Search Results (63)

Unmanned aerial vehicles (UAVs) increasingly demand higher flight speeds, longer endurance, improved payload capacity, and greater operational flexibility across a wide range of applications. While battery-electric propulsion systems dominate small UAV platforms, their limited energy density significantly constrains range and mission duration. Consequently, turbine-based propulsion systems, including micro turbojets, turboprops, turboshafts, and hybrid-electric gas turbine architectures, are attracting growing attention as alternatives for advanced UAV operations. Unlike previous reviews that primarily focus on propulsion technologies or individual subsystem performance, this review provides an integrated assessment of the multidisciplinary challenges associated with turbine-powered UAVs, encompassing thermal, structural, aerodynamic, acoustic, operational, and stealth considerations within a unified framework. The presented synthesis identifies current knowledge gaps and emerging research directions, providing a comprehensive reference for the design and development of next-generation turbine-powered unmanned aerial platforms. Full article

The rapid expansion of Unmanned Aerial Vehicle (UAV) applications in both civilian and military sectors has intensified the demand for propulsion systems capable of delivering higher speed, increased endurance, and improved payload capacity. While battery-electric propulsion remains dominant for small UAV platforms, its limited energy density restricts operational range and mission flexibility. As a result, micro gas engines have emerged as a viable alternative for applications requiring high power-to-weight ratios and sustained high-speed operation. This review examines the fundamentals, scaling effects, and classification of micro gas turbine propulsion systems used in UAV applications, with emphasis on micro turbojets and related hybrid configurations. The paper discusses the thermodynamic principles governing micro gas engines and analyzes the aerodynamic, thermal, and combustion challenges associated with miniaturization, including low Reynolds number effects, tip leakage losses, thermal management limitations, and combustion instability. Furthermore, the study reviews the operational characteristics and mission suitability of different propulsion architectures for reconnaissance UAVs, high-speed UAVs, including reconnaissance and loitering platforms, target drones, and hybrid-electric aerial platforms. Recent developments involving additive manufacturing, advanced control systems, recuperated cycles, and hybrid-electric integration are also evaluated as enabling technologies for next-generation UAV propulsion. The findings demonstrate that although micro gas turbines continue to face important efficiency and manufacturing challenges at reduced scales, they remain essential for mission profiles that exceed the capabilities of purely electric propulsion systems. Full article

The development of low-emission and reliable propulsion systems is essential for extending the operational capability of unmanned aerial vehicles (UAVs). Although aviation decarbonization is widely recognized as an important objective, it must be considered within the broader context of limited renewable-energy availability. Recent system-level analyses of transportation decarbonization have shown that the allocation of renewable electricity and sustainable fuels should prioritize sectors where direct electrification is most efficient, while hard-to-electrify sectors require alternative pathways. Aviation is one of the most difficult transport sectors to electrify because of strict energy-density requirements, especially for long-endurance airborne platforms. Therefore, sustainable liquid fuels and hybrid propulsion systems should not be considered universal replacements for electrification, but rather complementary solutions for applications where batteries alone cannot provide the required endurance, payload capacity or operational flexibility. In this context, the present study focuses on alcohol–kerosene blends for hybrid UAV power systems, where liquid-fuel energy density and partial emission reduction remain relevant engineering requirements. This work provides one of the first systematic experimental evaluations of ethanol–, butanol– and octanol–kerosene blends in a micro-turboprop engine operating as part of a hybrid UAV power-generation architecture. Unlike previous studies focused mainly on micro-turbojet thrust response, the present work evaluates the coupled influence of alcohol chain length and blending ratio on exhaust gas temperature, gaseous emissions, electrical output and operational stability under multi-load conditions representative of UAV operation. Jet-A and nine alcohol–kerosene blends containing 10%, 20% and 30% ethanol, butanol or octanol by volume were tested over four operating regimes, from idle to 2500 W electrical load. The results show that ethanol blends provided the strongest CO reduction, with E30 reducing CO by 24.9% relative to Jet-A under R3, while E10 offered the most balanced behavior across the full operating range. Higher ethanol fractions improved CO suppression but introduced NOx and low-load stability penalties. Octanol blends, particularly O20, exhibited the most kerosene-like and stable response, supporting reliable power delivery with reduced operational variability. Butanol blends showed intermediate behavior without providing a dominant advantage. A multi-criteria evaluation combining emissions, EGT behavior, relative performance, operational stability and cost identified E10 as the best overall compromise for hybrid UAV use. The study demonstrates that alcohol chain length produces nonlinear system-level effects in hybrid micro-turboprop architectures and provides an experimental basis for fuel selection in low-emission UAV power systems. Full article

