structural units in low-porosity, low-permeability fault-fracture reservoirs. Full article

In carbon sequestration in saline aquifers, many factors affect multiphase fluid migration and reservoir pressure change. This study developed a high-resolution three-dimensional numerical model to investigate large-scale CO₂ geological storage in the Ordovician carbonate aquifer of the Tarim Basin, China. This study focuses on the quantitative prediction of CO₂ plume migration, multiphase flow interactions between supercritical CO₂ and brine, and formation pressure evolution under coupled injection operations. Injection strategies were compared by constant rate (CR) and variable rate (VR) injection, and two caprock morphology-type selection by placing wells into monocline traps (wells 1/3/5) and anticline traps (wells 2/4) with varying limb dip angles and closure depths. The results demonstrate that both injection speed and caprock morphology strongly control CO₂ trapping evolution and storage security. At the end of the 500-year simulation, the dissolved-CO₂ migration distance followed the order CR > VR, indicating that, under the studied conditions, VR injection most effectively limited the lateral spread of dissolved CO₂ and thereby enhanced dissolved-CO₂ immobilization. In addition, CR and VR injection schedules have a subtle impact on long-term pressure change; Across all cases, formation pressure remained below the caprock breakthrough pressure. CR injection promotes the fastest CO₂ dissolution and pressure dissipation but yields the weakest long-term immobilization, whereas VR injection trades early dissolution rate for more effective plume containment. This result indicates that injection-strategy selection should be matched to dominant site controlled near-term pressure management versus long-term containment and to the trapping behavior imposed by caprock morphology. This study provides a mechanistically grounded optimization framework linking injection-speed control and caprock morphology to the coupled evolution of pressure-buildup safety and long-term CO₂ immobilization, supporting CCUS decision-making in the Tarim Basin. Full article

Accurately predicting the terminal state of shaped charge jets (SCJs) is crucial for optimizing their penetration performance. The core challenge lies in a deep understanding of the complete physical chain from shock compression to unloading expansion. This paper presents a hybrid analytical–numerical model for SCJ formation that incorporates a shock-pressure-driven unloading term. Unlike classical PER theory, the proposed model explicitly introduces an unloading term and derives a quantitative expression for the momentum conversion factor (Π_DMCF) to quantitatively characterize the momentum redistribution during collapse. Our analysis finds that Π_DMCF exhibits a typical S-shaped evolution law as the dimensionless Mach number (Ma) varies. This study uses a logistic function with two characteristic parameters, Ma₀ and k, to accurately fit the data. The research results indicate that the model parameters have clear physical connotations: Ma₀ characterizes the critical condition for the material to transition from “strength-dominated” to “kinetic-energy-dominated” behavior, while k reflects the degree of abrupt transition. After calibrating the model parameters using high-fidelity numerical simulations, the jet morphology and velocity data obtained from X-ray flash photography experiments are compared and verified, confirming that the model can significantly improve the prediction accuracy. Especially for Ti₅₅Al₂₀V₅Zr₅Nb₁₅ HEA, the prediction error in the jet velocity is less than 4%, and the theoretically predicted shock pressure is highly correlated with the numerical results (R² = 0.943). A further mechanistic analysis indicates that the proposed model successfully decodes the unique response of the HEA: its high dynamic strength results in a larger value, causing its momentum conversion efficiency to fall within a lower range under typical impact conditions. The theoretical framework constructed in this study provides a hybrid analytical–numerical and highly reliable theoretical tool for the accurate prediction of SCJs, as well as for the material selection and design of high-performance liners. Full article

Lattice structures have gained particular interest in the last years, because of the spread of Additive Manufacturing, which allowed their production with ease. These structures may be used as functionally graded materials for lightweighting in structural components, or they can be tailored to match the mechanical properties of bone tissue for orthopedic implants. To reduce the computational time and costs of structural simulation and optimization, this study presents a numerical homogenization to determine the main elastic constants of the BCCz lattice, over its relative density. Numerical simulations are carried out on a lattice with a nominal geometry, made from a homogeneous isotropic material. Results present charts and interpolating functions of the elastic constants of the lattice, over its relative density, that may help the designer in tailoring the lattice structure to the desired applications. Results show that the BCCz presents a substantial influence of the load direction on the mechanical properties, with the z direction showing superior properties than the transverse direction. This makes the BCCz lattice ideal for those structures where the main load directions are easily predictable. Full article

