System Performance Parameters Research Articles

Design of Regenerating Code Based on Security Level in Cloud Storage System

Cloud storage is an indispensable part of cloud computing solutions and the security of its stored data has become a key issue in the research and application of cloud storage systems. To solve this problem, this paper studies the anti-eavesdropping regenerating code technology for cloud storage systems, from the perspective of information theory. As opposed to the existing research ideas on regenerating code theory, that enable the system to obtain strong/weak security, this paper focuses on quantifying the relationship between security and system performance parameters, evaluating the system performance gains that can be obtained by appropriately reducing security, and designing regenerating code schemes with different information security levels to meet the personalized requirements of cloud storage customers. This paper puts forward a generalized matrix transposing method and applies it to the coding construction of fractional repetition codes. The scheme proposed in this paper will provide new ideas and methods for research on secure regenerating code technology in cloud storage systems.

Potential evaluation of an annular thermoelectric cooler driven by a dye-sensitized solar cell

For round shaped cooling space, a dye-sensitized solar cell (DSSC) is used to drive annular thermoelectric cooler (ATEC) to solve the performance deterioration caused by geometric mismatch, in which ATEC considers Seebeck effect, Peltier effect and Thomson effect. Considering all kinds of irreversible losses between DSSC and ATEC, the mathematical formula of performance parameters of hybrid system is derived. The effects of Schottky barrier height, thickness of TiO2 film, thickness of ATEC, number of thermoelectric elements and annular parameter of the ATEC on the cooling capacity and coefficient of performance (COP) of the coupling system are analyzed theoretically. The theoretical calculation results show that the maximum cooling capacity and COP of the hybrid system are 68.75 W/m2 and 0.071, respectively. In addition, it can be found that the positive Thomson effect had a positive influence on the thermoelectric cooling system. The findings of this work have the potential to provide fresh light of solar-driven annular thermoelectric cooler matching with actual round shaped cooled spaces.

Gear Profile Measurement Based on Thin Line Structured Light

In order to achieve the high precision 3D measurement of gear tooth profile, a telecentric optical measurement system with large FOV (field of view), WD (working distance) and measuring range was proposed, the system was applied to the gear tooth profile measurement experiment and obtained a good effect. Firstly, the composition and the principle of the system were introduced. The lights of projection and imaging with an angle of 45° from the normal line of the measured surface can reduce the requirement of DOF (depth of field) of the imaging objective lens. Secondly, in combine with the application background of small modulus gear tooth profile measurement, the magnification, NA (numerical aperture) and other optical parameters were calculated, the optical system was constructed and the system performance parameters are listed. Then, the calibration model of the system was demonstrated and the calibrations of depth pixel resolution and horizontal pixel resolution were completed through experiments. Finally, an involute artifact with a modulus of 2.9992mm and a total tooth profile deviation of 1.1μm was measured. The results show that the deviation of the total tooth profile measured is about 2μm, which is consistent with the calibration curve of the involute artifact.

Performance evaluation of urban drainage systems: an analytic hierarchy process approach for the Jaracati basin in Brazil

The efficiency of drainage systems in urban centers and especially in their peripheral regions has deserved more and more special attention since their interrelationship with social and environmental aspects is associated with the performance of drainage infrastructures. Integrated urban water management is a major factor in the effectiveness of an urban drainage system. In large cities, negative effects caused by the disorderly urbanization process, especially those related to rainwater runoff, are increasingly present. An evaluation model of urban drainage systems was generated using System Fragility Indicators (IFS) and their ranking via Analytic Hierarchy Process (AHP), in the Jaracati River basin, Maranhão State, Brazil. Indicators were measured through the evaluation of the system and the experience of specialists in drainage systems. The results obtained allowed for the consolidation of a model to support planning and decision-making in the assessment processes of measures to mitigate socio-environmental impacts involved in the urban drainage of a watershed, with performance indicators capable of guaranteeing the support of a model being verified measurement of performance parameters of urban drainage systems.