The high penetration of renewable energy has exposed integrated energy systems (IES) to stronger source-load uncertainties. Traditional scheduling methods that primarily pursue economic optimality often fail to account for system regulation margins, which may lead to excessive charging and discharging of energy storage systems, frequent fluctuations in unit output, and insufficient supply–demand matching capability under uncertain operating scenarios. To address these issues, this paper proposes a Flex-TD3 optimal scheduling method for IESs with embedded flexibility rules. First, a regional IES model incorporating photovoltaic generation, wind power, micro-gas turbines, gas boilers, electric chillers, waste heat recovery units, heat exchangers, and battery energy storage systems is established to describe the coupling relationships among electricity, heat, cooling, and gas flows, as well as the operational constraints of key devices. Second, active regulation flexibility indicators are constructed from the perspectives of system upward regulation capability, downward regulation capability, energy storage state health, and electro-thermal decoupling regulation margin. A comprehensive flexibility score is then formulated to characterize the system’s capability to cope with renewable energy fluctuations and load disturbances under the current operating state. Third, the flexibility indicators are embedded into the state space and reward function of the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, and a rule-based physical feasibility mapping mechanism is introduced to modify the raw scheduling actions generated by the agent according to device operational constraints, thereby enhancing the physical consistency and operational safety of the scheduling strategy. Case study results show that, compared with traditional optimal scheduling methods, the proposed method achieves better overall performance in terms of training convergence speed, operational economy, and scheduling stability. It can effectively reduce system operating costs, improve renewable energy accommodation capability, and decrease renewable energy curtailment, supply shortages, and constraint violations. Under uncertain scenarios involving renewable energy prediction errors, load disturbances, and high renewable energy penetration, the proposed method still maintains favorable scheduling performance, demonstrating its effectiveness and robustness. Full article

Advanced thermal management applications, including electronics cooling, battery systems, and micro heat exchangers, are increasingly requiring thermally conductive yet flexible polymer composites. Composite films containing total nanofiller loadings of 2.5, 5, 7.5, and 10 wt.% were systematically characterized using SEM, TGA, DSC, TT, and SSTM following ASTM C177-19. SEM analysis confirmed uniform dispersion and effective network formation of MWCNTs and GNPs within the TPU matrix at higher filler loadings. Thermal stability improved significantly, with the degradation onset temperature increasing from 319.2 °C for pure TPU to 369 °C for the TPU/MWCNT/GNP (90/5/5 wt.%) composite. DSC results revealed enhanced glass transition and melting temperatures, indicating improved thermal resistance and crystallinity. Mechanical testing showed a substantial increase in Young’s modulus, reaching 72.5 MPa for the 90/5/5 wt.% composite, corresponding to a 286.66% improvement over pure TPU. Most notably, steady-state thermal conductivity increased dramatically from 0.20 W/mK for pure TPU to 1.533 W/mK for the 90/5/5 wt.% composite, representing a 666.50% enhancement. The experimental results closely aligned with percolation-based theoretical models at higher filler concentrations. Overall, the developed hybrid nanofiller TPU composites demonstrate a synergistic improvement in thermal conductivity, mechanical strength, and thermal stability, making them promising candidates for flexible thermal management components in electronics, automotive, renewable energy, and biomedical applications. Full article

As a transformative force, the digital economy serves as a critical engine for driving high-quality economic development and fostering New Quality Productive Forces (NQPF)—characterized by high technology, high efficiency, and high quality. Viewing the establishment of China’s National Digital Economy Innovation and Development Pilot Zones as a quasi-natural experiment in economic system management, this study employs a Double Machine Learning (DML) framework to evaluate its systemic impact on A-share listed companies from 2015 to 2023. Unlike traditional linear models, the DML approach flexibly controls for high-dimensional confounding variables and functional form misspecification, thereby ensuring highly rigorous causal inference. The empirical results demonstrate that these pilot zones create an optimized “digital environment” that significantly enhances corporate NQPF, a conclusion that remains highly robust across a comprehensive battery of robustness and endogeneity tests. Mechanism analysis reveals three systemic transmission pathways through which the policy operates: optimizing factor allocation, deepening digital technology empowerment, and promoting green innovation and sustainability. Furthermore, heterogeneity analyses indicate that the policy’s efficacy varies significantly across corporate profiles, manifesting most prominently in non-state-owned enterprises, high-tech firms, and those located in eastern regions. These findings provide robust micro-level evidence for policymakers aiming to optimize digital economic systems and accelerate the systemic formation of advanced productive forces. Full article

