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Co-generation of energy derived from human movement is not new. Intentionally accumulating energy, from mass urban-mobility, provides opportunities to re-purpose power. However, when mass-mobility is predictable, yet not harnessed, this highlights critical gaps in application of interdisciplinary knowledge. This research highlights a novel application of geostatistical modelling for the built environment with the purpose of understanding where energy harvesting infrastructure should be located. The work presented argues that advanced Geostatistical methods can be implemented as an appropriate method to predict probability distribution, density, clustering of populations and mass-population mobility patterns from large-scale online distributed and heterogeneous data sets published by the Australian Urban Research Infrastructure Network. Where clear urban spatio-behavioural relationships of density and movement can be predicted – understanding such patterns supports cross-disciplinary city planning and decision-making. A data-informed – predictive spatial decision-making framework is proposed – facilitating the endeavour of cogenerating kinetic human energy within a prescribed space. This novel proposition could further sustainability strategies for compact living for cities such as in Perth, Western Australia which is increasingly economically and geographically pressured to densify. This research argues that surveillance data elucidate a capacity to interpret and understand impacts of densification strategies, efficacy of CCTV networks in existing and emerging cities.

In order to solve the problem of dynamic reconfiguration of distribution network connected with distributed generation (DG) and electric vehicle (EV), a dynamic reconfiguration method of distribution network based on time series symbolized segmentation is proposed. Firstly, the daily load curve of the distribution network accessed to distributed energy and electric vehicles is predicted. The time series of load is symbolized and the segmentation effect is evaluated by information entropy to determine the time division scheme. Then the model is established with the minimum daily operating loss cost as the objective function, and the distribution network is dynamically reconfigured according to the time division. The particle swarm optimization algorithm (PSO) based on branch group search is proposed to solve the model. Finally, the simulation calculation is carried out by using the IEEE33 node system as an example. The results show that the proposed method can reasonably divide the load time series, so that the daily opHrating loss cost of the distribution network after dynamic reconfiguration is reduced.

Abstract This study newly introduces a complementary cooperative algorithm considering generative adversarial network (GAN)-Conditional Latent Space (CLS) combined with bidirectional long short-term memory (BLSTM) for improved and efficient lithium-ion rechargeable battery state prediction. The GAN-CLS algorithm, which is an advanced method of GAN, can generate corresponding images from an input label description. Long short-term memory (LSTM) is a specific recurrent neural network (RNN) architecture that can predict sequences more accurately than conventional RNNs. In terms of battery state prediction, the combination of two methods (GAN-CLS and LSTM) surely provides more improved and efficient rechargeable battery state prediction in contrast to conventional state predictors. The procedure of this study is as follows. First, we propose methods to enhance the data from battery charge/discharge by converting prepared data to images; then, the GAN-CLS method is used to generate corresponding battery data from previous images. Subsequently, the generated data is used to train the BLSTM model. Finally, the trained model is used to predict the battery state. By various experiments and verification, it is concluded that the proposed study can be a good solution for rechargeable battery state prediction (reduction of the time cost 50 times in modeling and 20 times in train/test, provision of a more accurate prediction mean square error (MSE) smaller than 0.0025 and the average MSE less than 0.0013).

The growth of energy consumption has led to the depletion of fossil energy and the increasing greenhouse effect. In this case, low carbonization has become an important trend in the world’s energy development, in which clean energy occupies an important position. The uncertainties brought by the large-scale integration of wind power, photovoltaic and other renewable energy sources into the grid pose a serious challenge to system dispatch. The participation of demand response (DR) resources can flexibly cooperate with renewable energy, optimizing system dispatch and promoting renewable energy consumption. Thus, we propose a flexible DR scheduling strategy based on multiple response modes in this paper. We first present a DR resource operation model based on multivariate response modes. Then, the uncertainties are considered and dealt with by scenario generation and reduction technology. Finally, a day-head dispatch strategy considering flexible DR operation and wind power uncertainties is established. The simulation results show that the proposed strategy promotes wind power consumption and reduces system operation costs.

It is conventionally believed that there are no self-sustained thermoacoustic oscillations in the absence of acoustic modes in combustors. However, such oscillations (also known as intrinsic thermoacoustic instability) are recently found to occur in a premixed combustor with a mean flow present but no acoustic eigenmodes involved. Practical combustors are associated with entropy waves, pressure jump and mean flow, which are ignored in previous studies without justification. In this work, an entropy-involved energy measure is defined and used to study the stability behaviors of intrinsic thermoacoustic modes. The concepts and methods are exemplified with the classical time-delay n–τ unsteady heat release model. The intrinsic thermoacoustic eigenmodes are found to be related to not only a flame transfer/describing function but also the acoustic impedance at the flame, which is boundary-dependent. It is shown that the predicted frequency ωfr of the intrinsic modes and the critical gain nc depend on the ratio T¯2/T¯1 between the after- and before-combustion temperatures and the inlet mean flow Mach number M¯1. Comparison is then made between the present results and those available in literature. Good agreement is obtained for ωfr. Furthermore, the predicted stability of intrinsic modes based on calculated nc is found to agree well with direct numerical simulations (DNS). It is also interesting to show that as T¯2/T¯1→1, the critical gain as predicted from the previous models is nc→+∞, which means that all intrinsic eigenmodes are stable. However, the present works shows that nc→1.0. Further illustration is then performed by conducting case studies of measured flame transfer and describing functions in premixed combustors. The present work opens up an alternative but more applicable way to study intrinsic thermoacoustic oscillations via the entropy-involved energy measure.