Machine learning approach to predict the thermal performance of closed‐loop thermosyphon

AbstractThis paper presents the machine learning (ML) algorithm to predict the thermal performance of closed‐loop thermosyphon (CLT). The experimentation is carried out on the acetone‐charged CLT at different test conditions such as heat inputs, filling ratios, and adiabatic lengths. The test data is used to calculate the performance parameters such as thermal resistance, heat transfer coefficient, and effectiveness of the system. Based on the experimental dataset, the ML algorithms are developed to predict the performance parameters of the CLT system. The ML algorithms such as linear regression, decision tree (DT), random forest (RF), and lasso regression are used for the development of the prediction model. The hyperparameters are well‐tuned and optimized. The prediction measuring parameters (mean absolute error, R2) are analyzed carefully. It is noticed that the DT model outperformed the prediction of the other used models. The R2 score of the DT model was 98.504; whereas, the R2 scores of the RF model and linear regression model were about 94.76 and 92.17, respectively. This study will become a roadmap to the ML approach in the thermosyphon system.

Experimental comparison of R290 and R600a and prediction of performance with machine learning algorithms

The use of alternative refrigerants is among the popular topics of the refrigeration industry. In the first part of this study, thermodynamic performances of R290 and R600a gases were compared in a vapor compression refrigeration experiment setup. Although R600a caused an average of 33.44% less compressor power consumption compared to R290 refrigerant, R290 provided an average of 23.77% increase in COP (coefficient of performance), 82.55% in cooling capacity, and 20.99% increase in second law efficiency compared to R600a. In the second part of the study, the performance parameters of the refrigeration system were predicted with MLP (multi-layer perceptron), SVM (support vector machine), and DT (decision tree) machine learning algorithms. It was detected that the SVM method predicted all parameters with the least error. MAE (mean absolute error) values detected in the COP prediction with test set were 0.0317, 0.0324, and 0.0989 for SVM, MLP, and DT, respectively. Results revealed that performance of the refrigeration system increased when utilizing R290, and SVM was superior in prediction of performance indicators compared to other machine learning methods.

Combining renewable sources towards negative carbon emission hydrogen

Multi-energy systems that combine different energy sources and carriers to improve the overall technical, economic, and environmental performance can boost the energy transition. In this paper we posit an innovative multi-energy system for green hydrogen production that achieves negative carbon emissions by combining bio-fuel membrane-integrated steam reforming and renewable electricity electrolysis. The system produces green hydrogen and carbon dioxide, both at high purity. We use thermo-chemical models to determine the system performance and optimal working parameters. Specifically, we focus on its ability to achieve negative carbon emissions.The results show that in optimal operating conditions the system can capture up to 14.1 g of CO2 per MJ of stored hydrogen and achieves up to 70% storage efficiency. Therefore, we prove that a multi-energy system may reach the same efficiency of an average electrolyzer while implementing carbon capture. In the same optimal operating conditions the system converts 7.8 kg of biogas in 1 kg of hydrogen using 3.2 kg of oxygen coming from the production of 6.4 kg of hydrogen through the electrolyzer. With such ratios we estimate that the conversion of all the biogas produced in Europe with our system, could result in the installation of additional dedicated 800 GWp - 1280 GWp of photovoltaic power, or of 266 GWp - 532 GWp of wind power, without affecting the distribution grid and covering yearly the 45% of the worldwide hydrogen demand while removing from the atmosphere more than 2% of the European carbon dioxide emissions.

Energy and exergy analyses of a coal-fired micro-CHP system coupled engine as a domestic solution

In this study, a coal-fired stoker boiler and a beta-type rhombic-drive Stirling engine were manufactured and tested. The performance of the coal boiler with a Stirling engine has been investigated experimentally for the heating system of a three-storey house. The aims of this study are to investigate the Stirling engine's electricity generation potential and thermal energy production. Also, energy and exergy analyses of the micro-CHP system were performed to evaluate thermodynamic performance. Measurements were taken at 10-min intervals for three months. Thus, the temperature values, thermal power, thermal performance, efficiency, energy rates, exergy rates, and electrical parameters of the micro-CHP system can be monitored. The heating demand of the building was covered totally by the micro-CHP system, and the thermal power of the stoker boiler was reduced by approximately 1.29 kW due to the integration of the Stirling engine. The daily total of 2.6 kWh of electricity was generated with the Stirling engine and stored in the battery, and the rest of the electrical energy demand was taken from the grid. If the heating system is supported by PV, it can be operated off-grid. This study will provide insight into the usage of micro-CHP systems in residential applications.

