Energy Consumption Requirements Research Articles

Path optimization of underwater vehicles in multi-obstacle environment based on energy constraint strategy

Purpose To solve the problem that the underwater vehicles is difficult to turn and exit in a small range in the face of complex marine environment such as concave and ring under the limitation of its limitation of its shape and maximum steering angle, this paper aims to propose an improved ant colony algorithm based on trap filling strategy and energy consumption constraint strategy. Design/methodology/approach Firstly, on the basis of searching the global path, the disturbed terrain was pre-filled in the complex marine environments. Based on the energy constraint strategy, the ant colony algorithm was improved to make the search path of the underwater vehicle meet the requirements of the lowest energy consumption and the shortest path in the complex obstacle environment. Findings The simulation results showed that the modified grid environment diagram effectively reduced the redundancy search and improved the optimization efficiency. Aiming at the problem of “the shortest distance is not the lowest energy consumption” in the traditional path optimization algorithm, the energy consumption level was reduced by 26.41% after increasing the energy consumption constraint, although the path length and the number of inflection points were slightly higher than the shortest path constraint, which was more conducive to the navigation of underwater vehicles. Originality/value The method proposed in this paper is not only suitable for trajectory planning of underwater robots but also suitable for trajectory planning of land robots.

Impact of electrode arrangement and electrical connections on the power generation of ceramic membrane microbial fuel cell

AbstractSome of the most popular technologies for wastewater sanitation, still face serious limitations related to high energy consumption requirements. In this context, microbial fuel cells (MFCs) constitute a promising approach since they do not require aeration and produce electricity at the same time. Limitations for these devices, however, are related to the cost of the constituents and the functionality of the arrangement. In this work, a semi‐cylindrical ceramic MFC was designed and constructed using a low‐cost commercial ceramic handcraft as a membrane, carbon felt, carbon cloth, and carbon cloth/activated carbon in different arrangements for the anode and cathode components. The best results were obtained using carbon felt as an anode and a cathodic zone built with carbon felt in which void regions were filled with activated carbon. This arrangement produced 85 mWm−2 for each cell. Evaluating the performance of the MFC in a modular system with eight cells using a different number of separations inside the module and different electrical connections, resulting in a 4‐compartment module that produced 90 mWm−2 with one single module and 95 mWm−2 with a serial arrangement of two modules.

Simultaneous thermal zoning and demand control ventilation

Heating, Ventilation, and Air Conditioning systems play a crucial role in controlling indoor air conditions, including temperature, humidity, and CO₂ concentration, to ensure human comfort and safety. Thermal zoning and demand-controlled ventilation have demonstrated enhancements in comfort and energy efficiency. This study aims to develop a mathematical model for a Heating, Ventilation, and Air Conditioning system employing an estimated state-feedback control algorithm to optimize indoor comfort and energy savings while minimizing sensor requirements. The model encompasses dynamic equations governing CO₂ concentration and temperature in a five-room building. Temperature regulation is achieved by adjusting the airflow from a cooling unit with dampers, while CO₂ levels are managed by controlling the proportion of fresh air intake. To showcase the advantages of the proposed control approach, comparisons were made with alternative methods based on response performance, energy consumption, and sensor requirements. The estimated state-feedback control outperformed other approaches, requiring only one temperature and one CO₂ sensor at the mixed return. Additional occupancy sensors in each room are not necessary if an Extended Kalman filter is utilized. This technique exhibits scalability, adaptable to varying output variables and room configurations, and holds promise for broader implementation in the HVAC industry. By optimizing energy consumption and maintaining indoor air quality, this approach aligns with established sustainability metrics, promoting environmental responsibility. Compliance with health and safety audit standards ensures the fulfillment of social targets, while adherence to regulations and laws ensures governance targets are met.

