Search NASA⌕ Search

SEARCH · Search NASA

Results for “material point method”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
NASA NTRS1974

Analysis of longitudinal thickness-shear stiffness of a monolayer filamentary composite

The longitudinal thickness-shear behavior of a rectangular cross-section prism containing circular cross-section fibers embedded in a matrix is analyzed. The problem is formulated as a Saint-Venant flexure problem modeled by a tip-loaded cantilever composite beam and solution is obtained by the boundary-point least-squares method. It is shown that the longitudinal thickness-shear modulus is much greater than the in-plane shear modulus for a monofilament composite and therefore an independent analysis is necessary to predict the longitudinal thickness-shear behavior of the composite. Numerical results are presented in graphical form for various values of fiber volume fraction and material parameters typical of modern advanced composites.

Chang, S.↗
NASA NTRS1993

Nonlinear probabilistic finite element models of laminated composite shells

A probabilistic finite element analysis procedure for laminated composite shells has been developed. A total Lagrangian finite element formulation, employing a degenerated 3-D laminated composite shell with the full Green-Lagrange strains and first-order shear deformable kinematics, forms the modeling foundation. The first-order second-moment technique for probabilistic finite element analysis of random fields is employed and results are presented in the form of mean and variance of the structural response. The effects of material nonlinearity are included through the use of a rate-independent anisotropic plasticity formulation with the macroscopic point of view. Both ply-level and micromechanics-level random variables can be selected, the latter by means of the Aboudi micromechanics model. A number of sample problems are solved to verify the accuracy of the procedures developed and to quantify the variability of certain material type/structure combinations. Experimental data is compared in many cases, and the Monte Carlo simulation method is used to check the probabilistic results. In general, the procedure is quite effective in modeling the mean and variance response of the linear and nonlinear behavior of laminated composite shells.

Engelstad, S. P.↗
NASA NTRS1993

Analysis of Radiation-natural Convection Interactions in 1-g and low-g Environments using the Discrete Exchange Factor Method

A new numerical method is presented for the analysis of combined natural convection and radiation heat transfer with applications in many engineering situations such as materials processing, combustion and fire research. Because of the recent interest in the low gravity environment of space, attention is devoted to both 1-g and low-g applications. The two-dimensional mathematical model is represented by a set of coupled nonlinear integro-partial differential equations. Radiative exchange is formulated using the Discrete Exchange Factor method (DEF). This method considers point to point exchange and provides accurate results over a wide range of radiation parameters. Numerical results show that radiation significantly influences the flow and heat transfer in both low-g and 1-g applications. In the low-g environment, convection is weak, and radiation can easily become the dominant heat transfer mode. It is also shown that volumetric heating by radiation gives rise to an intricate cell pattern in the top heated enclosure.

Kassemi, M.↗
NASA NTRS1977

Impact conditions required for formation of melt by jetting in silicates

It is demonstrated that the process of jetting which occurs when particles collide at oblique angles may produce melt at much lower velocities than are required for melt production in head-on collisions. The minimum velocities of impact required for jetting in aluminum, bronzitite, dunite, and quartz are calculated by the method of shock polars. The analysis, which depends on stated assumptions about attained pressures and the occurrence of jetting, uses shock-velocity particle-velocity equations of state with three shock regimes. The treatment indicates that jetting should arise in bronzitite, dunite, and quartz at relative velocities as low as 1-2 km/sec. At such velocities material which passes near the stagnation point in the jet-forming region is subjected to sufficiently high pressures so that it is probably melted.