ANALISIS UNJUK KERJA PLTS CARPORT 37,8 KWP DI AREA PERKANTORAN KEMENTERIAN ESDM REPUBLIK INDONESIA JAKARTA PUSAT

The utilization of PV in Indonesia increased significantly. Based on data submitted by the Director General of EBTKE, the PV capacity value until first semester of 2021 is 217 MW. New challenge has emerged, namely maintaining performance of the PV system in good condition. Performance of the PV Carport system is analyzed by simulating PV using PVSyst software to see the potential value of electricity production, then calculating the performance parameters of the PV system such as Yield Factor (Yf), Yield Reference (Yr), Performance Ratio (PR) and Capacity Factor (CUF). Then compared with the results of electricity production and performance parameters of the PV system during operation for one year. The object of research on performance of the 37.8 kWp Carport PV system belonging to Ministry of Energy and Mineral Resources. Based on the PVSyst simulation results, the potential annual electrical energy from the Carport PV system is 53,910 kWh with performance parameters (Yf, Yr, PR, and CUF) respectively 1,426.19 kWh/kWp, 1,751.9 kWh/kW, 81.4% and 16%. The production value of the Carport PV system for a year in 2021 is 21,694.67 kWh with performance parameters (Yf, Yr, PR, and CUF) respectively of 573 kWh /kWp, 1,679.9 kWh/kW, 34% and 6.6%.

ANALISIS UNJUK KERJA PEMBANGKIT LISTRIK TENAGA SURYA (PLTS) ATAP ON-GRID 11,2 KWP DI RESIDENSIAL BUKIT GADING MEDITERANIA, JAKARTA UTARA

The implementation of PLTS as the utilization of EBT in Indonesia is still being developed. Government policy through KEN and RUEN targets a national PLTS capacity of 6.5 GW in 2025 and 45 GW in 2050. The PLTS system is implemented from the smallest scale, namely residential, with a new challenge, namely to maintain the performance of the PLTS system in good condition. The object of this research was carried out on an 11.2 kWp on-grid rooftop PV mini-grid system at the Bukit Gading Mediterania Residential. The performance of this rooftop PV system was analyzed using the PVsyst software to determine the potential for electricity production, then calculated the PV system performance parameters such as Yield Factor (Yf) , Yield Reference (Yr), Performance Ratio (PR) and Capacity Utilization Factor (CUF) of PLTS within one year of operation starting June 2021 – May 2022, which is then compared with the results of electricity production and the performance parameters of the PLTS system during operation one year from June 2021 – May 2022. The real production value of the 11.2 kWp on-Grid rooftop PV system for a year is 13,809.6 kWh with performance parameters (Yf Factor), Yield Reference (Yr), Performance Ratio (PR ) and Capacity Utilization Factor (CUF)) respectively are 1233.0 kWh/kWp, 1699.7 kWh/kW, 72.4% and 14.1%. For the PVSyst simulation results, the annual potential of electrical energy from the 11.2 kWp on-grid rooftop PLTS system is 15,649 kWh with performance parameters (Yf), Yield Reference (Yr), Performance Ratio (PR) and Capacity Utilization Factor (Yf). CUF)) respectively were 1,397.23 kWh/kWp, 1,691.3 kWh/kW, 82.6% and 16%.