Throughput and coverage based Base Station–Relay Station deployment for 5G cellular network

SummaryFifth generation cellular networks have high data rates and connectivity demands. The relaying approaches are widely used in cellular networks to improve coverage, user throughput, and capacity at low cost. The increasing data requirements of the mobile network increase energy consumption. Energy efficiency‐based approaches also impact network coverage and throughput. Therefore, developing an energy‐efficient network with optimal coverage and throughput is considered a challenging issue. It can be resolved with optimal deployment of Base Station (BS), Relay Station (RS), and minimizing power consumption. In this research, a joint clustering‐based deployment technique is proposed for BS–RS deployment by considering the network throughput and coverage ratio. After enabling optimal BS–RS deployment, the network traffic is estimated with an On‐demand real‐time traffic estimation framework (ODTE). It estimates user association values using Beacon frames. In addition, the Relay Assisted Base station Power Scheduling (RABPS) approach to minimize power consumption is included. Based on Erlang's probability, the RABPS algorithm is enhanced with sleep mode activation during off‐peak hours. The proposed 5G network ensures uninterrupted connectivity and energy efficiency with varying time slots of power scheduling. The throughput performance of the clustering‐based approach is increased with a coverage ratio of 88%. The simulation results show the superiority of the proposed 5G BS–RS deployment and power scheduling in terms of throughput, coverage ratio, and power consumption.

Interactions between bacteria and microalgae in microalgal-bacterial symbiotic wastewater treatment systems: mechanisms and influencing factors

Microalgal-bacterial symbiotic wastewater treatment systems (MBSWTSs) have received widespread attention due to their capacity to achieve high pollutant removal efficiency during wastewater treatment, with low energy consumption requirements, efficient carbon sequestration, and wastewater resource utilization. This paper provides an overview of the treatment performance and current research status of MBSWTSs, including a detailed summary of the mechanisms of nitrogen, phosphorus, and carbon removal by MBSWTSs and the interactions between bacteria and microalgae. In particular, this review focuses on the influence of operational parameters on the regulation of the symbiotic system, such as the microalgal:bacterial ratio, N:P ratio, external carbon source, dissolved oxygen concentration, aeration mode, and light availability. Among these factors, the microalgal:bacterial ratio, carbon source, and light availability have an important influence on microalgal-bacterial competition, as well as the trophic mode of the system, biomass production, and the capacity for the process to be practically applied on a large scale. MBSWTSs still have some challenging aspects that have hindered their development and application, such as the unknown mechanism of microalgal-bacterial co-metabolism, limited previous practical applications, algal contamination, and harvesting difficulties. To overcome these challenges, future research requires a multidisciplinary approach, incorporating life sciences, material science, and other disciplines. Comprehensive research should be conducted on the metabolic mechanisms of MBSWTSs, the optimization of process performance and waste resource utilization, providing a theoretical and practical foundation for the practical application of MBSWTSs.

MADRLOM: A Computation offloading mechanism for software-defined cloud-edge computing power network

Cloud-edge computing power network often exhibits complex and heterogeneous structures, posing several challenges to computation offloading that significantly impact network performance and the efficient utilization of computation resources. In this paper, we propose a cloud-edge computing power network architecture that efficiently integrates cloud and edge computing resources into a single network system using software-defined networking technology to support upper-layer business applications. In this context, existing computation offloading methods struggle with issues like users' personalized requirements for latency and energy consumption, as well as the inability to adapt to dynamically complex environments. To overcome these challenges, we introduce a computation offloading mechanism, MADRLOM, focusing on the network's heterogeneity and modeling the computation offloading problem at the cloud-edge end. We formalize the offloading problem as a Markov process and employ a multi-agent deep reinforcement learning algorithm based on priority experience replay sampling to address the planning problem of computation offloading, allowing user equipment to continuously optimize offloading strategies in response to environmental changes and achieve rational resource allocation. Through dynamic offloading strategies, computation tasks can be intelligently allocated to appropriate computing nodes, thereby achieving optimal resource utilization. We utilized the Mininet simulation platform to construct the experimental environment for the software-defined cloud-edge computing power network and compared it with several representative computation offloading strategies. The experimental results demonstrate that the MADRLOM significantly reduces the total system overhead in the software-defined cloud-edge computing network and shows excellent adaptability to environmental changes.

