arXiv subjects

Prabhat Kumar

Publications and source records attributed to Prabhat Kumar.

At least 55 records · Page 3Linked to original sources

arXiv2022

An improved Material Mask Overlay Strategy for the desired discreteness of pressure-loaded optimized topologies

This paper presents a Material Mask Overlay topology optimization approach with the improved material assignment at the element level for achieving the desired discreteness of the optimized designs for pressure-loaded problems. Hexagonal elements are employed to parametrize the design domain. Such elements provide nonsingular local connectivity; thus, checkerboard patterns and point connections inherently get subdued. Elliptical negative masks are used to find the optimized material layout. Each mask is represented via seven parameters that describe the location, shape, orientation, material dilation, and erosion variables of the mask. The latter two variables are systematically varied in conjunction with a grayscale measure constraint to achieve the solutions' sought 0-1 nature. Darcy's law with a drainage term is used to model the pressure load. The obtained pressure field is converted into the consistent nodal forces using Wachspress shape functions. Sensitivities of the objective and pressure load are evaluated using the adjoint-variable method. The efficacy and robustness of the approach are demonstrated by solving various pressure-loaded structures and pressure-driven compliant mechanisms. Compliance is minimized for loadbearing structures, whereas a multicriteria objective is minimized for mechanism designs. The boundary smoothing scheme is implemented within each optimization iteration to subdue the designs' undulated boundaries.

cs.CE↗

arXiv2022

HoneyTop90: A 90-line MATLAB code for topology optimization using honeycomb tessellation

This paper provides a simple, compact and efficient 90-line pedagogical MATLAB code for topology optimization using hexagonal elements (honeycomb tessellation). Hexagonal elements provide nonsingular connectivity between two juxtaposed elements and, thus, subdue checkerboard patterns and point connections inherently from the optimized designs. A novel approach to generate honeycomb tessellation is proposed. The element connectivity matrix and corresponding nodal coordinates array are determined in 5 (7) and 4 (6) lines, respectively. Two additional lines for the meshgrid generation are required for an even number of elements in the vertical direction. The code takes a fraction of a second to generate meshgrid information for the millions of hexagonal elements. Wachspress shape functions are employed for the finite element analysis, and compliance minimization is performed using the optimality criteria method. The provided Matlab code and its extensions are explained in detail. Options to run the optimization with and without filtering techniques are provided. Steps to include different boundary conditions, multiple load cases, active and passive regions, and a Heaviside projection filter are also discussed. The code is provided in Appendix~A, and it can also be downloaded along with supplementary materials from \url{https://github.com/PrabhatIn/HoneyTop90}.

cs.CE↗

arXiv2022

Topology optimization of stiff structures under self-weight for given volume using a smooth Heaviside function

This paper presents a density-based topology optimization approach to design structures under self-weight load. Such loads change their magnitude and/or location as the topology optimization advances and pose several unique challenges, e.g., non-monotonous behavior of compliance objective, parasitic effects of the low-stiffness elements, and unconstrained nature of the problems. The modified SIMP material scheme is employed with the three-field density representation technique (original, filtered, and projected design fields) to achieve optimized solutions~close~to~0-1. A novel mass density interpolation strategy is proposed using a smooth Heaviside function, which provides a continuous transition between solid and void states of elements and facilitates tuning of the non-monotonous behavior of the objective. A constraint that implicitly imposes a lower bound on the permitted volume is conceptualized using the maximum permitted mass and the current mass of the evolving design. Sensitivities of the objective and self-weight are evaluated using the adjoint-variable method. Compliance of the domain is minimized to achieve the optimized designs using the Method of Moving Asymptotes. The Efficacy and robustness of the presented approach are demonstrated by designing various 2D and 3D structures involving self-weight. The proposed approach maintains the constrained nature of the optimization problems and provides smooth and rapid objective convergence.

cs.CE↗

arXiv2022

Topology optimization for additive manufacturing with length scale, overhang, and building orientation constraints

