Abstract

In this study, taking an office building in Xi’an city as the research object, the heat load of the office building has been simulated using DeST. The rate of heat extraction from a 2000 m closed-loop geothermal well has also been simulated using COMSOL. The influences of the mass flowrate on the heat extraction rate and the average outlet temperature have been discussed. Combined with the downhole heat exchanger model, the heat pump model, and the thermal energy storage model, an integrated numerical simulation model for office building heating is established. The model is used to investigate the influence of different energy storage ratio on the system total cost, based on which the optimal energy storage ratio is determined. The results obtained from this study provide theoretical basis for improving geothermal energy utilization.

Abstract

In this study, we propose a reaction system for the low-temperature reduction of NO by H2 on carbon-based materials decorated with 5 wt.% Ni. This cost-effective catalyst system efficiently utilizes pyrolysis carbon-based materials and hydrogen. Additionally, it yields environmentally friendly products without requiring extra heat sources in practical SCR process.
Density functional theory elucidates the mechanism of NO heterogeneous reduction by H2 on Ni-decorated char. Two distinct reaction paths were identified, one involving the intermediate product N2O and the other not. These pathways exhibit different rate-determination steps and activation energies. Kinetic analysis indicates that the N2O byproduct pathway has a lower activation energy. Experimental results corroborate the theoretical findings.

Abstract

As an alternative refrigerant of R134a, natural refrigerant R290 has gradually been studied in the automotive air conditioning system. Due to its explosive characteristics, a certain safety hazard will be caused once leaks. This article studies the leakage and distribution of R290 under different working conditions through numerical simulation. The effects of leakage pores, charge amount, and leakage positions on R290 leaked diffusion. The results show that the leakage phase can be divided into a fast leakage stage, buffer stage, and diffusion phase. The leakage of the condenser is the most dangerous situation in the engine compartment leakage. Increasing the aperture and charge will increase the combustible area and duration of R290 leakage. Compared with the leakage diameter, the charge amount is more impact on the continuous duration of the internal flammable areas in the engine compartment.

Abstract

Energy quality analysis is a novel method to evaluate the effectiveness of mitigation on energy crisis and carbon emission. Entropy increase, as a developing energy quality evaluation theory, quantifies the energy unavailability caused by exergy loss in transmission and uncertainty of source and load, and provides theoretical support for decision making on planning and operational optimization of Integrated Energy System (IES). This article proposes the entropy state mechanism and Flow Hub (FH) model to solve the entropy state distribution of IES. Finally, a case study verifies the effectiveness of the proposed models, discussed the entropy increase balance between source and load.

Abstract

Borehole thermal energy storage (BTES) is a technology in which the thermal energy generated during non-heating seasons may be collected and stored in the soil for extraction in the heating season. However, the average soil temperature of BTES continues to decay with the heat extraction process, resulting in a serious mismatch between the heat extraction and the actual heat load. The variable flow operation of the BTES system allows for flexible adjustment of the heat extraction, increasing the heat flexibility of the BTES system. However, traditional heat transfer models of the BTES cannot quickly and accurately predict the outlet fluid temperature dynamically, making it difficult to match real-time heat load requirements through online regulation of the BTES system. The paper proposes a dynamic prediction method for outlet fluid temperature of the BTES system based on the multi-channel parallel neural network model. To train the neural network model, fluid temperature, flow rate, and multiple sets of soil temperature monitoring results from a large BTES project in Chifeng lasting 11,947 hours were used as the dataset. Randomly divide the dataset into 60% as the training dataset, 20% as the validation dataset, and 20% as the test dataset. The input layer of the basic model contains inlet fluid temperature, flow rate, and multiple sets of soil temperature; the outlet water temperature of the BTES is the output layer. The input features of the advanced model also include the inlet temperature, outlet temperature, and flow rate of the previous moment (hour). After training, the variance of the prediction error for the outlet temperature of the basic model and the advanced model is 0.93 (℃)2 and 0.27 (℃)2, respectively. The advanced model can rapidly and accurately predict the outlet temperature of the BTES, which implies that by continuously iterating with the model, the optimal flow rate can be found to match heat extraction with the real-time heat load.
The influence of changing the heat extraction flow rate of the BTES was also evaluated. The heat extraction of the BTES system increases rapidly with the heat extraction flow rate and then levels off, which emphasizes the importance of variable flow operation for the flexible operation of BTES systems.

