GRIET

Hyderabad, India
Hyderabad, India
SEARCH FILTERS
Time filter
Source Type

Venkata Suresh J.,GRIET | Bhramara P.,JNTUH College of Engineering
Materials Today: Proceedings | Year: 2017

Pulsating Heat Pipe (PHP) is a heat exchanger device which absorbs heat from evaporator region and transfers it to the condenser region. The flow in pipe is Multi-Phase flow. Vapour plugs and Liquid slugs are formed in PHP due to the capillary action.CFD modeling is done in ANSYS CFX with two turns of PHP and Methanol is used as the working fluid and a fill ratio of 60%. At evaporator boundary, heat flux that is equivalent to 10 W to 70 W is supplied and the condenser boundary is set as heat flux of range 4423 W/m 2 and in the adiabatic section heat flux is zero. The obtained CFD results are compared with the experimental paper [1].The CFD analysis is performed and the outputs of the simulations are plotted in graphs and contours. Decrease in the methanol temperature at the evaporator suggests that heat is carried away to the condenser part. Change in volume fractions of methanol and air in three regions viz. evaporator, adiabatic region and condenser reflects to the flow pattern of the fluid inside the PHP. © 2017 Elsevier Ltd. All rights reserved.


Prashanth Reddy K.,GRIET | Panitapu B.,JNTUCEH
Materials Today: Proceedings | Year: 2017

Decreasing the time of cooling phase in injection Moulding is of paramount importance in shortening the cycle time. Cooling phase takes more time when compared to other phases and is only stage which has scope of reducing cycle time and increasing production rate further. The given amount of heat energy that should be removed from melt is almost same in every cycle. The time taken to remove that heat energy is purely dependent on properties of materials across which the heat is being transferred from melt to coolant. This heat transfer generally occurs in convection-conduction-convection mode. In this paper Mould inserts across which heat is being dissipated, made of different materials are implemented and investigated in an injection Mould for a cosmetic product cap manufacture. Quality parameters like volumetric shrinkage and warpage are also observed along with time taken to reach ejection temperature. MoldFlow analysis tool is used to carry out the simulations with different Mould insert materials,The simulated results are found to be in good agreement with experimental values.The experimental work has been carried out at vasantha tool crafts pvt limited. However manufacturing of Mould inserts with desired materials may be expensive, implementing this for mass (large quantity) production orders in multi cavity Moulds turn out to be economical or profitable as the production rate increases without compromising in quality. © 2017 Elsevier Ltd. All rights reserved.


Praveen J.,GRIET | Vijaya Ramaraju V.,GRIET
Materials Today: Proceedings | Year: 2017

Electrical energy is one of the main resource for development of any country. As there are many ways to generate electrical energy, solar energy is one of green energy which is imperishable. As many research institutions are rigorously working on how to improve efficiency of Solar Photovoltaic cell so that we can generate more electrical energy per given area. Selection of different material such as CdTe,GaN,SiGaAs, Ge, InP, a-SiH,cSi will give variation in band gap, change in efficiency of photovoltaic cell. We need to solve supply demand problem by adding more generation. One method can be optimizing efficiency of solar cell by making multi- layer, multi junction with more materials which can effectively use complete solar spectrum to convert as electricity by changing band gap limits. This paper will present how different material will add to increase efficiency of solar cells. Another important reason for research towards improve in solar photovoltaic cell efficiency is rapid reduction of availability of conventional energy resources such as coal, oil and gas. © 2017 Published by Elsevier Ltd.


Gupta A.K.,BITS Pilani | Anirudh V.K.,BITS Pilani | Singh S.K.,GRIET
Materials and Design | Year: 2013

Strain, strain rate and temperature have a significant impact on the flow stress of a material. To study the impact of these factors on flow stress, quite a few empirical, semi-empirical constitutive models have been reported. In this work, four such models are being presented for estimation of flow stress on Austenitic Stainless Steel 316. While the Johnson Cook model, Modified Zerilli-Armstrong model are semi-empirical models, the Arrhenius type equation is a physical based equation. The Artificial Neural Network model on the other hand is trained based on the training data and employed to predict the flow stress on the testing data. The experiments for these data were conducted at various strain rates (0.1-0.0001s-1) and at various temperatures (323-623K). Values of stress were taken at strain intervals of 0.05 (from 0.05 to 0.3) to evaluate the material constants of the constitutive models. A comparative study on the reliability of the four models has also been made at the end. The correlation coefficient values observed were 0.9423 (JC model), 0.9879 (modified ZA model), 0.9852 (modified Arrhenius type equation) and 0.9930 (ANN model). © 2012 Elsevier Ltd.


Singh S.K.,GRIET | Gupta A.K.,BITS Pilani
CIRP Journal of Manufacturing Science and Technology | Year: 2010

In this paper, a new data mining technique support vector regression (SVR) is applied to predict the thickness along cup wall in hydro-mechanical deep drawing. After using the experimental results for training and testing, the model was applied to new data for prediction of thickness strains in hydro-mechanical deep drawing. The prediction results of SVR are compared with that of artificial neural network (ANN), finite element (FE) simulation and the experimental observations. The results are promising. It is found that SVR predicts the thickness variation in the drawn cups very accurately especially in the wall region. © 2010 CIRP.