This paper presents a density-based topology optimization approach considering additive manufacturing limitations. The presented method considers the minimum size of parts, the minimum size of cavities, the inability of printing overhanging parts without the use of sacrificial supporting structures, and the printing directions. These constraints are geometrically addressed and implemented. The minimum size on solid and void zones is imposed through a well-known filtering technique. The sacrificial support material is reduced using a constraint that limits the maximum overhang angle of parts by comparing the structural gradient with a critical reference slope. Due to the local nature of the gradient, the chosen restriction is prone to introduce parts that meet the structural slope but that may not be self-supporting. The restriction limits the maximum overhang angle for a user-defined printing direction, which could reduce structural performance if the orientation is not properly selected. To ease these challenges, a new approach to reduce the introduction of such non-self-supporting parts and a novel method that includes different printing directions in the maximum overhang angle constraint are presented. The proposed strategy for considering the minimum size of solid and void phases, maximum overhang angle, and printing direction, is illustrated by solving a set of 2D benchmark design problems including stiff structures and compliant mechanisms. We also provide MATLAB codes in the appendix for educational purposes and for replication of the results.

cs.CE↗

arXiv2022

SISL:Self-Supervised Image Signature Learning for Splicing Detection and Localization

Recent algorithms for image manipulation detection almost exclusively use deep network models. These approaches require either dense pixelwise groundtruth masks, camera ids, or image metadata to train the networks. On one hand, constructing a training set to represent the countless tampering possibilities is impractical. On the other hand, social media platforms or commercial applications are often constrained to remove camera ids as well as metadata from images. A self-supervised algorithm for training manipulation detection models without dense groundtruth or camera/image metadata would be extremely useful for many forensics applications. In this paper, we propose self-supervised approach for training splicing detection/localization models from frequency transforms of images. To identify the spliced regions, our deep network learns a representation to capture an image specific signature by enforcing (image) self consistency . We experimentally demonstrate that our proposed model can yield similar or better performances of multiple existing methods on standard datasets without relying on labels or metadata.

cs.CV↗

arXiv2021

SPACE: 3D Parallel Solvers for Vlasov-Maxwell and Vlasov-Poisson Equations for Relativistic Plasmas with Atomic Transformations

A parallel, relativistic, three-dimensional particle-in-cell code SPACE has been developed for the simulation of electromagnetic fields, relativistic particle beams, and plasmas. In addition to the standard second-order Particle-in-Cell (PIC) algorithm, SPACE includes efficient novel algorithms to resolve atomic physics processes such as multi-level ionization of plasma atoms, recombination, and electron attachment to dopants in dense neutral gases. SPACE also contains a highly adaptive particle-based method, called Adaptive Particle-in-Cloud (AP-Cloud), for solving the Vlasov-Poisson problems. It eliminates the traditional Cartesian mesh of PIC and replaces it with an adaptive octree data structure. The code's algorithms, structure, capabilities, parallelization strategy and performances have been discussed. Typical examples of SPACE applications to accelerator science and engineering problems are also presented.

physics.comp-ph↗

arXiv2021

On topology optimization of design-dependent pressure-loaded three-dimensional structures and compliant mechanisms

This paper presents a density-based topology optimization method for designing three-dimensional (3D) compliant mechanisms and loadbearing structures with design-dependent pressure loading. Instead of interface-tracking techniques, the Darcy law in conjunction with a drainage term is employed to obtain pressure field as a function of the design vector. To ensure continuous transition of pressure loads as the design evolves, the flow coefficient of a finite element is defined using a smooth Heaviside function. The obtained pressure field is converted into consistent nodal loads using a transformation matrix. The presented approach employs the standard finite element formulation and also, allows consistent and computationally inexpensive calculation of load sensitivities using the adjoint-variable method. For compliant mechanism design, a multi-criteria objective is minimized, whereas minimization of compliance is performed for designing loadbearing structures. Efficacy and robustness of the presented approach is demonstrated by designing various pressure-actuated 3D compliant mechanisms and structures.

cs.CE↗

arXiv2021

A numerical scheme for filter boundary conditions in topology optimization on regular and irregular meshes

