arXiv subjects

Prabhat Kumar

Publications and source records attributed to Prabhat Kumar.

At least 19 recordsLinked to original sources

arXiv2026

Topology optimization of multimaterial aircraft pylons using generalized shape function approach

As the primary structural component connecting the engine to the wing or fuselage, an aircraft pylon requires optimized structural efficiency; this paper provides topology optimization of multimaterial pylons using the generalized shape function (gSF) approach. The gSF method uses $n$ natural-coordinate design variables per element to provide optimized designs up to $2^n$ distinct material phases while promoting close to discrete material layouts in conjunction with the density and formulated Heaviside projection filters. Pylon structural compliance is minimized subject to volume constraints. Exploiting the geometric features of a typical pylon structure, multimaterial evolution is performed on a corresponding 2D design domain representing the midplane, with up to 14 candidate materials. The optimized two-dimensional layout is then extruded to achieve the corresponding three-dimensional optimized pylon structure. The Method of Moving Asymptotes is employed to achieve the final design variables. The resulting convergence histories exhibit smooth and stable objective minimization. The results highlight the capability of the multimaterial topology optimization framework to effectively optimized aircraft pylons with multiple candidate materials, without requiring a considerable expansion of the design variable set.

cs.CE↗

arXiv2026

Flap like a bat: Soft pneumatic wings for biomimetic ornithopters

This paper describes the development of a bio-inspired ornithopter wing mechanism that uses soft pneumatic actuators, such as PneuNet structures, in place of revolute joints, inspired by the complicated kinematics of bat flight. The proposed design employs pneumatically driven air pockets within a flexible wing skeleton to achieve smooth, lifelike flapping motion. The actuation system is controlled by 3/2-way solenoid valves and an air pump, which manipulate airflow and actuator deformation. We demonstrate, using ANSYS FEA software, the bending behavior of the PneuNet actuators under varying pressures to inform the wing's structural design. The implementation of soft robotics in the flapping-wing system offers potential advantages over traditional mechanisms, such as reduced mechanical complexity, lower acoustic signatures, enhanced adaptability, and wing morphing. This work demonstrates the feasibility of replacing rigid revolute joints with compliant pneumatic actuators, enabling more agile, silent, and biomimetic aerial robots.

cs.RO↗

arXiv2026

A $θ$-implicit hybrid particle-in-cell algorithm for kinetic simulations of magnetized plasmas

Hybrid particle-in-cell (PIC) models with kinetic ions and an inertialess electron fluid are widely used to simulate magnetized plasmas at ion scales, but explicit time advances are constrained by the whistler-wave CFL condition $\dt \propto Δx^2$, and the sub-cycling and artificial dissipation used to mitigate it carry accumulating costs. We present a $θ$-implicit hybrid PIC algorithm, implemented in the open-source GPU-capable code WarpX, in which fields and particles are advanced as a single nonlinear system solved by a particle-suppressed Jacobian-free Newton--Krylov method: the particles are nonlinearly eliminated, the electric field of the generalized Ohm's law is the sole unknown, and mass matrices remove all particle operations from the linear iterations. The electron pressure is advanced inside the nonlinear solve, and the scheme satisfies a discrete energy law whose single dissipation term vanishes at $θ= 1/2$ and damps only temporally under-resolved modes for $θ> 1/2$. The advance reproduces parallel and perpendicular wave dispersion including the ion Bernstein modes, conserves total energy to machine roundoff where the explicit advance heats secularly, does not develop the cold-beam finite-grid instability, and reproduces the $m=0$ instability of a Bennett pinch in RZ geometry. We conclude with 3D simulations of a field-reversed configuration initialized from a Grad--Shafranov equilibrium, comparing the explicit and implicit advances on the growth of the tilt instability and the preservation of trapped magnetic flux.

physics.comp-ph↗

arXiv2026

Multimaterial topology optimization using SIMP, DMO and gSF: A comparative study with honeycomb tessellations

This paper presents a comparative study of multimaterial topology optimization (MMTO) using the extended SIMP, Discrete Material Optimization (DMO), and generalized shape functions (gSF) approaches. The computational domain is discretized into hexagonal cells. Each cell is connected to its neighbors with edges. This characteristic reduces mesh-related effects on the optimized designs compared with conventional triangular and quadrilateral discretizations. Structural compliance is minimized at the prescribed volume fractions. The method of moving asymptotes is employed to update the design variables. The resulting optimized topologies are compared in terms of material distribution, structural performance, convergence behavior, and the characteristics of the obtained material interfaces. The comparative investigation provides insights into the working principles and performance of the extended SIMP, DMO, and gSF interpolation schemes for MMTO.

