Search Results (1,539)

Prefabricated lining is increasingly used in railway tunnels due to its advantages of environmental friendliness, high construction efficiency, and convenience. However, the existence of block joints weakens structural integrity, and the segmentation optimization of prefabricated lining remains a challenge especially for irregular lining. Based on the Yongfengcun tunnel in the Fuzhou Ganghou Railway Project, the nonlinear mechanical behaviors of joint stiffness were investigated under axial force, bending moment and shear force. A beam–spring model was established by considering the bending and shearing stiffness of block joints, and the mechanical behaviors were analyzed efficiently by Python 3.9 and ABAQUS 2025 for 572 segmentation schemes. Based on a Delphi questionnaire, three key indicators including horizontal convergence, bending moment amplitude and length variance were selected as independent optimization objectives. The stable Pareto frontier was obtained using the NSGA-II algorithm. Application in the Yongfengcun tunnel fully verified the effectiveness of the method. Full article

This study presents an integrated workflow for the multiobjective optimization of directional well trajectories in underground gas storage (UGS) reservoirs. A modular well-path construction model is developed, enabling flexible assembly of linear and curved segments in a local reference frame and their transformation into the reservoir. The optimization problem is formulated to simultaneously maximize working-gas capacity and minimize total drilling length for ten new directional wells. A calibrated UGS reservoir with more than 30 years of production history is used as the simulation environment, and solution quality is explored using the NSGA-II (non-dominated sorting genetic algorithm) evolutionary algorithm. The results reveal a diverse Pareto front of feasible designs. The best configurations achieve either an 8.6% reduction in total drilling length while still delivering a 2.12% capacity increase, or a 3.18% capacity enhancement at a modest drilling-length increase of 4%. These outcomes demonstrate that strategic redesign of well trajectories alone can deliver measurable improvements in UGS performance without modifying well controls or facility constraints. The proposed methodology provides a generalizable and computationally efficient framework for large-scale multiwell planning in UGS systems. Its modularity supports future extensions, including collision avoidance, perforation optimization, and adaptive well-control strategies. Full article

To enhance the low-carbon operational performance of integrated energy systems (IESs) under multi-source uncertainties, this study proposes a bilevel stochastic optimization framework incorporating a dynamic mean–CVaR risk model and a tiered carbon pricing mechanism. The upper level adopts an improved NSGA-II to jointly optimize economic cost, carbon emissions, and system flexibility through capacity planning decisions. The lower level performs scenario-based operation evaluation with a time-varying risk aversion coefficient, enabling differentiated risk responses across operating periods. A stepwise carbon price function and a capped carbon revenue mechanism are introduced to represent real carbon market regulations and avoid excessive emission reduction benefits. Multidimensional uncertainty scenarios—covering renewable variability, load fluctuations, and market price disturbances—are generated for risk-aware evaluation. Simulation results show that the proposed approach effectively reduces cost and emission volatility and achieves a more balanced trade-off between economy and low-carbon performance compared with conventional static-risk models. Sensitivity analyses further reveal that increased risk aversion shifts system operation strategies from economy-oriented to robustness-oriented modes, highlighting the importance of dynamic risk modeling and carbon policy design for future low-carbon multi-energy systems. Full article

This study develops and optimizes a hybrid plant that couples a recompression sCO₂ Brayton cycle to a central-tower particle receiver with a bottoming Organic Rankine Cycle (ORC), including environmental and exergy balances. The two scenarios revealed Pareto points that raised the exergy efficiency to 0.65 in winter and reduced the fuel flow to 15 kg/s. Scenario number two achieves an overall thermal efficiency of 0.50 with total daily emissions of 2520 t CO₂ and 2850 kg NO_x, enabling nearly constant net power. Exergy destruction is concentrated in the high-temperature recuperator (HTR) and ORC turbines (27% each) and the ORC condenser (25%). Compared to a non-optimized baseline, the best solutions increased the ORC and Brayton efficiencies by 6.8–12.66% and 33.4–33.5%, respectively; cut gas-turbine power by 34% and ORC power to 10%; and lowered daily CO₂ and NO_x emissions by 52%. The gains stem from the coordinated adjustments of key levers: lower gas-turbine inlet temperature (about 10%), reduced Brayton mass flow (23%), and tuned ORC turbine inlet pressure. Full article

