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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 1,207 records · Page 67

Variations in Cathodoluminescent Intensity of Spacecraft Materials Exposed to Energetic Electron Bombardment

Many contemporary spacecraft materials exhibit cathodoluminescence when exposed to electron flux from the space plasma environment. A quantitative, physics-based model has been developed to predict the intensity of the glow as a function of incident electron current density and energy, temperature, and intrinsic material properties. We present a comparative study of the absolute spectral radiance for several types of dielectric and composite materials based on this model which spans three orders of magnitude. Variations in intensity are contrasted for different electron environments, different sizes of samples and sample sets, different testing and analysis methods, and data acquired at different test facilities. Together, these results allow us to estimate the accuracy and precision to which laboratory studies may be able to determine the response of spacecraft materials in the actual space environment. It also provides guidance as to the distribution of emissions that may be expected for sets of similar flight hardware under similar environmental conditions.

plasma environment↗

Variations in Cathodoluminescent Intensity of Spacecraft Materials Exposed to Energetic Electron Bombardment

Many contemporary spacecraft materials exhibit cathodoluminescence when exposed to electron flux from the space plasma environment. A quantitative, physics-based model has been developed to predict the intensity of the glow as a function of incident electron current density and energy, temperature, and intrinsic material properties. We present a comparative study of the absolute spectral radiance for several types of dielectric and composite materials based on this model which spans three orders of magnitude. Variations in intensity are contrasted for different electron environments, different sizes of samples and sample sets, different testing and analysis methods, and data acquired at different test facilities. Together, these results allow us to estimate the accuracy and precision to which laboratory studies may be able to determine the response of spacecraft materials in the actual space environment. It also provides guidance as to the distribution of emissions that may be expected for sets of similar flight hardware under similar environmental conditions.

orders of magnitude↗

High-Temperature Extensometry and PdCr Temperature-Compensated Wire Resistance Strain Gages Compared

A detailed experimental evaluation is underway at the NASA Lewis Research Center to compare and contrast the performance of the PdCr/Pt dual-element temperature-compensated wire resistance strain gage with that of conventional high-temperature extensometry. The advanced PdCr gage, developed by researchers at Lewis, exhibits desirable properties and a relatively small and repeatable apparent strain to 800 C. This gage represents a significant advance in technology because existing commercial resistance strain gages are not reliable for quasi-static strain measurements above approx. 400 C. Various thermal and mechanical loading spectra are being applied by a high-temperature thermomechanical uniaxial testing system to evaluate the two strain-measurement systems. This is being done not only to compare and contrast the two strain sensors, but also to investigate the applicability of the PdCr strain gage to the coupon-level specimen testing environment typically employed when the high-temperature mechanical behavior of structural materials is characterized. Strain measurement capabilities to 800 C are being investigated with a nickel-base superalloy, Inconel 100 (IN 100), substrate material and application to TMC's is being examined with the model system, SCS-6/Ti-15-3. Furthermore, two gage application techniques are being investigated in the comparison study: namely, flame-sprayed and spot welding. The apparent strain responses of both the weldable and flame-sprayed PdCr wire strain gages were found to be cyclically repeatable on both IN 100 and SCS-6/Ti-15-3 [0]_8. In general, each gage exhibited some uniqueness with respect to apparent strain behavior. Gages mounted on the IN 100 specimens tended to show a repeatable apparent strain within the first few cycles, because the thermal response of IN 100 was stable. This was not the case, however, for the TMC specimens, which typically required several thermal cycles to stabilize the thermal strain response. Thus, progressive changes in the apparent strain behavior were corroborated by the extensometer, which unlike the mounted gage can distinguish quantitative changes in the material's thermal strain response. One specimen was instrumented with both a fixed and floating gage. From the difference in output of these two gages, the thermal expansion strains were calculated. These data, which are given in the figure, show excellent agreement with the values measured by the high-temperature extensometry.

