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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 487 records · Page 27

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NASA NTRS1991

Efficiency and threshold pump intensity of CW solar-pumped solid-state lasers

The authors consider the relation between the threshold pumping intensity, the material properties, the resonator parameters, and the ultimate slope efficiencies of various solid-state laser materials for solar pumping. They clarify the relation between the threshold pump intensity and the material parameters and the relation between the ultimate slope efficiency and the laser resonator parameters such that a design criterion for the solar-pumped solid-state laser can be established. Among the laser materials evaluated, alexandrite has the highest slope efficiency of about 12.6 percent; however, it does not seem to be practical for a solar-pumped laser application because of its high threshold pump intensity. Cr:Nd:GSGG is the most promising for solar-pumped lasing. Its threshold pump intensity is about 100 air-mass-zero (AM0) solar constants and its slope efficiency is about 12 percent when thermal deformation is completely prevented.

Hwang, In H.↗

DOE OSTI2020

Performance Limits of an Alternating Current Electroluminescent Device

Abstract The use of an alternating current (AC) voltage is a simple, versatile method of producing electroluminescence from generic emissive materials without the need for contact engineering. Recently, it was shown that AC‐driven, capacitive electroluminescent devices with carbon nanotube network contacts can be used to generate and study electroluminescence from a variety of molecular materials emitting in the infrared‐to‐ultraviolet range. Here, performance trade‐offs in these devices are studied through comprehensive device simulations and illustrative experiments, enhancing understanding of the mechanism and capability of electroluminescent devices based on alternating as opposed to direct current (DC) schemes. AC‐driven electroluminescent devices can overcome several limitations of conventional DC‐driven electroluminescent devices, including the requirement for proper alignment of material energy levels and the need to process emitting materials into uniform thin films. By simultaneously optimizing device geometry, driving parameters, and material characteristics, the performance of these devices can be tuned. Importantly, the turn‐on voltage of AC‐driven electroluminescent devices approaches the bandgap of the emitting material as the gate oxide thickness is scaled, and internally efficient electroluminescence can be achieved using low‐mobility single‐layer emitter films with varying thicknesses and energy barrier heights relative to the contact.

Wang, Vivian↗

DOE OSTI2023

Synthesis and Characterization of Carbon Microbeads

We report a microfluidic-based droplet generation platform for synthesizing micron-sized porous carbon microspheres. The setup employs carbon materials such as graphite, carbon nanotubes, graphene, fullerenes, and carbon black as starting materials. Custom composition, structure, and function are achieved through combinations of carbon materials, cross-linkers, and additives along with variations in process parameters. Carbon materials can be assembled into spheres with a mean diameter of units to hundreds of μm with relatively tight size distribution (<25% RSD). Pore structure and size (tens to hundreds of angstrom) can be modulated by incorporating porogen/coporogen dilutants during synthesis. The microbeads have excellent mechanical stability with an elastic modulus of hundreds of MPa. They can sustain high dynamic fluid flow pressures of up to 9000 psi. This work lays the foundation for synthesizing novel tailorable and customizable carbon microbeads. It opens avenues for applying these novel materials for composite and additive manufacturing, energy, life science, and biomedical applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2003

Material Properties Governing Co-Current Flame Spread: The Effect of Air Entrainment

A study on the effects of lateral air entrainment on an upward spreading flame has been conducted. The fuel is a flat PMMA plate of constant length and thickness but variable width. Video images and surface temperatures have allowed establishing the progression of the pyrolyis front and on the flame stand-off distance. These measurements have been incorporated into a theoretical formulation to establish characteristic mass transfer numbers ("B" numbers). The mass transfer number is deemed as a material related parameter that could be used to assess the potential of a material to sustain co-current flame spread. The experimental results show that the theoretical formulation fails to describe heat exchange between the flame and the surface. The discrepancies seem to be associated to lateral air entrainment that lifts the flame off the surface and leads to an over estimation of the local mass transfer number. Particle Image Velocimetry (PIV) measurements are in the process of being acquired. These measurements are intended to provide insight on the effect of air entrainment on the flame stand-off distance. A brief description of the methodology to be followed is presented here.

