Search Results (212)

Temperature control plays a critical role in mitigating the lifespan degradation mechanisms and ensuring thermal safety of lithium-ion batteries (LIBs) and proton exchange membrane fuel cells (PEMFCs). However, current energy management strategies (EMS) for fuel cell hybrid electric vehicles (FCHEVs) generally lack comprehensive thermal effect modeling and thermal runaway fault diagnosis, leading to irreversible aging and thermal runaway risks for LIBs and PEMFCs stacks under complex operating conditions. To address this challenge, this paper proposes a thermo-electrical co-optimization EMS incorporating thermal runaway fault diagnosis actuators, with the following innovations: firstly, a dual-layer framework integrates a temperature fault diagnosis-based penalty into the EMS and a real-time power regulator to suppress heat generation and constrain LIBs/PEMFCs output, achieving hierarchical thermal management and improved safety; secondly, the distributional soft actor–critic (DSAC)-based EMS incorporates energy consumption, state-of-health (SoH) degradation, and temperature fault diagnosis-based constraints into a composite penalty function, which regularizes the reward shaping and guides the policy toward efficient and safe operation; finally, a thermal safe constriction controller (TSCC) is designed to continuously monitor the temperature of power sources and automatically activate when temperatures exceed the optimal operating range. It intelligently identifies optimized actions that not only meet target power demands but also comply with safety constraints. Simulation results demonstrate that compared to DDPG, TD3, and SAC baseline strategies, DSAC-EMS achieves maximum reductions of 39.91% in energy consumption and 29.38% in SoH degradation. With the TSCC implementation, enhanced thermal safety is achieved, while the maximum energy-saving improvement reaches 25.29% and the maximum reduction in SoH degradation attains 20.32%. Full article

This study investigates the microstructure and mechanical properties of copper (Cu) and graphene/Cu (Gr/Cu) composites produced via high-pressure torsion (HPT) under 5 GPa at room temperature. Microstructural analysis revealed significant grain refinement, with average grain sizes of 0.39 μm for pure Cu and 0.35 μm for Gr/Cu composite. The Gr/Cu composite exhibited slightly higher microstrains and effective stacking fault energy (SFE). Tensile tests showed ultimate tensile strengths of 689 MPa (pure Cu) and 674 MPa (Gr/Cu), with the latter demonstrating improved ductility (~10% elongation). Ab initio calculations confirmed a 27% increase in SFE for Gr/Cu, aligning with experimental results. These findings highlight the potential of Gr/Cu composites for applications requiring high strength and efficient heat dissipation. Full article

This study employs first-principles calculations combined with the Special Quasirandom Structure (SQS) technique to investigate the impact of three interstitial elements C, N, and O, on the mechanical properties and stacking fault energy (SFE) of NiCoCr medium-entropy alloys. The results indicate that non-metallic O, C, and N tend to occupy octahedral interstitial sites, which can effectively release stress concentration and enhance the strength and deformability of the material. Differential charge density analysis shows that the dissolution of C, N, and O significantly alters the surrounding electronic environment, strengthening the interaction between solute atoms and metal atoms, thereby hindering dislocation glide and increasing the strength and hardness of the material. Elastic property analysis indicates that NiCoCr alloys doped with C, N, and O exhibit good ductility and anisotropic characteristics. Furthermore, the study of stacking fault energy reveals that the doping with C, N, and O can significantly increase the stacking fault energy of NiCoCr alloys, thereby optimizing their mechanical properties. These findings provide theoretical evidence for the design of advanced high-entropy alloys that combine high strength with good ductility. Full article

Voids have a crucial effect on the fatigue performance of materials. The general viewpoint is that voids, as possible sources of cracks, are harmful to the fatigue performance of materials. However, this study finds that microvoids enhance the low-cycle fatigue resistance of TiAl alloys, both in single crystal and polycrystal, using molecular dynamics simulations. Due to the difference between the simulation and test, the selected strain value is larger. It is found that during cyclic loading, Shockley partial dislocations preferentially nucleate around the microvoid in the single crystal, with stacking fault tetrahedra forming progressively to obstruct dislocation motion. The polycrystal model exhibits the synergistic effect of the microvoid–grain boundary, and the fatigue resistance is substantially enhanced through the combined mechanisms of Lomer–Cottrell lock formation, twin boundary migration, and phase transformation. In addition, simulation models with microvoids exhibit lower plastic strain energy density and enhance fatigue life compared to microvoid-free counterparts. The present study provides significant insights into designing γ-TiAl alloys through controlled microvoids to optimize fatigue resistance. Future work should include experimental validation to substantiate these computational findings. Full article