Source record↗

Applications of Nickelate perovskites for neuromorphic computing from electronic structure and Machine Learning

While the limit of Moore's law is presently being reached with current microelectronic technologies, we need to develop new paradigms that overcome this limitation. In that respect, neuromorphic computing is a concept that emulates the neural behavior and response of the human brain, and it has been recognized as a promising alternative approach. In this research project, we will perform multi-fidelity scale bridging to explore the potential use of materials with metal to insulator transition for neuromorphic applications. In particular, rare earth nickelates are promising for such purposes, as the transition in these materials is quite sensitive to a broad set of different external stimuli. Our multi-fidelity approach will bridge the high-fidelity electronic structure calculations with classical potentials. We will bridge dynamical mean field theory with a classical atomistic representation via a deep learning force field. The neural network is trained with energies, charges, and forces obtained by accurate electronic structure theories based on Dynamical Mean Field Theory. The configurational space is generated from known crystal phases, ab initio molecular dynamics with exchange-correlation functionals corrected with the Hubbard model, disordered phases with different concentrations of oxygen vacancies, and nonsymmetrical positions and induced strain by grain interfaces or contact with a substrate. Strategies to train the model with a reduced number of training examples are obtained from active learning methods, and new structures for improving the learning process are generated by using machine learning autoencoders. This classical potential will be validated through a diversity of electronic structure methods and represents an important step to combine the flexibility and accuracy of first-principles with the speed of classical potentials. The generated multi-fidelity surrogate model will be used to understand the role of strain, oxygen vacancies, proton doping, the variation of the crystal phase, substrate effects, vibrational effects as the octahedral rotation, grain boundaries and defect effects on the response of a Metal to Insulator Transition (MIT) in correlated materials. Long time and large-scale simulations will help understand the role of different stimuli to control the hysteresis of the MIT, as it has been experimentally suggested. Selected configurations will be analyzed with higher-level theories to provide an accurate electronic description and to study how the orbitals and charges are rearranged under different conditions.

36 MATERIALS SCIENCE↗

Materials response under hypersonic flow conditions probed by multi-modal diagnostics in a benchtop wind tunnel

In this LDRD “Materials response under hypersonic flow conditions probed by multi-modal diagnostics in a benchtop wind tunnel” a peer-reviewed lab-scale hypersonic wind tunnel (WT) was planned to be assembled in-house, along with time and space resolved and related flow and materials diagnostics to derive high quality measurements and the science that is able to inform the development of our modeling and simulation codes for the hypersonic regime. The baseline capability to achieve Mach 3 flow was scheduled to be completed in Q4 2020, in the first year of the LDRD. However due to the COVID-19 delays and travel restrictions starting in March, 2020 this task could not be completed to carry out the materials response studies in-house, or those planned with external partners that were engaged during this LDRD (NASA Langley, NASA Ames, Texas A&M university, and Virginia Tech). Still, using our hypersonic computational fluid simulation codes, many of the hypersonic components have been designed (for Mach 3, 5, and 7) and procured (or in procurement phase), and a new dedicated hypersonic laboratory was facilitized, 2 WT test sections have been manufactured, and a Mach 3 nozzle was 3D printed for prototyping. In parallel, unique materials diagnostics have been tested under static conditions and fully specified and awaiting validation in our partner facilities. Due to the new hypersonic initiative announced in January, 2020 we proposed a related scope increase in hypersonics research requiring energy interactions and new diagnostics well beyond what could be supported by an LDRD ER. A decision was made to end the current LDRD ER and formally propose a new LDRD SI that leverages the current effort, which will be starting in FY21. As a result of the uniqueness of this LDRD work, our early efforts have been encouraged and well received in the hypersonic research community, and is now part of multiple unsolicited internal and external project proposals expected to be funded in the coming years, which is encouraging to make LLNL a leader in experimentally probing extreme physics of local energetics critical to hypersonics.