In density-based topology optimization, design variables associated to the boundaries of the design domain require unique treatment to negate boundary effects arising from the filtering technique. An effective approach to deal with filtering boundary conditions is to extend the design domain beyond the borders using void densities. Although the approach is easy to implement, it introduces extra computational cost required to store the additional Finite Elements (FEs). This work proposes a numerical technique for the density filter that emulates an extension of the design domain, thus, it avoids boundary effects without demanding additional computational cost, besides it is very simple to implement on both regular and irregular meshes. The numerical scheme is demonstrated using the compliance minimization problem on two-dimensional design domains. In addition, this article presents a discussion regarding the use of a real extension of the design domain, where the Finite Element Analysis (FEA) and volume restriction are influenced. Through a quantitative study, it is shown that affecting FEA or volume restriction with an extension of the FE mesh promotes the disconnection of material from the boundaries of the design domain, which is interpreted as a numerical instability promoting convergence towards local optimums.

math.NA↗

arXiv2020

Space observation on detoxing the unhealthy air quality during COVID-19 pandemic in India

The purpose of this study has extremely dedicated to exposing the correlation between coronavirus pandemic and space observation on unhealthy air quality in India. The world has undergone lockdown to break the chain of coronavirus infection. The Air Quality Index (AQI) has started to improve after the commencement of lockdown due to industrial and transportation sectors temporally closed. This study compiled the data recently released by NASA (National Aeronautics and Space Administration), ESA (European Space Agency), and ISRO (Indian Space and Research Organization). In this paper, we have discussed the space observation on Nitrogen Dioxide (NO2), Aerosol Optical Depth (AOD), PM2.5, and PM10 influenced the air quality across the various region of India. We analyzed the detoxing of air quality before and during the lockdown period over the same time the frame of current and the previous year. The result has shown a positive impact on the detoxing of unhealthy air quality during lockdown stated as the emission of NO2 has reduced to 40% - 50% and optical level of aerosol indexed at low compared to the last 20 years in northern India.

physics.ao-ph↗

arXiv2020

On topology optimization of large deformation contact-aided shape morphing compliant mechanisms

A topology optimization approach for designing large deformation contact-aided shape morphing compliant mechanisms is presented. Such mechanisms can be used in varying operating conditions. Design domains are described by regular hexagonal elements. Negative circular masks are employed to perform dual task, i.e., to decide material states of each element and also, to generate rigid contact surfaces. Each mask is characterized by five design variables, which are mutated by a zero-order based hill-climbing optimizer. Geometric and material nonlinearities are considered. Continuity in normals to boundaries of the candidate designs is ensured using a boundary resolution and smoothing scheme. Nonlinear mechanical equilibrium equations are solved using the Newton-Raphson method. An updated Lagrange approach in association with segment-to-segment contact method is employed for the contact formulation. Both mutual and self contact modes are permitted. Efficacy of the approach is demonstrated by designing four contact-aided shape morphing compliant mechanisms for different desired curves. Performance of the deformed profiles is verified using a commercial software. The effect of frictional contact surface on the actual profile is also studied

cs.CE↗

arXiv2020

On Topology optimization with elliptical masks and honeycomb tessellation with explicit length scale constraints

Topology optimization using gradient search with negative and positive elliptical masks and honeycomb tessellation is presented. Through a novel skeletonization algorithm for topologies defined using filled and void hexagonal cells/elements, explicit minimum and maximum length scales are imposed on solid states in the solutions. An analytical example is presented which suggests that for a skeletonized topology, optimal solutions may not always exist for any specified volume fraction, minimum and maximum length scales, and that there may exist implicit interdependence between them. A Sequence for Length Scale (SLS) methodology is proposed wherein solutions are sought by specifying only the minimum and maximum length scales with volume fraction getting determined systematically. Through four benchmark problems in small deformation topology optimization, it is demonstrated that solutions by-and-large satisfy the length scale constraints though the latter may get violated at certain local sites. The proposed approach seems promising, noting especially that solutions, if rendered perfectly {\it black and white} with minimum length scale explicitly imposed and boundaries smoothened, are quite close in performance compared to the parent topologies. Attaining {\it volume distributed} topologies, wherein members are more or less of the same thickness, may also be possible with the proposed approach.