Artificial intelligence (AI) is now the computational core of smart building automation, acting across the entire cyber–physical stack. This review surveys peer-reviewed work on the integration of AI with indoor environmental quality (IEQ) and energy performance, distinguishing itself by presenting a holistic synthesis of the complete technological evolution from IoT sensors to generative AI. We uniquely frame this progression within a human-centric architecture that integrates digital twins of both the building (DT-B) and its occupants (DT-H), providing a forward-looking perspective on occupant comfort and energy management. We find that deep reinforcement learning (DRL) agents, often developed within physics-calibrated digital twins, reduce annual HVAC demand by 10–35% while maintaining an operative temperature within ±0.5 °C and CO₂ below 800 ppm. These comfort and IAQ targets are consistent with ASHRAE Standard 55 (thermal environmental conditions) and ASHRAE Standard 62.1 (ventilation for acceptable indoor air quality); keeping the operative temperature within ±0.5 °C of the setpoint and indoor CO₂ near or below ~800 ppm reflects commonly adopted control tolerances and per-person outdoor air supply objectives. Regarding energy impacts, simulation studies commonly report higher double-digit reductions, whereas real building deployments typically achieve single- to low-double-digit savings; we therefore report simulation and field results separately. Supervised learners, including gradient boosting and various neural networks, achieve 87–97% accuracy for short-term load, comfort, and fault forecasting. Furthermore, unsupervised models successfully mine large-scale telemetry for anomalies and occupancy patterns, enabling adaptive ventilation that can cut sick building complaints by 40%. Despite these gains, deployment is hindered by fragmented datasets, interoperability issues between legacy BAS and modern IoT devices, and the computer energy and privacy–security costs of large models. The key research priorities include (1) open, high-fidelity IEQ benchmarks; (2) energy-aware, on-device learning architectures; (3) privacy-preserving federated frameworks; (4) hybrid, physics-informed models to win operator trust. Addressing these challenges is pivotal for scaling AI from isolated pilots to trustworthy, human-centric building ecosystems. Full article

FeAl intermetallic compounds exhibit high application potential in high-voltage transmission lines to withstand external forces such as powerlines’ own gravity and wind force. The ordered crystal structure in FeAl intermetallic compounds endows materials with high strength, but the remarkable brittleness at room temperature restricts engineering applications. This contradiction is essentially closely related to the deformation mechanism at the nanoscale. Here, we performed molecular dynamics simulations to reveal anomalous grain size effects and deformation mechanisms in nanocrystalline FeAl intermetallic material. Models with grain sizes ranging from 6.2 to 17.4 nm were systematically investigated under uniaxial tensile stress. The study uncovers a distinctive inverse Hall-Petch relationship governing flow stress within the nanoscale regime. This behavior stems from high-density grain boundaries promoting dislocation annihilation over pile-up. Crucially, the material exhibits anomalous ductility at ultra-high strain rates due to stress-induced phase transformation dominating the plastic deformation. The nascent FCC phase accommodates strain through enhanced slip systems and inherent low stacking fault energy with the increasing phase fraction paralleling the stress plateau. Nanoconfinement suppresses the propagation of macroscopic defects while simultaneously suppressing room-temperature brittle fracture and inhibiting the rapid phase transformation pathways at extreme strain rates. These findings provide new theoretical foundations for designing high-strength and high-toughness intermetallic nanocompounds. Full article

A (NiCoCr)_99.25C_0.75 medium entropy alloy (MEA) was developed via laser powder bed fusion (LPBF) using pre-alloyed powder feedstock containing 0.75 at%C, followed by a precipitation heat treatment. The as-built alloy exhibited high density (>99.9%), columnar grains, fine substructures, and strong <111> texture. Heat treatment at 700 °C for 1 h promoted the precipitation of Cr-rich carbides (Cr₂₃C₆) along grain and substructure boundaries, which stabilized the microstructure through Zener pinning and the consumption of carbon from the matrix. The heat-treated alloy achieved excellent cryogenic tensile properties at 77 K, with a yield strength of 1230 MPa and an ultimate tensile strength of 1.6 GPa. Compared to previously reported LPBF-built NiCoCr-based MEAs, this alloy exhibited superior strength at both room and cryogenic temperatures, indicating its potential for structural applications in extreme environments. Deformation mechanisms at cryogenic temperature revealed abundant deformation twinning, stacking faults, and strong dislocation–precipitate interactions. These features contributed to dislocation locking, resulting in a work hardening rate higher than that observed at room temperature. This study demonstrates that carbon addition and heat treatment can effectively tune the stacking fault energy and stabilize substructures, leading to enhanced cryogenic mechanical performance of LPBF-built NiCoCr MEAs. Full article

