Increase In Power Consumption Research Articles

A novel pulse-jet cleaning method to effectively extend the lifetime of pleated filter and reduce dust emission

To solve the problem of poor cleaning effect of pleated filter, this study explored in detail the impact of various cleaning method on the filtration and cleaning performance of the filter media in clean-on-demand mode. Furthermore, the clogging mechanism of the filter and the deposition structure of dust on the filter media were analyzed based on the effective filtration area ratio and true resistance coefficient calculated by the proposed model. The results demonstrated that for the constant pulse-jet cleaning pressure (PJCP), increasing PJCP was more conducive to removing dust deposited on the filter media and improving regeneration efficiency, although higher PJCP increased power consumption. Different from the thick primary dust layer formed on the filter surface under the constant PJCP of 0.3 MPa, the dust was mainly deposited inside the filter media under the constant PJCP of 0.5 and 0.7 MPa. In contrast, stepwise increasing PJCP was more effective in cleaning and regenerating the filter media, which was reflected in further extending the filter lifetime, increasing the processing air volume and reducing the dust emission concentration with acceptable energy consumption. Moreover, the filter media mainly clogged itself and formed a medium thickness primary dust layer on the filter surface.

Design of Low Power Ternary Logic Encoder and ADC using CNTFET

Ternary logic has received substantial attention over the past decade due to its compensations of smaller chip area and interconnection compared to outmoded binary logic. Carbon Nanotube Field Effect Transistor (CNTFET) technology is widely used for ternary logic implementation due to its versatile threshold voltages. The increased power consumption in current designs utilizing CNFETs, as linked to binary logic, is attributed to elevated static power dissipation within the design. This work recommends alternative triple encoder designs with a focus on reducing the consumption of power. The model uses an additional Vdd/2 supply voltage to decrease static power dissipation and encoder power delay. Cadence virtuoso-based circuit simulations are implemented for the proposed encoder design.

The influence of wall effects on self-propelled performance of brown trout swimming

When benthic fish engage in predation, they often swim near the riverbank or close to hard rocks, where they are subjected to the combined effects of side and benthic walls. This study focuses on the brown trout and employs a three-dimensional numerical model to simulate the process of brown trout accelerating from a stationary state to a cruising state under the influence of wall effects. A self-developed subroutine algorithm is applied to solve the various hydrodynamic parameters of brown trout swimming. By varying the distance between the fish's center of gravity and the wall, this study explores the self-propelled performance and efficiency of fish swimming affected by the sidewall as well as the combined influence of the side and benthic walls. This study also reveals the hydrodynamic mechanism of wall effects that impact the performance of fish swimming in the body/caudal fin (BCF) mode. The results demonstrate that when the distance is less than 0.5 times the body length of the fish, swimming near the sidewall can enhance speed and thrust, but the swimming efficiency will reduce. Closer proximity to the benthic wall leads to increased power consumption and decreased efficiency, which is disadvantageous for fish swimming. The findings of this study reveal the unstable wall effects experienced by fish and offer insights for designing biomimetic underwater vehicles that leverage wall effects as well as for creating habitats that support fish swimming in the BCF mode.

Multiple hot spot cooling with flow boiling of HFE-7000 in a multichannel pin fin heat sink

