Influence of urban form on the performance of road pavement solar collector system: symmetrical and asymmetrical heights

Recent works have highlighted the importance of mitigating the urban heat island effect using innovative technologies. Several studies have emphasised the capabilities of the road pavement solar collector system to dissipate high temperature from the pavement/road surfaces not only to expand its lifecycle but also to reduce the Urban Heat Island effect. This study builds on previous research combining an urban configuration and a road pavement solar collector system in Computational Fluid Dynamics in order to understand the complicated connection of the urban environment and the road pavement. This study investigates the impact of the urban form on the performance of the road pavement solar collector focusing on comparing symmetrical and asymmetrical height of the urban street canyon. A tridimensional de-coupled simulation approach was used to simulate a macro domain (urban environment) and micro domain, which consists of road pavement solar collector pipes. ANSYS Fluent 15.0 was employed with the solar load model, Discrete Ordinate radiation model and Reynold Averaged Navier Stokes with standard k-epsilon equation. The simulation was carried out based on the summer month of June in Milan urban centre, Italy. Results showed a significant variation in the temperature results of road surface in comparing the three configurations. It was also found that there was a significant reduction in the road pavement solar collector system performance when taller building row was behind the first approaching building row. The method presented in this research could be useful for studying the system integration in various urban forms

Energy Consumption Prediction and Control Algorithm for Hybrid Electric Vehicles Based on an Equivalent Minimum Fuel Consumption Model

The development of hybrid technology can effectively solve the problems of the high pollution and energy consumption levels of automobiles. Therefore, an energy consumption prediction and control algorithm for hybrid vehicles based on a minimum equivalent fuel consumption model is proposed. The model’s battery power consumption is equivalent to the fuel consumption, and the sum of the engine fuel consumption and the battery equivalent fuel consumption is established as the objective function. By utilizing these factors, an innovative minimum equivalent fuel consumption model was constructed that could be used to measure the energy efficiency of hybrid vehicles. The longitudinal force result of braking force distribution control was obtained, as well as the energy consumption prediction structure of a hybrid electric vehicle. The rolling resistance, air resistance, and climbing resistance of the hybrid electric vehicles were calculated, and the energy consumption control algorithm for hybrid electric vehicles was constructed according to the calculation results. The experimental results indicated that under this research algorithm, the driving energy consumption of hybrid electric vehicles was relatively low and the energy consumption and energy efficiency measurements effectively met the actual demand, and the energy consumption prediction and control results were good

Recent Advances and Applications of AI-Based Mathematical Modeling in Predictive Control of Hybrid Electric Vehicle Energy Management in China

Artificial intelligence is widely used in mathematical modeling. The technical means in mathematical modeling are more and more diversified, especially the application of artificial intelligence algorithm greatly promotes the development of mathematical modeling. In recent years, because of its great influence on the fuel consumption, output power and exhaust performance of automobiles, the control strategy has become a research hotspot and focus in automobile R&D industry. Therefore, based on the relevant research results in recent years, after studying and analyzing the typical control strategies of hybrid vehicles, this paper finally puts forward the energy management strategy of hybrid vehicles based on model predictive control (MPC), and strives to contribute to the academic research of energy management strategies of hybrid vehicles

Research on the relationship between transmission efficiency and input torque of manual transmission

Based on the manual transmission of a micro car, this paper would present the analysis of the factors affecting the transmission efficiency (TE) and the calculation formula of TE. Accordingly, the calculation model of Matlab/Simulink TE would be built to figure out how TE varies with input torque. Meanwhile, a set of manual transmission test bench would be designed and used to verify the theoretical simulation results. It adopts a common DC bus energy feedback closed system which can feedback the power generated by the load motor to the grid through the DC bus so as to save the electricity and produce less pollution. Therefore, while the test bench can reflect the variation trend of TE about the manual transmission truly, it is comparatively reliable. Apart from being energy-saving, its unique versatility could definitely predict its exceptional potential. The data of TE obtained from the test bench are compared with the simulation result. It showed that the TE of bench test and simulation result are similar, though companied by less than 2% error difference which is within the allowable range. Most importantly, the bench test results proved the validity of the theoretical analysis statistically, which is of great necessity and significance to the research of TE

Using perceptron feed-forward Artificial Neural Network (ANN) for predicting the thermal conductivity of graphene oxide-Al2O3/water-ethylene glycol hybrid nanofluid

