Search Results (2,324)

The performance of haul trucks in open-pit mining is strongly affected by haul-road geometry, surface condition, rolling resistance, and operational traffic regimes. However, existing studies often consider road-surface mapping, vehicle dynamic response, and onboard telemetry as separate information streams, which limits the reproducible assessment of how road-related factors are associated with VIMS-derived fuel-use proxy burden, mechanical dynamic response, and transport-cycle instability. This study proposes a field-based, segment-level multisensor framework that integrates unmanned aerial vehicle/light detection and ranging (UAV/LiDAR) road-surface reconstruction, global positioning system/inertial measurement unit (GPS/IMU) trajectory and vibration data, and Caterpillar Vial Information Management System (VIMS) telemetry into a unified spatiotemporal analytical dataset. The methodological contribution consists in the synchronization of heterogeneous data sources at the road-segment level, the calculation of interpretable road-condition and vehicle-response indicators, and the statistical assessment of road-related effects while explicitly accounting for confounding factors such as longitudinal grade, payload state, speed regime, truck class, and operational variability. Unlike studies that use LiDAR mapping, vibration monitoring, or onboard telemetry as separate diagnostic channels, the proposed approach introduces a segment-level analytical framework in which road morphology, truck response, and operational penalties are aligned within the same spatial unit, interpreted under confounder-aware conditions, and verified through repeat-pass reproducibility and robustness checks. The framework was tested on haul roads around the Ekibastuz open-pit coal mine. The field analysis identifies road segments where degraded surface morphology, increased waviness, unfavorable longitudinal profile, and higher rolling resistance coincide with increased mechanical dynamic response, VIMS-derived fuel-use proxy burden, braking instability, and travel-time variability. The results are interpreted as controlled field-supported associations rather than as isolated causal effects. The proposed maintenance ranking should therefore be regarded as a decision-support output, while the operational effectiveness of specific repair interventions requires future before–after validation. Full article

The recovery of low- and medium-temperature waste heat using Organic Rankine Cycles (ORCs) is increasingly important for improving the efficiency and sustainability of industrial and energy systems. In compact ORC turboexpanders, high specific power output and large pressure ratios often require single- or two-stage turbines operating in transonic or supersonic regimes. Under these conditions, stator blade design is complicated by strong compressible-flow effects and, for organic working fluids, by real-gas thermodynamic behavior. Conventional supersonic stator design methods, such as the method of characteristics, are mainly applicable to the diverging supersonic portion of the blade passage, while the converging region is typically defined using empirical or heuristic prescriptions. This paper presents a physics-informed neural-network-based design method for supersonic turbine stator blades. The proposed framework generates the complete inter-blade passage, including both the converging and diverging regions, starting from a prescribed mean-line geometry and Mach number distribution. The velocity field is obtained by solving the governing equations of steady, inviscid, adiabatic, irrotational compressible flow within a PINN formulation. A hard boundary-condition strategy is used to impose the specified mean-line velocity distribution exactly, while real-fluid thermodynamic effects are incorporated through lookup tables for the speed of sound and density. The blade contours are then reconstructed from stream-function isolines predicted from the computed velocity field. The method is demonstrated for two working fluids: air, treated as a perfect gas, and toluene undergoing transcritical expansion. The resulting blade passages are first validated using inviscid CFD simulations, which show close agreement between the prescribed and computed mean-line Mach number distributions. Turbulent CFD simulations of the final blade cascades confirm smooth acceleration through the inter-blade passage, with no strong internal shocks and only weak fishtail shocks downstream of the trailing edge. For both fluids, the post-expansion ratio is approximately unity and the exit flow angle remains close to the prescribed blade metal angle, indicating well-matched supersonic stator designs. The results demonstrate that the proposed PINN-based design method provides a physically consistent approach for generating supersonic stator blade profiles for both ideal-gas and real-gas turbine applications. Full article