Electronics cooling applications require a high cooling performance due to increasing power consumption. Phase change heat transfer is a popular approach for offering high heat flux cooling. Controlling temperature distribution and mitigating boiling instabilities under high heat flux conditions present a significant challenge. While numerous studies investigated the effect of pin fins in a minichannel with single hotspots on boiling enhancement, few research studies are available for minichannels with multiple segmented hotspots. This study's primary objective is to dissipate high heat (1.4 kW) using flow boiling of a dielectric fluid, namely HFE-7000, which has lower thermophysical properties than water, to achieve a uniform temperature distribution, and to suppress boiling instabilities. The boiling performance of a pin fin multichannel heat sink with dimensions of 3 × 14 × 300 mm (H × W × L) subjected to 8 hotspots with uniform heating was investigated in this regard. Flow boiling experiments were performed over a wide range of input heat power from ∼0.1 to ∼1.4 kW with a coolant mass flux of 500 kg/m2 s in the three parallel minichannels having staggered elliptical pin fins with no tip clearance on each hotspot and distribution pins at the inlet. High-speed visualization was used to relate the heat transfer coefficient variations along the channel to flow patterns. The heat transfer performance and flow regimes at different locations were obtained and discussed. Thanks to the mixing effect of elliptical micro pin fins with optimum geometrical parameters along the heat sink, the presence of distribution pin fins at the inlet for suppressing boiling instabilities and early transition to stable annular flow, the temperature uniformity (∼2.3 °C, temperature difference between the inlet and outlet hotspots) could be achieved at the highest heat input. Heat transfer coefficient reduction upto 27 % was reported along the channel when comparing inlet (1st) and outlet (8th) hotspots.

UGV에서 효율적인 노면 모니터링을 위한 퓨전 센서 시스템

Road surface monitoring is essential for maintaining road environment safety through managing risk factors like rutting and crack detection. Using autonomous driving-based UGVs with high-performance 2D laser sensors enables more precise measurements. However, the increased energy consumption of these sensors is limited by constrained battery capacity. In this paper, we propose a fusion sensor system for efficient surface monitoring with UGVs. The proposed system combines color information from cameras and depth information from line laser sensors to accurately detect surface displacement. Furthermore, a dynamic sampling algorithm is applied to control the scanning frequency of line laser sensors based on the detection status of monitoring targets using camera sensors, reducing unnecessary energy consumption. A power consumption model of the fusion sensor system analyzes its energy efficiency considering various crack distributions and sensor characteristics in different mission environments. Performance analysis demonstrates that setting the power consumption of the line laser sensor to twice that of the saving state when in the active state increases power consumption efficiency by 13.3% compared to fixed sampling under the condition of =10, =10.

Extended EDAS Analysis for Multi-Criteria Decision-Making Based on Distributed Generation (DG) Technologies System

Recently, there has been a growing interest in distributed generation (DG) technologies, driven by various factors such as fuel price uncertainties, environmental constraints, and increasing power consumption along with transmission capacity shortages, in modern power systems. DG, which involves utilizing clean and renewable energy sources for power generation within the distribution system, has gained significant attention globally. Many developing countries, including Libya, are considering the adoption of DG technologies as part of their energy system expansion plans. Libya, located in North Africa and characterized by vast desert lands, has abundant solar radiation, making solar energy a promising and sustainable source of power. However, despite this energy potential, the southern part of Libya faces frequent power outages. In order to effectively maintain service quality, it is essential to conduct quantitative evaluation of wireless sensor networks. the evaluation of wireless sensor networks involves addressing the multiple attribute group decision-making (MAGDM) problem. To tackle the challenges of MAGDM, an extension of the classical EDAS (Evaluation based on Distance from Average Solution) method is proposed in this paper. The proposed method incorporates interval-valued intuitionistic fuzzy sets (IVIFSs), which provide a more flexible and comprehensive representation of uncertainty, to handle the complexities of MAGDM. The paper begins with a brief review of essential concepts related to IVIFSs. Then, the weights of attributes are determined using the CRITIC method. Subsequently, the IVIF-EDAS method is established by integrating the EDAS method with IVIFSs, and all the calculation procedures are described.

