Calculation Of Energy Consumption Research Articles

Research on Large Hybrid Electric Aircraft Based on Battery and Turbine-Electric

Hybrid electric aircraft use traditional engine and electric propulsion combinations to optimize aircraft architecture, improve propulsion efficiency, and reduce fuel consumption. As a new technology, the fuel and energy consumption calculation of hybrid electric aircraft is more complicated than traditional aircraft due to the usage of different energy forms. The purpose of this paper is to develop the analytical method for fuel and energy consumption for hybrid electric aircraft. This paper summarizes the working principle of hybrid electric aircraft, including the system architecture and power conversion mechanism. The calculation of fuel and energy consumption for hybrid electric aircraft is carried out in detail. In order to evaluate large hybrid electric aircraft, the architecture, based on energy flow, is established, and turbofan engine, electrical system, electric duct fan, and aerodynamic model characteristics are established. With a single-aisle aircraft as an example, the fuel and energy consumption under the 800 nautical mile range is performed. It shows that fuel consumption can be reduced by 10% and energy consumption by 4.7% compared with a traditional aircraft. The effects of different range and battery ratios are analyzed. The payload range for the hybrid electric aircraft is analyzed. The results show that even though the hybrid electric aircraft reduces the payload and range, it can significantly reduce fuel and energy consumption.

Fuzzy logic-supported building design for low-energy consumption in urban environments

Climate, building materials, occupancy patterns, and HVAC (heating, ventilation, and air conditioning) systems all interact in complex ways, making it difficult to design low-energy buildings. Thus, innovative architectural and engineering design strategies are required to meet the worldwide need to decrease building energy usage. To improve the calculation of energy consumption of buildings, this work introduces the FCR-BCS (fuzzy clustering rule-based building control systems), which integrates fuzzy logic concepts into computational simulations. FCR-BCS can contemplate real-world uncertainties and fluctuations using linguistic factors and approximate reasoning for more precise and trustworthy results in energy-efficient building design. This method's significance rests in its potential to significantly reduce energy use, advance sustainability, and improve urban residents' quality of life; architects and engineers can thus employ FCR-BCS to enhance the efficiency of HVAC systems and insulation. The outcomes of FCR-BCS simulation assessments show that it is capable of making buildings more energy efficient. The experimental outcomes demonstrate that the suggested model increases the sensitivity analysis by 99.4%, energy efficiency analysis by 99.8%, occupancy patterns analysis by 97.5%, temperature profile analysis by 98.8%, and energy consumption analysis by 99.6% compared to other existing models.

Particle Swarm Optimization for multi-chiller system: Capacity configuration and load distribution

This study applies Particle Swarm Optimization (PSO) to enhance the energy efficiency of a multi-chiller system in a large office building, with a focus on optimizing capacity configuration and load distribution. Given the rising demand for sustainable energy solutions in buildings, where HVAC systems, particularly chillers, account for a significant portion of energy consumption, this research aims to reduce energy use and improve system performance. EnergyPlus simulations, based on Baltimore weather data and a reference large office model, were conducted to build a baseline dataset. This dataset was then used in Python to calculate energy consumption and optimize load distribution and capacity configuration. PSO was applied in three stages: optimizing capacity configuration using five built-in EnergyPlus algorithms, optimizing load distribution, and simultaneously optimizing both. The results showed that optimizing capacity configuration alone improved performance, even using traditional load distribution methods. Load distribution optimization outperformed other algorithms and converged at a 0.7 Part Load Ratio (PLR) for staging. The integrated PSO application achieved an 11.3 % reduction in energy consumption, a 21.5 % improvement in Coefficient of Performance (COP), and a 12.8 % increase in the Seasonal Energy Efficiency Ratio (SEER) by precise operation of 15 different combinations throughout the entire load range. These results demonstrate the potential of PSO to significantly enhance the efficiency of multi-chiller systems, providing a novel approach to optimizing both capacity configuration and load distribution. This research contributes to a robust framework for improving energy performance in building systems and offers valuable insights for future sustainable energy solutions.

