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Dinesh Kumar

Publications and source records attributed to Dinesh Kumar.

At least 55 records · Page 3Linked to original sources
arXiv2022

Data-driven multi-scale modeling and robust optimization of composite structure with uncertainty quantification

It is important to accurately model materials' properties at lower length scales (micro-level) while translating the effects to the components and/or system level (macro-level) can significantly reduce the amount of experimentation required to develop new technologies. Robustness analysis of fuel and structural performance for harsh environments (such as power uprated reactor systems or aerospace applications) using machine learning-based multi-scale modeling and robust optimization under uncertainties are required. The fiber and matrix material characteristics are potential sources of uncertainty at the microscale. The stacking sequence (angles of stacking and thickness of layers) of composite layers causes meso-scale uncertainties. It is also possible for macro-scale uncertainties to arise from system properties, like the load or the initial conditions. This chapter demonstrates advanced data-driven methods and outlines the specific capability that must be developed/added for the multi-scale modeling of advanced composite materials. This chapter proposes a multi-scale modeling method for composite structures based on a finite element method (FEM) simulation driven by surrogate models/emulators based on microstructurally informed meso-scale materials models to study the impact of operational parameters/uncertainties using machine learning approaches. To ensure optimal composite materials, composite properties are optimized with respect to initial materials volume fraction using data-driven numerical algorithms.

cs.CY↗
arXiv2022

Practical Applications of Gaussian Process with Uncertainty Quantification and Sensitivity Analysis for Digital Twin for Accident Tolerant Fuel

The application of digital twin (DT) technology to the nuclear field is one of the challenges in the future development of nuclear energy. Possible applications of DT technology in the nuclear field are expected to be very wide: operate commercial nuclear reactors, monitor spent fuel storage and disposal facilities, and develop new nuclear systems. As U.S. Nuclear Regulatory Committee (NRC) recently announced, machine learning (ML) and artificial intelligence (AI) will be new domains in the nuclear field. Considering the data science perspective, Gaussian Process (GP) has proven to be an ML algorithm for modeling and simulation components of the digital twin framework, specifically for the accident tolerant fuel (ATF) concepts. ATF is one of the high-priority areas for both the U.S. Department of Energy (DOE) and NRC. GP's inherent treatment of lack of data, missing data, and data inconsistencies (noisy/erroneous data) present in the ATF concepts make it an attractive machine learning algorithm for implementation in the DT framework. This chapter focuses on the practical demonstration of GP and its applicability to the DT framework for predicting ATF.

physics.med-ph↗
arXiv2022

Reliability-Based Robust Design Optimization Method for Engineering Systems with Uncertainty Quantification

Robust optimization is a method for optimization under uncertainties in engineering systems and designs for applications ranging from aeronautics to nuclear. In a robust design process, parameter variability (or uncertainty) is incorporated into the engineering systems' optimization process to assure the systems' quality and reliability. This chapter focuses on a robust optimization approach for developing robust and reliable advanced systems and explains the framework for using uncertainty quantification and optimization techniques. For the uncertainty analysis, a polynomial chaos-based approach is combined with the optimization algorithms MOSA (Multi-Objective Simulated Annealing), and the process is discussed with a simplified test function. For the optimization process, gradient-free genetic algorithms are considered as the optimizer scans the whole design space, and the optimal values are not always dependent on the initial values.

stat.CO↗
arXiv2022

Uncertainty Quantification and Sensitivity analysis for Digital Twin Enabling Technology: Application for BISON Fuel Performance Code

To understand the potential of intelligent confirmatory tools, the U.S. Nuclear Regulatory Committee (NRC) initiated a future-focused research project to assess the regulatory viability of machine learning (ML) and artificial intelligence (AI)-driven Digital Twins (DTs) for nuclear power applications. Advanced accident tolerant fuel (ATF) is one of the priority focus areas of the U.S. Department of Energy (DOE). A DT framework can offer game-changing yet practical and informed solutions to the complex problem of qualifying advanced ATFs. Considering the regulatory standpoint of the modeling and simulation (M&S) aspect of DT, uncertainty quantification and sensitivity analysis are paramount to the DT framework's success in terms of multi-criteria and risk-informed decision-making. This chapter introduces the ML-based uncertainty quantification and sensitivity analysis methods while exhibiting actual applications to the finite element-based nuclear fuel performance code BISON.

