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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,279 records · Page 71
All sourcesNASA NTRSDOE OSTI
NASA NTRS2001

Equivalent-Continuum Modeling of Nano-Structured Materials

A method has been developed for modeling structure-property relationships of nano-structured materials. This method serves as a link between computational chemistry and solid mechanics by substituting discrete molecular structures with an equivalent-continuum model. It has been shown that this substitution may be accomplished by equating the vibrational potential energy of a nano-structured material with the strain energy of representative truss and continuum models. As an important example with direct application to the development and characterization of single-walled carbon nanotubes, the model has been applied to determine the effective continuum geometry of a graphene sheet. A representative volume element of the equivalent-continuum model has been developed with an effective thickness. This effective thickness has been shown to be similar to, but slightly smaller than, the interatomic spacing of graphite.

Odegard, Gregory M.↗
DOE OSTI2025

Autogenerating a Domain-Specific Question-Answering Data Set from a Thermoelectric Materials Database to Enable High-Performing BERT Models

We present a method for autogenerating a large domain-specific question-answering (QA) dataset from a thermoelectric materials database. We show that a small language model, BERT, once fine-tuned on this automatically generated dataset of 99,757 QA pairs about thermoelectric materials, affords better performance in the field of thermoelectric materials compared to a BERT model fine-tuned on the generic English-language QA data set, SQuAD-v2. We further show that mixing the two data sets (ours and SQuAD-v2), which have significantly different syntactic and semantic scopes, allows the BERT model to achieve even better performance. The best-performing BERT model fine-tuned on the mixed data set outperforms the models fine-tuned on the other two data sets by scoring an exact match of 67.93% and an F1 score of 72.29% when evaluated on our test data set. This has important implications as it demonstrates the ability to realize high-performing small language models, with modest computational resources, empowered by domain-specific materials data sets which can be generated according to our method.

biological databases↗
NASA NTRS1992

Military engine computational structures technology

Integrated High Performance Turbine Engine Technology Initiative (IHPTET) goals require a strong analytical base. Effective analysis of composite materials is critical to life analysis and structural optimization. Accurate life prediction for all material systems is critical. User friendly systems are also desirable. Post processing of results is very important. The IHPTET goal is to double turbine engine propulsion capability by the year 2003. Fifty percent of the goal will come from advanced materials and structures, the other 50 percent will come from increasing performance. Computer programs are listed.

Thomson, Daniel E.↗
DOE OSTI2025

Database of Nonaqueous Proton-Conducting Materials

This work presents the assembly of 48 papers, representing 74 different compounds and blends, into a machine-readable database of nonaqueous proton-conducting materials. SMILES was used to encode the chemical structures of the molecules, and we tabulated the reported proton conductivity, proton diffusion coefficient, and material composition for a total of 3152 data points. The data spans a broad range of temperatures ranging from -70 to 260 °C. To explore this landscape of nonaqueous proton conductors, DFT was used to calculate the proton affinity of 18 unique proton carriers. The results were then compared to the activation energy derived from fitting experimental data to the Arrhenius equation. It was found that while the widely recognized positive correlation between the activation energy and proton affinity may hold among closely related molecules, this correlation does not necessarily apply across a broader range of molecules. This work serves as an example of the potential analyses that can be conducted using literature data combined with emerging research tools in computation and data science to address specific materials design problems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

A detailed study of pre-heating effects in electron beam melting powder bed fusion process

Metal-based additive manufacturing processes, such as powder bed fusion with electron beam (PBF-EB) process, also referred to as electron beam melting (EBM), can produce high-density parts with minimal residual stresses due to the uniform and coherent preheating of the powder bed. However, understanding and controlling the multiple stages of preheating is required to enable the production of high-quality, consistent parts of various materials. This work presents a large-scale, multi-layer, three-dimensional numerical analysis focused on studying the preheating stages for predicting thermal history during the PBF-EB process. The model follows a continuous multi-stage cyclic process, that incorporates all the main stages of the PBF-EB process for 316 L stainless steel. This includes the gradual deposition of a new powder layer, the first and second preheating levels of the powder bed, and the energy deposition during melting (excluding the actual melt-pool behavior simulation). The model employs an adaptive time-scaling approach that automatically adjusts the energy deposition for each solution time-increment. This allows for localized changes in time-resolution over an otherwise computationally expensive multi-layer procedure. The material property variations are also taken into account, with an emphasis on the subtle irreversible changes in powder effective thermal conductivity after the two requisite preheating stages of the powder bed. This effect is studied using simplified conductivity models from the literature for partially sintered powder, validated by a dedicated experiment and numerical simulation. The large-scale model is then used to estimate the actual temperatures during first and second preheating levels for 316 L steel, which is not yet fully supported commercially for PBF-EB. Model predictions are corroborated by experiments, using and analyzing IR images, taken at the completion of each layer by the machine’s built-in infrared camera. The current model also incorporates a qualitative assessment for the effects of conductivity change during pre-heating, as well as evaluates the applicability of the time-scaling approach.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Role of Nuclear Science User Facilities (NSUF) in Nuclear Energy Materials Research

