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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

An Advanced Meso-Scale Peridynamic Modeling Technology using High-Performance Computing for Cost-Effective Product Design and Testing of Carbon Fiber Reinforced Polymer Composites in Light-weight Vehicles

We study a peridynamic composite modeling technology based on the discontinuous Galerkin finite element method, implemented in the commercial LS-DYNA software, for modeling and prediction of failure in carbon fiber reinforced polymer composites. The proposed technology is developed for the material failure analysis at the meso-scale, which provides the prevailing fiber-matrix interaction mechanism, without adoption of the representative volume element method and thus avoiding complicated numerical calibration procedures. Three types of experimental tests—in-plane coupon test, out-of-plane coupon test, and a component crash test—are simulated in a high-performance computing environment to assess the performance of the proposed peridynamic composite modeling technology.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Formulation and calibration of two-dimensional constitutive models for composite structures based on panel tests

Steel Plate Concrete (SC) composite members have been widely adopted because of its cost-efficiency and enhanced structural behavior. While researchers have attempted to study its in-plane shear behavior in the past twenty years, very limited number of large-scale pure shear tests were performed due to the challenge of experimental set-up and the availability of facilities. In this paper, a series of uniaxial loading tests and two full-scale pure shear panel tests of SC members were reported, on which the “mechanics-based Membrane Model of SC elements (MM-SC)” is developed. The MM-SC model is based on the fixed-angle crack formulation and the smeared-crack formulation, in which the experimental-based uniaxial constitutive models are implemented, considering the local buckling of faceplate, the tension stiffening of steel plate, the strength degradation of cracked concrete and the confinement effect of concrete. The proposed MM-SC model is subsequently incorporated into the object-oriented software OpenSEES. Finally, the simulation results of proposed model well predict the SC test observations in terms of critical branch points and structural behaviors, including initial stiffness, cracking strength, post-crack stiffness, yield strength, maximum strength, and failure modes.

42 ENGINEERING↗
DOE OSTI2018

K-3 Refractory Corrosion and Sulfate Solubility Model Enhancement (Final Report)

As higher waste loading LAW glasses are formulated, sulfate solubility and K-3 refractory corrosion are two properties that become increasingly constraining; however, WTP baseline models were not developed for these properties. In previous work for ORP, VSL/Atkins developed preliminary glass property-composition models that relate LAW glass composition to these two essential properties to support the implementation of the higher waste loading LAW glass formulations at the WTP. The development of the initial LAW glass property-composition models for K-3 refractory corrosion and sulfate solubility was completed in 2015. The present work scope focused on enhancement of these models and included augmentation of property-composition datasets for these two properties with newly available data, review of the updated datasets, and development of enhanced models.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Local composition activity coefficient model for mixed-gas adsorption equilibria

Taking into consideration the adsorbate–adsorbent interactions, a novel activity coefficient model is derived from the non-random two-liquid theory for mixed-gas adsorption equilibria. In contrast with the conventional activity coefficient models developed for bulk liquids, the new model correctly predicts negative deviations from ideality for adsorbed phase mixtures including azeotropic behavior exhibited by selected gas-adsorbent systems. Here, the requiring a single binary interaction parameter per adsorbate–adsorbate pair, the model successfully correlates wide varieties of binary adsorption isotherm data and it should be a powerful engineering thermodynamic tool in correlating and predicting mixed-gas adsorption equilibria.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Calcium Phosphate Constraints in HLW Glasses: Phosphate Liquidus Model