stat.AP↗
arXiv2022

A global analysis of $b \to s \ell \ell$ data in heavy and light $Z'$ models

We perform a model-independent global fit to all $b \to s \ell \ell$ data in the light of recent measurements of the lepton flavour universality violating (LFUV) observables $R_{K_S^0}$ and $R_{K^{*+}}$ as well as the updated measurements of observables in $B_s \to ϕμ^+ μ^-$ decay, by the LHCb collaboration. We obtain new physics (NP) solutions to the current anomalies in the data, assuming NP in the muon sector only. We find that the 1D NP scenarios $C_9^{\rm NP} <0 $ and $C_{9}^{\rm NP}=-C_{10}^{\rm NP}$ continue to be the most favoured ones. However, the significance of the then favoured scenario $C_{9}^{\rm NP}=-C'_{9}$ has reduced and the updated data now marginally prefers $C_{10}^{\rm NP}$ scenario over $C_{9}^{\rm NP}=-C'_{9}$. The 2D scenarios $(C_9^{\rm NP}, C_{10}^{\prime} )$, $(C_9^{\rm NP}, C_{9}^{\prime} )$ and $(C_9^{\rm NP}, C_{10}^{\rm NP} )$, continue to be favoured by the data in the listed order. We also analyse generic TeV scale $Z'$ models which can generate the favored 1D scenarios, $C_9^{\rm NP} $ and $C_9^{\rm NP} = -C_{10}^{\rm NP}$ along with the 2D NP scenarios $(C_9^{\rm NP}, C_{9}^{\prime} )$ and $(C_9^{\rm NP}, C_{10}^{\rm NP} )$. Using the additional constraints from $B_s -\bar{B_s}$ mixing and neutrino trident data, we find that all four models provide an equally good fit to the data. Further, we consider a model with a 25 MeV $Z'$ that couples to muons and has a $q^2$ dependent $b - s$ coupling. We also study the implications of the current data on the LFUV observable $R_ϕ$, $Q_{4,5}$ along with $R_{K^{(*)}}$ in the high $q^2$. We find that a precise measurements of these observables can provide a good discrimination between a few favored model-independent solutions, and have a potential to disentangle different heavy and light $Z'$ scenarios considered in this work.

hep-ph↗
arXiv2022

Digital Twin and Artificial Intelligence Incorporated With Surrogate Modeling for Hybrid and Sustainable Energy Systems

Surrogate modeling has brought about a revolution in computation in the branches of science and engineering. Backed by Artificial Intelligence, a surrogate model can present highly accurate results with a significant reduction in computation time than computer simulation of actual models. Surrogate modeling techniques have found their use in numerous branches of science and engineering, energy system modeling being one of them. Since the idea of hybrid and sustainable energy systems is spreading rapidly in the modern world for the paradigm of the smart energy shift, researchers are exploring the future application of artificial intelligence-based surrogate modeling in analyzing and optimizing hybrid energy systems. One of the promising technologies for assessing applicability for the energy system is the digital twin, which can leverage surrogate modeling. This work presents a comprehensive framework/review on Artificial Intelligence-driven surrogate modeling and its applications with a focus on the digital twin framework and energy systems. The role of machine learning and artificial intelligence in constructing an effective surrogate model is explained. After that, different surrogate models developed for different sustainable energy sources are presented. Finally, digital twin surrogate models and associated uncertainties are described.

cs.AI↗
arXiv2022

Leveraging Industry 4.0 -- Deep Learning, Surrogate Model and Transfer Learning with Uncertainty Quantification Incorporated into Digital Twin for Nuclear System

Industry 4.0 targets the conversion of the traditional industries into intelligent ones through technological revolution. This revolution is only possible through innovation, optimization, interconnection, and rapid decision-making capability. Numerical models are believed to be the key components of Industry 4.0, facilitating quick decision-making through simulations instead of costly experiments. However, numerical investigation of precise, high-fidelity models for optimization or decision-making is usually time-consuming and computationally expensive. In such instances, data-driven surrogate models are excellent substitutes for fast computational analysis and the probabilistic prediction of the output parameter for new input parameters. The emergence of Internet of Things (IoT) and Machine Learning (ML) has made the concept of surrogate modeling even more viable. However, these surrogate models contain intrinsic uncertainties, originate from modeling defects, or both. These uncertainties, if not quantified and minimized, can produce a skewed result. Therefore, proper implementation of uncertainty quantification techniques is crucial during optimization, cost reduction, or safety enhancement processes analysis. This chapter begins with a brief overview of the concept of surrogate modeling, transfer learning, IoT and digital twins. After that, a detailed overview of uncertainties, uncertainty quantification frameworks, and specifics of uncertainty quantification methodologies for a surrogate model linked to a digital twin is presented. Finally, the use of uncertainty quantification approaches in the nuclear industry has been addressed.