Kieffer, S. W.↗
DOE OSTI2026

Field-tailoring quantum materials via magneto-synthesis: metastable metallic and magnetically suppressed phases in a trimer iridate

We demonstrate that applying modest magnetic fields (< 0.1 T) during high-temperature crystal growth can profoundly alter the structure and ground state of a spin-orbit-coupled, antiferromagnetic trimer lattice. Using BaIrO₃ as a model system, whose ground state is intricately dictated by the trimer lattice, we show that magneto-synthesis , a field-assisted synthesis approach, stabilizes a structurally compressed, metastable metallic and magnetically suppressed phases inaccessible via conventional methods. These effects include a 0.85% reduction in unit cell, 4-order-of-magnitude decrease in resistivity, a 10-fold enhancement of the Sommerfeld coefficient, and the collapse of long-range magnetic order -- all intrinsic and bulk in origin. First-principles calculations confirm that the field-stabilized structure lies substantially above the ground state in energy, highlighting its metastable character. These large, coherent and correlated changes across multiple bulk properties, unlike those caused by dilute impurities, defects or off-stoichiometry, point to an intrinsic field-induced mechanism. The findings establish magneto-synthesis as a powerful new pathway for accessing non-equilibrium quantum phases in strongly correlated materials.

magneto-synthesis↗
NASA NTRSDate not supplied

NESC GN&C TDT Workshop on 2D Image Motion Optical Transfer Functions, Pointing Performance Analysis, and Requirements

What You Will Learn: The focus is on payload imaging performance due to pointing motion. Some historical background on pointing performance analysis is given. The Optical Transfer Function (OTF) and Modulation Transfer Function (MTF) are defined. The imaging performance due to pointing motion is measured by image motion optical transfer functions (IM OTF). IM OTFs are defined for displacement, smear, and jitter motions, which are all rigorously defined. Deterministic and Statistical IM OTFs are briefly derived and graphically illustrated and compared. The IM OTFs are parameterized by pointing error metrics(PEM), which are means and covariances of displacement, smear, and jitter. Emphasis is on procedures and algorithms to evaluate the image motion optical transfer functions and pointing error metrics. Three procedures are covered, which depend on whether the pointing error data is from time-domain simulation, frequency-domain analysis, or stochastic modeling. A method to evaluate the relative contribution of disturbance sources and to identify the most significant contributors is presented. The presentation includes pertinent discussion of flexible structures and control-structure interaction. No single book can adequately cover this subject, so a book is not required for the course. A list of selected articles, reports, documents, and books is provided for reference and further study. Mathis kept to the minimum necessary to convey principles; lengthy derivations are left to the reference material. Graphics are used to illustrate concepts. As with any such learning endeavor, the knowledge gained will be retained and strengthened through actual practice.c©2019–2021 Mark E. Pittelkau— 5

NASA Engineering and Safety Center (NESC)↗
DOE OSTI2025

Leveraging Natural Language Processing and Generative Models in Molecular Chemistry: Property Prediction and Novel Compound Generation

The accurate prediction of molecular properties is important for the rational design and the advancement of green chemistry and sustainable materials research. However, the predictive power of traditional computational chemistry methods is limited due to computational restrictions. Here, in this study, we examine an alternative approach to the accurate prediction of properties of organic compounds: natural language processing (NLP)-based molecular embedding. Using viscosity, partition coefficient (log P), and enthalpy of vaporization as test properties through a survey of comprehensive datasets comprising 5695 data points for viscosity, 25 870 data points for log P, and 2296 data points for enthalpy of vaporization. These are important properties for the design of greener, safer, and sustainable chemical processes. Models were trained using NLP methods such as Mol2vec and fine-tuned ChemBERTa, and results were compared with traditional input featurization techniques such as Morgan fingerprints and quantum chemistry derived sigma profiles and DFT features. Among the various machine learning models, Mol2vec demonstrated superior predictive capabilities, achieving the highest correlation coefficient (R 2 = 0.945) and lowest RMSE (0.106 mPa s) for viscosity, as well as high accuracy for log P and enthalpy of vaporization predictions. These findings establish the Mol2vec featurization technique, graph-convolutional neural networks (GCNN), and fine-tuned ChemBERTa model as powerful tools for predictive modeling of organic compounds properties, offering a significant improvement over previously used featurization techniques and opening up strategies for very-high-throughput computational screening. Finally, we integrated ML models with hybrid language-model-based generative adversarial networks (LM-GAN) to generate novel molecular sequences with desirable properties for different research applications. The ability to computationally design solvents with lower viscosity, lower log P, and lower enthalpy of vaporization offers a data-driven route to accelerating the discovery of sustainable alternatives to traditionally toxic solvents.

