In this work, we conducted 2-D numerical simulation to explore the characteristics and mechanism of temperature-driven switching and deflection of bistable flow crossing side-by-side tubes. The results
show that the temperature gradient has a notable effect on the deflection of flow through the gap between two tubes with the side-by-side arrangement when the ratio of pitch to dimeter is 1.5. The gap flow deflects to the tube side with a higher temperature. With the increase of temperature gradient between the two tubes, the deflection angle is larger and the deflection state can be kept stably for a longer time. The analysis on drag and lift demonstrates that the switching and deflection of bistable flow driven by temperature results from the effect of temperature gradient on the separation of flow boundary layer. This research provides another control mechanism of bistable flow, and enriches the theory research and the application on thermal control techniques.
In this paper, the molecular dynamic study (MDS) and experimental study are conducted to investigate the CO2 transmission discipline and to improve the carbon sequestration of the microalgae attached membrane photobioreactor (AM-PBR). First, for simulation part, the polyvinylidene difluoride (PVDF) membrane and polytetrafluoroethylene (PTFE) membrane are chosen for MDS. A three-layer simulation model is developed to describe the transmission process of CO2 from gas-liquid layer to microalgae cell. Then, several parameters including diffusion coefficient, concentration profile and velocity profile of CO2 is analyzed to compare the different membranes and working conditions. Finally, the experiments are conducted and the AM-PBR is designed to verified the simulation results. The results showed that the CO2 transmission and diffusion in PVDF membrane is better than PTFE membrane, and the average velocity of CO2 in PVDF system is 3-9 times higher than the velocity in PTFE system. For different working condition, the CO2 diffusion in 5% concentration is the highest comparing with other groups in both PVDF and PTFE systems. The experiments proved that the AM-PBR has higher microalgae growth rate than the normal PBRs. This work is a fundamental for the optimization of the PBR and membrane to improve the carbon sequestration rate.
It is a promising way to convert the excess renewable energy into hydrogen energy for storage. A two-layer optimization method considering the uncertainty of generation and load is proposed to determine the optimal placement and sizing of the hydrogen energy storage power station (HESS) in the power system with high penetration of renewable energy. The investment cost of the HESS and the operation cost of the power system with HESS are considered in the upper layer of the proposed method. Meanwhile, the Modified Backtracking Search Algorithm (MBSA) is utilized to determine the optimal placement and sizing of HESS in the upper-layer optimization. Based on the strategy formulated by the upper-layer, there is a goal that minimizes the operation cost of the power system with HESS in the lower-layer optimization. The robust optimization method is utilized to solve the optimal scheduling of the lower-layer optimization considering the uncertainty of systems. Finally, the simulation experiment in the IEEE 39-node system is performed to verify the effectiveness of the proposed two-layer optimization method. The simulation results show that the two-layer optimization method can effectively solve the optimal placement and sizing of HESS with consideration of uncertainty.
Combustion process can become more energy efficient and environment friendly if used with appropriate fuel additive. Discovery of fuel additive can be accelerated by applying hybrid approach of using of chemical kinetics and Machine Learning (ML). In this work we present a framework that takes the robustness of Machine Learning and accuracy of chemical kinetics to predict the effect of fuel additive on autoignition process. We present a case of making predictions for Ignition Delay Time (IDT) of biofuel n-butanol (C4H9OH) with several fuel additives. The proposed framework was able to predict IDT of autoignition with high accuracy when used with unseen additives. This framework highlights the potential of ML to exploit chemical mechanisms in exploring and developing the fuel additives to obtain the desirable autoignition characteristics.
Indoor daylight has significant influences on building energy use and occupant health, and studies suggest a targeted illuminance range to achieve an overall performance, the duration of which has been adopted to evaluate the urban design. In previous studies on urban form, solar availability, and daylight, there are two streams of research, namely, the parametric design, and the design optimization. As urban design often involves diverse urban form patterns, the former stream focuses on whether a uniform pattern or random pattern has better solar availability or daylight while the latter directly searches for the best performative design through optimization. However, in the parametric stream, the definitions and sampling for uniform and random design patterns were largely limited; in the optimization stream, the form parameters to optimize were often limited, and searching for the best design only provided one-sided information as design heuristics for generalization. Moreover, most of the studies in both streams focused on general solar availability and daylight, and it is largely unclear how urban form influences the comprehensive daylight duration metric. This study investigates the indoor daylight performance of different urban forms with two specific sets of questions: First, is random urban form better than regular urban form for daylight as stated in the first research stream? And second, what are the best and worst urban forms for daylight and how much performance difference they make, extended from the discussions in the second research stream? To answer these two sets of questions, this study used a grid-based hypothetical design setting as the test case. The study has two parts. In the first part, 500 parametric models were developed to compare daylight availability between five groups of urban form with different levels of uniformity/randomness. In the second part, designs with nearly best and worst daylight are generated using a genetic algorithm and compared. It was found that on average, randomness improves daylight duration, but some random forms perform poorly compared to uniform urban forms, which is echoed by a large performance variation of random urban forms from the optimization results. The findings provide a better understanding of design performance for daylight, which can be used as a general heuristic to inform urban design practice.