Energy efficient cluster routing protocol for wireless sensor networks using hybrid metaheuristic approache’s

The development of wireless sensor networks (WSNs) has been particularly notable in the context of smart computing, where various application areas have been identified. These networks consist of self-configured, small sensor nodes with battery power. However, sensors have limited energy and resources at their disposal. When unbalanced nodes exist within the network, it adversely affects the network’s lifetime due to increased power consumption. Designing efficient routing for wireless sensor networks remains a challenging task, particularly in terms of energy efficiency. Optimal solutions to address these challenges involve reducing node energy consumption through the implementation of clustering techniques. The current clustering schemes do not take into account node energy balancing, node density, and scalability when selecting low-energy nodes as cluster heads. The existing system is limited to exploring global opportunities within specified search zones. To enhance the performance of WSNs, a hybrid approach combining artificial bee colony (ABC) and ant colony optimization (ACO) has been developed. This approach helps in selecting an ideal cluster head from a group of terminals. Several factors are considered in the cluster head election, including residual energy at the nodes, distance to neighbors, distance to the base station, node degree, and node centrality. ACO determines the path between the cluster leader and the base station (BS) by selecting the most efficient route in terms of distance, remaining power, and node degrees. The performance of this proposed methodology has been analyzed in terms of energy consumption, network lifetime, and data packets at the base station. A comparison has been made between the outputs of the proposed methods and traditional benchmarking methods. The results reveal a substantial improvement in the average network lifetime, showcasing an increase of 40.50%, 33.17%, 25.00%, and 15.49% when compared to LEACH, Beecluster, iABC, and BeeSensor, respectively. In terms of alive nodes, our solution surpasses LEACH, Beecluster, iABC, and BeeSensor by 28.7%, 22.51%, 20.95%, and 12.47%, respectively. Additionally, the energy consumption of our approach proves to be significantly lower than LEACH, Beecluster, iABC, and BeeSensor, recording reductions of 47.25%, 32.40%, 27.38%, and 22.20%, respectively.

Impact of Ambient Temperature and Humidity on the Performance of Vapour Compression Air Conditioning System - Experimental and Numerical Investigation

Due to global warming, population growth, and urbanisation, demands for space cooling in buildings have significantly increased. Mechanical vapour compression cooling technology is relatively mature, well-understood and most commonly used globally. However, they suffer from high energy consumption and adverse environmental impact due to the refrigerants used. As ambient temperature and humidity have significant effects on the performance of vapour compression systems, this paper experimentally and numerically investigates their impact on the performance of vapour compression air conditioning system in terms of power consumption and cooling capacity. It was concluded that at ambient humidity of 55%, as the ambient temperature increases from 22 °C to 40 °C, the compressor temperature has increased from 46 °C to 59 °C which increased its power consumption from 751.2 to 935.52 Watts, giving an increase of 10.24 Watts for every one degree of ambient temperature increase. Also, as ambient humidity in-creased from 32% to 88%, the power consumption increased from 739.2 Watts to 853.2 Watts, an increase of 114 Watts. A validated CFD (Computational Fluid Dynamics) model was developed and predicted a loss in the enthalpy change from 11.7kJ/kg.d.a to 3.6kJ/kg.d.a. due to ambient temperature increasing from 22 °C to 40 °C and a loss of enthalpy change from 12.857kJ/kg.d.a to 5.524kJ/kg.d.a. due to increasing the ambient air humidity from 32% to 88%. This work high-lighted the impacts of ambient conditions on the performance of vapour compression cooling system in terms of increased power consumption and loss of cooling capacity.

Quality Enhancement with Frame-wise DLCNN using High Efficiency Video Coding in 5G Networks

