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264 results on '"Tripathi, Shivam"'

5. Role of dislocations on martensitic transformation temperatures and microstructure: A molecular dynamics study.

Author

Farache, David, Mishra, Saswat, Tripathi, Shivam, and Strachan, Alejandro

Subjects
MARTENSITIC transformations, ALLOYS, MICROSTRUCTURE, MOLECULAR dynamics, DISLOCATION structure, NICKEL-titanium alloys
Abstract

Microstructure and defects strongly affect martensitic transformations in metallic alloys. Significant progress has been made in understanding the atomic-level processes that control the role of grain boundaries and precipitates in these solid-to-solid phase transformations. Yet, the role of dislocations and their structures on martensitic transformation temperature and the resulting microstructure remains unclear. Therefore, we used large-scale molecular dynamics simulations to study the forward and reverse transformation of a martensitic material modeled after Ni63Al37 under cyclic thermal loading. The simulations reveal that dislocations in the austenite phase act as one-dimensional seeds for the martensite phase, which is present at temperatures significantly above the martensite start value. We find a reduction in the dislocation density during cyclic thermal loading, which results in the increase in martensite and austenite transition temperatures, in agreement with experiments. Importantly, we extracted a critical martensitic nuclei size for developing stable domains and found that relatively low dislocation densities are needed to grow independent martensitic variants resulting in a multi-domain structure. [ABSTRACT FROM AUTHOR]

Published
2024
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6. Numerical Simulation of Pressure Flow Due to Vertical Contraction on a Rigid Bed

Author

Majid, Sofi Aamir, Tripathi, Shivam, di Prisco, Marco, Series Editor, Chen, Sheng-Hong, Series Editor, Vayas, Ioannis, Series Editor, Kumar Shukla, Sanjay, Series Editor, Sharma, Anuj, Series Editor, Kumar, Nagesh, Series Editor, Wang, Chien Ming, Series Editor, Timbadiya, P. V., editor, Patel, P. L., editor, Singh, Vijay P., editor, and Barman, Bandita, editor

Published
2023
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7. Development of a Fog Index to Study Relationships Between Fog and Climate Variables

Author

Paurwal, Rakshit, Tripathi, Shivam, Bhattacharya, Arnab, di Prisco, Marco, Series Editor, Chen, Sheng-Hong, Series Editor, Vayas, Ioannis, Series Editor, Kumar Shukla, Sanjay, Series Editor, Sharma, Anuj, Series Editor, Kumar, Nagesh, Series Editor, Wang, Chien Ming, Series Editor, Timbadiya, P. V., editor, Singh, Vijay P., editor, and Sharma, Priyank J., editor

Published
2023
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14. Experimental Study on Pressure Flow Due to Vertical Contraction

Author

Majid, Sofi Aamir, Tripathi, Shivam, di Prisco, Marco, Series Editor, Chen, Sheng-Hong, Series Editor, Vayas, Ioannis, Series Editor, Kumar Shukla, Sanjay, Series Editor, Sharma, Anuj, Series Editor, Kumar, Nagesh, Series Editor, Wang, Chien Ming, Series Editor, Dikshit, Anil Kumar, editor, Narasimhan, Balaji, editor, Kumar, Bimlesh, editor, and Patel, Ajey Kumar, editor

Published
2022
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15. Efficiency Improvement for Regenerative Braking System for a Vehicular Model Using Supercapacitor

Author

Singh, Sangeeta, Tiwari, Kushagra Pani, Shahi, Ananya, Singh, Marut Nandan, Tripathi, Shivam, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Tomar, Anuradha, editor, Malik, Hasmat, editor, Kumar, Pramod, editor, and Iqbal, Atif, editor

Published
2022
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16. Effect of Soil Structure Interaction on Seismic Response of Buildings

Author

Shekhar, Sudhanshu, Tripathi, Shivam Mani, Ram, Shri, di Prisco, Marco, Series Editor, Chen, Sheng-Hong, Series Editor, Vayas, Ioannis, Series Editor, Kumar Shukla, Sanjay, Series Editor, Sharma, Anuj, Series Editor, Kumar, Nagesh, Series Editor, Wang, Chien Ming, Series Editor, Choudhary, Awdhesh Kumar, editor, Mondal, Somenath, editor, Metya, Subhadeep, editor, and Babu, G. L. Sivakumar, editor

Published
2022
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18. Electronic, mechanical, and vibrational properties of calcium lanthanum sulfide solid solution: A DFT study.

