scholarly journals Numerical and Experimental Study on the Performance of Thermoelectric Radiant Panel for Space Heating

Materials ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 550 ◽  
Author(s):  
Hansol Lim ◽  
Jae-Weon Jeong
Keyword(s):  
Energy Consumption ◽  
Prediction Models ◽  
Thermoelectric Module ◽  
Coefficient Of Performance ◽  
Joule Effect ◽  
Heating Capacity ◽  
Simulation Based ◽  
Radiant Panel ◽  
And Performance ◽  
Heating Mode

The purpose of this study is to investigate the suitable operation and performance of a thermoelectric radiant panel (TERP) in the heating operation. First, the hypothesis was suggested that the heating operation of TERP can operate without a heat source at the cold side according to theoretical considerations. To prove this hypothesis, the thermal behavior of the TERP was investigated during the heating operation using a numerical simulation based on the finite difference method. The results indicated that it is possible to heat the radiant panel using a thermoelectric module without fan operation via the Joule effect. A mockup model of the TERP was constructed, and the numerical model and hypothesis were validated in experiment 1. Moreover, experiment 2 was performed to evaluate the necessity of fan operation in the heating operation of TERP regarding energy consumption. The results revealed that the TERP without fan operation showed the higher coefficient of performance (COP) in the heating season. After determining the suitable heating operation of the TERP, prediction models for the heating capacity and power consumption of the TERP were developed using the response surface methodology. Both models exhibited good R2 values of >0.94 and were validated within 10% error bounds in experimental cases. These prediction models are expected to be utilized in whole-building simulation programs for estimating the energy consumption of TERPs in the heating mode.

Experimental Analysis on Vapour Compression Refrigeration System Using Nanolubricant with HFC-134a Refrigerant

Nano Hybrids ◽  
2015 ◽  
Vol 9 ◽  
pp. 33-43 ◽  
Author(s):  
A. Manoj Babu ◽  
S. Nallusamy ◽  
K. Rajan
Keyword(s):  
Energy Consumption ◽  
System Performance ◽  
Mineral Oil ◽  
Coefficient Of Performance ◽  
Refrigeration System ◽  
Hfc 134A ◽  
Reduce Energy Consumption ◽  
And Performance ◽  
Vapour Compression ◽  
Better Than

This paper investigates the reliability and performance of a refrigeration system using nanolubricant with 1, 1, 1, 2-Tetrafluoroethane (HFC-134a) refrigerant. Mineral Oil (MO) is mixed with nanoparticles such as Titanium Dioxide (TiO2) and Aluminium Oxide (Al2O3). These mixtures were used as the lubricant instead of Polyolester (POE) oil in the HFC-134a refrigeration system as HFC-134a does not compatible with raw mineral oil. An investigation was done on compatibility of mineral oil and nanoparticles mixture at 0.1 and 0.2 grams / litre with HFC-134a refrigerant. To carry out this investigation, an experimental setup was designed and fabricated in the lab. The refrigeration system performance with the nanolubricant was investigated by using energy consumption test. The results indicate that HFC-134a and mineral oil with above mentioned nanoparticles works normally and safely in the refrigeration system. The refrigeration system performance was better than the HFC-134a and POE oil system. Thus nanolubricant (Mixture of Mineral Oil (MO) and nanoParticles) can be used in refrigeration system to considerably reduce energy consumption and better Coefficient of Performance (COP).

Design and Performance Analysis on CO2 Combined System in Supermarket

2012 ◽  
Vol 433-440 ◽  
pp. 1219-1225
Author(s):  
Jing Hong Ning ◽  
Sheng Chun Liu
Keyword(s):  
Energy Consumption ◽  
Waste Heat ◽  
Hot Water ◽  
Coefficient Of Performance ◽  
Working Fluid ◽  
Space Heating ◽  
Combined System ◽  
Space Cooling ◽  
Gas Cooling ◽  
And Performance

This paper reports a combined space cooling, space heating, water heating and food refrigeration system (named CO2 combined system) in supermarket. This system using CO2 as the working fluid consists of a two-stage CO2 transcritical cycle used for food refrigeration, a single-stage CO2 transcritical cycle for space cooling in summer and space heating in winter. The waste heat emitted from the CO2 gas cooling in food refrigeration cycle and space cooling and space heating cycles is recovered by heat recover exchanger and is used to provide hot water for space heating and for general usage, such as the catering, the washing and the toilet facilities in the supermarket. So this CO2 combined system improves the coefficient of performance, decreases the energy consumption as well as reduces the heat pollution. Moreover, this CO2 combined system is compared with typical conventional supermarket technology, the results show that the energy consumption of CO2 combined system is reduced largely and energy efficiency is increased obviously. It can be concluded that the CO2 combined system has a good future for protecting environment and saving energy.