On using artificial neural network models for a thermodynamically-balanced humidification-dehumidification system: Design and rating analysis

This research focuses on rating and sizing a thermodynamically-balanced humidification-dehumidification (HDH) system with zero, single, and double extractions of humid air from the humidifier to the dehumidifier. In order to implement thermodynamic balancing appropriately, optimal thermal design is essential to be estimated via a reliable and straightforward model. Thus, this study suggests an artificial neural network to predict the system performance and size parameters. The artificial neural network is trained, tested, and validated using a rich data set created to cover possible operating ranges. The considered performance parameters are gained output ratio, water-to-air mass flow rate ratio, recovery ratio, seawater and air temperatures, energy effectiveness, and critical enthalpy pinch, and the design parameters involve humidifier volume and dehumidifier area. Four inputs are used: seawater inlet temperature, heating temperature, enthalpy pinch, and extractions count. The developed model works best for inlet temperature, heating temperature, enthalpy pinch, and extractions count within 20 – 40 °C, 60 – 80 °C, 1 – critical enthalpy pinch in kJ kgd-1, and 0 – 2 extractions, respectively. The results indicate that the developed artificial neural network has high accuracy in predicting the performance and design parameters, demonstrating minimum accuracy of 98.3%, 96.4%, and 98.9% for zero, single, and double extractions, respectively. Furthermore, the generated neural network is validated against numerical and experimental data from the literature, showing a maximum discrepancy percentage of less than 4%. Thus, the neural network provided by this study is helpful for HDH desalination engineers, designers, researchers, and scientists.

DETERMINATION OF OPTIMUM FLUID FOR DIFFERENT HEAT SOURCE TEMPERATURES BASED ON MULTI-OBJECTIVE FUNCTIONS IN THE ORGANIC RANKİNE CYCLE

In this study, the optimum fluid was determined by using Non-dominated Sorting Genetic Algorithm-II (NSGA-II) within the scope of Organic Rankine Cycles (ORC) low temperature applications. Heat source temperatures are taken as 90, 100 and 110 °C. Fluid optimization was performed by comparing the performance of 8 fluids from 4 different categories under different criteria (dry-R601 and R601a, isentropic-R141b and R123, wet-R152a and R134a, new generations-R1234yf and R1234ze). Objective functions have been established under the parameters of Energy, Exergy, Economy and Environment (4E). In ORC systems, every organic fluid has certain advantages and disadvantages. It is seen that the studies on organic fluid selection meet a single goal from the system performance parameters. However, it has been observed that the turbine power performance is not at the desired level due to the required evaporator capacity of the fluid, which performs well in terms of thermal efficiency in ORC systems. Therefore, it is necessary to determine the percentage of organic fluid that can be used by optimizing it under different objective functions. In this study, the optimum fluid was determined for ORCs operating under 90, 100 and 110 °C heat source temperatures by evaluating different objective functions together.

Optimal Interplanetary Transfer of Solar Wind Ion Focusing Thruster-Based Spacecraft

The Solar Wind Ion Focusing Thruster (SWIFT) is a highly-innovative propellantless propulsion concept, recently proposed by Gemmer and Mazzoleni. In its nominal configuration, a SWIFT consists of a conically-shaped mesh of positively-charged conducting tethers, with its vertex linked to the spacecraft and its axis oriented towards the Sun. The SWIFT collects and filters the solar wind plasma and suitably directs the positive ions, which are then accelerated by an ion thruster. Such a device is theoretically able to generate a deep-space propulsive acceleration that comes, in part, from the solar wind dynamic pressure impinging on the conical grid and, in part, from the positive ion beam. In particular, the orientation of the ion beam may be chosen in such a way as to set the resultant propulsive acceleration and steer the spacecraft. The aim of this paper is to analyze the performance of a SWIFT-propelled spacecraft in an orbit-to-orbit two-dimensional interplanetary transfer. To that end, some mission scenarios are studied, in an optimal framework, by minimizing the total flight time necessary for the spacecraft to complete the transfer as a function of the propulsion system performance parameters. Numerical simulations are used to compare the optimal flight times calculated in simplified Earth–Venus and Earth–Mars transfers with those obtained by considering other propellantless propulsion systems.

