arXiv subjects

Vinod Kumar

Publications and source records attributed to Vinod Kumar.

At least 19 recordsLinked to original sources

arXiv2026

Revisiting In-Medium QCD Effects on Spin-Polarized Strange Quark Stars

We investigate the properties of exotic Strange Quark Matter with spin polarization and the complex configuration of Strange Quark Stars using a phenomenological MIT Bag Model, enhanced by incorporating a QCD informed running strange quark mass dependent on the chemical potential. The effective mass using quasiparticle approach is utilized to understand the framework of SQM. The resulting Equation of State is constructed for two distinct parameter sets, yielding an energy per baryon below the iron limit for stable configurations and thus supporting the Bodmer Witten Terazawa hypothesis that SQM could be the actual ground state of exotic matter. This framework is then used to address the Tolman Oppenheimer Volkoff equations to study the effect of spin polarization on stellar properties. The model predicts that the maximum stellar mass increases with the degree of spin polarization, a result that diverges from previous constant mass models. Furthermore, the model predictions for mass, radius, and surface redshift are in excellent agreement with observational constraints for the compact object Vela X one. This agreement validates our theoretical approach and strengthens the candidacy of Vela X one as a Strange Quark Star. Overall, the model results highlight the importance of in medium QCD effects in describing dense matter.

hep-ph↗

arXiv2026

Thermodynamical analysis of QGP using effective PNJL model with Quasiparticle approach

We study the thermodynamics of the quark-gluon plasma using an effective Two flavor Polyakov Nambu Jona Lasinio (PNJL) model extended by a quasiparticle description for quarks and gluons, incorporating temperature dependent quark masses within the PNJL framework. Two variants, Quasiparticle Model-I and Quasiparticle Model-II, are implemented to investigate bulk thermodynamic observables such as pressure, energy density, entropy density, specific heat, and the speed of sound. The combined framework yields a robust baseline for the description of hot QGP dynamics in the high temperature regime at vanishing chemical potential and zero magnetic field. Systematic comparison with lattice QCD results shows an excellent agreement and clear improvement over conventional PNJL implementations. We observe that both variants complement each other, offering mutually consistent insight into quasiparticle mass effects and medium response in the deconfined phase. This mutual consistency validates the physical foundation of the overall quasiparticle mechanism, reinforcing the credibility of the calculated Equation of State. Finally, the quasiparticle model extension improves PNJL from a descriptive tool to a more qualitative phenomenological approach, enabling an improved description of the strong interacting quark-gluon plasma.

hep-ph↗

arXiv2026

Optical Response of a screw dislocated GaAs Quantum Wire: Temperature and Pressure Effects

We investigate the influence of a screw dislocation, characterized by the dislocation parameter, on the optical response of a parabolic GaAs cylindrical quantum wire under the combined effects of temperature, hydrostatic pressure, and the axial magnetic field. Using a torsion-modified metric together with pressure- and temperature-dependent material properties, namely the effective mass and dielectric permittivity, we obtain exact solutions of the Schr\"odinger equation in terms of Whittaker functions. The screw dislocation introduces a \(k_z\)-dependent coupling that breaks the symmetry between the angular momentum states \(m\) and \(-m\) and modifies the centrifugal term in the effective potential. Based on the resulting eigenstates, we evaluate the linear and third-order nonlinear optical absorption coefficients, as well as the corresponding refractive index changes, for the dipole-allowed transitions \(m = 0 \to +1\) and \(m = 0 \to -1\). Our results show that increasing the dislocation parameter produces a pronounced redshift and suppresses the resonance amplitude for the \(m = 0 \to +1\) transition, whereas the \(m = 0 \to -1\) transition exhibits a blueshift accompanied by peak enhancement. We further find that increasing temperature shifts the resonances toward higher photon energies and enhances their amplitudes, while hydrostatic pressure causes a redshift and reduces the peak intensity for both transitions. In addition, the magnetic field strengthens the optical response and induces a blueshift for the \(m = 0 \to +1\) transition, whereas the opposite behavior is obtained for the \(m = 0 \to -1\) transition. We have also examined the behavior of the refractive index changes, which exhibit analogous asymmetric dependence on the dislocation parameter.