Optimizing Drone Delivery Paths from Shared Bases: A Location-Routing Problem with Realistic Energy Constraints

Recently, Amazon patented fulfillment centers for drones on a large scale in densely populated areas. A network of such shared centers can be used for landing and launching drones as an alternative to the traditional private bases in near future. This paper studies how a user of such a shared delivery infrastructure can optimally determine the required bases among the existing bases to perform her delivery tasks. To this end, a location-routing problem is studied where the problem determines the drone launching centers and their routes to deliver parcels. A realistic energy function is used that incorporates the effect of load weight for calculating energy consumption in all flight phases including take-off, level flight, hovering, and landing. Although the energy consumption is nonlinear, the problem is formulated as a mixed-integer linear programming model and strengthened by valid inequalities and a pre-processing algorithm that enables us to solve instances with up to 100 customers using off-the-shelf optimization solvers. Moreover, a heuristic method is presented to solve instances with up to 200 customers. Results indicate the importance of incorporating the load-dependent energy formula in contrast to using fixed flight duration constraints for drones. The value of allowing multiple visits on each trip versus only simple back-and-forth trips is also assessed. The advantage of using an integrated location-routing approach is shown over the sequential approach in which the locations of bases are decided first and the routes are constructed next. Moreover, to manage future demand uncertainty, the model is extended for the case that a number of demand scenarios are given.

Performance assessment of a multi-level shuttle system: an optimisation-embedded simulation study on time and energy consumption

ABSTRACT In response to the dynamic demands of modern e-commerce and supply chains, this paper addresses the critical need for adaptability and efficiency through the implementation of automated storage and retrieval systems. By using an optimisation-embedded simulation methodology, the proposed study focuses on a multi-level shuttle system designed to overcome the inherent limitations of traditional storage systems. A comprehensive discrete event simulation model is developed to evaluate the performance of the multi-level shuttle system. This model includes calculations of travel time and energy consumption, taking also into account stochastic handling times associated with repetitive tasks. In addition, a dynamic programming algorithm is integrated in the simulation model to optimise the sequencing of storage and retrieval missions performed by the handling machine. The study systematically investigates various design and operational parameters, including warehouse configurations and capacity, simultaneous handling of multiple unit loads, and the dimensions of the optimisation sessions. For all configurations tested, the research demonstrates operational advantages in the simultaneous handling of two unit loads. Theoretical contributions extend the current state of simulation models for automated warehousing systems. For practitioners, the study provides empirical insights to support decision making in the configuration of automated warehouses.

Destruction of perfluorooctane sulfonic acid (PFOS) in gas sparging incorporated UV-indole reductive treatment system – Benefits and challenges

Ultraviolet (UV) reductive treatment systems that generate hydrated electrons (eaq-) have emerged as a promising technology for the destruction of chemically inert per- and polyfluoroalkyl substances (PFAS). Here, we report on the evaluation of an indole derivative-based UV reductive treatment system that utilizes the amphipathic properties of PFAS at the gas-water interface (via nitrogen (N2) sparging) for more energy-efficient destruction of perfluorooctane sulfonic acid (PFOS). Results from this work illustrated that N2 sparging within UV systems can enhance the degradation and defluorination of PFOS compared to non-sparged conditions, but their overall treatment efficiency is low to industry standard. The inadequate system performance is likely originated from the insufficient accumulation of electron sources at the gas-water interface and their low water solubility level. In addition, carbonate species, which are ubiquitous in natural water and commonly applied as buffers in UV reductive treatment systems, negatively impact PFOS defluorination when indole is the electron source. The species-specific quenching imposed by carbonate species (e.g., HCO3- > H2CO3*) indicates that naturally occurring constituents and varying reactor conditions can substantially influence the remediation of PFOS. Other notable findings in this work include: 1) gramine, a cationic indole derivative, was able to remove > 99 % PFOS mass via electrostatic interaction within 0.5 h of reaction, signifying the electron source's structural property importance in UV reductive treatment systems, and 2) energy consumption calculations showed indole species are less energy-efficient as electron sources for PFOS destruction comparing to sulfite-iodide, but performance tradeoffs exist in both systems. The results of this work revealed both the benefits and challenges of utilizing N2 sparging and indole derivatives in UV-PFAS reductive treatment processes and provided critical information needed to improve the prediction and design of similar PFAS destruction technologies.

