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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 1,441 records · Page 80
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

Computational simulation of acoustic fatigue for hot composite structures

This paper presents predictive methods/codes for computational simulation of acoustic fatigue resistance of hot composite structures subjected to acoustic excitation emanating from an adjacent vibrating component. Select codes developed over the past two decades at the NASA Lewis Research Center are used. The codes include computation of (1) acoustic noise generated from a vibrating component, (2) degradation in material properties of the composite laminate at use temperature, (3) dynamic response of acoustically excited hot multilayered composite structure, (4) degradation in the first-ply strength of the excited structure due to acoustic loading, and (5) acoustic fatigue resistance of the excited structure, including propulsion environment. Effects of the laminate lay-up and environment on the acoustic fatigue life are evaluated. The results show that, by keeping the angled plies on the outer surface of the laminate, a substantial increase in the acoustic fatigue life is obtained. The effect of environment (temperature and moisure) is to relieve the residual stresses leading to an increase in the acoustic fatigue life of the excited panel.

Singhal, S. N.↗
NASA NTRS2012

Real-Time X-ray Radiography Diagnostics of Components in Solid Rocket Motors

Solid rocket motors (SRMs) typically use nozzle materials which are required to maintain their shape as well as insulate the underlying support structure during the motor operation. In addition, SRMs need internal insulation materials to protect the motor case from the harsh environment resulting from the combustion of solid propellant. In the nozzle, typical materials consist of high density graphite, carbon-carbon composites and carbon phenolic composites. Internal insulation of the motor cases is typically a composite material with carbon, asbestos, Kevlar, or silica fibers in an ablative matrix such as EPDM or NBR. For both nozzle and internal insulation materials, the charring process occurs when the hot combustion products heat the material intensely. The pyrolysis of the matrix material takes away a portion of the thermal energy near the wall surface and leaves behind a char layer. The fiber reinforcement retains the porous char layer which provides continued thermal protection from the hot combustion products. It is of great interest to characterize both the total erosion rates of the material and the char layer thickness. By better understanding of the erosion process for a particular ablative material in a specific flow environment, the required insulation material thickness can be properly selected. The recession rates of internal insulation and nozzle materials of SRMs are typically determined by testing in some sort of simulated environment; either arc-jet testing, flame torch testing, or subscale SRMs of different size. Material recession rates are deduced by comparison of pre- and post-test measurements and then averaging over the duration of the test. However, these averaging techniques cannot be used to determine the instantaneous recession rates of the material. Knowledge of the variation in recession rates in response to the instantaneous flow conditions during the motor operation is of great importance. For example, in many SRM configurations the recession of the solid propellant grain can drastically alter the flow-field and effect the recession of internal insulation and nozzle materials. Simultaneous measurement of the overall erosion rate, the development of the char layer, and the recession of the char-virgin interface during the motor operation can be rather difficult. While invasive techniques have been used with limited success, they have serious drawbacks. Break wires or make wire sensors can be installed into a sufficient number of locations in the charring material from which a time history of the charring surface can be deduced. These sensors fundamentally alter the local structure of the material in which they are imbedded. Also, the location of these sensors within the material is not known precisely without the use of an X-ray. To determine instantaneous recession rates, real-time X-ray radiography (X-ray RTR) has been utilized in several SRM experiments at PSU. The X-ray RTR system discussed in this paper consists of an X-ray source, X-ray image intensifier, and CCD camera connected to a capture computer. The system has been used to examine the ablation process of internal insulation as well as nozzle material erosion in a subscale SRM. The X-ray source is rated to 320 kV at 10 mA and has both a large (5.5 mm) and small (3.0 mm) focal spot. The lead-lined cesium iodide X-ray image intensifier produces an image which is captured by a CCD camera with a 1,000 x 1,000 pixel resolution. To produce accurate imagery of the object of interest, the alignment of the X-ray source to the X-ray image intensifier is crucial. The image sequences captured during the operation of an SRM are then processed to enhance the quality of the images. This procedure allows for computer software to extract data on the total erosion rate and the char layer thickness. Figure 1 Error! Reference source not found.shows a sequence of images captured during the operation the subscale SRM with the X-ray RTR system. The X-rayTR system, alignment procedure, uncertainty determination, and image analysis process will be discussed in detail in the full manuscript.