Abstract In AP1000 plant, the Automatic Depressurization System (ADS) 1–3 stages operate to discharge the high-temperature and high-pressure steam from the Reactor Coolant System (RCS) primary side to the large heat sink tank In-containment Refueling Water Storage Tank (IRWST) in accidental conditions. The key equipment’s specific shape and arrangement lead to the complicate flow and heat transfer characteristics in IRWST. In the present work, an overall scaled IRWST&ADS sparger experiment has been built up. The thermocouples matrix, flowmeters, pressure transmitters, heat flux sensors, Particle Image Velocimetry (PIV) technique, and high speed camera are employed for the measurements of the key thermal and flow parameters. The local steam jets condensation phenomena as well as the overall flow and thermal behavior are investigated. The experimental results indicate that the thermal stratification phenomenon is obvious in IRWST. The criteria of Richardson Number and Stratification Number are utilized to predict and evaluate the thermal stratification extent, respectively. An improved ADS arrangement design is further proposed to reduce the thermal stratification. Moreover, the multi-holes lumped “steam condensation column” is modeled with characteristic parameters, then the steam condensation heat transfer coefficient range in chugging condensation process is estimated. The experimental results provide practical engineering application reference for the effective operation of the passive safety system in AP1000 plant.

Biomass is a promising alternative energy source for fossil fuel with the advantages of abundance, renewability, environmental friendliness, etc. This makes the development of biomass technology be of great potential and interesting. The experiments of biomass fast pyrolysis were performed in a microquartz reactor for rice husk (RH), corn stalk (CS) and birch wood (BW), and scanning electron microscope (SEM), energy dispersive spectrometer, and Raman microscope were then applied to analyze the collected chars. The average char yields of RH, CS, and BW pyrolyzed at 800 °C were 29.64%, 18.67%, and 8.64%, respectively. The morphological structures of RH and CS were mainly reserved in chars, while the raw surface textures of BW disappeared during the fast pyrolysis. The silicon concentrations in RH char and CS char were much higher than BW char, and the graphitization degree of CS char was the lowest among the three biomass chars.

Abstract In this paper, we investigate distributed thermal-electrochemical modeling of a Lithium-Ion battery cell to include the effect of temperature distribution across the thickness of the cell as a first step to study the module level temperature distribution at high charging rates. Most recent works have focused on lumped thermal models for a Li-Ion cell which ignore any temperature differential across cell thickness. However, even a small temperature differential across cell thickness at the cell level can contribute to significant temperature differential in the thickness direction of stacked-up Li-Ion cells at the module level. Such temperature differential can potentially impact the battery charging control system, especially at high charging rates. Here, the thermal-electrochemical partial differential and algebraic equations for a Li-ion cell are solved via a spatial finite difference method. Simulation results show that the temperature differentials over the cell thickness at the cell level are not insignificant, particularly at high charging rates.

AbstractAs the unique rotating component, the impeller is the core component of a nuclear reactor coolant pump (RCP), the dynamic properties of the impeller are critical for the safe operation of the whole reactor. The purpose of this study was to shed comprehensive light on the pressure pulsation and modal properties of a scaled RCP impeller via experimental and numerical methods. The numerical model was validated by an experiment connecting the pressure pulsation signals at the diffuser inlet, and a good agreement was obtained between the numerical and experimental results. Pressure pulsation acting on the impeller's blade is mainly dominated by the impeller rotating frequency, the vane passing frequency, and the double blade‐passing frequency, and the pressure pulsation acting on the blade's pressure surface is more intense than on the suction surface. The modal properties were obtained via the modal test and numerical methods with the impeller suspended as a free body in the air and submerged inside water. The reduction in the impeller natural frequencies was between 31.63% and 37.77% for the corresponding mode shape due to the added mass effect of the fluid. Based on the pressure pulsation characteristics acting on the impeller and the natural frequency of the impeller, it is considered there is no risk of resonance in the impeller. Finally, it is expected that the present work can provide scientific guidance to avoid hydraulic resonance in nuclear reactor coolant pumps.