arXiv subjects

Krishna Kumar

Publications and source records attributed to Krishna Kumar.

At least 55 records · Page 3Linked to original sources

arXiv2023

Optoelectronic properties of silver doped copper oxide thin films

Thin films have found a wide variety of applications because of the substantial improvement in their properties as compared to bulk metals. Metal oxide thin films are increasingly being used in various fields and are especially important in functional applications. They can be either p- or n-type in nature depending on the materials, dopants, and preparation route. Copper oxide is an example of a p-type metal oxide, which finds application in solar cells, photo-electrochemical cells, gas sensors, supercapacitors, and thermoelectric touch detectors. Both copper (I) and copper (II) oxides can be grown with the lower valence state oxide stable at low temperature and the higher valence state obtained by annealing at higher temperatures. In this work, we modify the optical and electrical properties of copper oxide thin films, by doping of silver through a thermal evaporation process route. Copper is thermally evaporated onto the substrate and silver is co-evaporated during this process. The films are then annealed in ambient under various conditions to obtain copper oxide. Structural and functional comparison is made between undoped and silver doped copper oxide thin films, prepared under the same conditions. Thermal evaporation is a simple route for obtaining doped metal oxides and the process can be extended to a variety of other systems as well.

physics.app-ph↗

arXiv2023

Geotechnical Parrot Tales (GPT): Harnessing Large Language Models in geotechnical engineering

The widespread adoption of large language models (LLMs), such as OpenAI's ChatGPT, could revolutionize various industries, including geotechnical engineering. However, GPT models can sometimes generate plausible-sounding but false outputs, leading to hallucinations. In this article, we discuss the importance of prompt engineering in mitigating these risks and harnessing the full potential of GPT for geotechnical applications. We explore the challenges and pitfalls associated with LLMs and highlight the role of context in ensuring accurate and valuable responses. Furthermore, we examine the development of context-specific search engines and the potential of LLMs to become a natural interface for complex tasks, such as data analysis and design. We also develop a unified interface using natural language to handle complex geotechnical engineering tasks and data analysis. By integrating GPT into geotechnical engineering workflows, professionals can streamline their work and develop sustainable and resilient infrastructure systems for the future.

cs.CL↗

arXiv2023

Audio classification using ML methods

Machine Learning systems have achieved outstanding performance in different domains. In this paper machine learning methods have been applied to classification task to classify music genre. The code shows how to extract features from audio files and classify them using supervised learning into 2 genres namely classical and metal. Algorithms used are LogisticRegression, SVC using different kernals (linear, sigmoid, rbf and poly), KNeighborsClassifier , RandomForestClassifier, DecisionTreeClassifier and GaussianNB.

cs.SD↗

arXiv2023

Enabling knowledge discovery in natural hazard engineering datasets on DesignSafe

Data-driven discoveries require identifying relevant data relationships from a sea of complex, unstructured, and heterogeneous scientific data. We propose a hybrid methodology that extracts metadata and leverages scientific domain knowledge to synthesize a new dataset from the original to construct knowledge graphs. We demonstrate our approach's effectiveness through a case study on the natural hazard engineering dataset on ``LEAP Liquefaction'' hosted on DesignSafe. Traditional lexical search on DesignSafe is limited in uncovering hidden relationships within the data. Our knowledge graph enables complex queries and fosters new scientific insights by accurately identifying relevant entities and establishing their relationships within the dataset. This innovative implementation can transform the landscape of data-driven discoveries across various scientific domains.

physics.geo-ph↗

arXiv2023

Using explainability to design physics-aware CNNs for solving subsurface inverse problems

