Comprehensive Analysis Method of Acquiring Wall Heat Fluxes in Rotating Detonation Combustors

Accurate perception of the combustor thermal environment is crucial for thermal protection design of a rotating detonation combustor (RDC). In this study, a comprehensive analysis method is established to calculate the non-uniform heat flux distribution of the RDC by utilizing the measured temperature distribution of the combustor outer wall obtained by the high-speed infrared thermal imager. Firstly, in order to determine the inverse heat flux solving method, a physical model based on the geometric characteristics of the RDC is constructed and its thermal conductive process is simulated, given by different heat flux boundary conditions. Then the wall heat fluxes are inversely calculated by the Levenberg-Marquardt (L-M) method based on the above numerical data. Results show that the L-M method can obtain more accurate heat flux distribution even in the zones with large heat flux gradients, considering the axial heat conduction within the combustor outer wall caused by the non-uniform heat flux. Finally, the wall heat flux distribution is analyzed coupling the L-M method together with the experimental measurements in kerosene two-phase RDC. The analyses show that the highest temperature of combustor outer surface and the highest wall heat flux occurs within the region of 20mm from the combustor head, which corresponds to nearly 14% of the combustor length. With the increase of axial distance, the heat flux is rapidly reduced, and then the heat flux distribution is more uniform at the downstream region of the combustor. The heat flux peak and thermal heat rate are positively correlated with the combustor equivalence ratio in the range between 0.44 and 0.64.Comment: 26 pages, 21 figure

Intelligent demand response for industrial energy management considering thermostatically controlled loads and EVs

In this paper, an intelligent energy management framework with demand response capability was proposed for industrial facilities. The framework consists of multiple components, including industrial processes modeled by the state task network (STN) method, thermostatically controlled loads (TCLs) like the heating, ventilation and air conditioning (HVAC) system with chilled water storage (CWS), renewable generation like photovoltaic (PV) arrays and electric vehicles (EVs). These components were firstly modeled and the operation of them is then optimized in time-of-use (TOU) pricing schemes. Factors that affect several components at the same time, e.g. the number of workers, are considered. The optimization is formulated as a mixed integer linear programming (MILP) problem. A general tire manufacturing facility was investigated as the case study. Simulation results show that the proposed intelligent industrial energy management (IIEM) with DR is able to effectively utilize the flexibility contained in all parts of the facility and reduce the electricity costs as well as the peak demand of the facility, while satisfying all the operating constraints

A Novel Energy Management Optimization Method for Commercial Users Based on Hybrid Simulation of Electricity Market Bidding

Energy management and utilization for commercial users is becoming increasingly intelligent and refined, fostering a closer and growing connection with the electricity market. In this paper, a novel energy management optimization theoretical framework for commercial users is proposed based on the hybrid simulation of electricity market bidding. The hybrid simulation model based on Multi-Agent Simulation (MAS) with reinforcement learning and System Dynamic Simulation (SDS) is established to solve the problem using a single simulation method: it cannot adjust the clearing price when considering the whole market; considering the uncertainty of Electric Vehicles (EVs) travel and Lighting Loads (LLs), the multi-objective optimization model of energy management for commercial users is constructed to minimize the total energy cost of commercial users, as well as maximize the lighting comfort of indoor office staff, which compensates for the lack of the single-objective optimization of the power consumption for commercial users. A multi-objective optimization model of energy management for commercial users is established based on the hybrid simulation of electricity market bidding. By running the multi-objective optimization model based on hybrid simulation, the results show that the proposed method can realize the optimization of energy management for commercial users considering electricity market bidding

A Robust Optimization Strategy for Domestic Electric Water Heater Load Scheduling under Uncertainties

In this paper, a robust optimization strategy is developed to handle the uncertainties for domestic electric water heater load scheduling. At first, the uncertain parameters, including hot water demand and ambient temperature, are described as the intervals, and are further divided into different robust levels in order to control the degree of the conservatism. Based on this, traditional load scheduling problem is rebuilt by bringing the intervals and robust levels into the constraints, and are thus transformed into the equivalent deterministic optimization problem, which can be solved by existing tools. Simulation results demonstrate that the schedules obtained under different robust levels are of complete robustness. Furthermore, in order to offer users the most optimal robust level, the trade-off between the electricity bill and conservatism degree are also discussed

HDAC2- and EZH2-Mediated Histone Modifications Induce PDK1 Expression through miR-148a Downregulation in Breast Cancer Progression and Adriamycin Resistance

Background: Breast cancer has one of highest morbidity and mortality rates for women. Abnormalities regarding epigenetics modification and pyruvate dehydrogenase kinase 1 (PDK1)-induced unusual metabolism contribute to breast cancer progression and chemotherapy resistance. However, the role and mechanism of epigenetic change in regulating PDK1 in breast cancer remains to be elucidated. Methods: Gene set enrichment analysis (GSEA) and Pearson’s correlation analysis were performed to analyze the relationship between histone deacetylase 2 (HDAC2), enhancer of zeste homologue 2 (EZH2), and PDK1 in database and human breast cancer tissues. Dual luciferase reporters were used to test the regulation between PDK1 and miR-148a. HDAC2 and EZH2 were found to regulate miR-148a expression through Western blotting assays, qRT-PCR and co-immunoprecipitation assays. The effects of PDK1 and miR-148a in breast cancer were investigated by immunofluorescence (IF) assay, Transwell assay and flow cytometry assay. The roles of miR-148a/PDK1 in tumor growth were investigated in vivo. Results: We found that PDK1 expression was upregulated by epigenetic alterations mediated by HDAC2 and EZH2. At the post-transcriptional level, PDK1 was a new direct target of miR-148a and was upregulated in breast cancer cells due to miR-148a suppression. PDK1 overexpression partly reversed the biological function of miR-148a—including miR-148a’s ability to increase cell sensitivity to Adriamycin (ADR) treatment—inhibiting cell glycolysis, invasion and epithelial–mesenchymal transition (EMT), and inducing apoptosis and repressing tumor growth. Furthermore, we identified a novel mechanism: DNMT1 directly bound to EZH2 and recruited EZH2 and HDAC2 complexes to the promoter region of miR-148a, leading to miR-148a downregulation. In breast cancer tissues, HDAC2 and EZH2 protein expression levels also were inversely correlated with levels of miR-148a expression. Conclusion: Our study found a new regulatory mechanism in which EZH2 and HDAC2 mediate PDK1 upregulation by silencing miR-148a expression to regulate cancer development and Adriamycin resistance. These new findings suggest that the HDAC2/EZH2/miR-148a/PDK1 axis is a novel mechanism for regulating cancer development and is a potentially promising target for therapeutic options in the future