36 MATERIALS SCIENCE↗

Characterization and Analysis of Triaxially Braided Polymer Composites under Static and Impact Loads

In order to design impact resistant aerospace components made of triaxially-braided polymer matrix composite materials, a need exists to have reliable impact simulation methods and a detailed understanding of the material behavior. Traditional test methods and specimen designs have yielded unrealistic material property data due to material mechanisms such as edge damage. To overcome these deficiencies, various alternative testing geometries such as notched flat coupons have been examined to alleviate difficulties observed with standard test methods. The results from the coupon level tests have been used to characterize and validate a macro level finite element-based model which can be used to simulate the mechanical and impact response of the braided composites. In the analytical model, the triaxial braid unit cell is approximated by using four parallel laminated composites, each with a different fiber layup, which roughly simulates the braid architecture. In the analysis, each of these laminated composites is modeled as a shell element. Currently, each shell element is considered to be a smeared homogeneous material. Simplified micromechanics techniques and lamination theory are used to determine the equivalent stiffness properties of each shell element, and results from the coupon level tests on the braided composite are used to back out the strength properties of each shell element. Recent improvements to the model include the incorporation of strain rate effects into the model. Simulations of ballistic impact tests have been carried out to investigate and verify the analysis approach.

Goldberg, Robert K.↗

Establishment of computerized numerical databases on thermophysical and other properties of molten as well as solid materials and data evaluation and validation for generating recommended reliable reference data

The Center for Information and Numerical Data Analysis and Synthesis, (CINDAS), measures and maintains databases on thermophysical, thermoradiative, mechanical, optical, electronic, ablation, and physical properties of materials. Emphasis is on aerospace structural materials especially composites and on infrared detector/sensor materials. Within CINDAS, the Department of Defense sponsors at Purdue several centers: the High Temperature Material Information Analysis Center (HTMIAC), the Ceramics Information Analysis Center (CIAC) and the Metals Information Analysis Center (MIAC). The responsibilities of CINDAS are extremely broad encompassing basic and applied research, measurement of the properties of thin wires and thin foils as well as bulk materials, acquisition and search of world-wide literature, critical evaluation of data, generation of estimated values to fill data voids, investigation of constitutive, structural, processing, environmental, and rapid heating and loading effects, and dissemination of data. Liquids, gases, molten materials and solids are all considered. The responsibility of maintaining widely used databases includes data evaluation, analysis, correlation, and synthesis. Material property data recorded on the literature are often conflicting, diverging, and subject to large uncertainties. It is admittedly difficult to accurately measure materials properties. Systematic and random errors both enter. Some errors result from lack of characterization of the material itself (impurity effects). In some cases assumed boundary conditions corresponding to a theoretical model are not obtained in the experiments. Stray heat flows and losses must be accounted for. Some experimental methods are inappropriate and in other cases appropriate methods are carried out with poor technique. Conflicts in data may be resolved by curve fitting of the data to theoretical or empirical models or correlation in terms of various affecting parameters. Reasons (e.g. phase transitions) must be found for unusual dependence or any anomaly. Such critical evaluation involves knowledge of theory, experience in measurement, familiarity with metallurgy (microstructural behavior) and not inconsiderable judgment. An examination of typical data compiled and analyzed by CINDAS shows that the thermal conductivity of a material reported in the literature may vary by a factor of two of more; the range of reported values increases as temperature increases reflecting the difficulty of high temperature measurements. Often only estimates of melt behavior are available, despite the importance of melt properties in modeling, welding, or other solidification processes. There may be only a few measurements available for properties such as kinematic viscosity, even for widely used materials such as stainless steel. In the face of such a paucity of existing data and in a national environment where too few new data are being generated it is nonetheless the responsibility of CINDAS to select and disseminate recommended values of a wide variety of thermophysical properties.

Ho, C. Y.↗

Nonlinear viscoelastic characterization of polymer materials using a dynamic-mechanical methodology

Polymer materials retrieved from LDEF exhibit nonlinear constitutive behavior; thus the authors present a method to characterize nonlinear viscoelastic behavior using measurements from dynamic (oscillatory) mechanical tests. Frequency-derived measurements are transformed into time-domain properties providing the capability to predict long term material performance without a lengthy experimentation program. Results are presented for thin-film high-performance polymer materials used in the fabrication of high-altitude scientific balloons. Predictions based upon a linear test and analysis approach are shown to deteriorate for moderate to high stress levels expected for extended applications. Tests verify that nonlinear viscoelastic response is induced by large stresses. Hence, an approach is developed in which the stress-dependent behavior is examined in a manner analogous to modeling temperature-dependent behavior with time-temperature correspondence and superposition principles. The development leads to time-stress correspondence and superposition of measurements obtained through dynamic mechanical tests. Predictions of material behavior using measurements based upon linear and nonlinear approaches are compared with experimental results obtained from traditional creep tests. Excellent agreement is shown for the nonlinear model.