Cortopassi, A. C.↗
NASA NTRS2018

Dynamic Impact Testing and Model Development in Support of NASA's Advanced Composites Program

The purpose of this paper is to provide an executive overview of the HEDI effort for NASA's Advanced Composites Program and establish the foundation for the remaining papers to follow in the 2018 SciTech special session NASA ACC High Energy Dynamic Impact. The paper summarizes the work done for the Advanced Composites Program to advance our understanding of the behavior of composite materials during high energy impact events and to advance the ability of analytical tools to provide predictive simulations. The experimental program carried out at GRC is summarized and a status on the current development state for MAT213 will be provided. Future work will be discussed as the HEDI effort transitions from fundamental analysis and testing to investigating sub-component structural concept response to impact events.

Melis, Matthew E.↗
NASA NTRS2018

Dynamic Impact Testing and Model Development in Support of NASA's Advanced Composites Program

The purpose of this presntation is to provide an executive overview of the HEDI effort for NASAs Advanced Composites Program and establish the foundation for the remaining papers to follow in the 2018 SciTech special session NASA ACC High Energy Dynamic Impact. The paper summarizes the work done for the Advanced Composites Program to advance our understanding of the behavior of composite materials during high energy impact events and to advance the ability of analytical tools to provide predictive simulations. The experimental program carried out at GRC is summarized and a status on the current development state for MAT213 will be provided. Future work will be discussed as the HEDI effort transitions from fundamental analysis and testing to investigating sub-component structural concept response to impact events.

Melis, Matthew E.↗
NASA NTRS2018

High Strain Rate Response of Adhesively Bonded Fiber-Reinforced Composite Joints – A Computational Study to Guide Experimental Design

Adhesively bonded carbon fiber-reinforced epoxy composite laminates are widely used in aerospace applications. During a high energy impact event, these laminates are often subjected to high strain rate loading. However, the influence of high strain rate loading on the response of these composite joints is not well understood. Computational finite element (FE) modeling and simulations are conducted to guide the design of high strain rate experiments. Two different experimental designs based on split Hopkinson bar were numerically modeled to simulate Mode I and Mode II types loading in the composite. In addition, the computational approach adopted in this study helps in understanding the high strain rate response of adhesively bonded composite joints subjected to nominally Mode I and Mode II loading. The modeling approach consists of a ply-level 3D FE model, a progressive damage constitutive model for the composite material behavior and a cohesive tie-break contact element for interlaminar delamination.

Ravindran, Suraj↗
DOE OSTI2022

Verification of Upcoming MCNP Features For Estimating Nuclear Data Sensitivities in Fixed Source Simulations [Abstract]

Predictive simulation codes, like the Monte Carlo N-Particle (MCNP) transport code, are used throughout the nuclear community. These simulations are based on nuclear data. Maximizing the accuracy and precision of nuclear data maximizes the accuracy and precision of the overall simulation. This is imperative to applications that rely on simulations. For example, improving nuclear data for special nuclear material improves simulation accuracy in stockpile stewardship applications, which results in larger safety margins and decreased operational costs. The improvement and validation of nuclear data is completed through integral benchmark experiments. Past benchmarks have primarily been limited to focus on the effective multiplication factor ($\kappa$ eff ); broadening the purview of benchmarks beyond $\kappa$ eff -dependent nuclear data addresses nuclear data deficiencies. Different response types depend on different areas of nuclear data. This dependence is quantified as nuclear data sensitivity: the change in response due to perturbation of a contributing parameter. The larger the nuclear data sensitivity of a response, the more the experiment is influenced by the uncertainties of the nuclear data. The optimization of nuclear data sensitivities in future benchmarks would result in more detailed validation of lesser studied areas of nuclear data. Currently, direct sensitivity capabilities are not easily found for all experiment types and parameters. An MCNP tool to directly estimate the cross section sensitivities of tallied values is under development. Additionally, updates have been made to the perturbation feature of MCNP, which can be used in a less direct approach to estimating sensitivities. This work verifies these features to estimate nuclear data sensitivities in fixed source simulations of a 4.5-kg sphere of alpha- phase weapons-grade plutonium surrounded by differing amounts of copper and polyethylene. Integrated estimates made using MCNP’s tools were found to statistically agree with integrated estimates made from manual perturbation of nuclear data proving the validity of the MCNP tools.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials Using MALAMUTE

Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy, aims to develop and qualify additively-manufactured materials for nuclear applications. The key challenges to these efforts are the microstructural variabilities observed on the AM products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-through-put experimental and modeling techniques to accelerate the qualification efforts. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the AM process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture the microstructural variabilities is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning modeling capabilities to develop a digital twin for AM that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation for AM materials. The melting and subsequent solidification that occurs during the AM process is a complex phenomenon that requires multiscale multiphysics analysis. Idaho National Laboratory’s (INL) Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for AM materials in an efficient, reliable, and cost-effective way. This work package focuses on understanding the role of process variabilities on the various microstructural characteristics of the AM materials. Microstructures unique to AM materials, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. In fiscal year (FY) 24, we significantly advanced upon our work in the last fiscal year, both on physics-based and ML models. The alloy solidification model available in MOOSE has been extended to incorporate the thermodynamic properties and free energy relevant to 316SS. The model demonstrates the Cr segregation that occurs during solidifcation. It is demonstrated that rate of solidification and solute segregation is primarily influence by the cooling rate dictating the level of freezing. This work captures the microstructural variabilities at the subgrain level that are often missing in the part-scale models. With an aim to connect the microstructural evolution model to realistic process conditions, a reduced order model is developed for predicting the thermal conditions around meltpool from high-fidelity process simulations. Furthermore, machine learning approach is used to accelerate the temperature prediction during the AM process. In the following years, MALAMUTE will be used to connect different aspects of the models and quantitatively predict the microstructural evolution. The developed ML-based surrogate model will consider the process conditions as the input to predict the microstructural features in a cost-effective way. The generated microstructures can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work help identify the key microstructural features at the subgrain level that are significant in property/performance prediction of the AM products. This work will provide inputs to the large-scale process variability models to reevaluate and validate assumptions/simplifications made in the part-scale models. Furthermore, through active learning this work will help identify the data need from both modeling and experimental sides for development of a robust digital twin for AM.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Numerical Simulations of Cratering Effects in Adobe

This paper outlines a new simplified approach to developing a material model for adobe. The approach is to fit the equation of state (EOS) using a Mie-Gruneisen (MGR) analytical model with a P-Alpha compaction law, and to fit the pressure-dependent yield surface with the Geological Yield Surface (GEO) modeled in CTH using well characterized adobe. By identifying key parameters that governed material response, this simplified modeling approach aimed to increase the understanding of the shock compaction and compression behavior of adobe. The new simplified model for adobe represented in this paper replicated the features of past experimental penetration data. At low velocities the penetration behavior of steel spheres into adobe is captured by Stokes law, where the drag coefficient is inversely proportional to the Reynolds number. Each inherently different adobe material investigated had a separate linear region with the slope equal to the inverse of the coefficient of drag multiplied by impact velocity. A transition region following the Stokes region was identified in each adobe material, where the penetration depth was constant with increasing impact velocity. This penetration depth limit was shown to be dependent upon the yield strength of the adobe and inversely proportional to the initial density. Finally, examining the sensitivity of the penetration depth to the key model parameters, the material model for adobe was adjusted to provide the best fit to the experimental penetration data. In addition, a simplified water content, or initial saturation of the adobe, was introduced as another relevant parameter to characterize the response. Using this simple material model for adobe, validated with experimental penetration data, the response of adobe targets to hypervelocity impact of a variety of projectile types can be reliably predicted.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Using machine-learning to understand complex microstructural effects on the mechanical behavior of Ti-6Al-4V alloys

Structural materials properties are highly dependent on their microstructure. Their microstructure is in turn affected by multiple fabrication and thermo-mechanical treatment parameters, all of which conform a highly-dimensional parametric space with often hidden correlations that are difficult to extract by experimentation alone. This is particularly true for alloys of the dual-phase Ti-6Al-4V family, with their greatly complex and rich microstructures, which combine several intrinsic length scales associated with multiple grain and subgrain structures, grains with different crystal lattices (α and β phases), and complex chemistry. In this paper we use a comprehensive set of machine learning techniques to develop predictive tools relating the yield strength and hardening rate of these alloys to a set of input parameters covering extensive ranges. The data generator is a finite-element crystal plasticity model for polycrystal deformation that takes into account slip anisotropy and employs standard dislocation evolution models for the α and β phases of Ti-based alloys. Our dataset includes over two thousand independent simulations and is used to train the machine learning models, which are then used to establish correlations between microstructural parameters and the alloys’ mechanical response. Our results point to the most influential parameters affecting yield strength and hardening rate, information that can then be used to guide experimental synthesis and characterization efforts to save time and resources.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Quasi-two-dimensional ferromagnetism and anisotropic interlayer couplings in the magnetic topological insulator MnBi 2 Te 4

