Optimization Of Energy Consumption Research Articles

BIM-based energy consumption analysis for residential buildings: effects of orientation, size, and type of windows

Buildings have a significant environmental impact due to their reliance on nonrenewable energy sources. To address this concern, computer software is utilized to estimate building energy requirements. Building Information Modeling (BIM) has emerged as a valuable tool in this process, with BIM 6D offering a particularly beneficial feature. BIM 6D provides a comprehensive energy model of the building, simulating its actual energy performance. By analyzing natural and artificial lighting systems, particularly daylighting, BIM 6D can assist in optimizing energy efficiency. Despite being underutilized, the potential of this feature to enhance building sustainability is noteworthy. BIM 6D facilitates well-informed design and operational decisions for both new constructions and the renovation of existing buildings. The study examines design criteria such as building orientation, window orientation, size, and type to optimize energy consumption in a bungalow in the Sangli region of Maharashtra, underscoring the importance of energy-efficient buildings to local communities. Energy analysis and optimization are conducted using the Autodesk Insight tool. The methodology involves creating 3D models with Autodesk Revit Architecture software and performing various analyses, including Building Energy Modeling (BEM) simulations, Illuminance Analysis, Solar Access, and Daylight Autonomy assessments. The analysis findings indicate that low-e glass windows of small size and oriented between 0° and 90° significantly enhance energy efficiency under certain conditions, while changing the window type does not affect the analysis. The paper concludes with recommendations for window orientation and size to enhance energy performance in residential buildings, contributing to the broader goal of sustainable and energy-efficient construction practices. This study provides valuable insights for designing energy-efficient residential buildings in rural areas, addressing awareness gaps, and promoting sustainable construction practices.

Investigation of effect of recycled polyester ratio and draw frame passages on flame retardancy of chenille fabric properties

PurposeIn this study, both flame retardancy and sustainability properties were combined in the chenille yarn so that functional and sustainable upholstery fabrics can be produced with optimum energy consumption. The chenille yarn samples were produced from recycled polyester (rPET)-blended binder yarns and fully drawn yarn (FDY) polyester pile yarn. While the pile component of the chenille samples was kept constant, the lock yarns were manufactured as 50% r-PET-50% virgin polyester (PES), 100% r-PET and 100% PES. The binder yarns were also produced in two groups. In the first group, a 2-passage draw frame was used, while a 3-passage draw frame was used in the second group. The chenille yarn samples were applied flame-retardant (FR) finishing.Design/methodology/approachThe knitted upholstery fabric samples were produced from chenille yarn samples via flat knitting machines. The knitted fabrics were applied flame retardancy, bursting strength and abrasion resistance tests according to related standards.FindingsThe test results were analysed, and so the effect of r-PET ratio and draw frame passage number on upholstery fabric flame retardancy, bursting strength and abrasion resistance performance properties were revealed. It was concluded that the number of draw frame passages and the recycled fibre content have no important effect on the flame retardancy. On the other hand, it has been observed that the bursting strength decreases as the recycled fibre content increases and bursting distention results are close to each other. It was seen that the draw frame passage number has no significant effect on both bursting strength and distention. It was revealed that both r-PET fibre content and draw frame passage number have significant effect on abrasion resistance. The samples with 2 draw frame passages provide higher abrasion resistance compared to those with 3 draw frame passage. The lowest abrasion resistance was obtained with 100% r-PET fibre content and the highest abrasion resistance was obtained with 50% r-PET-50% PES.Originality/valueThe results will provide enough knowledge for sustainable chenille yarn design. The recycled fibre content and draw frame passage number can be optimised, and so sustainable chenille yarns can be produced with less energy consumption. Future studies will involve producing chenille yarns with different linear densities and varying draw frame combinations, such as 1, 2 and 3 passages.