Coutin, Mickael↗

DOE OSTI2022

Li-ion Battery Material phase prediction through Hierarchical Curriculum Learning

Li-ion Batteries (LIB), one of the most efficient energy storage devices, are widely adopted in many industrial applications. Imaging data of these battery electrodes obtained from X-ray tomography can explain the distribution of material constituents and allow reconstructions to study electron transport pathways. Therefore, it can eventually help quantify various associated properties of electrodes (e.g., volume-specific surface area, porosity) which determine the performance of batteries. However, these images often suffer from low image contrast between multiple material constituents , making it difficult for humans to distinguish and characterize these constituents through visualization. A minor error in detecting distributions among the material constituents can lead to a high error in the calculated parameters of material properties.We present a novel hierarchical curriculum learning framework to address the complex task of estimating material constituent distribution in battery electrodes. To provide spatially smooth prediction, our framework comprises three modules: (i) an uncertainty-aware model trained to yield inferences conditioned upon global knowledge of material distribution, (ii) a technique to capture relatively more fine-grained (local) distributional signals, (iii) an aggregator to appropriately fuse the local and global effects towards obtaining the final distribution.

Tabassum, Anika↗

NASA NTRS1980

Material and process constraints for a flat interface in the Bridgman-Stockbarger technique

The functional relationships between various material and process parameters necessary for a flat interface in the Bridgman-Stockbarger technique are given. In particular, two boundary condition cases are considered. The findings imply that the material and process parameters cannot be arbitrary if a flat melt-solid interface is to be achieved. The sensitivity of one parameter to changes in another parameter suggests great care should be taken in obtaining the various material parameters.

Foster, L. M.↗

DOE OSTI2026

Models and Algorithms for Equilibrium Analysis of Mixed-Material Nucleic Acid Systems

Dynamic programming algorithms within the NUPACK software suite enable analysis of equilibrium base-pairing properties for complex and test tube ensembles containing arbitrary numbers of interacting nucleic acid strands. Currently, calculations are limited to single-material systems that are either all-RNA or all-DNA. Here, to enable analysis of mixed-material systems that are critical for modern applications in vitro, in situ, and in vivo, we develop physical models and dynamic programming algorithms that allow the material of the system to be specified at nucleotide resolution. Free energy parameter sets are constructed for both RNA/DNA and RNA/2'OMe-RNA mixed-material systems by combining available empirical mixed-material parameters with single-material parameter sets to enable treatment of the full complex and test tube ensembles. New dynamic programming recursions account for the material of each nucleotide throughout the recursive process. For a complex with N nucleotides, the mixed-material dynamic programming algorithms maintain the O(N 3 ) time complexity of the single-material algorithms, enabling efficient calculation of diverse physical quantities over complex and test tube ensembles (e.g., complex partition function, equilibrium complex concentrations, equilibrium base-pairing probabilities, minimum free energy secondary structure(s), and Boltzmann-sampled secondary structures) at a cost increase of roughly 2.0-3.5×. The results of existing single-material algorithms are exactly reproduced when applying the new mixed-material algorithms to single-material systems. Accuracy is significantly enhanced using mixed-material models and algorithms to predict RNA/DNA and RNA/2'OMe-RNA duplex melting temperatures from the experimental literature as well as RNA/DNA melt profiles from new experiments. In conclusion, mixed-material analyses can be performed online using the NUPACK web app (www.nupack.org) or locally using the NUPACK Python module.

2′OMe-RNA↗

DOE OSTI2020

Scripting ToolKit (STK) 2.0 for Granta Database: Model Calibration Examples [Slides]

Multiple stress cushion (SX-358) uniaxial compression tests are performed. There are 640 (= 4 cycles x 160 tests) compression test curves coming to Granta. Find CHIP-Foam material model parameters. Efficient method is needed to process test data to calibrate custom material models.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1989

Tailoring of composite links for optimal damped elasto-dynamic performance

A method is developed for the optimal design of composite links based on dynamic performance criteria directly related to structural modal damping and dynamic stiffness. An integrated mechanics theory correlates structural composite damping to the parameters of basic composite material systems, laminate parameters, link shape, and modal deformations. The inclusion of modal properties allows the selective minimization of vibrations associated with specific modes. Ply angles and fiber volumes are tailored to obtain optimal combinations of damping and stiffness. Applications to simple composite links indicate wide margins for trade-offs and illustrate the importance of various design variables to the optimal design.

Saravanos, D. A.↗

NASA NTRS1989

Tailoring of composite links for optimal damped elasto-dynamic performance

Saravanos, D. A.↗

NASA NTRS1992

Determining the pH of Mars from the Viking labelled release reabsorption effect

The acid-base properties and redox potentials of solids are two of the more fundamental chemical parameters characterizing a material. Knowledge of these parameters for martian regolith fines would be of considerable value in determining what specific compounds are present and making judgements on what reactions are possible.

