Search Results (721)

A jet flow mixer is a novel agitator type widely used in the industry. However, scientific research has yet to be conducted on this impeller type. In this study, six types of fluids with various properties widely used in the paint industry were chosen to calculate the positioning of the jet flow mixer in the tank. Calculations were performed using computational fluid dynamics (CFD) software and validated using literature data. Simulations were conducted to consider the inside of the jet flow mixer and the inside of the tank. The initial calculations made for jet flow mixers allowed the determination of volume flow and power numbers for three types of mixers (propeller agitator and Pitched Blade Turbine with three and four blades). Those parameters were then used in subsequent calculations, obtaining the optimal inclination angle of the agitator and power consumption for each considered case. The jet flow mixer with a propeller impeller positioned at an angle of 45° proved to be the choice to achieve the best results. Full article

Data over sound (DoS) is an established technique that has experienced a resurgence in recent years, finding applications in areas such as contactless payments, device pairing, authentication, presence detection, toys, and offline data transfer. This study introduces CardiaWhisper, a system that extends the DoS concept to the medical domain by using a medical data-over-sound (MDoS) framework. CardiaWhisper integrates wearable biomedical sensors with home care systems, edge or IoT gateways, and telemedical networks or cloud platforms. Using a transmitter device, vital signs such as ECG (electrocardiogram) signals, PPG (photoplethysmogram) signals, RR (respiratory rate), and ACC (acceleration/movement) are sensed, conditioned, encoded, and acoustically transmitted to a nearby receiver—typically a smartphone, tablet, or other gadget—and can be further relayed to edge and cloud infrastructures. As a case study, this paper presents the real-time transmission and processing of ECG signals. The transmitter integrates an ECG sensing module, an encoder (either a PLL-based FM modulator chip or a microcontroller), and a sound emitter in the form of a standard piezoelectric speaker. The receiver, in the form of a mobile phone, tablet, or desktop computer, captures the acoustic signal via its built-in microphone and executes software routines to decode the data. It then enables a range of control and visualization functions for both local and remote users. Emphasis is placed on describing the system architecture and its key components, as well as the software methodologies used for signal decoding on the receiver side, where several algorithms are implemented using open-source, platform-independent technologies, such as JavaScript, HTML, and CSS. While the main focus is on the transmission of analog data, digital data transmission is also illustrated. The CardiaWhisper system is evaluated across several performance parameters, including functionality, complexity, speed, noise immunity, power consumption, range, and cost-efficiency. Quantitative measurements of the signal-to-noise ratio (SNR) were performed in various realistic indoor scenarios, including different distances, obstacles, and noise environments. Preliminary results are presented, along with a discussion of design challenges, limitations, and feasible applications. Our experience demonstrates that CardiaWhisper provides a low-power, eco-friendly alternative to traditional RF or Bluetooth-based medical wearables in various applications. Full article

In wireless communications and radio frequency courses, Software-Defined Radios (SDRs) offer students hands-on experience with software-based signal processing on programmable hardware platforms such as Field Programmable Gate Arrays (FPGAs). While some remote SDR laboratories enable students to access real hardware, they typically lack support for Partial Reconfiguration (PR)—a powerful FPGA capability that allows sections of a design to be reconfigured at runtime without disrupting the main system operation. This capability enhances real-time adaptability and optimizes resource utilization, making it highly relevant for modern SDR applications. This study addresses this gap by extending an existing SDR remote lab to support PR, enabling students to explore reconfigurable hardware design within a remote learning environment. Two integration architectures were developed: one based on a graphical user interface (UI) and another utilizing a command-line workflow, both accessible via a web browser. Preliminary experiments using Red Pitaya SDR platforms—reportedly the first use of these devices for educational PR exploration—examined the impact of PR on logic resource utilization and total power consumption across three levels of design complexity. These results were compared to equivalent static FPGA designs performing the same functionality without PR. By making PR experimentation accessible through a remote platform, this work enhances STEM education by bridging advanced FPGA techniques with practical learning. It will equip students with industry-relevant skills for developing agile, resource-efficient wireless systems and foster a deeper understanding of adaptive hardware design. Full article