Energy management strategy for extended range electric logistics vehicle based on type-2 fuzzy control

To overcome the limitations of fuzzy energy management strategy (EMS) in describing uncertainty, this paper proposes an EMS based on type-2 fuzzy (T2F), which optimizes energy consumption for extended range electric logistics vehicle (ERELV). Firstly, establish an energy consumption simulation model for ERELV. Then, an EMS based on T2F controller is designed. The strategy takes the state of charge (SOC) and the demand power as the input, and the range extender power as the output. This paper analyses the influence of T2F control strategy on the energy consumption of the ERELV, and uses the model-based design method to verify the EMS in the Hardware-in-the-loop (HIL) simulation test. The test results show that compared with the traditional fuzzy control strategy, the T2F strategy can reduce the engine’s energy loss by 12.58%. Finally, actual fuel consumption tests were conducted on the established ERELV model. By comparing the results of C-WTVC and CHTC-HT operating conditions, the model was verified to meet the research requirements of EMSs in this paper and has practical significance.

Assessing the Influence of Occupancy Factors on Energy Performance in US Small Office Buildings

Office buildings are responsible for about 35% of the total electricity in the US and over 70% of building energy consumption occurs during occupancy periods. Therefore, understanding occupancy behavior is crucial for reducing building energy consumption. However, given the stochastic nature of occupant behavior, identifying which occupancy parameters have the most impact on energy consumption poses a considerable challenge. This study aims to investigate and quantify the impact of various occupancy parameters on the energy performance of a US small-sized office building using an EnergyPlus-based nationwide energy simulation. First, dynamic occupancy schedules are created based on different occupancy parameters using an agent-based model. Next, the generated dynamic occupancy schedules are integrated into a small office building model from the Department of Energy’s prototypes. This creates a dataset of occupancy parameters and building energy performance across various climate zones. Finally, various feature selection and statistical analysis methods are applied to the generated dataset. This helps identify significant occupancy parameters and quantify their impact on building energy performance across different climate zones. According to the results of the study, buildings located in cool marine, mixed marine, and warm marine climate zones had lower total energy consumption compared to other zones. Additionally, feature selection methods identified “Occupant Density” as the primary significant variable impacting energy consumption, across all climate zones. These findings offer valuable insights into the influential occupancy parameters across various climate zones, highlighting the importance of tailoring occupancy schedules to enhance energy efficiency. They provide practical guidance that can be directly applied to optimize energy consumption and achieve significant energy savings in small office settings with different weather conditions.

FMORT: The Meta-Heuristic routing method by integrating index parameters to optimize energy consumption and real execution time using FANET

Decreasing energy consumption in Unmanned Aerial Vehicles (UAVs) while simultaneously enhancing their reliability and processing capabilities is considered a fundamental challenge. The routing mechanisms employed in Flying Ad Hoc Networks (FANETs) are more complex compared to those in Mobile Ad Hoc Networks (MANETs) and Vehicular Ad Hoc Networks (VANETs), a challenge addressed by the FMORT method. To tackle these complex routing challenges, clustering techniques that utilize hybrid Meta-heuristic algorithms can be applied. Data analysis within the FMORT framework identified factors influencing service integration, leading to a reduction in redundant request transmissions and overall redundancy in the proposed method. The identification of food sources in the hybrid Meta-heuristic algorithm of the FMORT method is achieved through the integration of the Sparrow and Dragonfly algorithms. These algorithms work simultaneously to increase energy efficiency and increase network lifetime. This strategy optimizes information exchange by selecting an intelligent threshold detector and categorizing inputs, thereby minimizing node mobility. As a result, it improves performance metrics and decreases delivery costs, energy consumption, and delays. In the proposed method, a balanced performance is achieved by comparing existing methods in terms of transmission delay, Packet Delivery Ratio( PDR), throughput, and energy consumption. Simulation results show that the FMORT approach provides effective and stable outcomes in terms of reliability, decreased delays, and improved packet delivery rates. The FMORT framework includes principles for neighbor selection, determining suitable cluster heads, and scoring based on the average Euclidean distance. Additionally, it manages topology access, ensures proper distribution, guarantees data connectivity, and accurately categorizes inputs. By optimizing the sensitivity rate, this method minimizes the average delays and meekly values input data through effective load balancing. Key parameters considered for real time optimization of overall performance include the number of cluster heads during re-clustering, the ratio of request-to-acknowledgment packet transmission, node, and network lifetime, end-to-end delay, and energy consumption. Ultimately, the simulation results show that compared to the MWCRSF algorithm, the average optimization of index parameters,% 0.73 decrease in energy consumption,% 2.23 network lifetime, 1.35 re-cluster construction time and also% 0.11 re-cluster lifetime has increased.

