Search Results (4,334)

Enhancing the real-world performance of sustainably designed and certified green buildings remains a significant challenge, particularly in hot climates where efforts to improve thermal comfort often conflict with energy efficiency goals. In the United Arab Emirates (UAE), even newly constructed facilities with green building certifications present opportunities for retrofitting and performance optimization. This study investigates the energy and thermal comfort performance of a LEED Gold-certified, mixed-use university campus in Dubai through a calibrated digital twin developed using IES thermal modelling software. The analysis evaluated existing sustainable design strategies alongside three retrofit energy conservation measures (ECMs): (1) improved building envelope U-values, (2) installation of additional daylight sensors, and (3) optimization of fan coil unit efficiency. Simulation results demonstrated that the three ECMs collectively achieved a total reduction of 15% in annual energy consumption. Thermal comfort was assessed using operative temperature distributions, Predicted Mean Vote (PMV), and Predicted Percentage of Dissatisfaction (PPD) metrics. While fan coil optimization yielded the highest energy savings, it led to less favorable comfort outcomes. In contrast, enhancing envelope U-values maintained indoor conditions consistently within ASHRAE-recommended comfort zones. To further support energy reduction and progress toward Net Zero targets, the study also evaluated the integration of a 228.87 kW rooftop solar photovoltaic (PV) system, which offset 8.09% of the campus’s annual energy demand. By applying data-driven thermal modelling to assess retrofit impacts on both energy performance and occupant comfort in a certified green building, this study addresses a critical gap in the literature and offers a replicable framework for advancing building performance in hot climate regions. Full article

The Union of the Comoros, located in the Indian Ocean, faces persistent energy challenges due to its geographic isolation, heavy dependence on imported fossil fuels, and underdeveloped electricity infrastructure. This study investigates the techno-economic optimization of a hybrid microgrid designed to supply electricity to a rural village in Grande Comore. The proposed system integrates photovoltaic (PV) panels, wind turbines, a diesel generator, and battery storage. Detailed modeling and simulation were conducted using HOMER Energy, accompanied by a sensitivity analysis on solar irradiance, wind speed, and diesel price. The results indicate that the optimal configuration consists solely of PV and battery storage, meeting 100% of the annual electricity demand with a competitive levelized cost of energy (LCOE) of 0.563 USD/kWh and zero greenhouse gas emissions. Solar PV contributes over 99% of the total energy production, while wind and diesel components remain unused under optimal conditions. Furthermore, the system generates a substantial energy surplus of 63.7%, which could be leveraged for community applications such as water pumping, public lighting, or future system expansion. This study highlights the technical viability, economic competitiveness, and environmental sustainability of 100% solar microgrids for non-interconnected island territories. The approach provides a practical and replicable decision-support framework for decentralized energy planning in remote and vulnerable regions. Full article

The growing need for high-efficiency and reliable propulsion systems in naval applications, particularly within the evolving landscape of submarine warfare, has led to an increased interest in multiphase Permanent Magnet AC motors. This study presents a modelling and simulation approach for a 3MW, seventeen-phase Permanent Magnet AC motor designed for submarine propulsion, integrating an AI-based drive control system. Despite the advantages of multiphase motors, such as higher power density and enhanced fault tolerance, significant challenges remain in achieving precise torque and variable speed, especially for externally mounted motors operating under deep-sea conditions. Existing control strategies often struggle with the inherent nonlinearities, unmodelled dynamics, and extreme environmental variations (e.g., pressure, temperature affecting oil viscosity and motor parameters) characteristic of such demanding deep-sea applications, leading to suboptimal performance and compromised reliability. Addressing this gap, this research investigates advanced control methodologies to enhance the performance of such motors. A MATLAB/Simulink framework was developed to model the motor, whose drive system leverages an AI-optimised dual fuzzy-PID controller refined using the Harmony Search Algorithm. Additionally, a combination of Indirect Field-Oriented Control (IFOC) and Space Vector PWM strategies are implemented to optimise inverter switching sequences for precise output modulation. Simulation results demonstrate significant improvements in torque response and control accuracy, validating the efficacy of the proposed system. The results highlight the role of AI-based propulsion systems in revolutionising submarine manoeuvrability and energy efficiency. In particular, during a test case involving a speed transition from 75 RPM to 900 RPM, the proposed AI-based controller achieves a near-zero overshoot compared to an initial control scheme that exhibits 75.89% overshoot. Full article