Refrigeration systems with minimized refrigerant charge: System design and performance

2005 ◽  
Vol 219 (2) ◽  
pp. 127-137 ◽  
Author(s):  
F Primal ◽  
P Lundqvist
Keyword(s):  
System Design ◽  
Heat Pump ◽  
Operating Conditions ◽  
Coefficient Of Performance ◽  
Charge System ◽  
Safety Issues ◽  
Heating Capacity ◽  
And Performance ◽  
Lubrication Oils ◽  
Additional Charge

Independently of the choice of refrigerant, environmental and/or safety issues can be minimized by reducing leakage and the amount of refrigerant charge in heat pump or refrigeration systems, preferably both. In the investigation reported here, a laboratory test rig was built, simulating a water-to-water heat pump with a heating capacity of 5 kW. The system was designed to minimize the charge of refrigerant mainly by use of minichannel aluminium heat exchangers and a compact system design. It was shown that the system could be run with 200 g of propane at typical domestic heat pump operating conditions without reduction in the heating coefficient of performance (COP1) compared with a traditional design. Additional charge reduction is possible by selecting proper compressor lubrication oils or by employing a compressor simply using less lubrication oil.

Experimental studies on direct expansion solar thermal heat pump systems using R430A as a substitute to R134a

2021 ◽  
pp. 095440892110209
Author(s):  
M Mohanraj ◽  
I M Kartheheyan
Keyword(s):  
Global Warming ◽  
Energy Consumption ◽  
Heat Pump ◽  
Energy Performance ◽  
Solar Thermal ◽  
Coefficient Of Performance ◽  
Direct Expansion ◽  
Pump System ◽  
Discharge Temperature ◽  
Heating Capacity

The use of halogen-based refrigerants in heat pump applications is restricted because of their high global warming potential (GWP). Therefore, it is necessary to identify a low GWP substitute for heat pump applications. This article presents the energy performance of a direct expansion solar thermal heat pump system (DXSTHPS) using R430A as an environmentally friendly substitute to phase out R134a. The effects of ambient parameters on compressor discharge temperature, compressor energy consumption, condenser heating capacity and coefficient of performance (COP) of a DXSTHPS using R134a and R430A are estimated and compared. Moreover, the total equivalent global warming impacts (TEGWI) of a DXSTHPS using R134a and R430A are evaluated. The results showed that the R430A has 0.7–1.9% lower compressor energy consumption than R134a. The condenser heating capacity and COP of a DXSTHPS using R430A are higher than R134a by 4.6–8.7% and 5.1–10.2%, respectively. The compressor discharge temperature observed in a DXSTHPS using R430A is 5.8 °C higher than R134a. The lubricant physical properties are retained at higher compressor operating temperatures, ensuring compressor reliability. The DXSTHPS using R430A has 4.2–12.9% lower TEGWI due to its lower GWP with lower compressor energy consumption than R134a.

scholarly journals Machine learning-based energy consumption modeling and comparison of H.264/AVC and google VP8 encoders

2021 ◽  
Vol 11 (2) ◽  
pp. 1303
Author(s):  
Yousef O. Sharrab ◽  
Mohammad Alsmirat ◽  
Bilal Hawashin ◽  
Nabil Sarhan
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Performance Metrics ◽  
Prediction Models ◽  
Video Encoding ◽  
Video Sequences ◽  
Learning Approaches ◽  
Video Encoder ◽  
Wide Range ◽  
And Performance

Advancement of the prediction models used in a variety of fields is a result of the contribution of machine learning approaches. Utilizing such modeling in feature engineering is exceptionally imperative and required. In this research, we show how to utilize machine learning to save time in research experiments, where we save more than five thousand hours of measuring the energy consumption of encoding recordings. Since measuring the energy consumption has got to be done by humans and since we require more than eleven thousand experiments to cover all the combinations of video sequences, video bit_rate, and video encoding settings, we utilize machine learning to model the energy consumption utilizing linear regression. VP8 codec has been offered by Google as an open video encoder in an effort to replace the popular MPEG-4 Part 10, known as H.264/AVC video encoder standard. This research model energy consumption and describes the major differences between H.264/AVC and VP8 encoders in terms of energy consumption and performance through experiments that are based on machine learning modeling. Twenty-nine raw video sequences are used, offering a wide range of resolutions and contents, with the frame sizes ranging from QCIF(176x144) to 2160p(3840x2160). For fairness in comparison analysis, we use seven settings in VP8 encoder and fifteen types of tuning in H.264/AVC. The settings cover various video qualities. The performance metrics include video qualities, encoding time, and encoding energy consumption.