cs.CE↗

arXiv2026

Tilt-Rotor Quadcopter with Learned Residual Control and Mode-Dependent State Estimation

Tilt-rotor quadcopters redirect thrust for translation without banking, but complicate modeling, state estimation, and disturbance rejection. This paper presents a simulation-based flight stack for a \SI{2.00}{\kilo\gram} tilt-rotor quadcopter. The system uses a six-degree-of-freedom nonlinear model with first-order motor dynamics and two-axis rotor tilt, parameterized using a 3D CAD model. A cascaded PD controller commands forward and lateral tilt angles. A 14-state EKF fuses sensor data across hover, early-transition, and transition modes using a hysteretic (Schmitt-trigger) classifier to prevent covariance discontinuities. Because the proportional position loop leaves a steady-state offset under constant wind, a small bounded neural residual is added on top of the unmodified PD controller. It is trained by behavioral cloning from a teacher with privileged access to the true wind, while the deployed network never observes wind. In simulation, the EKF achieves a post-settling position root mean square error (RMSE) of \SI{0.226}{\metre} (horizontal) and \SI{0.03}{\metre} (vertical) and an attitude RMSE below \SI{0.8}{\degree}, and it estimates a wind velocity of $(\num{2.0}, \num{-1.2})\,\unit{\metre\per\second}$ to within \SI{0.15}{\metre\per\second} per axis. The residual reduces steady-state station-keeping error under wind by approximately \SI{49}{\percent}, from \SI{0.117}{\metre} to \SI{0.060}{\metre} at \SI{1.5}{\metre\per\second} and from \SI{0.319}{\metre} to \SI{0.162}{\metre} at an out-of-distribution \SI{3.9}{\metre\per\second}. The framework provides a basis for hardware-in-the-loop and flight-test studies.

cs.RO↗

arXiv2026

Inner Products and Banach Algebra structures on Bicomplex Numbers and Their Associated Spaces

In this paper, we introduce various types of inner products and norms on the bicomplex number system $\mathbb{C}_2$, the bicomplex vector space ${\mathbb{C}_2}^{n}$, the space of bicomplex matrices ${C_2}^{m \times n}$, and the space of bicomplex polynomials $\mathbb{C}_2[ξ]_n$. We investigate the relationships among these inner products and norms, and establish several results. Furthermore, we prove that $\mathbb{C}_2$ and ${\mathbb{C}_2}^{n}$ are Banach algebras and Hilbert spaces. These results provide a unified framework for the study of inner product structures and normed linear spaces over bicomplex numbers and their associated spaces.

math.RT↗

arXiv2026

TO-mdiSPAs: Topology Optimization of multi-directional Soft Pneumatic Actuators

Soft pneumatic actuators (SPAs) are highly promising and have been extensively explored within the field of soft robotics. Under pneumatic pressure loads, an SPA undergoes bending deformation to perform specific tasks. A multi- or omni-directional SPA can bend and move in any direction within a 3D space, leveraging its multiple degrees of freedom to realize various mechanical functions. This work presents a systematic methodology using topology optimization (TO) to achieve an optimized design for a multi-directional SPAs. To ensure manufacturing robustness, a three-field TO formulation considering blueprint, dilated, and eroded designs is implemented. Additionally, Darcy's law, incorporating a drainage term, is used to model the design-dependent nature of the pneumatic loading. A min-max optimization problem is formulated based on the target output deformations of the SPA unit and solved using the Method of Moving Asymptotes. The optimization yields a high-performing, unconventional geometric design. Finally, numerical simulations demonstrate that the optimized SPA successfully achieves versatile multi-directional movements.

cs.RO↗

arXiv2026

A Comparative Study on Robust Topology Optimization of Design-Dependent Pressure-Actuated Compliant Mechanisms with Quadrilateral Elements

This paper presents a comparative study of compliant mechanisms generated using a robust topology optimization technique involving design-dependent pressure loads. Design domains are parameterized using standard and higher-order quadrilateral elements. Both eroded and blueprint configurations are considered. A min-max optimization model combined with an output-spring method is employed to extremize the mechanisms' output displacements. A volume and a strain energy constraint are applied to the blueprint and the eroded designs, respectively. The optimization process is executed using the method of moving asymptotes. Numerical experiments are performed to optimize the pressure-actuated inverter and gripper mechanisms using Q4, Q8, and Q9 elements, and the results are compared. The research highlights how quadrilateral element selection influences both the resulting topologies and performance characteristics.