To address the poor cutting stability and deterioration of hole quality caused by the inherent trade-off between chip evacuation performance and drill-body stiffness in U-drilling, a multi-objective optimization framework was established. The design variables were the core thicknesses ${\mathrm{L}}_1$ and ${\mathrm{L}}_2$ of the inner and outer chip flutes, the inner and outer offset angles ${\mathrm{\theta }}_1$ and ${\mathrm{\theta }}_2$, and the inner and outer helix angles ${\mathrm{\beta }}_1$ and ${\mathrm{\beta }}_2$. The objectives were to maximize the chip evacuation force and minimize the drill-body strain (which serves as an equivalent indicator of maximizing drill-body stiffness). The chip evacuation force was rapidly evaluated using a mechanistic chip evacuation force model derived from mechanism-based analysis. The drill-body strain was efficiently predicted using a GA–BP neural-network surrogate model. An NSGA-II algorithm combined with the entropy-weighted TOPSIS method was employed to solve the optimization problem, yielding the optimal parameter combination for the U-drill chip-flute geometry. The results show that drilling experiments on 42CrMo under the optimal structural parameter combination reduced the cutting forces in the $x$, $y$, and $z$ directions by approximately 11.2%, 13.1%, and 11.8%, respectively. The root-mean-square acceleration in the $x$ and $y$-directions decreased by about 17.3% and 22.9%, respectively. These improvements effectively enhanced the hole-wall surface roughness and hole diameter accuracy, and further improved chip evacuation smoothness and cutting stability of the U-drill. Full article

In recent years, vortex spinning has garnered significant attention owing to its high efficiency and superior yarn quality. However, the drafting process involves multiple interrelated parameters, and different combinations of parameters can considerably influence subsequent spinning performance. To address this, the present study introduces a novel hybrid optimization algorithm to enhance spinning quality by rationalizing the coordination of drafting parameters. First, orthogonal experiments were conducted with the draft ratio and roller center distance as variables, using the mean grayscale value and grayscale standard deviation of the post-experiment silver images as multi-objective functions to evaluate drafting effectiveness. Subsequently, a regression model between drafting parameters and drafting outcomes was constructed using the Particle Swarm Optimization–Backpropagation Neural Network (PSO-BP) algorithm, followed by multi-objective optimization via the Non-dominated Sorting Genetic Algorithm II (NSGA-II) genetic algorithm to obtain a Pareto-optimal solution set. Finally, the entropy-weighted Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method was applied to comprehensively evaluate the Pareto-optimal set and determine the optimal combination of process parameters. The results demonstrate that, under the optimal parameter combination, the deviation between the measured quality indicators of the drafted sliver and the predicted values remains within 6%, confirming the effectiveness of the proposed model as a viable approach for optimizing drafting parameter configurations. Full article

This study addresses the challenges of coordinating spatio-temporal water allocation to optimize water productivity and reduce carbon emissions in water resource management, particularly the lack of high-resolution, integrated optimization frameworks capable of simultaneously tackling water scarcity and greenhouse gas (GHG) emissions. We propose a modeling approach for large-scale regional rice irrigation that explicitly represents the physical-process-based relationships among irrigation water, yield, and methane (CH₄) emissions. Using GIS, a grid-based simulation domain was constructed at a 500 m × 500 m resolution, and the GIS-DSSAT and GIS-DNDC models were employed to simulate yield and CH₄ emissions under varying irrigation amounts. The Random Forest algorithm—selected for its ability to capture complex nonlinear interactions—was used to establish the response surfaces linking irrigation water, yield, and CH₄ emissions. A spatio-temporal irrigation optimization model was then developed to simultaneously reduce CH₄ emissions and enhance water productivity. This methodology was applied to the Sanjiang Plain in Heilongjiang Province, where the NSGA-II algorithm was used to derive optimal irrigation schemes for rice cultivation across 408,264 grid cells. The results revealed quadratic nonlinear relationships between irrigation water amount, yield, and CH₄ emissions. Compared to the conventional irrigation practice in the region, which typically involves 15–20 flood irrigation events per season, the optimized irrigation schedule comprised 7–14 events—with 12 events accounting for 42% of the cases—and an irrigation duration ranging from day 137 to 256. This led to a 10.3% reduction in total irrigation volume, a 9.6% decrease in CH₄ emissions per unit yield, and a 21.8% increase in water productivity. This study provides valuable decision support for optimizing regional water allocation and developing rice cultivation strategies that improve productivity while reducing emissions. Full article

To address the strongly coupled and highly nonlinear optimization problems arising from the increasing system complexity, optimization objectives, and variable dimensions in practical engineering applications, this paper proposes a multi-strategy enhanced NSGA-III algorithm (MSNSGA-III) by introducing K-means clustering, an adaptive hybrid operator, and an assistant evolutionary population strategy on the basis of the NSGA-III algorithm. This algorithm overcomes the performance limitations of the original algorithm in large-scale search with multiple variables. By employing the DTLZ test functions with different variable dimensions and conducting comparisons with six other representative algorithms, the proposed algorithm is proven to have strong competitiveness in terms of diversity and convergence speed. To reflect the superiority of the algorithm in practical applications, this paper establishes a variable-thickness optimization model for the morphing leading edge. By adopting the spline curve-based optimization variable control strategy and the MSNSGA-III algorithm, the optimal thickness distribution of the leading edge skin is obtained. The results show that, compared with the leading edge with a fixed skin thickness of 1.5 mm, the optimized variable thickness skin leading edge achieves 43.6% improvement in shape maintaining accuracy, 40.9% improvement in deformation accuracy, and 17.5% reduction in driving force. Full article