MnBi 2 Te 4 (MBT) is a promising van der Waals layered antiferromagnetic (AFM) topological insulator that combines a topologically nontrivial inverted Bi-Te band gap with ferromagnetic (FM) layers of Mn ions. Here, the inelastic neutron scattering on single crystals reported here describes rather complex magnetism in MBT. The magnetic anisotropy that controls the bulk and surface magnetic field response of MBT is found to have contributions from both single-ion and interlayer two-ion terms. A description of the quasi-two-dimensional intralayer FM spin waves requires long-range, competing FM and AFM interactions and anomalous damping. While this might suggest carrier-mediated magnetic coupling, abinitio calculations in insulating MBT also find long-range interactions, and classical spin dynamics simulations suggest that magnetic vacancies are at least partially responsible for observations of anomalous damping near the zone boundary.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Essential role of magnetic frustration in the phase diagrams of doped cobaltites

Doped perovskite cobaltites (e.g., La 1–x Sr x CoO 3 ) have been extensively studied for their spin-state physics, electronic inhomogeneity, and insulator-metal transitions. Ferromagnetically interacting spin-state polarons emerge at low x in the phase diagram of these compounds, eventually yielding long-range ferromagnetism. The onset of long-range ferromagnetism (x≈0.18) is substantially delayed relative to polaron percolation (x≈0.05), however, generating a troubling inconsistency. Here, in this study, Monte Carlo simulations of a disordered classical spin model are used to establish that previously ignored magnetic frustration is responsible for this effect, enabling faithful reproduction of the magnetic phase diagram.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Progress Towards Development of High Temperature Ultrasonic Single Element Transducer

Current work proposes BSPT[(1-x)BiScO{sub 3-x}PbTiO{sub 3}] piezoelectric material for the use till 300 deg. C. Using physics based modeling approach, it has been demonstrated that the amplitude loss due to temperature increase is only 2 dB from room temperature to 300 deg. C. In the present work, the effect of adhesive, nickel face-plate and the backing material acoustic impedance on the dynamic response of the transducer is also discussed. Using the observations from modelling, the development of transducer components is presented with initial experimental data. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Incorporating bubble growth volume feedback to improve simulation of the response of a structure containing liquid and gas to sudden energy input

The SNS target module is a stainless-steel structure that contains and directs mercury. The mercury is struck with short, intense proton pulses to create neutrons. The pulses deposit energy and cause loads on the target structure and mercury cavitation. Helium bubbles are introduced into the mercury to reduce loads and cavitation. This gas complicates the prediction of the target module’s physical response. The current method used to simulate the structural response to the proton pulse incorporates a simple but effective approach to approximate mercury cavitation effects. This method cannot account for the changes from injected non-condensable gas. The ability to model the target vessel response is a prerequisite for estimating its life. Known approaches for simulating a bubbly fluid mixture’s response are computationally expensive and impractical for detailed engineering models. A constitutive model is proposed for target mercury with injected gas bubbles, with the intent of providing more accurate mercury–vessel response behavior without the computing costs required to track individual bubbles. The model’s assumptions and a computational model embodiment are described. The bubbly mercury computational model is subjected to test problems to assess its potential utility. Here, the model was able to better predict the effect of gas on wave propagation.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

MPACT Safeguards Modeling: FY25 Update

Sandia National Laboratories develops and maintains several open-source software packages to support material accountancy analyses. This includes the Material Accountancy Performance Indicator Toolkit (MAPIT), the Fissile Facility Flow Modeler (F3M) and the Separation and Safeguards Performance Model Library (SSPM-L). MAPIT is responsible for performing statistical safeguards analyses on bulk and itemized data from nuclear fuel cycle facilities and can operate on real or synthetic data. MAPIT is the only open-source software for such analyses. F3M is a library of modules, built in MATLAB Simulink, that contain pre made blocks to represent different generic fuel cycle processes. These blocks can be used together in a modular fashion to represent and simulate nuclear fuel cycle processes with the goal of improving facility-level accountancy during the design phase. F3M is also an open-source library. Finally, the SSPM-L library is a series of completed models built from F3M. The library includes facility models such as a generic PUREX facility and a fuel fabrication facility. The SSPM-L library is not open source, but is available to collaborators with a relevant use case. These tools include modeling and simulation pipelines to simulate nuclear fuel cycle facilities and the underlying software needed to simulate measurement uncertainty and perform statistical analyses. Together, these tools can perform end-to-end nuclear material accountancy analyses. This report documents the various improvements made to these tools in FY25. Specifically, we added new statistical test, new statistical modeling capabilities, new fuel cycle facility models, and launched a new open-source model component library.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2017