cs.CE↗

arXiv2026

Topology optimization with buckling constraints: Adaptive eigenvalue aggregation and modality identification

This paper presents novel approaches to tackle two key challenges in topology optimization problems with buckling constraints: determining how many eigenvalues to aggregate and identifying the eigenvalue modality at the optimal design. We demonstrate the mathematical inconsistencies that arise when choosing an arbitrary fixed number of eigenvalues for aggregation. Specifically, if the multiplicity of the critical eigenvalues is smaller than the chosen subset size, coalescence with higher-order eigenvalues can occur. Neglecting these during aggregation causes incorrect sensitivities, while simply increasing the preselected eigenvalue count unnecessarily raises computational costs. To resolve this issue, we propose an approach that dynamically determines the exact number of eigenvalues required at each optimization iteration. The proposed approach reduces computation time significantly while maintaining competitive performance relative to the conventional method across the numerical experiments presented. Additionally, accurately predicting modality at the optimal point is crucial for understanding the buckled mode shapes of optimized designs. A new optimization formulation is proposed using the difference between eigenvalues to capture the true eigenvalue modality. This formulation is first validated on a classical 1D clamped column, successfully predicting both the bimodal solution and the optimal eigenvectors. The approach demonstrates that the optimal solutions for the wall reinforcement and shear-loaded hinged plate problems for the given set of parameters are trimodal and tetramodal, respectively.

cs.CE↗

arXiv2026

A Generalized Shape Function Approach for Multimaterial Topology Optimization

This paper presents a generalized shape function (gSF) approach for multi-material topology optimization that utilizes a compact design space to produce optimized configurations featuring a large number of materials. Building upon 1D (linear), 2D (bilinear), and 3D (trilinear) shape functions, generalized nD (n-linear) shape functions are conceptualized to map the multi-material simplex domain. Natural coordinates of these shape functions are considered the design variables used to determine the material densities. These densities are mathematically proven to satisfy the essential barycentric properties, guaranteeing a physically valid material interpolation space. Furthermore, we demonstrate that applying density filtering directly to the natural coordinates is mathematically equivalent to filtering the densities themselves, and that the tailored projection scheme preserves these vital barycentric properties in the projected states. The versatility, efficacy, and success of the gSF approach are demonstrated across various 2D and 3D stiff-structure (SS) and compliant-mechanism (CM) design problems. Strain energy is minimized for SS, whereas a multicriteria objective is minimized for CMs with given volume constraints. Sensitivity analysis is performed using the adjoint variable method, and the optimization problem is solved using the method of moving asymptotes. Results for SSs and CMs in 2D and 3D, respectively, up to 24 and 15 different materials, are presented. Objective history plots indicate smooth convergence. The proposed approach removes practical restrictions on the number of candidate materials, offering excellent scalability for large-scale engineering applications.

cs.CE↗

arXiv2026

Nitrospinics as a platform from orbital-torque memory to artificial intelligence

The exploration of energy-efficient and functional spintronics has attracted considerable attention. Orbital transport has opened new pathways for current-induced torque generation beyond the conventional spin transport based on the spin Hall effect. In addition, artificial intelligence computing has been demonstrated using spintronic devices. Further progress of these devices can be anticipated through the development of unique and functional materials beyond the existing heavy metals and topological systems with strong spin-orbit coupling. The nitride materials exhibit unique chemical, magnetic, and structural versatility, including antiferromagnetism, high thermal stability, and compatibility with diverse device architectures. Here, we propose Nitrospinics as a conceptual and functional framework that exploits nitride materials for applications ranging from orbital-torque-based spintronic devices to artificial intelligence hardware. Using Cr2N, a two-dimensional nitride MXene with an atomic layered structure, as a prototype system, we discuss how nitrogen contributes to the structural stability, the orbital torque generation, and the interfacial orbital and spin conversion. We further outline key challenges and opportunities toward establishing nitride-based materials for next-generation computing technologies.

cond-mat.mtrl-sci↗

arXiv2026

TiBCLaG: Hybrid design approach for a trigger-induced bistable compliant laparoscopic grasper