We present a novel method of using explainability techniques to design physics-aware neural networks. We demonstrate our approach by developing a convolutional neural network (CNN) for solving an inverse problem for shallow subsurface imaging. Although CNNs have gained popularity in recent years across many fields, the development of CNNs remains an art, as there are no clear guidelines regarding the selection of hyperparameters that will yield the best network. While optimization algorithms may be used to select hyperparameters automatically, these methods focus on developing networks with high predictive accuracy while disregarding model explainability (descriptive accuracy). However, the field of Explainable Artificial Intelligence (XAI) addresses the absence of model explainability by providing tools that allow developers to evaluate the internal logic of neural networks. In this study, we use the explainability methods Score-CAM and Deep SHAP to select hyperparameters, such as kernel sizes and network depth, to develop a physics-aware CNN for shallow subsurface imaging. We begin with a relatively deep Encoder-Decoder network, which uses surface wave dispersion images as inputs and generates 2D shear wave velocity subsurface images as outputs. Through model explanations, we ultimately find that a shallow CNN using two convolutional layers with an atypical kernel size of 3x1 yields comparable predictive accuracy but with increased descriptive accuracy. We also show that explainability methods can be used to evaluate the network's complexity and decision-making. We believe this method can be used to develop neural networks with high predictive accuracy while also providing inherent explainability.

cs.LG↗

arXiv2023

Multiphase lattice Boltzmann modeling of cyclic water retention behavior in unsaturated sand based on X-ray Computed Tomography

The water retention curve (WRC) defines the relationship between matric suction and saturation and is a key function for determining the hydro-mechanical behavior of unsaturated soils. We investigate possible microscopic origins of the water retention behavior of granular soils using both Computed Tomography (CT) experiment and multiphase lattice Boltzmann Method (LBM). We conduct a CT experiment on Hamburg sand to obtain its WRC and then run LBM simulations based on the CT grain skeleton. The multiphase LBM simulations capture the hysteresis and pore-scale behaviors of WRC observed in the CT experiment. Using LBM, we observe that the spatial distribution and morphology of gas clusters varies between drainage and imbibition paths and is the underlying source of the hysteresis. During drainage, gas clusters congregate at the grain surface; the local suction increases when gas clusters enter through small pore openings and decreases when gas clusters enter through large pore openings. Whereas, during imbibition, gas clusters disperse in the liquid; the local suction decreases uniformly. Large pores empty first during drainage and small pores fill first during imbibition. The pore-based WRC shows that an increase in pore size causes a decrease in suction during drainage and imbibition, and an increase in hysteresis.

physics.geo-ph↗

arXiv2023

Development of a stable two-phase contact MPM algorithm for saturated soil-structure interaction problems

The simulation of soil-structure interaction problems involving two-phase materials poses significant challenges in geotechnical engineering. These challenges arise due to differences in material stiffnesses, the interaction between multiple phases, high bulk modulus of pore fluid, and low permeability. The conventional explicit time integration scheme is limited by its conditional stability, necessitating small time step sizes and resulting in pressure oscillations under rapid loading conditions. To address these issues, we propose a stable two-phase contact algorithm within the Material Point Method (MPM) framework for soil-structure interaction problems. Our algorithm models the soil as a fully saturated porous media with incompressible pore fluid. We introduce three main advancements over conventional MPM methods. We employ Chorin's projection method to solve coupled formulations and reduce numerical oscillations. By implicitly handling a diffusion term, our algorithm permits larger stable time step sizes, independent of the bulk modulus and permeability of the pore fluid. Lastly, We integrate a rigid algorithm to model solid bodies accurately and a precise contact detection algorithm. We provide detailed formulations and time increment processes of the two-phase contact MPM algorithm. Furthermore, we compare the proposed algorithm with Finite Element Method (FEM) and explicit MPM to assess its accuracy and performance in simulating coupled hydro-mechanical problems. The two-phase contact algorithm offers a more stable and efficient approach to simulate soil-structure interaction problems.

math.NA↗

arXiv2023

Quantification of geogrid lateral restraint using transparent sand and deep learning-based image segmentation

An experimental technique is presented to quantify the lateral restraint provided by a geogrid embedded in granular soil at the particle level. Repeated load triaxial tests were done on transparent sand specimens with geosynthetic inclusions simulating geogrids. Particle outlines on laser illuminated planes through the specimens were segmented using a deep learning-based segmentation algorithm. The particle outlines were characterized in terms of Fourier shape descriptors and tracked across sequentially captured images. The accuracy of the particle displacement measurements was validated against Digital Image Correlation (DIC) measurements. In addition, the method's resolution and repeatability is presented. Based on the measured particle displacements and rotations, a state boundary line between probable and improbable particle motions was identified for each test. The size of the zone of probable motions could be used to quantify the lateral restraint provided by the inclusions. Overall, the tests results revealed that the geosynthetic inclusions restricted both particle displacements and rotations. However, the particle displacements were found to be restrained more significantly than the rotations. Finally, a unique relationship was found between the magnitude of the permanent strains of the specimens and the size of the zone of probable motions.