Relative fuel property variation of gas-pressurized and conventional torrefaction for biochar performance assessment

Exploring biochar from gas-pressurized torrefaction for solid biofuel production is of great concern for the process performance assessment. This study conducts several relative indexes of biochars produced from gas-pressurized and conventional torrefaction, identifying the merits of the former. The results suggest that a higher pressure is more feasible for reducing biochar volume and enhancing energy efficiency. The results of the relative higher heating value (1.01–1.07) and relative enhancement factor (1.00–1.06) indicate that gas-pressurized torrefaction is more efficient for energy density improvement. Furthermore, gas-pressurized torrefaction dramatically facilitates biochar grindability, with the relative Hardgrove grindability index value ranging from 1.20 to 2.40. The proximate and elemental analysis results suggest that a higher pressure facilitates biochar carbonization and deoxygenation with a higher carbonized component. The energy consumption, efficiency, and expense calculation results imply that gas-pressurized torrefaction is more energy-efficient and cost-saving, with the relative upgrading energy index range of 1.00–1.12.

Optimizing the Integration of Building Materials, Energy Consumption, and Economic Factors in Rural Houses of Cold Regions: A Pathway

Limited material options and economic conditions significantly restrict the potential for energy efficiency improvements in rural houses in China’s cold regions. It is worth exploring how to propose suitable energy-saving renovation plans for rural houses in cold regions under practical constraints. By using Grasshopper within Rhinoceros 8 software, an algorithm integrates material selection, energy consumption calculations, and economic analysis. The method efficiently generates thermal optimization schemes, providing insights into energy use, costs, and payback periods. In a case study of a typical rural house in Daqing City, the optimized scheme achieved over 70% energy savings compared to traditional homes, with renovation costs amounting to less than 40% of residents’ annual income and a 2-year payback period. This significant improvement highlights the potential of the proposed method in enhancing the energy efficiency and economic viability of rural house renovations.

Research on energy consumption evaluation and energy-saving design of cranes in service based on structure-mechanism coupling

Aiming at the problem that the life cycle energy consumption index of hoisting machinery is difficult to be objectively quantified, a crane energy consumption evaluation calculation method based on the working cycle of the service period is proposed, the typical service working mode of the crane is analyzed, and the working cycle process of the mechanism is clarified. On this basis, the stress cycle characteristics of the crane structure during the service period are determined based on the coupling relationship between the mechanism and the structure, which lays the foundation for accurately evaluating the life index of the crane. According to the relationship between the load rate and effective power of each mechanism during the service period of the crane, the calculation method for the evaluation and calculation of the structure energy consumption during the service period of the crane is studied. Finally, the crane is designed based on the concept of absolute service safety, and an optimal design model of the main girder structure of the crane with the goal of the lowest energy consumption in service is established. Using the research method of this paper to study a certain type of bridge crane, the energy consumption of the hoisting machinery during its service period is not only related to the accumulation of the operating characteristics of the mechanism but also related to the characteristic parameters such as the structure self-weight. The comprehensive evaluation of energy consumption provides an effective quantitative method and a reliable green design theory for crane design.