In this paper, Artificial Neural Network (ANN) was used to investigate the influence of temperature and volume fraction of nanoparticles on the thermal conductivity of Graphene oxide-Al2O3/Water-Ethylene glycol hybrid nanofluid. Nanofluids were prepared with the volume fraction of nanoparticles 0.1, 0.2, 0.4, 0.8, and 1.6% in the temperature range of 25–55 °C. The nanofluid's thermal conductivity results were extracted from six different volume fractions of nanoparticles and seven different temperatures. Then, to generalize the data and obtain a function, the Perceptron feed-forward ANN was used, simulating the output parameter. The outcomes show that the ANN is well trained using the trainbr algorithm and has an average of 1.67e-6 for MSE and a correlation coefficient of 0.999 for thermal conductivity. Finally, we conclude that the effect of increasing the temperature of nanofluid is less against the volume fraction of nanoparticles, especially in low concentrations. This effect is negligible and in the absence of nanoparticles, increasing the temperature from 20 °C to 55 °C leads to an enhance in thermal conductivity of about 6%. However, at high concentrations of nanoparticles, increasing the temperature leads to further thermal conductivity. At volume fraction nanoparticles 1.6%, increasing the temperature from 20 °C to 55 °C increases the thermal conductivity from 0.45 to 0.54 W/m.K

Deciphering the Molecular Targets and Mechanisms of HGWD in the Treatment of Rheumatoid Arthritis via Network Pharmacology and Molecular Docking

Background. Huangqi Guizhi Wuwu Decoction (HGWD) has been applied in the treatment of joint pain for more than 1000 years in China. Currently, most physicians use HGWD to treat rheumatoid arthritis (RA), and it has proved to have high efficacy. Therefore, it is necessary to explore the potential mechanism of action of HGWD in RA treatment based on network pharmacology and molecular docking methods. Methods. The active compounds of HGWD were collected, and their targets were identified from the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP) and DrugBank database, respectively. The RA-related targets were retrieved by analyzing the differentially expressed genes between RA patients and healthy individuals. Subsequently, the compound-target network of HGWD was constructed and visualized through Cytoscape 3.8.0 software. Protein-protein interaction (PPI) network was constructed to explore the potential mechanisms of HGWD on RA using the plugin BisoGenet of Cytoscape 3.8.0 software. Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed in R software (Bioconductor, clusterProfiler). Afterward, molecular docking was used to analyze the binding force of the top 10 active compounds with target proteins of VCAM1, CTNNB1, and JUN. Results. Cumulatively, 790 active compounds and 1006 targets of HGWD were identified. A total of 4570 differentially expressed genes of RA with a p value  0.5 were collected. Moreover, 739 GO entries of HGWD on RA were identified, and 79 pathways were screened based on GO and KEGG analysis. The core target gene of HGWD in RA treatment was JUN. Other key target genes included FOS, CCND1, IL6, E2F2, and ICAM1. It was confirmed that the TNF signaling pathway and IL-17 signaling pathway are important pathways of HGWD in the treatment of RA. The molecular docking results revealed that the top 10 active compounds of HGWD had a strong binding to the target proteins of VCAM1, CTNNB1, and JUN. Conclusion. HGWD has important active compounds such as quercetin, kaempferol, and beta-sitosterol, which exert its therapeutic effect on multiple targets and multiple pathways

Nitrogenous Fertilizer Levels Affect the Physicochemical Properties of Sorghum Starch

Nitrogen is a key factor affecting sorghum growth and grain quality. This experiment was designed to investigate the physicochemical properties of sorghum starch in four sorghum varieties (Liaoza 10, Liaoza 19, Jinza 31, and Jinza 34) under four nitrogen levels: 0 kg/ha urea (N1), 300 kg/ha urea as base fertilizer (N2), 300 kg/ha urea as topdressing at the jointing stage (N3), and 450 kg/ha urea as topdressing at the jointing stage (N4). The results showed that grain size and amylose content increased with increasing nitrogen fertilizer level, peaking at N3. The peak viscosity, final viscosity, gelatinization temperature, initial temperature, final temperature, and enthalpy value increased with the nitrogenous fertilizer level, peaking at N3. The application of nitrogen fertilizer at the jointing period significantly increased the above indicators. However, excess nitrogen at the jointing period (N4) can significantly reduce the above indicators, thus changing the physicochemical properties and structure of sorghum starch. Overall, nitrogen significantly affects the structure and physicochemical properties of sorghum starch