physics.geo-ph↗

arXiv2022

GNS: A generalizable Graph Neural Network-based simulator for particulate and fluid modeling

We develop a PyTorch-based Graph Network Simulator (GNS) that learns physics and predicts the flow behavior of particulate and fluid systems. GNS discretizes the domain with nodes representing a collection of material points and the links connecting the nodes representing the local interaction between particles or clusters of particles. The GNS learns the interaction laws through message passing on the graph. GNS has three components: (a) Encoder, which embeds particle information to a latent graph, the edges are learned functions; (b) Processor, which allows data propagation and computes the nodal interactions across steps; and (c) Decoder, which extracts the relevant dynamics (e.g., particle acceleration) from the graph. We introduce physics-inspired simple inductive biases, such as an inertial frame that allows learning algorithms to prioritize one solution (constant gravitational acceleration) over another, reducing learning time. The GNS implementation uses semi-implicit Euler integration to update the next state based on the predicted accelerations. GNS trained on trajectory data is generalizable to predict particle kinematics in complex boundary conditions not seen during training. The trained model accurately predicts within a 5\% error of its associated material point method (MPM) simulation. The predictions are 5,000x faster than traditional MPM simulations (2.5 hours for MPM simulations versus 20 s for GNS simulation of granular flow). GNS surrogates are popular for solving optimization, control, critical-region prediction for in situ viz, and inverse-type problems. The GNS code is available under the open-source MIT license at https://github.com/geoelements/gns.

cs.LG↗

arXiv2022

A Frequency-Velocity CNN for Developing Near-Surface 2D Vs Images from Linear-Array, Active-Source Wavefield Measurements

This paper presents a frequency-velocity convolutional neural network (CNN) for rapid, non-invasive 2D shear wave velocity (Vs) imaging of near-surface geo-materials. Operating in the frequency-velocity domain allows for significant flexibility in the linear-array, active-source experimental testing configurations used for generating the CNN input, which are normalized dispersion images. Unlike wavefield images, normalized dispersion images are relatively insensitive to the experimental testing configuration, accommodating various source types, source offsets, numbers of receivers, and receiver spacings. We demonstrate the effectiveness of the frequency-velocity CNN by applying it to a classic near-surface geophysics problem, namely, imaging a two-layer, undulating, soil-over-bedrock interface. This problem was recently investigated in our group by developing a time-distance CNN, which showed great promise but lacked flexibility in utilizing different field-testing configurations. Herein, the new frequency-velocity CNN is shown to have comparable accuracy to the time-distance CNN while providing greater flexibility to handle varied field applications. The frequency-velocity CNN was trained, validated, and tested using 100,000 synthetic near-surface models. The ability of the proposed frequency-velocity CNN to generalize across various acquisition configurations is first tested using synthetic near-surface models with different acquisition configurations from that of the training set, and then applied to experimental field data collected at the Hornsby Bend site in Austin, Texas, USA. When fully developed for a wider range of geological conditions, the proposed CNN may ultimately be used as a rapid, end-to-end alternative for current pseudo-2D surface wave imaging techniques or to develop starting models for full waveform inversion.

cs.LG↗

arXiv2022

Minority Report: A Graph Network Oracle for In Situ Visualization

In situ visualization techniques are hampered by a lack of foresight: crucial simulation phenomena can be missed due to a poor sampling rate or insufficient detail at critical timesteps. Keeping a human in the loop is impractical, and defining statistical triggers can be difficult. This paper demonstrates the potential for using a machine-learning-based simulation surrogate as an oracle to identify expected critical regions of a large-scale simulation. These critical regions are used to drive the in situ analysis, providing greater data fidelity and analysis resolution with an equivalent I/O budget to a traditional in situ framework. We develop a distributed asynchronous in situ visualization by integrating TACC Galaxy with CB-Geo MPM for material point simulation of granular flows. We employ a PyTorch-based 3D Graph Network Simulator (GNS) trained on granular flow problems as an oracle to predict the dynamics of granular flows. Critical regions of interests are manually tagged in GNS for in situ rendering in MPM.