About 50 million gallons of high-level mixed waste is currently stored in underground tanks at the United States Department of Energy’s (DOE’s) Hanford site in the State of Washington. The Hanford Tank Waste Treatment and Immobilization Plant (WTP) will provide DOE’s Office of River Protection (ORP) with a means of treating this waste by vitrification for subsequent disposal. The tank waste will be separated into low- and high-activity waste fractions, which will then be vitrified respectively into Immobilized Low Activity Waste (ILAW) and Immobilized High Level Waste (IHLW) products. The ILAW product will be disposed in an engineered facility on the Hanford site while the IHLW product is designed for acceptance into a national geological disposal facility for high-level nuclear waste. The ILAW and IHLW products must meet a variety of requirements before they can be accepted for disposal. Glass property-composition models will be used to control the vitrification process at the WTP to ensure that the glass meets product quality and processability requirements. The quality and the validity ranges of the property-composition models and the supporting database will greatly influence waste treatment operations, including the waste loading that is achievable in the glass for each waste composition. The current baseline models for operation of the WTP were developed under the Bechtel National, Inc. (BNI) WTP contract. These models were based on LAW and HLW glass property data over a range of compositions that was guided primarily by the WTP contract and the set of tanks specified therein. The HLW testing was directed towards waste streams from the then-planned early feed tanks for the WTP (i.e., AZ-101, AZ-102, C-106/AY-102, and C-104/AY-101). These wastes are high in iron (AZ-101, AZ-102, and C-106/AY-102) or thorium (C-104/AY-101). While the baseline models may be adequate for commissioning of the facility and processing of the specified tanks, refinements and extensions or augmentations of these models will be required to process the balance of the tank waste inventory. The overall mission of the WTP will require the immobilization of tank waste compositions that are variously dominated by mixtures of aluminum, chromium, bismuth, iron, phosphorous, zirconium, and sulfur compounds as waste-loading-limiting components. Further, the WTP contract includes minimum waste loading requirements that are generally rather modest while loadings significantly higher than those in the current baseline are likely achievable. In order to achieve these higher waste loadings and to expand the applicable composition regions, new models will be needed to support the optimized operation of the WTP. Since the development of the baseline WTP models, ORP directed a program of work at the Vitreous State Laboratory (VSL) to examine various approaches for optimization of the performance of the WTP, including increasing the waste loadings in the LAW and HLW glass products. That work involved HLW rich in Al, Al+Na, Cr, Bi, S, Zr, Sr, P and Fe, in contrast to old baseline models that were based mostly on HLW tanks that were high in Fe, with one other that was also high in Th. Glass property data collected under the ORP program were compiled and combined with data collected to develop the WTP models. The combined databases have been used to update pre-existing HLW composition models or to develop new models for properties such as Product Consistency Test (PCT) responses, spinel crystallization (one-percent crystal fraction temperature, T 1% ), melt viscosity, melt electrical conductivity, nepheline formation upon slow cooling (i.e., Canister Centerline Cooling [CCC]), and Toxicity Characteristic Leach Procedure (TCLP) Cd response. While spinel is typically the most commonly occurring phase in HLW glasses, other phases can be limiting depending on the waste and glass composition. For example, various phosphate phases can become important as the phosphorus content increases. Lithium phosphate crystallization can impact PCT releases of HLW glasses, while calcium phosphate phases forming in the cold-cap can deleteriously impact feed processing in the melter. At present, however, no models exist that relate glass composition and crystallization of phosphate phases in general and calcium phosphate in particular. The present work was therefore directed towards the initial development of such models. Future work may be directed at studying amorphous phase separation in high phosphate HLW glasses. Within the River Protection Project, feed compositions are projected using the Hanford Tank Waste Operations Simulator (HTWOS), a dynamic computer model that tracks the waste through storage, retrieval, feed staging, and treatment processes. A review of the HTWOS data shows that WTP will process 4106 batches of HLW glass for the overall mission (System Plan Rev. 6, ORP-11242), each with its own waste feed and glass composition. Some of the projected HTWOS feed batches are expected to have relatively high concentrations of phosphorous compared to typical HLW streams. In the 4106 batches of HLW for the overall WTP mission P 2 O 5 varies from 0.59 wt% to 13.00 wt%, with 95 batches containing at least 10 wt% P 2 O 5 . Occurrences of phosphate crystals have commonly been noted in glasses with high bismuth and phosphorus contents. In addition, recently collected data from a set of statistically designed HLW matrix glasses prepared and characterized to support enhancement of the HLW glass property-composition models identified apatite in a number of glass samples. However, the current HLW model for crystallization (one-percent crystal fraction temperature) is applicable only to spinel crystallization in glasses. Previous projections of waste loadings and glass volume (such as those in System Plan Rev. 6, ORP-11242) have used preliminary constraints for calcium phosphate crystallization that are based on the limited available data. Additional data and improved constraints or, preferably, models that relate glass composition to crystallization of phosphate-containing phases, are therefore needed to support HLW vitrification at the WTP. The present study focuses on the development of such a model based on data from previously characterized glasses and a set of newly fabricated glasses.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2014

Simplified 2DEG carrier concentration model for composite barrier AlGaN/GaN HEMT

The self consistent solution of Schrodinger and Poisson equations is used along with the total charge depletion model and applied with a novel approach of composite AlGaN barrier based HEMT heterostructure. The solution leaded to a completely new analytical model for Fermi energy level vs. 2DEG carrier concentration. This was eventually used to demonstrate a new analytical model for the temperature dependent 2DEG carrier concentration in AlGaN/GaN HEMT.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