Implementation of monitoring and certification with the developed China heavy-duty vehicle energy consumption and carbon emission calculation model

Owing to escalating concerns regarding global warming, there has been a heightened focus on greenhouse gases emitted by vehicles. To effectively monitor and certify the fuel consumption and CO2 emissions of heavy-duty vehicles, this study proposes a calculation model named the China Heavy-Duty Vehicle Energy Consumption and Carbon Emission Calculation Model (CHECM). The CHECM is a simulation tool based on longitudinal dynamics. A classification learner was utilised to obtain shifting strategies, achieving accuracies of 92.9% and 93.5% under regulated driving cycles. A fuel-consumption model was incorporated to predict the transient performance of the engine and transmission. In addition, the Sobol method was used to assess the sensitivities of rolling resistance, air drag and rotational mass conversion coefficients to the driving force and a method was proposed to obtain the road resistance correction factor. The test results of three China-6 heavy-duty vehicles over two regulatory test cycles were obtained and used for model accuracy evaluation. The results showed that the deviations between the measured and calculated fuel consumption were 1.25–3.57%, whereas those between the measured and calculated CO2 emissions using the Chinese World Transient Vehicle Cycle were 3.23–4.16%. The CHECM has the potential to accurately replicate various driving conditions and vehicle configurations, particularly when specific sources of uncertainty are constrained.

Spatio-temporal distribution and peak prediction of energy consumption and carbon emissions of residential buildings in China

With the acceleration of urbanization and the improvement of people's living standards, the carbon emissions of residential buildings (BCE) in China have been increasing, hindering the achievement of carbon peaking and carbon neutrality goals. It has become increasingly urgent and important to analyze and grasp the dynamic trends of BCE. Given the notable regional variations in carbon emissions, it is necessary to delve into the distribution characteristics of BCE and the underlying factors influencing them. First, based on the emission factor method, the energy consumption and carbon emissions calculation models are established. Next, the main influencing factors are obtained by improving Kaya's constant equation. Meanwhile, the GA-PPC-Kmeans model is established to divide BCE into different regions. Then, the STIRPAT-Ridge model is built to predict the peak carbon emissions. Finally, a combination of static and dynamic methods is used to forecast peaks under three scenarios, which are low-carbon (LC) scenario, baseline (BA) scenario, and high-carbon (HC) scenario. The results show that five pivotal factors significantly impact BCE in China, which are energy carbon emission intensity (ECEI), energy intensity (EI), economic density (ED), residential housing level (RHL) and population size(P). Furthermore, BCE is categorized into five distinct regions: low-carbon demonstration area, energy efficiency improvement area, carbon concentration and innovation area, high-carbon optimization area, and carbon poverty development area. Under BA scenario, the peaks for BCE in these regions converge primarily around 2040 and 2045. This study provides a regional research perspective for China's residential buildings to reach the peak of carbon emissions, and serves as a reference for formulating carbon emission reduction plans in different regions.

Privacy protection of communication networks using fully homomorphic encryption based on network slicing and attributes

At present, social networks have become an indispensable medium in people's daily life and work. However, concerns about personal privacy leakage and identity information theft have also emerged. Therefore, a communication network system based on network slicing is constructed to strengthen the protection of communication network privacy. The chameleon hash algorithm is used to optimize attribute-based encryption and enhance the privacy protection of communication networks. On the basis of optimizing the combination of attribute encryption and homomorphic encryption,, a communication network privacy protection method using homomorphic encryption for network slicing and attribute is designed. The results show that the designed network energy consumption is low, the average energy consumption calculation is reduced by 8.69%, and the average energy consumption calculation is reduced by 14.3%. During data transmission, the throughput of the designed network can reach about 700 Mbps at each stage, which has a high efficiency.. The above results demonstrate that the designed communication network provides effective privacy protection. Encrypted data can be decrypted and tracked in the event of any security incident. This is to protect user privacy and provide strong technical support for communication network security.

The Impact of Renewable Energy on Low Energy Consumption in Civil Engineering Construction

As the main representative of new energy, photovoltaic energy is widely used in civil engineering construction, its purpose is to reduce the energy consumption in the project, there is a deviation in the integration of new energy and civil engineering, and it cannot effectively play its own advantages. Continuously analyze the changes in the energy consumption values of civil engineering. Then, the influencing factors of building energy consumption were introduced, and the covariance matrix was constructed, and the engineering construction benefit was taken as the ultimate goal. When calculating energy consumption, the dual benefits of building benefits are fully considered, and environmental protection and engineering energy consumption are estimated to reduce the error of energy consumption calculation. The results show that new energy can reduce the high energy consumption value in the project, improve the light transmittance, enhance the conversion rate of sunlight, and provide support for the transportation, lighting and thermal insulation of the building, with an increase rate of 10~25%, and the new energy can reduce the cost of civil engineering, with a reduction rate of 19~26%, and improve the economic and social benefits, with an increase of more than 10%. Therefore, new energy can reduce the energy and energy consumption of civil engineering, and expand the scope and application depth of new energy development.