In this study, we selected the production processes and main products of three typical chemical enterprises in Shanghai, namely SH Petrochemical (part of the oil-refining sector), SK Ethylene, and HS Chlor-Alkali, to quantitatively assess the synergistic effects across technology, policy, and emission mechanisms. The localized air pollutant levels and greenhouse gas emissions of the three enterprises were calculated. The synergistic effects between the end-of-pipe emission reductions for air pollutants and greenhouse gas emissions were analyzed using the pollutant reduction synergistic and cross-elasticity coefficients, including technology comparisons (e.g., acrylonitrile gas incineration (AOGI) technology vs. traditional flare). Based on these data, we used the SimaPro software and the CML-IA model to conduct a life cycle environmental impact assessment regarding the production and upstream processes of their unit products. By combining the life cycle method and the scenario simulation method, we predicted the trends in the environmental impacts of the three chemical enterprises after the implementation of low-carbon development policies in the chemical industry in 2030. We also quantified the synergistic effects of localized air pollutant and greenhouse gas (GHG) emission reductions within the low-carbon development scenario by using cross-elasticity coefficients based on life cycle environmental impacts. The research results show that, for every ton of air pollutant reduced through end-of-pipe treatment measures, the HS Chlor-Alkali enterprise would increase its maximum CO₂ emissions, amounting to about 80 tons. For SK Ethylene, the synergistic coefficient for VOC reduction and CO₂ emissions when using AOGI thermal incineration technology is superior to that for traditional flare thermal incineration. The activities of the three enterprises had an impact on several environmental indicators, particularly the fossil fuel resource depletion potential, accounting for 69.48%, 53.94%, and 34.23% of their total environmental impact loads, respectively. The scenario simulations indicate that, in a low-carbon development scenario, the overall environmental impact loads of SH Petrochemical (refining sector), SK Ethylene, and HS Chlor-Alkali would decrease by 3~5%. This result suggests that optimizing the upstream power structure, using “green hydrogen” instead of “grey hydrogen” in hydrogenation units within refining enterprises, and reducing the consumption of electricity and steam in the production processes of ethylene and chlor-alkali are effective measures in reducing carbon emissions in the chemical industry. The quantification of the synergies based on life cycle environmental impacts revealed that there are relatively strong synergies for air pollutant and GHG emission reductions in the oil-refining industry, while the chlor-alkali industry has the weakest synergies. Full article

The energy consumption of current vertical-lifting underwater monitoring devices mainly falls into two categories: one fully supplied by battery packs; and the other partially by battery packs, with the rest from ocean thermal energy. Constrained by battery capacity, their operation time is limited, making long-term remote operations difficult. This study focuses on a device powered entirely by ocean thermal energy, which realizes the absorption and storage of energy through a phase change heat-exchange system, significantly extending its operation cycle and working area. A composite phase change material of n-hexadecane and graphite with a volume ratio of 9:1 is used. The Fluent software 2022 R1, based on the enthalpy-porosity method, simulates the phase change process of the device to analyze the effects of different structures and seawater temperatures. Results show that with the same phase change material volume and inner diameter of the cylindrical heat exchanger, a smaller outer diameter yields better phase change performance. Lower seawater temperature facilitates solidification. Due to natural convection in the liquid phase, the melting time is 520 s and solidification time is 4800 s, with the melting rate far exceeding the solidification rate. Full article

As a fundamental technology in environmental perception, sound source localization (SSL) plays a critical role in public safety, marine exploration, and smart home systems. However, traditional methods such as beamforming and time-delay estimation rely on manually designed physical models and idealized assumptions, which struggle to meet practical demands in dynamic and complex scenarios. Recent advancements in deep learning have revolutionized SSL by leveraging its end-to-end feature adaptability, cross-scenario generalization capabilities, and data-driven modeling, significantly enhancing localization robustness and accuracy in challenging environments. This review systematically examines the progress of deep learning-based SSL across three critical domains: marine environments, indoor reverberant spaces, and unmanned aerial vehicle (UAV) monitoring. In marine scenarios, complex-valued convolutional networks combined with adversarial transfer learning mitigate environmental mismatch and multipath interference through phase information fusion and domain adaptation strategies. For indoor high-reverberation conditions, attention mechanisms and multimodal fusion architectures achieve precise localization under low signal-to-noise ratios by adaptively weighting critical acoustic features. In UAV surveillance, lightweight models integrated with spatiotemporal Transformers address dynamic modeling of non-stationary noise spectra and edge computing efficiency constraints. Despite these advancements, current approaches face three core challenges: the insufficient integration of physical principles, prohibitive data annotation costs, and the trade-off between real-time performance and accuracy. Future research should prioritize physics-informed modeling to embed acoustic propagation mechanisms, unsupervised domain adaptation to reduce reliance on labeled data, and sensor-algorithm co-design to optimize hardware-software synergy. These directions aim to propel SSL toward intelligent systems characterized by high precision, strong robustness, and low power consumption. This work provides both theoretical foundations and technical references for algorithm selection and practical implementation in complex real-world scenarios. Full article