ChemBERTa↗
DOE OSTI2025

Exact signed distance fields using parallel Fast Sweeping Method

Signed distance fields are often used in multiphysics simulations to track material interfaces. We present a simple methodology based on the fast sweeping method to generate the exact signed distance from triangular meshes and linear paths on Cartesian grids. The methodology propagates the closest primitive to the boundary to the rest of the domain following the characteristics. A local upwind criterion is used to decide between the new and existing closest primitive at each grid point while capturing the correct sign of the global function. The methodology has optimal computational complexity and runs efficiently in distributed-memory architectures. We include 2D and 3D test cases along with a resolution study up to 0.512 trillion zones and 1,000 computer cores. The solution strategy can also be applied to other types of meshes or collections of primitives.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Massive all-atom analysis of 2D materials with quantum properties (Final report)

Improvements in microscopy have enabled the acquisition of data at a scale that is difficult to process manually, making automated machine learning approaches to analyzing experimental images essential. In this project, we developed and applied machine learning (ML) workflows for atomic resolution scanning transmission electron microscopy (STEM) images. This development included improving both methodology as well as generating user-friendly codes. We developed machine learning architectures which, after training, automatically identify the location and types of defects throughout a material. We used these data to produce class-averaged images of 2D atomic coordinates with up to 0.3 pm precision, uncovering the structure and oscillations of long-range strain fields around point defects in WSe 2-2x Te 2x . We also resolved a long-standing problem in this field in the training of ML models, a lack of labeled experimental data, by developing a cycle-GAN that transformed simulated-generated labeled data into labeled data indistinguishable from experiment and therefore suitable for training. This removed the remaining parts of the ML data processing workflow where human intervention was still critical and therefore a bottleneck to working at scale. Codes have been developed and released for this full machine learning workflow. ML approaches to partially automate STEM acquisition were also developed. Finally we applied ML and other advanced data processing methods to several materials science problems in two-dimensional materials, including studying the evolution of hyperuniformity with defect concentration in WSe2, understanding phase transformations in transition metal dichalcogenides during in-situ heating in the STEM, and exploring how 2D interfaces transform from twisted into aligned structures.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Roadmap on methods and software for electronic structure based simulations in chemistry and materials

This Roadmap article provides a succinct, comprehensive overview of the state of electronic structure methods and software for molecular and materials simulations. Seventeen distinct sections collect insights by 51 leading scientists in the field. Each contribution addresses the status of a particular area, as well as current challenges and anticipated future advances, with a particular eye towards software related aspects and providing key references for further reading. Foundational sections cover density functional theory and its implementation in real-world simulation frameworks, Green's function based many-body perturbation theory, wave-function based and stochastic electronic structure approaches, relativistic effects and semiempirical electronic structure theory approaches. Subsequent sections cover nuclear quantum effects, real-time propagation of the electronic structure, challenges for computational spectroscopy simulations, and exploration of complex potential energy surfaces. The final sections summarize practical aspects, including computational workflows for complex simulation tasks, the impact of current and future high-performance computing architectures, software engineering practices, education and training to maintain and broaden the community, as well as the status of and needs for electronic structure based modeling from the vantage point of industry environments. Overall, the field of electronic structure software and method development continues to unlock immense opportunities for future scientific discovery, based on the growing ability of computations to reveal complex phenomena, processes and properties that are determined by the make-up of matter at the atomic scale, with high precision.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Quantifying the Diversity of an Atmospheric Aerosol Population in an Arctic Oil Field on a Single‐Particle Level