cs.GR↗

arXiv2022

A machine learning approach to predicting pore pressure response in liquefiable sands under cyclic loading

Shear stress history controls the pore pressure response in liquefiable soils. The excess pore pressure does not increase under cyclic loading when shear stress amplitude is lower than the peak prior amplitude -- the shielding effect. Many sophisticated constitutive models fail to capture the shielding effect observed in the cyclic liquefaction experiments. We develop a data-driven machine learning model based on the LSTM neural network to capture the liquefaction response of soils under cyclic loading. The LSTM model is trained on 12 laboratory cyclic simple shear tests on Nevada sand in loose and dense conditions subjected to different cyclic simple shear loading conditions. The LSTM model features include the relative density of soil and the previous stress history to predict the pore water pressure response. The LSTM model successfully replicates the pore pressure response for three cyclic simple test results considering the shielding and density effects.

physics.geo-ph↗

arXiv2022

Hybrid Finite Element and Material Point Method to Simulate Granular Column Collapse from Failure Initiation to Runout

The performance evaluation of a potentially unstable slope involves two key components: the initiation of the slope failure and the post-failure runout. The Finite Element Method (FEM) excels at modeling the initiation of instability but quickly loses accuracy in modeling large-deformation problems due to mesh distortion. Hence, the FEM is unable to accurately model post-failure slope runout. Hybrid Eulerian-Lagrangian methods, such as the Material Point Method (MPM), offer a promising alternative for solving large-deformation problems, because particles can move freely across a background mesh, allowing for large deformation without computational issues. However, the use of moving material points in MPM for integration rather than the fixed Gauss points of the FEM reduces the accuracy of MPM in predicting stress distribution and thus failure initiation. We have created a hybrid method by initiating a failure simulation in FEM and subsequently transferring the coordinates, velocities, and stresses to MPM particles to model the runout behavior, combining the strength of both methods. We demonstrate the capability of the hybrid approach by simulating the collapse of a frictional granular column, comparing it to an empirical solution, and evaluating a suitable time to transfer from FEM to MPM by trialing multiple iterations with transfers at different stages of the collapse.

physics.geo-ph↗

arXiv2022

Investigating the source of hysteresis in the Soil-Water Characteristic Curve using the multiphase lattice Boltzmann method

The soil-water characteristic curve (SWCC) is the most fundamental relationship in unsaturated soil mechanics, relating the amount of water in the soil to the corresponding matric suction. From experimental evidence, it is known that SWCC exhibits hysteresis (i.e. wetting/drying path dependence). Various factors have been proposed as contributors to SWCC hysteresis, including air entrapment, contact angle hysteresis, ink-bottle effect, and change of soil fabric due to swelling and shrinkage, however, the significance of their contribution is debated. From our pore-scale numerical simulations, using the multiphase lattice Boltzmann method, we see that even when controlling for all these factors SWCC hysteresis still occurs, indicating that there is some underlying source that is not accounted for in these factors. We find this underlying source by comparing the liquid/gas phase distributions for simulated wetting and drying experiments of 2D and 3D granular packings. We see that during wetting (i.e. pore filling) many liquid bridges expand simultaneously and join together to fill the pores from the smallest to the largest, allowing menisci with larger radii of curvature (lower matric suction). Whereas, during drying (i.e. pore emptying), only the limited existing gas clusters can expand, which become constrained by the size of the pore openings surrounding them and result in menisci with smaller radii of curvature (higher matric suction).

cond-mat.soft↗

arXiv2022

Effect of color reconnection and hadronic re-scattering on underlying events in pp collisions at LHC energies