TDCOSMO - XVI. Measurement of the Hubble constant from the lensed quasar WGD 2038–4008

Time-delay cosmography is a powerful technique to constrain cosmological parameters, particularly the Hubble constant (H0). The TDCOSMO Collaboration is performing an ongoing analysis of lensed quasars to constrain cosmology using this method. In this work, we obtain constraints from the lensed quasar WGD 2038−4008 using new time-delay measurements and previous mass models by TDCOSMO. This is the first TDCOSMO lens to incorporate multiple lens modeling codes and the full time-delay covariance matrix into the cosmological inference. The models are fixed before the time delay is measured, and the analysis is performed blinded with respect to the cosmological parameters to prevent unconscious experimenter bias. We obtain DΔ t = 1.68−0.38+0.40 Gpc using two families of mass models, a power-law describing the total mass distribution, and a composite model of baryons and dark matter, although the composite model is disfavored due to kinematics constraints. In a flat ΛCDM cosmology, we constrain the Hubble constant to be H0 = 65−14+23 km s−1 Mpc−1. The dominant source of uncertainty comes from the time delays, due to the low variability of the quasar. Future long-term monitoring, especially in the era of the Vera C. Rubin Observatory’s Legacy Survey of Space and Time, could catch stronger quasar variability and further reduce the uncertainties. This system will be incorporated into an upcoming hierarchical analysis of the entire TDCOSMO sample, and improved time delays and spatially-resolved stellar kinematics could strengthen the constraints from this system in the future.Key words: gravitational lensing: strong / cosmological parameters / distance scale⋆ Corresponding author; kcwong19@gmail.com.⋆⋆ NHFP Einstein fellow.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Woven ceramic matrix composite surrogate model based on physics-informed recurrent neural network

A recurrent neural network (RNN) based surrogate model is developed to emulate the nonlinear constitutive behavior of woven ceramic matrix composites (CMCs) driven by matrix damage at multiple length scales. Physics-informed constraints are introduced into the surrogate model through regularization to ground the prediction in physics and improve its predictive capabilities. Training data is generated using the multiscale generalized method of cells (MSGMC) approach coupled with a matrix damage model. This coupling permits simulating the nonlinear behavior of woven CMCs based on constituent response at the micro-, meso-, and macroscales. The multiscale repeating unit cell is loaded under non-monotonic conditions including multiple load / unload cycles and tension / compression. The fiber volume fraction as well as the intra- and intertow void volume fractions are also varied in the generation of training data. Therefore, the RNN-based surrogate model is tasked with predicting, as a function of variable input strain sequence and fiber and void volume fractions, the resulting stress versus strain response while satisfying physical constraints such as positive semi-definiteness of the tangent stiffness matrix and linear elastic unloading. Further, the trained surrogate model effectively matches the stress versus strain response and successfully predicts the tangent modulus throughout the loading regime. Neural network based surrogate models can offer efficient alternatives to running computationally intensive multiscale material models to simulate the nonlinear response of large structural models. Therefore the presented work provides evidence towards the feasibility of developing, training, and running such models for CMCs with complex architectures, nonlinear multiaxial material response, and under non-monotonic loading conditions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Additive Manufacturing with Cellulose-Based Composites: Materials, Modeling, and Applications

Recent advances in large-scale additive manufacturing (AM) with polymer-based composites have enabled efficient production of high-performance materials. Cellulose nanomaterials (CNMs) have emerged as bio-based feedstocks due to their exceptional strength and sustainability. However, challenges such as hornification and poor dispersion in polymer matrices still limit large-scale CNM–polymer composite manufacturing, requiring novel strategies. Here, this review outlines an approach starting with atomic-level simulations to link molecular composition to key parameters like bulk density, viscosity, and modulus. These simulations provide data for finite element analysis (FEA), which informs large-scale experiments and reduces the need for extensive trials. The strategy explores how atomic interactions impact the morphology, adhesion, and mechanical properties of CNM-based composites in AM processes. The review also discusses current developments in AM, along with predictions of mechanical and thermal properties for structural applications, packaging, flexible electronics, and hydrogel scaffolds. By integrating experimental findings with molecular dynamics (MD) simulations and finite element modeling (FEM), valuable insights for material design, process optimization, and performance enhancement in CNM-based AM are provided to address ongoing challenges.

36 MATERIALS SCIENCE↗
DOE OSTI2013

A micro to macro approach to polymer matrix composites damage modeling : final LDRD report.

