Search Results (1,874)

This paper presents fully transient analytical solutions for organic contaminant transport through composite liner systems consisting of a geomembrane (GMB) underlain by either a compacted clay liner (CCL) or a geosynthetic clay liner (GCL). The proposed solutions simultaneously account for three key mechanisms, namely advection due to GMB defects and wrinkles, first-order degradation, and thermodiffusion induced by temperature gradients. The solutions provide steady-state temperature distribution, transient contaminant concentration profiles, mass flux, and cumulative mass outflow at the base of the liner. The analytical solutions are rigorously verified against experimental thermodiffusion data from the literature, an existing analytical solution, and a numerical model using COMSOL Multiphysics 5.4. A parametric study is conducted to investigate the effects of thermodiffusion, Soret coefficient, and thermal conductivity on benzene transport. Results show that thermodiffusion substantially increases benzene outflow; neglecting it may lead to unconservative liner design. The benzene transport rate increases almost linearly with the Soret coefficient. The thermal conductivity of the GMB and GCL significantly affects benzene transport in the GMB/GCL system, while the thermal conductivities of the GMB and CCL have negligible effects on the GMB/CCL system. The proposed analytical solutions enable rapid parametric analysis, preliminary liner design, environmental risk assessment, and early warning. By enabling rapid comparison of GMB/CCL versus GMB/GCL systems (material selection), optimization of clay layer thickness for required breakthrough time (thickness optimization), and quantification of thermal effects on contaminant flux (temperature control), the solutions serve as an efficient tool for sustainable liner design. Full article

Constant-speed control of hydraulic retarders is essential for improving driving safety and reducing driver workload on long downhill roads. For heavy-duty battery electric trucks (BETs), regenerative braking provides a fast-response braking source and enables energy recovery, offering the potential to improve both speed regulation and energy efficiency. This study proposes a two-mode constant-speed downhill control framework for BETs. In the retarder braking mode, a variable-argument proportional–integral–derivative (VAPID) controller is employed to regulate the hydraulic retarder, with its parameters optimized by an improved seeker optimization algorithm (ISOA). In the cooperative braking mode, a parallel dual-controller structure is adopted, where the retarder is governed by ISOA-VAPID and regenerative braking is regulated by a fuzzy-tuned PD controller according to real-time battery states. To further improve energy recovery, an optimization-based AMT gear-shifting schedule and coordinated strategy are incorporated. The proposed framework is validated through offline simulations, sensitivity analysis, and driver-in-the-loop experiments under constant-slope, variable-slope, and real-world downhill road conditions. Results show that the retarder braking mode outperforms benchmark methods in steady-state accuracy and dynamic response. In the cooperative braking mode, braking energy is effectively recovered while the battery charging load under unfavorable battery states is reduced. Moreover, AMT gear shifting improves energy recovery efficiency with negligible influence on constant-speed performance. Full article

Network intrusion detection systems (NIDSs) deployed in dynamic environments face concept drift from evolving attacks and traffic patterns, causing model reliability to degrade over time. Continual learning (CL) offers an adaptive solution, yet many methods misalign drift detection, memory updating, and optimization: drift is often judged with low-dimensional statistics, while adaptation occurs in representation space, limiting consistency under buffer constraints. To address concept drift in non-stationary network traffic and catastrophic forgetting during online intrusion detection updates, we propose a continual-learning framework built upon SSF that combines feature-space Gaussian-kernel Maximum Mean Discrepancy (MMD) drift detection with gradient-matching coresets for memory admission. The proposed framework retains strategic forgetting and steady-state distillation while replacing low-dimensional drift tests with feature-space MMD and mask-based selection with gradient-matching coresets, thereby improving incremental updates under a limited memory budget. On NSL-KDD and UNSW-NB15 under a unified multi-seed streaming protocol, the proposed method improves detection performance and knowledge retention. Experimental results demonstrate that gradient-matching coreset selection is the primary contributor to the observed performance improvements, while the effectiveness of MMD-based drift scheduling and strategic forgetting depends on the underlying data distribution and drift-trigger threshold. The proposed framework employs batch-level MMD scheduling to coordinate memory admission and online optimization, providing a practical path toward robust continual intrusion detection. Full article