In the present situation, applications related to multimedia are discovered to be comfortable with the use of video. The number of end consumers who use video continues to rise every day. People are presently searching for videos with better quality among the ones that are currently available there. This results in the launch and dissemination of HD (high definition) videos. Ultra high-definition (UHD) videos are becoming more and more popular as a result of this advancement and need. However, as video communication keeps expanding, there is an upsurge in network traffic because of the limited bandwidth, especially among smart cities. Different advancement codecs have been suggested to deal with the data stream to overcome this hazardous circumstance. However, the fact that modern UHD videos have huge amounts of data makes the available codecs even more complicated. UHD videos can be processed with the latest improvement codec, H.265/High-efficiency video coding (HEVC). Nevertheless, it is impacted by increased power consumption and intricate calculations. Limitations in the codec's functionality confine its use to specific applications, preventing its application in wireless, mobile, or portable settings. Hence, this research concentrates on implementing frame-level quality enhancement through a deep learning network known as FQE-Net. The deep learning convolutional neural network (DLCNN) is specifically crafted to manage films with resolutions up to 16K. Its primary objectives include reducing complexity, minimizing artifacts, enhancing the efficiency of the HEVC codec, and compacting energy consumption. To achieve superior efficiency, it is imperative to replace the DWT transforms within the HEVC codec with a DLCNN model. Additionally, incorporating the Content Block Search Algorithm for Motion Estimation and Compensation, alongside filtering techniques like Sample Adaptive Filter and Deblocking Filter, becomes essential. The simulation results showed that the suggested FQE-Net performed better than the conventional techniques.

Microheater Topology for Advanced Gas Sensor Applications with Carbyne-Enriched Nanomaterials

The response characteristics of carbyne-enriched surface-acoustic-wave (SAW)-based gas sensors utilizing meander and rectangular microheater topologies were investigated to assess their desorption and recovery properties. Comparative analysis of contact resistance and interface capacitance before and after heating revealed minimal deviation in contact resistance, signifying strong thermal stability in the carbyne-enriched layer. However, the interface capacitance varied with the microheater size. Our analysis reveals that a small meander microheater configuration (line width: 300 µm) facilitates efficient sensor recovery at ethanol concentration measurements in the range of 180–680 ppm, maintaining a low deviation in time delay across different concentrations (~2.3%), resulting in a narrow hysteresis and linear sensor response. Conversely, the large meander microheater (line width: 450 µm) and rectangular dense microheater induce irreversible changes in the sensing structure, leading to a widened hysteresis at higher concentrations and increased power consumption. Recovery patterns display substantial deviations from initial values at different concentration levels. Higher concentrations exhibit broader hysteresis, while lower concentrations show narrower hysteresis loops, compared to the small meander microheater. The study offers insights into desorption rates, power consumption variations, and recovery behaviors related to different microheater configurations. It demonstrates the importance of microheater topology selection in tailoring recovery properties and response characteristics, contributing to the advancement of carbyne-based sensor technology.

A new nano-design of configurable logic module based on coplanar reversible adder and modified Fredkin gates using quantum technology

Quantum Dot Cellular Automata (QCA) and reversible logic have emerged as promising alternatives to conventional CMOS technology, offering several advantages, such as ultra-dense structures and ultra-low-power consumption. Among the crucial components of processors, the Arithmetic Logic Unit (ALU) has witnessed significant advancements in reversible computing, leading to energy-efficient and high-speed computing systems, particularly beneficial for Digital Signal Processing (DSP) applications. Conventional ALUs, reliant on irreversible logic, encounter energy inefficiencies due to information loss during computations, resulting in increased power consumption. Moreover, they may face limitations in processing speed, impacting real-time processing capabilities, especially for complex DSP tasks involving intensive arithmetic and logic operations. In response to these challenges, a research paper presents a pioneering approach, proposing a novel reversible ALU design using QCA nanotechnology. The proposed design ingeniously incorporates Modified Fredkin (MF) gates, and a coplanar reversible full adder based on the HNG gate, skillfully leveraging the unique features of QCA nanotechnology to optimize the ALU's energy-efficient and high-speed performance for DSP applications. This revolutionary QCA reversible ALU comprises 330 QCA cells arranged in a compact 0.41 μm2 area, skillfully realized through the coplanar clock-zone-based crossover approach. Its core computational elements, the three MF gates, and the innovative coplanar reversible full adder empower the ALU to execute a remarkable array of 20 distinct arithmetic and logic operations, showcasing its versatility in handling diverse DSP tasks. The proposed structure undergoes extensive simulations utilizing QCADesigner version 2.0.3 to confirm its performance. The evaluation results manifest substantial improvements compared to previous designs, boasting a 30% reduction in area occupancy, a 20% decrement in cell count, a 10% reduction in latency, and a 10% decrease in quantum cost compared to the best-known previous structure. These compelling outcomes solidify the potential of the proposed reversible ALU as a transformative advancement in energy-efficient and high-speed computing for DSP applications.