As the Arctic rapidly warms, sea ice extent is decreasing and oil and gas extraction activities are expanding. Local combustion emissions affect the Arctic atmospheric aerosol chemical mixing state (the distribution of chemical species across the aerosol population), which impacts climate-relevant properties. Bulk and single-particle measurements of submicron aerosols were conducted at Oliktok Point, Alaska within the North Slope of Alaska oil fields. In this work, we quantify aerosol diversity using online single-particle mass spectrometry data (32,880 individual particles), offline single-particle microscopy data (20,912 individual particles), and online bulk aerosol mass spectrometry and aethalometer data. This method was used to derive individual particle mass fractions for both refractory and non-refractory material within distinct particle types. Single-particle, average single-particle, and bulk population diversities (D i , D α , D γ , respectively) and mixing state indices (χ) were calculated for the data set. Calculated D i values were generally low (2.2 ± 0.6), as individual particle masses were dominated by a few chemical species of interest. Aged aerosol particles (those internally mixed with nitrate and/or sulfate) exhibited higher D i values (>3) compared to recently emitted (fresh) aerosol particles. During oil field plume periods, D α values approached three due to the abundance of diesel combustion particles, which were rich in sulfate, black carbon, and organic aerosol. Overall, the submicron aerosol population within the Arctic oil fields was found to be relatively externally mixed (χ < 50%), due to the constant local emissions within oil fields combining with background aerosol and locally emitted sea spray aerosol at the coastal site.

Wu, J.↗
NASA NTRS1981

A quantitative method for photovoltaic encapsulation system optimization

It is pointed out that the design of encapsulation systems for flat plate photovoltaic modules requires the fulfillment of conflicting design requirements. An investigation was conducted with the objective to find an approach which will make it possible to determine a system with optimum characteristics. The results of the thermal, optical, structural, and electrical isolation analyses performed in the investigation indicate the major factors in the design of terrestrial photovoltaic modules. For defect-free materials, minimum encapsulation thicknesses are determined primarily by structural considerations. Cell temperature is not strongly affected by encapsulant thickness or thermal conductivity. The emissivity of module surfaces exerts a significant influence on cell temperature. Encapsulants should be elastomeric, and ribs are required on substrate modules. Aluminum is unsuitable as a substrate material. Antireflection coating is required on cell surfaces.

Garcia, A., III↗
DOE OSTI2025

Using Separation-Enhanced Isotope Ratio Mass Spectrometry to Enable Increased Renewable Carbon Content in Transportation Fuels (CRADA 525)

Stable isotope ratio measurements of carbon atoms using isotope ratio mass spectrometry (IRMS) can be an effective tool for quantifying biogenic carbon in co-processed fuels, with results approaching the precision and accuracy of accelerator mass spectrometry (AMS). The lower cost of an IRMS may enable deployment to refineries, improving access and analysis turnaround times (≤2 hours), and, by extension, provide data that can allow process optimization to maximize renewable carbon in desired refinery products. This project explored the integration of chemical separation with IRMS analyses to enable highly detailed tracking of biogenic carbon into fuel product streams separated by boiling point range, chemical class, or specific compound. Forty-nine fuels and fuel components of fossil and biogenic origin, spanning gasoline and diesel boiling point ranges, were received from three refiners and were analyzed for their δ 13 C values via IRMS. Results spanned a 13 C range from ca. 10‰ to 44‰ and reflect materials derived from sustainable sources (e.g., C4 or C3 plants, animal-based pathways, syngas) or from fossil-derived fuels. Common ranges are approximately 18‰ to 9‰ and approximately 30‰ to 20‰ for C4 and C3 plants, respectively, and approximately 34‰ to 24‰ and approximately 70‰ to 33‰ for petroleum-derived fuels and methane, respectively. Fuel-like standards were developed and tested using direct-injection elemental analyzer (EA) IRMS for liquid fuels. This method was compared with the published methods, yielding statistically similar results. Four blend curve sets were produced ranging from 0% to 100% of a fuel containing biogenic carbon, focusing on 0% to 10% biogenic carbon. Linear fits were the most applicable for two of the four blend curve sets; however, two sets were found to exhibit slightly quadratic behavior, which was more pronounced in low biogenic blend samples, necessitating second-order fits. The origin of the slight quadratic behavior remains unclear; however, the discussion points to possible interpretations. CanmetENERGY thoroughly characterized a majority of the samples using one- and two-dimensional gas chromatography (GC and GC×GC, respectively) and other analyses. Selected samples were subjected to solid phase extraction (SPE) for saturate, olefin, aromatic, and polar (SOAP) analysis, and the resulting solvent-diluted fractions containing saturates and aromatics were returned to Pacific Northwest National Laboratory (PNNL), where the solvent was removed via evaporation or physical separation using GC techniques. Characterization and separations provided an understanding of saturate and aromatic content, as well as boiling point ranges for each sample and sample fraction. Samples resulting from SPE were examined using EA-IRMS and gas chromatography combustion IRMS (GC-C-IRMS) analyses. Both approaches suggest that the range in values between end-members can be increased by selecting the paraffinic or aromatic fraction of the end-member or by selecting among individual compounds resulting from GC separation of the paraffinic fractions. Considerable work remains to put these approaches into practice and statistically validate the benefit for using a fraction or individual compound over bulk analysis of a sample. However, initial results suggest that separations provide advantages for samples having blend ratios of less than 10% biogenic blendstocks. 13 C results showed statistically similar biofuel blend results to those obtained at PNNL, although additional work is needed to obtain better reproducibility. Select samples were sent to Los Alamos National Laboratory (LANL) for IRMS measurements and Beta Analytics for AMS measurements. This work suggests that IRMS and AMS yield closely comparable results and in some circumstances, IRMS could serve as a surrogate for AMS. While additional work is needed to better resolve statistical advantages for separations and better show the comparable nature of IRMS and AMS in both the biogenic carbon analysis of bulk chemical classes, initial results from this study suggest that these should be pursued in order to proliferate this approach for quantifying biogenic carbon in transportation fuels to the refinery level, thereby potentially enabling process optimization in co-processing scenarios.