The bolted rotor system functions as a critical component in aero-engines and gas turbines. Additionally, the misalignment fault is a typical and common fault in bolted rotor systems. Nevertheless, current research on bolted rotor systems has not covered misalignment faults. Therefore, a mathematical model of bolted rotor systems incorporating misalignment faults is established in this work. The nonlinear dynamics of bolted rotor systems involving misalignment are investigated by the comparison of the frequency amplitude responses, waterfall diagrams, rotor orbits and time-varying stiffness. Moreover, an in-depth analysis is conducted on the variations in vibration behaviors of rotor systems under different misalignment degrees. Finally, the proposed model is examined using rotor-rig tests conducted under aligned and misaligned conditions. A consistent observation from the numerical and test results is that the 2× frequency resonance speed does not equate precisely to 0.5 times the critical speed. In addition, the 2× component undergoes a sudden change as the misalignment level rises. Full article

Accurate thrust control is vital for efficient gas turbine engine operation, yet direct in-flight thrust measurement is unavailable. Most engines rely on speed control schedules, which suffer from unplanned dead zones and poor throttle-to-thrust linearity—compromising pilot handling and fuel economy. We propose a robust thrust control schedule to improve the thrust-to-throttle relationship. It integrates two intermediate/idle thrust estimators, a degradation estimator, and a reference thrust estimator. The first intermediate/idle thrust estimator provides baseline estimates of intermediate and idle thrust. The degradation estimator compensates thrust deviations caused by engine degradation. The second intermediate/idle thrust estimator updates its estimates online using compensated estimates to track degradation. The reference thrust estimator then uses outputs from the second estimator and linearly interpolates the current power lever angle to determine reference thrust. The proposed schedule is evaluated under the considered degradation conditions. CLM-based simulations evaluate its effectiveness during the early-stage degradation period, while the micro-turbojet ground experiment demonstrates the proof-of-concept implementation of the outer thrust-loop structure. Full article

Aero-engine safety and prognostics and health management (PHM) rely on robust vibration-based fault diagnosis. However, many deep learning studies on rotating machinery are evaluated under random train–test splits that mix hardware instances and may obscure the domain shift faced in deployment. This paper presents a protocol-driven end-to-end baseline for multi-sensor aero-engine-relevant vibration diagnosis on the HIT inter-shaft bearing benchmark. Six synchronous vibration channels are segmented into fixed-length windows, standardized using source-domain statistics, and classified by a compact 1D CNN backbone with and without squeeze-and-excitation (SE) channel attention. A deeper ResNet1D baseline is further introduced to examine whether increasing backbone capacity improves cross-bearing generalization under the same source-only training protocol. We compare random segment-level splits with bearing-level cross-splits that hold out entire bearings as unseen target domains, and we report deployment-oriented indicators including balanced accuracy, false-alarm rate (FAR), and miss rate over five random seeds. Under random splits, the compact CNN baseline reaches near-ceiling test accuracy, confirming that the benchmark is readily separable under in-domain interpolation. In contrast, cross-bearing evaluation reveals severe degradation: in the representative split, the baseline CNN accuracy collapses to approximately 15% with near-zero normal-class recall, while ResNet1D improves fault sensitivity but still retains a high FAR above 88%. Additional cross-bearing permutations further show that this degradation is not attributable to a single unfavorable source–target split. These findings indicate that, under the tested source-only backbones and protocols, distribution mismatch is a dominant bottleneck for deployment-ready cross-bearing diagnosis. The results establish a reproducible baseline for protocol-driven evaluation in aero-engine PHM and motivate future work on domain adaptation, domain generalization, calibration, and sequential decision logic. Full article

As the offshore wind industry expands, improving operation and maintenance (O & M) efficiency while reducing the levelized cost of electricity (LCOE) has become increasingly important. This study develops an intelligent inspection route optimization framework for 21 offshore wind turbines located in the Changhua offshore wind farm of Taiwan. The framework integrates Geographic Information System (GIS) spatial information, dynamic sea-state conditions, labor costs based on Taiwan’s Labor Standards Act, and vessel fuel consumption into a comprehensive cost evaluation model. Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) were applied to two practical scenarios: full-field routine inspections and targeted maintenance missions. Experimental results show that PSO achieved the shortest route for full-field inspections, reducing the travel distance to 20.125 km compared with 23.976 km obtained by GA. In contrast, for targeted maintenance involving eight turbines, GA generated a shorter route of 5.719 km, outperforming PSO’s 6.456 km. For the scenarios investigated in this study, PSO showed superior performance in the 21-turbine inspection task, whereas GA achieved better results in the 8-turbine maintenance task. The proposed framework provides an effective decision-support tool for offshore wind farm O & M planning, improving maintenance efficiency while reducing operational costs. Full article