Since 1 January 2024, newly produced heavy-duty trailers are subject to the assessment of their performance regarding CO₂ and fuel consumption according to Implementing Regulation (EU) 2022/1362. The method is based on the already established approach for the CO₂ and energy consumption evaluation of trucks and buses, i.e., applying a combination of component testing and vehicle simulation using the software VECTO (Vehicle Energy Consumption calculation TOol). For the evaluation of trailers, generic conventional towing vehicles in combination with the specific CO₂ and fuel consumption-relevant properties of the trailer, such as mass, aerodynamics, rolling resistance etc., are simulated in the “VECTO Trailer” software. The corresponding results are used in the European HDV CO₂ standards with which manufacturers must comply to avoid penalty payments (2030: −10% for semitrailers and −7.5% for trailers compared with the baseline year 2025). Methodology and legislation are currently being extended to also cover the effects of electrified trailers (trailers with an electrified axle and/or electrically supplied auxiliaries) on CO₂, electrical energy consumption, and electric range extension (special use case in combination with a battery-electric towing vehicle). This publication gives an overview of the developed regulatory framework and methods to be implemented in a future extension of VECTO Trailer as well as a comparison of different e-trailer configurations and usage scenarios regarding their impact on CO₂, energy consumption, and electric range by applying the developed methods in a preliminary potential analysis. Results from this analysis indicate that e-trailers that use small batteries (5–50 kWh) to power electric refrigeration units achieve a CO₂ reduction of 5–10%, depending primarily on battery capacity. In contrast, e-trailers designed for propulsion support with larger batteries (50–500 kWh) and e-axle(s) (50–500 kW) demonstrate a reduction potential of up to 40%, largely determined by battery capacity and e-axle rating. Despite their reduction potential, market acceptance of e-trailers remains uncertain as the higher number of trailers compared with towing vehicles could lead to slow adoption, especially of the more expensive configurations. Full article

Nowadays, there are many studies that have been conducted in order to reduce the emissions of modern reciprocating engines without, at the same time, having a negative impact on the performance characteristics. One method to accomplish that is by using ethanol-supplemented fuels instead of conventional gasoline. On the other side of the spectrum, spark timing is one of the most important parameters that affects the combustion mechanism inside a reciprocating engine and is basically controlled by the ignition advance of the engine. Therefore, the main purpose of this study is to investigate the effect of spark timing alteration on the performance characteristics and emissions of a modern reciprocating, naturally aspirated, aircraft SI engine (i.e., ROTAX 912s), operated under four different engine operating points (i.e., combination of engine speed and throttle opening), by using ethanol-supplemented fuel. The implementation of the aforementioned method is achieved through the use of an advanced simulating software (i.e., GT-POWER), which provides the user with the possibility to completely design a piston engine and parameterize it, by using a comprehensive single-zone phenomenological model, for any operating conditions in the entire range of its operating points. The predictive ability of the designed engine model is evaluated by comparing the results with the experimental values obtained from the technical manuals of the engine. For all test cases examined in the present work, the results are affiliated with important performance characteristics, i.e., brake power, brake torque, and brake-specific fuel consumption, as well as specific NO and CO concentrations. Thus, the primary objectives of this study were to examine and evaluate the results of the combination of using ethanol-supplemented fuel instead of gasoline and the alteration of the spark timing, to asses their effects on the basic performance characteristics and emissions of the aforementioned type of engine. By examining the results of this study, it is revealed that the increase in the ethanol concentration in the gasoline–ethanol fuel blend combined with the increase in the ignition advance might be an auspicious solution in order to meliorate both the performance and the environmental behavior of a naturally aspirated SI aircraft piston engine. In a nutshell, the outcoming results of this research show that the combination of the two methods examined may be a valuable solution if applied to existing reciprocating SI engines. Full article