The presence of strong clutter remains a critical challenge for radar system target detection. Traditional clutter suppression techniques such as Doppler-based filters often fail to extract low-velocity targets from clutter. To address this limitation, this paper proposes an adaptive singular value decomposition (A-SVD) method utilizing support vector machines (SVM). The proposed approach leverages the augmented implicitly restarted Lanczos bidiagonalization (AIRLB) algorithm to decompose echo matrices into different subspaces, which are then characterized in relation to Doppler frequency, energy, and correlation. These features are employed to classify the clutter subspaces using an SVM classifier, which solves the problem of selecting the SVD threshold. The clutter subspaces are suppressed by zeroing out corresponding singular values, and the matrix is then recomposed by the rest of the subspaces to recover the echo. Experiments on simulated and real datasets show that the proposed method achieves an average improvement factor (IF) above 40 dB and reduces runtime by over 85% in most scenarios. Full article

This study proposes the SMR Smart Net-Zero City (SSNC) framework—a scalable model for achieving carbon neutrality by integrating Small Modular Reactors (SMRs), renewable energy sources, and sector coupling within a microgrid architecture. As deploying renewables alone would require economically and technically impractical energy storage systems, SMRs provide a reliable and flexible baseload power source. Sector coupling systems—such as hydrogen production and heat generation—enhance grid stability by absorbing surplus energy and supporting the decarbonization of non-electric sectors. The core contribution of this study lies in its real-time data emulation framework, which overcomes a critical limitation in the current energy landscape: the absence of operational data for future technologies such as SMRs and their coupled hydrogen production systems. As these technologies are still in the pre-commercial stage, direct physical integration and validation are not yet feasible. To address this, the researchers leveraged real-time data from an existing commercial microgrid, specifically focusing on the import of grid electricity during energy shortfalls and export during solar surpluses. These patterns were repurposed to simulate the real-time operational behavior of future SMRs (ProxySMR) and sector coupling loads. This physically grounded simulation approach enables high-fidelity approximation of unavailable technologies and introduces a novel methodology to characterize their dynamic response within operational contexts. A key element of the SSNC control logic is a day–night strategy: maximum SMR output and minimal hydrogen production at night, and minimal SMR output with maximum hydrogen production during the day—balancing supply and demand while maintaining high SMR utilization for economic efficiency. The SSNC testbed was validated through a seven-day continuous operation in Busan, demonstrating stable performance and approximately 75% SMR utilization, thereby supporting the feasibility of this proxy-based method. Importantly, to the best of our knowledge, this study represents the first publicly reported attempt to emulate the real-time dynamics of a net-zero city concept based on not-yet-commercial SMRs and sector coupling systems using live operational data. This simulation-based framework offers a forward-looking, data-driven pathway to inform the development and control of next-generation carbon-neutral energy systems. Full article

The transition to green energy in the transport sector is becoming a priority in the context of global climate challenges and the European Green Deal. This paper investigates the development of alternative fuelling stations, particularly electric vehicle (EV) charging infrastructure and hydrogen stations, across EU countries with a focus on Poland. It combines a policy and technology overview with a quantitative scientific analysis, offering a multidimensional perspective on green infrastructure deployment. A Pearson correlation analysis reveals significant links between charging station density and both GDP per capita and the share of renewable energy. The study introduces an original Infrastructure Accessibility Index (IAI) to compare infrastructure availability across EU member states and models Poland’s EV charging station demand up to 2030 under multiple growth scenarios. Furthermore, the article provides a comprehensive overview of biofuels, including first-, second-, and third-generation technologies, and highlights recent advances in hydrogen and renewable electricity integration. Emphasis is placed on life cycle considerations, energy source sustainability, and economic implications. The findings support policy development toward zero-emission mobility and the decarbonisation of transport systems, offering recommendations for infrastructure expansion and energy diversification strategies. Full article