All-optical image denoising using a diffractive visual processor

Image denoising, one of the essential inverse problems, targets to remove noise/artifacts from input images. In general, digital image denoising algorithms, executed on computers, present latency due to several iterations implemented in, e.g., graphics processing units (GPUs). While deep learning-enabled methods can operate non-iteratively, they also introduce latency and impose a significant computational burden, leading to increased power consumption. Here, we introduce an analog diffractive image denoiser to all-optically and non-iteratively clean various forms of noise and artifacts from input images – implemented at the speed of light propagation within a thin diffractive visual processor that axially spans <250 × λ, where λ is the wavelength of light. This all-optical image denoiser comprises passive transmissive layers optimized using deep learning to physically scatter the optical modes that represent various noise features, causing them to miss the output image Field-of-View (FoV) while retaining the object features of interest. Our results show that these diffractive denoisers can efficiently remove salt and pepper noise and image rendering-related spatial artifacts from input phase or intensity images while achieving an output power efficiency of ~30–40%. We experimentally demonstrated the effectiveness of this analog denoiser architecture using a 3D-printed diffractive visual processor operating at the terahertz spectrum. Owing to their speed, power-efficiency, and minimal computational overhead, all-optical diffractive denoisers can be transformative for various image display and projection systems, including, e.g., holographic displays.

LSTM-Aided Selective Beam Tracking in Multi-Cell Scenario for mmWave Wireless Systems

Millimeter wave systems rely on narrow beams (beamforming) and dense cell deployments for reliable communication. Tracking these beams from multiple cells can increase power consumption and signaling overhead. Therefore, a mobile needs to selectively and smartly track beams under power/overhead constraints. In this paper, we propose a fully data-driven, long short-term memory (LSTM)-based, selective link tracking approach. These approaches are developed for both fixed and adaptive power/overhead constraints, which also predict the magnitude of the best performing beam. The algorithms are validated in simulations of a 28GHz 5G New Radio (NR)-like system in an urban area with realistic navigation routes utilizing detailed ray-tracing. The simulations demonstrate that the proposed methods outperform classic and deep reinforcement learning (RL) approaches in terms of tracking accuracy, power saving and overhead for both analog and digital beamforming architectures. We also argue that the prediction of the proposed method can be easily performed on a digital signal processor of a modern chipset with minimal resource consumption.

Green and Sustainable Industrial Internet of Things Systems Leveraging Wake-Up Radio to Enable On-Demand IoT Communication

The industrial Internet of things (IIoT) is a major lever in Industry 4.0 development, where reducing the carbon footprint and energy consumption has become crucial for modern companies. Today’s IIoT device infrastructure wastes large amounts of energy on wireless communication, limiting device lifetime and increasing power consumption and battery requirements. Communication capabilities seriously affect the responsiveness and availability of autonomous IoT devices when collecting data and retrieving commands to/from higher-level applications. Thus, the objective of optimizing communication remains paramount; in addition to typical optimization methods, such as algorithms and protocols, a new concept is emerging, known as wake-up radio (WuR). WuR provides novel on-demand radio communication schemes that can increase device efficiency. By expanding the lifespan of IoT devices while maintaining high reactivity and communication performance, the WuR approach paves the way for a “place-and-forget” IoT device deployment methodology that combines a small carbon footprint with an extended lifetime and highly responsive functionality. WuR technology, when applied to IoT devices, facilitates green IIoT, thereby enabling the emergence of a novel on-demand IoT (OD-IoT) concept. This article presents an analysis of the state-of-the-art WuR technology within the green IoT paradigm and details the OD-IoT concept. Furthermore, this review provides an overview of WuR applications and their impact on the IIoT, including relevant industry use cases. Finally, we describe our experimental performance evaluation of a WuR-enabled device that is commercially available off the shelf. Specifically, we focused on the communication range and energy consumption, successfully demonstrating the applicability of WuR and the strong potential that it has and the benefits that it offers for sustainable IIoT systems.