Multi-Objective Flight Control for Drag Minimization and Load Alleviation of High-Aspect Ratio Flexible Wing Aircraft

As aircraft wings become much more flexible due to the use of light-weight composites material, adverse aerodynamics at off-design performance can result from changes in wing shapes due to aeroelastic deflections. Increased drag, hence increased fuel burn, is a potential consequence. Without means for aeroelastic compensation, the benefit of weight reduction from the use of light-weight material could be offset by less optimal aerodynamic performance at off-design flight conditions. Performance Adaptive Aeroelastic Wing (PAAW) technology can potentially address these technical challenges for future flexible wing transports. PAAW technology leverages multi-disciplinary solutions to maximize the aerodynamic performance payoff of future adaptive wing design, while addressing simultaneously operational constraints that can prevent the optimal aerodynamic performance from being realized. These operational constraints include reduced flutter margins, increased airframe responses to gust and maneuver loads, pilot handling qualities, and ride qualities. All of these constraints while seeking the optimal aerodynamic performance present themselves as a multi-objective flight control problem. The paper presents a multi-objective flight control approach based on a drag-cognizant optimal control method. A concept of virtual control, which was previously introduced, is implemented to address the pair-wise flap motion constraints imposed by the elastomer material. This method is shown to be able to satisfy the constraints. Real-time drag minimization control is considered to be an important consideration for PAAW technology. Drag minimization control has many technical challenges such as sensing and control. An initial outline of a real-time drag minimization control has already been developed and will be further investigated in the future. A simulation study of a multi-objective flight control for a flight path angle command with aeroelastic mode suppression and drag minimization demonstrates the effectiveness of the proposed solution. In-flight structural loads are also an important consideration. As wing flexibility increases, maneuver load and gust load responses can be significant and therefore can pose safety and flight control concerns. In this paper, we will extend the multi-objective flight control framework to include load alleviation control. The study will focus initially on maneuver load minimization control, and then subsequently will address gust load alleviation control in future work.

Aerolasticity↗
DOE OSTI2015

Low Insertion Loss and Highly Sensitive SH-SAW Sensors Based on 36° YX LiTaO 3 Through the Incorporation of Filled Microcavities

Reduction in power consumption and improvement in mass sensitivity are important considerations for surface acoustic wave (SAW) devices used in various sensing applications. Detection of minute quantities of a particular species (clinical sensing) and power requirements (wireless sensing) are two key metrics that must be optimized. In this paper, a 3-D finite element model (FEM) was employed to compare insertion loss (IL) and mass sensitivity of SAW sensors having microcavities filled with ZnO and nanocrystalline diamond to a standard two-port SAW design. Initial simulation results show that ZnO filled cavities (depth = 5 mu m) were most effective at reducing power loss Delta IL = (6.03 dB) by increasing particle displacement (acousto-electric to mechanical transduction) at the output transducer. A 100-pg/cm(2) load was applied to the sensing area of each device to evaluate mass sensitivity. Our simulations suggest that ZnO filled cavities with shallow depth (2.5 mu m) have the greatest sensitivity. The FEM simulations are used to understand the acoustic wave propagation in microcavity-based SAW sensors. The observed enhancement in mass sensitivity and power transfer is attributed to waveguiding effects and constructive interference of the scattered acoustic waves from the microcavities. Devices fabricated with microcavities similar to 1 mu m deep decreased IL by 3.306 dB compared with a standard SAW device. Additional simulations were conducted for each device configuration using the same depth in order to make a direct comparison between measured and simulated results. Our findings offer encouraging prospects for designing low IL highly sensitive microcavity-based SAW biosensors.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Metamagnetism of few-layer topological antiferromagnets

MnBi 2 Te 4 (MBT) materials are a promising class of antiferromagnetic topological insulators whose films provide access to novel and technologically important topological phases, including quantum anomalous Hall states and axion insulators. MBT device behavior is expected to be sensitive to the various collinear and noncollinear magnetic phases that are accessible in applied magnetic fields. Here, we use classical Monte Carlo simulations and electronic structure models to calculate the ground state magnetic phase diagram as well as topological and optical properties for few-layer films with up to six septuple layers. Using magnetic interaction parameters appropriate for MBT, we find that it is possible to prepare a variety of different magnetic stacking sequences, some of which have sufficient symmetry to disallow nonreciprocal optical response and Hall transport coefficients. Other stacking arrangements do yield large Faraday and Kerr signals, even when the ground state Chern number vanishes.

36 MATERIALS SCIENCE↗