Local Calibration of Pavement Mechanistic-Empirical Faulting Reliability using Pavement Management Data

2021 ◽  
pp. 036119812110013
Author(s):  
Lucio Salles de Salles ◽  
Lev Khazanovich
Keyword(s):  
Model Calibration ◽  
Prediction Models ◽  
Plain Concrete ◽  
Performance Model ◽  
Pavement Management ◽  
Pavement Design ◽  
Reliability Model ◽  
Transverse Joint ◽  
Design Characteristics ◽  
And Performance

The Pavement ME transverse joint faulting model incorporates mechanistic theories that predict development of joint faulting in jointed plain concrete pavements (JPCP). The model is calibrated using the Long-Term Pavement Performance database. However, the Mechanistic-Empirical Pavement Design Guide (MEPDG) encourages transportation agencies, such as state departments of transportation, to perform local calibrations of the faulting model included in Pavement ME. Model calibration is a complicated and effort-intensive process that requires high-quality pavement design and performance data. Pavement management data—which is collected regularly and in large amounts—may present higher variability than is desired for faulting performance model calibration. The MEPDG performance prediction models predict pavement distresses with 50% reliability. JPCP are usually designed for high levels of faulting reliability to reduce likelihood of excessive faulting. For design, improving the faulting reliability model is as important as improving the faulting prediction model. This paper proposes a calibration of the Pavement ME reliability model using pavement management system (PMS) data. It illustrates the proposed approach using PMS data from Pennsylvania Department of Transportation. Results show an increase in accuracy for faulting predictions using the new reliability model with various design characteristics. Moreover, the new reliability model allows design of JPCP considering higher levels of traffic because of the less conservative predictions.

scholarly journals Fabrication of a Silicide Thermoelectric Module Employing Fractional Factorial Design Principles

2021 ◽  
Author(s):  
Joachim S. Graff ◽  
Raphael Schuler ◽  
Xin Song ◽  
Gustavo Castillo-Hernandez ◽  
Gunstein Skomedal ◽  
...  
Keyword(s):  
Factorial Design ◽  
Fractional Factorial Design ◽  
Waste Heat ◽  
Thermoelectric Module ◽  
Fractional Factorial ◽  
Taguchi Methods ◽  
Dimensional Parameter ◽  
Cu Metallization ◽  
Manganese Silicide ◽  
And Performance

AbstractThermoelectric modules can be used in waste heat harvesting, sensing, and cooling applications. Here, we report on the fabrication and performance of a four-leg module based on abundant silicide materials. While previously optimized Mg2Si0.3Sn0.675Bi0.025 is used as the n-type leg, we employ a fractional factorial design based on the Taguchi methods mapping out a four-dimensional parameter space among Mnx-εMoεSi1.75−δGeδ higher manganese silicide compositions for the p-type material. The module is assembled using a scalable fabrication process, using a Cu metallization layer and a Pb-based soldering paste. The maximum power output density of 53 μW cm–2 is achieved at a hot-side temperature of 250 °C and a temperature difference of 100 °C. This low thermoelectric output is related to the high contact resistance between the thermoelectric materials and the metallic contacts, underlining the importance of improved metallization schemes for thermoelectric module assembly.

scholarly journals Mobility-Included DNN Partition Offloading from Mobile Devices to Edge Clouds

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 229
Author(s):  
Xianzhong Tian ◽  
Juan Zhu ◽  
Ting Xu ◽  
Yanjun Li
Keyword(s):  
Neural Networks ◽  
Energy Consumption ◽  
Mobile Devices ◽  
Wireless Network ◽  
Deep Neural Networks ◽  
Mobile User ◽  
Computation Offloading ◽  
Long Latency ◽  
Total Latency ◽  
And Performance