Organic synaptic transistor showing ultralow energy consumption with a microscale channel by laser ablation

Low energy consumption per synaptic event is important for artificial synapses in applications of highly integrated and large-scale neuromorphic computing systems. Reducing the channel length of a synaptic transistor is an effective method to achieve this goal because such devices can work under low operating voltage and current. In this Letter, we use femtosecond laser ablation to fabricate a microscale slit in an Ag film as the channel of an organic synaptic transistor to obtain low energy consumption. The length of the shortest channel is only 1.6 μm. As a result, the device could be driven by a 50 μV drain bias voltage while output 855 pA excitatory postsynaptic current under a gate spike of 50 mV and 30 ms. The calculated energy consumption per synaptic event is 1.28 fJ, which is comparable to that of a biological synapse (1–10 fJ per synaptic event). Femtosecond laser ablation has been demonstrated a rapid and effective process for the fabrication of microscale channel with high resolution for synaptic transistor, showing large potential for the development of neuromorphic electronics.

МОДЕЛЮВАННЯ ЕНЕРГОЕФЕКТИВНОСТІ ЖИТЛОВОЇ БУДІВЛІ ЗА УМОВ РІЗНИХ ДЖЕРЕЛ ТЕПЛОВОЇ ЕНЕРГІЇ

The paper presents the results of modeling the process of energy consumption in public, residential, and commercial buildings as a result of the use of various sources of thermal energy. Buildings with such energy sources as: a) a low-temperature gas boiler with a capacity of up to 120 kW for the needs of heat supply of a residential building and freon air conditioning systems for the needs of cooling the building; b) a gas condensing boiler with a thermal capacity of up to 120 kW with a temperature regime of 55/45°C for heat supply and freon air conditioning systems for cooling; c) reversible heat pumping unit of the “ground-water” type with a temperature regime of 35/28°C for the needs of the heating and cooling system and a reversible heat pumping machine with a regime of 55/45°C for the needs of hot water supply; d) a reversible air-to-air heat pump for heating and cooling systems and electric boilers for hot water supply; e) a biomass solid fuel boiler with manual fuel loading with a heat output of more than 100 kW for heat supply and freon systems for cooling; f) a fuel pellet boiler with automatic mechanized fuel loading with a heat output of more than 100 kW for building heating and cooling and freon systems for cooling; g) centralized heating system of the building. The results of calculations of the specific primary energy consumption and specific greenhouse gas emissions for each of the above heat sources are presented. It is established that biomass boilers have the lowest values of primary energy consumption and specific greenhouse gas emissions. The lowest efficiency is achieved by a district heating system with a centralized energy source. The modeling was conducted for the climatic conditions of Vinnytsia. At the same time, the disadvantage of using solid fuel boilers is the limitation of their use in densely populated areas, which is explained by the complexity of flue gas cleaning and regular biomass supply. Therefore, it has been determined that the most efficient source of thermal energy for buildings of various types to achieve the standardized energy efficiency indicators is a ground-to-water heat pump system. This installation, along with providing buildings with thermal energy, allows for cooling in the warm season, operating in reverse mode.

Energy Consumption Calculation of Civil Buildings in Regional Integrated Energy Systems: A Review of Characteristics, Methods and Application Prospects

Civil buildings play a critical role in urban energy consumption. The energy consumption of civil buildings significantly affects energy allocation and conservation management within regional integrated energy systems (RIESs). This paper first analyzes the influencing factors of civil building energy consumption, as well as the energy consumption characteristics of different types of buildings such as office buildings, shopping malls, hospitals, hotels, and residential buildings. Subsequently, it reviews methodologies for calculating operational energy consumption, offering valuable insights for the optimization and strategic adjustments of an RIES. Finally, the paper assesses the application potential of these calculation methods within an RIES and discusses the future development trend of calculating civil building energy consumption.