Digital Economy and Urban Carbon Emissions: A Quasi-Natural Experiment of the National Big Data Comprehensive Pilot Zone in China

In this study, we assess the impact of digital economy development on urban carbon emissions using a difference-in-differences (DID) approach based on the quasi-natural experiment of the national big data comprehensive pilot zone (NBDCPZ) policy and panel data from 168 prefecture-level cities in China from 2006 to 2019. We find that the establishment of NBDCPZ has a significant carbon emission reduction effect. This finding is robust to various sensitivity tests. Further mechanism validation illustrates that the construction of NBDCPZ promotes the regional digital development level and reduces urban carbon emissions through three channels: improving energy efficiency to optimize energy consumption, increasing green total factor productivity, and promoting the clustering of high-tech industries. The heterogeneity analysis concludes that the carbon emission reduction effect of NBDCPZ policy varies with different regional locations and different city characteristics. Moreover, using spatial models, we find that the positive impact is characterized by spatial spillover effect. The findings provide detailed empirical evidence to formulate urban development strategies for enabling carbon reduction of the digital economy. JEL Classification: O30, Q53, Q55, Q58, R10

Energy-efficient and reliable coordinated scheduling for water distribution systems: enhancing hydraulic conditions and water quality

ABSTRACT Multi-objective operation optimization of water distribution systems (WDSs) in large metropolitan areas is essential; however, it is complex and time-consuming. Effective and reliable scheduling of WDSs can significantly enhance the efficiency and reliability of urban water resources management, which requires further research. This paper develops an optimization method combining genetic algorithm and multiple criteria analysis that coordinates energy conservation, hydraulic condition improvement, and water age optimization in WDSs. Results showed that the optimal scheduling method could achieve a 23.0, 16.7, and 2.5% decrease in energy consumption, and water supply volume with unsatisfied pressure, and average water age, respectively. To achieve optimal results, this study indicated that it is crucial to properly allocate water supply volume and pressure across multiple drinking water treatment plants. This finding provides important guidance for water utilities in scheduling. Furthermore, the emergency scenario analysis shows that the newly proposed method can significantly enhance the social and economic sustainability of WDSs through the coordinated optimization of energy consumption, hydraulic conditions, and water age.

Transportation of Liquefied Natural Gas (LNG) via Long-Distance Low-Temperature Pipeline and Cascade Utilization of Cold Energy

In addition to methane, liquefied natural gas (LNG) typically contains valuable light hydrocarbons such as ethane, propane, and butane. Establishing LNG receiving stations for imported LNG can effectively alleviate regional energy supply-demand conflicts, optimize energy consumption structures, promote sustainable regional economic development, and improve ecological environments. The technology for long-distance low-temperature pipeline transportation of LNG facilitates the efficient transfer and cascading utilization of the cold energy contained in LNG, allowing for deep coupling with cold networks and natural gas pipelines, thereby promoting the integrated development of LNG, cold energy, and natural gas transportation. This paper systematically summarizes the current status of LNG long-distance low-temperature pipeline transportation technology, cold energy cascading utilization technology, pipeline regulation technology, and multi-network integration technology. It analyzes the key challenges these technologies face under new strategies and looks ahead to future development trends, aiming to provide scientific insights and directions for the application of LNG long-distance low-temperature transportation and multi-network integration in China.

