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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 1,135 records · Page 63
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A renewable lignin-based thermoplastic adhesive for steel joining

Adhesive bonding of metals has become increasingly relevant in recent years due to the demand for reducing weight and improving performance in structural applications such as automobiles and aerospace. We developed renewable thermoplastic adhesives from technical organosolv lignin isolated from hardwood biomass and acrylonitrile butadiene co-polymer rubber (NBR) for joining steel substrates. NBR33, NBR41 and NBR51 with acrylonitrile molar ratios of 33, 41 and 51%, respectively, were blended with lignin to form two-phase thermoplastic adhesives, and their adhesion, viscoelastic and surface properties were measured. Lignin content in the compositions were varied, ranging from 40% to 80% (w/w), to alter toughness, stiffness, and surface energy characteristics of the material. Better interaction or reactivity between the lignin and NBR phases was observed with greater nitrile content in NBR, leading to greater modulus and stiffness of the adhesive. Simultaneously, increasing the proportion of lignin reduced toughness and improved stiffness, with the highest adhesive strength of 13.1 MPa measured in a 60% lignin loading ratio with NBR51. Surface energy measurements revealed that total surface energy (sum of polar and dispersive surface energy) raised with lignin loading, suggesting that both surface energy and matrix strength play a critical role in the adhesive properties of the synthesized materials. A finite element-based cohesive zone model (CZM) was developed and implemented to study the failure strength of the adhesively bonded joint. Here, this study demonstrates the viability of lignin as a valuable building block for adhesives, not only due to its inherent chemical structure and rigidity, but also for its surface energy characteristics.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Investigation of crossed-twin structure formation in magnesium and magnesium alloys

In this work, the effect of alloying addition on the propensity for twin-twin interactions to transform into crossed-twin structures in magnesium alloys is investigated. A full-field elasto-viscoplastic fast Fourier transform (EVP-FFT) framework combined with a discrete twin model and dislocation density-based hardening law for slip strengths is used to calculate the micromechanical fields in the crystalline matrix around the interacting twins. AZ31 and MgLi alloys are selected along with pure Mg to study the influence of plastic anisotropy in connection with alloying elements. These alloys were selected since their plastic anisotropy measure, which is defined as the ratio between the critical resolved shear stress for pyramidal $\langle$c +a$\rangle$ and basal $\langle$a$\rangle$ slip modes, spanned a wide range. To quantify the role of twin thicknesses, we probe a range of impinging twin thicknesses while fixing the recipient twin thickness. The analysis reveals that: (i) the local driving stress for crossed twin structure formation generated from the interaction of the two twins is lower in a low plastically anisotropic material, like a MgLi alloy, than a high plastically anisotropic material like pure Mg and (ii) the critical impinging twin thickness needed to form the crossed twin structure in pure Mg, AZ31 and MgLi alloys is ~0.5, ~0.75 and ~1.5 times the recipient twin thickness. We propose a relationship between the tendency for crossed twin structure formation and the experimentally observed higher ductility in MgLi alloys compared to pure Mg. Finally, one key implication of the findings is that crossed- twin structure formation can be hindered and the ductility of magnesium alloy thereby improved by properly choosing alloying elements that lower the slip strength for pyramidal $\langle$c + a$\rangle$ slip.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Elucidating the impact of fiber source on polypropylene/hemp composite performance for the automotive industry

Given their high strength-to-weight ratio, there is an ever-increasing volume of plastics being used in the automotive industry as plastics aid in the charge to lightweight vehicles for improved fuel efficiency. However, these plastics are often landfilled at their end-of-life, which has given rise to the demand for sustainable materials and waste management alternatives compared to purely synthetic systems. Natural fiber composites have been explored as a viable material option to reduce the environmental impact of plastic use in automobiles while simultaneously ensuring the part performance is not sacrificed. Herein, we explored the use of hemp/polypropylene (PP) composites in which US-sourced hemp is compared to internationally sourced and industrially available hemp. There appears to be a minimal impact on the composite properties regardless of fiber sourcing, and the addition of a natural filler to the PP matrix results in up to a 367% increase in Young's modulus, 126% increase in heat deflection temperature, and comparable water uptake performance. It should be noted that the natural filler addition does increase density by up to 13% due to the higher density of natural fibers compared to the low-density PP matrix. A modified rule of mixtures calculation revealed that the composite materials produced in this study demonstrated good agreement with analytical modeling. Finally, a screening analysis was performed exploring the transportation of hemp fibers, and the results build a strong case for regionalized manufacturing of automotive parts.