cond-mat.mes-hall↗

arXiv2025

Verification of Convergent-Divergent Nozzle Designs in Propulsion Aerospace Applications

The performance of convergent and divergent nozzles is critical in aerospace propulsion systems, where the efficient expansion of high-temperature, high-pressure gases directly impacts thrust generation. In this study, we investigate a series of nozzle geometries using numerical simulations in ANSYS Fluent, guided by classical compressible flow theory, initially developed by Ludwig Prandtl. The governing equations of conservation of mass, momentum, and energy are solved under steady-state conditions, with emphasis on shock formation, boundary-layer effects, and Mach number distributions across the nozzle throat and divergent section. Parametric analyses are conducted to evaluate the influence of nozzle contour, area ratio, and throat geometry on flow acceleration and thrust coefficient. The results demonstrate close agreement with theoretical predictions of isentropic compressible flow while also highlighting deviations due to viscous and three-dimensional effects. These findings provide design insights for optimizing nozzle performance across propulsion applications, from launch vehicles to high-speed air-breathing systems. We obtained absolute error differences of 2.05 percent, 6.03 percent, and 9.9 percent in the throat temperature measurements for the RL10B2, SSME-40k, and A-1 nozzles, respectively.

physics.flu-dyn↗

arXiv2025

Computational Aerothermal Framework and Analysis of Stetson Mach 6 Blunt Cone

Accurately predicting aerothermal behavior is paramount for the effective design of hypersonic vehicles, as aerodynamic heating plays a pivotal role in influencing performance metrics and structural integrity. This study introduces a computational aerothermal framework and analyzes a blunt cone subjected to Mach 6 conditions, drawing inspiration from Stetson foundational experimental work published in 1983. While the findings offer significant insights into the phenomena at play, the study highlights an urgent necessity for integrating chemical kinetics to comprehensively capture non-equilibrium effects, thereby enhancing the predictive accuracy of computational fluid dynamics (CFD) simulations. This research implements a one-way coupling method between CFD simulations and heat conduction analysis, facilitating a thorough investigation of surface heat transfer characteristics. The numerical results elucidate discrete roughness elements' impact on surface heating and fluid dynamics within high-speed airflow. Furthermore, the investigation underscores the critical importance of accounting for non-equilibrium thermochemical effects in aerothermal modeling to bolster the accuracy of high-enthalpy flow simulations. By refining predictive computational tools and deepening understanding of hypersonic aerothermal mechanisms, this research lays a robust groundwork for future experimental and computational endeavors, significantly contributing to advancing high-speed flight applications.

physics.flu-dyn↗

arXiv2025

Detecting secondary-phase in bainite microstructure through deep-learning based single-shot approach

Relating properties and processing conditions to multiphase microstructures begins with identifying the constituent phases. In bainite, distinguishing the secondary phases is an arduous task, owing to their intricate morphology. In this work, deep-learning techniques deployed as object-detection algorithms are extended to realise martensite-austenite (MA) islands in bainite microstructures, which noticeably affect their mechanical properties. Having explored different techniques, an extensively trained regression-based algorithm is developed to identify the MA islands. This approach effectively detects the secondary phases in a single-shot framework without altering the micrograph dimensions. The identified technique enables scalable, automated detection of secondary phase in bainitic steels. This extension of the detection algorithm is suitably prefaced by an analysis exposing the inadequacy of conventional classification approaches in relating the processing conditions and composition to the bainite microstructures with secondary phases.

cond-mat.mtrl-sci↗

arXiv2024

Current State of Atmospheric Turbulence Cascades

Turbulence cascade has been modeled using various methods; the one we have used applies to a more exact representation of turbulence where people use the multifractal representation. The nature of the energy dissipation is usually governed by partial differential equations that have been described, such as Navier-Stokes Equations, although usually in climate modeling, the Kolmogorov turbulence cascading approximation leads towards an isotropic representation. In recent years, Meneveau et al. have proposed to go away from Kolmogorov assumptions and propose multifractal models where we can account for a new anisotropic representation. Our research aims to use Direct Numerical Simulations (DNS) from the JHU Turbulence Database and Large Eddy Simulations (LES) we simulated using OpenFOAM to predict how accurate these simulations are in replicating Meneveau experimental procedures with numerical simulations using the same rigorous mathematical approaches. Modeling turbulence cascading using higher fidelity data will advance the field and produce faster and better remote sensing metrics. We have written computer code to analyze DNS and LES data and study the multifractal nature of energy dissipation. The box-counting method is used to identify the multifractal dimension spectrum of the DNS and LES data in every direction to follow Meneveau work to represent turbulence-cascading effects in the atmosphere better.

