Liquid air energy storage (LAES) is promising for decarbonizing the power network. Fluids are popular as both cold recovery and storage media with the benefits of no additional heat exchangers and straightforward control strategy. Methanol and propane are required to work together as single fluid is not able to work in such a wide temperature range of 85-300 K. This leads to a four-tank configuration, making the cold storage bulky and complex. To address this issue, this paper investigates various fluids and it is found that their temperature range could be extended when they are under pressure (i.e., pressurized fluids). This makes it possible to recover and store the cold energy from liquid air by single pressurized fluid with a two-tank configuration. Therefore, a compact LAES configuration is proposed with pressurized propane (1 MPa) as an example for cold recovery and storage. A new concept of cold storage density is discussed for the first time to show how much cold energy is stored per unit. The Simulation results show that the proposed LAES system increases the volumetric cold storage density by ~52%, saves the capital cost of cold storage by 37%, and shortens the simple payback period of the system by 1.13-67.72%, compared with the traditional LAES system with fluids-based cold storage. This study will provide a feasible way to simplify the LAES system and improve the economic benefits.
Erythritol as a phase change material has a main disadvantage: low thermal conductivity. In this paper, we proposed a novel erythritol/graphene composite phase change material and its thermal properties were predicted by molecular dynamics method. The effects of graphene mass fraction, size and number of layers on thermal conductivity were analyzed. The mechanism of graphene on thermal conductivity was revealed from the microscopic point of view. The results show that graphene can efficiently improve the thermal conductivity of the composites, thus improving the thermal properties of erythritol. The thermal conductivity of the composites increases with the increase of graphene amount, size and number of layers. When the mass fraction of graphene increased to 8 wt%, the thermal conductivity is doubled. This study can provide guidance for the design and application of erythritol-based composite phase change materials.
Energy analysis and energy evaluation are the important basis of efficiency management of hospital building. The purpose of this paper is to summarize the scientific and applicability of the evaluation methods and indicators of hospital energy consumption in China. Based on the analysis of energy consumption characteristics of different types of hospitals, the simple normalization method and multiple regression model were used to evaluate the building energy consumption of hospitals, and the conclusions of the two were compared. Based on the data analysis of 30 sample hospitals in Shanghai, this paper analyzes the defects in the evaluation and classification method of Chinese hospital buildings and the quota energy consumption index. This study found that at the present stage, the classification of hospital energy consumption evaluation in China is not clear, and the evaluation conclusions obtained from different indicators are inconsistent. On this basis, this paper puts forward the development needs of hospital energy consumption analysis and energy saving evaluation in the classification of hospital types, selection of evaluation indexes and dynamic evaluation methods, in order to solve the above problems effectively.
Self-excited thermoacoustic oscillations usually occur in many practical systems such as rocket motors, gas turbines and cryogenic distribution systems. We study the dynamics of a self-excited cryogenic thermoacoustic system subjected to acoustic forcing. The effect of the driving phase on the amplitude of forced oscillation is analyzed. The results show that the variation of driving phase has not affected the maximum amplitude of pressure oscillation. The coupling oscillation characteristics are clarified through the phase portraits. By analyzing the data with Poincare map, we found a range of nonlinear dynamics, including (i) a shifting of the 1-period oscillation towards k-period oscillation as the forcing phase increases; (ii) an accompanying transition from single-frequency model to two-frequency model. The results suggest that such oscillators can be used to represent thermoacoustic selfexcited systems subjected to similar forcing.
To address the problems of low performance efficiency and high energy consumption of conventional fuel cell test systems, this study proposes a novel fuel cell test system. This test system integrates hydrogen circulation and recovery preheating, and uses a condenser to collect water generated from the stack, realizing the integrated gas-heat-water utilization of the system. The thermodynamic model of the system is also established, and the performance of the two systems are compared and evaluated using exergy analysis. The exergy loss distribution of each auxiliary component in the system as well as the net power, parasitic power, and exergy efficiency of the system are determined. The results show that the fuel cell stack, exhaust gas emission and bubble humidifiers are the locations with the largest losses in both systems, and the performance of the stack and the waste heat recovery of exhaust gas the system should be improved. When the systems are operating at 1A/cm2, the exergy loss of the novel system is 96kW, which is 28% lower than the conventional system, the net power output of the system is 80kW, which is 19% higher than conventional system, and the exergy efficiency of the system is 39%, which is 32% higher than the conventional system, while saving 309kg of humidified water per hour. Therefore, the proposed novel system can significantly improve the system performance and overall operating efficiency. The analysis of the two systems can provide a novel direction for further performance improvement of the fuel cell test system.