Distributed energy systems (DESs) are crucial for renewable deployment, but decentralised generation substantially increases flexibility requirements. Flexibility is framed as a system property that emerges from the coordinated operation of demand, storage and dispatchable generation across multi-energy carriers. Demand response schemes and demand-side management can provide flexibility, but their effective potential is constrained by user participation. Sector-coupling strategies and energy storage systems enable temporal and cross-sector decoupling between renewable generation and demand. Electrochemical batteries are technically mature and well suited for short-term balancing, but costs and environmental impacts are significant. Power-to-Heat with heat pumps and thermal energy storage is a cost-effective solution, especially when combined with low-temperature district heating. Electric vehicles, when operated under smart-charging and vehicle-to-grid schemes, can shift large charging demands feeding energy into the grid, facing battery degradation and infrastructure costs. Power-to-Gas and Power-to-X use hydrogen and electrofuels as long-term storage but are penalised by low round-trip efficiencies and significant capital costs if power-to-power with fuel cells is applied. On the supply side, micro-CHP can provide dispatchable capacity when fuelled by renewable fuels and combined with seasonal storage. Costs and efficiencies are strongly scale-dependent, and markets, regulation, digital infrastructure and social acceptance are key enablers of flexibility. Full article

The miniaturisation of electronic devices has intensified the demand for compact, high-performance lithium-ion batteries. This review synthesises recent progress in microscale battery development, focusing on microfabrication techniques, nanostructured materials, porosity-engineered architectures, and strategies for reducing non-active components. It explores both top–down and bottom–up fabrication methods, the integration of nanomaterials, the role of gradient electrode architectures in enhancing ion transport and energy density, along with strategies to reduce non-active components, such as separators and current collectors, to maximise volumetric efficiency. Advances in top–down and bottom–up fabrication methods, including photolithography, laser structuring, screen printing, spray coating, mechanical structuring, and 3D printing, enable precise control over electrode geometry and enhance ion transport and material utilisation. Nanostructured anodes, cathodes, electrolytes, and separators further improve conductivity, mechanical stability, and cycling performance. Gradient porosity designs optimise ion distribution in thick electrodes, while innovations in ultra-thin separators and lightweight current collectors support higher energy density. Remaining challenges relate to scalability, mechanical robustness, and long-term stability, especially in fully integrated micro-battery architectures. Future development will rely on hybrid fabrication methods, advanced material compatibility, and data-driven optimisation to bridge laboratory innovations with practical applications. By integrating microfabrication and nanoscale engineering, next-generation LIBs can deliver high energy density and long operational lifetimes for miniaturised and flexible electronic systems. Full article

This research evaluates Battery Energy Storage Systems (BESS) and Compressed Air Vessels (CAV) as complementary solutions for enhancing micro-grid resilience, flexibility, and sustainability. BESS units ranging from 5 to 400 kWh were modeled using a Nonlinear Autoregressive Neural Network with Exogenous Inputs (NARX) neural network, achieving high SOC prediction accuracy with R² > 0.98 and MSE as low as 0.13 kWh². Larger batteries (400–800 kWh) effectively reduced grid purchases and redistributed surplus energy, improving system efficiency. CAVs were tested in pumped-storage mode, achieving 33.9–57.1% efficiency under 0.5–2 bar and high head conditions, offering long-duration, low-degradation storage. Waterhammer-induced CAV storage demonstrated reliable pressure capture when Reynolds number ≤ 75,000 and Volume Fraction Ratio, VFR > 11%, with a prototype reaching 6142 kW and 170 kWh at 50% air volume. CAVs proved modular, scalable, and environmentally robust, suitable for both energy and water management. Hybrid systems combining BESS and CAVs offer strategic advantages in balancing renewable intermittency. Machine learning and hydraulic modeling support intelligent control and adaptive dispatch. Together, these technologies enable future-ready micro-grids aligned with sustainability and grid stability goals. Full article