Iridium, with its excellent high-temperature chemical inertness, is a preferred cladding material for radioisotope batteries. However, its inherent room-temperature brittleness severely restricts its application. In this research, pure Ir and six Ir-W solid solutions (Ir₃₁W₁ to Ir₂₆W₆) were modeled. The effects of W on the elastic properties, generalized stacking fault energy, and bonding properties of Ir solid solution alloys were investigated by first-principles simulation, aiming to find a way to overcome the intrinsic brittleness of Ir. With the W concentration increasing from 0 to 18.75 at %, the calculated Cauchy pressure (C₁₂–C₄₄) increases from −22 to 5 GPa, Pugh’s ratio (B/G) increases from 1.60 to 1.72, the intrinsic stacking fault energy reduces from 337.80 to 21.16 mJ/m², and the unstable stacking fault energy reduces from 636.90 to 547.39 mJ/m². According to these results, it is predicted that the addition of W improves the toughness of iridium alloys. The alloying of W weakens the covalency properties of the Ir-Ir bond (the ICOHP value increases from −0.8512 to −0.7923 eV). These phenomena result in a decrease in the energy barrier for grain slip. Full article

Stacking fault tetrahedra (SFTs) are typically considered improbable in high stacking fault energy metals like aluminum. Using molecular statics and dynamics simulations, we reveal the formation, growth, and transformation of SFTs in aluminum via vacancy aggregation. Three types—perfect, truncated, and defective SFTs—are characterized by their structure, formation energy, and binding energy across a range of vacancy cluster sizes. Formation energies of perfect and truncated SFTs follow a scaling relation; beyond a critical size, truncated SFTs become thermodynamically favored, indicating a size-dependent transformation pathway. Binding energy and structure evolution exhibit quasi-periodic behavior, where vacancies initially adsorb at the vertices or the midpoints of the edges of a perfect SFT, then aggregate along one facet, triggering fault nucleation and a binding energy jump as the system reconstructs into a new perfect SFT. Molecular dynamics simulations further confirm the SFT nucleation and growth via vacancy aggregation, consistent with thermodynamic predictions. SFTs exhibit notable thermal mobility, enabling coalescence and evolution into vacancy-type dislocation loops. BCC-like $V_5$ clusters are identified as potential nucleation precursors. These findings explain the nanoscale, low-temperature nature of SFTs in aluminum and offer new insights into defect evolution and control in FCC metals. Full article

The Qiangtang Basin (Tibetan Plateau) poses significant geophysical challenges for seismic exploration due to near-surface widespread permafrost and steeply dipping Mesozoic strata induced by the Cenozoic Indo-Eurasian collision. These seismic geological conditions considerably contribute to lower signal-to-noise ratios (SNRs) with complex wavefields, to some extent reducing the reliability of conventional seismic imaging and structural interpretation. To address this, the common-reflection-surface (CRS) stack method, derived from optical paraxial ray theory, is implemented to transcend horizontal layer model constraints, offering substantial improvements in high-SNR prestack gather generation and prestack depth migration (PSDM) imaging, notably for permafrost zones. Using 2D seismic data from the basin, we detailedly compare the CRS stack with conventional SNR enhancement techniques—common midpoint (CMP) FlexBinning, prestack random noise attenuation (PreRNA), and dip moveout (DMO)—evaluating both theoretical foundations and practical performance. The result reveals that CRS-processed prestack gathers yield superior SNR optimization and signal preservation, enabling more robust PSDM velocity model building, while comparative imaging demonstrates enhanced diffraction energy—particularly at medium (20–40%) and long (40–60%) offsets—critical for resolving faults and stratigraphic discontinuities in PSDM. This integrated validation establishes CRS stacking as an effective preprocessing foundation for the depth-domain imaging of complex permafrost geology, providing critical improvements in seismic structural resolution and reduced interpretation uncertainty for hydrocarbon exploration in permafrost-bearing basins. Full article

This study investigates the influence of plastic deformation and temperature on the formation of mechanically induced martensite and the associated changes in hardness in AISI 304 austenitic stainless steel. Cold rolling was performed at three temperatures (20 °C, 0 °C, and −196 °C) and various degrees of deformation (10–70%). Microstructural changes, including the formation of ε and α′ martensite, were characterized using X-ray diffraction (XRD) and electron backscatter diffraction (EBSD). The results confirm that martensitic transformation proceeds via the γ → ε → α′ sequence, with transformation rates and martensite fractions increasing at lower temperatures and higher strains. The stacking fault energy of 25.9 mJ/m² favors this transformation pathway. Transformation rates of α′ martensite fractions significantly increased at lower temperatures and higher strains, 91.8% α′ martensite was observed at just 30% deformation at −196 °C. Hardness measurements revealed a strong correlation with martensite content: strain hardening dominated at lower deformations, while martensite formation became the primary hardening mechanism at higher deformations, especially at cryogenic temperatures. The highest hardness (551 HV) was observed in samples deformed to 70% at −196 °C. The findings provide insights into optimizing the mechanical properties of AISI 304 stainless steel through controlled deformation and temperature conditions. Full article

