Search Results (242)

Wet cooling towers are widely used for low-energy thermal management and ventilation support; however, high-fidelity simulations are computationally expensive for large design studies. This work develops a physics-based static reduced-order model for a two-dimensional axisymmetric counterflow wet cooling tower derived from computational fluid dynamics (CFD) simulations coupled with a user-defined source-term formulation for heat and mass transfer in the fill region. A design of experiments based on advanced Latin hypercube sampling generated 210 configurations, of which 168 valid simulations were retained. The active inputs included tower diameter, fill height, inlet air mass flow rate, inlet air temperature, inlet humidity ratio, inlet water mass flow rate, and inlet water temperature, while the cooling range and evaporation rate were selected as target outputs. Five surrogate families were compared by cross-validation. Kriging was statistically most accurate, with $R_{CV}^2$ values of 0.9999 and 0.9998 for the cooling range and evaporation rate, respectively. Second-order quadratic polynomial models were selected as the engineering reduced order model (ROM) because they capture non-linear boundary curvatures with accuracy, achieving $R_{CV}^2\ge 0.9989$ and root mean square errors of 0.0426 K and 0.00042 kg/s while preserving an explicit, directly implementable algebraic form. Sensitivity analysis indicated that the inlet water temperature and air mass flow rate are dominant factors within the sampled domain. Full article

Surrogate-assisted optimization can reduce the number of repeated solid rocket motor (SRM) simulations, but a globally accurate surrogate is not necessarily reliable where design decisions are made: near active constraints. This study develops a low/medium-fidelity surrogate workflow for SRM performance prediction and constraint-robust screening. The low-fidelity response is obtained from a zero-dimensional internal-ballistics solver, and the medium-fidelity response is obtained from SrmStudio, an engineering-level SRM internal-ballistics design and simulation software. Residual correction and autoregressive Co-Kriging (AR1) models are assessed through global holdout accuracy, boundary-focused subsets, calibrated predictive intervals, same-budget enrichment comparisons, and SrmStudio consistency replay. Although the global coefficient of determination exceeds 0.99, the burn-time-boundary and pressure-exponent-sensitive subset MAPEs are approximately 28–47% higher than the final five-response global mean; the separation-boundary and high-pressure subsets do not exhibit the same increase. Calibrated 90% AR1 intervals achieve 90.8–96.7% coverage on the boundary test set and identify regions in which mean-only feasibility decisions are less reliable. A direct weight search further shows how sampling performance changes with the score composition after a boundary pool has been identified. Tolerance-boundary augmentation reduces the local thrust-window prediction error from 0.802 to 0.062 percentage points. Within the adopted simulation hierarchy, SrmStudio replay identifies a numerically promising candidate whose mean thrust increases from the SrmStudio baseline of 3866.67 N to 4000.67 N, a simulator-relative improvement of 3.47%, while satisfying the nominal constraints. Robustness analysis shows that the pressure exponent n is the limiting uncertainty, with an admissible calibration boundary of only ±0.191%. Full article

Sustainable agricultural production requires environmentally friendly weed management solutions. Steam-based thermal weed control is a promising alternative to conventional herbicide-based weed management. However, optimizing steam delivery systems remains computationally expensive due to the repeated simulation-based design evaluations required. This paper presents a design optimization framework for a novel root-targeted steam injection module intended for integration into an autonomous agricultural platform for sustainable thermal weed management. The mechanical behavior of different nozzle geometries was evaluated using finite element analysis using the ABAQUS/CAE software, generating the simulation dataset used for optimization. To reduce the computational cost associated with repeated finite element simulations, a Kriging surrogate model was constructed from this dataset and coupled with an evolutionary optimization algorithm to identify the optimal nozzle geometry. The evaluated nozzle geometries exhibited maximum stresses ranging from 5.12 to 20.41 N/mm². The stress value predicted by the Kriging model for the optimized configuration was validated through an additional finite element simulation, showing a deviation of only 6.6%. Furthermore, an artificial neural network model implemented in PyTorch 2.7.1 was trained on the simulation dataset and used as an independent validation tool to estimate the stress corresponding to the optimized nozzle geometry, with a prediction deviating by approximately 5.4% from the corresponding finite element result. The proposed approach significantly reduces computational cost while maintaining high accuracy in stress prediction, enabling the identification of a structurally reliable nozzle geometry for sustainable herbicide-free thermal weed management. Full article

