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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Finite Element Simulation of Compression Molding of Woven Fabric Carbon Fiber/Epoxy Composites: Part I Material Model Development

Woven fabric carbon fiber/epoxy composites made through compression molding are one of the promising choices of material for the vehicle light-weighting strategy. Previous studies have shown that the processing conditions can have substantial influence on the performance of this type of the material. Therefore the optimization of the compression molding process is of great importance to the manufacturing practice. An efficient way to achieve the optimized design of this process would be through conducting finite element (FE) simulations of compression molding for woven fabric carbon fiber/epoxy composites. However, performing such simulation remains a challenging task for FE as multiple types of physics are involved during the compression molding process, including the epoxy resin curing and the complex mechanical behavior of woven fabric structure. In the present study, the FE simulation of the compression molding process of resin based woven fabric composites at continuum level is conducted, which is enabled by the implementation of an integrated material modeling methodology in LS-Dyna. Specifically, the chemo-thermo-mechanical problem of compression molding is solved through the coupling of three material models, i.e., one thermal model for temperature history in the resin, one mechanical model to update the curing-dependent properties of the resin and another mechanical model to simulate the behavior of the woven fabric composites. Preliminary simulations of the carbon fiber/epoxy woven fabric composites in LS-Dyna are presented as a demonstration, while validations and models with real part geometry are planned in the future work.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Systematic Construction of Time-Dependent Hamiltonians for Microwave-Driven Josephson Circuits

Time-dependent electromagnetic drives are fundamental for controlling complex quantum systems, including superconducting Josephson circuits. In these devices, accurate time-dependent Hamiltonian models are imperative for predicting their dynamics and designing high-fidelity quantum operations. Existing numerical methods, such as black-box quantization (BBQ) and energy-participation ratio (EPR), excel at modeling the static Hamiltonians of Josephson circuits. However, these techniques do not fully capture the behavior of driven circuits stimulated by external microwave drives, nor do they include a generalized approach to account for the inevitable noise and dissipation that enter through microwave ports. Here, we introduce numerical techniques that leverage classical microwave simulations, efficiently executable in finite-element solvers, to obtain the time-dependent Hamiltonian of microwave-driven superconducting circuits with arbitrary geometries under charge, flux, or mixed electromagnetic modulation. Importantly, our techniques do not rely on a lumped-element description of the superconducting circuit, in contrast to previous approaches to tackling this problem. We demonstrate the versatility of our approach by characterizing the driven properties of realistic circuit devices in complex electromagnetic environments, including coherent dynamics due to charge and flux modulation, as well as drive-induced relaxation and dephasing. Our techniques offer a powerful toolbox for optimizing circuit designs and advancing practical applications in superconducting quantum computing.

Lu, Yao [Yale U.; Yale U. (main); Fermilab] (ORCID↗
DOE OSTI2022

Finite-volume pionless effective field theory for few-nucleon systems with differentiable programming

Finite-volume pionless effective field theory provides an efficient framework for the extrapolation of nuclear spectra and matrix elements calculated at finite volume in lattice QCD to infinite volume, and to nuclei with larger atomic number. In this work, it is demonstrated how this framework may be implemented via a set of correlated Gaussian wave functions optimized using differentiable programming and via solution of a generalized eigenvalue problem. This approach is shown to be significantly more efficient than a stochastic implementation of the variational method based on the same form of correlated Gaussian wave functions, yielding comparably accurate representations of the ground-state wave functions with an order of magnitude fewer terms. The efficiency of representation allows such calculations to be extended to larger systems than in previous work. Further, the method is demonstrated through calculations of the binding energies of nuclei with atomic number A ϵ {2,3,4} in finite volume, matched to lattice QCD calculations at quark masses corresponding to m π = 806 MeV, and infinite-volume effective field theory calculations of A ϵ {2,3,4,5,6} systems based on this matching.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

Asymmetrical Magnet Shape Optimization Based on S-C Mapping for Torque Profile Mitigation in Unidirectional Application of SPMS Machine

Surface-mounted Permanent Magnet Synchronous (SPMS) machines are widely used in applications that require low torque ripple, reduced vibration and acoustic noise. For those applications of SPMSM in which only unidirectional operation is needed, asymmetric design optimization show to have significant advantages over symmetric designs. In this paper, an analytical method based on Schwarz -Christoffel mapping is presented for asymmetrical magnet shape optimization in SPMS machine. In this method, permanent magnet is divided into finite cells so that each cell can be assigned with either air (OFF) or magnet (ON). The Optimization problem is solved using Genetic Algorithms (GAs). In order to remove non-manufacturable shapes, the cluster of materials and cleaning method is used and applied into magnet cells. The analytical method can significantly reduce the computation time used by optimization process as compared with methods based on Finite Element Analysis (FEA). This method is applied to a 12-pole SPMS machine. Validation of the S-C mapping method and final results are carried out using 2D and 3D finite element analysis. Here, the results obtained demonstrate that asymmetrical magnet shape improves cogging torque and torque ripple performance of SPMS machine by a significant margin.