The increasing need for efficient energy consumption monitoring, driven by economic and environmental concerns, has made Non-Intrusive Load Monitoring (NILM) a cost-effective alternative to traditional measurement methods. Despite its progress since the 1980s, NILM still lacks standardized benchmarks, limiting objective performance comparisons. This study introduces several key contributions: (1) the development of five new converters with 13-digit timestamp support and harmonic inclusion, improving the data collection accuracy by up to 25%; (2) the implementation of an advanced disaggregation software, achieving a 10–15% increase in the F1-score for certain appliances; (3) a detailed analysis of harmonics’ impact on NILM, reducing the Mean Normalized Error in Assigned Power by up to 40%; and (4) the design of open-source measurement hardware to enhance reproducibility. This study also evaluates open hardware platforms and compares five common household appliances using NILM Toolkit metrics. Results demonstrate that open hardware and software foster reproducibility and accelerate innovation in NILM. The proposed approach contributes to a standardized and scalable NILM framework, facilitating real-world applications in energy management and smart grid optimization. Full article

Efficient real-time isotope identification is a critical challenge in nuclear spectroscopy, with important applications such as radiation monitoring, nuclear waste management, and medical imaging. This work presents a novel approach for isotope classification using a System-on-Chip FPGA, integrating hardware-accelerated principal component analysis (PCA) for feature extraction and a software-based random forest classifier. The system leverages the FPGA’s parallel processing capabilities to implement PCA, reducing the dimensionality of digitized nuclear signals and optimizing computational efficiency. A key feature of the design is its ability to perform real-time classification without storing ADC samples, directly processing nuclear pulse data as it is acquired. The extracted features are classified by a random forest model running on the embedded microprocessor. PCA quantization is applied to minimize power consumption and resource usage without compromising accuracy. The experimental validation was conducted using datasets from high-resolution pulse-shape digitization, including closely matched isotope pairs (¹²C/¹³C, ³⁶Ar/⁴⁰Ar, and ⁸⁰Kr/⁸⁴Kr). The results demonstrate that the proposed SoC FPGA system significantly outperforms conventional software-only implementations, reducing latency while maintaining classification accuracy above 98%. This study provides a scalable, precise, and energy-efficient solution for real-time isotope identification. Full article

Underwater acoustic sensor networks, crucial for marine monitoring, face significant challenges, including limited bandwidth, high delay, and severe energy constraints. Addressing these limitations requires an energy-efficient design to ensure network survivability, reliability, and reduced operational costs. This paper proposes an artificial intelligence-driven framework aimed at enhancing energy efficiency and sustainability in applications of marine wildlife monitoring in underwater sensor networks, according to the vision of implementing an underwater acoustic sensor network. The framework integrates intelligent computing directly into underwater sensor nodes, employing lightweight AI models to locally classify marine species. Transmitting only classification results, instead of raw data, significantly reduces data volume, thus conserving energy. Additionally, a software-defined radio methodology dynamically adapts transmission parameters such as modulation schemes, packet length, and transmission power to further minimize energy consumption and environmental disruption. GNU Radio simulations evaluate the framework effectiveness using metrics like energy consumption, bit error rate, throughput, and delay. Adaptive transmission strategies implicitly ensure reduced energy usage as compared to non-adaptive transmission solutions employing fixed communication parameters. The results illustrate the framework ability to effectively balance energy efficiency, performance, and ecological impact. This research contributes directly to ongoing development in sustainable and energy-efficient underwater wireless sensor network design and deployment. Full article

This paper explores the optimal configuration strategies for building-integrated photovoltaic (BIPV) systems in response to the low-carbon transformation needs of semi-outdoor substations, aiming to reconcile the contradiction between photovoltaic (PV) power generation efficiency and indoor environmental control in industrial buildings. Taking a 220 kV semi-outdoor substation of the China Southern Power Grid as a case study, a building energy consumption–PV power generation coupling model was established using EnergyPlus software. The impacts of three PV wall constructions and different building orientations on a transformer room and an air-conditioned living space were analyzed. The results show the EPS-filled PV structure offers superior passive thermal performance and cooling energy savings, making it more suitable for substation applications with high thermal loads. Building orientation plays a decisive role in the net energy performance, with an east–west alignment significantly enhancing the PV module’s output and energy efficiency due to better solar exposure. Based on current component costs, electricity prices, and subsidies, the BIPV system demonstrates a moderate annual return, though the relatively long payback period presents a challenge for widespread adoption. East–west orientations offer better returns due to their higher solar exposure. It is recommended to adopt east–west layouts in EPS-filled PV construction to optimize both energy performance and economic performance, while further shortening the payback period through technical and policy support. This study provides an optimized design path for industrial BIPV module integration and aids power infrastructure’s low-carbon shift. Full article