Plumb, Robert C.↗

DOE OSTI2020

Deuterium retention in Be-D co-deposits formed over an ITER relevant parameter space

Be-D co-deposit samples are produced over an extended range of plasma-material interaction parameters reflective of the conditions expected in ITER deposition dominant locations. Deposition temperature, D impact energy, atomic Be deposition rate, and D2 neutral pressure were varied in the ranges 373–623 K, ~2–100 eV, 1–10 × 10 19 m 2 s –1 , and 0.27–13.3 Pa respectively. Thermal desorption spectrometry was carried out on samples, subsequently, to measure the D release profile and retention. In this work, it was found that the D retention in samples agrees well with the existing GDT D/Be scaling over the extended parameter space, and that increased D impact energy and D2 pressure can lead to the observation of a sharp low-temperature release peak at ~440 K in desorption data. Desorption data without the presence of the sharp release mechanism are modeled with TMAP-7 utilizing three traps of de-trapping energies, 0.8, 0.9 and 1.0 eV.

36 MATERIALS SCIENCE↗

NASA NTRS1998

Deterministic Multiaxial Creep and Creep Rupture Enhancements for CARES/Creep Integrated Design Code

High temperature and long duration applications of monolithic ceramics can place their failure mode in the creep rupture regime. A previous model advanced by the authors described a methodology by which the creep rupture life of a loaded component can be predicted. That model was based on the life fraction damage accumulation rule in association with the modified Monkman-Grant creep rupture criterion. However, that model did not take into account the deteriorating state of the material due to creep damage (e.g., cavitation) as time elapsed. In addition, the material creep parameters used in that life prediction methodology, were based on uniaxial creep curves displaying primary and secondary creep behavior, with no tertiary regime. The objective of this paper is to present a creep life prediction methodology based on a modified form of the Kachanov-Rabotnov continuum damage mechanics (CDM) theory. In this theory, the uniaxial creep rate is described in terms of sum, temperature, time, and the current state of material damage. This scalar damage state parameter is basically an abstract measure of the current state of material damage due to creep deformation. The damage rate is assumed to vary with stress, temperature, time, and the current state of damage itself. Multiaxial creep and creep rupture formulations of the CDM approach are presented in this paper. Parameter estimation methodologies based on nonlinear regression analysis are also described for both, isothermal constant stress states and anisothermal variable stress conditions This creep life prediction methodology was preliminarily added to the integrated design code CARES/Creep (Ceramics Analysis and Reliability Evaluation of Structures/Creep), which is a postprocessor program to commercially available finite element analysis (FEA) packages. Two examples, showing comparisons between experimental and predicted creep lives of ceramic specimens, are used to demonstrate the viability of Ns methodology and the CARES/Creep program.

Jadaan, Osama M.↗

NASA NTRS1998

Deterministic and Probabilistic Creep and Creep Rupture Enhancement to CARES/Creep: Multiaxial Creep Life Prediction of Ceramic Structures Using Continuum Damage Mechanics and the Finite Element Method

High temperature and long duration applications of monolithic ceramics can place their failure mode in the creep rupture regime. A previous model advanced by the authors described a methodology by which the creep rupture life of a loaded component can be predicted. That model was based on the life fraction damage accumulation rule in association with the modified Monkman-Grant creep ripture criterion However, that model did not take into account the deteriorating state of the material due to creep damage (e.g., cavitation) as time elapsed. In addition, the material creep parameters used in that life prediction methodology, were based on uniaxial creep curves displaying primary and secondary creep behavior, with no tertiary regime. The objective of this paper is to present a creep life prediction methodology based on a modified form of the Kachanov-Rabotnov continuum damage mechanics (CDM) theory. In this theory, the uniaxial creep rate is described in terms of stress, temperature, time, and the current state of material damage. This scalar damage state parameter is basically an abstract measure of the current state of material damage due to creep deformation. The damage rate is assumed to vary with stress, temperature, time, and the current state of damage itself. Multiaxial creep and creep rupture formulations of the CDM approach are presented in this paper. Parameter estimation methodologies based on nonlinear regression analysis are also described for both, isothermal constant stress states and anisothermal variable stress conditions This creep life prediction methodology was preliminarily added to the integrated design code CARES/Creep (Ceramics Analysis and Reliability Evaluation of Structures/Creep), which is a postprocessor program to commercially available finite element analysis (FEA) packages. Two examples, showing comparisons between experimental and predicted creep lives of ceramic specimens, are used to demonstrate the viability of this methodology and the CARES/Creep program.