The Nuclear Science User Facilities (NSUF) is one of a diverse number of U.S. Department of Energy (DOE) user facilities established to provide researchers with the most advanced tools of modern science. The NSUF is unique and represents a consortium of capabilities distributed across the U.S. at twenty-one institutions. The NSUF is centered at and managed from the Idaho National Laboratory (INL), where it was originally founded, but it coordinates activities at twenty “partner” institutions that include universities, the Center for Advanced Energy Studies (CAES), national laboratories, and a nuclear industry vendor. These institutions have capabilities that include neutron, ion, and gamma irradiation, hot cells, advanced materials characterization equipment, and high-performance computing resources. Many of these capabilities were beyond reach for most researchers before NSUF. The NSUF provides researchers to access these capabilities at no cost to nuclear energy researchers to produce the highest quality research results to increase understanding of advanced nuclear energy technologies important to DOE-NE and support national priorities by adapting to the needs of DOE-NE programs, industry, and new innovative concepts for sustainable nuclear future.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2000

Accelerating Innovation: Turning Goals into Reality

The success of NASA's programs depends upon innovation, which is recognized by several characteristics. All aspects of a program including tools, processes, materials, subsystems, vehicles, and operations should be evaluated to determine possible innovations which might be implemented. Several examples are presented of ways in which innovation has substantially furthered the goals of NASA. The specific fields mentioned include high performance computing, advanced technologies for aerospace system design, advanced materials and manufacturing processes, neural based flight control, linear aerospike engines, advanced space propulsion systems, high altitude and long duration autonomous flights, advanced vehicle concepts, advanced space propulsion systems, as well as advanced weather information. A final list details the perceived ways in which NASA can benefit from continued innovation in such ways as partnering with the private sector.

VanDalsem, William R.↗
DOE OSTI2025

AI-powered municipal solid waste management: a comprehensive review from generation to utilization

The accumulation of municipal solid waste (MSW) continues to rise due to burgeoning population, rapid global urbanization and economic growth, intensifying ecological concerns associated with landfills and greenhouse gas (GHG) emissions. Over the past 2 decades, global waste generation has surged by 50%, with one-third remaining uncollected and about 70% sent to landfills. This review examines the critical role of integrating emerging technologies, such as advanced sensors and artificial intelligence (AI), into end-to-end MSW management to alleviate landfill burdens. The suitability of various AI tools for different stages of MSW management is assessed, alongside the deployment of advanced sensors including hyperspectral cameras, computer vision systems, and internet of things (IoT) devices for material identification. Applications of genetic algorithms and reinforcement learning for optimizing collection routes, reducing costs, and lowering emissions are highlighted. Life cycle assessment (LCA) across all stages of MSW management is also reviewed, along with future trends in leveraging generative AI, natural language processing (NLP), and agent-based AI systems to analyze waste generation patterns and public sentiment. Efficient collection and handling can be enhanced through route optimization with geographic information systems and real-time bin-level monitoring. Furthermore, sensor-embedded, real-time object detection systems paired with robotics enable material characterization and automated sorting, thereby lowering costs and diverting waste from landfills into value-added products for diverse industrial sectors including packaging, chemicals, textiles, metals and glass, transportation, and electronics industries. Without intervention, global waste is projected to reach 4.54 billion tons by 2050, contributing direct economic costs of $\$$400 billion and roughly 2.38 billion tons of CO 2 -equivalent emissions annually. This review demonstrates how AI-driven, end-to-end solutions for MSW management can mitigate economic and environmental challenges, while directly supporting the United Nations Sustainable Development (UNDP) goals related to innovation and infrastructure (SDG 9), sustainable cities (SDG 11), responsible consumption and production (SDG 12), and climate action (SDG 13).

09 BIOMASS FUELS↗
DOE OSTI2024

Heterostructural Rocksalt-Wurtzite Interfaces

Data and code accompanying the following publication: Sharad Mahatara and Stephan Lany, "Heterostructural interface engineering for ultrawide gap nitrides from first principles: TaC/AlN and TaC/GaN rocksalt-wurtzite interfaces," Physical Review Applied 22, 054044 (2024). https://doi.org/10.1103/PhysRevApplied.22.054044

36 MATERIALS SCIENCE↗
NASA NTRS1992

Two-dimensional finite element model of the pultrusion process

Composite materials used in the fabrication of industrial products/components are under constant development. Applications vary widely from consumer products to high-performance aerospace components. The pultrusion process is one of the important methods of production of composite materials. In order to develop a fundamental understanding of this process, a computational model employing the finite element method is developed which enables a prediction of the material temperature and degree-of-cure at any time during the process. The model is comprehensive; it can readily be employed to perform parametric studies of the process and to aid in the development of efficient design procedures for this type of material system. Comparisons are made between model predictions and experimental results and good agreement is observed.