physics.flu-dyn↗

arXiv2024

Computational Analysis of the Temperature Profile Developed for a Hot Zone of 2500{\deg}C in an Induction Furnace

Temperature gradients developed at ultra-high temperatures create a challenge for temperature measurements that are required for material processing. At ultra-high temperatures, the components of the system can react and change phases depending on their thermodynamic stability. These reactions change the system's physical properties, such as thermal conductivity and fluidity. This phenomenon complicates the extrapolation of temperature measurements, as they depend on the thermal conductivity of multiple insulating layers. The proposed model is an induction furnace employing an electromagnetic field to generate heat reaching 2500 degrees Celsius. A heat transfer simulation applying the finite element method determined temperatures and verified experimentally at key locations on the surface of the experimental setup within the furnace. The computed temperature profile of cylindrical graphite crucibles embedded in a larger cylindrical graphite body surrounded by zirconia grog is determined. Compared to experimental results, the simulation showed a percentage error of approximately 3.4 percent, confirming its accuracy.

physics.comp-ph↗

arXiv2024

Partition of Unity Physics-Informed Neural Networks (POU-PINNs): An Unsupervised Framework for Physics-Informed Domain Decomposition and Mixtures of Experts

Physics-informed neural networks (PINNs) commonly address ill-posed inverse problems by uncovering unknown physics. This study presents a novel unsupervised learning framework that identifies spatial subdomains with specific governing physics. It uses the partition of unity networks (POUs) to divide the space into subdomains, assigning unique nonlinear model parameters to each, which are integrated into the physics model. A vital feature of this method is a physics residual-based loss function that detects variations in physical properties without requiring labeled data. This approach enables the discovery of spatial decompositions and nonlinear parameters in partial differential equations (PDEs), optimizing the solution space by dividing it into subdomains and improving accuracy. Its effectiveness is demonstrated through applications in porous media thermal ablation and ice-sheet modeling, showcasing its potential for tackling real-world physics challenges.

cs.LG↗

arXiv2024

Designing an Optimal Scoop for Holloman High-Speed Test Track Water Braking Mechanism using Computational Fluid Dynamics

Specializing in high-speed testing, Holloman High-Speed Test Track (HHSTT) uses water braking to stop vehicles on the test track. This method takes advantage of the higher density of water, compared to air, to increase braking capability through momentum exchange by increasing the water content in that section at the end of the track. By studying water braking using computational fluid dynamics (CFD), the forces acting on tracked vehicles can be approximated and prepared before actual testing through numerical simulations. In this study, emphasis will be placed on the brake component of the tracked sled, which is responsible for interacting with water to brake. By discretizing a volume space around our brake, we accelerate the water and air to simulate the brake coupling relatively. The multiphase flow model uses the governing equations of the gas and liquid phases with the finite volume method to perform 3D simulations. By adjusting the air and water inlet velocity, it is possible to simulate HHSTT sled tests at various operating speeds.

physics.flu-dyn↗

arXiv2024

Computational Investigation of Roughness Effects on Boundary Layer Transition for Stetson's Blunt Cone at Mach 6

In this aerothermal study, we performed a two-dimensional steady-state Computational Fluid Dynamics (CFD) and heat conduction simulation at Mach 6. The key to our methodology was a one-way coupling between CFD surface temperature as a boundary condition and the calculation of the heat transfer flux and temperatures inside the solid stainless-steel body of a nose geometry. This approach allowed us to gain insight into surface heat transfer signatures with corresponding fluid flow regimes, such as the one experienced in laminar fluid flow. We have also examined this heat transfer under roughness values encountered in Stetson's studies at the Wright-Patterson Air Force Base Ludwig tube. To validate our findings, we have performed this type of work on a blunt cone, specifically for the U.S. Air Force. The research focuses on predicting transition onset using laminar correlations derived from Stetson's experimental studies, examining the role of discrete roughness elements. Findings emphasize the importance of incorporating non-equilibrium effects in future computational frameworks to enhance predictive accuracy for high-speed aerodynamic applications.