The latest results in Deep Neural Networks (DNNs) have greatly improved the accuracy and performance of a variety of intelligent applications. However, running such computation-intensive DNN-based applications on resource-constrained mobile devices definitely leads to long latency and huge energy consumption. The traditional way is performing DNNs in the central cloud, but it requires significant amounts of data to be transferred to the cloud over the wireless network and also results in long latency. To solve this problem, offloading partial DNN computation to edge clouds has been proposed, to realize the collaborative execution between mobile devices and edge clouds. In addition, the mobility of mobile devices is easily to cause the computation offloading failure. In this paper, we develop a mobility-included DNN partition offloading algorithm (MDPO) to adapt to user’s mobility. The objective of MDPO is minimizing the total latency of completing a DNN job when the mobile user is moving. The MDPO algorithm is suitable for both DNNs with chain topology and graphic topology. We evaluate the performance of our proposed MDPO compared to local-only execution and edge-only execution, experiments show that MDPO significantly reduces the total latency and improves the performance of DNN, and MDPO can adjust well to different network conditions.

scholarly journals A Novel Load Forecasting Approach Based on Smart Meter Data Using Advance Preprocessing and Hybrid Deep Learning

2021 ◽  
Vol 11 (6) ◽  
pp. 2742
Author(s):  
Fatih Ünal ◽  
Abdulaziz Almalaq ◽  
Sami Ekici
Keyword(s):  
Deep Learning ◽  
Energy Consumption ◽  
Prediction Models ◽  
Short Term Memory ◽  
Critical Role ◽  
Load Forecasting ◽  
Unified Framework ◽  
Short Term ◽  
Planning And Scheduling ◽  
Short Term Load Forecasting

Short-term load forecasting models play a critical role in distribution companies in making effective decisions in their planning and scheduling for production and load balancing. Unlike aggregated load forecasting at the distribution level or substations, forecasting load profiles of many end-users at the customer-level, thanks to smart meters, is a complicated problem due to the high variability and uncertainty of load consumptions as well as customer privacy issues. In terms of customers’ short-term load forecasting, these models include a high level of nonlinearity between input data and output predictions, demanding more robustness, higher prediction accuracy, and generalizability. In this paper, we develop an advanced preprocessing technique coupled with a hybrid sequential learning-based energy forecasting model that employs a convolution neural network (CNN) and bidirectional long short-term memory (BLSTM) within a unified framework for accurate energy consumption prediction. The energy consumption outliers and feature clustering are extracted at the advanced preprocessing stage. The novel hybrid deep learning approach based on data features coding and decoding is implemented in the prediction stage. The proposed approach is tested and validated using real-world datasets in Turkey, and the results outperformed the traditional prediction models compared in this paper.

scholarly journals Power and Performance Evaluation of Memory-Intensive Applications

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4089
Author(s):  
Kaiqiang Zhang ◽  
Dongyang Ou ◽  
Congfeng Jiang ◽  
Yeliang Qiu ◽  
Longchuan Yan
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Power Consumption ◽  
Job Scheduling ◽  
Memory System ◽  
Processor Core ◽  
Memory Efficiency ◽  
And Performance ◽  
Reasonable Use ◽  
Server System

In terms of power and energy consumption, DRAMs play a key role in a modern server system as well as processors. Although power-aware scheduling is based on the proportion of energy between DRAM and other components, when running memory-intensive applications, the energy consumption of the whole server system will be significantly affected by the non-energy proportion of DRAM. Furthermore, modern servers usually use NUMA architecture to replace the original SMP architecture to increase its memory bandwidth. It is of great significance to study the energy efficiency of these two different memory architectures. Therefore, in order to explore the power consumption characteristics of servers under memory-intensive workload, this paper evaluates the power consumption and performance of memory-intensive applications in different generations of real rack servers. Through analysis, we find that: (1) Workload intensity and concurrent execution threads affects server power consumption, but a fully utilized memory system may not necessarily bring good energy efficiency indicators. (2) Even if the memory system is not fully utilized, the memory capacity of each processor core has a significant impact on application performance and server power consumption. (3) When running memory-intensive applications, memory utilization is not always a good indicator of server power consumption. (4) The reasonable use of the NUMA architecture will improve the memory energy efficiency significantly. The experimental results show that reasonable use of NUMA architecture can improve memory efficiency by 16% compared with SMP architecture, while unreasonable use of NUMA architecture reduces memory efficiency by 13%. The findings we present in this paper provide useful insights and guidance for system designers and data center operators to help them in energy-efficiency-aware job scheduling and energy conservation.

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