This study investigates the effectiveness of passive design in low-rise residential buildings located in arid desert climates, using the Dubai Solar Decathlon Middle East (SDME) competition as a case study. This full-scale experiment offers a unique opportunity to evaluate design solutions under controlled, realistic conditions; prescriptive, modeled performance; and monitored performance assessments. The prescriptive assessment reviews geometry, orientation, envelope thermal properties, and shading. Most houses adopt compact forms, with envelope-to-volume and envelope-to-floor area ratios averaging 1 and 3.7, respectively, and window-to-wall ratios of approximately 17%, favoring north-facing openings to optimize daylight while reducing heat gain. Shading is strategically applied, horizontal on south façades and vertical on east and west. The thermal properties significantly exceed the local code requirements, with wall performance up to 80% better than that mandated. The modeled assessment uses Building Energy Models (BEMs) to simulate the impact of prescriptive measures on energy performance. Three variations are applied: assigning minimum local code requirements to all the houses to isolate the geometry (baseline); removing shading; and applying actual envelope properties. Geometry alone accounts for up to 60% of the variation in cooling intensity; shading reduces loads by 6.5%, and enhanced envelopes lower demand by 14%. The monitored assessment uses contest-period data. Indoor temperatures remain stable (22–25 °C) despite outdoor fluctuations. Energy use confirms that houses with good designs and airtightness have lower cooling loads. Airtightness varies widely (avg. 14.5 m³/h/m²), with some well-designed houses underperforming due to construction flaws. These findings highlight the critical role of passive design as the first layer for improving the energy performance of the built environment and advancing toward net-zero targets, specifically in arid desert climates. Full article

In this work, the biosorption potential of Spirulina sp. as an effective and eco-friendly biosorbent for the removal of Ni(II) and Pb(II) ions from aqueous solutions was investigated. Detailed characterization of the biosorbent was carried out, including surface morphology, chemical composition, particle size, zeta potential, crystallinity, zero-point charge, and functional group analysis. Batch tests were performed to determine the kinetic constants and adsorption equilibrium of the studied ions. The adsorption behavior of Spirulina sp. was described using six adsorption isotherms. The best fit was obtained for the Redlich-Peterson and Langmuir isotherms, indicating that monolayer adsorption occurred. The maximum biosorption capacities for Ni(II) and Pb(II) were 20.8 mg·g⁻¹ and 93.5 mg·g⁻¹, respectively, using a biosorbent dose of 10 g·L⁻¹, initial metal concentrations ranging from 50 to 5000 mg·L⁻¹, at pH 6, 20 °C, and a contact time of 120 min. Low values of the mean free energy of adsorption (E) in the Dubinin–Radushkevich and Temkin model (0.3 and 0.1 kJ·mol⁻¹ for Pb(II) and 0.35 and 0.23 kJ·mol⁻¹ for Ni(II)) indicate the dominance of physical processes in the ion binding mechanism. The adsorption of Pb(II) ions was more effective than that of Ni(II) ions across the entire range of tested concentrations. At low initial concentrations, the removal of Pb(II) reached 94%, while for Ni(II) it was 80%. Full article

This paper analyzes the availability of lithium resources required to support a global decarbonized energy system featuring electrical energy storage based on lithium iron phosphate (LFP) batteries. A net-zero carbon grid consisting of existing nuclear and hydro capacity, with the balance being a 50/50 mix of wind and solar power generation, is assumed to satisfy projected world electrical demand in 2050, incorporating the electrification of transportation. The battery electrical storage capacity needed to support this grid is estimated and translated into the required number of nominal 10 MWh LFP storage plants similar to the ones currently in operation. The total lithium required for the global storage system is determined from the number of nominal plants and the inventory of lithium in each plant. The energy required to refine this amount of lithium is accounted for in the estimation of the total lithium requirement. Comparison of the estimated lithium requirements with known global lithium resources indicates that a global storage system consisting only of LFP plants would require only around 12.3% of currently known lithium reserves in a high-economic-growth scenario. The overall cost for a global LFP-based grid-scale energy storage system is estimated to be approximately USD 17 trillion. Full article