physics.flu-dyn↗

arXiv2024

A Novel MOSFET based Single Event Latchup Detection, Current Limiting & Self Power Cycling circuit for Spacecraft systems

Single Event Latch-up (SEL) is one of the prime concerns for CMOS ICs used in space systems. Galactic Cosmic Rays or Solar Energetic Particles (SEP) may trigger the parasitic latch up circuit in CMOS ICs and cause increase in current beyond the safe limits thereby presenting a threat of permanent failure of the IC. Mitigation of the SEL is always a challenging task. The conventional mitigation approaches inherently introduce some response time which presents an uncertainty because during this response time the current may exceed the safe current limits. This paper presents a novel circuit based on MOSFETs which provides end-to-end complete solution of detecting SEL, limiting the current below the set threshold and executing power cycling to restore the normal functioning of the CMOS IC. The proposed circuit has been simulated in MULTISIM and the simulation results match very well with the expected behavior of (i)current limiting and (ii) the total time duration taken in power cycling to bring the SEL sensitive device back to its normal operational state. This circuit can be harnessed by spacecraft system designers to overcome the catastrophic threat of SEL posed by space radiation environment.

eess.SY↗

arXiv2024

Empowering Abilities: Increasing Representation of Students with Disabilities in the STEM Field

The ExploreSTEM Summer Camps 2023 were designed to deliver inclusive STEM education to students aged 14 to 22 years with disabilities. This paper presents a thorough examination of the 2023 camp program, emphasizing the pivotal role of inclusive STEM education in potentially shaping students' personal and academic trajectories. The curriculum encompassed four weeklong fundamental STEM domains: Internet of Things (IoT), Computational Engineering, Artificial Intelligence (AI), and Augmented and Virtual Reality (AR/VR). Within Camp 1, students actively engaged with Dash robots, employing dedicated programming environments to command actions and gather sensor data, fostering interactions with the IoT platform and facilitating seamless data transmission. Camp 2 was dedicated to acquainting students with foundational computational engineering principles, establishing a robust framework for comprehending intricate engineering concepts. Camp 3 commenced with insightful presentations elucidating AI applications across multifaceted industries, including engineering, healthcare, and education, illuminating AI's pervasive influence on contemporary society. The primary aim of Camp 4 was to introduce students to the immersive domains of AR and VR, showcasing their applications beyond conventional STEM disciplines into everyday life experiences. The amalgamation of informative presentations, interactive activities, and a nurturing learning environment cultivated an engaging and enriching experience for all participants. By embracing inclusivity and harnessing innovative pedagogical approaches, the ExploreSTEM Summer Camps empowered students to explore, innovate, and excel within the dynamic realm of STEM education.

econ.GN↗

arXiv2024

The inadequacy of the geometric features of MA islands in relating bainite microstructures to composition and processing conditions

Achieving desired properties in bainite steels with MA islands demands understanding the affect of processing conditions and composition on their size and morphology. Generally, this understanding is gained by studying the change in the size and morphology of the MA islands with composition and the processing conditions. In the present work, around 8500 MA islands dispersed across of approximately 1500 bainite microstructures are investigated to comprehend the influence of composition and heat treatment cycle on the geometric features. The geometric features considered in this study include polygon area metric, compactness and aspect ratio. A thorough statistical analysis of these features across bainite steels of different compositions and processing conditions unravel that, though there are minor changes, no characteristic variation is introduced in the size and morphology of the MA islands. In other words, the distribution of the various forms of MA islands are almost identical in bainite steels of different chemistry and heat treatment, thereby indicating the inadequacy of geometric features in explicating the affect of processing conditions on microstructure.

cond-mat.mtrl-sci↗

arXiv2024

Interpretable MA-island clusters and fingerprints relating bainite microstructures to composition and processing temperature