Machine learning holds a lot of promise for quickly and correctly assessing building energy performance at urban level. However, due to the lack of data for minority types of buildings, unfavorable results are produced sometimes. Therefore, this study proposes a concise approach to generate enough data for training machine learning models while avoiding overfitting. Superior results are obtained. The importance of variables is analyzed using urban open data sets, which are valuable to data collectors and publishers in decision-making.
To solve significant differences in the performance of solar-air source heat pump systems under different weather conditions for an office building of scientific research in Shanghai, it proposes to classify the meteor-ological data by cluster analysis in this paper. Mainly, it uses solar insolation and outdoor temperature as two primary indicators with ten secondary indicators for further analysis. By this means, this study classifies 90 days of winter meteorological data in Shanghai into eight categories. Data standardization, factor analysis, and k-means clustering are the critical methods, and Bayes discriminant verifies the correct rate of 98.9% in the paper. Furthermore, it selects the typical day of every selected class to analyze the heat pump operating time effect on the system COP. Finally, the maximum system COP was used to determine the heat pump operating time as the performance optimization target. Meaning-fully, the intra-class daily data was verified to prove that the clustering result was highly accurate and reliable for further research and provides a solid related system control strategy.
This study presents a vision-based deep learning approach for detecting and recognising occupant’ activities and window opening behaviour to help control the heating, ventilation, and air-conditioning (HVAC) system according to space’s actual thermal and ventilation requirements. A convolutional neural network (CNN) model was developed, trained, and deployed to a camera for real-time detection. The results of an experimental test within the case study building indicated an overall detection accuracy of 92.72% for occupancy activities and 87.74% for window operations. Real-time detection and recognition provided the generation of the deep learning influenced profiles (DLIP) used as input for building energy simulation to evaluate the impact of the approach on energy demand and indoor air quality. The present work assesses the importance of the proposed approach for predicting indoor air quality and comfort while optimising building HVAC operations to provide an effective demand-controlled ventilation strategy.
The Net-Zero targets to 2050-2060 set by the major economies to face the global environmental challenges need a multi-disciplinary approach. Circular Economy is one of the main pillars to reach these targets, by redefining the growth models, moving towards a sustainable approach, and decoupling economic activities from the consumption of finite resources.
Higher Education Institutions have the responsibility to set-up capacity building paths with the primary role of providing the next generation of university students with the tools to develop and fully implement existing strategies, and to propose new ideas for new disrupting technologies.
The Project BBChina, funded by the European Commission under the ERASMUS+ CBHE program, dealt with this issue by establishing a Master Program on Bio-Based Circular Economy in three Chinese Universities, whose first edition started in September 2019, and presently starting with its third edition. This paper presents the path that led to the Program implementation, its structure and results, including the implementation of soft skills, represented by a specifically developed Entrepreneurship Course.
The topic will be further discussed by invited representatives from both the Academic and the Industrial world during the “Applied Energy Symposium 2021: Low carbon cities and urban energy systems” within the panel titled “Interdisciplinary Higher Education for a Resilient Circular Economy”.
Great thermal management challenges have to be considered that the thermal design power (TDP) of central processing unit (CPU) in the high-performance computer cluster has reached 400 W with package heat flux of 25 W/cm². In this study, the three-dimensional numerical model of I-type microchannel heat sink (MCHS) with uniform heat source (400W) applied to the fin area was established to explore its fluid flow and temperature distribution. Three dimensionless parameters, ϕC (0.111, 0.148, 0.167, 0.185, 0.222), ϕW (0, 0.5, 1) and ϕL (0, 0.5, 1) were employed to comprehensively analyze the effects of the header shapes on the flow distribution in MCHS based on the Mal-distribution Factor (MF).
The numerical results showed that the variation of flow rate in MCHS behaves an overall trend of gradually decreasing from the middle channels to the channels on both sides due to the I-type inlet/outlet arrangement. Besides, the difference of flow rate among the microchannels in MCHS becomes relatively gentle because of ϕC increasing. It is found that rectangular header (ϕW=1, ϕL=1) performs best in flow distribution but triangular header (ϕW=0, ϕL=0) performs worst. These results indicate that the strategy to improve the flow distribution in the microchannels is to increase the value of the three dimensionless parameters. Summarizing the results of fluid flow distribution, temperature maximum and pressure drop obtained from all simulation calculations, the optimal I-type MCHS header design is with ϕC=0.185, ϕW=1 and ϕL=1. This study could provide a specific instruction for the design of the practical I-type MCHS for high TDP CPUs liquid cooling.