Internal cracks during slab bending and straightening are continuous-casting defects. Existing caster curves rely on plastic deformation, while curvature variation is concentrated within short sections, causing excessive strain rates and increasing the risk of internal straightening cracks. This study proposes a novel method for slab straightening through creep deformation and develops a curve for an R9300 caster by connecting cubic transition curves with cycloidal main segments. High-temperature tensile and constant-stress creep tests of Q345C steel were combined with transient thermal simulation and geometric strain-rate calculations. Under constraints on caster height, minimum curvature radius, and steady-state creep rate, the optimized parameters were a = 2600 mm and t = 3.6 rad. The curve eliminates the circular-arc section and ensures continuous position, tangent, and curvature. Its bending and straightening sections are each 9379 mm long, increases of 8349 and 7859 mm, respectively, while caster height increases by only 0.47 m. At the internal 1200 °C isotherm, the maximum strain rates are $6.75\times {10}^{-5} {\mathrm{s}}^{-1}$ and $5.19\times {10}^{-5} {\mathrm{s}}^{-1}$, reductions of 82.2% and 81.1% relative to the conventional caster. Both remain below the steady-state creep rate of $7.45\times {10}^{-5} {\mathrm{s}}^{-1}$ under ±10% secondary-cooling and ±10 °C casting-temperature fluctuations. The curve alleviates deformation concentration and enables the slab region at 1200 °C and above to bend and straighten through creep deformation. Full article

Pipeline corrosion critically threatens the safe operation of chemical industrial park pipeline networks, making accurate corrosion growth prediction essential for preventing catastrophic failures. Mechanistic models assume steady states, which conflict with in-service corrosion dynamics; data-driven approaches, however, presume complete datasets but frequently face missing parameters due to sensor failures and limited samples from costly inspections, increasing the risk of noise and overfitting. In this paper, the TinyTransCorrosion model was proposed, which is a lightweight Transformer-based corrosion growth prediction model specifically designed for small-sample scenarios. A cross-validation residual analysis is employed for data cleaning, while five physical embedding features are constructed to encode domain knowledge and compensate for missing parameters. A compact Transformer encoder containing only 6433 parameters was adopted, and a corrosion gating mechanism along with a classification (CLS) token was introduced to achieve efficient feature interaction. Evaluated on a real-world pipeline inspection dataset with 215 records, TinyTransCorrosion attains an R² of 0.6411 and an MAE of 0.1608 mm, outperforming nine conventional baseline models, including Mean predictor, Linear Regression, Ridge, SVR-RBF, Random Forest, XGBoost, LSTM, MLP, and CNN. While these results are limited to a single-site dataset and require external validation on independent multi-source data, the proposed lightweight physics-guided architecture demonstrates promising predictive capability for small-sample pipeline corrosion assessment, with model error approaching the metrological limit imposed by field ultrasonic gauge accuracy. It provides an acceptable pathway for prioritizing inspection intervals and optimizing maintenance scheduling in resource-constrained industrial settings. Full article

Under the “dual carbon” strategy, the organic Rankine cycle (ORC) represents a key technology for efficiently recovering low-grade industrial waste heat in inland factories. This study addresses ORC operational instability caused by seasonal temperature fluctuations and high cooling-water costs in inland regions. An ORC system powered by industrial waste heat is investigated. A steady-state simulation model is developed in Aspen Plus to analyze the effects of expansion pressure, condensation pressure, cooling-water flow rate, and working-fluid flow rate on system performance, and to filtrate parameters to maximize economic returns. The results demonstrate that optimal expansion pressure yields maximum net shaft power. Condensation pressure and the cooling-water flow rate are closely linked, necessitating a balance between power generation revenue and cooling costs. An optimal range for working-fluid flow rate is also identified. Seasonal temperature variations significantly influence system performance. Higher summer temperatures increase condensation pressure and reduce revenue, while lower winter temperatures enhance revenue when filtrated parameters are used. This research provides theoretical and technical references for achieving efficient, cost-effective, year-round ORC operation in inland factories. In the case study, the analysis for seasonal temperature variations increases the total annual revenue by CNY 2.217 million, with an annual average system efficiency of 10.59%. Full article