High-power electric air heaters are key charging components in air thermal energy storage systems, but the dominant heat-loss regions and retrofit basis of existing devices remain unclear. In this study, a three-dimensional conjugate heat-transfer model was developed for an existing 1200 kW vertical electric air heater and validated using three steady-state experimental cases, with a maximum outlet-temperature deviation of 2.17%. Based on the validated model, temperature-field characteristics and segmental heat-loss distributions were analyzed under different mass flow rates. The results show that heat loss was highly non-uniform: Segments 2 and 3 accounted for 37.26% and 54.51% of the total heat loss, respectively, contributing 91.77% in total. A targeted local retrofit scheme was, therefore, proposed by filling the non-flowing inner-cylinder region in Segments 2 and 3 with glass wool and enhancing insulation near local cooling boundaries. After optimization, the average total heat loss decreased from 31.94 kW to 17.69 kW, corresponding to a 44.6% reduction. Under the rated condition, the outlet temperature increased from 1421.6 K to 1482.0 K, providing 584.8 kWh of additional effective thermal storage per cycle and an estimated payback period of 399 d. This study provides a diagnosis-guided retrofit approach for existing high-power electric air heaters. Full article

Decarbonizing heavy-duty logistics requires powertrains that integrate novel topology design, degradation-aware optimization, and robust dynamic performance under real-world operational loads. While solid oxide fuel cells offer high efficiency, their application in transportation is hindered by thermal fatigue. This study proposes a novel hybrid powertrain topology integrating a metal-supported solid oxide fuel cell (SOFC), a micro gas turbine (MGT), and an aluminum–silicon phase change material (PCM) thermal buffer. A high-fidelity dynamic model is developed and coupled with a multi-objective optimization framework to size the PCM buffer and battery pack, balancing capital expenditure and system lifetime. Furthermore, a degradation-aware energy management strategy based on a thermal state-of-charge metric is introduced. Simulations over a 10 h dynamic drive cycle indicate that the optimal configuration (120 kg PCM, 80 kWh battery) extends the SOFC’s simulated remaining useful life to 38,400 h, a 2.5-fold improvement over unbuffered systems. Concurrently, the proposed energy management strategy reduces the MGT mechanical wear index by 98% compared to conventional load-following strategies. The system demonstrates robust performance across ambient temperatures from −20 °C to +45 °C and achieves a 22% reduction in projected capital expenditure compared to standard proton exchange membrane fuel cell powertrains. This topology offers a highly durable and economically viable pathway for next-generation zero-emission heavy-duty vehicles. This work addresses a critical gap in the literature: the lack of integrated thermal buffering and degradation-aware control strategies for high-temperature fuel cell systems in dynamic vehicular applications. By coupling a physical latent heat buffer with a novel Thermal-SOC-proportional Energy Management Strategy, the proposed architecture directly targets the primary degradation mechanisms that have historically impeded SOFC commercialization in heavy-duty transport. Full article

Accurately evaluating how aero-engine performance supports upper-level capability remains a challenging issue in the digital planning, demonstration, and design of complex equipment systems-of-systems. Existing studies mainly rely on two-level analyses at the subsystem and system-of-systems levels, which are insufficient to characterize the cross-level transmission relationships among the aero-engine, aircraft performance, and overall capability. To address this limitation, this paper proposes a multilayer network-based contribution evaluation method for aero-engines oriented toward digital equipment planning and demonstration. First, a three-layer evaluation index system is constructed, including the overall capability layer, the aircraft performance layer, and the aero-engine performance layer, based on the OODA loop concept and aviation physical constraints. This provides a structured and traceable basis for cross-level requirement decomposition and scheme evaluation. Second, by integrating expert prior judgment with mechanism-based sensitivity analysis, the interrelationships among indicators at different layers are quantified, and a multilayer evaluation index network is established. Third, topological structure analysis is employed to identify key indicators in the aero-engine layer, and a cascading propagation model is introduced to evaluate the supporting roles and contribution rates of both individual indicators and the overall aero-engine layer with respect to the overall capability layer. Simulation results show that the proposed method can effectively reveal the structural characteristics, propagation paths, and dynamic influence patterns of aero-engine-layer indicators within the multilayer network. The proposed method provides methodological support for digital equipment planning, scheme demonstration, design optimization, and capability-oriented decision-making of aero-engines. Full article