Industrial laparoscopic graspers rely on multi-link rigid mechanisms manufactured to tight tolerances, resulting in high manufacturing and assembly costs. This work presents the design and proof-of-concept validation of a monolithic, fully compliant, bistable, laparoscopic grasper that eliminates the need for multiple rigid links, thereby reducing part count. The device integrates a compliant trigger and a compliant gripper end-effector, coupled via a control push-rod, to achieve stable grasping without continuous user input. A hybrid approach combining an analytical model, iterative prototyping, and finite element analyses is employed to design the laparoscopic grasper. The trigger mechanism is synthesized using a Two-Element Beam Constraint Model as a design framework to control the deformation and stiffness of I-beam-like elements. This technique enables elastic energy storage while preventing snap-through instability. The end-effector is designed as a compliant gripper to achieve adaptive grasping through elastic deformation. The laparoscopic design presented here is fabricated using fused deposition 3D printing. A comparative study of the jaws' opening and closing performance is presented using nonlinear finite element analysis and experimentation. The fabricated prototype demonstrates reliable bistable actuation, confirming the feasibility of such compliant laparoscopic grasper architectures.

cs.RO↗

arXiv2026

Soft Pneumatic Grippers: Topology optimization, 3D-printing and Experimental validation

Typically, heuristic/trial-based approaches are used to design soft pneumatic grippers (SPGs). This paper presents a systematic topology optimization framework for developing SPGs. The design-dependent nature of actuating load is modeled using Darcy's law with an added drainage term. A 2D soft arm unit is then optimized as a compliant mechanism under pneumatic loading. To ensure the design is robust and manufacturable, the problem is formulated as a min-max optimization, where output deformations of blueprint and eroded designs are considered. A volume constraint is imposed on the blueprint part, while a strain-energy constraint is enforced on the eroded part. The Method of Moving Asymptotes is employed to solve optimization problems. The optimized 2D part is extruded suitably to generate a 3D unit. Ten such 3D units are assembled to create a gripper arm. Both the optimized 2D unit and the corresponding gripper arm outperform their conventional rectangular designs under pneumatic loading, demonstrating the efficacy of the proposed approach. The arms are fabricated using the SLA printing technique. Numerical and experimental results are compared at different pneumatic loads. Four 3D-printed arms are integrated with a supporting structure to form the SPG. The gripping action of the SPG is demonstrated on objects with different weights, sizes, structures, stiffnesses, and shapes.

cs.RO↗

arXiv2026

TO-SoFiT: Topology Optimization of Hydraulic Soft Fish Tail Design for programmable undulating locomotion

Soft robots leverage compliant materials to generate motion through controlled elastic deformation, making them ideal for delicate tasks such as underwater exploration and biomimetic marine systems. Although hydraulic/pneumatic actuation remains pivotal for such systems, the lack of systematic design frameworks has hindered the development of robots capable of complex 3D motion, such as fish-like swimming. This work introduces a topology optimization method to automate the design of a hydraulic soft fish tail, explicitly addressing the design-dependent coupling between fluidic actuation and structural deformation. We use a Darcy law-based model augmented with a drainage term to simulate spatially varying hydraulic pressure loads, translating these into consistent nodal forces via finite element analysis. The employed robust multi-criteria optimization formulation balances deformation efficiency, fluid-structure interaction, geometric manufacturability, and required stiffness for optimizing a bioinspired soft fish tail for 3D swimming kinematics. The optimized tail topology is incorporated into a pneumatic network actuator and computationally validated under various hydraulic loads, achieving tunable undulatory amplitudes and multiaxis bending for depth adjustment. The optimized 2D tail outperforms its rectangular counterpart. By cascading optimized tail segments, we demonstrate programmable swimming patterns in soft robotic fish tails at different hydraulic loads. This work advances the systematic codesign of hydraulic actuators and soft structures, offering a pathway to automate underwater robots with optimized design and vertebrate-like agility in confined aquatic environments. Our implementations and simulations are publicly available at 'https://github.com/PrabhatIn/TO-SoFiT'.

cs.RO↗

arXiv2026

MagneX: A High-Performance, GPU-Enabled, Data-Driven Micromagnetics Solver for Spintronics