Realising the affect of composition and processing condition on bainite microstructures is often challenging, owing to the intricate distribution of the constituent phases. In this work, scanning electron micrographs of non-isothermally transformed bainite, with martensite-austenite (MA) islands, are analysed to relate the microstructures to the composition and quench-stop temperature. The inadequacy of the MA-islands' geometric features, namely aspect ratio, polygon area and compactness, in establishing this relation is made evident from Kullback-Leibler (KL) divergence at the outset. Clustering the bainite microstructures, following a combination of feature extraction and dimensionality reduction, further fails to realise the affect of composition and processing temperature. Deep-learning analysis of the individual MA islands, in contrast to the bainite microstructures, yields interpretable clusters with characteristically distinct size and morphology. These five clusters, referred to as fine- and coarse-dendrite, fine- and coarse-polygon and elongated, are exceptionally discernible and can be adopted to describe any MA island. Characterising the bainite microstructures, based on the distribution of the interpretable MA-island clusters, generates \textit{fingerprints} that sufficiently relates the composition and processing conditions with the microstructures.

cond-mat.mtrl-sci↗

arXiv2024

Role of time-varying magnetic field on QGP equation of state

The phase diagram of quantum chromodynamics (QCD) and its associated thermodynamic properties of quark gluon plasma (QGP) are studied in the presence of time dependent magnetic field. The study plays a pivotal role in the field of cosmology, astrophysics, and heavy ion collisions. In order to explore the structure of quark gluon plasma to deal with the dynamics of quarks and gluons, we investigate the equation of state (EoS) not only in the environment of static magnetic field but also in the presence of time-varying magnetic fields. So, for determining the equation of state of QGP at non zero magnetic fields, we revisited our earlier model where the effect of time varying magnetic field was not taken into consideration. Using the phenomenological model, some appealing features are noticed depending upon the three different scales; effective mass of quark, temperature, and time independent as well as time-dependent magnetic field. Earlier the effective mass of quark was incorporated in our calculations and in the current work, it is modified for static and time-varying magnetic fields. Thermodynamic observables including pressure, energy density, entropy, etc. are calculated for a wide range of temperature and time-dependent as well as time-independent magnetic fields. Finally, we claim that the EoS are highly affected in the presence of a magnetic field. Our results are notable compared to other approaches and found to be advantageous for the measurement of QGP equation of state. These crucial findings with and without time-varying magnetic field could have phenomenological implications in various sectors of high energy physics.

hep-ph↗

arXiv2023

Theory of hypersurfaces of a Finsler space with generalized square metric

The emergence of generalized square metrics in Finsler geometry can be attributed to various classification concerning ({\alpha}, \beta})-metrics. They have excellent geometric properties in Finsler geometry. Within the scope of this research paper, we have conducted an investigation into the generalized square metric denoted as F(x,y)=({\alpha}(x,y)+\beta}(x,y))^(n+1)/({\alpha}^n (x,y)) focusing specifically on its application to the Finslerian hypersurface. Furthermore, the classification and existence of first, second, and third kind of hyperplanes of the Finsler manifold has been established.

math.DG↗

arXiv2023

Tracking an Untracked Space Debris After an Inelastic Collision Using Physics Informed Neural Network

With the sustained rise in satellite deployment in Low Earth Orbits, the collision risk from untracked space debris is also increasing. Often small-sized space debris (below 10 cm) are hard to track using the existing state-of-the-art methods. However, knowing such space debris' trajectory is crucial to avoid future collisions. We present a Physics Informed Neural Network (PINN) - based approach for estimation of the trajectory of space debris after a collision event between active satellite and space debris. In this work, we have simulated 8565 inelastic collision events between active satellites and space debris. Using the velocities of the colliding objects before the collision, we calculate the post-collision velocities and record the observations. The state (position and velocity), coefficient of restitution, and mass estimation of un-tracked space debris after an inelastic collision event along with the tracked active satellite can be posed as an optimization problem by observing the deviation of the active satellite from the trajectory. We have applied the classical optimization method, the Lagrange multiplier approach, for solving the above optimization problem and observed that its state estimation is not satisfactory as the system is under-determined. Subsequently, we have designed Deep Neural network-based methods and Physics Informed Neural Network (PINN )based methods for solving the above optimization problem. We have compared the performance of the models using root mean square error (RMSE) and interquartile range of the predictions. It has been observed that the PINN-based methods provide a better prediction for position, velocity, mass and coefficient of restitution of the space debris compared to other methods.

astro-ph.EP↗