42 ENGINEERING↗
DOE OSTI2014

Redwing: A MOOSE application for coupling MPACT and BISON

Fuel performance and whole core neutron transport programs are often used to analyze fuel behavior as it is depleted in a reactor. For fuel performance programs, internal models provide the local intra-pin power density, fast neutron flux, burnup, and fission rate density, which are needed for a fuel performance analysis. The fuel performance internal models have a number of limitations. These include effects on the intra-pin power distribution by nearby assembly elements, such as water channels and control rods, and the further limitation of applicability to a specified fuel type such as low enriched UO2. In addition, whole core neutron transport codes need an accurate intra-pin temperature distribution in order to calculate neutron cross sections. Fuel performance simulations are able to model the intra-pin fuel displacement as the fuel expands and densifies. These displacements must be accurately modeled in order to capture the eventual mechanical contact of the fuel and the clad; the correct radial gap width is needed for an accurate calculation of the temperature distribution of the fuel rod. Redwing is a MOOSE-based application that enables coupling between MPACT and BISON for transport and fuel performance coupling. MPACT is a 3D neutron transport and reactor core simulator based on the method of characteristics (MOC). The development of MPACT began at the University of Michigan (UM) and now is under the joint development of ORNL and UM as part of the DOE CASL Simulation Hub. MPACT is able to model the effects of local assembly elements and is able calculate intra-pin quantities such as the local power density on a volumetric mesh for any fuel type. BISON is a fuel performance application of Multi-physics Object Oriented Simulation Environment (MOOSE), which is under development at Idaho National Laboratory. BISON is able to solve the nonlinearly coupled mechanical deformation and heat transfer finite element equations that model a fuel element as it is depleted in a nuclear reactor. Redwing couples BISON and MPACT in a single application. Redwing maps and transfers the individual intra-pin quantities such as fission rate density, power density, and fast neutron flux from the MPACT volumetric mesh to the individual BISON finite element meshes. For a two-way coupling Redwing maps and transfers the individual pin temperature field and axially dependent coolant densities from the BISON mesh to the MPACT volumetric mesh. Details of the mapping are given. Redwing advances the simulation with the MPACT solution for each depletion time step and then advances the multiple BISON simulations for fuel performance calculations. Sub-cycle advancement can be applied to the individual BISON simulations and allows multiple time steps to be applied to the fuel performance simulations. Currently, only loose coupling where data from a previous time step is applied to the current time step is performed.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2017

A Lagrangian discontinuous Galerkin hydrodynamic method

Here, we present a new Lagrangian discontinuous Galerkin (DG) hydrodynamic method for solving the two-dimensional gas dynamic equations on unstructured hybrid meshes. The physical conservation laws for the momentum and total energy are discretized using a DG method based on linear Taylor expansions. Three different approaches are investigated for calculating the density variation over the element. The first approach evolves a Taylor expansion of the specific volume field. The second approach follows certain finite element methods and uses the strong mass conservation to calculate the density field at a location inside the element or on the element surface. The third approach evolves a Taylor expansion of the density field. The nodal velocity, and the corresponding forces, are explicitly calculated by solving a multidirectional approximate Riemann problem. An effective limiting strategy is presented that ensures monotonicity of the primitive variables. This new Lagrangian DG hydrodynamic method conserves mass, momentum, and total energy. Results from a suite of test problems are presented to demonstrate the robustness and expected second-order accuracy of this new method.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Modeling gas breakthrough and flow phenomena through engineered barrier systems using a discrete fracture approach

Compacted clays are being considered to build engineered barrier systems (EBS) intended for the safe isolation of high-level nuclear waste (HLW) and spent nuclear fuel (SNF). The corrosion of the metallic canister containing the HLW/SNF will lead to the generation and buildup of the gas pressure in the more internal part of the clay buffer. This phenomenon would eventually trigger the formation and propagation of fractures in the clay barrier, jeopardizing its safety functions. Here, in this work, we propose to use the fragmentation technique (MFT) to model evolving fractures in clays triggered by gas pressurization. The MFT has been successfully used to model the formation of fractures in concrete, drying cracks in soil, hydraulic and thermo-fractures in rocks. In this work, we extend the MFT to deal with multiphase fluid flow in deformable porous media, and we upgraded a fully coupled computer finite element code using the extended technique. The proposed approach is first verified against analytical solutions and is then applied to model gas breakthrough experiments in clays. A very satisfactory performance of the method is observed in all the analyses, showing the potential of the MFT to tackle multiphase flow problems in deformable porous media with evolving discontinuities.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2015