Energy Management System and Control of Plug-in Hybrid Electric Vehicle Charging Stations in a Grid-Connected Microgrid

In the complex environment of microgrid deployments targeted at geographic regions, the seamless integration of renewable energy sources meets a variety of essential challenges. These include the unpredictable nature of renewable energy, characterized by intermittent energy generation, as well as ongoing fluctuations in load demand, the vulnerabilities present in distribution network failures, and the unpredictability that results from unfavorable weather conditions. These unexpected events work together to disturb the delicate balance between energy supply and demand, raising the alarming threat of system instability and, in the worst cases, the sudden advent of damaging blackouts. To address this issue, a fuzzy logic-based energy management system has been developed to monitor, manage, and optimize energy consumption in microgrids. This study focuses on the control of diesel generators and utility grids in a grid-connected microgrid which manages and evaluates numerous energy consumption and distribution features within a specified system, e.g., building or a microgrid. An energy management system is suggested based on fuzzy logic as a swift fix for complications with effective and competent resource management, and its presentation is compared with both the grid-connected and off-grid modes of the microgrid. In the end, the results exhibit that the proposed controller outclasses the predictable controllers in dropping sudden variations that arise during the addition of sources of renewable energy, supporting the refurbishment of the constant system.

Collaborative Optimization Framework for Coupled Power and Transportation Energy Systems Incorporating Integrated Demand Responses and Electric Vehicle Battery State-of-Charge

The growing adoption of electric vehicles (EVs) and advancements in dynamic wireless charging (DWC) technology have strengthened the interdependence between power distribution networks (PDNs) and electrified transportation networks (ETNs), leading to the emergence of coupled power and transportation energy systems (CPTESs). This development introduces new challenges, particularly as DWC technology shifts EV charging demand from residential plug-in charging to charging-while-driving during commuting hours, causing simultaneous congestion in both ETNs and PDNs during peak times. The present work addresses this issue by developing a collaborative optimization framework for CPTESs that incorporates integrated demand responses (IDRs) and EVs battery state-of-charge (SOC). In the ETN, a multiperiod traffic assignment model with time-shiftable traffic demands (MTA-TSTD) is established to optimize travelers’ routes and departure times while capturing traffic flow distribution. Meanwhile, effective path generation models with EVs battery SOC are proposed to optimize charging energy during driving and construct the effective path sets for MTA-TSTD. In the PDN, a multiperiod optimal power flow model with time-shiftable power demands (MOPF-TSPD) is formulated to schedule local generators and flexible power demands while calculating the power flow distribution. To enhance temporal and spatial coordination in CPTESs, a distributed coordinated operation model considering IDRs is proposed, aiming to optimize energy consumption, alleviate congestion, and ensure system safety. Finally, an adaptive effective path generation algorithm and an ETN–PDN interaction algorithm are devised to efficiently solve these models. Numerical results on two test systems validate the effectiveness of the proposed models and algorithms.

Energy-Saving Multi-Agent Deep Reinforcement Learning Algorithm for Drone Routing Problem.

With the rapid advancement of drone technology, the efficient distribution of drones has garnered significant attention. Central to this discourse is the energy consumption of drones, a critical metric for assessing energy-efficient distribution strategies. Accordingly, this study delves into the energy consumption factors affecting drone distribution. A primary challenge in drone distribution lies in devising optimal, energy-efficient routes for drones. However, traditional routing algorithms, predominantly heuristic-based, exhibit certain limitations. These algorithms often rely on heuristic rules and expert knowledge, which can constrain their ability to escape local optima. Motivated by these shortcomings, we propose a novel multi-agent deep reinforcement learning algorithm that integrates a drone energy consumption model, namely EMADRL. The EMADRL algorithm first formulates the drone routing problem within a multi-agent reinforcement learning framework. It subsequently designs a strategy network model comprising multiple agent networks, tailored to address the node adjacency and masking complexities typical of multi-depot vehicle routing problem. Training utilizes strategy gradient algorithms and attention mechanisms. Furthermore, local and sampling search strategies are introduced to enhance solution quality. Extensive experimentation demonstrates that EMADRL consistently achieves high-quality solutions swiftly. A comparative analysis against contemporary algorithms reveals EMADRL's superior energy efficiency, with average energy savings of 5.96% and maximum savings reaching 12.45%. Thus, this approach offers a promising new avenue for optimizing energy consumption in last-mile distribution scenarios.

