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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

In-Silico Design of Next Generation Cellulose-Derived Packaging Materials (CRADA Final Report)

Developing sustainable solutions for single-use packaging is an important objective to combat the environmental crisis of plastics pollution. Most embodiments of cellulose-based packaging materials, including CellophaneTM, are completely biodegradable in both terrestrial and marine environments. However, petroleum-derived alternatives offer some performance advantages for metrics such as moisture barriers and mechanical properties. This project leverages molecular dynamics simulation to investigate how molecular modifications to cellulose-based polymer assemblies impact their material properties. An important performance criterion for the modified materials was to retain biodegradability; thus, modifications by naturally occurring, biodegradable additives were the focus of this study. Specifically, we developed models with xylan and lignin of varying monomeric compositions into the cellulose matrix. The mechanical properties were investigated by performing stress-strain simulations, and the water barrier and hydrophobicity were investigated by simulating the water contact angle. Our findings indicate that the incorporation of xylan into the cellulose matrix tends to increase the mechanical properties with an optimal loading of ~27 wt%. We also predict that orienting the nanoscale directionality of the xylan chains such that they are perpendicular to the cellulose fibrils will dramatically increase mechanical strength. In contrast, the incorporation of lignin tends to weaken the composite at all loadings investigated. Simulations of water contact angle predicted that coating polymers on the surface of the cellulose assembly creates a more hydrophobic surface than incorporating them throughout the matrix. Of the coatings investigated, lignin resulted in the most hydrophobic surface, followed by pectin and keratin, which both imparted modest increases in hydrophobicity. Future experimental work done by Futamura will focus on designing material prototypes to capitalize on the predictions of performance enhancement obtained from molecular modeling. While substantial progress was made by the simulations performed in this project, there still exists a vast parameter space that we were unable to investigate, including branching, functional group decoration, and degree of polymerization of polymer additives. However, the methods developed in this initial investigation will facilitate more rapid evaluation of the impact of molecular characteristics on the performance of biopolymer composite materials and thereby accelerate future materials discovery efforts in this area.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Classification and Localization of Fracture-Hit Events in Low-Frequency Distributed Acoustic Sensing Strain Rate with Convolutional Neural Networks

Summary Distributed acoustic sensing (DAS) has been used in the oil and gas industry as an advanced technology for surveillance and diagnostics. Operators use DAS to monitor hydraulic fracturing activities, examine well stimulation efficacy, and estimate complex fracture system geometries. Particularly, low-frequency DAS can detect geomechanical events such as fracture hits because hydraulic fractures propagate and create strain rate variations in the rock. Analysis of DAS data today is mostly done post-job and subject to interpretation methods. However, the continuous and dense data stream generated live by DAS poses the opportunity for more efficient and accurate real-time data-driven analysis. The objective of this study is to develop a machine learning-based workflow that can identify and locate fracture-hit events in simulated strain rate responses correlated with low-frequency DAS data. In this paper, “fracture hit” refers to a hydraulic fracture originating from a stimulated well intersecting an offset well. We start with building a single fracture propagation model to produce strain rate patterns observed at a hypothetical monitoring well. This model is used to generate two sets of strain rate responses with one set containing fracture-hit events. The labeled synthetic data are then used to train a custom convolutional neural network (CNN) model for identifying the presence of fracture-hit events. The same model is trained again for locating the event with the output layer of the model replaced with linear units. We achieved near-perfect predictions for both event classification and localization. These promising results prove the feasibility of using CNN for real-time event detection from fiber-optic sensing data. Additionally, we use edge detection techniques to recognize fracture-hit event patterns in strain rate images. The fracture-hit location can be identified using recognized pixels in the image. The accuracy of edge detection-based location identification is also plausible, but edge detection is dependent on the assumption of pattern shape and image quality, hence it is less robust compared to CNN models. This comparison further supports the need for CNN applications in image-based real-time fiber-optic sensing event detection.