Author

Nishibuchi, George Maxwell, Tripathi, Shivam, Mishra, Saswat, Rivero-Baleine, Clara, and Strachan, Alejandro

Subjects
*SOLID solutions, *LANTHANUM, *CRYSTALS, *DENSITY functional theory, *POINT defects
Abstract

The combination of optical and mechanical properties of calcium lanthanum sulfide (CLS) makes it an attractive material for optical applications. CLS is a γ -phase La2S3/CaS solid solution above 50:50 La2S3/CaS. Experiments have characterized the effect of composition on the structural, thermal, and optical properties. However, the effect of impurities and other defects, such as porosity, on resulting properties is difficult to deconvolute. We used the density functional theory (DFT) simulations to characterize the effect of La2S3/CaS relative fractions on structural, electronic, mechanical, and vibrational properties of single crystalline CLS solid solutions. We find a decrease in the stiffness with CaS fraction and confirm the experimentally observed increase in the lattice parameter with La2S3 fraction. We also characterized the point defects, including sulfur vacancies, oxygen substituting sulfur, oxygen, and sulfur impurities on lanthanum sublattice vacant sites, and sulfur substituting calcium (vS, OS, OLa, and SLa, respectively). Our defect studies in 90:10 CLS find the neutral charge state configuration to be energetically favorable for all tested configurations except for the sulfur vacancies (vS). [ABSTRACT FROM AUTHOR]

Published
2023
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22. Study of Temporal Behaviour of Homogeneity Maps for Estimating Representative Area of a Ground Sample Using Remote Sensing

Author

Deshpande, Prasad J., Sure, Anudeep, Dikshit, Onkar, Tripathi, Shivam, di Prisco, Marco, Series Editor, Chen, Sheng-Hong, Series Editor, Vayas, Ioannis, Series Editor, Kumar Shukla, Sanjay, Series Editor, Sharma, Anuj, Series Editor, Kumar, Nagesh, Series Editor, Wang, Chien Ming, Series Editor, Bhuiyan, Chandrashekhar, editor, Flügel, Wolfgang-Albert, editor, and Jain, Sharad Kumar, editor

Published
2021
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23. Three-Dimensional Numerical Simulation of Pressure-Flow Scour

Author

Majid, Sofi Aamir, Tripathi, Shivam, di Prisco, Marco, Series Editor, Chen, Sheng-Hong, Series Editor, Vayas, Ioannis, Series Editor, Kumar Shukla, Sanjay, Series Editor, Sharma, Anuj, Series Editor, Kumar, Nagesh, Series Editor, Wang, Chien Ming, Series Editor, Bhuiyan, Chandrashekhar, editor, Flügel, Wolfgang-Albert, editor, and Jain, Sharad Kumar, editor

Published
2021
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28. Impact of calibrating a low-cost capacitance-based soil moisture sensor on AquaCrop model performance

Author

Adla, Soham (author), Bruckmaier, Felix (author), Arias-Rodriguez, Leonardo F. (author), Tripathi, Shivam (author), Pande, S. (author), Disse, Markus (author), Adla, Soham (author), Bruckmaier, Felix (author), Arias-Rodriguez, Leonardo F. (author), Tripathi, Shivam (author), Pande, S. (author), and Disse, Markus (author)