Kotkunde N.,BITS Pilani | Deole A.D.,BITS Pilani | Gupta A.K.,BITS Pilani | Singh S.K.,GRIET
Materials and Design | Year: 2014

An accurate prediction of flow behavior of metals considering the combined effects of strain, strain rate and temperature is essential for understanding flow response of metals. Isothermal uniaxial tensile tests have been performed from 323K to 673K at an interval of 50K and strain rates 10-5, 10-4, 10-3 and 10-2s-1. In this study prediction of flow behavior of Ti-6Al-4V alloy sheet is done using four constitutive models namely; Johnson-Cook (JC), Fields-Backofen (FB), Khan-Huang-Liang (KHL) and Mechanical Threshold Stress (MTS). The predictions of these constitutive models are compared using statistical measures like correlation coefficient (R), average absolute error (δ) and its standard deviation (δ). Analysis of statistical measures revealed that FB model has more deviation from the experimental values. Whereas, the predictions of all other models (JC, KHL, and MTS) are very close to the experimental results. JC and KHL are better models for predicting the flow stress. However, considering the fact that MTS model is a physical based model, MTS model is preferred over other models. © 2013 Elsevier Ltd.


Kotkunde N.,BITS Pilani | Krishnamurthy H.N.,BITS Pilani | Puranik P.,BITS Pilani | Gupta A.K.,BITS Pilani | Singh S.K.,GRIET
Materials and Design | Year: 2014

A reliable and accurate prediction of flow behavior of metals in industrial forming process considering the coupled effects of strain, strain rate and temperature is crucial in understanding the workability of the metal and optimizing parameters for hot forming process. In this study, the tensile fracture behavior of the Ti-6Al-4V alloy is examined with scanning electron microscope (SEM) over the range of magnifications. SEM study revealed that microvoids and shallow dimples are observed at the fracture surface which indicates the fracture is predominately ductile in nature. Also, an investigation on flow behavior of Ti-6Al-4V alloy is done using constitutive models. Four constitutive models; modified Johnson-Cook (m-JC), modified Arrhenius type equations (m-Arr), modified Zerilli-Armstrong (m-ZA) and Rusinek-Klepaczko (RK) models are developed to predict the flow stress. The predictions of these constitutive models are compared with each other using statistical measures like correlation coefficient, average absolute error and its standard deviation. Comparing the statistical measures, m-Arr model is a better model for predicting the flow stress, but considering the fact that m-ZA model is a physical based model, m-ZA model is preferred over the m-Arr model. © 2013 Elsevier Ltd.


Gupta A.K.,BITS Pilani | Singh S.K.,GRIET | Reddy S.,University of New South Wales | Hariharan G.,National Institute of Technology Warangal
Materials and Design | Year: 2012

Flow stress during hot deformation depends mainly on the strain, strain rate and temperature, and shows a complex nonlinear relationship with them. A number of semi empirical models were reported by others to predict the flow stress during deformation. In this work, an artificial neural network is used for the estimation of flow stress of austenitic stainless steel 316 particularly in dynamic strain aging regime that occurs at certain strain rates and certain temperatures and varies flow stress behavior of metal being deformed. Based on the input variables strain, strain rate and temperature, this work attempts to develop a back propagation neural network model to predict the flow stress as output. In the first stage, the appearance and terminal of dynamic strain aging are determined with the aid of tensile testing at various temperatures and strain rates and subsequently for the serrated flow domain an artificial neural network is constructed. The whole experimental data is randomly divided in two parts: 90% data as training data and 10% data as testing data. The artificial neural network is successfully trained based on the training data and employed to predict the flow stress values for the testing data, which were compared with the experimental values. It was found that the maximum percentage error between predicted and experimental data is less than 8.67% and the correlation coefficient between them is 0.9955, which shows that predicted flow stress by artificial neural network is in good agreement with experimental results. The comparison between the two sets of results indicates the reliability of the predictions. © 2011 Elsevier Ltd.


Singh S.K.,GRIET | Mahesh K.,GRIET | Gupta A.K.,Mechanical Engineering Group
Materials and Design | Year: 2010

Blue brittle region also known as dynamic strain ageing (DSA) regime is very important in the materials because in this region material properties behave in very unpredictable ways. In this work, Artificial Neural Network (ANN) models are developed for the prediction of mechanical properties such as yield strength (YS), ultimate tensile strength (UTS), % elongation, strength coefficient (K) and strain hardening exponent (n) for the extra deep drawn (EDD) quality steel in blue brittle region. To calculate the mechanical properties at elevated temperatures, experiments were conducted at the interval of 25 °C from room temperature till 700 °C in three rolling directions. Based on the experimental results, the blue brittle region for EDD steel is identified between 350 °C and 450 °C and ANN model is trained in all the three rolling directions. Trained up ANN model is tested with the experimental results at two different temperatures with in blue brittle region. Experimental and modeling errors in the prediction of mechanical properties are found within the permissible range. © 2009 Elsevier Ltd. All rights reserved.


Singh S.K.,GRIET
International Journal of Material Forming | Year: 2010

Anisotropy and strain rate sensitivity index (m) plays a very important role in the formability of materials. In the present investigation strain ratios in 0°, 45° and 90° to the rolling direction and the strain rate sensitivity index were calculated at different temperatures. After developing the data from experiments, Artificial Neural Network (ANN) models are trained for different properties. Trained ANN models are used to calculate different strain ratios and sensitivity index at unknown temperatures. The results are promising and the percentage error in ANN prediction is found to be around 10%. © 2010 Springer/ESAFORM.

Loading GRIET collaborators
Loading GRIET collaborators