Engineering↗
DOE OSTI2020

A continuum framework for coupled solid deformation–fluid flow through anisotropic elastoplastic porous media

We present a continuum framework for coupled solid deformation–fluid flow in anisotropic elastoplastic porous media. A thermodynamic formulation of the coupled processes gives rise to an anisotropic Biot tensor that is a function of drained elastic tangent moduli tensor of the solid skeleton and the intrinsic bulk modulus of the solid constituent. Two effective stress measures emerge from the formulation, namely, σ', which is energy-conjugate to the elastic strain, and σ'', which is energy-conjugate to the plastic strain. For the special case of transverse isotropy that is commonly encountered in natural rocks, the Biot tensor can be expressed in terms of its normal and tangential components to the bedding plane, along with a microstructure tensor. Apart from its thermodynamic consistency, an advantage of this new formulation is that standard mixed finite element formulation can be employed to discretize the domain and solve initial boundary-value problems. We conduct plane strain simulations of coupled solid deformation–fluid flow in a transversely isotropic porous medium to demonstrate the impacts of material anisotropy, stress history, and the Biot tensor on the system response.

42 ENGINEERING↗
DOE OSTI2020

Mesoscale informed parameter estimation through machine learning: A case-study in fracture modeling

Scale bridging is a critical need in computational sciences, where the modeling community has developed accurate physics models from first principles, of processes at lower length and time scales that influence the behavior at the higher scales of interest. However, it is not computationally feasible to incorporate all of the lower length scale physics directly into upscaled models. This is an area where machine learning has shown promise in building emulators of the lower length scale models, which incur a mere fraction of the computational cost of the original higher fidelity models. We demonstrate the use of machine learning using an example in materials science estimating continuum scale parameters by emulating, with uncertainties, complicated mesoscale physics. Additionally, we describe a new framework to emulate the fine scale physics, especially in the presence of microstructures, using machine learning, and showcase its usefulness by providing an example from modeling fracture propagation. Our approach can be thought of as a data-driven dimension reduction technique that yields probabilistic emulators. Our results show well-calibrated predictions for the quantities of interests in a low-strain simulation of fracture propagation at the mesoscale level. Furthermore, on average, we achieve ~10% relative errors on time-varying quantities like total damage and maximum stresses. Successfully replicating mesoscale scale physics within the continuum models is a crucial step towards predictive capability in multi-scale problems.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Hybridization and deconfinement in colloidal quantum dot molecules

The structural, electronic, and optical properties of CdSe/CdS core–shell colloidal quantum dot molecules, a new class of coupled quantum dot dimers, are explored using atomistic approaches. Unlike the case of dimers grown by molecular beam epitaxy, simulated strain profile maps of free-standing colloidal dimers show negligible additional strain resulting from the attachment. Further, the electronic properties of the relaxed dimers are described within a semiempirical pseudopotential model combined with the Bethe–Salpeter equation within the static screening approximation to account for electron–hole correlations. The interplay of strain, hybridization (tunneling splitting), quantum confinement, and electron–hole binding energies on the optical properties is analyzed and discussed. The effects of the dimensions of the neck connecting the two quantum dot building blocks, as well as the shell thickness, are studied.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Material Model Parameters Optimization in Liquid Mercury Target Dynamics Simulation With Machine Learning Surrogates

A pulsed spallation target is subjected to very short (∼0.7μs) but intense loads (23.3 kJ) from repeated proton pulses, which knock away neutrons from the mercury atoms’ nuclei for a wide range application in physics, engineering, medicine, petroleum exploration, biology, chemistry, etc. The effect of this pulsed loading on the stainless-steel target module which contains the flowing mercury target material is difficult to predict not only due to its short but intense explosive-like physical reaction, but also the nonlinear material behavior of the liquid mercury in the structure. Injecting small helium bubbles in the mercury has been an efficient method of mitigating the pressure wave at high power level stage. However, prediction of the resultant loading on the target is more difficult when helium gas is intentionally injected into the mercury. A 2-phase material model that incorporates the Rayleigh-Plesset (R-P) model is expected to address this complex multi-physics dynamics problem by including the bubble dynamics in the liquid mercury. A parameter sensitivity study was firstly employed to understand their impact on the simulation strains. The investigated parameters included E, μ, γ, σ, n, VFgas, and gas cumulative volume curve control parameters a and b. Verification and validation results from sparse polynomial expansions (SPE) method and directional Gaussian smoothing (DGS) optimization show that the surrogate model had training error of ∼7% and validation error of ∼15%, indicating that machine learning methods and surrogate models can help optimize the uncertain parameters in the complex 2-phase material model. This approach is expected to fill the knowledge gap between unknown liquid-gas mixture material model and measured vessel strain responses.