The fairing, as an auxiliary structure attached to the deck body, plays an important role in mitigating the hydrodynamic loads on the deck. In this study, we first conducted a series of experiments to investigate the hydrodynamic performance of a three-girder deck under two different configurations, i.e., a bare deck and a deck with a circular fairing corresponding to a quarter-cylinder. Experimental observations showed that the effectiveness of this fairing is highly dependent on the flow conditions, generally leading to a reduction in drag coefficients while having a limited impact on lift coefficients. Subsequently, we focused on a representative hydraulic condition, which was simulated numerically using a CFD model implemented in OpenFOAM. The validated CFD model was then adopted to simulate the hydraulic characteristics of the system under identical hydraulic conditions (i.e., the same discharge and downstream water level) but with different fairing geometries, parameterized by a generalized hyperelliptic function. The drag coefficients obtained from simulations were used to build Kriging surrogate models, linking fairing geometry to force coefficients. This enabled efficient exploration of the design space and the identification of the optimal shape. Compared with the bare deck configuration, the optimized fairing reduced the drag coefficient by 15.5%, performing comparably to a circular fairing (14.0%). Flow field analysis confirmed that the presence of the fairing locally alters the flow characteristics. The optimized fairing was then tested under different hydraulic conditions to evaluate its robustness. The results showed that it is generally effective under critical conditions, confirming its performance stability. Full article

Piston engines are the core power units of large unmanned aerial vehicles (UAVs), and the characteristics of their thermal-fluid working processes directly affect the power performance and operational reliability of UAVs. To address the difficulty of simultaneously ensuring simulation accuracy and computational efficiency in conventional piston engine models, a lightweight simulation method for the thermal-fluid modeling of piston engines is proposed. A Rotax 915 piston engine for large UAVs was selected as the target engine. A one-dimensional thermal-fluid working process model was first established in GT-Power, and a Kriging surrogate model was then constructed to develop a lightweight simulation framework, enabling the fast and accurate prediction of key engine response parameters. The proposed framework enables the rapid prediction of engine performance, propeller load, and lubrication responses under UAV mission-related operating conditions. The surrogate model delivered favorable prediction accuracy against the GT-Power model (R² > 0.95, MAPE < 5%); flight data verification (excluding cold start) presented a MAPE of 11% and 27% for exhaust gas temperature and lubricating oil pressure, respectively. The proposed lightweight model was validated against actual UAV flight data, and the results show that it can effectively capture the variation trends of exhaust gas temperature and lubricating oil pressure. Further simulation results indicate that with increasing altitude, engine power decreases whereas brake specific fuel consumption (BSFC) increases. With increasing engine speed, engine power increases, BSFC decreases, and lubricating oil pressure declines. Under different flight phases, the takeoff phase shows the highest engine power and BSFC, the cruise phase corresponds to the maximum propeller torque and thrust, and the descent phase exhibits the lowest overall load level. The results provide a useful basis for performance evaluation, condition monitoring, and digital-twin-oriented modeling of piston engines for large UAVs. Full article

