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This paper focuses on the influence of ramp locations upstream of a strut-based scramjet combustor under reacting flow conditions that are numerically investigated. In contrast, a computational study is adopted using Reynolds Averaged Navier Stokes (RANS) equations with the Shear Stress Transport (SST) k-ω turbulence model. The numerical results of the Deutsches Zentrum für Luft- und Raumfahrt or German Aerospace Centre (DLR) scramjet model are validated with the reported experimental values that show compliance within the range, indicating that the adopted simulation method can be extended for other investigations as well. The performance of the ramps in the strut-based scramjet combustor is analyzed based on parameters such as wall pressures, combustion efficiency and total pressure loss at various axial locations of the combustor. From the numerical shadowgraph, more shock interactions are observed upstream of the strut injection region for the ramp cases, which decelerates the flow downstream, and additional shock reflections with less intensity are also noticed when compared with the DLR scramjet model. The shock reflection due to the ramps enhances the hydrogen distribution in the spatial direction. The ignition delay is noticed for ramp combustors due to the deceleration of flow compared to the baseline strut only scramjet combustor. However, a higher flame temperature is observed with the ramp combustor. Because more shock interactions arise from the ramps, a marginal increase in the total pressure loss is observed for ramp combustors when compared to the baseline model.

The utilization of energy is on the rise in current trends due to increasing consumptions by households. Smart buildings, on the other hand, aim to optimize energy, and hence, the aim of the study is to forecast the cost of energy consumption in smart buildings by effectively addressing the minimal energy consumption. However, smart buildings are restricted, with limited power access and capacity associated with Heating, Ventilation and Air Conditioning (HVAC) units. It further suffers from low communication capability due to device limitations. In this paper, a balanced deep learning architecture is used to offer solutions to address these constraints. The deep learning algorithm considers three constraints, such as a multi-objective optimization problem and a fitness function, to resolve the price management problem and high-level energy consumption in HVAC systems. The study analyzes and optimizes the consumption of power in smart buildings by the HVAC systems in terms of power loss, price management and reactive power. Experiments are conducted over various scenarios to check the integrity of the system over various smart buildings and in high-rise buildings. The results are compared in terms of various HVAC devices on various metrics and communication protocols, where the proposed system is considered more effective than other methods. The results of the Li-Fi communication protocols show improved results compared to the other communication protocols.

The energy market is gradually changing from centralized trading to peer-to-peer trading due to the tremendous increase in a microgrid with green energy resources. When more generating units are included in the microgrid, the possibilities of more reactive power flows exist in the system that leads to high transmission loss which has to be optimized. The reactive power is one of the essential ancillary services in the microgrid towards preserving the voltage in the transmission and distribution line. The major contribution of the paper is towards managing the ancillary service in the distributed energy network economically and technically. This study aims to estimate and optimize the power loss, reactive power, and price management as well. Towards optimization, the self-balanced differential evolution algorithm (SBDE) is used in this study. A distribution system operator is involved in coordinating the sellers and buyers. The proposed layered microgrid architecture uses the blockchain technology for reactive power price management by providing transparency and security among peers. The process of converging various transactions into a block and adding in the distributed blockchain is illustrated. Multiple transactions are performed by using the proposed methodology, giving efficient energy transaction. The results show that the power loss is minimized using SBDE algorithm for different cases. Additionally, the study has demonstrated the price allocation of the optimal reactive power obtained from providers. The blockchain technology embedded in reactive power pricing will play a significant role in the evolution of traditional power distribution systems to active distribution networks.