Underlying events dominate most of the hadronic activity in p$-$p collisions and are spanned from perturbative to non-perturbative QCD, having a sensitivity ranging from the multi-scale to very low-x scale physics. A detailed understanding of such events plays a crucial role in the accurate understanding of Standard Model ()SM and Beyond Standard Model physics. The underlying event activities has been studied within the framework of Pythia 8 Monte Carlo model, considering the underlying events observables mean charged particle multiplicity density , $\langle d^{2}N /dηdϕ\rangle$ and mean scalar $p_T$ sum, $\langle d^{2} \sum p_{T} /dηdϕ\rangle$ as a function of leading charged particle in towards, away, and transverse region of p$-$p collisions at $\sqrt{s}$ = 2.76, 7 and 13 TeV. The towards, away, and transverse regions have been defined on an azimuthal plane relative to leading particle in p$-$p collisions. The energy dependence of underlying events and their activities in the central and forward region has also been studied. The effect of hadronic re-scattering, color reconnection, and rope hadronization mechanism implemented in Pythia 8 has been studied in details to gain insight into the different processes contributing to underlying events in soft sector.

hep-ph↗

arXiv2022

In situ visualization of regional-scale natural hazards with Galaxy and Material Point Method

Visualizing regional-scale landslides is essential to conveying the threat of natural hazards to stakeholders and policymakers. Traditional visualization techniques are restricted to post-processing a limited subset of simulation data and are not scalable to rendering regional-scale models. In situ visualization is a technique of rendering simulation data in real-time, i.e., rendering visuals in tandem while the simulation is running. This study develops a scalable N:M interface architecture to visualize regional-scale landslides. We demonstrate the scalability of the architecture by simulating the long runout of the 2014 Oso landslide using the Material Point Method coupled with the Galaxy ray tracing engine rendering 4.2 million material points as spheres. In situ visualization has an amortized runtime increase of 2% compared to non-visualized simulations. The developed approach can achieve in situ visualization of regional-scale landslides with minimal impact on the simulation process.

physics.geo-ph↗

arXiv2021

Conductive and convective heat transfer in inductive heating of subsea buried pipelines

Inductive heating with high-voltage cables reduces the risk of hydrate formation by raising the temperature of the production fluid in pipelines. Heating the pipeline results in losing a certain fraction of the heat to the surrounding soil through conduction or convection-dominated flow through the soil. However, the amount of heat lost in conduction versus convection and the transition from conduction to convection-dominated heat loss remains unknown. Soil permeability, temperature gradient between cable and mudline, and burial depth influence the mode of heat transfer and the amount of heat lost. We study the dominant mode of heat transfer in pipelines with inductive heating using 2D Finite Difference analysis under different soil and environmental conditions. Low permeability soils primarily exhibit conductive heat transfer, thus losing minimum heat to the surrounding soil. In contrast, convective flow drives a significant fraction of the heat away from the pipeline and towards the ground surface for highly permeable soils, barely heating the fluid in the pipe. We identify a critical Rayleigh-Darcy number of 1 as the controlling value separating conduction and convection-dominated heat transfer. An increase in burial depth deteriorates the heating efficiency in convection-dominated high permeability soils, while it remains unaffected in conduction-dominated low permeability soils.

physics.geo-ph↗

arXiv2021

Effect Of Slope Angle On The Runout Evolution of Granular Column Collapse for Varying Initial Volumes

In nature, submarine slope failures usually carry thousands of cubic-meters of sediments across extremely long distances and cause tsunamis and damages to offshore structures. This paper uses the granular column collapse experiment to investigate the effect of slope angle on the runout behavior of submarine granular landslides for different initial volumes. A two-dimensional coupled lattice Boltzman and discrete element method (LBM-DEM) approach is adopted for numerically modeling the granular column collapse. Columns with four different slope angles and six different volumes are modelled under both dry and submerged conditions. The effects of hydrodynamic interactions, including the generation of excess pore pressures, hydroplaning, and drag forces and formation of turbulent vortices, are used to explain the difference in the runout behavior of the submerged columns compared to the dry columns. The results show that at any given slope angle, there is a threshold volume above which the submerged columns have a larger final runout compared to their dry counterpart, and this threshold volume decreases with slope angle.

physics.geo-ph↗