Capabilities are developed, verified and validated to generate constitutive responses using material and geometric measurements with representative volume elements (RVE). The geometrically accurate RVEs are used for determining elastic properties and damage initiation and propagation analysis. Finite element modeling of the meso-structure over the distribution of characterizing measurements is automated and various boundary conditions are applied. Plain and harness weave composites are investigated. Continuum yarn damage, softening behavior and an elastic-plastic matrix are combined with known materials and geometries in order to estimate the macroscopic response as characterized by a set of orthotropic material parameters. Damage mechanics and coupling effects are investigated and macroscopic material models are demonstrated and discussed. Prediction of the elastic, damage, and failure behavior of woven composites will aid in macroscopic constitutive characterization for modeling and optimizing advanced composite systems.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Size effect in stainless steel thin wires under tension

Miniaturization of modern devices drives the exploration into how materials behave in small dimensions. However, the mechanical behaviors of micro-scale specimens under varied loading conditions are still challenging to characterize and predict. In this work, specimen size-dependent weakening effect was experimentally revealed in 316L stainless steel thin wires under tension with wire diameter decreasing from 200 μm down to 61 μm. The underlying mechanisms were primarily associated with surface grain softening and strain localization, which were largely dependent on the number of grains across wire diameter. Through an analog composite model with surface and interior grains that follows the Hall-Petch relation, the extent of surface grain softening was found to coincide with the flow stress reduction due to lack of boundary strengthening. Further using elasto-plastic self-consistent polycrystal modeling, the stress-strain behaviors of interior and surface grains were well described in analogy to those of bulk materials with similar-size grains and extremely large grains, respectively. Subsequently, the analog composite model well simulated the specimen size-dependent weakening effect under tension. This article provided more insights into the size effect exploration and proposed a practical model for describing size-dependent mechanical behaviors of small-scale materials to be applied to miniaturized devices.

316L stainless steel↗
DOE OSTI2016

Local structure, composition, and crystallization mechanism of a model two-phase “composite nanoglass”

We report a detailed study of the local composition and structure of a model, bi-phasic nanoglass with nominal stoichiometry Cu{sub 55}Nb{sub 45}. Three dimensional atom probe data suggest a nanoscale-phase-separated glassy structure having well defined Cu-rich and Nb-rich regions with a characteristic length scale of ≈3 nm. However, extended x-ray absorption fine structure analysis indicates subtle differences in the local environments of Cu and Nb. While the Cu atoms displayed a strong tendency to cluster and negligible structural order beyond the first coordination shell, the Nb atoms had a larger fraction of unlike neighbors (higher chemical order) and a distinctly better-ordered structural environment (higher topological order). This provides the first experimental indication that metallic glass formation may occur due to frustration arising from the competition between chemical ordering and clustering. These observations are complemented by classical as well as ab initio molecular dynamics simulations. Our study indicates that these nanoscale phase-separated glasses are quite distinct from the single phase nanoglasses (studied by Gleiter and others) in the following three respects: (i) they contain at least two structurally and compositionally distinct, nanodispersed, glassy phases, (ii) these phases are separated by comparatively sharp inter-phase boundaries, and (iii) thermally induced crystallization occurs via a complex, multi-step mechanism. Such materials, therefore, appear to constitute a new class of disordered systems that may be called a composite nanoglass.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

A novel methodology to integrate outcomes regarding perioperative pain experience into a composite score: Prediction model development and validation

Abstract Background An integrated score that globally assesses perioperative pain experience and rationally weights each component has not yet been developed. Methods A development dataset specific to adult Chinese patients undergoing orthopaedic surgery was obtained from PAIN OUT (1985 qualified patients of 2244). A more recent validation dataset obeying the same conditions was obtained from the Chinese Anaesthesia Shared‐database Platform (1004 qualified patients of 1032). Outcomes were assessed using the International Pain Outcomes Questionnaire (IPO‐Q), which comprises key patient‐level outcomes of perioperative pain management, including pain experience and perceptions of care. Using principal component analysis and regression models, a composite score (CS) was inferred to integrate pain experience. The discrimination of the CS for dissatisfaction and desire for more pain treatment was compared with that of the worst pain score. Results A CS was developed from the 12 items of the IPO‐Q regarding pain experience. The weight for calculating the CS was worst pain 11, least pain 17, time spent in severe pain 11, interference with activity in bed 9, interference with breathing deeply or coughing 10, interference with sleep 9, anxiety 12, helplessness 12, nausea 0, drowsiness 2, itch 5 and dizziness 2. In external validation, the CS indicated superior discrimination to the worst pain in predicting dissatisfaction ( p < 0.001) and desire for more pain treatment ( p < 0.001). Conclusions This study introduced a methodology to integrate outcomes regarding perioperative pain experience into a CS, which was based on the weight of each item. Significance This novel methodology sheds additional light on the riveting issue of carefully integrating several measures into a composite endpoint, which may be useful for quality improvement purposes when addressing the impact of a change in clinical practice.