Hubbard, Amber M. [Oak Ridge National Laboratory (↗
DOE OSTI2021

Analytical modeling of the evolution of the nonlinearity parameter of sensitized stainless steel

Austenitic stainless steels are subject to the precipitation of chromium carbides (M 23 C 6 ) during exposure to high temperatures, causing these alloys to be susceptible to intergranular corrosion due to chromium depletion along grain boundaries. The acoustic nonlinearity parameter, β, shows sensitivity to the formation of carbides in these alloys. The Thermo-Calc TC-PRISMA module was used to model the nucleation and growth of grain boundary M 23 C 6 carbides. The model was verified with scanning transmission electron microscopy analysis that allowed measurements of the grain boundary precipitates. The paper introduces a reduced-order model of the acoustic nonlinearity based on the formation of misfit dislocations at the interface of the grain boundary precipitate and matrix to explain the change in β during isothermal aging. Furthermore, a direct relationship between the radius of the M 23 C 6 grain boundary carbides and β was observed and verified with nonlinear ultrasound measurements on 304L and 316L stainless steels.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Characterizing random-singlet state in two-dimensional frustrated quantum magnets and implications for the double perovskite Sr 2 CuTe 1 – x W x O 6

Motivated by the experimental observation of a nonmagnetic phase in compounds with frustration and disorder, we study the ground state of a spin-1/2 square-lattice Heisenberg model with randomly distributed nearest-neighbor J 1 and next-nearest-neighbor J 2 couplings. By using the density matrix renormalization group (DMRG) calculation on a cylinder system with a circumference of up to ten lattice sites, we identify a disordered phase between the Néel and stripe magnetic phase with growing J 2 /J 1 in the presence of strong bond randomness. The vanished spin-freezing parameter indicates the absence of spin-glass order. The large-scale DMRG results unveil the size-scaling behaviors of the spin-freezing parameter, the power-law decay of the average spin correlation, and the exponential decay of the typical spin correlation, which all agree with the corresponding behavior in the one-dimensional random-singlet (RS) state and characterize the RS nature of this disordered phase. The DMRG simulation also provides insights and opportunities for characterizing a class of nonmagnetic states in two-dimensional frustrated magnets with disorder. Here, we also compare with existing experiments and suggest more measurements for understanding the spin-liquid-like behaviors in the double perovskite Sr 2 CuTe 1–x W x O 6 .

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Triaxiality and the nature of low-energy excitations in Ge 76

The deformation properties of the low-lying states in 76 Ge have been investigated following a safe energy Coulomb excitation measurement with the GRETINA tracking array and CHICO2 heavy-ion counter at the ATLAS accelerator facility at Argonne National Laboratory. A comprehensive set of transition and static E2 matrix elements were extracted from the measured differential Coulomb cross-sections, and compared with results of configuration interaction shell-model calculations and computations carried out within the framework of the generalized triaxial rotor model. The remarkable agreement between the calculated and experimental data supports a near-maximum triaxial deformation for the ground state of 76 Ge. Additionally, the degree of softness of the asymmetry in 76 Ge and 76 Se was investigated using rotational invariants generated from configuration interaction shell-model wave functions computed with the jj44b and JUN45 effective interactions. The resulting invariants are shown to be consistent with a stiff triaxial deformation in 76 Ge and a predominantly soft triaxial potential for 76 Se, in agreement with the conclusions of recent works by this collaboration.

59 ≤ A ≤ 89↗
DOE OSTI2024

GPU Accelerated Sparse Cholesky Factorization

The solution of sparse symmetric positive definite linear systems is an important computational kernel in large-scale scientific and engineering modeling and simulation. We will solve the linear systems using a direct method, in which a Cholesky factorization of the coefficient matrix is performed using a right-looking approach and the resulting triangular factors are used to compute the solution. Sparse Cholesky factorization is compute intensive. In this work we investigate techniques for reducing the factorization time in sparse Cholesky factorization by offloading some of the dense matrix operations on a GPU. We will describe the techniques we have considered. We achieved up to 4x speedup compared to the CPU-only version.