To tackle the intermittency of renewable energy and realize the repurposing of abandoned oil and gas wells, this study proposes a compressed air energy storage (CAES)-based cogeneration system integrated with geothermal energy and abandoned oil and gas wells, and conducts a five-dimensional comprehensive analysis covering exergy, exergoeconomic, exergoenvironmental, economic and environmental performance. The optimal operating parameters are determined as air compressed to 200 bar, an ORC turbine inlet pressure of 16 bar and an inlet temperature of 110 °C. The system’s annual total power generation is 2,971,416.5 kWh during low-power daytime operation, and 20,131,785 kWh during high-power nighttime operation. Compared with conventional CAES systems, the proposed system reduces total exergy destruction by 4121.35 kW and increases exergy efficiency from 48.49% to 63.38%. Coolers, geothermal heat exchangers and compressors are the main sources of exergy destruction cost and capital investment, while COM1, HE1 and HOT1 are the key components causing environmental impacts. The system realizes cogeneration of power, hydrogen and pure water, with a static payback period of about 5.4 years and significantly reduced TEWI value at elevated turbine inlet pressure. This system achieves multi-objective synergies in energy efficiency, economy and environment, providing a feasible scheme for the green repurposing of abandoned oil and gas wells and cascaded utilization of renewable energy. Full article

Structural static testing is paramount for validating the structural integrity of critical aerospace components. However, conventional test rigs are often constrained to fixed loading axes and frequently induce parasitic torques. Accurate reproduction of aero-engine pylon flight loads therefore requires a mechanism that combines omnidirectional vector loading, high stiffness, and efficient force transmission. Achieving these coupled requirements is primarily a geometric synthesis problem, yet the associated workspace, stiffness, and load–capacity indices are nonlinear, mutually coupled, and expensive to evaluate over dense pose samples. To address this optimization bottleneck, this work develops a task-specific 6-UPS loading mechanism and a bio-inspired sensitivity-weighted NSGA-II algorithm for its geometric synthesis. Inspired by gene/locus-specific heterogeneity in biological evolution, the algorithm assigns variable-wise search intensities according to design-variable sensitivities, which are estimated using Multivariate Adaptive Regression Splines (MARS). In this way, influential design genes receive stronger local exploitation, whereas less sensitive ones retain broader exploration. Numerical simulations demonstrate that the proposed approach reduces computation time from about 30 h to 3 h relative to direct optimization with the baseline NSGA-II, while simultaneously improving workspace, stiffness, and load-carrying capacity. A hybrid physical prototype was further tested under 240 loaded pose conditions; the system maintained force magnitude errors below 0.64% (63.42 N) and directional deviations below 1.15°. These results support the efficacy of the proposed bio-inspired optimization-based design methodology for high-fidelity static testing of aero-engine pylons under the adopted hybrid setup. Full article

Detonation-based combustion systems for application in gas turbines (GTs) have received growing attention in recent decades. Such a technology leads to higher thermodynamic cycle efficiency compared to the conventional deflagrative solution as a result of pressure rise occurring during the heat addition process. This study aims to implement pressure gain combustion (PGC) into a thermodynamic cycle where the main compressor is operated at a lower pressure ratio compared to the Brayton–Joule cycle. In detail, this study focuses on the impact of the secondary air system (SAS) compressor, which is necessary to correctly feed the blade cooling circuits with adequate pressure as well as to deliver high-pressure air for cooling the PGC system. A parametric analysis based on different amounts of cooling air to the PGC system is proposed and discussed. In detail, the power demand by the SAS compressor can be as high as 5–6% of the net PGC GT power output, with maximum demands calculated in the range from 16 to 22 MW for a 335 MW F-class gas turbine. These figures are significant because the higher they are, the greater the risk of reducing the performance advantage introduced by the pressure gain combustion. In addition, the effects of SAS compressor efficiency are investigated and a preliminary assessment of both size and rotational speed of the SAS compressor is proposed as well. Full article