Modular multilevel converters (MMCs) are widely used in power-conversion applications, including distributed energy storage integration, because of their scalability, high efficiency, and reduced harmonic distortion. Integrating battery storage systems into MMC submodules using dual active bridge (DAB) converters provides electrical isolation and reduces voltage stress, harmonics, and common-mode issues. However, voltage fluctuations due to the battery state of charge can compromise the zero-voltage switching (ZVS) operation of a DAB and increase the reactive power circulation, leading to higher losses and reduced system performance. To address these challenges, this study investigated an active control strategy for submodule voltage regulation in an MMC with DAB-based battery integration. Assuming single-phase-shift modulation, two control strategies were evaluated. The first strategy regulated the DAB voltage on one side to match the battery voltage on the other, scaled by the high-frequency transformer turns ratio, which facilitated the ZVS operation and reduced the reactive power. The second strategy optimized this voltage to minimize the total power-conversion losses. The proposed control strategies improved the efficiency, particularly at low power levels, achieving several percentage points of improvement compared to maintaining a constant voltage. Full article

This study aims to develop an optimal underwriting strategy for affordable (H1 and M1) and premium (L1 and M2) electric vehicles (EVs), balancing financial risk and sustainability commitments. The research is motivated by regulatory pressures, risk management needs, and sustainability goals, necessitating an adaptation of traditional underwriting models. The study employs a modified Delphi method with industry experts to identify key risk factors, including accident risk, repair costs, battery safety, driver behavior, and PCAF carbon impact. A sensitivity analysis was conducted to examine premium adjustments under different risk scenarios, categorizing EVs into four risk segments: Low-Risk, Low-Carbon (L1); Medium-Risk, Low-Carbon (M1); Medium-Risk, High-Carbon (M2); and High-Risk, High-Carbon (H1). Findings indicate that premium EVs (L1 and M2) exhibit lower volatility in underwriting costs, benefiting from advanced safety features, lower accident rates, and reduced carbon attribution penalties. Conversely, budget EVs (H1 and M1) experience higher premium fluctuations due to greater accident risks, costly repairs, and higher carbon costs under PCAF implementation. The worst-case scenario showed a 14.5% premium increase, while the best-case scenario led to a 10.5% premium reduction. The study recommends prioritizing premium EVs for insurance coverage due to their lower underwriting risks and carbon efficiency. For budget EVs, insurers should implement selective underwriting based on safety features, driver risk profiling, and energy efficiency. Additionally, incentive-based pricing such as telematics discounts, green repair incentives, and low-carbon charging rewards can mitigate financial risks and align with net-zero insurance commitments. This research provides a structured framework for insurers to optimize EV underwriting while ensuring long-term profitability and regulatory compliance. Full article

Deep neural network (DNN) hardware (HW) accelerators have achieved great success in improving DNNs’ performance and efficiency. One key reason is the dataflow in executing a DNN layer, including on-chip data partitioning, computation parallelism, and scheduling policy, which have large impacts on latency and energy consumption. Unlike prior works that required considerable efforts from HW engineers to design suitable dataflows for different DNNs, this work proposes an efficient data-centric approach, named Dataflow Code Propagation (DCP), to automatically find the optimal dataflow for DNN layers in seconds without human effort. It has several attractive benefits that prior studies lack, including the following: (i) We translate the HW dataflow configuration into a code representation in a unified dataflow coding space, which can be optimized by back-propagating gradients given a DNN layer or network. (ii) DCP learns a neural predictor to efficiently update the dataflow codes towards the desired gradient directions to minimize various optimization objectives, e.g., latency and energy. (iii) It can be easily generalized to unseen HW configurations in a zero-shot or few-shot learning manner. For example, without using additional training data, Extensive experiments on several representative models such as MobileNet, ResNet, and ViT show that DCP outperforms its counterparts in various settings. Full article