Predicting the Ammonia Synthesis Performance of Plasma Catalysis Using an Artificial Neural Network Model

Ammonia synthesis in a plasma catalysis system coupling dielectric barrier discharge and an alumina-loaded ruthenium catalyst was investigated. The discharge energy, the N2 to H2 ratio, and the reactant flow rate greatly affect the production of NH3 and the efficiency of energy. Higher ammonia synthesis rates and higher energy yields were obtained at high discharge powers and flow rates. An artificial neural network (ANN) was built to describe the influence of operating parameters on the NH3 synthesis performance, including ammonia synthesis rate and energy yield. The proposed ANN model was trained using experimental data. The results showed the N2 to H2 ratio was the most impactful parameter with a relative importance of 41.7% on the model, followed by the flow rate and discharge power of 32 and 26.3%, respectively. This ANN model can effectively help to optimize the operating parameters of the plasma catalysis system for NH3 synthesis and predict the catalysis performance under specific situations.

Predictive investigation of the combined heat storage system using at NPPs with VVER

The increase in the share of nuclear power plants in the energy systems leads to the need for their participation in covering the irregularities in the electrical load schedules. NPPs unloading is economically unsustainable. Combining NPPs with storage systems can be a solution to this problem, as it will allow the reactors to operate at a constant capacity utilization factor, while excess energy will be stored during load dip hours and converted into electrical energy during peak load hours. The authors have developed a thermal storage system based on the combination of a phase change accumulator with hot and cold water tanks. The system also contains a steam turbine to generate electricity during peak hours using stored energy. Earlier the authors proved that a low-power turbine can be used to supply power to the NPP auxiliary needs with a blackout using only residual heat energy. In this paper, a methodology for determining the necessary operating parameters of the storage system for using under the required conditions is presented. The investigation of economic efficiency was carried out on the example of a range of electricity tariffs for the world’s energy systems. Accumulated net present value ranged from $0 to $85 million.

A novel two-stage compression air-source heat pump cycle combining space heating, cooling, and domestic hot water production

To improve the efficiency of HVAC system, a novel air-source heat pump based on the two-stage compression cycle is proposed. The heat pump shows the multi-function of producing the domestic hot water year-round, space cooling water in summer, and space heating water in winter. The theoretical model is developed to analyze the performance of heat pump under various conditions. Four high GWP refrigerants, including R134a, R32, R407, R410A, and four low GWP refrigerants, including R1234ze(E), R600a, R152a, R290, are compared and analyzed under various conditions. Besides, the effects of environment and system operation parameters, such as domestic hot water demand ratio, evaporating and condensing temperatures, on system performance are investigated. The major results are concluded: A multi-functional air-source heat pump shows high annual efficiency by switching both the four-way valve and tee valve. The low GWP refrigerant R152a shows the best performance among the selected refrigerants, especially for summer conditions, and the COP is higher than the R1234ze(E), R134a, R600a, R32, R407C, R410a, and R290, R152a by 10.4 to 9.0 %, 8.3 to 7.9 %, 5.6 to 4.0 %, 9.1 to 13.0 %, 20.9 to 23.3 %, 29.8 to 36.5 %, and 11.0 % under the selected condition, respectively. Compared with summer conditions, R152a improves COP slightly under winter conditions, especially for R600a and R134a (less than 5 % under the selected conditions). Besides, under the summer conditions, the system performance increases first and then decreases with the increasing domestic hot water demand. The maximum COP reaches 6.7 while the summer domestic hot water load ratio (SLR) equals 0.5 and the environment air temperature equals 25 °C. The heat pump also shows a good performance under winter conditions. The minimum COP reaches 2.4 with the air temperature of −25 °C and the winter domestic hot water load ratio (WLR) of unity. The result of this study hopes to promote the further improvement of the air-source heat pump and HVAC system.