cs.LG↗
arXiv2022

Machine Learning and Artificial Intelligence-Driven Multi-Scale Modeling for High Burnup Accident-Tolerant Fuels for Light Water-Based SMR Applications

The concept of small modular reactor has changed the outlook for tackling future energy crises. This new reactor technology is very promising considering its lower investment requirements, modularity, design simplicity, and enhanced safety features. The application of artificial intelligence-driven multi-scale modeling (neutronics, thermal hydraulics, fuel performance, etc.) incorporating Digital Twin and associated uncertainties in the research of small modular reactors is a recent concept. In this work, a comprehensive study is conducted on the multiscale modeling of accident-tolerant fuels. The application of these fuels in the light water-based small modular reactors is explored. This chapter also focuses on the application of machine learning and artificial intelligence in the design optimization, control, and monitoring of small modular reactors. Finally, a brief assessment of the research gap on the application of artificial intelligence to the development of high burnup composite accident-tolerant fuels is provided. Necessary actions to fulfill these gaps are also discussed.

eess.SY↗
arXiv2022

Unusual Magnetotransport from two dimensional Dirac Fermions in Pd$_{3}$Bi$_{2}$Se$_{2}$

Pd$_{3}$Bi$_{2}$Se$_{2}$ has been proposed to be topologically non-trivial in nature. However, evidence of its non-trivial behavior is still unexplored. We report the growth and magneto-transport study of Pd$_{3}$Bi$_{2}$Se$_{2}$ thin films, revealing for the first time the contribution of two-dimensional (2D) topological surface states. We observe exceptional non-saturated linear magnetoresistance which results from Dirac fermions inhabiting the lowest Landau level in the quantum limit. The transverse magnetoresistance changes from a semi-classical weak-field $B^{2}$ dependence to a high-field $B$ dependence at a critical field $B^{\star}$. It is found that $B^{\star} \propto T^2$, which is expected from the Landau level splitting of a linear energy dispersion. In addition, the magnetoconductivity shows signatures of 2D weak anti-localization (WAL). These novel magnetotransport signatures evince the presence of 2D Dirac fermions in Pd$_{3}$Bi$_{2}$Se$_{2}$ thin films.

cond-mat.str-el↗
arXiv2022

Road map through the desert with scalars

In the context of the gauge coupling unification, we present a comprehensive analysis of the extensions of the Standard Model with vector-like fermions and scalars. We find 145 models that satisfy the unification condition, which are distinguishable by the number of new particles in the spectrum and by their transformation properties under the gauge symmetry group of the Standard Model. For all models we derive lower bounds on the exotic fermion and scalar masses, stemming from the measurement of the strong gauge coupling scale dependence, from the heavy stable charged particle searches, and from the electroweak precision tests. We also discuss the potential of testing the unification scenarios at the future 100 TeV collider and in the proton decay experiments. We show that many models can already be excluded based on the current data, while many others will be entirely probed in the coming years.

hep-ph↗
arXiv2022

Impact of $b \to s \ell \ell$ anomalies on rare charm decays in non-universal $Z'$ models

In this work, we study the impact of $b \to s \ell \ell$, $B_s - \bar{B_s}$ mixing and neutrino trident measurements on observables in decays induced by $c \to u $ transition in the context of a non-universal $Z'$ model which generates $C^{\rm NP}_{9} <0$ and $C^{\rm NP}_9 = - \,C^{\rm NP}_{10} $ new physics scenarios at the tree level. We inspect the effects on $D^0 \to π^0 ν\barν$, $D^+ \to π^+ ν\barν$ and $B_c \to B^+ ν\barν $ decays which are induced by the quark level transition $c \to u ν\barν$. The fact that the branching ratios of these decays are negligible in the standard model (SM) and the long distance effects are relatively smaller in comparison to their charged dileptons counterparts, they are considered to provide genuine null-tests of SM. Therefore the observation of these modes at the level of current as well as planned experimental sensitivities would imply unambiguous signature of new physics. Using the constraints on $Z'$ couplings coming from a combined fit to $b \to s \ell \ell$, $ΔM_s$ and neutrino trident data, we find that any meaningful enhancement over the SM value is ruled out in the considered framework. The same is true for $D - \bar{D}$ mixing observable $ΔM_D$ along with $D^0 \to μ^+ μ^-$ and $D^+ \to π^+ μ^+ μ^-$ decay modes which are induced through $c \to u μ^+ μ^-$ transition.