High-RAP asphalt mixtures are highly susceptible to fatigue brittle fracture. This study investigated the fatigue damage evolution of basalt fiber (BF)-reinforced high-RAP mixtures. Four-point bending fatigue tests were performed to evaluate the stiffness damage factor and the ratio of cumulative dissipated energy change $R_{CEDC}$. The ExpDec2 model and a second-derivative criterion were utilized to quantify steady-state energy dissipation and partition damage stages. Results indicate that BF extended fatigue life, yielding a 55% improvement at 70% RAP content. Model evaluations revealed that in the initial stage, increasing RAP delayed the critical damage inflection point due to accelerated non-steady damage, whereas BF shifted inflection points forward by constraining early damage accumulation. In the stable propagation stage, BF reduced the energy dissipation rates of 50% and 70% RAP mixtures by 44% and 22%, respectively. These quantitative results provide essential performance data regarding the effect of BF on the fatigue life and energy dissipation of high-RAP mixtures, serving as a practical reference for the design and durability optimization of sustainable asphalt pavements. Full article

Against the backdrop of the transition toward power systems with high shares of renewable energy and power electronics and the rapid growth of urban load, large receiving-end urban grids are fed by multiple line-commutated-converter HVDC (LCC-HVDC) links, so that their maximum receiving capability is frequently limited by the static-voltage-stability margin. To assess the receiving capability of such large urban grids, this paper proposes a method for evaluating the maximum receiving capability of a receiving-end urban grid that incorporates a Modular-Multilevel-Converter-based multi-terminal embedded DC (MMC-MTEDC) system. First, a quasi-steady-state model of the receiving-end urban grid with LCC infeed and an embedded MMC-MTEDC system, in which the DC-network equations characterize the mutual coupling among the AC active-power injections of the receiving-end converter stations, is established. Second, an augmented extended Jacobian that incorporates the MMC control equations and the DC power-flow equations is constructed; its minimum singular value is adopted as the static-voltage-stability index, and the corresponding sensitivities are derived to reveal the mechanism by which the receiving capability is formed. On this basis, a unit-commitment optimization model that centers on the stability-margin constraint and accounts for the converter-capability curve, the bus-voltage limits, and the line-loading limits is built; the model is solved iteratively by a column-and-constraint-generation (CCG) method, and the feasibility of the unit commitment is used to estimate the maximum receiving capability. A modified IEEE 39-bus system is used as a case study, which quantitatively verifies the effectiveness of the MMC-MTEDC in enhancing the receiving capability of the receiving-end urban grid. Full article

This study presents a computationally efficient quasi-one-dimensional mathematical model based on the traveling wave method to investigate hydraulic shock attenuation using a gas-hydraulic damper in pipeline systems. Unlike conventional models, this formulation accounts for fluid compressibility and incorporates a non-linear boundary condition strictly satisfying gas mass conservation within the damper. The model was successfully validated against a MATLAB 2024 Simulink benchmark, demonstrating a maximum pressure amplitude discrepancy of only 5–8%. A parametric analysis evaluated the effects of damper volume, initial gas pressure, and pipe diameter on surge suppression. Results show that insufficient damper volume causes extreme negative pressure drops, risking severe cavitation and fluid column separation. Conversely, excessive volume induces “over-damping,” undesirably increasing system inertia and delaying steady-state recovery. Crucially, scaling analysis reveals that a damper optimized for a specific pipe diameter loses efficacy in larger pipes, as the flow’s kinetic energy scales with the diameter’s square. This model provides a robust, precise computational tool for the optimal and safe design of pipeline networks. Full article