Optimization-Based Control for a Large-Scale Electrical Vertical Take-Off and Landing during an Aircraft’s Vertical Take-Off and Landing Phase with Variable-Pitch Propellers

The UAV industry has witnessed an unprecedented boom in recent years. Among various kinds of UAV platforms, the vertical take-off and landing (VTOL) aircraft with fixed-wing configurations has received more and more attention due to its flexibility and long-distance flying abilities. However, due to the fact that the advance ratio of regular propeller systems during the cruise phase is significantly higher than that during the VTOL phase, a variable-pitch propeller system is proposed and designed which can be applied without additional propulsion mechanisms during both flying stages. Thus, a VTOL aircraft platform is proposed based on the propulsion system constructed of variable-pitch propellers, and appropriate control manners are precisely analyzed, especially during its VTOL phase. As a basic propulsion system, a nonlinear model for variable-pitch propellers is constructed, and an optimization-based control allocation module is developed because of its multi-solution and high-order characteristics. Finally, the objective function is designed according to the stability and energy consumption requirements. Simulation experiments demonstrate that the proposed controller is able to lower energy consumption and maintain the stability of the aircraft while tracking aggressive trajectories for large-scale VTOLs with noises at the same time.

Real-Time Human Activity Recognition on Embedded Equipment: A Comparative Study

As living standards improve, the growing demand for energy, comfort, and health monitoring drives the increased importance of innovative solutions. Real-time recognition of human activities (HAR) in smart homes is of significant relevance, offering varied applications to improve the quality of life of fragile individuals. These applications include facilitating autonomy at home for vulnerable people, early detection of deviations or disruptions in lifestyle habits, and immediate alerting in the event of critical situations. The first objective of this work is to develop a real-time HAR algorithm in embedded equipment. The proposed approach incorporates the event dynamic windowing based on space-temporal correlation and the knowledge of activity trigger sensors to recognize activities in the case of a record of new events. The second objective is to approach the HAR task from the perspective of edge computing. In concrete terms, this involves implementing a HAR algorithm in a “home box”, a low-power, low-cost computer, while guaranteeing performance in terms of accuracy and processing time. To achieve this goal, a HAR algorithm was first developed to perform these recognition tasks in real-time. Then, the proposed algorithm is ported on three hardware architectures to be compared: (i) a NUCLEO-H753ZI microcontroller from ST-Microelectronics using two programming languages, C language and MicroPython; (ii) an ESP32 microcontroller, often used for smart-home devices; and (iii) a Raspberry-PI, optimizing it to maintain accuracy of classification of activities with a requirement of processing time, memory resources, and energy consumption. The experimental results show that the proposed algorithm can be effectively implemented on a constrained resource hardware architecture. This could allow the design of an embedded system for real-time human activity recognition.

Delay-sensitive energy-efficient routing scheme for the Wireless Sensor Network with path-constrained mobile sink

The use of mobile sinks (MSs) can address several challenges for traditional wireless sensor networks (WSNs), such as energy holes, meanwhile it also introduces some new issues. Especially, for the path constraint MS, the fixed trajectory and uncontrollable moving speed results in significant delay and even buffer overflows. Existing research mainly focused on delay-tolerant applications which are unsuitable for delay-sensitive data or adopted the greedy policy to collect data which is inefficient in energy. To minimize the energy consumption and guarantee delay requirement simultaneously, we propose a delay-sensitive and energy-efficient routing scheme. Firstly, to obtain the MS position, we propose a K-nearest neighbor-based prediction model, which can reduce the energy and delay overhead of the location update. Based on the prediction result, an efficient cluster routing scheme is designed. To ensure both search speed and the optimality of the cluster head (CH), we improve the Reed-Deer algorithm (RDA) as IRDA, and use the IRDA to find out CHs. To remove the redundancy of the CH, a removal method is proposed. After constructing clusters, a method is used to determine the next-hop of the CH by using the predication position of the MS, the received timestamp and the delay requirement of the data. A fault tolerance mechanism is supplied to deal the predication error and other unexpected situations. By applying IRDA and the prediction model, the proposed routing scheme avoids the frequent construction of new routes, reduces the hops of the end-to-end path, minimizes network energy consumption, and ensures timely data transmission. The comparison results indicates that the proposed protocol can effectively improve the network performance while guarantee delay requirement. Especially in realistic scenario, the proposed algorithm can effectively reduce energy consumption and the end-to-end delay and improve the packet delivery ratio.