09 BIOMASS FUELS↗
NASA NTRS2009

In-Situ Measurement of Hall Thruster Erosion Using a Fiber Optic Regression Probe

One potential life-limiting mechanism in a Hall thruster is the erosion of the ceramic material comprising the discharge channel. This is especially true for missions that require long thrusting periods and can be problematic for lifetime qualification, especially when attempting to qualify a thruster by analysis rather than a test lasting the full duration of the mission. In addition to lifetime, several analytical and numerical models include electrode erosion as a mechanism contributing to enhanced transport properties. However, there is still a great deal of dispute over the importance of erosion to transport in Hall thrusters. The capability to perform an in-situ measurement of discharge channel erosion is useful in addressing both the lifetime and transport concerns. An in-situ measurement would allow for real-time data regarding the erosion rates at different operating points, providing a quick method for empirically anchoring any analysis geared towards lifetime qualification. Erosion rate data over a thruster s operating envelope would also be useful in the modeling of the detailed physics inside the discharge chamber. There are many different sensors and techniques that have been employed to quantify discharge channel erosion in Hall thrusters. Snapshots of the wear pattern can be obtained at regular shutdown intervals using laser profilometry. Many non-intrusive techniques of varying complexity and sensitivity have been employed to detect the time-varying presence of erosion products in the thruster plume. These include the use quartz crystal microbalances, emission spectroscopy, laser induced flourescence, and cavity ring-down spectroscopy. While these techniques can provide a very accurate picture of the level of eroded material in the thruster plume, it is more difficult to use them to determine the location from which the material was eroded. Furthermore, none of the methods cited provide a true in-situ measure of erosion at the channel surface while the thruster is in operation (i.e. none yield a continuous channel erosion measurement). A recent fundamental sensor development effort has led to a novel regression, erosion, and ablation sensor technology (REAST). The REAST sensor allows for measurement of real-time surface erosion rates at a discrete surface location. The sensor was tested using a linear Hall thruster geometry (see Fig. 1), which served as a means of producing plasma erosion of a ceramic discharge chamber. The mass flow rate, discharge voltage, and applied magnetic field strength could be varied, allowing for erosion measurements over a broad thruster operating envelope. Results are presented demonstrating the ability of the REAST sensor to capture not only the insulator erosion rates but also changes in these rates as a function of the discharge parameters.