Karsavuran, M Ozan↗
DOE OSTI2023

Optimization of Conductive Fins to Minimize UO2 Fuel Temperature and Radial Temperature Gradient

To further the development of low-enriched uranium fuels, precedence has been placed on delivering the same amount of power while lowering the fuel temperature and radial temperature gradient. To address this, modeling efforts have resulted in a novel design featuring conductive fins of varying thermal conductivities and geometries inserted into the fuel matrix. These conductive inserts were not allowed to exceed 6% of the original fuel volume. This constraint was imposed due to other designs displacing 10% of fuel volume. A parametric study was performed that consisted of 2.56 million BISON simulations involving varying fin characteristics (i.e., fin thermal conductivity, number, and geometry) to determine the optimal geometric configuration for a desired amount of fuel volume displaced. The results from this study show that the thickness and length of each fin affect the fuel temperature and temperature gradient more than varying the number and thermal conductivity of the fins. The parametric study resulted in the development of an optimized combination to produce the lowest peak fuel temperature, lowest radial temperature gradient, and highest temperature reduction for the amount of original fuel volume displaced. The simulations presented in this work will eventually be compared with irradiation experiments of similar fuel designs at Idaho National Laboratory’s Advanced Test Reactor.

Paaren, Kyle M.↗
DOE OSTI2025

Why Is Attention Sparse In Particle Transformer?

Transformer-based models have achieved state-of-the-art performance in jet tagging at the CERN Large Hadron Collider (LHC), with the Particle Transformer (ParT) representing a leading example of such models. A striking feature of ParT is its sparse, nearly binary, attention structure, raising questions about the origin of this behavior and whether it encodes physically meaningful correlations. In this work, we investigate the source of ParT's sparse attention by comparing models trained on multiple benchmark datasets and examine the relative contributions of the attention term and the physics-inspired interaction matrix before softmax. We find that binary sparsity arises primarily from the attention mechanism itself, with the interaction matrix playing a secondary role. Moreove, we show that ParT is able to identify key jet substructure elements, such as leptons in semileptonic top decays, even without explicit particle identification inputs. These results provide new insight into the interpretability of transformer-based jet taggers and clarify the conditions under which sparse attention patterns emerge in ParT.

Legge, Timothy [UC, San Diego]↗
DOE OSTI2021

Bayesian learning of orthogonal embeddings for multi-fidelity Gaussian Processes

Uncertainty propagation in complex engineering systems often poses significant computational challenges related to modeling and quantifying probability distributions of model outputs, as those emerge as the result of various sources of uncertainty that are inherent in the system under investigation. Gaussian Processes regression (GPs) is a robust meta-modeling technique that allows for fast model prediction and exploration of response surfaces. Multi-fidelity variations of GPs further leverage information from cheap and low fidelity model simulations in order to improve their predictive performance on the high fidelity model. In order to cope with the high volume of data required to train GPs in high dimensional design spaces, a common practice is to introduce latent design variables that are typically projections of the original input space to a lower dimensional subspace, and therefore substitute the problem of learning the initial high dimensional mapping, with that of training a GP on a low dimensional space. Here in this paper, we present a Bayesian approach to identify optimal transformations that map the input points to low dimensional latent variables. The \projection" mapping consists of an orthonormal matrix that is considered a priori unknown and needs to be inferred jointly with the GP parameters, conditioned on the available training data. The proposed Bayesian inference scheme relies on a two-step iterative algorithm that samples from the marginal posteriors of the GP parameters and the projection matrix respectively, both using Markov Chain Monte Carlo (MCMC) sampling. In order to take into account the orthogonality constraints imposed on the orthonormal projection matrix, a Geodesic Monte Carlo sampling algorithm is employed, that is suitable for exploiting probability measures on manifolds. We extend the proposed framework to multi-fidelity models using GPs including the scenarios of training multiple outputs together. We validate our framework on three synthetic problems with a known lower-dimensional subspace. The benefits of our proposed framework, are illustrated on the computationally challenging aerodynamic optimization of a last-stage blade for an industrial gas turbine, where we study the effect of an 85-dimensional shape parameterization of a three-dimensional airfoil on two output quantities of interest, specifically on the aerodynamic efficiency and the degree of reaction

42 ENGINEERING↗
DOE OSTI2022

Calabi-Yau CFTs and random matrices

Using numerical methods for finding Ricci-flat metrics, we explore the spectrum of local operators in two-dimensional conformal field theories defined by sigma models on Calabi-Yau targets at large volume. Focusing on the examples of K3 and the quintic, we show that the spectrum, averaged over a region in complex structure moduli space, possesses the same statistical properties as the Gaussian orthogonal ensemble of random matrix theory.