Algebraic function operator expectation value based quantum eigenstate determination: A case of twisted or bent Hamiltonian, or, a spatially univariate quantum system on a curved space

Recent studies on quantum evolutionary problems in Demiralp’s group have arrived at a stage where the construction of an expectation value formula for a given algebraic function operator depending on only position operator becomes possible. It has also been shown that this formula turns into an algebraic recursion amongst some finite number of consecutive elements in a set of expectation values of an appropriately chosen basis set over the natural number powers of the position operator as long as the function under consideration and the system Hamiltonian are both autonomous. This recursion corresponds to a denumerable infinite number of algebraic equations whose solutions can or can not be obtained analytically. This idea is not completely original. There are many recursive relations amongst the expectation values of the natural number powers of position operator. However, those recursions may not be always efficient to get the system energy values and especially the eigenstate wavefunctions. The present approach is somehow improved and generalized form of those expansions. We focus on this issue for a specific system where the Hamiltonian is defined on the coordinate of a curved space instead of the Cartesian one.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Clifford Circuit Optimization with Templates and Symbolic Pauli Gates

The Clifford group is a finite subgroup of the unitary group generated by the Hadamard, the CNOT, and the Phase gates. This group plays a prominent role in quantum error correction, randomized benchmarking protocols, and the study of entanglement. Here we consider the problem of finding a short quantum circuit implementing a given Clifford group element. Our methods aim to minimize the entangling gate count assuming all-to-all qubit connectivity. First, we consider circuit optimization based on template matching and design Clifford-specific templates that leverage the ability to factor out Pauli and SWAP gates. Second, we introduce a symbolic peephole optimization method. It works by projecting the full circuit onto a small subset of qubits and optimally recompiling the projected subcircuit via dynamic programming. CNOT gates coupling the chosen subset of qubits with the remaining qubits are expressed using symbolic Pauli gates. Software implementation of these methods finds circuits that are only 0.2% away from optimal for 6 qubits and reduces the two-qubit gate count in circuits with up to 64 qubits by 64.7% on average, compared with the Aaronson-Gottesman canonical form.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

An iterative phase-space explicit discontinuous Galerkin method for stellar radiative transfer in extended atmospheres

In this work, a phase-space discontinuous Galerkin (PSDG) method is presented for the solution of stellar radiative transfer problems. It allows for greater adaptivity than competing methods without sacrificing generality. The method is extensively tested on a spherically symmetric, static, inverse-power-law scattering atmosphere. Results for different sizes of atmospheres and intensities of scattering agreed with asymptotic values. The exponentially decaying behavior of the radiative field in the diffusive-transparent transition region, and the forward peaking behavior at the surface of extended atmospheres were accurately captured. The integrodifferential equation of radiation transfer is solved iteratively by alternating between the radiative pressure equation and the original equation with the integral term treated as an energy density source term. In each iteration, the equations are solved via an explicit, flux-conserving, discontinuous Galerkin method. Finite elements are ordered in wave fronts perpendicular to the characteristic curves so that elemental linear algebraic systems are solved quickly by sweeping the phase space element by element. Two implementations of a diffusive boundary condition at the origin are demonstrated wherein the finite discontinuity in the radiation intensity is accurately captured by the proposed method. This allows for a consistent mechanism to preserve photon luminosity. The method was proved to be robust and fast, and a case is made for the adequacy of parallel processing. In addition to classical two-dimensional plots, results of normalized radiation intensity were mapped onto a log-polar surface exhibiting all distinguishing features of the problem studied.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