This paper addresses the challenges of life prediction and reliability assessment for ball screws under complex operating conditions by proposing a reliability prediction model that incorporates full-life fatigue damage. First, a full-life fatigue life prediction model encompassing the three stages of crack initiation, propagation, and fatigue cumulative spalling is developed. This model comprehensively considers the effects of material properties, geometric parameters, and loading history, enabling a systematic description of the fatigue process of ball screws from initial use to final failure. Based on this life prediction model, an enhanced adaptive Kriging–Monte Carlo simulation (E-AK-MCS) method is introduced to construct a surrogate model, which efficiently solves the high-dimensional nonlinear limit state function, thereby enabling accurate reliability assessment and parameter sensitivity analysis. Experimental results demonstrate that the proposed model achieves an average life prediction accuracy of 94.15% for the 8020 and 5005 specification ball screws, indicating its preliminary engineering applicability under the tested conditions. Reliability analysis indicates that ball diameter fracture toughness, and initial crack size are key factors influencing service reliability. This research provides systematic theoretical methods and technical support for the accurate life prediction, reliability design, and process optimization of ball screws. Full article

To achieve coordinated optimization of lightweighting and static–dynamic performance for a skateboard chassis, this paper proposes a multi-objective optimization method based on Universal Kriging, NSGA-II, and TOPSIS. A three-dimensional parametric model of the skateboard chassis structure for electric commercial vehicles was established based on a modular design philosophy, and modal and static analyses under four typical operating conditions were conducted. Six key variables were identified through parameter sensitivity analysis, and a surrogate model was constructed using the optimal Latin hypercube sampling method and the Universal Kriging model. The NSGA-II algorithm was used to solve the multi-objective optimization model and obtain a set of Pareto optimal solutions, from which the optimal compromise solution was selected using the entropy-weighted TOPSIS multi-criteria decision-making method. The optimized design achieved the objectives of reducing the skateboard chassis mass by 2.02%, decreasing the maximum deformation under bending conditions by 19.16%, and increasing the first-order natural frequency by 2.24%, thereby effectively improving lightweight, stiffness, and dynamic response performance of the skateboard chassis. This method integrates modular design with multi-objective optimization, providing a theoretical framework and technical pathway for the structural optimization design of a skateboard chassis. Full article

The toroidal propeller, as a high-performance propulsor with a unique geometric configuration, presents challenges in parameterizing its complex geometry and conducting design optimization. This paper proposes a hybrid Free-Form Deformation (FFD) based parametric method, which integrates global FFD control with local parameters to achieve flexible and efficient description of the complex surfaces of toroidal propellers. Building upon this, an automated design framework integrating Computational Fluid Dynamics (CFD), a Kriging surrogate model, and a data-driven optimization algorithm is constructed to explore a high-dimensional design space comprising 14 variables. The goal is to minimize torque while satisfying thrust and geometric constraints. Optimization results show that the optimized propeller achieves approximately 3.63% higher propulsive efficiency at the design condition and requires about 4.32% less power for the required thrust, compared with the best design from Design of Experiments (DOE) sampling. Further flow field analysis reveals that the optimized design achieves a more gradual pressure distribution, which effectively suppresses flow separation and cavitation risk, thereby maintaining better performance across a wider operational range. This study provides a systematic parametric modeling method and optimization strategy for the efficient design of toroidal propellers, demonstrating clear engineering application value. Full article

In electric vehicle thermal management systems, direct measurement of the internal motor temperature is difficult. Therefore, the coolant pressure drop is an important indicator for estimating the motor thermal state. However, manufacturing and operating uncertainties in water-cooled interior permanent magnet synchronous motors (IPMSMs) can cause variability in cooling performance and pressure drop, requiring a reliability-based design approach. In this study, reliability-based design optimization (RBDO) is performed by considering manufacturing tolerances in the cooling channels and uncertainty in the inlet coolant flow rate. Based on coupled electromagnetic–thermal–fluid analysis and Kriging surrogate models, RBDO is applied to minimize the maximum temperature while satisfying the allowable pressure-drop limit at a target reliability level. The proposed RBDO improves the probability of satisfying the pressure-drop constraint from 54.1% in the baseline design to 99.9%, while increasing the mean maximum temperature by only 0.17 K. These results indicate that RBDO can improve the reliability of the pressure-drop constraint in IPMSM water-cooling systems under practical manufacturing and operating uncertainties, with only a limited change in thermal performance. Full article