The current study explores the changes in beef quality following partial freezing and during superchilled storage, alongside chilled storage comparisons. Kinetic models were developed to predict changes in colour difference (∆E), thiobarbituric acid−reactive substances (TBARS), total volatile basic nitrogen (TVB−N), drip loss and firmness. Beef samples were partially frozen in an air blast freezer at −30 °C for 9 min prior to storage at −5 °C, −4 °C, −2.8 °C, −1.8 °C. Chilled beef samples were directly stored at 2 °C and 6 °C without partial freezing. All samples were stored for 21 days. The lightness (L*), redness (a*), yellowness (b*) and colour difference (∆E) were significantly lower in superchilled storage samples compared to chilled storage samples. The pH of beef samples increased gradually over time (p < 0.05). TBARS, TVB−N and drip loss increased while firmness decreased with the increase in storage time in both storage conditions (p < 0.05). Overall, beef quality was affected by both storage duration and temperature. Firmness followed the first order kinetic model; drip loss, TVB−N, TBARS and colour difference (∆E) fitted the zero−order kinetic model. Temperature dependence was adequately modelled using Arrhenius−type equation with the activation energy values of 110.111, 52.870, 68.553, 119.480, 47.301 kJ/mol for drip loss, firmness, TBARS, TVB−N and colour difference (∆E), respectively. The models demonstrated strong predictive performance, with RMSE and MAPE values within ±10%. The developed kinetic models successfully predicted quality changes within the −5 °C to 6 °C temperature range. Full article

The wave spectrum, as a key statistical feature describing wave energy distribution, is crucial for understanding wave propagation mechanisms and supporting ocean engineering applications. This study, based on ERA5 reanalysis spectrum data, proposes a model combining CNN and xLSTM for rapid gridded wave spectrum prediction over the Bohai and Yellow Seas domain. It uses 2D gridded spectrum data rather than a spectrum at specific points as input and analyzes the impact of various input factors at different time lags on wave development. The results show that incorporating water depth and mean sea level pressure significantly reduces errors. The model performs well across seasons with the seasonal spatial average root mean square error (SARMSE) of spectral energy remaining below 0.040 m²·s and RMSEs for significant wave height (SWH) and mean wave period (MWP) of 0.138 m and 1.331 s, respectively. At individual points, the spectral density bias is near zero, correlation coefficients range from 0.95 to 0.98, and the peak frequency RMSE is between 0.03 and 0.04 Hz. During a typical cold wave event, the model accurately reproduces the energy evolution and peak frequency shift. Buoy observations confirm that the model effectively tracks significant wave height trends under varying conditions. Moreover, applying a frequency-weighted loss function enhances the model’s ability to capture high-frequency spectral components, further improving prediction accuracy. Overall, the proposed method shows strong performance in spectrum prediction and provides a valuable approach for regional wave spectrum modeling. Full article

Dual-active bridge (DAB) converters have emerged as a preferred topology in electric vehicle charging and energy storage applications, owing to their structurally symmetric configuration and intrinsic galvanic isolation capabilities. However, conventional triple-phase shift (TPS) control strategies face significant challenges in maintaining high efficiency across ultra-wide output voltage and load ranges. To exploit the inherent structural symmetry of the DAB topology, a symmetric optimization strategy based on triple-phase shift (SOS-TPS) is proposed. The method specifically targets the forward buck operating mode, where an optimization framework is established to minimize the root mean square (RMS) current of the inductor, thereby addressing both switching and conduction losses. The formulation explicitly incorporates zero-voltage switching (ZVS) constraints and operating mode conditions. By employing the Karush–Kuhn–Tucker (KKT) conditions in conjunction with the Lagrange multiplier method (LMM), the refined control trajectories corresponding to various power levels are analytically derived, enabling efficient modulation across the entire operating range. In the medium-power region, full-switch ZVS is inherently satisfied. In the low-power operation, full-switch ZVS is achieved by introducing a modulation factor λ, and a selection principle for λ is established. For high-power operation, the strategy transitions to a conventional single-phase shift (SPS) modulation. Furthermore, by exploiting the inherent symmetry of the DAB topology, the proposed method reveals the symmetric property of modulation control. The modulation strategy for the forward boost mode can be efficiently derived through a duty cycle and voltage gain mapping, eliminating the need for re-derivation. To validate the effectiveness of the proposed SOS-TPS strategy, a 2.3 kW experimental prototype was developed. The measured results demonstrate that the method ensures ZVS for all switches under the full load range, supports ultra-wide voltage conversion capability, substantially suppresses RMS current, and achieves a maximum efficiency of 97.3%. Full article