Lin, Lianshan↗
NASA NTRS2011

Fracture Mechanics Analyses of Subsurface Defects in Reinforced Carbon-Carbon Joggles Subjected to Thermo-Mechanical Loads

Coating spallation events have been observed along the slip-side joggle region of the Space Shuttle Orbiter wing-leading-edge panels. One potential contributor to the spallation event is a pressure build up within subsurface voids or defects due to volatiles or water vapor entrapped during fabrication, refurbishment, or normal operational use. The influence of entrapped pressure on the thermo-mechanical fracture-mechanics response of reinforced carbon-carbon with subsurface defects is studied. Plane-strain simulations with embedded subsurface defects are performed to characterize the fracture mechanics response for a given defect length when subjected to combined elevated-temperature and subsurface-defect pressure loadings to simulate the unvented defect condition. Various subsurface defect locations of a fixed-length substrate defect are examined for elevated temperature conditions. Fracture mechanics results suggest that entrapped pressure combined with local elevated temperatures have the potential to cause subsurface defect growth and possibly contribute to further material separation or even spallation. For this anomaly to occur, several unusual circumstances would be required making such an outcome unlikely but plausible.

Knight, Norman F., Jr.↗
DOE OSTI2022

Proxy-Based Bayesian Inversion Of Poroelastic Simulations To Interpret Strain Tensor Data Measured During Well Testing

The long runtimes of 3D poroelastic numerical simulators makes it impractical to interpret deformation datasets using many inversion schemes. Recent advances in instrumentation have made it possible to measure the strain tensor during well testing, but the lack of robust inversion methods is limiting the ability to interpret these data. We have developed an inversion workflow that reduces the number of computations required to complete a Bayesian inversion using DREAMzs. The workflow trains a KNN model using output from the poroelastic simulator, and then uses the KNN model as a proxy for the simulator during inversion. The workflow also includes a strategy for ensuring the results from the proxy model converge to the results from the simulator, ensuring the accuracy of the final results. An idealized example configured to represent a well test in a deep aquifer is used to verify that the workflow correctly identifies parameters and characterizes noise. Field data measured using strainmeters during an injection test at an oil reservoir in Oklahoma are used to evaluate performance with a real dataset. The workflow identified 265 history matching solutions out of 1240 total simulation runs (21% acceptance ratio), and the results are used to characterize posterior parameter distribution and evaluate the prediction uncertainty. This approach makes it feasible to invert strain data measured during well testing and this has the potential to improve the characterization of aquifers and reservoirs.

Roudini, Soheil↗
NASA NTRS2010

Strain System for the Motion Base Shuttle Mission Simulator

The Motion Base Shuttle Mission Simulator (MBSMS) Strain System is an innovative engineering tool used to monitor the stresses applied to the MBSMS motion platform tilt pivot frames during motion simulations in real time. The Strain System comprises hardware and software produced by several different companies. The system utilizes a series of strain gages, accelerometers, orientation sensor, rotational meter, scanners, computer, and software packages working in unison. By monitoring and recording the inputs applied to the simulator, data can be analyzed if weld cracks or other problems are found during routine simulator inspections. This will help engineers diagnose problems as well as aid in repair solutions for both current as well as potential problems.

Huber, David C.↗
DOE OSTI2022

DISCOVR strain pipeline screening – Part II: Winter and summer season areal productivities and biomass compositional shifts in climate-simulation photobioreactor cultures

Assessing the seasonal biomass productivity and compositional shift dynamics under simulated outdoor culture conditions of the top 22 algae strains selected during Tier I flask screening is an important step in the further prioritization of strains with regard to outdoor pond cultivation. These top 22 strains were subjected to Tier II testing in the PNNL Laboratory Environmental Algae Pond Simulator (LEAPS) photobioreactors, simulating light and temperature conditions of 20 cm deep outdoor ponds during the Arizona winter and summer season. All strains were grown in two consecutive nutrient-replete batch culture experiments at their particular optimal medium salinity to quantify their respective seasonal linear-phase areal biomass productivities. To determine biomass compositional shifts in response to nutrient-depletion, the LEAPS cultures were allowed to enter a 9-day nutrient depletion phase at the end of the second batch run. The following strains were evaluated in winter-season climate-simulated cultures and are listed in the order from highest (7.9 g m -2 day -1 ) to lowest (2.3 g m -2 day -1 ) areal N-replete biomass productivity: Monoraphidium minutum 26B-AM, Tetraselmis striata LANL1001, Chlorella vulgaris LRB AZ-1201, Micractinium reisseri NREL14-F2, Monoraphidium sp. MONOR1, Chlorella vulgaris NREL4-C12, Scenedesmus obliquus UTEX393, Scenedesmus acutus LRB-AP-0401, Nannochloropsis oceanica CCAP849/10, and Stichococcus minutus CCALA727. The following strains were evaluated in summer-season climate-simulated cultures and are listed in the order from highest (31.8 g m -2 day -1 ) to lowest (14.2 g m -2 day -1 ) areal N-replete biomass productivity: Picochlorum renovo NREL39-A8, Scenedesmus obliquus UTEX393, Porphyridium cruentum CCMP675, Picochlorum celeri TG2-WT-CSM/EMRE, Chlorella sorokiniana DOE1116, Stichococcus minor CCMP819, Picochlorum oklahomensis CCMP2329, Chlorella sorokiniana DOE1412 (UTEXB3016), Scenedesmus rubescens NREL46B-D3, Picochlorum soloecismus DOE101, Tetraselmis striata LANL1001, Scenedesmus obliquus DOE 0152.z, and Agmenellum quadruplicatum UTEX2268. All LEAPS cultures experienced a significant reduction in areal biomass productivity in response to nutrient-depletion, from 7-16% in the winter season simulation and up to 1-60% in the summer season simulation. Finally, for 10 of the strains tested, the carbohydrate content more than doubled upon nutrient depletion, and for 9 strains, the lipid content increased by over 50% of the initial content.