HOSS: an implementation of the combined finite-discrete element method

Nearly thirty years since its inception, the combined finite-discrete element method (FDEM) has made remarkable strides in becoming a mainstream analysis tool within the field of Computational Mechanics. FDEM was developed to effectively “bridge the gap” between two disparate Computational Mechanics approaches known as the finite and discrete element meth-ods. At Los Alamos National Laboratory (LANL) researchers developed the Hybrid Optimization Software Suite (HOSS) as a hybrid multi-physics platform, based on FDEM, for the simulation of solid material behavior complemented with the latest technological enhancements for full fluid–solid interaction. Furthermore, in HOSS, several newly developed FDEM algorithms have been implemented that yield more accurate material deformation formulations, inter-particle interaction solvers, and fracture and fragmentation solutions. Additionally, an explicit computational fluid dynamics solver and a novel fluid–solid interaction algorithms have been fully integrated (as opposed to coupled) into the HOSS’ solid mechanical solver, allowing for the study of an even wider range of problems. Advancements such as this are leading HOSS to become a tool of choice for multi-physics problems. Finally, HOSS has been successfully applied by a myriad of researchers for analysis in rock mechanics, oil and gas industries, engineering application (structural, mechanical and biomedical engineering), mining, blast loading, high velocity impact, as well as seismic and acoustic analysis. This paper intends to summarize the latest development and application efforts for HOSS.

58 GEOSCIENCES↗
DOE OSTI2019

High-Voltage Charging-Induced Strain, Heterogeneity, and Micro-Cracks in Secondary Particles of a Nickel-Rich Layered Cathode Material

Nickel-rich layered materials LiNi 1-x-y Mn x Co y O 2 are promising candidates for high-energy-density lithium-ion battery cathodes. Unfortunately, they suffer from capacity fading upon cycling, especially with high-voltage charging. It is critical to have a mechanistic understanding of such fade. Synchrotron-based techniques (including scattering, spectroscopy, and microcopy) and finite element analysis are utilized to understand the LiNi 0.6 Mn 0.2 Co 0.2 O 2 material from structural, chemical, morphological, and mechanical points of view. The lattice structural changes are shown to be relatively reversible during cycling, even when 4.9 V charging is applied. However, local disorder and strain are induced by high-voltage charging. Nano-resolution 3D transmission X-ray microscopy data analyzed by machine learning methodology reveal that high-voltage charging induced significant oxidation state inhomogeneities in the cycled particles. Regions at the surface have a rock salt–type structure with lower oxidation state and build up the impedance, while regions with higher oxidization state are scattered in the bulk and are likely deactivated during cycling. In addition, the development of micro-cracks is highly dependent on the pristine state morphology and cycling conditions. Hollow particles seem to be more robust against stress-induced cracks than the solid ones, suggesting that morphology engineering can be effective in mitigating the crack problem in these materials.

25 ENERGY STORAGE↗
DOE OSTI2026

A robust framework for frictional fault contact in geological formations using a stabilized augmented Lagrangian approach

Numerical simulations are essential to evaluate the performance and safety of engineered subsurface systems such as geological carbon storage sites, enhanced geothermal fields, and oil and gas reservoirs. A key challenge lies in accurately modeling the frictional contact behavior along fault surfaces. This problem involves inequality constraints that arise from the physics of frictional slip, requiring specialized numerical methods to handle the resulting highly nonlinear and path-dependent behavior. Here, in this work, we address this challenge using an Augmented Lagrangian Method (ALM) implemented via the Uzawa algorithm. The formulation employs mixed finite element spaces, combining low-order piecewise linear displacements within the 3D domain cells with piecewise constant tractions defined on the fault surfaces. Furthermore, to ensure stability and satisfy the inf-sup condition, the discrete displacement space is enriched with face bubble functions on both sides of the contact interfaces. This approach offers several advantages over other stabilization techniques that rely on additional terms, and it integrates naturally in the Uzawa framework.

58 GEOSCIENCES↗
DOE OSTI2022

Tunable phononic bandgap materials designed via topology optimization

Topology optimization is used to design phononic bandgap materials that are tunable by mechanical deformation. A periodic media is considered, which due to the assumption of length scale separation, allows the dispersion relations to be obtained by analyzing a single unit cell subjected to Floquet–Bloch boundary conditions. A finite macroscopic deformation is applied to the unit cell to affect its geometry and hence dispersion. We tune the dispersion–deformation relation to our liking by solving a topology optimization problem using nonlinear programming. The adjoint method is employed to compute the sensitivities, and the non-differentiability of degenerate eigenvalues is avoided using symmetric polynomials. Several tunable phononic crystal designs are presented. Also, a verification analysis is performed, wherein the optimized design is interpreted and analyzed using a conforming finite element mesh.