Strganac, Thomas W.↗

PathTrace and MPVEASI: A Path Analysis Comparative Validation Study

Developed in 2018, PathTrace is a software package built with the intention of making path analysis simple and intuitive. PathTrace is a top-down pathway analysis software where a user is able to explore vulnerable pathways into a facility. The intention of utilizing a software tool like PathTrace is to characterize an existing physical protection system (PPS) and to upgrade the system to achieve a high level of response interruption, or probability of interruption (P I ) of the adversary. There are four steps for conducting path analysis using PathTrace. The first step is to identify an image to use to build the model and scale the model within PathTrace using a section of known distance (wall or fence perimeter, for example). The scaling process will produce a grid of cells through which the user is able to build a model. The second step is to fill out the grid of cells with four categories of materials: Barriers, Detection Areas, Jumps, and Targets. These materials apply associated delay and detection values to the cells in which they are applied. The third step is to represent the adversary and response forces. The adversaries are represented by their capabilities in interacting with the materials identified in step two, and the response is represented by how quickly they will be able to respond to an adversary attack. Finally, the user is able to take all of the information from the previous three steps and perform a Most Vulnerable Path (MVP) analysis. In this stage, the user is able to visualize vulnerable adversary pathways and reason about how to upgrade these pathways to provide a high level of P I .

97 MATHEMATICS AND COMPUTING↗

Hydration and Restructuring of Polar Polymer Interfaces: Implications in Antifouling and Responsive Materials

Manipulating polymer interfaces is crucial for understanding how structure influences function in applications spanning biofouling prevention to energy storage. Moreover, observing how polymers adapt their microscopic structure to changes in their local environment can reveal essential properties that govern their performance in such applications, providing key insights into how to design more effective interfaces. Here, in this study, a series of “grafting-from” polymer brushes with side chains varying in charge, are probed by sum frequency generation (SFG) and modeled using all-atom molecular dynamics (MD) simulations to elucidate how chemical makeup and charge mediate interfacial restructuring in dry versus hydrated states. Results show that charge, in progressing from nonpolar to cationic to zwitterionic, results in dramatic changes in interfacial structure and overall hydration. While net neutral systems, regardless of bulk phase polarity, show minimal interfacial water structuring, the cationic species exhibits strong bulk water signals from the surface potential. Meanwhile, the polymer brushes themselves restructure in water differently independent of charge, impacting the functional groups that are presented to the aqueous phase. Nonpolar and cationic species for instance undergo a change in alkyl group orientations to accommodate hydrating water molecules, whereas the zwitterionic polymer becomes completely disordered in water. Overall, the structure-based behavior trends presented herein have implications in antifouling applications and responsive material interfaces.

biointerface↗

Quadrupolar Kondo effect in uranium heavy-electron materials?

The possibility of an electric quadrupole Kondo effect for a non-Kramers doublet on a uranium (U) ion is a cubic metallic host is demonstrated by model calculations showing a Kondo upturn in the resistivity, universal quenching of the quadrupolar moment, and a heavy-electron anomaly in the electronic specific heat. With inclusion of excited crystal-field levels, some of the unusual magnetic-response data in the heavy-electron superconductor UBe13 may be understood. Structural phase transitions at unprecedented low temperatures may occur in U-based heavy-electron materials.