cs.CE↗

arXiv2020

Evolution of the self-injection process in the transition of an LWFA from self-modulation to blowout regime

Long wavelength infrared (LWIR) laser driven plasma wakefield accelerators are investigated here in the self-modulated laser wakefield acceleration (SM-LWFA) and blowout regimes using 3D Particle-in-Cell simulations. The simulation results show that in SM-LWFA regime, self-injection arises with wave breaking, whereas in the blowout regime, self-injection is not observed under the simulation conditions. The wave breaking process in SM-LWFA regime occurs at a field strength that is significantly below the 1D wave-breaking threshold. This process intensifies at higher laser power and plasma density and is suppressed at low plasma densities ($\leq 1\times10^{17}$ $cm^{-3}$ here). The produced electrons show spatial modulations with a period matching that of the laser wavelength, which is a clear signature of direct laser acceleration (DLA).

physics.plasm-ph↗

arXiv2020

Topology Optimization of Fluidic Pressure Loaded Structures and Compliant Mechanisms using the Darcy Method

In various applications, design problems involving structures and compliant mechanisms experience fluidic pressure loads. During topology optimization of such design problems, these loads adapt their direction and location with the evolution of the design, which poses various challenges. A new density-based topology optimization approach using Darcy's law in conjunction with a drainage term is presented to provide a continuous and consistent treatment of design-dependent fluidic pressure loads. The porosity of each finite element and its drainage term are related to its density variable using a Heaviside function, yielding a smooth transition between the solid and void phases. A design-dependent pressure field is established using Darcy's law and the associated PDE is solved using the finite element method. Further, the obtained pressure field is used to determine the consistent nodal loads. The approach provides a computationally inexpensive evaluation of load sensitivities using the adjoint-variable method. To show the efficacy and robustness of the proposed method, numerical examples related to fluidic pressure loaded stiff structures and small-deformation compliant mechanisms are solved. For the structures, compliance is minimized, whereas for the mechanisms a multi-criteria objective is minimized with given resource constraints.

cs.CE↗

arXiv2020

Damping-like Torque in Monolayer 1T-TaS$_2$

A damping-like spin orbit torque (SOT) is a prerequisite for ultralow power spin logic devices. Here, we report on the damping-like SOT in just one monolayer of the conducting transition metal dichalcogenide (TMD) TaS$_2$ interfaced with a NiFe (Py) ferromagnetic layer. The charge-spin conversion efficiency is found to be 0.25$\pm$0.03 and the spin Hall conductivity (2.63 $\times$ 10$^5$ $\frac{\hbar}{2e}$ $Ω^{-1}$ m$^{-1}$) is found to be superior to values reported for other TMDs. The origin of this large damping-like SOT can be found in the interfacial properties of the TaS$_2$/Py heterostructure, and the experimental findings are complemented by the results from density functional theory calculations. The dominance of damping-like torque demonstrated in our study provides a promising path for designing next generation conducting TMD based low-powered quantum memory devices.

cond-mat.mes-hall↗

arXiv2020

Detecting Face2Face Facial Reenactment in Videos

Visual content has become the primary source of information, as evident in the billions of images and videos, shared and uploaded on the Internet every single day. This has led to an increase in alterations in images and videos to make them more informative and eye-catching for the viewers worldwide. Some of these alterations are simple, like copy-move, and are easily detectable, while other sophisticated alterations like reenactment based DeepFakes are hard to detect. Reenactment alterations allow the source to change the target expressions and create photo-realistic images and videos. While technology can be potentially used for several applications, the malicious usage of automatic reenactment has a very large social implication. It is therefore important to develop detection techniques to distinguish real images and videos with the altered ones. This research proposes a learning-based algorithm for detecting reenactment based alterations. The proposed algorithm uses a multi-stream network that learns regional artifacts and provides a robust performance at various compression levels. We also propose a loss function for the balanced learning of the streams for the proposed network. The performance is evaluated on the publicly available FaceForensics dataset. The results show state-of-the-art classification accuracy of 99.96%, 99.10%, and 91.20% for no, easy, and hard compression factors, respectively.