Wearable devices can be developed using hardware platforms such as Application Specific Integrated Circuits (ASICs), Graphics Processing Units (GPUs), Digital Signal Processors (DSPs), Micro controller Units (MCUs), or Field Programmable Gate Arrays (FPGAs), each with distinct advantages and limitations. ASICs offer high efficiency but lack flexibility. GPUs excel in parallel processing but consume significant power. DSPs are optimized for signal processing but are limited in versatility. CPUs provide low power consumption but lack computational power. FPGAs are highly flexible, enabling powerful parallel processing at lower energy costs than GPUs but with higher resource demands than ASICs. The combined use of FPGAs and CPUs balances power efficiency and computational capability, making it ideal for wearable systems requiring complex algorithms in far-edge computing, where data processing occurs onboard the device. This approach promotes green electronics, extending battery life and reducing user inconvenience. The primary goal of this work was to develop a versatile framework, similar to existing software development frameworks, but specifically tailored for mixed FPGA/MCU platforms. The framework was validated through a real-world use case, demonstrating significant improvements in execution speed and power consumption. These results confirm its effectiveness in developing green and smart wearable systems. Full article

This study focuses on the pre-treatment cleaning technology for the polyimide (PI) substrate of flexible nine-in-one microsensors. The environmentally friendly cleaning agent B, developed by Sea Energe, was innovatively used to replace traditional organic solutions (acetone, methanol, and isopropyl alcohol) to verify its feasibility and application potential in Micro-Electro-Mechanical Systems (MEMS) processes. Cleaning agent B, developed by Sea Energe, was used for the first time to clean the PI substrate of flexible nine-in-one microsensors, and the flexible nine-in-one microsensor was used as a verification platform to compare the cleaning performance with traditional organic solutions (acetone, methanol, and isopropyl alcohol). The experimental results proved that cleaning agent B developed by Sea Energe effectively removed contamination from the PI substrate surface while avoiding the environmental impact and process compatibility issues associated with traditional organic solvents. To verify its reliability, the developed flexible nine-in-one microsensor was embedded in the hydrogen end flow channel of a hydrogen/vanadium redox flow battery (HVRFB) to perform real-time monitoring of multiple parameters, including hydrogen concentration, voltage, current, conductivity, temperature, humidity, flow, pressure, and pH. The experimental results proved that using cleaning agent B, developed by Sea Energe, to clean the PI substrate and the subsequent flexible nine-in-one microsensor resulted in comparable operational stability and measurement accuracy to traditional organic solution (acetone, methanol, and isopropyl alcohol) cleaning processes. This experimental result verifies that cleaning agent B, developed by Sea Energe, not only has an excellent cleaning effect, but also meets the requirements for highly reliable microsensor development, potentially offering an alternative solution for the future introduction of green processes into semiconductors, MEMSs, and various application fields. Full article

This paper presents the reliability assessment of an IoT-based sensor node designed for detecting combustible gas leaks in residential environments. Building on a previously published design that integrates low-power micromachined (Micro-Electro-Mechanical Systems, MEMS) pellistors and electrochemical Volatile Organic Compounds (VOC) sensors, this study evaluates the node’s long-term robustness and stability under both realistic and accelerated operating conditions. The system employs a dual-sensor strategy in which the VOC sensor acts as a sentinel, activating the pellistor only when necessary, thereby optimizing power consumption and extending battery life. BLE and LoRa communication capabilities support flexible deployment and real-time data transmission. To ensure suitability for safety-critical applications, we conducted comprehensive reliability testing, including accelerated life tests and environmental stress testing in compliance with IEC 60068 standards. The results confirm the system’s ability to maintain consistent performance and data integrity under thermal, mechanical, and chemical stress, demonstrating its robustness for prolonged operation in demanding environments. Overall, this work underscores the importance of rigorous reliability validation for IoT-based safety devices and positions the proposed solution as a significant step toward enhancing residential gas safety, with potential applications in broader industrial monitoring scenarios. Full article