Mass concrete design presents a significant challenge due to the inherent conflicts among key performance metrics: high compressive strength, low heat of hydration, and low water absorption (a key durability indicator). Traditional trial-and-error methods are inefficient and fail to systematically navigate these complex trade-offs. To address this, this study proposes a data-driven multi-objective optimization framework for mass concrete mix design. A comprehensive experimental dataset of 64 mixtures was established by varying the water-to-binder ratio (0.40–0.55), fly ash content (0–120 kg/m³), and slag content (0–120 kg/m³), with cement content fixed at 400 kg/m³. Kriging surrogate models were developed to accurately map the nonlinear relationships between these design variables and the three performance responses. These models were then integrated with the NSGA-II algorithm to generate a Pareto-optimal front of solutions. The framework’s predictive accuracy and generalization capability were rigorously validated through out-of-sample experiments, demonstrating prediction errors consistently below 10%. The results provide a quantified map of feasible engineering compromises, enabling engineers to select tailored mixtures for specific project priorities, such as low-heat mixes for dams or high-strength mixes for foundations. Full article

Historically, in the initial stages of solid rocket motor (SRM) development, performance parameters, such as specific impulse, total impulse, mass, and thrust, have been prioritized, with cost considerations often treated as secondary. Consequently, SRM performance optimization under cost constraints has emerged as a central objective in aerospace propulsion. To address this gap, this study establishes a cost–performance evaluation model for SRMs. A Kriging surrogate model, the Non-dominated Sorting Genetic Algorithm II (NSGA-II), and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) are leveraged to minimize the manufacturing cost and maximize the terminal velocity of SRM engines, subject to constraints associated with the maximum operating pressure of the combustion chamber and burn time. First, a cost–performance calculation model for an SRM is developed and validated. Subsequently, Pearson correlation analysis and Sobol-based global sensitivity analysis are combined to reduce the dimensionality of the design parameters, and optimal Latin hypercube sampling is used to generate the training samples. Building on this foundation, a Kriging surrogate model is constructed. The cost–performance model of the SRM is subjected to multi-objective optimization using NSGA-II and TOPSIS to support decision-making. The results indicate that the proposed cost–performance calculation model achieves an error below 5%, demonstrating high accuracy. Among the design parameters, the combustion chamber length, nozzle outlet area, and expansion ratio significantly influence the cost and performance of SRMs. The surrogate models exhibit strong predictive accuracy, with coefficients of determination exceeding 0.9. The optimized TOPSIS scheme yields a performance improvement of 10.94% with a cost increase of 4.15% compared with the reference scheme. In summary, the cost–performance evaluation and optimization framework established in this work provides quantitative decision support for SRM design under cost constraints, and the integrated methodology can be extended to other aerospace propulsion systems or complex engineering equipment. This contributes to achieving synergistic optimization of performance and cost under resource limitations, and offers practical guidance for advancing affordability-driven design in propulsion engineering. Full article

The surging need for public security risk mitigation has placed stricter demands on the modernization of emergency response capacities. Unmanned aircraft systems (UASs) offer a promising solution for liquid anti-riot agent dispersion, yet the complex interaction between rotor-induced downwash and droplet trajectories makes nozzle layout optimization a significant challenge. To address the prohibitive computational costs of traditional Computational Fluid Dynamics (CFD) and the limitations of single-objective optimization, this study proposes an integrated “simulation–modeling–optimization–decision” framework. First, a linear nozzle layout was identified as superior to the traditional circular arrangement, achieving a 44.8% increase in deposition rate. Subsequently, Optimal Latin Hypercube Sampling (OLHS) and CFD simulations were combined to construct high-precision Kriging surrogate models for three key indicators: deposition rate, uniformity, and coverage rate. The NSGA-II algorithm was then employed to solve the multi-objective trade-off, followed by the entropy-weighted TOPSIS method to identify the optimal engineering solution. Results indicate that nozzle count is the dominant system-level variable under the constant per-nozzle flow-rate condition, showing strong positive correlations with all performance indicators. The identified optimal configuration (6 nozzles with a 1.88 m boom length) achieved a 66.1% increase in deposition rate and an 18.7% increase in coverage rate compared to the original circular layout. Furthermore, the surrogate-based framework improved optimization efficiency to 296% compared to full factorial methods. This study provides a scientific theoretical basis and a highly efficient technical pathway for the structural design of high-performance UAV spray systems. Full article

