C. V. Raman Global University is an engineering and management institution located in Bhubaneswar, Odisha, India. It was established in 1997 as C.V. Raman College of Engineering by Sanjib Kumar Rout under C.V. Raman Group of Institutions. The institute is accredited by the National Board of Accreditation (NBA) and is certified to ISO 9001:2000 quality.The institute is rated "A" by National Assessment and Accreditation Council (NAAC) and is affiliated to Biju Patnaik University of Technology (BPUT). On 29 February 2020, the college was granted university status and was renamed C. V. Raman Global University. V.
Hydrogen is one of the major pillars of the low-carbon economy, but its wide application is limited by difficulties in storage. Metal hydrides (MHs) represent excellent candidates for storage since they are characterized by high volumetric density, reversibility, and safety. This article provides a thorough and integrative review of novel generation MH storage technologies based on intermetallic, complex, magnesium, and chemical hydrides with special focus on thermodynamics and kinetics of these processes. Major restrictions, including high temperatures of desorption, slow kinetics, and cycling instability, are discussed in conjunction with current advanced approaches to address the issue of MH properties improvement, such as catalyst addition, nanoscale modifications, composite materials, and HEA engineering. Special attention is paid to innovative HEA materials, which can improve hydrogen mobility and binding energies due to composition engineering and lattice distortion. In addition, a rapid growth in the use of artificial intelligence algorithms for the fast development of new materials with tailored features and accurate hydrogen storage property prediction is described. Relevance to practice is supported by examples involving hydrogen fuel cells for transport and space applications. Although MH-based storage technologies still have certain drawbacks, such as heat management, material stability, and environmental aspects, they have great promise as reliable and scalable platforms for hydrogen storage.
Liquefied natural gas (LNG) regasification releases a significant amount of cold energy. As a result, the use of LNG for cold energy has gained attention in both academic and engineering studies. In the case of high-pressure LNG (meeting the demands of the gas supply networks following regasification), applying the organic Rankine cycle and seawater as a heat source results in a significant exergy loss and relatively poor power generation. The purpose of this work is to propose a cryogenic power production system based on the direct expansion cycle for the effective use of LNG cold and pressure energy. The proposed system utilizes the low-temperature condensate from the heat recovery steam generator served as a low-grade heat source to reheat the re-gasified LNG to eliminate the use of seawater as a heat source. The LNG flowrate m(center dot)LNG, temperature T4, and pressure P4 at the expander inlet were selected for sensitivity analysis. Then, a multi-objective optimization technique using response surface methodology is employed to maximize the thermal efficiency eta th and exergy efficiency eta ex, and minimize the exergy destruction. Analysis of variance is used to verify the model adequacy, and the constructed model capacity to accurately predict the output responses is examined. Sensitivity analysis is used to recognize and rank different key parameters in order of relevance. The proposed system design demonstrated eta ex increased up to 15.43% and eta th of 16.2% at m(center dot)LNG/P4 of 100 kg/s/100 bar.
Scrapped and damaged high speed cutting tools collected from the machine shop without the addition of alloying elements are casted using centrifugal casting due to its added advantages in mechanical properties over gravity casting. The Cast product is then cut into pieces by wirecut electro-discharge machining process to prepare the tool shank, and then the turning tool tip is developed by grinding the tool shank using tool cutter grinding machine. After grinding, the tool is hardened by proper heat treatment process to get required hardness for cutting. The developed tool cutting ability is evaluated by comparing it with the existing HSS M2 tool during finish turning on mild steel workpieces with varying feed, keeping other parameters constant. The comparison study was conducted on the basis of tool wear, cutting force required for machining, surface finish of workpiece and chip formation. The developed tool showed better results than the existing tool in terms of crater wear and surface roughness for all cutting conditions. However, a slight increase in cutting force components and flank wear is observed for the developed tool after 0.12 mm/rev. Moreover, a similar pattern of chip formation is evidenced for both tools at all cutting conditions. The cutting ability of both developed and existing tool is comparable, claiming this novel technique is a viable method for cutting tool production.
This paper presents an overview of Deep Reinforcement Learning (DRL) control for the voltage regulation of the Dual Active Bridge (DAB) converter. The DRL control has become a model-free control option for power converters operating with dynamic conditions, parametric uncertainties, real-time disturbances, and system instability. The DRL control has been implemented with a Simulink model of a DAB converter for voltage regulation using the Reinforcement Learning Designer app. This app can create, train, and simulate a DDPG agent for a predefined environment i.e., a DAB converter operated by phase shift modulation. The RL Designer app reduces the complexity of designing a lengthy algorithm for creating and training a DRL agent. The MATLAB/Simulink results showcase the accurate tracking of the DAB converter's output voltage under different phase-shift modulations. The experimental validation of DRL control for regulating DAB converter output voltage was done using Real Time Digital Simulator (RTDS). Further, a comparative analysis of DRL agents has been conducted to prove the efficient performance of the actor-critic policy-based DDPG agent over other value-based agents.
In nature, bioconvection generated by motile microorganisms may offer great opportunities in various applications such as environmental engineering, renewable energy technologies, and biomedical applications. In this paper, for the enrichment of heat transport and the regulation of bioconvection, a trihybrid nanofluid composed of gold (Au), silver (Ag), and multi-walled carbon nanotube (MWCNT) nanoparticles are considered within an MHD flow over a stretching cylinder. Unlike common single-nanoparticle nanofluids, trihybrid nanofluids utilize the synergistic behavior of multiple nanoparticles to achieve superior thermal conductivity, improved energy transport, and increased stability of fluids. The various key physical mechanisms incorporated here are thermal radiation, internal heat generation or absorption, electroosmotic effects and electromagnetic forces, to evaluate their impacts on microbial motility and bio-convective flow behavior. Furthermore, an activation energy-based chemical reaction is considered to show the moderation in microbial activity and enhancement in system performance. This set of coupled nonlinear partial differential equations is reduced to a system of ODEs by invoking proper similarity transformations and solvednumericallyviatheGalerkinfinite element method (G-FEM). Both multiple linear and quadratic regression analyses have been performed to develop the predictive models. It has been observed that the quadratic model presents more reliable and accurate predictions compared to the linear one. In general, the results show that bioconvection can be effectively controlled through adjusting parameters of the bioconvective Schmidt number, Peclet number, and Biot number to enhance heat and mass transfer characteristics of the system.