Hackett, Robert M.↗
NASA NTRS1995

A finite element propagation model for extracting normal incidence impedance in nonprogressive acoustic wave fields

A propagation model method for extracting the normal incidence impedance of an acoustic material installed as a finite length segment in a wall of a duct carrying a nonprogressive wave field is presented. The method recasts the determination of the unknown impedance as the minimization of the normalized wall pressure error function. A finite element propagation model is combined with a coarse/fine grid impedance plane search technique to extract the impedance of the material. Results are presented for three different materials for which the impedance is known. For each material, the input data required for the prediction scheme was computed from modal theory and then contaminated by random error. The finite element method reproduces the known impedance of each material almost exactly for random errors typical of those found in many measurement environments. Thus, the method developed here provides a means for determining the impedance of materials in a nonprogressirve wave environment such as that usually encountered in a commercial aircraft engine and most laboratory settings.

Watson, Willie R.↗
NASA NTRS2009

An Overview of the State of the Art in Atomistic and Multiscale Simulation of Fracture

The emerging field of nanomechanics is providing a new focus in the study of the mechanics of materials, particularly in simulating fundamental atomic mechanisms involved in the initiation and evolution of damage. Simulating fundamental material processes using first principles in physics strongly motivates the formulation of computational multiscale methods to link macroscopic failure to the underlying atomic processes from which all material behavior originates. This report gives an overview of the state of the art in applying concurrent and sequential multiscale methods to analyze damage and failure mechanisms across length scales.

Saether, Erik↗
NASA NTRS2014

Material and Thickness Grading for Aeroelastic Tailoring of the Common Research Model Wing Box

This work quantifies the potential aeroelastic benefits of tailoring a full-scale wing box structure using tailored thickness distributions, material distributions, or both simultaneously. These tailoring schemes are considered for the wing skins, the spars, and the ribs. Material grading utilizes a spatially-continuous blend of two metals: Al and Al+SiC. Thicknesses and material fraction variables are specified at the 4 corners of the wing box, and a bilinear interpolation is used to compute these parameters for the interior of the planform. Pareto fronts detailing the conflict between static aeroelastic stresses and dynamic flutter boundaries are computed with a genetic algorithm. In some cases, a true material grading is found to be superior to a single-material structure.

Stanford, Bret K.↗
NASA NTRSDate not supplied

In-situ Imaging of Pyrolyzing Aerospace Materials

Tracking morphological changes of materials during heating is crucial to understand its response in fire protection, biofuel production, thermal protection systems (TPS) for hypersonic flight. As materials are heated, they undergo physical and chemical changes due to water loss, stretching or shrinking, pyrolysis and chemical reactions in the ambient environment. The effects of these changes can have a profound impact on the material’s performance, indicated by changes in on the porosity and volume. While materials such as wood shrink as they pyrolyze and lose mass, others swell due to their inherent characteristics when exposed to heat [1]. This study focuses on experiments conducted at the Advanced Light Source (ALS) beamline 8.3.2, where in situ micro-computed tomography (µ-CT) is performed on materials as they are being pyrolyzed. Through in situ µ-CT, the change in total volume and porosity can be obtained in real-time, allowing for better understanding of the underlying thermophysical and chemical processes as a function of temperature. This study also focuses on the implementation of the Porous Microstructure Analysis software (PuMA) [2] to obtain thermal conductivity, permeability, and other properties of the material from the 3D tomographies. The information gained from these tomographies will supplement microscale model development of material morphological change and will aid macroscale modeling for high-temperature applications. For this study, Room Temperature Vulcanizing silicone (RTV) [3-5] is heated from room temperature to 1000°C using an infrared lamp heating system, and tomographies are continuously collected as the sample is heated. The tomographies are then segmented to obtain solid and void phases, from which estimates of pore size, porosity and total volume are extracted as a function of temperature. PuMA is deployed on the segmented tomographies to obtain thermal conductivity, permeability, and other properties as a function of temperature. Preliminary results show that RTV first intumesces (swells) as pyrolysis begins, due to build-up of pyrolysis gases in closed pores, and then shrinks significantly as more open pores are formed and the pyrolysis gases outgas. Pore network visualization of the tomographies using OpenPNM [6] showed the increase in pore connectivity with increase in temperature. Future work will focus on using PuMA to obtain macroscopic properties of RTV as a function of temperature.