Accurate prediction of radio wave propagation in evaporation ducts is critical for radar systems but faces significant environmental uncertainties. This study presents an uncertainty quantification and global sensitivity analysis framework comparing three surrogate models: Polynomial Chaos Expansion, Ordinary Kriging, and Polynomial-Chaos Kriging. Using a parabolic equation solver, we quantify how five parameters—mean duct height, duct height slope, potential refractivity gradient, frequency, and root mean square (RMS) wave height—affect propagation loss. We assess predictive accuracy, perform Sobol-based sensitivity analysis, and explore how surrogate performance relates to the normalized frequency V, a parameter characterizing modal complexity. Results show that Kriging consistently outperforms the others: its local interpolation capability proves essential for capturing rapid spatial oscillations caused by multimode interference. We observe a statistically significant negative correlation between Kriging’s prediction error and V, suggesting that its local interpolation becomes increasingly advantageous as the modal complexity of the field (quantified by V) increases. This provides a physically interpretable, though not yet predictive, link between surrogate model choice and the underlying propagation physics. Sensitivity analysis reveals that mean duct height dominates uncertainty at short-to-medium ranges, while the potential refractivity gradient becomes increasingly influential at longer ranges. RMS wave height exhibits localized effects near multipath nulls, particularly at higher frequencies. These findings provide quantitative guidance for prioritizing environmental measurements and offer a physically interpretable basis for surrogate model selection in evaporation duct problems. Full article

Pump-as-turbine (PAT) units have been widely used for energy recovery in water-supply networks, petrochemical systems, and small hydropower applications; however, their turbine-mode performance is often limited because most commercial pumps are originally designed for pumping conditions. To improve the hydraulic performance of a low-specific-speed PAT, this study developed a surrogate-assisted multi-objective optimization framework combining three-dimensional computational fluid dynamics (CFD), design of experiments, a Kriging surrogate model, and a multi-objective genetic algorithm. Five key impeller geometric parameters, including blade inlet angles, blade wrap angles, and impeller outlet diameter, were selected as design variables, and turbine-mode efficiency was maximized under a head constraint of H ≥ 24 m at the rated condition of 1450 r/min. The results showed that the optimized design increased efficiency from 72.34% to 84.42% while satisfying the head requirement. Comparative analyses of pressure and velocity fields in the impeller and volute further revealed that the performance improvement was mainly associated with enhanced flow-field uniformity and reduced local hydraulic losses. A dedicated PAT test rig was finally established to experimentally validate the optimized design. Full article

Distributed optimal design brings significant solutions for experiment optimization in complex engineering design problems. A Kriging-based augmented Lagrangian Method is proposed with the help of the Multi-fidelity Hamiltonian Kriging (MHK) surrogate model. The Multi-fidelity Hamiltonian Kriging-based Augmented Lagrangian Method (MHK-ALM) uses subsystem surrogate models constructed from multi-fidelity data to speed up the inner loop solution of ALM, while also reducing the iterations of the outer loop of ALM. The MHK-ALM is illustrated with one numerical simulation of a multi-fidelity constrained NASA speed reducer problem, demonstrated with a multidisciplinary design optimization of a solid-propellant ballistic missile. The engineering application of the multidisciplinary design optimization (MDO) problem shows that the proposed method can perform precisely over certain advanced surrogate-based optimization frameworks. The MHK-ALM can be applied for any other distributed optimal design problems where one need complex subsystem decomposition. Full article