42 ENGINEERING↗
DOE OSTI2024

Efficient mapping between void shapes and stress fields using Deep Convolutional Neural Networks with sparse data

Establishing fast and accurate structure-to-property relationships is an important component in the design and discovery of advanced materials. Physics-based simulation models like the finite element method (FEM) are often used to predict deformation, stress, and strain fields as a function of material microstructure in material and structural systems. Such models may be computationally expensive and time intensive if the underlying physics of the system is complex. This limits their application to solve inverse design problems and identify structures that maximize performance. In such scenarios, surrogate models are employed to make the forward mapping computationally efficient to evaluate. However, the high dimensionality of the input microstructure and the output field of interest often renders such surrogate models inefficient, especially when dealing with sparse data. Deep convolutional neural network (CNN) based surrogate models have shown great promise in handling such high-dimensional problems. In this paper, a single ellipsoidal void structure under a uniaxial tensile load represented by a linear elastic, high-dimensional and expensive-to-query, FEM model. We consider two deep CNN architectures, a modified convolutional autoencoder framework with a fully connected bottleneck and a UNet CNN, and compare their accuracy in predicting the von Mises stress field for any given input void shape in the FEM model. Additionally, a sensitivity analysis study is performed using the two approaches, where the variation in the prediction accuracy on unseen test data is studied through numerical experiments by varying the number of training samples from 20 to 100.

surrogate modeling; convolutional neural networks;↗
DOE OSTI2017

Uncertainty propagation of p-boxes using sparse polynomial chaos expansions

In modern engineering, physical processes are modelled and analysed using advanced computer simulations, such as finite element models. Furthermore, concepts of reliability analysis and robust design are becoming popular, hence, making efficient quantification and propagation of uncertainties an important aspect. In this context, a typical workflow includes the characterization of the uncertainty in the input variables. In this paper, input variables are modelled by probability-boxes (p-boxes), accounting for both aleatory and epistemic uncertainty. The propagation of p-boxes leads to p-boxes of the output of the computational model. A two-level meta-modelling approach is proposed using non-intrusive sparse polynomial chaos expansions to surrogate the exact computational model and, hence, to facilitate the uncertainty quantification analysis. The capabilities of the proposed approach are illustrated through applications using a benchmark analytical function and two realistic engineering problem settings. They show that the proposed two-level approach allows for an accurate estimation of the statistics of the response quantity of interest using a small number of evaluations of the exact computational model. This is crucial in cases where the computational costs are dominated by the runs of high-fidelity computational models.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Space-Time Block Preconditioning for Incompressible Flow

Parallel-in-time methods have become increasingly popular in the simulation of time-dependent numerical PDEs, allowing for the efficient use of additional message passing interface processes when spatial parallelism saturates. Most methods treat the solution and parallelism in space and time separately. In contrast, all-at-once methods solve the full space-time system directly, largely treating time as simply another spatial dimension. All-at-once methods offer a number of benefits over separate treatment of space and time, most notably significantly increased parallelism and faster time to solution (when applicable). However, the development of fast, scalable all-at-once methods has largely been limited to time-dependent (advection-)diffusion problems. This paper introduces the concept of space-time block preconditioning for the all-at-once solution of incompressible flow. By extending well-known concepts of spatial block preconditioning to the space-time setting, we develop a block preconditioner whose application requires the solution of a space-time (advection-)diffusion equation in the velocity block, coupled with a pressure Schur complement approximation consisting of independent spatial solves at each time-step, and a space-time matrix-vector multiplication. The new method is tested on four classical models in incompressible flow. Finally, the results indicate perfect scalability in refinement of spatial and temporal mesh spacing, perfect scalability in nonlinear Picard iteration count when applied to a nonlinear Navier--Stokes problem, and minimal overhead in terms of number of preconditioner applications compared with sequential time-stepping.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Learning Robust Marking Policies for Adaptive Mesh Refinement

Here in this work, we revisit the marking decisions made in the standard adaptive finite element method (AFEM). Experience shows that a naïve marking policy leads to inefficient use of computational resources for adaptive mesh refinement (AMR). Consequently, using AMR in practice often involves ad-hoc or time-consuming offline parameter tuning to set appropriate parameters for the marking subroutine. To address these practical concerns, we recast AMR as a Markov decision process in which refinement parameters can be selected on-the-fly at run time, without the need for pre-tuning by expert users. In this new paradigm, the refinement parameters are also chosen adaptively via a marking policy that can be optimized using methods from reinforcement learning. We use the Poisson equation to demonstrate our techniques on h- and hp-refinement benchmark problems, and our experiments suggest that superior marking policies remain undiscovered for many classical AFEM applications. Furthermore, an unexpected observation from this work is that marking policies trained on one family of PDEs are sometimes robust enough to perform well on problems far outside the training family. For illustration, we show that a simple hp-refinement policy trained on 2D domains with only a single re-entrant corner can be deployed on far more complicated 2D domains, and even 3D domains, without significant performance loss. For reproduction and broader adoption, we accompany this work with an open-source implementation of our methods.

97 MATHEMATICS AND COMPUTING↗