Tomography↗
NASA NTRS1989

Advanced methods for 3-D inelastic structural analysis for hot engine structures

Three-dimensional Inelastic Analysis Methods are described. These methods were incorporated into a series of new computer codes embodying a progression of mathematical models (mechanics of materials, specialty finite element, boundary element) for streamlined analysis of hot engine structures such as: (1) combustor liners, (2) turbine blades, and (3) turbine vanes. These models address the effects of high temperatures and thermal/mechanical loadings on the local (stress/strain) and global (displacements, frequencies, amplitudes, buckling) structural behavior of the three respective components. The methods and the three computer codes, referred to as MOMM (Mechanics Of Materials Model), MHOST (MARC-Hot Section Technology), and BEST3D (Boundary Element Stress Technology), have been developed and are briefly described.

Chamis, C. C.↗
DOE OSTI2025

Structural origin of disorder-induced ion conduction in NaFePO 4 cathode materials

Diffusion in NaFePO 4 can be enhanced through amorphization. Based on computations using DFT and machine learning potentials, we ascribe this phenomenon to the formation of less constrained Na-ion environments upon disordering. Most modern battery technologies depend on solid-state crystalline cathode materials. However, some of these materials are constrained by the low ionic conductivity of their most stable phases. An example of this is maricite (NaFePO 4 ). Interestingly, experiments have shown that maricite can improve its rate capability through disordering (amorphization). However, experimental characterization of amorphous cathode materials remains a major challenge, hindering a clear understanding of the structural origin of the disorder-induced improvement in sodium-ion mobility. To address this, we here employ molecular dynamics simulations by first training a machine learning potential for NaFePO 4 based on the atomic cluster expansion approach and a batch active learning potential parameterization scheme. This potential is then applied to explore the structural and dynamical properties of NaFePO 4 glasses as cathode materials. Specifically, we investigate the effect of glass structure on sodium-ion diffusion, revealing the relative influences of short-range and medium-range order features. We find significant heterogeneity in sodium-ion diffusivity in the glass, with fast-conducting ions residing in less constrained atomic environments with fewer P and Fe neighbors. These more mobile ions are also surrounded by larger ring-type structures. Overall, the results and developed approach present promising avenues for developing high-performance glassy cathodes for next-generation batteries.

Christensen, Rasmus↗
NASA NTRS2004

Fluid Structure Interaction in a Turbine Blade

An unsteady, three dimensional Navier-Stokes solution in rotating frame formulation for turbomachinery applications is presented. Casting the governing equations in a rotating frame enabled the freezing of grid motion and resulted in substantial savings in computer time. The turbine blade was computationally simulated and probabilistically evaluated in view of several uncertainties in the aerodynamic, structural, material and thermal variables that govern the turbine blade. The interconnection between the computational fluid dynamics code and finite element structural analysis code was necessary to couple the thermal profiles with the structural design. The stresses and their variations were evaluated at critical points on the Turbine blade. Cumulative distribution functions and sensitivity factors were computed for stress responses due to aerodynamic, geometric, mechanical and thermal random variables.

Gorla, Rama S. R.↗
NASA NTRS2011

Ascent Heating Thermal Analysis on Spacecraft Adaptor Fairings

When the Crew Exploration Vehicle (CEV) is launched, the spacecraft adaptor (SA) fairings that cover the CEV service module (SM) are exposed to aero heating. Thermal analysis is performed to compute the fairing temperatures and to investigate whether the temperatures are within the material limits for nominal ascent aeroheating case. The ascent heating is analyzed by using computational fluid dynamics (CFD) and engineering codes at Marshall Space Flight Center. The aeroheating environment data used for this work is known as Thermal Environment 3 (TE3) heating data. One of the major concerns is with the SA fairings covering the CEV SM and the SM/crew launch vehicle (CLV) flange interface. The TE3 heating rate is a function of time, wall temperature, and the spatial locations. The implementation of the TE3 heating rate as boundary conditions in the thermal analysis becomes challenging. The ascent heating thermal analysis on SA fairings and SM/CLV flange interface are performed using two commercial software packages: Cullimore & Ring (C&R) Thermal Desktop (TD) 5.1 and MSC Patran 2007r1 b. TD is the pre-and post-processor for SINDA, which is a finite-difference-based solver. In TD, the geometry is built and meshed, the boundary conditions are defined, and then SINDA is used to compute temperatures. MSC Pthermal is a finite-element- based thermal solver. MSC Patran is the pre- and post-processor for Pthermal. Regarding the boundary conditions, the convection, contact resistance, and heat load can be imposed in different ways in both programs. These two software packages are used to build the thermal model for the same analysis to validate each other and show the differences in the modeling details.

Wang, Xiao Yen↗