Polzin, Kurt↗
DOE OSTI2024

Fully Recyclable CFRP Particles (CRADA 519)

The proposed resin-enhanced materials, which utilize fully recycled CFRP (carbon-fiber-reinforced polymer) particles, are enabled by PARC's proprietary Chemically Linked Particles Networks (13 granted patents and patent applications). PNNL and PARC have collaborated to develop manufacturing methods, such as vacuum-assisted wet compression molding and filament winding, for fabricating fiber-reinforced composites with the PARC matrix. PNNL has conducted various mechanical, physical, and thermal characterizations to compare their differences between the composites with baseline and PARC matrices. Those characterizations include density, void volume fraction, optical micrography, uniaxial tension, four-point bending/flexure, interlaminar shear, coefficient of thermal expansion, glass transition temperature, and curing kinetics.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Spatial variation of the low-temperature micro-photoluminescence of THM-grown CdZnTeSe

The quaternary compound CdZnTeSe (CZTS) has emerged as a next-generation detector material. Unlike CdZnTe (CZT), CZTS has the distinct advantages of little or no sub-grain boundary networks and a much lower concentration of Te inclusions, plus better compositional homogeneity. Thus, the material is expected to offer better spatial charge-transport homogeneity compared to CZT. However, an inhomogeneous distribution of point defects in both CZT and CZTS, such as Cd vacancies and residual impurities, can impose additional spatial inhomogeneity of the resistivity, carrier mobilities, and carrier lifetimes of the material. To investigate the spatial distribution of such defects in CZTS, low-temperature photoluminescence (PL) spectroscopic studies were performed at different positions along a single crystalline CZTS sample surface, which was grown by the traveling heater method (THM). In conclusion, the intensity variation of the PL emission of excitons bound to a neutral acceptor defect (A 0 , X), which is likely dominated by an acceptor like Cu-related level, with respect to the PL emission line from a neutral donor-bound exciton (D 0 , X) exhibited significant spatial variation, while the peak energy positions were approximately uniform due to the high compositional and bandgap homogeneity of the material.

II-VI semiconductor↗
DOE OSTI2026

GBOpt: Grain boundary structure optimization using Monte Carlo and evolutionary algorithms

Polycrystalline materials are made of many small crystals separated by grain boundaries (GBs), whose atomic structure strongly influences material properties. Because the structure of a GB determines its properties, the optimal structure must be known in order to determine those impacts. There are many ways of placing atoms in the GB region, but the optimal structure is defined as the one that gives the lowest value of a target property (typically energy). GB structure optimization has been successfully demonstrated using stochastic and evolutionary methods, but no reusable, community-maintained open-source workflow has been developed. GBOpt (Grain Boundary Optimization) is an open-source Python package that creates that workflow, where we have presently implemented two approaches: Markov Chain Monte Carlo, and genetic algorithm based on elite selection. We demonstrate this capability by successfully reproducing the known optimal structures of a specific GB in two materials, and point interested readers to the GitHub repository for additional examples, including optimization for different properties. Both of the implemented approaches recovered the known structures, with the genetic algorithm approach finding the optimal structure faster on average.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

Isostructural electronic transition in MoS 2 probed by solid-state high-harmonic generation spectroscopy

Studying materials under extreme pressure in diamond anvil cells (DACs) is key to discovering emergent states of matter, yet no method currently allows the direct measurement of the electronic structure in this environment. Solid-state high-harmonic generation (sHHG) offers a unique all-optical window into the electronic structure of materials. We demonstrate sHHG spectroscopy inside a DAC by probing 2H-MoS 2 , up to 30 GPa, revealing a pressure-induced crossover of the lowest direct bandgap from the K-point to the Γ-point. This transition manifests as a sharp minimum in harmonic intensity and a 30° rotation of the sHHG polarization anisotropy, despite the absence of a structural phase change. First-principles simulations attribute these features to interference between competing excitation pathways at distinct points in the Brillouin zone. Our results establish sHHG as a sensitive probe of electronic transitions at high pressure, enabling access to quantum phenomena that evade detection by conventional techniques.

Nebgen, Bailey R. [University of California, Berke↗