Edge collaborative caching solution based on improved NSGA II algorithm in Internet of Vehicles

With the rapid development of the Internet of Vehicles technology and the continuous advancement of intelligent transportation, vehicle network traffic is growing exponentially. Traditional caching solutions transmit content through the core network, causing significant latency. A vehicle-cloud collaborative edge caching network architecture was proposed to address this type of problem. Using this architecture, both the on-board terminals and cloud/edge servers could provide computing services. A communication model, caching model, latency model, energy consumption model, load model and multi-objective optimization problem model were designed. The edge collaborative caching solution based on the improved NSGA II algorithm was proposed. By caching some services to edge nodes and nearby vehicles, reducing content access latency and improving resource utilization. The results indicate that the caching solution outperforms the comparison scheme in terms of the comprehensive costs of latency, energy consumption, and load balancing in simulation experiments. It can meet the caching requirements of low latency and low energy consumption for in-vehicle applications, laying a solid foundation for achieving more efficient and reliable connected vehicles and autonomous driving.

Metasurface computing components that support dual channel parallel processing and provide full type logic gate options

The logic gate components based on electromagnetic waves are crucial for building a digital computing platform with batch computing and low-cost energy consumption requirements. However, most of the reported logic gates have only one computational channel and have a single function. In order to improve the channel capacity and versatility of logic gates, we design a simple and compact graphene metasurface for dual channel parallel computing, with each channel's function being switchable across all types of logic functions. In the design, we combine the advantages of reconfigurable metasurfaces and polarization sensitive metasurfaces. Based on the polarization sensitive characteristics, we integrate two polarization logic gates into a single device. These two polarization logic gates can work simultaneously, and the crosstalk between them is 0. Based on the reconfigurable feature, the functions of both logic gates can be freely selected from NOT, AND, NAND, OR, NOR, XOR and XNOR. So, this component can achieve simultaneous operation of any two logical functions, and there are 49 combinations available. Our design philosophy provides inspiration for achieving ultra fast and high-density integrated signal processing.

Deep Reinforcement Learning-Based Energy Consumption Optimization for Peer-to-Peer (P2P) Communication in Wireless Sensor Networks.

The fast development of the sensors in the wireless sensor networks (WSN) brings a big challenge of low energy consumption requirements, and Peer-to-peer (P2P) communication becomes the important way to break this bottleneck. However, the interference caused by different sensors sharing the spectrum and the power limitations seriously constrains the improvement of WSN. Therefore, in this paper, we proposed a deep reinforcement learning-based energy consumption optimization for P2P communication in WSN. Specifically, P2P sensors (PUs) are considered agents to share the spectrum of authorized sensors (AUs). An authorized sensor has permission to access specific data or systems, while a P2P sensor directly communicates with other sensors without needing a central server. One involves permission, the other is direct communication between sensors. Each agent can control the power and select the resources to avoid interference. Moreover, we use a double deep Q network (DDQN) algorithm to help the agent learn more detailed features of the interference. Simulation results show that the proposed algorithm can obtain a higher performance than the deep Q network scheme and the traditional algorithm, which can effectively lower the energy consumption for P2P communication in WSN.