superstring vacua↗
DOE OSTI2023

SAS4A/SASSYS-1 Commercial Grade Dedication Example Report for a Generic Sodium Pool-Type Fast Reactor Application

In the U.S., a key component of the commercialization of advanced reactors is completion of a license application, which must ultimately be approved by the Nuclear Regulatory Commission (NRC). The approval of the license application by the NRC is contingent on satisfactory demonstration of the design basis and the response of the advanced reactor design to transient and accident scenarios using accepted codes and methods. This report describes the qualification and dedication requirements that the advanced reactor safety analysis system software SAS4A/SASSYS-1 are expected to need to fulfill to be used for sodium-cooled pool-type fast reactor licensing. The qualification and dedication requirements are identified through performance critical characteristics and evaluation model acceptance criteria representative of the advanced reactor design considered for licensing. This document captures, additionally, the verification process developed to demonstrate that the software fulfills the qualification and dedication requirements for a generic sodium-cooled pool-type fast reactor as part of the commercial grade dedication process. Like most software that has primarily existed in the research and development space, the most significant challenge facing SAS4A/SASSYS-1 for use in a licensing framework is the availability of a documentation basis describing the code pedigree. SAS4A/SASSYS-1 has been used for licensing of the fast flux test facility (FFTF) and the JOYO sodium-cooled fast reactor in Japan, as well as the design of the CRBR Plant. However, the historical verification and validation (V&V) activities supporting SAS4A/SASSYS-1 development do not align with modern software quality assurance (SQA) and V&V requirements. Two approaches to use of SAS4A/SASSYS-1 in a commercial licensing framework have been identified: commercial-grade dedication (CGD) and software qualification. The methods and requirements prescribed in the ASME NQA-1-2008/2009 Standard and Regulatory Guide 1.203 on the evaluation model development and assessment process (EMDAP) have been used as guidance to define the CGD and qualification processes, respectively. A qualification and dedication requirements matrix has been developed which utilizes fundamental software verification. In this process, software verification is defined as a software quality process aimed at defining software requirement specifications, developing software design documentation, and performing and documenting acceptance testing of the code against requirements. A key element of software qualification and dedication includes determination of software acceptance with respect to critical characteristics relevant to the functional requirements of the software. To assist with identification of cross-cutting transient phenomena and functional requirements, domestic SFR vendor designs have been reviewed to identify a reference SFR design. For this report, the reference design is defined as a pool-type reactor with metal alloy fuel, a liquid-metal intermediate heat transport system, and passive decay heat rejection systems. Given this reference, a series of high-level cross-cutting phenomena was identified for a general class of single-fault undercooling or reactivity insertion transients that scopes the design basis space, with the goal of assisting with prioritization of documentation development efforts for key transient models in SAS: 1) Reactivity feedback response prior to scram; 2) System-wide thermal inertia; 3) Transition in natural circulation flow regime in heat removal systems; 4) Decay heat generation; 5) Steady-state fuel characterization; 5) Clad/fuel behavior at elevated temperatures; 6) Point kinetics and decay heat; 7) Pump coastdown behavior; 8) Core flow redistribution in loss of forced convection; 9) Pool stratification. As a demonstration of CGD of SAS4A/SASSYS-1 for a sodium pool reactor, a software qualification and dedication gap analysis as it relates to code documentation has been performed. This effort leverages the framework established as part of the SAS4A/SASSYS-1 SQA Program. This CGD demonstration provides a framework that vendors can build upon to demonstrate the applicability of the SAS4A/SASSYS-1 software for licensing a sodium-cooled pool-type fast reactor.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

Distributed non-negative matrix factorization with determination of the number of latent features

The holistic analysis and understanding of the latent (that is, not directly observable) variables and patterns buried in large datasets is crucial for data-driven science, decision making and emergency response. Such exploratory analyses require devising unsupervised learning methods for data mining and extraction of the latent features, and non-negative matrix factorization (NMF) is one of the prominent such methods. NMF is based on compute-intense non-convex constrained minimization, which, for large datasets requires fast and distributed algorithms. However, current parallel implementations of NMF fail to estimate the number of latent features. In practice, identifying these features is both difficult and significant for pattern recognition and latent feature analysis, especially for large dense matrices. Here, we introduce a distributed NMF algorithm coupled with distributed custom clustering followed by a stability analysis on dense data, which we call DnMFk, to determine the number of latent variables. The results on synthetic data and the classical Swimmer data set demonstrate the accuracy of model determination while scaling nearly linearly across multiple processors for large data. Further, we employ DnMFk to determine the number of hidden features from a terabyte matrix.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Jacobian-based model diagnostics and application to equation oriented modeling of a carbon capture system