Dynamic Multi-Sleeping Control with Diverse Quality-of-Service Requirements in Sixth-Generation Networks Using Federated Learning

The intensive deployment of sixth-generation (6G) base stations is expected to greatly enhance network service capabilities, offering significantly higher throughput and lower latency compared to previous generations. However, this advancement is accompanied by a notable increase in the number of network elements, leading to increased power consumption. This not only worsens carbon emissions but also significantly raises operational costs for network operators. To address the challenges arising from this surge in network energy consumption, there is a growing focus on innovative energy-saving technologies designed for 6G networks. These technologies involve strategies for dynamically adjusting the operational status of base stations, such as activating sleep modes during periods of low demand, to optimize energy use while maintaining network performance and efficiency. Furthermore, integrating artificial intelligence into the network’s operational framework is being explored to establish a more energy-efficient, sustainable, and cost-effective 6G network. In this paper, we propose a small base station sleeping control scheme in heterogeneous dense small cell networks based on federated reinforcement learning, which enables the small base stations to dynamically enter appropriate sleep modes, to reduce power consumption while ensuring users’ quality-of-service (QoS) requirements. In our scheme, double deep Q-learning is used to solve the complex non-convex base station sleeping control problem. To tackle the dynamic changes in QoS requirements caused by user mobility, small base stations share local models with the macro base station, which acts as the central control unit, via the X2 interface. The macro base station aggregates local models into a global model and then distributes the global model to each base station for the next round of training. By alternately performing model training, aggregation, and updating, each base station in the network can dynamically adapt to changes in QoS requirements brought about by user mobility. Simulations show that compared with methods based on distributed deep Q-learning, our proposed scheme effectively reduces the performance fluctuations caused by user handover and achieves lower network energy consumption while guaranteeing users’ QoS requirements.

Efficient energy management in smart grids with finite horizon Q-learning

Efficient energy distribution in smart grids is an important problem driven by the need to manage increasing power consumption across the globe. This problem has been studied in the past using different frameworks including Markov Decision Processes (MDP) and Reinforcement Learning. However, existing algorithms in Reinforcement Learning Theory largely deal with infinite horizon decision making. On the other hand, smart grid problems are inherently finite horizon in nature and so can best be addressed using Finite Horizon algorithms. We therefore analyze the smart grid setup using a finite horizon MDP model and develop, for the first time, a finite horizon version of the popular Q-learning algorithm. We observe that our algorithm shows good empirical performance on the smart grid setting. We also theoretically analyze the full convergence of the algorithm by proving both the stability and the convergence of the same. Our analysis of stability and convergence of finite horizon Q-learning is based entirely on the ordinary differential equations (O.D.E) method. Apart from smart grids, we additionally demonstrate the performance of our algorithm on a setting of random MDPs indicating that the algorithm is more generally applicable and can be studied on other settings in the future.

Economic Transformations in Urban Industries during Energy Transition: Novel Tradeoff Technique for Balancing Energy Consumption

Mobile terminal devices are becoming more prevalent owing to the growth of the Internet of Things (IoT). Mobile device terminals generate large amounts of information, which results in increased power consumption (PC) and delays. Nevertheless, the short delay often leads to increased PC. The present study proposes a tradeoff technique for balancing PC and delay so as to achieve optimum PC and delay in a smart grid as a very large scale system. The first objective is to explain how a delay threshold reduces delay in the market. Following that, the delay and PC of the mobile terminal layer, fog node layer, and cloud server layer with lineup view are described. The energy optimization issue is solved via computing the optimum workload of the layers using nonlinear programming. A cloud-fog solidarity planning scheme has been modelled for reducing PC. In addition, it is possible to create an algorithm that completes work when the nodes depart. The experimental outcomes show a reduction of about 22% in energy use, as well as a reduction in delay of 12.5% over the prime come prime served method. Integrating economic considerations, the study reveals a cloud-fog solidarity planning scheme aimed at dropping energy consumption and optimizing delay, resulting in a 22% decrease in energy use and a 12.5% reduction in delay compared to the prime come prime served method in the context of urban industries' energy transition.