Hydrolysate derived from sugarcane bagasse by hydrothermal and enzymatic treatment as an external carbon source for wastewater nitrogen removal

Research on the preparation of denitrifying carbon sources from agricultural wastes persists with problems of environmentally unfriendly pretreatment methods and unstable carbon release. In this study, a hydrolysis method without secondary waste liquid generation was used and the obtained hydrolysate was applied as a denitrifying carbon source. Firstly, the optimum hydrothermal condition of sugarcane bagasse (SCB) was explored, which showed a higher reducing sugar concentration (13.68 g/L) and COD concentration (20,691.67 mg/L) at 200 °C, 40 min with relatively low energy consumption (EC) requirement (1.81 kW·h). Afterward, all three bio-additives (tea saponins, rhamnolipids, and whey proteins) were found to enhance enzymatic hydrolysis, with whey proteins being the most effective, increasing the reducing sugar and COD concentrations by 29.40 % and 17.51 %, respectively. The enzymatic reaction was optimized using the following conditions: whey protein concentration of 0.2 %, pH 5.0, temperature 50 °C, cellulase to the β-glucosidase ratio of 4:1, and total enzyme amount of 30 mg. Moreover, the nitrogen removal effect of SCB hydrolysate as a carbon source was determined, and the average TN removal was 98.23 % at C/N of 5 and HRT of 8. Finally, the microbial community was analyzed, and the dominant phylum was denitrifying bacteria (Proteobacteria), and organic matter utilizing bacteria (Bacteroidetes, Acidobacteriota, and Patescibacteria), and the relative abundance of the latter both improved with increasing C/N, which further demonstrated the feasibility of SCB hydrolysate as a carbon source. This research provides a reference for the application of agricultural waste hydrolysate in wastewater nitrogen removal.

The Optimal Size of a Heterogeneous Air Taxi Fleet in Advanced Air Mobility: A Traffic Demand and Flight Scheduling Approach

Introducing Advanced Air Mobility (AAM) as a novel transportation mode poses unique challenges due to limited practical and empirical data. One of these challenges involves accurately estimating future passenger demand and the required number of air taxis, given uncertainties in modal shift dynamics, induced traffic patterns, and long-term price elasticity. In our study, we use mobility data obtained from a Dresden traffic survey and modal shift rates to estimate the demand for AAM air taxi operations for this regional use case. We organize these operations into an air taxi rotation schedule using a Mixed Integer Linear Programming (MILP) optimization model and set a tolerance for slight deviations from the requested arrival times for higher productivity. The resulting schedule aids in determining the AAM fleet size while accounting for flight performance, energy consumption, and battery charging requirements tailored to three distinct types of air taxi fleets. According to our case study, the methodology produces feasible and high-quality air taxi flight rotations within an efficient computational time of 1.5 h. The approach provides extensive insights into air taxi utilization, charging durations at various locations, and assists in fleet planning that adapts to varying, potentially uncertain, traffic demands. Our findings reveal an average productivity of 12 trips per day per air taxi, covering distances from 13 to 99 km. These outcomes contribute to a sustainable, business-focused implementation of AAM while highlighting the interaction between operational parameters and overall system performance and contributing to vertiport capacity considerations.