Forced periodic operation (FPO) has emerged as a promising process intensification strategy for catalytic reactors. In this study, the nonlinear frequency response (NFR) methodology was applied to investigate square-wave FPO of an isothermal CSTR for methanol synthesis. The analysis focused on periodic modulation of the inlet CO and flow rate, considering both single-input and simultaneous-input forcing. The reactor response was evaluated using higher-order frequency response functions (FRFs) to quantify the non-periodic component responsible for time-averaged process improvement. The results showed that individual modulation of either inlet CO or flow rate does not provide significant improvement in reactor performance and may even reduce methanol productivity. In contrast, simultaneous modulation generates a strong positive nonlinear interaction that substantially improves reactor performance. Under optimal forcing conditions, methanol productivity increased from 336.9 mmol min⁻¹${\mathrm{k}\mathrm{g}}_{\mathrm{c}\mathrm{a}\mathrm{t}}^{-1}$ at steady-state to 553.6 mmol min⁻¹${\mathrm{k}\mathrm{g}}_{\mathrm{c}\mathrm{a}\mathrm{t}}^{-1}$, corresponding to a 64.3% improvement. Compared with previously reported cosine forcing, square-wave modulation nearly doubled the attainable productivity improvement while also improving hydrogen utilisation efficiency. The results indicate that square-wave FPO represents an effective intensification strategy for methanol synthesis in a laboratory-scale isothermal CSTR and confirm the capability of the NFR methodology for the a priori evaluation and optimisation of periodically operated catalytic reactor systems. Full article

Addressing the deficiencies of data-driven heating, ventilation, and air conditioning (HVAC) models in multi-objective joint prediction, long-horizon prediction accuracy, and uncertainty quantification, this study proposes a heterogeneous deep ensemble framework. Through co-simulation technology, a multi-operating-condition operational dataset for a typical variable air volume (VAV)–Reheat system was constructed. By adopting an operating-condition-level partitioning strategy, the input features were strictly decoupled into historical system states, internal loads, and external control commands. On this basis, the framework integrates the advantages of Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRU), and Temporal Convolutional Networks (TCN). Utilizing an inverse-variance weighting mechanism, it achieves the synchronous and accurate prediction of 11-dimensional targets, encompassing total system power, five-zone temperatures, and five-zone CO₂ concentrations. Simultaneously, the prediction divergence among the ensemble models is utilized to quantify epistemic uncertainty, and a variance scanning mechanism is introduced to calibrate the prediction confidence intervals. The results indicate that within a 6 h prediction horizon, the absolute errors of the model in power, temperature, and CO₂ are reduced by 80.17%, 63–72%, and 75–80.62%, respectively, compared to the physical baseline. On the independent test set, the actual coverage rate of the calibrated prediction intervals approaches 90%, exhibiting an adaptive enveloping variation characterized by tightening during steady states and widening during transients. The proposed heterogeneous ensemble framework demonstrates superior prediction accuracy and long-horizon prediction stability, providing reliable model support for multi-objective optimal operation and robust control of buildings. Full article

Bio-inspired cellular metamaterials manufactured using additive manufacturing technologies provide a promising route for controlling thermal transport properties through architecture rather than through the intrinsic properties of the constituent material. This study investigates steady-state heat transfer in open-cell lattice structures comprising 20 different lattice metamaterial specimens representing various classes of cellular architecture. These include Kelvin, auxetic, BCCZ, BCC, cube, Z-cuboctahedron, diamond, FCC, FBCCXYZ, FCCZ, FBCC, G7, isostructure, octahedron, octet structure, rhombohedral dodecahedron, truncated cuboctahedron and truncated cube, all of which are made from polymer materials. The investigated architectures were inspired by functional principles observed in natural cellular systems, including cancellous bone, wood, coral skeletons, and other biological porous materials, where efficient transport processes are achieved through optimized material distribution and interconnected cellular networks. A theoretical model combining conduction through the lattice skeleton, radiative heat transfer within pores and potential convective contributions was developed using homogenization theory and representative volume element analysis. The experiment confirmed the main hypothesis of this study as described by the mathematical model. Experimental validation also confirmed that the homogenization model correctly predicts the thermal conductivity of open-cell lattice structures in highly porous materials with a porosity of around 0.95. The results demonstrate the potential of biomimetic cellular design for the development of lightweight thermal-management materials with programmable thermal transport properties. Full article