09 BIOMASS FUELS↗
DOE OSTI2025

A generalizable machine learning-assisted fast Fourier transform algorithm to simulate the large strain phenomena in polycrystalline materials

Machine learning methods have shown initial promise in constitutive modeling for single crystals or homogenized polycrystals, delivering notable computational efficiency. However, existing machine learning-based constitutive models often lack generalizability, limiting their application across diverse boundary value problems. This study introduces a thermodynamics-informed artificial neural network model to accelerate rate-tangent crystal plasticity fast Fourier transform simulations for cross-scale deformation behaviors of polycrystals under complex loading. Our model integrates microstructural variability and local interactions effectively. To address local effects in each grain, we employ K-means clustering to group Gauss points within the microstructure into clusters assumed to be in similar mechanical states. This approach, based on self-clustering analysis, extends model scope from macroscopic stress response to the granular level, capturing mechanical responses and orientation evolution across grains. This reduces the number of nonlinear problems to solve, with cluster responses propagated throughout each group. The thermodynamics-based artificial neural network-extracted features are further processed using local material state clusters to account for history-dependent deformation and evolving microstructures. Additionally, representative volume element simulations with rate-tangent crystal plasticity fast Fourier transform provide reliable datasets for model training. The proposed model demonstrates high efficiency, accuracy, self-consistency, and enhanced generalizability in predicting strain–stress responses and orientation evolution at both individual grain and aggregate scales under complex loading conditions, such as biaxial tension and arbitrary loading scenarios.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Strain localization criteria for viscoplastic geomaterials

This report presents a viscoplastic localization criterion to detect quasi-instantaneous (i.e., load-induced) and delayed (creep-induced) strain localization in rate-dependent solids. The study is based on the theory of controllability and a viscoplastic description of the mechanical response. Analytical precursors of unstable states are defined through systems of ordinary differential equations (OEDs). The use of the proposed criteria is illustrated at the material point level through a set of strain localization analyses simulating active strain localization of a porous rock. In addition, full-field finite element simulations of compression tests conducted under various pressures are reported to demonstrate the role of local unstable viscoplasticity in the spontaneous propagation of deformation bands under stationary boundary conditions. The study shows that the viscoplastic localization criterion maintains a negative sign as long as the behavior is unstable, that is, the rate of deformation is accelerating. The sign switch coincides with the transition to decelerating deformation. The analyses revealed that pulses of overstress always emerge in correspondence with the growth of unstable behavior, and the peak matches the transition to stable behavior. The local responses recovered from full-field analyses were consistent with those observed in analyses at material point level and the predictions of the presented theory.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Utah FORGE Project 3-2417: Simulations for Distributed Acoustic Sensing Strain Signatures as an Indicator of Fracture Connectivity