Energy and water budget of rice under different establishment methods

A field study aimed to investigate the influence of different rice establishment methods on energy and water budget of rice was conducted during kharif season of 2022. The experiment was carried out using a randomized block design with three replications. Seven treatments were examined, including three transplanting methods viz., SRI, mechanical transplanting and conventional transplanting, and four direct seeded rice (DSR) methods viz., wet DSR (drum seeding), wet DSR (broadcasting), dry converted wet rice (broadcasting) and dry converted wet rice (line sowing). Results revealed that chemical fertilizers, irrigation water, land preparation and seeding or transplanting operations constituted ~80% of the input energy in all rice establishment methods. Among the rice establishment methods, conventional transplanting exhibited the highest input energy of 21723.9 MJ ha-1, while dry converted wet rice (line sowing) recorded the lower input energy of 18394.3 MJ ha-1. In terms of output energy, wet DSR (drum seeding) recorded the highest value of 141781.1 MJ ha-1, whereas dry converted wet rice (broadcasting) registered the lowest value of 77223.2 MJ ha-1. The output energy of wet DSR (drum seeding) was comparable to that of SRI and dry converted wet rice (line sowing). Similarly, dry converted wet rice (line sowing) exhibited higher irrigation water productivity (0.73 kg m-3) and total water productivity (0.30 kg m-3) with a reduced water requirement of 630 mm. Compared to conventional transplanting, input energy consumption and irrigation water requirement was lower by 18.1% and 51.9% in dry converted wet rice (line sowing), 17.4% and 51.9% in dry converted wet rice (broadcasting), 16.9%, 36.7% in SRI and 9.13%, 27.6% in wet DSR (drum seeding) respectively. Thus, the adoption of wet DSR (drum seeding), SRI and dry converted wet rice (line sowing) is recommended for energy and water efficient rice production in Telangana.

Efficient Removal of Chromium and Lead from Tanneries Effluent of Korangi Industrial Area Karachi Using Rotating Disk Mesh as Anode Electrode Electrocoagulation

The aim of this study was to examine the best electrode design for the electrocoagulation process with the best removal performance, reduced passivation on the electrode surface, and lower energy consumption requirements for removing Chromium (Cr) and Lead (Pb) from leather tannery effluent. Three different electrodes were compared: non-rotating disk electrode (NRDE), rotating disk electrode (RDE) and rotating disk mesh electrode (RDME). All electrodes were used to observe a reduction in passivation on the electrode surface and its effects on the removal performance of Cr and Pb. The material used for the electrodes was iron. The maximum removal efficiency obtained was Cr = 87.9% and Pb = 97.5% under the following operating conditions: pH = 7, treatment time = 90 min, current density = 6.57 mA/cm², and RPM = 80. The results show that the electrical energy requirement for treating chromium and lead using RDME was 4.5 kWh/m³, which was found to be lower than the energy requirement observed in various other studies for treating tannery effluent. According to the results, RDME shows the highest removal performance with lower specific energy consumption compared to NRDE and RDE. RDME can be efficiently employed at a larger scale for treating leather tannery effluent.

Research Progress of Acoustic Energy Harvesters Based on Nanogenerators

In the wake of the rapid development of the Internet of Things (IoT) and artificial intelligence (AI) technology, a huge amount of wireless sensing nodes (WSNs) is urgently in demand in all aspects of daily production and living, such as smart cities, smart transportation, and intelligent monitoring. However, it has become one of the crucial challenges in the development of IoT technology to fulfill the requirements of energy consumption for enormous amounts of WSNs. Therefore, it not only possesses great potential to realize in situ power supply of WSNs by harvesting environmental energy but also can address the problems of durability, maintenance, and cost that exist in battery power supply. In particular, acoustic energy is ubiquitous in the environment but not efficiently utilized. Meanwhile, piezoelectric nanogenerators (PENG) and triboelectric nanogenerators (TENG) are capable of accomplishing efficient conversion of broadband, high entropy, and weak energy as the new energy conversion technology. Therefore, the basic principle of acoustic energy harvester based on nanogenerators (NGs) is firstly discussed. Then, the advances of acoustic energy harvesters based on NGs in the viewpoint of acoustic energy resonator structures are systematically reviewed for the first time. This review not only covers the working mechanism, structural design, and application scenarios of acoustic energy harvesters but also explores the advances in ultrasonic energy harvesting based on NGs. Finally, existing challenges and prospects for future development are systematically discussed, which will facilitate the development of acoustic energy harvesting based on NGs.