This study introduces the ’Black-Backed Jackal Optimization’ (BBJO), a nature-inspired meta-heuristic algorithm designed for complex, non-linear, and high-dimensional search spaces. The fundamental mathematical model of BBJO relies on the opportunistic hunting behavior and survivability strategies of the black-backed jackal (Lupulella mesomelas). We use non-linear energy decrease and adaptive Lévy flight to maintain the equilibrium of the search. This allows the algorithm to scan large areas first, then zoom in with a high degree of precision once it has identified a suitable location. This configuration prevents the algorithm from getting stuck on a suboptimal local solution, which is a frequent danger during searches in complex spaces. BBJO has been validated against 23 standard benchmark functions, demonstrating significantly greater accuracy than Particle Swarm Optimization (PSO) on complex and large-scale search spaces. On fixed-size domains ($F_{21}$–$F_{23}$), the BBJO algorithm achieved a 100% success rate with zero standard deviation, surpassing the Grey Wolf Optimizer (GWO) and Differential Evolution (DE), which frequently suffered from structural stagnation. Visual convergence study shows that BBJO efficiently identifies optimal search regions early in the iteration budget, saving time compared to traditional linear decay models. BBJO optimizes fuzzy inference systems (FISs) for two practical applications: autonomous car speed control and industrial furnace regulation. Experimental results indicate that BBJO significantly decreased cumulative penalties and improved steady-state error reduction compared to baseline configurations and established meta-heuristic methods. The results show that BBJO is a reliable and useful technique for engineering optimization. Full article

The deadbeat predictive control (DPC) method has received increasing research interest in the grid-forming converter control strategy, due to its advantages of fast response in emergency grid scenarios and great potential in utilizing a system multi-time-step predictive optimization strategy. However, the voltage–current double-loop DPC of a grid-forming converter is sensitive to the filter inductance and capacitance parameters, resulting in a steady-state tracking error under parameter mismatch conditions. To address this issue, this manuscript proposes a double closed-loop steady-state error compensation strategy for grid-forming converters using double-loop DPC. Based on an analysis of the DPC algorithm and the mechanism of performance degradation caused by parameter mismatch, compensation terms are designed for the inner current loop and outer voltage loop respectively. The compensation terms are constructed from the feedback errors, effectively and rapidly suppressing the performance degradation caused by parameter mismatch, without introducing complex observers that may degrade the system dynamic response speed. A simulation model, which includes both the physical model of the electrical circuit and the discrete-time controller with sample-and-hold characteristics, is established to verify the proposed control strategy under different operating conditions, including load transient and inductor parameter mismatch. The results demonstrate that the proposed compensation method significantly reduces the steady-state tracking error caused by parameter mismatch while preserving the fast dynamic response characteristic of DPC, thereby substantially improving the accuracy of active power output and enhancing the system’s robustness against parameter deviations. Full article

In response to the challenges encountered in high-throughput resilience assessment of zonal ship central cooling systems, including numerical stiffness in physics-based dynamic models, abnormal solver termination, and insufficient continuity in batch simulation campaigns, a physics-informed co-simulation framework for resilience-oriented assessment is proposed. With control–physics orthogonal decoupling as its core, the framework separates the control-scheduling layer from the thermo-hydraulic solver at the software-execution level, while retaining information exchange through standardized interfaces. In addition, physics constraint-based pre-filtering, process-level fault isolation, and automatic recovery mechanisms are integrated to improve the robustness and continuity of automated batch assessment. A hierarchical reduced-order thermo-hydraulic model of the zonal ship central cooling system is established. Subsequently, the numerical stiffness characteristics of the fluid network and heat-transfer units under valve topology switching conditions are analyzed. A standalone C++ solver kernel is generated from a Simulink prototype model, and a Java/Web-based collaborative scheduling platform is constructed. Cross-environment consistency tests show that the C++ solver reproduces the Simulink prototype results under representative fast hydraulic and slow thermal scenarios, with steady-state and transient discrepancies below 0.05% and 1.08%, respectively. Physics constraint-based pre-filtering intercepted 42.6% of infeasible samples and reduced the total wall-clock runtime of the tested optimization task by approximately 38%. In 1000 fault-injection tests, the process-isolation mechanism isolated 12 abnormal solver terminations, while the main scheduling process remained alive and the remaining batch tasks were completed under the tested conditions. Finally, an abrupt pulse thermal-load increase in the forward zone was used as a representative scenario to demonstrate automatic extraction of temperature trajectories and quantitative evaluation using the cumulative temperature-exceedance severity (CTS) index. The results indicate that the proposed framework can support offline resilience-oriented assessment, reconfiguration-strategy screening, and batch evaluation of shipboard fluid–thermal systems under the tested conditions. Full article