Jadaan, Osama M.↗

DOE OSTI2024

A mechanistic interpretation of Nelson curves for PVP failures under high temperature hydrogen attack

As an empirically established design criterion, Nelson curves that relate the service temperature and the allowable hydrogen partial pressure have been developed and utilized for more than sixty years in pressure vessels and piping (PVP) safety design. Despite a relatively clear thermodynamic understanding of the high-temperature-hydrogen-attack (HTHA) problem, the detailed fracture process on the microstructural length scales, however, remains elusive, and a quantitative assessment of the PVP lifetime under HTHA from the available creep fracture dataset is still not possible. This work develops a microstructure-informed and micromechanics-based model by incorporating a synergy between hydrogen transport and intergranular-cavity-based fracture process. Based on the available creep lifetime data of C-0.5Mo steels, we are able to calibrate material constitutive parameters, and then conduct nonlinear finite element simulations that reveal a real-time stress-induced hydrogen diffusional transport along grain boundaries, coupled with a microstructure-explicit failure process, from which Nelson curves can be computed. Such failure analyses allow us to delineate two distinct regimes on the Nelson curves, i.e., dislocation-creep-controlled or grain boundary diffusion-assisted cavity growth. More importantly, we found that a small change of the pipe thickness and applied stresses can significantly shift these lifetime curves. However, these two parameters are usually not provided in Nelson curves, thus limiting their usage in material selection and safety design. In conclusion, this discrepancy can clearly be mitigated by extensive parametric studies from our micromechanical modeling/simulation framework.

36 MATERIALS SCIENCE↗

DOE OSTI2023

ASME Section III, Division 5, High Temperature Reactors

Section III, Division 5 stands ready to support near-term deployment of advanced reactors. Progress on improving design rules, extending design lifetimes, adding more materials, and adding advanced component fabrication methods. Lessons-learned from Alloy 617 Code Case effort have streamlined the balloting workflow for Class A material code cases. After requester submitted material design parameters and supporting data package to ASME, Division 5 could turn around a material code case in about three Code Week cycles (less than a year). Data requirements for new materials are described in Division 5, “Nonmandatory Appendix HBB-Y, Guidelines For Design Data Needs For New Materials”.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

Machine learning-driven design and self-sensing capabilities of automotive bumper lattices for adaptive impact response

We present a novel approach to design an automotive bumper energy absorber using carbon fiber reinforced polymer composites, optimized to meet conflicting performance requirements for two distinct impact scenarios. The design must satisfy both a low-speed (2.5 mph) pendulum intrusion test, simulating vehicle-to-vehicle collisions, and a high-speed (25 mph) leg flexion test, replicating pedestrian impacts. These tests demand opposing deformation characteristics: high flexibility (deformation < 85 mm) for the former and high stiffness (deformation < 22 mm) for the latter. To address these contradictory requirements, we developed a machine learning (ML) framework for inverse optimization of lattice designs and material selection. Unlike traditional iterative design processes, our ML model directly outputs optimal design parameters and material choices based on target performance inputs. The energy absorber was fabricated using advanced additive manufacturing techniques, including extrusion deposition and digital light processing. The integration of carbon fibers provides multifunctionality to the bumper structure, enabling self-sensing capabilities through changes in electrical resistivity under compression. This electrical response demonstrates high repeatability under multiple cycles at 2% compression and exhibits distinct signatures during crack formation under high deformation. This research offers adaptive performance through innovative design methodologies and smart material integration. The approach has potential applications in various fields requiring adaptive energy absorption and real-time structural health monitoring.

Chawla, Komal [ORNL] (ORCID:0000000190327565)↗

DOE OSTI2024

Leverage modern artificial intelligence (AI) enabled systems for waste reduction

Manufacturing industries continue to face challenges in reducing waste, as upstream strategies such as source reduction and product redesign require a deeper understanding of processes compared to conventional recycling methods. Recent advancements in artificial intelligence (AI) and machine learning (ML) have opened new opportunities to integrate modern computational techniques with traditional waste minimization strategies. This paper explores AI-enabled approaches for product redesign, source reduction, and recycling that can significantly reduce waste generation while improving efficiency and sustainability. AI-driven material substitution and lightweighting in product design enable discovery of novel materials with optimized properties, reducing waste without compromising performance. Reinforcement learning models optimize process parameters, raw material specifications, and machine sequencing to minimize production losses, while Industrial Internet of Things (IIoT) systems paired with AI analytics enhance real-time waste tracking, predictive maintenance, and quality inspection. Furthermore, AI-based demand forecasting and production planning reduce overproduction and excess inventory, as demonstrated in industrial applications. In recycling, ML-powered pattern recognition and robotic sorting technologies achieve higher accuracy in waste segregation, directly improving recycling efficiency. Complementary solutions such as smart bins and AI-enabled waste pickup scheduling optimize collection logistics, reducing both costs and emissions. Although implementation requires upfront investment in infrastructure and training, the long-term benefits include higher material efficiency, reduced waste, improved product quality, and stronger sustainability outcomes across the supply chain. By leveraging AI-enabled systems, manufacturers can align waste minimization efforts with circular economy principles, creating scalable solutions for both industry and society.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