cs.CV↗

arXiv2019

Structural and magnetic properties of co-sputtered Fe0.8C0.2 thin films

We studied the structural and magnetic properties of \FeC~thin films deposited by co-sputtering of Fe and C targets in a direct current magnetron sputtering (dcMS) process at a substrate temperature (\Ts) of 300, 523 and 773\,K. The structure and morphology was measured using x-ray diffraction (XRD), x-ray absorption near edge spectroscopy (XANES) at Fe $L$ and C $K$-edges and atomic/magnetic force microscopy (AFM, MFM), respectively. An ultrathin (3\,nm) $^{57}$\FeC~layer, placed between relatively thick \FeC~layers was used to estimate Fe self-diffusion taking place during growth at different \Ts~using depth profiling measurements. Such $^{57}$\FeC~layer was also used for $^{57}$Fe conversion electron Mössbauer spectroscopy (CEMS) and nuclear resonance scattering (NRS) measurements, yielding the magnetic structure of this ultrathin layer. We found from XRD measurements that the structure formed at low \Ts~(300\,K) is analogous to Fe-based amorphous alloy and at high \Ts~(773\,K), pre-dominantly a \tifc~phase has been formed. Interestingly, at an intermediate \Ts~(523\,K), a clear presence of \tefc~(along with \tifc~and Fe) can be seen from the NRS spectra. The microstructure obtained from AFM images was found to be in agreement with XRD results. MFM images also agrees well with NRS results as the presence of multi-magnetic components can be clearly seen in the sample grown at \Ts~= 523\,K. The information about the hybridization between Fe and C, obtained from Fe $L$ and C $K$-edges XANES also supports the results obtained from other measurements. In essence, from this work, experimental realization of \tefc~has been demonstrated. It can be anticipated that by further fine-tuning the deposition conditions, even single phase \tefc~phase can be realized which hitherto remains an experimental challenge.

cond-mat.mtrl-sci↗

arXiv2019

Compliant Fluidic Control Structures: Concept and Synthesis Approach

The concept and synthesis approach for planar Compliant Fluidic Control Structures (CFCSs), monolithic flexible continua with embedded functional pores, is presented in this manuscript. Such structures are envisioned to find application in biomedicine as tunable microuidic devices for drug/nutrient delivery. The functional pores enlarge and/or contract upondeformation of the compliant structure in response to external stimuli, facilitating the regulated control of fluid/nutrient/drug transport. A thickness design variable based topology optimization problem is formulated to generate effective designs of these structures. An objective based on hydraulic diameter(s) is conceptualized, and it is extremized using a gradient based optimizer. Both geometrical and material nonlinearities are considered. The nonlinear behaviour of employed hyperelastic material is modeled via the Arruda-Boyce constitutive material model. Large-displacement finite element analysis is performed using the updated Lagrangian formulation in plane-stress setting. The proposed synthesis approach is applied to various CFCSs for a variety of fluidic control functionalities. The optimized designs of various CFCSs with single and/or multiple functional pores are fabricated via a Polydimethylsiloxane (PDMS) soft lithography process, using a high precision 3D printed mold and their performances are compared. with the numerical predictions.

cs.CE↗

arXiv2018

A Reinforcement Learning Approach to Target Tracking in a Camera Network

Target tracking in a camera network is an important task for surveillance and scene understanding. The task is challenging due to disjoint views and illumination variation in different cameras. In this direction, many graph-based methods were proposed using appearance-based features. However, the appearance information fades with high illumination variation in the different camera FOVs. We, in this paper, use spatial and temporal information as the state of the target to learn a policy that predicts the next camera given the current state. The policy is trained using Q-learning and it does not assume any information about the topology of the camera network. We will show that the policy learns the camera network topology. We demonstrate the performance of the proposed method on the NLPR MCT dataset.

cs.CV↗