hep-ph↗
arXiv2021

Large Microwave Inductance of Granular Boron-Doped Diamond Superconducting Films

Boron-doped diamond granular thin films are known to exhibit superconductivity with an optimal critical temperature of Tc = 7.2K. Here we report the measured complex surface impedance of Boron-doped diamond films in the microwave frequency range using a resonant technique. Experimentally measured inductance values are in good agreement with estimates obtained from the normal state sheet resistance of the material. The magnetic penetration depth temperature dependence is consistent with that of a fully-gapped s-wave superconductor. Boron-doped diamond films should find application where high kinetic inductance is needed, such as microwave kinetic inductance detectors and quantum impedance devices.

cond-mat.supr-con↗
arXiv2021

Continuing search for new physics in $b \to s μμ$ decays: two operators at a time

The anomalies in the measurements of observables involving $b \to s μμ$ decays, namely $R_K$, $R_{K^*}$, $P_5^{\prime}$, and $B_s^ϕ$, may be addressed by adding lepton-universality-violating new physics contributions to the effective operators ${\cal O}_9, {\cal O}_{10}, {\cal O}^\prime_9, {\cal O}^\prime_{10}$. We analyze all the scenarios where the new physics contributes to a pair of these operators at a time. We perform a global fit to all relevant data in the $b \to s$ sector to estimate the corresponding new Wilson coefficients, $C_9^{\rm NP}, C_{10}^{\rm NP}, C_9^\prime, C_{10}^\prime$. In the light of the new data on $R_K$ and $R_{K^*}$ presented in Moriond 2019, we find that the scenarios with new physics contributions to the ($C_9^{\rm NP}$, $C_9^\prime$) or ($C_9^{\rm NP}$, $C_{10}^\prime$) pair remain the most favored ones. On the other hand, though the competing scenario ($C_9^{\rm NP}$, $C_{10}^{\rm NP}$) remains attractive, its advantage above the SM reduces significantly due to the tension that emerges between the $R_K$ and $R_{K^*}$ measurements with the new data. The movement of the $R_K$ measurement towards unity would also result in the re-emergence of the one-parameter scenario $C_9^{\rm NP} = -C_9^\prime$.

hep-ph↗
arXiv2021

Vesicle dynamics in large amplitude oscillatory extensional flow

Although the behavior of fluid-filled vesicles in steady flows has been extensively studied, far less is understood regarding the shape dynamics of vesicles in time-dependent oscillatory flows. Here, we investigate the nonlinear dynamics of vesicles in large amplitude oscillatory extensional (LAOE) flows using both experiments and boundary integral (BI) simulations. Our results characterize the transient membrane deformations, dynamical regimes, and stress response of vesicles in LAOE in terms of reduced volume (vesicle asphericity), capillary number ($\Ca$, dimensionless flow strength), and Deborah number ($\De$, dimensionless flow frequency). Results from single vesicle experiments are found to be in good agreement with BI simulations across a wide range of parameters. Our results reveal three distinct dynamical regimes based on vesicle deformation: pulsating, reorienting, and symmetrical regimes. We construct phase diagrams characterizing the transition of vesicle shapes between pulsating, reorienting, and symmetrical regimes within the two-dimensional Pipkin space defined by $\De$ and $\Ca$. Contrary to observations on clean Newtonian droplets, vesicles do not reach a maximum length twice per strain rate cycle in the reorienting and pulsating regimes. The distinct dynamics observed in each regime result from a competition between the flow frequency, flow time scale, and membrane deformation timescale. By calculating the particle stresslet, we quantify the nonlinear relationship between average vesicle stress and strain rate. Additionally, we present results on tubular vesicles that undergo shape transformation over several strain cycles. Broadly, our work provides new information regarding the transient dynamics of vesicles in time-dependent flows that directly informs bulk suspension rheology.

