Euler Flow Computations on Non-Matching Unstructured Meshes

Advanced fluid solvers to predict aerodynamic performance-coupled treatment of multiple fields are described. The interaction between the fluid and structural components in the bladed regions of the engine is investigated with respect to known blade failures caused by either flutter or forced vibrations. Methods are developed to describe aeroelastic phenomena for internal flows in turbomachinery by accounting for the increased geometric complexity, mutual interaction between adjacent structural components and presence of thermal and geometric loading. The computer code developed solves the full three dimensional aeroelastic problem of-stage. The results obtained show that flow computations can be performed on non-matching finite-volume unstructured meshes with second order spatial accuracy

Computational simulations of latent heat thermal energy storage systems - with innovative and first-principles based simulation for the underlying unsteady melting (and solidification) processes

This thesis develops an effective modeling and simulation procedure for a specific thermal energy storage system commonly used and recommended for various applications (such as an auxiliary energy storage system for solar heating based Rankine cycle power plant). This thermal energy storage system transfers heat from a hot fluid (termed as heat transfer fluid - HTF) flowing in a tube to the surrounding phase change material (PCM). Through unsteady melting or freezing process, the PCM absorbs or releases thermal energy in the form of latent heat. Both scientific and engineering information is obtained by the proposed first-principle based modeling and simulation procedure. On the scientific side, the approach accurately tracks the moving melt-front (modeled as a sharp liquid-solid interface) and provides all necessary information about the time-varying heat-flow rates, temperature profiles, stored thermal energy, etc. On the engineering side, the proposed approach is unique in its ability to accurately solve – both individually and collectively – all the conjugate unsteady heat transfer problems for each of the components of the thermal storage system. This yields critical system level information on the various time-varying effectiveness and efficiency parameters for the thermal storage system

Role of Neural Network in Mobile Ad Hoc Networks for Mobility Prediction

The MANETs differ from traditional networks in a lot of aspects, such as high channel error rates, unusual channel features, frequent link breaks, and intense link layer contentions. These characteristics significantly reduce network connectivity, which affects overall network latency, network overhead, network throughput (i.e. the amount of data successfully transferred via a MANETs in a predetermined amount of time), and packet delivery ratio (PDR). For effective network resources preparation and organization in MANETs, the mobility forecast of MN and units is essential. This effectiveness would allow for better planning and higher overall quality - of - service, including reliable facility availability and efficient management of energy. In this research, we suggest to use ELMs, which are renowned for their ability to approximate anything, to model and forecast the mobility of each node in a MANET. Mobility-aware topology control methods and location-assisted routing both leverage mobility prediction in MANETs. It is assumed that each MN taking part in these protocols is aware of its current mobility data, including location, velocity, and movements direction angle. This approach predicts both the locations of future nodes and the distances between subsequent nodes. The interaction or relationship between the Cartesian longitude and latitude of the erratic nodes is better captured by ELMs than by multilayer perceptron’s, resulting in mobility prediction that is based on several conventional mobility models that is more precise and realistic

A Study on the Benefits of Sustainable Management Practices and its Association with Talent Retention in Star Category Hotels

Sustainable management encompasses ideas from sustainability and synthesizes them with the concepts of management. This paper focuses on studying the benefits related to sustainable management practices that are adopted and followed in the hotel industry with specific reference to Star Category Hotels of Pune Region and its association with retention of the talented employees in the organisation.The study had reviewed the practices adopted by star category hotels and its association with retaining the talented bunch of employees. The research design for these factors is then explained further, which is supported by a Chi-Square test as a tool of data interpretation. A structured questionnaire was circulated amongst the managerial level employees in star hotels of the Pune region and responses were collected. The study had revealed that employees do believe that parameters covered under the benefits of the talent retention are mutually beneficial for the organisation as well as the employees

Role of Deep Learning in Mobile Ad-hoc Networks

The portable capability of MANETs has specially delighted in an unexpected expansion. A massive need for dynamic ad-hoc basis networking continues to be created by advancements in hardware design, high-speed growth in the wireless network communications infrastructure, and increased user requirements for node mobility and regional delivery processes. There are several challenging issues in mobile ad-hoc networks, such as machine learning method cannot analyze features like node mobility, channel variation, channel interference because of the absence of deep neural layers. Due to decentralized nature of mobile ad hoc networks, its necessitate to concentrate over some extremely serious issues like stability, scalability, routing based problems such as network congestion, optimal path selection, etc. and security