IoT-based framework for optimizing energy efficiency and reliability in acoustic sensor networks using mobile sinks

In today’s world, there is an increasing demand for environmental monitoring, surveillance, and oceanographic research, which poses challenges in improving energy efficiency and data transfer reliability in Acoustic Sensor Networks. Existing methods face hurdles due to limited energy resources and unreliable data transmission. We propose a Reliable and Energy-Efficient Framework with Sink Mobility (REEFSM) to address these issues. This framework optimizes energy consumption and enhances data reliability by incorporating advanced energy management strategies such as adaptive duty cycling and efficient data transmission mechanisms by minimizing forwarding nodes. Simulation results demonstrate that REEFSM reduces energy consumption by up to 43% and increases data reliability by 35% compared to protocols like EERBCR and DEADS. REEFSM ensures zero dead nodes, minimizes packet drops, and maintains high data accuracy throughout the simulation. This research outcome provides a sustainable and reliable solution for energy-efficient data collection in underwater environments. The future research directions, including integrating autonomous path planning, energy harvesting, and machine learning techniques, hold great potential for further advancements in the field.

An investigation on energy-saving scheduling algorithm of wireless monitoring sensors in oil and gas pipeline networks

With the rapid development of the oil and gas industry, monitoring the safety and efficiency of pipeline networks has become particularly important. In this context, Wireless Sensor Networks (WSNs) are widely used for monitoring oil and gas pipelines due to their flexible deployment and cost-effectiveness. However, since sensor nodes typically rely on limited battery power, extending the network’s lifecycle and improving energy utilization efficiency have become focal points of research. Therefore, this paper proposes an energy-saving scheduling algorithm based on transformer networks, aimed at optimizing energy consumption and data transmission efficiency of wireless monitoring sensors in oil and gas pipelines. Firstly, this study designs a deep learning-based Transformer model that learns from historical data on energy consumption patterns and environmental variables to predict the energy and data transmission needs of each sensor node. Secondly, based on the prediction results, this algorithm employs a dynamic scheduling strategy that automatically adjusts the sensor’s operational mode and communication frequency according to the node’s energy status and task urgency. Additionally, we have validated the effectiveness of the proposed algorithm through field tests and simulation experiments. According to the experimental results, our model has higher efficiency in energy saving. Compared with Convolutional Neural Networks, Recurrent Neural Networks and Graph Neural Networks, the total energy consumption of sensor networks under the model scheduling in this paper was reduced by 6.7%, 33.4% and 26.3%, respectively. Our algorithms improve the energy efficiency and stability of the monitoring system and provide important technical support for future intelligent pipeline monitoring systems. We hope this paper will inspire future scientific research in this field.

An energy-environment coupled simulation framework for multi-scale and multi-facet evaluation of data center

This paper focuses on analyzing the thermal environment and optimizing energy consumption in data centers, which has largely omitted from previous studies. The thermal environmental of data centers is simulated and analyzed by utilizing the established “server-rack-room” multi-scale heat transfer numerical model. Based on this, the coupling simulation model of thermal environment and energy consumption in data centers is proposed to explore the relationship between them, and the corresponding optimization strategy is put forward. The energy consumption simulated by energy-environment coupled model and non-coupled model can reach a discrepancy of over 30 %, which indicates that the thermal environment impacts the power consumption of the data center significantly. Besides, the effect of several operational parameters of air conditioning system on the thermal environment and energy consumption of data center is analyzed. Through the particle swarm optimization algorithm, the optimal system parameters, which meet the requirements of thermal environment and lead to the lowest energy consumption, are found for typical cities in different climatic regions. The recommended settings include the supply air temperature of about 24 ∼ 26 ℃, the air supply volume of 8 m3/s, and the temperature difference of about 3.5 ℃ between the supply air temperature and the supply chilled water temperature. Under the optimized parameter setting, the highest temperature of server decreases to below 71 ℃, and the energy saving rate is more than 6 %.