To improve the optimization performance of the original Bounty Hunter Optimizer (BHO) in complex search environments, this paper proposes a quantum computing and adaptive mechanism-based BHO, named QCAMBHO. The proposed algorithm integrates three complementary strategies: quantum-computing-enhanced initialization, adaptive Lévy flight, and an adaptive differential operator. These mechanisms are designed to improve population diversity, strengthen global exploration, and enhance later-stage exploitation. The performance of QCAMBHO is evaluated on the CEC2017 and CEC2022 benchmark test suites. Experimental results show that QCAMBHO achieves competitive or superior optimization performance compared with several advanced algorithms in terms of convergence accuracy, stability, and robustness. Ablation experiments further confirm the positive contribution of each strategy and the synergistic effect of their integration. To examine its practical applicability, QCAMBHO is further used to optimize the key parameters of Kernel Extreme Learning Machine (KELM), and a QCAMBHO-KELM model is constructed for enterprise bankruptcy prediction. The results show that QCAMBHO-KELM achieves better overall classification performance than BHO-KELM and other comparison models across multiple evaluation metrics, including accuracy, Matthews correlation coefficient, sensitivity, specificity, precision, recall, and F1-score. These findings indicate that QCAMBHO not only provides an effective optimizer for complex numerical problems but also offers a promising decision-support tool for improving the accuracy and reliability of enterprise bankruptcy early warning. Full article

Rotary steerable drilling technology is fundamentally aimed at achieving precise wellbore trajectory control. As a representative directional tool, a dynamic push-the-bit RSS generates steering force during rotary drilling through the interaction between its extendable steering pads and the borehole wall, and it is distinguished from static push-the-bit RSS by the rotational friction that develops at the pad–wall interface. To further clarify the influence of friction on the resultant steering force and the build-up rate, this study develops a steering-force optimization model that explicitly incorporates tangential friction, validates the model, and then conducts numerical simulations to examine how PDC bit design parameters and formation properties affect the build-up rate. The results indicate that the friction-aware optimization model can achieve a higher build-up rate. Quantitatively, relative to the friction-free allocation model that is commonly used as the baseline in push-the-bit BUR prediction, the friction-aware formulation increases the final lateral displacement from approximately 28.4 to 30.6 mm in the analytical comparison (+7.7%) and from approximately 24.3 to 26.9 mm in the full-scale finite-element comparison (+10.7%) over the same steering-force action time. In soft formations with a low internal friction angle, a bit design combining a moderate gauge-protection dimension, an appropriate inner cone angle, and a large crown radius can effectively enhance lateral cutting and steering-force transmission, thereby improving build capability and trajectory stability. These findings provide a theoretical basis for improving build-rate efficiency in push-the-bit rotary steerable drilling systems. Full article

Reservoir computing (RC) is a simple and highly efficient artificial neural network. For such a network, only the output connection weights need training, effectively reducing computational complexity. Optoelectronic time-delayed RC is typically based on an optoelectronic oscillator (OEO) with simultaneous broadband processing capabilities for both optical and electrical signals, while being readily implementable based on existing technologies. In this work, a new OEO-based RC (OEO-RC) using an electroabsorption modulated laser (EML) is designed, and the electroabsorption modulator (EAM) integrated in the EML serves as a nonlinear node. This scheme simplifies the architecture of an OEO-RC. And it is validated by using two typical tasks of the NARMA 10 time series prediction and the handwritten digit image recognition. Numerical results demonstrate that with optimized hyperparameters, this EML-based OEO-RC exhibits a comparable performance compared with some existing photonic time-delayed RCs. Full article