A Combined Power and Ejector Refrigeration Cycle System for Thermal Energy Recovery from the Waste Heat of Internal Combustion Engine

In this paper, due to the importance of energy recovery from internal combustion engines and the increasing human growing need for power and refrigeration, a two-cycle power and refrigeration system consisting of an organic Rankine cycle and an ejector refrigeration cycle is investigated analytically. The proposed system with the ability to use the ejector<span dir="RTL"> as an effective and economical system, can produce simultaneous power and refrigeration by drawing thermal energy from the exhaust gas and engine coolant in the form of a novel cycle. This study analyzes the proposed novel two-cycle Rankine-ejector refrigeration system (TCReRS) compared to a two-cycle Rankine-absorption refrigeration system (TCRARS). It was concluded that the TCReRS with higher output and a COP of about 69.99%, is a useful and promising system under the considered condition. The proposed system uses environmentally friendly R1233zd and R32 fluids as operating fluids in Rankine power and ejector refrigeration cycles, respectively. In addition, the effect of system performance parameters has been investigated and optimized.

Multicriteria Design and Operation Optimization of a Solar-Assisted Geothermal Heat Pump System

This work focuses on the determination of the design and operation parameters of a thermal system depending on the optimization objective set. Its main objective and contribution concern the proposal of a generalized methodological structure involving multiobjective optimization techniques aimed at providing a solution to a practical problem, such as the design and dimensioning of a solar thermal system. The analysis is based on system operation data provided by a dynamic simulation model, leading to the development of multiple surrogate models of the thermal system. The thermal system surrogate models correlate the desired optimization objectives with thermal system design and operation parameters while additional surrogate models of the Pareto frontiers are generated. The implementation of the methodology is demonstrated through the optimal design and operation parameter dimensioning of a solar-assisted geothermal heat pump that provides domestic hot water loads of an office building. Essentially, energy consumption is optimized for a desired domestic hot water thermal load coverage. Implementation of reverse-engineering methods allows the determination of the system parameters representing the optimized criteria.

Statistical Method of Low Frequency Oscillations Analysis in Power Systems Based on Phasor Measurements

This study aims to develop and test a new accelerated method for analyzing low-frequency oscillations in power systems using phasor measurements. The proposed method is based on the use of mathematical statistics methods that do not require significant computing power and have high reliability. Changes in the structure of power generation and integration of control devices based on power electronics cause low-frequency oscillations of power system operation parameters that present a threat. These changes result in a reduction in the total inertia of power systems with the subsequent impact on the operation of automatic voltage regulators and power system stabilizers, the purpose of which is to damp low-frequency oscillations. We conduct a careful review of the existing methods for low-frequency oscillations analysis in power systems to identify the gaps in the literature and design a new method to address the issues. The proposed method is tested on real-life data that was obtained during a disturbance with a transient event. Estimation of the low-frequency oscillation parameters was carried out, and the potential threat posed by these phenomena was examined. The implementation of the proposed algorithm for analyzing low-frequency oscillations is done using the Matlab programming language. Evaluation of the proposed algorithm is performed on physical data obtained during real transient processes occurring at large power plants.

Privacy preserving Federated Learning framework for IoMT based big data analysis using edge computing

The current industrial scenario has witnessed the application of several artificial intelligence-based technologies for mining and processing IoMT-based big data. An emerging distributed machine learning paradigm, Federated Learning (FL), has been widely applied in IoMT-based systems as a measure to overcome the issues associated with incorporating AI into such lightweight distributed computing systems while tackling privacy issues as well. However, extensive research has identified that classical FL is still prone to privacy threats due to data leakage and the chances of adversarial attacks during gradient transfer operations. Inspired by these issues, we propose a privacy-preserving framework (Fed_select) that ensures user anonymity in IoMT-based environments for big data analysis under the FL scheme. Fed_Select utilizes alternative minimization to limit gradients and participants in system training to decrease points of system vulnerability. The framework works on an edge computing-based architecture which ensures user anonymity via the employment of hybrid encryption techniques along with added benefits of load reduction at the central server. Also, a Laplacian noise-based differential privacy is employed on the shared attributes for security enhancement that adds confidentiality to the transferred data even during adversarial scenarios. Experimental results on standard datasets showcase that the change in the volume of gradients shared and the number of participants is not proportional to the variation in various system performance parameters. Specifically, an idealistic range of client and gradient-sharing fractions along with the appropriate value of noise for differential privacy implementation is determined. Additionally, we analyze the system from a security perspective as well as compare it with other schemes.