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A numerical study was conducted to investigate the flow and heat transfer characteristics of a supersonic second throat exhaust diffuser for high-altitude simulations. The numerical results were satisfactorily validated by the experimental results. A subscale diffuser using nitrogen was utilized to investigate starting pressure and pressure variation in the diffuser wall. Based on the validated numerical method, the flow and heat transfer characteristics of the diffuser using burnt gas were evaluated by changing operating pressure and geometric shape. During normal diffuser operation without cooling, high-temperature regions of over 3000 K appeared, particularly near the wall and in the diffuser diverging section. After cooling, the flow and pressure distribution characteristics did not differ significantly from those of the adiabatic condition, but the temperature in the subsonic flow section decreased by more than 1000 K. Furthermore, the tendency of the heat flux from the diffuser internal flow to the wall was similar to that of the pressure variations, and it increased with operating pressure. It was confirmed that the heat fluxes of the supersonic and subsonic flows in the diffuser were proportional to the operating pressure to the 0.8 and −1.7 power, respectively. In addition, in the second throat region after separation, the heat flux could be scaled to the Mach number ratio before and after the largest oblique shock wave because the largest shock train affected the heat flux of the diffuser wall.

Biomass and renewable resources are becoming substitutes for fossil-based resources, providing opportunities for more sustainable environmental management and reductions in environmental damage. This paper studies the prospects for wood pellet production in Kazakhstan through the lens of business model adjustment in a microenterprise in Kazakhstan. This study focuses on answering the following questions: (1) How do microenterprises propose, create, deliver and capture value through business models in the wood industry? (2) What are the opportunities and challenges relating to these business models in the context of wood pellet production in Kazakhstan? Kazakhstan has a high potential for biomass production, providing a particularly interesting case for analysing how microenterprises can tap into this potential to create value. This paper combines an analysis of bioenergy and forestry trends with a qualitative case study. The analysis of the business model is based on Osterwalder’s business model canvas. The value proposition of the enterprise studied herein is to provide a local biomass-based alternative to fossil fuels. The overall growth of wood-based industries in Kazakhstan and the national movement towards renewable energy create favourable prospects for microenterprises engaged in the production of wood pellets; however, these industries are also characterised by high institutional and regulatory dependencies.

The increasing demand for global energy consumption expedites major opportunities for the innovation of green energy technologies. Addressing the issue of sustainable energy is highly crucial for societies in order to maintain secure and balanced future progress in the economy and ecologically. Recently, there has been a growing interest in the development of improved and efficient sustainable energy technologies that are capable of reducing the global environmental footprint. The growing knowledge of hybrid techniques contributes to a decrease in the use of environmental resources while generating energy. However, various factors including the availability of natural resources, and different economic policies restrict the development of sustainable energies. Water and energy are the two major aspects for progressing towards a sustainable future. Recently, membrane-based technologies have begun to play an essential role in the advancement of sustainable energy and water demands. In this review article, the opportunities for membrane technologies dealing with water and energy sustainability have been analyzed.

This paper presents a novel method for online power quality data analysis in transmission networks using a machine learning-based classifier. The proposed classifier has a bundle structure based on the enhanced version of the Extreme Learning Machine (ELM). Due to its fast response and easy-to-build architecture, the ELM is an appropriate machine learning model for power quality analysis. The sparse Bayesian ELM and weighted ELM have been embedded into the proposed bundle learning machine. The case study includes real field signals obtained from the Turkish electricity transmission system. Most actual events like voltage sag, voltage swell, interruption, and harmonics have been detected using the proposed algorithm. For validation purposes, the ELM algorithm is compared with state-of-the-art methods such as artificial neural network and least squares support vector machine.

In the development of deep geological repositories (DGRs), performance assessment modeling is used to evaluate the integrity and performance of the engineered and geological barriers for thousands or millions of years of evolution of the disposal system. To evaluate the suitability of a site for a DGR, geoscientific data from dedicated site investigation programs are integrated into site-specific assessments. This paper presents the development and implementation of a modeling workflow aimed at comparing three potential siting areas for a DGR in Switzerland from the viewpoint of long-term safety and technical feasibility. The workflow follows the guidelines of the national regulator addressing safety relevant criteria such as the barrier efficiency of the host rock and its mechanical and chemical integrity in response to repository-induced influences and the long-term stability of the repository site over geological scales. In the regulatory requirements, the role of parametric, conceptual, and scenario uncertainty has been identified as an issue of special importance in the site selection process. The assessment approach comprises a portfolio of numerical models for the simulation of solute, gas and heat transport in the repository nearfield. The modeling was performed with deterministic as well as probabilistic variants integrated in an indicator-based approach that allows the consistent comparison of the candidate sites using quantitative dimensionless performance indices. The model-based assessment of the sites allows a traceable, transparent, and verifiable implementation of the site selection process.

The impact of moisture ingress on the surface of copper indium gallium diselenide (CIGS) solar cells was studied. While industry-scale modules are encapsulated in specialized polymers and glass, over time, the glass can break and the encapsulant can degrade. During such conditions, water can potentially degrade the interior layers and decrease performance. The first layer the water will come in contact with is the transparent conductive oxide (TCO) layer. To simulate the impact of this moisture ingress, complete devices were immersed in deionized water. To identify the potential sources of degradation, a common window layer for CIGS devices—a bilayer of intrinsic zinc oxide (i-ZnO) and conductive indium tin oxide (ITO)—was deposited. The thin films were then analyzed both pre and post water soaking. To determine the extent of ingress, dynamic secondary ion mass spectroscopy (SIMS) was performed on completed devices to analyze impurity diffusion (predominantly sodium and potassium) in the devices. The results were compared to device measurements, and indicated a degradation of device efficiency (mostly fill factor, contrary to previous studies), potentially due to a modification of the alkali profile.

To reduce the influence of wind power output uncertainty on power system stability, demand response (DRPs) and energy storage systems (ESSs) are introduced while solving scheduling optimization problems. To simulate wind power scenarios, this paper uses Latin Hypercube Sampling (LHS) to generate the initial scenario set and constructs a scenario reduction strategy based on Kantorovich distance. Since DRPs and ESSs can influence the distribution of demand load, this paper constructs a joint scheduling optimization model for wind power, ESSs and DRPs under the objective of minimizing total coal cost, and constraints of power demand and supply balance, users’ demand elasticity, thermal units’ startup-shutdown, thermal units’ output power climbing and wind power backup service. To analyze the influences of ESSs and DRPs on system wind power consumption capacity, example simulation is made in a 10 thermal units system with a 1000 MW wind farm and 400 MW energy storage systems under four simulation scenarios. The simulation results show that the introduction of DRPs and ESSs could promote system wind power consumption capacity with significantly economic and environment benefits, which include less coal consumption and less pollutant emission; and the optimization effect reaches the optimum when DRPs and ESSs are both introduced.