Intelligent Swarm: Concept, Design and Validation of Self-Organized UAVs Based on Leader–Followers Paradigm for Autonomous Mission Planning

Unmanned Aerial Vehicles (UAVs), commonly known as drones, are omnipresent and have grown in popularity due to their wide potential use in many civilian sectors. Equipped with sophisticated sensors and communication devices, drones can potentially form a multi-UAV system, also called an autonomous swarm, in which UAVs work together with little or no operator control. According to the complexity of the mission and coverage area, swarm operations require important considerations regarding the intelligence and self-organization of the UAVs. Factors including the types of drones, the communication protocol and architecture, task planning, consensus control, and many other swarm mobility considerations must be investigated. While several papers highlight the use cases for UAV swarms, there is a lack of research that addresses in depth the challenges posed by deploying an intelligent UAV swarm. Against this backdrop, we propose a computation framework of a self-organized swarm for autonomous and collaborative missions. The proposed approach is based on the Leader–Followers paradigm, which involves the distribution of ROS nodes among follower UAVs, while leaders perform supervision. Additionally, we have integrated background services that autonomously manage the complexities relating to task coordination, control policy, and failure management. In comparison with several research efforts, the proposed multi-UAV system is more autonomous and resilient since it can recover swiftly from system failure. It is also reliable and has been deployed on real UAVs for outdoor survey missions. This validates the applicability of the theoretical underpinnings of the proposed swarming concept. Experimental tests carried out as part of an area coverage mission with 6 quadcopters (2 leaders and 4 followers) reveal that the proposed swarming concept is very promising and inspiring for aerial vehicle technology. Compared with the conventional planning approach, the results are highly satisfactory, highlighting a significant gain in terms of flight time, and enabling missions to be achieved rapidly while optimizing energy consumption. This gives the advantage of exploring large areas without having to make frequent downtime to recharge and/or charge the batteries. This manuscript has the potential to be extremely useful for future research into the application of unmanned swarms for autonomous missions.

Optimising energy consumption in Nano‐cryptography: Quantum cellular automata‐based multiplexer/demultiplexer design

Optimising energy consumption in Nano‐cryptography: Quantum cellular automata‐based multiplexer/demultiplexer design

Indoor occupancy monitoring using environmental feature fusion and semi-supervised machine learning models

Smart buildings optimize energy consumption and occupant comfort through Heating, Ventilation and Air Conditioning, and lighting management. Nevertheless, large venues require data fusion techniques to improve analysis and forecasting. This study aims to evaluate the effectiveness of using different feature fusion techniques, environmental sensors, and semi-supervised learning to estimate indoor occupancy in a 230 m2 office. Using five Internet of Things devices measuring air temperature, relative humidity, and barometric pressure, data was collected for 99 days with 6800 entries (on average) and only 14% labeled. Eight feature selection methods were evaluated along with three supervised and two semi-supervised classification methods. Results indicate that the Chi-squared-based approach for feature fusion outperformed others. Similarly, the semi-supervised Self-Training model achieved better performance than the supervised methods. This research shows that combining semi-supervised learning and data fusion allows for estimating the occupancy level in large indoor spaces with high accuracy and low labeling costs. Highlights This study pioneers in exploring semi-supervised learning and distinct feature fusion methods for estimating indoor occupancy levels in a 230 m 2 open office using only Internet of Things (IoT) environmental sensors (air temperature, relative humidity, and barometric pressure). A comprehensive comparison of statistical methods, feature selection, and dimensionality reduction techniques are conducted to determine their ability to generate robust feature fusion sets. The feature fusion selected through the Chi-squared test stood out with a high accuracy F1-score (average of 0.95) and an average accuracy of 0.99. The Self-Training model reached the best performance from semi-supervised learning, with an average F1-Score of 0.90 and an average accuracy of 0.97, based on a dataset with a large proportion of unlabelled data (16,847 entries) and only 9367 labels. For supervised learning, Random Forest achieved a high accuracy (average of 0.98) and F1-score (average of 0.93) across various feature sets.