Reliability of FET-type gas sensor with asymmetric air-gap micro-heater structure considering thermoelectric effect

Poly-silicon has a wide range of applications because of its interesting properties in the thermoelectric effect. We propose an optimal field-effect transistor-type (FET-type) gas sensor platform with an embedded poly-silicon micro-heater designed considering both power consumption and reliability. Since the poly-silicon micro-heater has a thermoelectric effect, the reliability of the micro-heater can be improved without a significant increase in power consumption by adopting asymmetric etch-holes (finally asymmetric air-gap) along the heater electrode. The sensor with the shorter etch-hole (10 µm) around one electrode of the heater electrode increases power consumption by only 1.22 times but can increase lifetime by 14.2 times. The heat distribution and contact temperature of symmetric and asymmetric air-gap heaters are compared using COMSOL Multiphysics. Sensors with different lengths of etch-holes are fabricated, and the thermal response and reliability of the fabricated sensors are measured and analyzed. In addition, in a sensor in which a thin film of indium oxide is deposited as a sensing material on the gas sensor platform, the effect of air-gap design change on the gas sensing characteristics is investigated. Depending on the polarity of the voltage applied between the two terminals of the heater, the difference in void formation and damage at the heater contacts is confirmed by TEM and EDS analysis.

Improvement of interfacial thermal resistance between TIMs and copper for better thermal management

Due to the increasing power consumption of modern electronic devices and rapid rise in heat generation, how to efficiently manage the heat dissipation becomes a challenge. The previous studies have shown the importance of interface treatment for better thermal management while the mechanism is lacking yet. Here, we study the interface thermal transport between the thermal interface materials (TIMs) and the copper substrate using both experimental measurements and theoretical calculations. The results show that after interface treatment, the thermal contact resistance between the graphite materials and copper decreases from 87.96 K · mm2 · W−1 to 20.26 K · mm2 · W−1. For the practical testing, a difference up to 7 ○C is demonstrated between the treated and untreated samples, indicating the potential of reduction of thermal contact resistance for the heat dissipation in electronic devices, which is verified in our theoretical calculation as well. Our study provides an applicable approach to enhancing the thermal stability in electronic devices by investigating the impact of the interfacial thermal resistance on heat dissipation.

Evaluation of Water-Cooling Effect in Hydrogen-Fed SOFC for High-Efficiency Combined System Design

Abstract If pure hydrogen is used as a fuel in high-temperature fuel cells, waste heat must be removed by air cooling, which requires increased power consumption for supplying excess air. This study presents a hydrogen-fed solid oxide fuel cell (SOFC) that uses water instead of air for stack cooling and improved system performance. A novel SOFC system with energy cascade utilization is also proposed using cooling water as the working fluid for a steam turbine. Water cooling for the SOFC stack cooling reduced the stack power and efficiency but significantly reduced the power consumption for supplying excess air by more than 60%. Under ambient SOFC operating pressure, the net power and efficiency of the proposed system were increased by 25.6% and 12.2%p compared to the air-cooled system, respectively. At an SOFC operating pressure of 1000 kPa, the proposed hybrid system with energy cascade utilization achieved improvements of 10.2% in net power and 7.5%p in net efficiency, leading to efficiency higher than 73%. This study is significant in that it proposes a novel high-efficiency SOFC system with energy cascade utilization by using two-phase water as a cooling medium and working fluid of steam turbine.