Jiang, Bailin↗
DOE OSTI2021

Crack fault diagnosis of rotating machine in nuclear power plant based on ensemble learning

Crack faults in rotating machines can cause machine shutdown or scrapping, endangering the normal operation and safety of nuclear power plants. Intelligent diagnostic techniques based on machine learning have the potential to diagnose crack faults. However, problems such as scarcity of field fault data and high noise of plant measurements pose challenges to the application of machine learning. Here this study proposes an ensemble learning approach to mitigate the negative impacts of the problems. Ensemble learning is a strategy for combining multiple machine learning models into a composite model. The basic idea of ensemble learning is that even if one model makes a mistake, other models can correct it. Case studies based on bearing and gear system fault experiments show that the proposed ensemble learning models have better diagnostic results than the single model in the presence of noise and small data.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2017

Numerical Analysis of Experiments on Thermally Induced Dissociation of Methane Hydrates in Porous Media

Numerical simulation is essential for the prediction and evaluation of hydrocarbon reservoir performance. Numerical simulators developed for the description of the behavior of hydrates under production and the corresponding flow of fluids and heat accounting for all known processes are powerful, but they need validation through comparison to field or experimental data in order to instill confidence in their predictions. In this study, we analyze by means of numerical simulation the results of an experiment of methane hydrate dissociation by thermal stimulation in unconsolidated porous media heated through the vessel walls. The physics captured by the model include multicomponent heat and mass transfer, multiphase flow through porous media, and the phase behavior of the CH 4 + H 2 O system involved in methane hydrate formation and dissociation. The set of governing equations consists of the mass and energy conservation equations coupled with constitutive relationships, i.e., the dissolution of gas in H 2 O, relative permeability and capillary pressure models, composite thermal conductivity models, and methane hydrate phase equilibria. The model geometry describes accurately the hydrate reactor used in a recent experimental study investigating methane hydrate dissociation behavior [Chong et. al. Appl. Energy 2016, 177, 409-421]. The cumulative gas production is estimated and validated against three tests of experimental data involving different boundary temperatures, showing a good agreement between observations and numerical predictions. The predicted evolution of the spatial distributions of different phases over time shows that hydrate dissociation progresses inward from the reactor boundary to the center, methane gas accumulates to the top of the reactor because of buoyancy, and water migrates down to the bottom of the reactor because of gravity. A sharp hydrate dissociation front is predicted, and the estimated location of hydrate dissociation front suggests a linear relationship with the square root of time. A sensitivity analysis on the thermal conductivity of sand under fully saturated conditions is conducted to elucidate its effect on the gas production behavior. In addition, the energy efficiency ratio computed from the simulation of this boundary-wall heating technique varies from 14.0 to 16.2. Deviations between observations and predictions of the evolution of the temperature profile are attributed to initial heterogeneous distribution of the hydrate phase in the hydrate reactor.

42 ENGINEERING↗
DOE OSTI2021

Gravitation and Mesh Adaption

The Gravitation and Mesh Adaptation (GaMA) toolbox consists of a set of Matlab classes for modeling the environment around asteroids and comets. A variety of gravitational models and supporting algorithms from are consolidated alongside a custom meshing utility tailored for the application. This allows the user to work within a single streamlined environment to import and manipulate surface definitions, probe the dynamical environment, integrate trajectories, and post-process results. For other applications, modified surface meshes can be exported. Gravity Models: 1) Werner's analytic polyhedron model 2) Mascon model 3) Gottlieb's spherical harmonic model 4) Approximate polyhedron models 5) Curvilinear surface models 6) Custom composite models Meshing Features: 1) Array-based half-edge data structure 2) Supports curvilinear surface definitions up to degree 4 3) Ray-tracing 4) Coarsening 5) Feature-based refinement 6) Projection 7) Smoothing 8) Mesh quality validity tests 9) Mesh repair Additional Features: 1) Solar radiation pressure model 2) Distant 3rd body model 3) Collision detection 4) Trajectory integration and post-processing 5) Visualization of surface fields

Pearl, Jason↗