Optimization operation of data Center's distributed air conditioning system based on supply-demand matching

A supply-demand matching optimization control of data center's distributed air conditioning system was investigated. For the demand side, an estimation model of a server's cooling air parameters was proposed based on server’ thermal resistance. Test results showed that thermal resistances of servers with power being 6–11 kW were 5.5–9 °C/kW. With the server's heat resistance, chip's temperature and power being known, cooling air outlet temperature and air flow rate can be calculated for different air supply temperature. For the supply side, a mathematic model of the distributed air conditioning system, that combines free cooling and mechanical cooling, was established to calculated energy consumption. Using the above two models, the optimal cooling air supply temperature, which leads to the least power consumption of the air conditioning system can be calculated. Performances of the optimized control method were compared with the conventional control method. Results showed that energy consumption could be saved by 94.4 %, 47.6 % and 31.3 % under the natural cooling mode, the hybrid cooling mode, and the active cooling mode, respectively. Annual energy saving ratios after using the optimized control method in the data center located in Harbin and Kunming were 62.2 % and 45.7 %, respectively.

Network energy use not directly proportional to data volume: The power model approach for more reliable network energy consumption calculations

AbstractIt is commonly assumed that data volume and network energy consumption are directly proportional, a notion perpetuated by numerous studies and media coverage. This paper challenges this assumption, offering a comprehensive examination of network operations to explain why the relationship between energy consumption and data volume is nonlinear. The power model approach is explored as an alternative methodology for calculating network energy consumption providing a more reliable representation of network energy use. The power model demonstrates that simple energy intensity calculations, expressed as kilowatt hours per gigabyte of data, are insufficient for accurately estimating real‐world network energy consumption.

ВИЗНАЧЕННЯ ОПТИМАЛЬНИХ ПАРАМЕТРІВ ФІЛЬТРАЦІЙНОГО СУШІННЯ ЯЧМІННОЇ ПИВНОЇ ДРОБИНИ

In this work, the calculation of specific energy consumption for the process of the barley brewer’s spent grain filtration drying was investigated. It has been determined that the lowest total energy consumption for the evaporation of 1 kg of moisture during the filtration drying of barley brewer’s spent grain from the initial moisture content of the material ω1 = 77.88 % (wt.) to the final value ω2 = 10 % (wt.) is 14898.087 kJ/kg H2O or 4.138 kW/kg H2O for the following process parameters: the height of the layer of dried material H = 120 mm, the thermal agent temperature T = 90 °C, the thermal agent velocity v0 = 1.81 m/sec. Determining the optimal process parameters at which the lowest energy costs for drying the material are possible is important for the design of drying equipment.

Distributed Energy Dispatch for Geo-Data Centers Port Microgrid

With the development of port automation and artificial intelligence, coordination with multi-geographic data centers (Geo-DCs) has become a viable solution to address the issue of limited port computing resources. This study proposes a distributed energy dispatch method for the port microgrid coordinated with Geo-DCs (Geo-DCPM), aimed at reducing port carbon emissions and operational costs. Consider the single point of failure problem and high construction costs of centralized data centers. Geo-DCs are first introduced to solve the problem of insufficient computing resources in ports. An energy consumption calculation model for Geo-DCs is established, considering the data load delay constraint and the data space transfer constraint caused by specific delay-sensitive loads in the port microgrid. Then, an energy dispatch model (EDM) is constructed for the Geo-DCPM, taking into account carbon capture costs. Moreover, based on mixed-integer linear programming, a distributed algorithm is proposed to solve the EDM problem. Finally, the simulation results verify the effectiveness of the proposed method. Compared with the centralized algorithm, the packet loss rate of the distributed algorithm combined with Geo-DCs is significantly lower, reduced by about 70%.