In order to reduce the density and alloy cost of austenitic stainless steel, this study designed Fe-0.35C-12Cr-5Ni-(0,2,4)Al-(6,10)Mn (wt.%) stainless steels with different Al and Mn contents. The effects of Al and Mn contents on the microstructure, deformation behavior, and mechanical properties were investigated using microstructural analyses, quasi-static tensile tests, and Charpy impact tests. The results showed that an increase in Al content led to the formation of austeniteferrite duplex microstructure, while an increase in Mn content reduced the ferrite fraction. In the Al-free steel, the deformation mechanism was deformation-induced α′-martensitic transformation. When the Al content increased to 2 wt.%, the deformation mechanism was primarily mechanical twinning due to the increased stacking fault energy caused by Al. This resulted in a lower tensile strength but better toughness. When the Al content was further increased to 4 wt.%, the proportion of mechanical twinning decreased. The presence of ferrite led to cleavage at the fracture surface. The cleavage fracture explained the low elongation and toughness of duplex stainless steels. However, the elongation and toughness were enhanced with the increase in Mn content. Full article

Aero-engine turbine vanes made from directionally solidified nickel-based superalloys often fail with crack formation from the external wall of cooling channels. Therefore, this study simulates the compressive load on the external wall of the vane and conducts a sequence of creep–fatigue evaluations at 980 °C to investigate the creep–fatigue damage mechanisms of a directionally solidified superalloy and to assess its life. It is found that at low strain ranges, creep damage is dominant, with creep cavities forming inside the specimen and fatigue sources mostly distributed in the specimen interior. As the strain range increases, the damage mechanism transitions from creep-dominated to creep–fatigue coupled damage, with cracks nucleating preferentially on the surface and exhibiting a characteristic of multiple fatigue sources. In the longitudinal (L) specimen, dislocations in multiple orientations of the {111}<110> slip system are activated simultaneously, interacting within the γ channels to form dislocation networks, and dislocations shear through the γ′ phase via antiphase boundary (APB) pairs. In the transverse (T) specimen, stacking intrinsic stacking faults (SISFs) accumulate within the limited {111}<112> slip systems, subsequently forming a dislocation slip band. The modified creep–fatigue life prediction model, incorporating strain energy dissipation and stress relaxation mechanisms, demonstrates an accurate fatigue life prediction under creep–fatigue coupling, with a prediction accuracy within an error band of 1.86 times. Full article

Photovoltaic (PV) systems are integral to global renewable energy generation, yet their efficiency and reliability are frequently compromised by undetected faults, leading to significant energy losses, increased maintenance costs, and reduced operational lifespans. To address these challenges, this study proposes a novel hybrid deep learning framework that combines Stacked Sparse Auto-Encoders (SSAE) for autonomous feature extraction with an Optimized-Multi-Layer Perceptron (OMLP) for precise fault classification. The SSAE extracts high-dimensional fault features from raw operational data, while the OMLP leverages these features to classify faults with exceptional accuracy. The model was rigorously validated using real-world PV datasets, encompassing diverse fault types such as partial shading, open circuits, and module degradation under dynamic environmental conditions. Results demonstrate state-of-the-art performance, with the model achieving 99.82% accuracy, 99.7% precision, 99.4% sensitivity, and 100% specificity, outperforming traditional machine learning and deep learning approaches. These findings highlight the framework’s robustness and reliability in real-world applications. By significantly enhancing fault detection accuracy and computational efficiency, the proposed approach optimizes PV system performance, reduces operational costs, and supports sustainable energy production. This study concludes that the hybrid SSAE-Optimized MLP model represents a scalable and efficient solution for improving the reliability and longevity of renewable energy infrastructure, setting a new benchmark for intelligent maintenance strategies in the field. Full article

As a promising candidate for next-generation aviation structural materials, lightweight refractory high entropy alloys (HEAs) exhibit high strength, low density, and excellent high-temperature performance. In this study, we investigated the influence of local chemical ordering on the properties of Ti–V–Zr–Nb–Al HEAs using Monte Carlo (MC) simulations based on density functional theory (DFT) calculations. We established that the chemical short-range ordering (SRO) in Ti–V–Zr–Nb–Al HEAs increases with the Al content, resulting in a gradual increase in stacking fault energy (SFE). This theoretical investigation suggests that SRO can be utilized to tailor the performance of HEAs, thereby providing guidance for the scientific design of macroscopic mechanical properties. Full article