Ultrasonic vibration-assisted grinding using cubic boron nitride (CBN) wheels has emerged as an effective approach for improving surface integrity and machining efficiency in hard-to-machine materials. However, achieving a desirable balance between surface roughness and material removal rate remains a critical challenge due to their inherently conflicting nature. In this study, a multi-objective optimization framework is proposed to simultaneously minimize surface roughness (Ra) and maximize material removal rate (MRR) in external cylindrical CBN grinding performed on a computer numerical control (CNC) milling machine under ultrasonic vibration assistance. Gaussian process regression models were first developed to accurately represent the nonlinear relationships between machining parameters and the target responses. These surrogate models were subsequently integrated with the non-dominated sorting genetic algorithm II (NSGA-II) to generate a set of Pareto-optimal solutions. The convergence behavior of the optimization process was evaluated using the hypervolume indicator, confirming fast and stable convergence. The resulting Pareto front clearly illustrates the trade-off between Ra and MRR, and a knee point solution was identified as a practical compromise for industrial application. The optimized results demonstrate that ultrasonic vibration-assisted CBN grinding can significantly enhance machining performance while maintaining acceptable surface quality. The proposed methodology provides an effective decision-support tool for multi-objective process optimization in advanced grinding applications. Full article

Rapid urban expansion poses growing challenges for balancing carbon emissions (CE), economic development, and ecological protection, particularly in coastal urban agglomerations. Although optimization–simulation approaches have been widely applied, explicit consideration of low-carbon objectives remains limited. To address this gap, this study proposes an integrated non-dominated sorting genetic algorithm III (NSGA-III)–patch-generating land use simulation (PLUS) framework that combines multi-objective optimization with spatially explicit land-use simulation. Using multi-temporal land-use datasets (2000–2020) from the Guangdong–Hong Kong–Macao Greater Bay Area (GBA), this research examined spatiotemporal land-use transitions and their co-evolution with CE, ecosystem services value (ESV), and GDP under five development scenarios. The results show that construction land expanded by 78% from 2000 to 2020, largely through cropland conversion, which pushed CE upward to 335.4 Mt. For 2030, the Low Carbon Emission scenario reduces CE by 11.8 Mt compared with the natural development scenario. The Balanced Development scenario maintains economic growth while limiting CE increases and stabilizing ESV. Spatially, scenario differences are limited in extent. Over 93% of areas remain unchanged, and variations are mainly concentrated in peri-urban corridors around the Guangzhou–Foshan core. Overall, the NSGA-III–PLUS framework provides a structured approach for coordinating carbon mitigation and land-use planning in rapidly urbanizing coastal areas. Full article

The Energy-Efficient Hybrid Flow Shop Scheduling Problem poses a significant multi-objective optimization challenge, necessitating the simultaneous minimization of conflicting objectives: Total Tardiness, Total Energy Cost, and Carbon Trading Cost. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is a classic algorithm in the field of multi-objective optimization. However, this algorithm frequently lacks the adaptive capability required to navigate high-dimensional solution spaces, often trapping the search in local optima, particularly when constrained by practical energy states of heterogeneous machines. To address these complexities, this study proposes a hybrid algorithm, named QGN, integrating Q-learning, the Grey Wolf Optimizer (GWO), and the NSGA-II. Specifically, QGN algorithm integrates NSGA-II for robust diversity maintenance with GWO for high-precision intensification. Unlike static hybrid methods, QGN employs a Q-learning agent as an adaptive controller to dynamically balance global exploration and local refinement, providing a theoretically grounded response to the rugged search landscape created by machine heterogeneity. Comprehensive experimental validation across diverse production scenarios confirms that QGN significantly outperforms baselines, including NSGA-II, Jaya, and Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D), as well as the state-of-the-art Q-learning- and GVNS-driven NSGA-II (QVNS) algorithm, in terms of both convergence and diversity. The results indicate that the proposed algorithm yields superior solution dominance, generates a substantially larger set of non-dominated solutions, and maintains a more uniform distribution along the Pareto front. Full article