This dataset encompasses simulations of strain signatures from both hydraulically connected and "near-miss" fractures in enhanced geothermal systems (EGS). The files and results are presented from the perspective of digital acoustic sensing's (DAS) potential to differentiate the two fracture types. This dataset was acquired by the FOGMORE R&D project (Fiber Optic Geophysical MOnitoring of Reservoir Evolution), under Utah FORGE R&D Project 3-2417. Included are simulation and results via MatLab and COMSOL files, as well as a thesis and paper summarizing the results. Some stimulated fractures may be incomplete, approaching but not intersecting the production well. These "near-miss" fractures can be addressed in future stimulation stages or re-stimulated to complete the connection. We propose the use of fiber optic distributed acoustic sensing (DAS) as a method by which near-miss stimulated fractures may be identified and distinguished from hydraulically connected fractures. The low-frequency sub-nanostrain signatures of both complete and near-miss fractures in DAS data are simulated in this study using a hydrogeomechanical discrete fracture network model. The spatial distribution of strain was found to be an accurate indicator. However, this indicator must be evaluated in the context of DAS gauge length and spatial sampling. These simulations are a precursor to tests conducted at FORGE in 2023.

15 GEOTHERMAL ENERGY↗
NASA NTRS1999

Formation and Migration Energies of Interstitials in Silicon Under Strain Conditions

Simulation calculations are conducted for Si substrates to analyze formation and diffusion energies of interstitials under strain condition using statics methods .based on a Stillinger-Weber type potential function. Defects in the vicinity of the surface region and in the bulk are examined, and the role played by compressive and tensile strains on the energetics of interstitials is investigated. Results indicate that strain alters defect energetics which, in turn, modifies their diffusion characteristics.

Halicioglu, Timur↗
DOE OSTI2022

Insight into ideal shear strength of Ni-based dilute alloys using first-principles calculations and correlational analysis

Here the present work examines the effect of alloying elements (denoted X) on the ideal shear strength for 26 dilute Ni-based alloys, Ni11X, as determined by first-principles calculations of pure alias shear deformations. The variations in ideal shear strength are quantitatively explored with correlational analysis techniques, showing the importance of atomic properties such as size and electronegativity. The shear moduli of the alloys are affirmed to show a strong linear relationship with their ideal shear strengths, while the shear moduli of the individual alloying elements were not indicative of alloy shear strength. Through combination with available ideal shear strength data on Mg alloys, a potential application of the Ni alloy data is demonstrated in the search for a set of atomic features suitable for machine learning applications to mechanical properties. As another illustration, the calculated Ni ideal shear strengths play a key role in a predictive multiscale framework for deformation behavior of single crystal alloys at large strains, as shown by simulated stress–strain curves.

36 MATERIALS SCIENCE↗
NASA NTRS2002

The Stress-strain Behavior of Polymer-Nanotube Composites from Molecular Dynamics Simulations

Stress-strain curves of polymer-carbon nanotube composites are derived from molecular dynamics simulations of a single-walled carbon nanotube embedded in polyethylene. A comparison is made between the response to mechanical loading of a composite with a long, continuous nanotube (replicated via periodic boundary conditions) and the response of a composite with a short, discontinuous nanotube. Both composites are mechanically loaded in the direction of and transverse to the NT axis. The long-nanotube composite shows an increase in the stiffness relative to the polymer and behaves anisotropically under the different loading conditions. The short-nanotube composite shows no enhancement relative to the polymer, most probably because of its low aspect ratio. The stress-strain curves are compared with rule-of-mixtures predictions.

Frankland, S. J. V.↗
DOE OSTI2022

Quantifying deformation during Zry-4 burst testing: a comparison of BISON and a combined in-situ digital image correlation and infrared thermography method

The development and verification of nuclear fuel-cladding performance codes require extensive testing to establish empirical correlations, necessitating more accelerated testing methods that can replace large validation studies. Here, in the present work, a technique was developed and implemented to experimentally quantify the relationship between temperature, cladding internal pressure, and strain in-situ during a burst test, with the specific aim of generating data for code validation and model refinement. Digital image correlation was used to measure hoop, axial, and radial strain, and infrared thermography to quantify axial temperature gradients. Several key experimental modifications to traditional LOCA burst testing were necessary, but are shown to effectively have little impact on burst temperatures. The time/temperature dependent pressure and time/spatially dependent thermal gradient data were used with BISON to simulate cladding burst and strain rates. Although the measured DIC strains reach appreciable amounts at slightly lower temperatures than BISON simulated strains, there is good agreement between the measured and simulated strains in terms of magnitude and rates leading up to burst. However, in the few seconds prior to and during burst, the BISON simulated strains are significantly lower than the measured strains, indicating the potential for model improvement. The combined experiment and simulation technique may be applied to any new cladding concept to accelerate fuel qualification.

36 MATERIALS SCIENCE↗