It can be difficult to identify the specific variables or equations responsible for convergence issues in large mathematical programming models. The Institute for the Design of Advanced Energy Systems Integrated Platform (IDAES-IP) contains a tool to identify poorly scaled constraints and variables by searching for rows and columns of the Jacobian matrix with small L2-norms. A singular value decomposition is then performed to identify degenerate sets of equations and remaining scaling issues. Here, this work presents a flowsheet developed for post-combustion carbon capture using a monoethanolamine (MEA) solvent system as a case study. This work takes the reader through the entire process of model diagnostics and reformulation, from a basic introduction to the mathematics behind these model diagnostics to the reformulations necessary to make the model numerically robust, including a significantly modified enhancement factor model.

IDAES↗
DOE OSTI2022

A fractional model for anomalous diffusion with increased variability: Analysis, algorithms and applications to interface problems

Fractional equations have become the model of choice in several applications where heterogeneities at the microstructure result in anomalous diffusive behavior at the macroscale. Here, we introduce a new fractional operator characterized by a doubly-variable fractional order and possibly truncated interactions. Under certain conditions on the model parameters and on the regularity of the fractional order we show that the corresponding Poisson problem is well-posed. Additionally, we introduce a finite element discretization and describe an efficient implementation of the finite-element matrix assembly in the case of piecewise constant fractional order. Through several numerical tests, we illustrate the improved descriptive power of this new operator across media interfaces. Furthermore, we present one-dimensional and two-dimensional h-convergence results that show that the variable-order model has the same convergence behavior as the constant-order model.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

The St. Benedict Facility: Probing Fundamental Symmetries through Mixed Mirror $β$-Decays

Precise measurements of nuclear beta decays provide a unique insight into the Standard Model due to their connection to the electroweak interaction. These decays help constrain the unitarity or non-unitarity of the Cabibbo–Kobayashi–Maskawa (CKM) quark mixing matrix, and can uniquely probe the existence of exotic scalar or tensor currents. Of these decays, superallowed mixed mirror transitions have been the least well-studied, in part due to the absence of data on their Fermi to Gamow-Teller mixing ratios ($ρ$). At the Nuclear Science Laboratory (NSL) at the University of Notre Dame, the Superallowed Transition Beta-Neutrino Decay Ion Coincidence Trap (St. Benedict) is being constructed to determine the ρ for various mirror decays via a measurement of the beta–neutrino angular correlation parameter ($α_{βν}$) to a relative precision of 0.5%. In this work, we present an overview of the St. Benedict facility and the impact it will have on various Beyond the Standard Model studies, including an expanded sensitivity study of $ρ$ for various mirror nuclei accessible to the facility. A feasibility evaluation is also presented that indicates the measurement goals for many mirror nuclei, which are currently attainable in a week of radioactive beam delivery at the NSL.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Magnetic phase transitions in quantum spin-orbital liquids

Here, we investigate the spin and orbital correlations of a superexchange model with spin $\textit{S}$ = 1 and orbital $\textit{L}$ = 1 relevant for $5d^4$ transition-metal Mott insulators, using exact diagonalization and density matrix renormalization group (DMRG). For spin-orbit coupling λ = 0, the orbitals are in an entangled state that is decoupled from the spins. We find two phases with increasing λ: (I) the $\textit{S}$2 phase with two peaks in the structure factor for λ ≤ λ$_{c1}$ ≈ 0.34$\textit{J}$ where $\textit{J}$ is the ferromagnetic exchange; and, (II) the $\textit{S}$1 phase for λ$_{c1}$ < λ ≤ λ$_{c2}$ ≈ 1.2$\textit{J}$ with emergent antiferromagnetic correlations. Both $\textit{S}$1 and $\textit{S}$2 phases are shown to exhibit power-law correlations, indicative of a gapless spectrum. Upon increasing λ > λ$_{c2}$ leads to a product state of local spin-orbital singlets that exhibit exponential decay of correlations, indicative of a gapped phase. We obtain insights into the phases from the well-known Uimin-Lai-Sutherland model in an external field that provides an approximate description of our model within mean-field theory.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