Coordinated Ship Welding with Optimal Lazy Robot Ratio and Energy Consumption via Reinforcement Learning

Ship welding is a crucial part of ship building, requiring higher levels of robot coordination and working efficiency than ever before. To this end, this paper studies the coordinated ship-welding task, which involves multi-robot welding of multiple weld lines consisting of synchronous ones to be executed by a pair of robots and normal ones that can be executed by one robot. To evaluate working efficiency, the objectives of optimal lazy robot ratio and energy consumption were considered, which are tackled by the proposed dynamic Kuhn–Munkres-based model-free policy gradient (DKM-MFPG) reinforcement learning algorithm. In DKM-MFPG, a dynamic Kuhn–Munkres (DKM) dispatcher is designed based on weld line and co-welding robot position information obtained by the wireless sensors, such that robots always have dispatched weld lines in real-time and the lazy robot ratio is 0. Simultaneously, a model-free policy gradient (MFPG) based on reinforcement learning is designed to achieve the energy-optimal motion control for all robots. The optimal lazy robot ratio of the DKM dispatcher and the network convergence of MFPG are theoretically analyzed. Furthermore, the performance of DKM-MFPG is simulated with variant settings of welding scenarios and compared with baseline optimization methods. Compared to the four baselines, DKM-MFPG owns a slight performance advantage within 1% on energy consumption and reduces the average lazy robot ratio by 11.30%, 10.99%, 8.27%, and 10.39%.

Application of FFBP neural network to the prediction of swirling flow

Since ancient times, man has been seeking to improve in the field of transportation and energy consumption, as the combination of optimal energy consumption and not polluting the environment in exchange for obtaining a better return made him constantly searching for optimal methods in the field of anxiety and industry based on combustion. The swirl or vortex flow is one of the most important discoveries of the last century seeking to develop combustion, as research is still focused on it in many aspects, whether in terms of the shape of the rotational areas it forms. This study aims to determine the extent to which artificial intelligence can predict the characteristics of the Swirl flow. The model adopted experimental data of the Swirl flow, both descriptive and positional data as inputs and horizontal, vertical, and kinetic energy as outputs for several positions within the combustion chamber. The results indicate that the model can capture the spatial characteristics of the vortex flow field and the combined Learning of the relationship between the input and output parameters. The results of the velocity density distribution and the position of the vortex center of the vortex flow field agree well with the experimental results. The generated prediction model has good prediction accuracy based on previously observed datasets and can reconstruct the vortex flow field. In addition, it can perform inductive prediction on a previously unseen dataset and has some generalization ability. Finally, this study suggests that many potential architectures have applications based on the induction prediction model created here.

Designing of Ecological Model Predictive Control Algorithm for Electric Trucks Platoons with Optimal Energy Consumption

Abstract In response to the problem of motor vehicle exhaust emissions pollution, the development of new energy in China’s truck industry will be an inevitable trend. At present, the penetration rate and market share of pure electric trucks are constantly increasing. The research on electric truck platoons has strong practical significance. This article focuses on the ecological control strategy of pure electric truck platoons. Firstly, a platoon model of four electric trucks is established in Trucksim. The leading vehicle designs an ecological optimal speed dynamic programming algorithm, and in terms of following vehicle tracking control, the battery SOC state motor speed is considered to be improved, Designed an ecological model predictive control algorithm for electric truck platoon with optimal energy consumption. Using real road slope data in the mining area, a Trucksim/Matlab/Simulink joint simulation was conducted. The simulation results showed that compared with traditional constant speed cruise control, the proposed ecological predictive cruise control algorithm for pure electric commercial vehicle platoons saved 9.2577% of the overall battery SOC.