In order to comprehensively investigate the multiphysics coupling in spintronic devices, it is essential to parallelize and utilize GPU-acceleration to address the spatial and temporal disparities inherent in the relevant physics. Additionally, the use of cutting-edge time integration libraries as well as machine learning (ML) approaches to replace and potentially accelerate expensive computational routines are attractive capabilities to enhance modeling capabilities moving forward. Leveraging the Exascale Computing Project software framework AMReX, as well as SUNDIALS time-integration libraries and python-based ML workflows, we have developed an open-source micromagnetics modeling tool called MagneX. This tool incorporates various crucial magnetic coupling mechanisms, including Zeeman coupling, demagnetization coupling, crystalline anisotropy interaction, exchange coupling, and Dzyaloshinskii-Moriya interaction (DMI) coupling. We demonstrate the GPU performance and scalability of the code and rigorously validate MagneX's functionality using the mumag standard problems and widely-accepted DMI benchmarks. Furthermore, we demonstrate the data-driven capability of MagneX by replacing the computationally-expensive demagnetization physics with neural network libraries trained from our simulation data. With the capacity to explore complete physical interactions, this innovative approach offers a promising pathway to better understand and develop fully integrated spintronic and electronic systems.

cs.CE↗

arXiv2025

ROAD: Reflective Optimization via Automated Debugging for Zero-Shot Agent Alignment

Automatic Prompt Optimization (APO) has emerged as a critical technique for enhancing Large Language Model (LLM) performance, yet current state-of-the-art methods typically rely on large, labeled gold-standard development sets to compute fitness scores for evolutionary or Reinforcement Learning (RL) approaches. In real-world software engineering, however, such curated datasets are rarely available during the initial cold start of agent development, where engineers instead face messy production logs and evolving failure modes. We present ROAD (Reflective Optimization via Automated Debugging), a novel framework that bypasses the need for refined datasets by treating optimization as a dynamic debugging investigation rather than a stochastic search. Unlike traditional mutation strategies, ROAD utilizes a specialized multi-agent architecture, comprising an Analyzer for root-cause analysis, an Optimizer for pattern aggregation, and a Coach for strategy integration, to convert unstructured failure logs into robust, structured Decision Tree Protocols. We evaluated ROAD across both a standardized academic benchmark and a live production Knowledge Management engine. Experimental results demonstrate that ROAD is highly sample-efficient, achieving a 5.6 percent increase in success rate (73.6 percent to 79.2 percent) and a 3.8 percent increase in search accuracy within just three automated iterations. Furthermore, on complex reasoning tasks in the retail domain, ROAD improved agent performance by approximately 19 percent relative to the baseline. These findings suggest that mimicking the human engineering loop of failure analysis and patching offers a viable, data-efficient alternative to resource-intensive RL training for deploying reliable LLM agents.

cs.AI↗

arXiv2025

PyHexTop: a compact Python code for topology optimization using hexagonal elements

Python serves as an open-source and cost-effective alternative to the MATLAB programming language. This paper introduces a concise topology optimization Python code, named ``\texttt{PyHexTop}," primarily intended for educational purposes. Code employs hexagonal elements to parameterize design domains as such elements provide checkerboard-free optimized design naturally. \texttt{PyHexTop} is developed based on the ``\texttt{HoneyTop90}" MATLAB code~\cite{kumar2023honeytop90} and uses the \texttt{NumPy} and \texttt{SciPy} libraries. Code is straightforward and easily comprehensible, proving a helpful tool that can help people new in the topology optimization field to learn and explore. \texttt{PyHexTop} is specifically tailored to address compliance minimization with specified volume constraints. The paper provides a detailed explanation of the code for solving the Messerschmitt-Bolkow-Blohm beam and extensions to solve problems different problems. The code is publicly shared at: https://github.com/PrabhatIn/PyHexTop

cs.CE↗

arXiv2025

TOaCNN: Adaptive Convolutional Neural Network for Multidisciplinary Topology Optimization

This paper presents an adaptive convolutional neural network (CNN) architecture that can automate diverse topology optimization (TO) problems having different underlying physics. The architecture uses the encoder-decoder networks with dense layers in the middle which includes an additional adaptive layer to capture complex geometrical features. The network is trained using the dataset obtained from the three open-source TO codes involving different physics. The robustness and success of the presented adaptive CNN are demonstrated on compliance minimization problems with constant and design-dependent loads and material bulk modulus optimization. The architecture takes the user's input of the volume fraction. It instantly generates optimized designs resembling their counterparts obtained via open-source TO codes with negligible performance and volume fraction error.

cs.CE↗