With the wide adoption of renewable energy resources in the power grid, energy storage systems have drawn significant attention to improving the stability and efficiency of the power grid. Among various storage systems, Liquid Air Energy Storage (LAES) has a promising future due to its intrinsic advantages. However, the modeling of a LAES is a complex issue, and existing approaches based on principles have a heavy computational load. To facilitate modeling of LAES, this study focused on data-driven modeling with machine learning and conducted a comparative analysis for several popular methods, including K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Deep Neural Networks (DNN). With LAES as the study case, data-driven models were built based on the data generated by its first-principal model developed with the Aspen HYSYS simulation software. For the selected machine learning methods, the modeling accuracy and running time were compared, showing that the DNN achieved the best performance compared to the others.
Artificial neural network (ANN) models were developed to predict milk cooling, milk harvesting and water heating electricity consumption using data collected from 56 pasture-based Irish dairy farms. The methodology employed variable selection, outlier detection, hyper-parameter tuning and nested cross-validation. The ANN models were developed to predict monthly electricity use, while monthly predicted values were also aggregated and assessed at farm- and catchment-levels. Model input variables were constrained to stock and milk production, infrastructural equipment and farm management details. The ANN algorithm predicted monthly electricity consumption for milk harvesting with an error of 22% (relative prediction error), milk cooling to within 24% and water heating to within 31%. Prediction errors reduced to 16%, 12% and 9%, respectively when predicted values were aggregated at the farm-level. In addition, significant reductions in prediction errors were calculated when milk harvesting (0.8%), milk cooling (1.8%), and water heating (1.9%) predictions were aggregated at the catchment-level. This demonstrates the potential effectiveness of the developed ANN models as tools for macro-level simulations.
Conventional scenario-based analysis is not able to accurately and comprehensively evaluate the capability of a distribution network to integrate increasing demand and distributed generation (DG) due to their significant uncertainties. To solve this problem, feasible operation region (FOR) was defined and studied, which provides an effective way to obtain the whole picture of hosting capacity of a distribution network. The analytical expressions of thermal boundaries of FOR in a radial distribution network were obtained through theoretical deduction. To validate the obtained thermal boundaries, a point-wise simulation procedure for generating the cross-sections of FOR in two-dimensional power injection space was proposed. An 11kV radial distribution network from the United Kingdom Generic Distribution System (UKGDS) was used for the case study. The results show that the derived thermal boundaries can well approximate the real thermal boundaries of FOR. Moreover, these thermal boundaries of FOR are more accurate than those calculated by a method proposed in a previous study, especially when considering independent reactive power injections in the distribution network.
This study simultaneously investigated the effects of lignite as an additive in biogas applications and the suitability of the obtained digestate to produce organic nitrogen fertilizer. Lignite was added to the anaerobic digestion of liquid dairy manure without an external inoculum at a rate of 3.75 w.-% and a particle size of (1250-2500) μm. Maximum methane production rate, methane potential, and lag phase time were determined using the modified Gompertz model, respectively. Subsequently, the digestate was separated into liquid and solid phases, and nitrogen distribution and loadings were examined. Lignite addition increased the max. methane production rate by +34.7% and decreased the lag phase time by -15.17%. The absolute amount of nitrogen attached to the solid phase, which can be obtained from 1 t digestate after centrifugation, increased by 95.36% from 0.767 kgN/tD (DM) to 1.499 kgN/tD (DM + L) for lignite addition.
A mathematical model of a fuel combustion boiler suitable for system level studies of a heating system and adaptable to different combustible fuels is presented. The model considers the thermophysical properties of the flue gas and of the heat transfer fluid, mass flow rates and temperatures. To provide confidence into the modelling approach, the model was built in MATLAB/Simulink and simulation results were compared with results obtained with Apros. Results exhibited a good agreement between both software platforms under different mass flow rate conditions when methane and hydrogen were employed as fuels.