Abstract

Sensor data and agro-hydrological modeling have been combined to improve irrigation management. Crop water models simulating crop growth and production in response to the soil-water environment need to be parsimonious in terms of structure, inputs and parameters to be applied in data scarce regions. Irrigation management using soil moisture sensors requires them to be site-calibrated, low-cost, and maintainable. Therefore, there is a need for parsimonious crop modeling combined with low-cost soil moisture sensing without losing predictive capability. This study calibrated the low-cost capacitance-based Spectrum Inc. SM100 soil moisture sensor using multiple least squares and machine learning models, with both laboratory and field data. The best calibration technique, field-based piece-wise linear regression (calibration r2 = 0.76, RMSE = 3.13 %, validation r2 = 0.67, RMSE = 4.57 %), was used to study the effect of sensor calibration on the performance of the FAO AquaCrop Open Source (AquaCrop-OS) model by calibrating its soil hydraulic parameters. This approach was tested during the wheat cropping season in 2018, in Kanpur (India), in the Indo-Gangetic plains, resulting in some best practices regarding sensor calibration being recommended. The soil moisture sensor was calibrated best in field conditions against a secondary standard sensor (UGT GmbH. SMT100) taken as a reference (r2 = 0.67, RMSE = 4.57 %), followed by laboratory calibration against gravimetric soil moisture using the dry-down (r2 = 0.66, RMSE = 5.26 %) and wet-up curves respectively (r2 = 0.62, RMSE = 6.29 %). Moreover, model overfitting with machine learning algorithms led to poor field validation performance. The soil moisture simulation of AquaCrop-OS improved significantly by incorporating raw reference sensor and calibrated low-cost sensor data. There were non-significant impacts on biomass simulation, but water productivity improved sign, Water Resources

Published
2024
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29. SA-CNN: Dynamic Scene Classification using Convolutional Neural Networks

Author

Gangopadhyay, Aalok, Tripathi, Shivam Mani, Jindal, Ishan, and Raman, Shanmuganathan

Subjects
Computer Science - Computer Vision and Pattern Recognition, I.5.4, I.4.8
Abstract

The task of classifying videos of natural dynamic scenes into appropriate classes has gained lot of attention in recent years. The problem especially becomes challenging when the camera used to capture the video is dynamic. In this paper, we analyse the performance of statistical aggregation (SA) techniques on various pre-trained convolutional neural network(CNN) models to address this problem. The proposed approach works by extracting CNN activation features for a number of frames in a video and then uses an aggregation scheme in order to obtain a robust feature descriptor for the video. We show through results that the proposed approach performs better than the-state-of-the arts for the Maryland and YUPenn dataset. The final descriptor obtained is powerful enough to distinguish among dynamic scenes and is even capable of addressing the scenario where the camera motion is dominant and the scene dynamics are complex. Further, this paper shows an extensive study on the performance of various aggregation methods and their combinations. We compare the proposed approach with other dynamic scene classification algorithms on two publicly available datasets - Maryland and YUPenn to demonstrate the superior performance of the proposed approach.

Published
2015

49. Modeling environment-dependent atomic-level properties in complex-concentrated alloys.

Author

Farnell, Mackinzie S., McClure, Zachary D., Tripathi, Shivam, and Strachan, Alejandro

Subjects
ALLOYS, RADIATION tolerance, ATOMIC structure, RADIATION damage, ATOMIC models
Abstract

Complex-concentrated-alloys (CCAs) are of interest for a range of applications due to a host of desirable properties, including high-temperature strength and tolerance to radiation damage. Their multi-principal component nature results in a vast number of possible atomic environments with the associated variability in chemistry and structure. This atomic-level variability is central to the unique properties of these alloys but makes their modeling challenging. We combine atomistic simulations using many body potentials with machine learning to develop predictive models of various atomic properties of CrFeCoNiCu-based CCAs: relaxed vacancy formation energy, atomic-level cohesive energy, pressure, and volume. A fingerprint of the local atomic environments is obtained combining invariants associated with the local atomic geometry and periodic-table information of the atoms involved. Importantly, all descriptors are based on the unrelaxed atomic structure; thus, they are computationally inexpensive to compute. This enables the incorporation of these models into macroscopic simulations. The models show good accuracy and we explore their ability to extrapolate to compositions and elements not used during training. [ABSTRACT FROM AUTHOR]

Published
2022
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