Porous scaffolds fabricated via fused deposition modeling (FDM) are promising for bone tissue engineering, but their mechanical performance and geometric fidelity are governed by complex interactions between process parameters and architectural design. This study presents a multi-objective optimization framework for poly (lactic acid) (PLA) scaffolds based on three triply periodic minimal surface (TPMS) topologies—Gyroid, Primitive, and Diamond. A Box–Behnken design combined with response surface methodology was used to model compressive strength, elastic modulus, yield strength, energy absorption density, and discrepancies in volume and porosity as functions of layer thickness (0.05–0.15 mm), extrusion temperature (210–220 °C), and target porosity (50–70%). The resulting quadratic models exhibited strong predictive capability (R² > 77%, with most >90%) and were validated experimentally at extreme parameter combinations, yielding relative errors below 10% for 83% of measurements. Multi-objective optimization using NSGA-II, coupled with principal component analysis and correlation-based objective reduction, revealed that the six original objectives collapse to topology-specific essential pairs: absorbed energy density and porosity discrepancy for Gyroid; Young’s modulus and volume discrepancy for Primitive; and Young’s modulus and porosity discrepancy for Diamond. The generated Pareto fronts quantify the inherent trade-off between mechanical performance and geometric fidelity for each topology, providing designers with explicit decision maps. This framework enables rational, application-driven selection of printing parameters and scaffold architecture, advancing the clinical translation of patient-specific FDM-printed bone scaffolds. Full article

Hydrogen storage vessels are critical components in hydrogen energy systems, and improving their manufacturing efficiency and structural performance is essential for next-generation Type IV vessel designs. Compared with conventional wet filament winding, towpreg dry filament winding offers higher efficiency, reduced environmental impact, and better adaptability to complex structures. In this study, key process parameters, including winding tension, heating temperature, and winding speed were systematically optimized using the tensile strength and interlaminar shear strength of NOL ring specimens as evaluation metrics. A response surface methodology (RSM) regression model was established to correlate process variables with mechanical properties, followed by multi-objective optimization using the non-dominated sorting genetic algorithm II (NSGA-II) and final parameter selection through the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The results indicate that shear strength is primarily affected by heating temperature, whereas tensile strength is mainly governed by winding tension. The optimal parameter combination (79 N, 360 °C, and 11 m/min) yielded tensile and shear strengths of 2462.2 MPa and 64.4 MPa, respectively, with prediction errors below 0.5%. A 9 L Type IV hydrogen storage vessel manufactured under these conditions showed approximately 15.4% lower mass and about 17% higher gravimetric hydrogen storage efficiency than a comparable wet wound vessel. Full article

The problem of multi-criteria selection of network security configurations (NSC) under resource constraints and the necessity to comply with information security (IS) policies is addressed in this study. A formal mathematical model of the problem has been developed, encompassing the definition of a set of possible security mechanism configurations, the formalization of objective functions reflecting security levels, throughput, and deployment costs, and the introduction of constraints on feasible solutions. The NSGA-II (Non-dominated Sorting Genetic Algorithm II) optimization algorithm is employed to generate a set of Pareto-optimal solutions, ensuring uniform coverage of compromise configurations. A software package implemented in Python 3 incorporates modules for population generation, fitness evaluation, selection, crossover, mutation operators, and result visualization. Computational experiments (CE) were conducted to validate the effectiveness of the proposed approach. The evolution dynamics of the Pareto hypervolume were analyzed, the uniformity of solution distribution in the objective space was studied, and the impact of algorithm parameters on convergence to the optimal solution was examined. The results demonstrate that the proposed methodology enables the formation of NSC sets that achieve a balanced trade-off between security, throughput, and IS system deployment costs. Full article

The optimization of the MOSFET gate drive parameters is crucial for the trade-off between switching loss and electromagnetic interference (EMI). However, the nonlinear coupling among gate drive parameters, board-level parasitic, and switching performance limits the effectiveness of traditional MOSFET drive design methods. This paper proposes an adaptive tuning framework based on the proximal policy optimization (PPO) algorithm. An analytical switching model incorporating board-level parasitics is first derived to analyze the coupling between drive parameters and switching performance. The optimization problem is then formulated as a Markov decision process (MDP). Within this framework, domain randomization is applied during training. This enables the agent to learn a generalizable optimization strategy that remains robust across the varying parasitic inductances encountered in different PCB layouts. Compared to the traditional Non-dominated Sorting Genetic Algorithm II (NSGA-II), the proposed method uses the trained policy for direct inference. This reduces computation time by $98.7\%$ while maintaining a multi-objective performance difference within $10.06\%$. In addition, hardware verification shows a 10.7% average deviation between the measured and simulated results. These results demonstrate that the proposed method provides an efficient and scalable solution for MOSFET gate drive optimization. Full article