Offshore wind energy offers abundant resources and significant potential for large-scale development. Efficient design of collection systems is critical to the economic viability of offshore wind farms (OWFs). This study proposes an upper–lower level topology optimization framework based on the Artificial Lemming Algorithm (ALA) to address the complexity arising from large numbers of wind turbines (WTs). At the upper level, wind turbines can be partitioned into different numbers of regions according to practical engineering requirements using the Radial Fuzzy C-Means (RFCM) clustering algorithm. At the lower level, the ALA is applied to optimize the collection system topology within each region, aiming to minimize total construction cost while satisfying operational constraints. A case study involving a 75-WT offshore wind farm is conducted. Comparative simulations against various heuristic algorithms including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Differential Evolution (DE) show that the proposed method achieves faster convergence, lower total costs and greater robustness. Specifically, the ALA reduces the best cost by 9.9% and improves average runtime by 28.5%, indicating its advantages in best-cost search and computational efficiency in the tested case. In addition, based on 10 independent runs, the ALA achieves the lowest median cost of $6684\times {10}^4$ CNY, with an interquartile range of 6593–$6813\times {10}^4$ CNY and a cost range of 6362–$7087\times {10}^4$ CNY. Overall, the proposed framework provides a practical optimization approach for obtaining low-cost feasible collection-system layouts in the studied offshore wind farm case. Full article

In this work, Direct Numerical Simulation (DNS) investigates the combustion behaviour of a reactive transverse lean premixed jet of an ammonia blend (10% NH₃, 11% H₂, 16% O₂ and 63% N₂ by volume) injected through a rectangular nozzle in a pre-heated non-vitiated air crossflow at a pressure of 5 bar. The configuration has been chosen from a Reynolds-Averaged Navier–Stokes (RANS) test campaign to ensure low NO and low unburned fuel, while maintaining a high temperature profile at the turbine inlet. The DNS shows that the flame stabilises on the leeward side of the rectangular jet, within and downstream of the recirculation region, while high scalar dissipation and short residence times prevent persistent anchoring on the windward side. Joint statistics reveal that the reaction does not follow a constant equivalence ratio path, since intermediate progress states are shifted towards leaner mixtures by entrainment, dilution and differential diffusion. The strongest heat-release and displacement-speed events occur in localised regions where mixture state, stretch and flame-front geometry act jointly. The displacement-speed budget is mainly controlled by the chemical source term, with diffusion reducing the net propagation speed and stratification-induced cross terms remaining small. Under intense stretch, positively curved flame elements exhibit larger displacement speeds, indicating a coupled effect of curvature, preferential diffusion and local radical transport. NO formation is dominated by fuel-nitrogen chemistry: HNO and NH₂ are the main NO-producing routes, whereas N₂ and N₂O provide the dominant NO-sink channels. The DNS predicts an outlet-averaged NO level of 400 dppm, while extended-domain RANS calculations indicate that longer residence times could reduce it below 100 dppm. Full article

The increasing demand for higher efficiency and lower emissions in aircraft gas turbines motivates investigation of alternative thermodynamic cycle architectures. This study assesses the performance and nitrogen oxides (NOx) emission behavior of a triple-spool, separate-exhaust turbofan engine equipped with an interstage turbine burner (ITB). A baseline engine representative of the RB211 Trent 892 is first modeled at maximum takeoff, sea-level static conditions and verified against publicly available takeoff reference data. The cycle is then modified by introducing an isobaric secondary combustion process between the high-pressure and intermediate-pressure turbines. The effects of fan pressure ratio, bypass ratio, overall pressure ratio, high-pressure turbine inlet temperature, and ITB exit temperature are examined using two-parameter response surface sweeps. Main combustor NOx is estimated using an RQL-type cycle correlation, while the ITB contribution is represented using an engineering source–sink model accounting for new NOx formation and partial reburning of upstream NOx. The baseline model predicts specific thrust, thrust-specific fuel consumption (TSFC), and NOx emission index (EINOx) within ±8% of reference values. At a selected ITB operating point, specific thrust increases by 1.98%, TSFC increases by 9.84%, thermal efficiency decreases by 2.56%, and the adopted engineering source–sink model predicts a 20.03% reduction in fuel flow-weighted EINOx. The corresponding takeoff-mode NOx-per-thrust indicator decreases by approximately 12.1%. These results indicate that ITB integration introduces a coupled performance–emissions trade-off and should not be evaluated solely as a thrust augmentation method. Full article