cond-mat.soft↗
arXiv2020

Classification with 2-D Convolutional Neural Networks for breast cancer diagnosis

Breast cancer is the most common cancer in women. Classification of cancer/non-cancer patients with clinical records requires high sensitivity and specificity for an acceptable diagnosis test. The state-of-the-art classification model - Convolutional Neural Network (CNN), however, cannot be used with clinical data that are represented in 1-D format. CNN has been designed to work on a set of 2-D matrices whose elements show some correlation with neighboring elements such as in image data. Conversely, the data examples represented as a set of 1-D vectors -- apart from the time series data -- cannot be used with CNN, but with other classification models such as Artificial Neural Networks or RandomForest. We have proposed some novel preprocessing methods of data wrangling that transform a 1-D data vector, to a 2-D graphical image with appropriate correlations among the fields to be processed on CNN. We tested our methods on Wisconsin Original Breast Cancer (WBC) and Wisconsin Diagnostic Breast Cancer (WDBC) datasets. To our knowledge, this work is novel on non-image to image data transformation for the non-time series data. The transformed data processed with CNN using VGGnet-16 shows competitive results for the WBC dataset and outperforms other known methods for the WDBC dataset.

cs.CV↗
arXiv2020

Predictions for $B_s \to \bar{K}^* \ell \,\ell$ in non-universal $Z'$ models

The lepton flavor universality violating (LFUV) measurements $R_K$ and $R_{K^*}$ in $B$ meson decays can be accounted for in non-universal $Z'$ models. We constrain the couplings of these $Z'$ models by performing a global fit to correlated $b \to s \ell \ell$ and $b \to d \ell \ell $ processes, and calculate their possible implications for $B_s \to \bar{K}^*\ell \ell$ observables. For real new physics (NP) couplings, the 1-$σ$ favored parameters allow the corresponding LFUV ratio $R_{K^*}^{(s)}$ in $B_s \to \bar{K}^*\ell \ell$ to range between 0.8 -- 1.2 at low $q^2$. Complex NP couplings improve the best fit only marginally, however they allow a significant enhancement of the branching ratio, while increasing the range of $R_{K^*}^{(s)}$ at low $q^2$ to 0.8 -- 1.8. We find that NP could cause zero-crossing in the forward-backward asymmetry $A_{FB}$ to shift towards lower $q^2$ values, and enhancement in the magnitude of integrated $A_{FB}$. The $CP$ asymmetry $A_{CP}$ may be suppressed and even change sign. The simultaneous measurements of integrated $R_{K^*}^{(s)}$ and $A_{CP}$ values to 0.1 and 1% respectively, would help in constraining the effective NP Wilson coefficient $C_9$ in $ b \to d μμ$ interactions.

hep-ph↗
arXiv2020

Wohlleben Effect and Emergent Pi junctions in superconducting Boron doped Diamond thin films

Diamond is an excellent band insulator. However, boron (B) doping is known to induce superconductivity. We present two interesting effects in superconducting B doped diamond (BDD) thin films: i) Wohlleben effect (paramagnetic Meissner effect, PME) and ii) a low field spin glass like susceptibility anomaly. We have performed electrical and magnetic measurements (under pressure in one sample) at dopings (1.4 , 2.6 and 3.6) X 1021 cm-3, in a temperature range 2 - 10 K. PME, a low field anomaly in inhomogeneous superconductors could arise from flux trapping, flux compression, or for non-trivial reason such as emergent Josephson Pi junctions. Joint occurrence of PME and spin glass type anomalies points to possible emergence of Pi junctions. BDD is a disordered s-wave superconductor; and Pi junctions could be produced by spin flip scattering of spin half moments when present at weak superconducting regions (Bulaevski et al. 1978). A frustrated network of 0 and Pi junctions will result (Kusmartsev et al. 1992) in a distribution of spontaneous equilibrium supercurrents, a phase glass state. Anderson localized spin half spinons embedded in a metallic fluid (two fluid model of Bhatt et al.) could create Pi junction by spin flip scattering. Our findings are consistent with presence of Pi junctions, invoked to explain their (Bhattacharyya et al.) observation of certain resistance anomaly in BDD.

cond-mat.supr-con↗