MIT-World Peace University (MIT-WPU) is a top educational institute located in Pune, India. It is recognized within&nbsp;UGC under Government of Maharashtra Act No. XXXV 2017.&nbsp;

It is part of the MIT group of institutions (established in 1983) which covers 10+ campuses across India and has more than 50,000+ students enrolled across various disciplines at a given point of time. The institute houses 15 schools ranging from Engineering &amp; Technology, Design, Pharmacy, Commerce, Governance among others. It offers undergraduate, postgraduate and PhD programs among different disciplines.&nbsp;

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MIT World Peace University

Engineering and Technology

Erosion wear is a severe issue in different industrial applications results in economic costs and operational failure. In most of the industry pipelines are used to handle the slurry during industrial process, the slurry handling pipeline is failure due to erosion wear. The slurry pot test rig is developed and fabricated for investigating the erosion wear rate of different material and solid particles used in the slurry pipeline with various working conditions. The various influencing parameters such as solid particle flow velocity, impact angle, solid particle size, and concentration are responsible for the rate of material loss and erosion wear mechanisms. The present study is based on experimental work and its validation of results of slurry erosion wear for slurry handling pipeline using slurry pot test rig and CFD simulation. For experimentation, mild steel is used as pipeline material and coal bottom ash is used as solid particles for simulating slurry handling pipeline use in a thermal power plant. The experimentation is performed on the slurry pot test rig. The computational fluid dynamics (CFD) tool is used to predict and simulate the experimental results of the slurry pot test rig. The simulation results compared with experimental results and check the percentage of errors with both the results. The simulation results show a similar kind of trend for erosion wear behavior of mild steel as per the experimental results. Simulation results show the average error up to±21% variation with the experimental result. The simulation results give the position of erosion wear over the sample surface. © 2022, Walailak University. All rights reserved.

World University root node

School of Mechanical Engineering

Trends in Sciences

CFD Simulation and Experimental Results Validation of Slurry Erosion Wear using Slurry Pot Test Rig

Due to the countless drop in friction, lubrication, as well as accumulation problems, Compliant tools have become a perfect replacement for rigid - joints in micro and nano applications and have increasing importance in industrial fields. Compliant mechanisms transform force or motion through the deformation of their structural members. Compliant mechanisms have gained popularity in micro and nano-engineering, aerospace, smart structures, Opto - mechatronics, healthcare, and semiconductor equipment. The significant demand for compliant mechanisms in a variability of applications encouraged the development of design methods. Along with the developed designs, actuators and sensors continuously evolved to acquire precise positioning. In micro-positioning, a large group of XY stage actuators are found, but nowadays, actuators with stage XYZ and stage XYθ (3 - DOF) are popular, especially in a robotics application. Furthermore, the sensors also evolved more sensitive and reliable. The current compliant system comes with the integrated actuator and embedded sensing element. This paper briefly overviews the development of topology optimization techniques, sensors, and actuators for the past decades. © 2022 Author(s).

AIP Conference Proceedings

Design and development of compliant mechanisms using various sensors and actuators: A review

Lecture Notes in Mechanical Engineering

Investigations on Efficient Designs of Domestic Cooking Pots

Materials

The Joining of Copper to Stainless Steel by Solid-State Welding Processes: A Review

School of Computer Engineering & Technology

2022 6th International Conference on Computing, Communication, Control and Automation, ICCUBEA 2022

Using Drone Technology for Fruit Orchard Management and Waste Reduction

Smart Innovation, Systems and Technologies

Challenges of Introducing Lean Six Sigma, IoT in Industry 4.0, and Supply Chain Management: A Review

There are many applications and sectors in today's era where noise pollution is reaching very high levels. This prolonged noise exposure leads to a detrimental health effect. This may cause loss of concentration, working efficiency, headache, increased blood pressure level, annoyance, and even workplace accidents. Hence, the need to reduce the high noise level is becoming a significant issue. Varieties of absorbing materials are being used in the recent age for noise reduction and sound attenuation. Most of the acoustic enclosures consist of polyurethane foam as a sound absorber. The noise control is achieved by modifying the noise source characteristics, path modification, or muffling at the receiver. The best way to control noise is noise absorption, where acoustic materials are designed to absorb sound. Polyurethane foam is widely accepted because of its good properties like wide absorption frequency range, structural stabilization, low cost, easy handling, and moisture resistance. It has been observed that the sound absorption coefficient (SAC) depends on the thickness of polyurethane foam and the air gap. This paper discusses the different acoustic properties of various acoustic structures consisting of the perforated panel, air gap, and polyurethane foam for the frequency range of 100-4000 Hz. Different combinations of acoustic structures are proposed to observe the effect of absorptivity in terms of sound attenuation. The effect of layer sequence on the acoustic absorption of the structure has been investigated. The relation between the thickness of the material, air gap, and SAC are also analyzed. FEA can accurately analyze the absorption characteristics at normal incidence of sound-absorbing structure. The acoustic structure consisting of polyurethane foam gives the maximum value of SAC for a frequency range of 2300-4000 Hz. The acoustic structure consisting of the perforated panel, air gap, and polyurethane foam give the maximum SAC value for the lower frequency range of 100 to 2300 Hz. In the current study, it has been seen that the acoustic structure consisting of polyurethane foam with an air gap gives the minimum value of SAC. The SAC varies with the air gap and thickness of the foam. The study affirmed that using the perforated panels in acoustic structure gives the maximum value of SAC in the lower frequency range. The SAC of a material is calculated with an analytical method and verified with the experimental method and FEA software. For verification of SAC for different acoustic structures, COMSOL Multiphysics 5.5 results are seen to be in good agreement with a maximum difference of 8.1 % with that of the experimental results. Copyright © 2022 by ASME.

ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE)

EFFECT OF AIR GAP, THICKNESS OF POLYURETHANE (PU) FOAM, AND PERFORATED PANEL ON SOUND ABSORPTION COEFFICIENT FOR ACOUSTIC STRUCTURES

Understanding and solving fluid-related problems pose a significant demand for computationally inexpensive flow simulations, which the conventional fluid mechanics approach fails to satisfy. Thus, researchers choose to incorporate fluid mechanics with Machine Learning (ML) as a possible solution. This technology provides several tools and algorithms that help with prediction-based decision making, building optimized control theories, experience-based learning, and many more, all of which depend upon available data. Since its inception, the field of fluid mechanics has generated a lot in terms of experimental and simulation data. Hence, we can apply Machine Learning to extract meaningful information from fluid flow databases. Complex domains of fluid mechanics such as turbulence modeling, active flow control, and optimization all seek to gain from such a multidisciplinary approach. However, these domains from the world of fluids pose new problems for the data science world. These newly found complexities encourage engineers to create more robust learning models than conventional ones. Thus, a blend of fluid mechanics and machine learning creates a powerful and vastly complex field of study that will help completely revolutionize current research and industrial applications. This paper covers research from the earliest to some of the most recent ML algorithms and provides a brief overview of ways these algorithms complement the field of fluid mechanics. Three case studies—turbulence closure modeling using ML, flow control and manipulation using ML, and aerodynamic shape optimization, are used to explain this. To help better understand these applications, the underlying fundamentals of supervised, semi-supervised, and unsupervised learning models and some of the most widely used algorithms are also under consideration in the paper. The paper, thus, covers in great depth both the fields of fluids and ML. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Optimization of Fluid Modeling and Flow Control Processes Using Machine Learning: A Brief Review

Water-flooding is a necessity for improved extraction of crude from the existing reservoir. However, scale deposition is a challenge to be mitigated due to incompatibility among injected and formation water during water-flooding operations. Sulfates and carbonate scales of barium, calcium, and strontium are commonly encountered in diversified oil fields. Such scale deposition inside the pores of reservoir rock leads to a reduction in effective permeability, which ultimately affects the fluid flow and, thus, results in a decrease in overall productivity. Hence it is important to predict the impact of scale deposition on permeability reduction. In the present study, a generalized model using a multivariate regression approach was developed to estimate the reduction of permeability due to barium, calcium, and strontium sulfate scale deposition based on important variables, namely temperature, pressure, and brine concentration and pore volume injected. Experimental data of sulfate scale deposition in Malaysian sandstone reservoirs from published literature were utilized for modeling purposes. The developed simple generalized model can predict final permeability caused by scale deposition with good accuracy (<10 % relative error) compared to experimental data. Moreover, a sliding window regression approach was used to improve its performance. Models were subsequently validated using a new data-set of sandstone reservoirs available from open literature. © 2022, Petroleum and Coal. All Rights Reserved.

School of Chemical Engineering

School of Petroleum Engineering

Petroleum and Coal

Prediction of Reducing Permeability due to Scale Deposition during Water-flood-ing Operations of Crude Reservoirs

Cutting fluids are meant to reduce the frictional wear and to dissipate the heat generated at the tool–workpiece interface, during machining operations. Conventionally applied cutting fluids are mineral-based oils, which pose grave environmental and health hazards. The traditional flood cooling systems result in cleaning and disposal issues apart from a lot of water consumption. In recent years, research work is focused on exploring sustainable techniques with the use of biodegradable oils as cutting fluids and the supply concepts like minimum quantity lubrication (MQL). Vegetable oils, apart from being bio degradable, have strong potential by virtue of their superior lubrication properties. Moreover, addition of nanoparticles in vegetable oils exhibits better performance. The present work conducts experimental-based investigations of machining SS410 for minimum quantity lubrication (MQL) condition. Molybdenum disulfide (MoS2) nanoparticles with palm oil and coconut oil are used as cutting fluids. The experimentations are carried out under selected conditions, and machining performance is measured. Analysis is done to identify the combination for optimum machining performance. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.