For every tractor test carried out on a concrete road under defined conditions, the Nebraska Tractor Test Laboratory (NTTL) provides values of the specific volumetric fuel efficiency (SVFE) in unit of kWh/L). Because soil tillage is a highly energy-intensive process and the energy consumption of tillage operations is a significant component of a farm budget, there is a growing amount of attention being given to the examination of the SVFE for tillage operations. Nonetheless, the study of the tillage process and a scientific approach to the tillage process are becoming more and more dependent on scientific modeling. Therefore, in this study based on real-tillage field operation, an artificial neural network (ANN) model was built to predict SVFE. This study aimed to confirm that the ANN model could incorporate 10 inputs for prediction: initial soil moisture content, draft force, initial soil bulk density, sand, silt, and clay proportions in the soil tractor power, plow width, tillage depth, and tillage speed. The Qnet v2000, as an ANN simulation software, was employed for the simulation of the SVFE. In this regard, 20,000 runs of Qnet v2000 were completed for the training and testing stages. The anticipated results displayed that the determination coefficient (R²) was larger than 0.96; using the training dataset, R² was 0.982 and using the testing dataset, R² was 0.9741, indicating that the recognition of a full ANN model makes it likely to reply to essential enquiries that were previously unanswerable regarding the impact of working and soil conditions on the SVFE of a tractor–tillage implement system. Additionally, sensitivity analyses were completed to specify which modeled parameters were more sensitive to the factors using the obtained ANN model. According to the sensitivity analysis, SVFE was more affected by changes in the tillage speed (21.07%), silt content in the soil (15.56%), draft force (11.01%), and clay content in the soil (10.86%). Predicting SVFE can lead to more appropriate decisions on tractor–chisel plow combination management. Therefore, it is highly advisable to use the newly created ANN model to appropriately manage SVFE to reduce tractor–tillage implement energy dissipation. Additionally, suitable management of some variables, for example, tillage depth, tillage speed, and soil moisture content, can help enhance fuel consumption in the tractor–tillage implementation system. Full article

This paper discusses the development of an Integrated Building Energy Model (IBEM) in MATLAB (R2024b) for a university campus building. In the general context of the development of integrated energy district models to guide the evolution and planning of smart energy grids for increased efficiency, resilience, and sustainability, this work describes in detail the development and use of an IBEM for a university campus building featuring a heat pump-based heating/cooling system and PV generation. The IBEM seamlessly integrates thermal and electrical aspects into a complete physical description of the energy performance of a smart building, thus distinguishing itself from co-simulation approaches in which different specialized tools are applied to the two aspects and connected at the level of data exchange. Also, the model, thanks to its physical, white-box nature, can be instanced repeatedly within the comprehensive electrical micro-grid model in which it belongs, with a straightforward change of case-specific parameter settings. The model incorporates a heat pump-based heating/cooling system and photovoltaic generation. The model’s components, including load modeling, heating/cooling system simulation, and heat pump implementation are described in detail. Simulation results illustrate the building’s detailed power consumption and thermal behavior throughout a sample year. Since the building model (along with the whole campus micro-grid model) is implemented in the MATLAB Simulink environment, it is fully portable and exploitable within a large, world-wide user community, including researchers, utility companies, and educational institutions. This aspect is particularly relevant considering that most studies in the literature employ co-simulation environments involving multiple simulation software, which increases the framework’s complexity and presents challenges in models’ synchronization and validation. Full article

Focusing on the power consumption of a regional multi-energy system with the characteristics of energy congestion in students’ dormitory buildings in the hot summer and warm winter regions of southern China, a practical regional multi-energy system consisting of three subsystems, namely an integrated screw chiller (ISC), a screw ground-source heat pump (SGSHP), and an air-source heat pump (ASHP), was optimised by the operation control strategy. The system’s power consumption and cooling/heating load characteristics during operation were analysed, and changes in the terminal air-conditioning load were simulated. Based on the dynamic cooling and heating load of the building, a two-stage loading strategy was proposed for optimising the system operation. Taking the load demand matching requirement of the system output and the terminal load demand as constraints, a simulation model of the system was developed using TRNSYS 16 software, and the changes in power consumption and the cooling/heating capacity before and after optimisation were analysed. The results show that the optimised system reduced annual power consumption by approximately 19% and increased condensation heat recovery by about 2.3%. The optimised operation control strategy was aligned well with the terminal cooling and heating demands. Full article