Abstract

In this paper, based on analyzing the existing LNG cold energy utilization power generation system and aiming at the existing problems, and also combined with using geothermal resources, seawater and seawater-source heat pump systems, a multi-energy complementary LNG cold energy power generation model is established. In the process of LNG gasification, the proposed system uses seawater as the high-temperature heat source, and LNG as the low-temperature heat source to generate electricity first, and then combines with green energy heating, such as using geothermal or seawater-source heat pump, to meet the heat load requirement in the LNG gasification process. Several kinds of organic working fluids were selected to maximize the net power output and the cycle thermal efficiency, so as to achieve the best operating effect. The system parameters are optimized and the influences of the parameters, such as the inlet and outlet pressures of the turbine, on the system performance have been analyzed. It is found that, using propane as the working fluid, the power generation system can generate 2.47MW electricity in winter and 4.25MW in summer. The optimal evaporation and condensing pressures are 7.3 bar and 0.85 bar respectively, corresponding to a power cycle thermal efficiency of 13.34%. The results show that the proposed system can meet the heat load required for LNG heating and achieve the goal of energy saving, as well as reducing the emission of C02, which is of great significance for promoting the realization of carbon neutrality Future investment needs to be carried out to further analyze the techno-economics of the proposed system.

Abstract

To satisfy the requirements of the renewable energy systems’ construction and development, as well as reducing the challenge got from large-scale renewable energy integration, this paper made some contributions based on a hydropower-photovoltaic (PV)- storage system (HPSS). The capacity ratio of storage system, hydropower and PV, is optimized aiming to smoothly utilize the renewable power output.
First of all, two application scenarios are created. One scenario is focusing on the power output smoothing effort during the piecewised time durations, which is defined as Step Output Power Scenario (SOPS) in this paper; the other scenario is power output shaving in high-frequency segment based on full-time duration. It is defined as Smooth Out the Power Fluctuation Scenario (SOPFS) in this paper. Finally, based on the above two scenarios, multi-objective energy storage capacity optimization is proposed. In addition, the integration value of each generation scheduling period in SOPS is optimized.
The measured data from hydro-PV power stations in Lancang River Energy Base is applied, which shows that the proposed method can effectively alleviate the stochastic fluctuations of the renewable energy power output. What’s more, compared with the stand-alone PV system, the integration percentage of renewable energy increased by 21.45%. The HPSS which utilizes energy storage units with capacity of 1,500 MWh is more economical than the stand-alone power generation system.

Abstract

With the proposal of carbon neutrality goals, the necessity of reducing carbon in the aviation industry has become increasingly prominent. The advanced aero propulsion technology of adaptive cycle engines poses a new challenge to the traditional engine, which has received widespread attention and high expectations from people. Adaptive cycle engines can change the engine’s thermal cycle through adjusting variable geometry schedules to improve engine efficiency, reduce fuel consumption, and increase aircraft cruise time under different missions. In this paper, the optimization variables, constraints, and optimization goals are proposed, and the mathematical performance optimization models of the adaptive cycle engine and the mixed flow turbofan engine are established. Compared with the traditional engines, the control laws of each variable geometry modulation are analyzed in terms of performance matching mechanism, and the regulation necessity of all the variable geometries is discussed. The optimal throttling characteristics lines of the adaptive cycle engine in different bypass modes are calculated by the particle swarm optimization method. The advantageous thrust ranges of the adaptive cycle engine in different operating modes are derived, and the design principles of split ratios is given according to the optimization resultsr.

Abstract

Hydrogen safety is intrinsic to the popularization and application of hydrogen energy. Leakage is a major source of hydrogen-related safety accidents, so the research on leakage is crucial. The visual calibration method can quickly visualize the concentration distribution in the area of hydrogen leakage, but the accuracy of the visualized images needs to be improved. To solve the problem, this paper proposes a multi-source data fusion method based on a deep learning framework, which reconstructs the concentration distribution of the hydrogen leakage and obtains a reconstructed concentration distribution image. Firstly, the leakage images are obtained from the schlieren visualization experiment and using the calibration equations for concentration identification. The visualization experiment is simulated by using ANSYS Fluent, and the simulation result was analyzed and studied with the visualization experiment result. Then, the concentration data obtained from the simulation is used for the training, optimization and validation of the multilayer perceptron neural network, and the axial concentration data obtained from the visualization experiments was used as input to the net to obtain the radial concentration data of the reconstructed visualization image, and the attenuation law of radial concentration at three sections were analyzed. From the result, the reconstructed visualization image by this data fusion method can well reflect the concentration distribution of hydrogen leakage.

Abstract

In this study, a closed-loop geothermal system (CLGS) using downhole heat exchanger (DHE) was proposed to extract heat from the HDR resources in order to avoid the problems associated with the current EGS technology. An increasing-pressure endothermic power cycle (IPEPC), with an increasing-pressure endothermic process in the DHE and CO2-based mixture as working fluid, has been developed for the geothermal wells with depths ranging from 2 km to 4 km. The mixture types and mass ratio of the working fluid, and the operation parameters of the IPEPC have been optimized simultaneously. The power generation performance of the IPEPC has been compared with that of organic Rankine cycle (ORC) and that of flash-steam system under the same geothermal conditions. When the DHE length (L) is 2000 m, the net power output of the IPEPC is 17.5%, 98.4% and 120% higher than that of the t-CO2, SF and ORC respectively; whereas it is only 7.4%, 9.1% and 21.7% if the L is 3000 m. Results obtained in this study are of engineering guiding significance for hot dry rock power generation.