Kelp derived hierarchically porous carbon aerogels with ultrahigh surface area for high-energy-density supercapacitor in aqueous electrolyte

Nowadays, obtaining porous carbons from abundant and sustainable biomass as electrode materials for energy storage has been attracting tremendous attentions, to meet the requirement of energy consumption and environmental remediation. In this study, hierarchically porous carbon aerogels (HPCAs) with desirable micro-, meso- and macropores were obtained via decoloration, freeze-drying and subsequent carbonizing-activating process using kelp as sustainable, low-cost and massive precursors by adjusting the activation temperature. The effect of temperature on the pore structures and electrochemical performance of HPCAs were investigated in depth. At 800 °C, the as-obtained HPCA-800 had an appropriate combination of specific surface area and hierarchically porous networks, rendering the optimal capability of electrolyte ion's accumulation and transportation. The HPCA-800 exhibited highest specific capacitance of 278 F g−1 in 6 M KOH and 1965 F g−1 and 1 M KOH + 0.06 M K3Fe (CN)6 electrolyte, respectively, which are superior to those of the reported carbon aerogels. Moreover, an ultrahigh energy density of 93 W h kg−1 at high power density of 2109 W kg−1 was achieved in the redox electrolyte. The energy storage mechanism was also elucidated. The HPCAs from kelp should be promising and competitive electrode materials for constructing high-energy-density supercapacitor, owing to their outstanding supercapacitor performance together with the superior advantages of raw materials including abundance, low-cost and renewability.

Cooling and anti-friction effects of vortex tube cryogenic minimum quantity lubrication in milling of 3D printed iron-nickel alloy

3D printed iron-nickel alloy is a promising engineering material with superior properties, which is widely applied in aerospace, nuclear and petrochemical industries. However, it is rather a difficult-to-cut material due to the insufficient cooling and lubrication, and the cost of tool usage is extremely high even with TiAlN coated or TiCN coated tools. Although some scholars have attempted to conduct the eco-friendly VT-cMQL (Vortex Tube-cryogenic Minimum Quantity Lubrication) system for machining iron-nickel alloy, this outdated VT-cMQL in Parallel mode still has huge shortcomings in terms of lubrication and anti-friction effects, energy consumption, and minimum hardware requirements. To solve this problem, this work proposed a new series VT-cMQL system in forms of Tandem I and Tandem II. The results show that the structure of new VT-cMQL system in Tandem I mode is simple, that only requires 1 air filter, 1 air compressor, 2 sections of pipe, and no T-joint is needed. The lowest cold flow temperature is −4.7 °C when the spray pressure, spraying distance, pipe length, and oil supply are 1.5 bar, 10 mm, 30 cm, and 45 ml/h, respectively. The cooling and anti-friction effect of VT-cMQL has little impact on milling tool's adhesion or oxidation, the main contribution is to further reduce abrasive wear. From the perspective of iron-nickel alloy industrial production, the new proposed VT-cMQL system can significantly reduce hardware configuration and improve the utilization of cryogenic compressed air.

Increased Dynamic Drivetrain Performance by Implementing a Modular Design with Decentralized Control Architecture

This paper assesses the energy consumption, control performance, and application-specific functional requirements of a modular drivetrain in comparison to a benchmark drivetrain. A decentralised control architecture has been developed and validated using mechanical plant models. Simscape models have been validated with data from an experimental setup including an equivalent modular and benchmark drivetrain. In addition, the control strategy has been implemented and validated on the experimental setup. The results prove the ability of the control strategy to synchronize the motion of the different sliders, resulting in crank position tracking errors below 0.032 radians on the setup. The model and experimental data show an increased performance of the modular drivetrain compared to the benchmark drivetrain in terms of energy consumption, control performance, and functional requirements. The modular drivetrain is especially advantageous for machines running highly dynamic motion profiles due to the reduced inertia. For such motion profiles, an increased position tracking of up to 84% has been measured. In addition, it is shown that the modular drivetrain root mean square (RMS) torque is reduced with 32% compared to the benchmark drivetrain. However, these mechanical energy savings are partly counteracted by the higher motor losses seen in the modular drivetrain, resulting in potential electrical energy savings of around 29%.