Continuous monitoring of environmental and physiological parameters is essential for early diagnostics, real-time decision making, and intelligent system adaptation. Recent advancements in bio-microelectromechanical systems (BioMEMS) sensors have significantly enhanced our ability to track key metrics in real time. However, continuous monitoring demands sustainable energy supply solutions, especially for on-site energy replenishment in areas with limited resources. Artificial intelligence (AI), particularly large language models, offers new avenues for interpreting the vast amounts of data generated by these sensors. Despite this potential, fully integrated systems that combine self-powered BioMEMS sensing with AI-based analytics remain in the early stages of development. This review first examines the evolution of BioMEMS sensors, focusing on advances in sensing materials, micro/nano-scale architectures, and fabrication techniques that enable high sensitivity, flexibility, and biocompatibility for continuous monitoring applications. We then examine recent advances in energy harvesting technologies, such as piezoelectric nanogenerators, triboelectric nanogenerators and moisture electricity generators, which enable self-powered BioMEMS sensors to operate continuously and reducereliance on traditional batteries. Finally, we discuss the role of AI in BioMEMS sensing, particularly in predictive analytics, to analyze continuous monitoring data, identify patterns, trends, and anomalies, and transform this data into actionable insights. This comprehensive analysis aims to provide a roadmap for future continuous BioMEMS sensing, revealing the potential unlocked by combining materials science, energy harvesting, and artificial intelligence. Full article

In this paper, the dispatch for the industrial micro-grid with an integrated photovoltaic-gas-manufacturing facility system considering carbon emissions and operation costs is investigated. Two kinds of energy, electricity and natural gas, are contained in the integer energy system, in which the electricity mainly comes from the PV panels and the utility electricity network, and the natural gas mainly comes from the utility gas network. In addition, electricity and natural gas can be converted into each other. Four kinds of loads, electricity load, gas load, heating load and cooling load, need to be satisfied, in which the electricity load can be divided into fixed load and flexible load. The flexible load comes from the scheduling for manufacturing facilities, and the scheduling of manufacturing facilities is modeled as a kind of deferable load to be integrated into the energy system. Moreover, daily operation costs and carbon emissions are considered in the decision, and the deviation preference strategy is used to solve this multi-objective optimization problem. Finally, a case study with a lithium-ion battery assembly system is proposed. According to the results, it can be found that the proposed model can help managers realize effective scheduling of the industrial micro-grid. Full article

DC micro-grids are emerging as a promising solution for efficiently integrating renewable energy into power systems. These systems offer increased flexibility and enhanced energy management, making them ideal for applications such as heat pump (HP) systems. However, the integration of intermittent renewable energy sources with optimal energy management in these micro-grids poses significant challenges. This paper proposes a novel control strategy designed specifically to improve the performance of DC micro-grids. The strategy enhances energy management by leveraging an environmental mission profile that includes real-time measurements for energy generation and heat pump performance evaluation. This micro-grid application for heat pumps integrates photovoltaic (PV) systems, wind generators (WGs), DC-DC converters, and battery energy storage (BS) systems. The proposed control strategy employs an intelligent maximum power point tracking (MPPT) approach that uses optimization algorithms to finely adjust interactions among the subsystems, including renewable energy sources, storage batteries, and the load (heat pump). The main objective of this strategy is to maximize energy production, improve system stability, and reduce operating costs. To achieve this, it considers factors such as heating and cooling demand, power fluctuations from renewable sources, and the MPPT requirements of the PV system. Simulations over one year, based on real meteorological data (average irradiance of 500 W/m², average annual wind speed of 5 m/s, temperatures between 2 and 27 °C), and carried out with Matlab/Simulink R2022a, have shown that the proposed model predictive control (MPC) strategy significantly improves the performance of DC micro-grids, particularly for heat pump applications. This strategy ensures a stable DC bus voltage (±1% around 500 V) and maintains the state of charge (SoC) of batteries between 40% and 78%, extending their service life by 20%. Compared with conventional methods, it improves energy efficiency by 15%, reduces operating costs by 30%, and cuts CO₂; emissions by 25%. By incorporating this control strategy, DC micro-grids offer a sustainable and reliable solution for heat pump applications, contributing to the transition towards a cleaner and more resilient energy system. This approach also opens new possibilities for renewable energy integration into power grids, providing intelligent and efficient energy management at the local level. Full article