The shrinkage strain, occurring upon UV curing and aging, leads to non-uniform dimensional changes that can compromise the part’s final geometry. This study investigates the deformation of green parts during the stereolithography process. Based on experimental measurements, a finite element model (FEM) is developed to account for different phenomena contributing to the structural distortion of the part, like polymerization shrinkage and the adhesion between the part and the build platform during printing. In addition, the time dependency of the degree of conversion is also considered to integrate the aging of green parts, and elastoplastic material behavior is also considered to include non-reversible deformations. This novel model makes it possible to predict stress generation during the stereolithography process and simulate part warping over time. The resulting simulations provided a numerical validation for part shapes observed experimentally, as well as insights to better understand the deformation mechanisms and optimize the dimensional fidelity of stereolithography-manufactured components. Full article

In the context of the global transition towards energy systems with a high share of renewable energy, efficient and large-scale energy storage technologies are essential for improving the stability and flexibility of power grids. Phase change thermal energy storage has attracted considerable attention because of its high energy density and nearly isothermal heat release capability. However, its practical application remains constrained by the intrinsically low thermal conductivity of phase change materials (PCMs). For instance, 0.2–0.3 W/m·K for organic paraffins, 0.15–0.35 W/m·K for fatty acids, and 0.5–1.0 W/m·K for salt hydrates lead to slow charging and discharging rates. Incorporating metal foams into PCMs to form composite PCMs has emerged as a promising strategy, as metal foams can significantly improve effective thermal conductivity and enhance internal heat transfer. This paper systematically reviews recent advances in metal foam-enhanced phase change thermal energy storage, with particular emphasis on numerical modeling and structural optimization. First, the heat transfer enhancement mechanisms of metal foam/PCM composites are analyzed, together with the key performance indicators used to evaluate thermal storage performance. Second, material-level developments are reviewed, including pore structure parameters, interfacial engineering, and advanced fabrication methods. The review then discusses current structural design strategies, such as graded pore structures and partially filled configurations, as well as hybrid enhancement methods that combine passive and active heat transfer enhancement. Particular attention is paid to numerical modeling approaches at both pore and system scales, which are used to predict and optimize thermal behavior. In addition, optimization methods, including topology optimization, machine learning, and genetic algorithms, are examined for their potential to inversely design foam structures with tailored thermal performance. Finally, the key challenges in this field are summarized, and future research directions are proposed. These include multi-scale intelligent design, integration with complementary thermal management technologies, and the development of scalable solutions for engineering applications. This review aims to provide a systematic reference for achieving performance breakthroughs and promoting the practical deployment of phase change thermal energy storage technologies. Full article

The spacecraft reachable domain has become increasingly important for orbital game analysis due to growing on-orbit activities such as servicing, debris removal, and space situational awareness. This paper provides a comprehensive review of reachable domain theory and its applications in orbital games. A unified mathematical framework is established through three complementary classification dimensions: spatial attributes that distinguish absolute from relative reachable domains, temporal attributes that differentiate free-time from fixed-time reachable domains, and informational attributes that contrast deterministic and predictive reachable domains. Solution methods are systematically reviewed according to this taxonomy, covering analytical and semi-analytical methods, numerical optimization approaches, and geometric and sampling methods for spatial-scale reachable domains, as well as linearized ellipsoidal approximation, exact envelope determination, and fast analytical approximation for time-scale reachable domains. Applications are examined through three representative scenarios: one-on-one pursuit-evasion games, multi-agent cooperative games, and threat-avoidance and defense games. Key limitations of existing approaches are identified, including modeling fidelity, computational efficiency, and scalability under uncertainty. Future research directions are outlined to address these challenges. Full article

In time-delay reservoir computing (TDRC), mask signal generation techniques in the input layer remain a key factor limiting system integration. In this study, we propose an optical mask generation scheme based on steady–quasi-periodic state switching (S-QPS) dynamics in a semiconductor laser with optical feedback. Experimentally generated S-QPS signals are applied to a TDRC system as mask signals, and the system performance is evaluated using the Santa Fe chaotic time-series prediction task. S-QPS signals are numerically generated based on the Lang–Kobayashi rate equations. The optimal normalized mean square error is 0.027. An analysis of the factors affecting system performance is carried out. The results indicate that period offset has a limited impact on system performance. In contrast, noise-induced amplitude fluctuations have a more pronounced impact. These results provide insights into the use and optimization of S-QPS signals for optical mask generation in TDRC systems. Full article