Cox, D. L.↗

Technology Readiness Assessment for HEEET TPS

The HEEET project was conceived to develop a heatshield with a high performance ablative thermal protection material that can withstand the extreme entry environment produced as a result of rapid deceleration during high speed entry into Venus, Saturn, Uranus or higher speed entry into Earth's atmosphere. Successful maturation of HEEET supports future New Frontiers and Discovery AO's, as well as Flagship and directed missions in the longer term. In addition, HEEET has the potential to evolve and to support re-entry to Earth, for missions such as Mars Sample Return.The primary goal of the HEEET Project was to develop an ablative TPS heat-shield based on woven TPS technology to Technology Readiness Level (TRL) 6. Key evidence to support the TRL evaluation includes: Demonstration of reproducible manufacturing of a dual layer material over a range of thicknesses and integrated on to a heatshield engineering test unit at a scale that is applicable to near term Discovery as the highest priority and future NF missions as secondary priority set of missions. Demonstration of predictable and stable performance of the dual layer TPS over a range of entry environments that are applicable to near term Discovery and NF missions of interest to SMD.Includes completion of coupon arc jet and laser testing and development of a mid-fidelity thermal response model that correlates with test results. Demonstration of flight heatshield system design for a range of sizes and loads that are relevant to near term Discovery and NF missions of interest to SMD. Includes completion of structural testing to validate analytic thermal/structural models and development of a material property database. Includes structural testing of a ~1m Engineering Test Unit under relevant entry loads.

Gage, Peter↗

Discrete-Direct Model Calibration and Uncertainty Propagation Method Confirmed on Multi-Parameter Plasticity Model Calibrated to Sparse Random Field Data

A discrete direct (DD) model calibration and uncertainty propagation approach is explained and demonstrated on a 4-parameter Johnson-Cook (J-C) strain-rate dependent material strength model for an aluminum alloy. The methodology's performance is characterized in many trials involving four random realizations of strain-rate dependent material-test data curves per trial, drawn from a large synthetic population. The J-C model is calibrated to particular combinations of the data curves to obtain calibration parameter sets which are then propagated to “Can Crush” structural model predictions to produce samples of predicted response variability. These are processed with appropriate sparse-sample uncertainty quantification (UQ) methods to estimate various statistics of response with an appropriate level of conservatism. This is tested on 16 output quantities (von Mises stresses and equivalent plastic strains) and it is shown that important statistics of the true variabilities of the 16 quantities are bounded with a high success rate that is reasonably predictable and controllable. The DD approach has several advantages over other calibration-UQ approaches like Bayesian inference for capturing and utilizing the information obtained from typically small numbers of replicate experiments in model calibration situations—especially when sparse replicate functional data are involved like force–displacement curves from material tests. The DD methodology is straightforward and efficient for calibration and propagation problems involving aleatory and epistemic uncertainties in calibration experiments, models, and procedures.

42 ENGINEERING↗

Analysis of Sensory/Active Piezoelectric Composite Structures in Thermal Environments

Although there has been extensive development of analytical methods for modeling the behavior of piezoelectric structures, only a limited amount of research has been performed concerning the implications of thermal effects on both the active and sensory response of smart structures. Thermal effects become important when the piezoelectric structure has to operate in either extremely hot or cold temperature environments. Consequently, the purpose of this paper is to extend the previously developed discrete layer formulation of Saravanos and Heyliger to account for the coupled mechanical, electrical, and thermal response in modern smart composite beams. The mechanics accounts for thermal effects which may arise in the elastic and piezoelectric media at the material level through the constitutive equations. The displacements, electric potentials, and temperatures are introduced as state variables, allowing them to be modeled as variable fields through the laminate thickness. This unified representation leads to an inherent capability to model both the active compensation of thermal distortions in smart structures and the resultant sensory voltage when thermal loads are applied. The corresponding finite element formulation is developed and numerical results demonstrate the ability to model both the active and sensory modes of composite beams with heterogeneous plies with attached piezoelectric layers under thermal loadings.

Lee, Ho-Jun↗

Challenges in Tracking Waste Reduction Performance Improvement in Manufacturing Plants

The recently released Circularity Gap Report 2023 by the Circle Economy Foundation states that the circularity score for the global economy is declining. The US Environmental Protection Agency (EPA) tracked municipal solid waste from 1960 to 2018 and found that 50% of the waste was destined for landfills. EPA estimates that US industry is responsible for 2.7 Gt of solid nonhazardous waste annually in the US mostly linear economy model. The circular economy framework aims to decouple economic value generation from the extraction of virgin materials from nature. The linear model of material extraction and disposal at the end of life is highly unsustainable. Manufacturing companies are adopting ambitious waste reduction targets to achieve sustainability. Through the Better Plants program, US DOE has established the Waste Reduction Network, which offers technical assistance to partners to achieve their ambitious waste reduction goals. Basic requirements of establishing a target include identifying a baseline, quantifying waste performance, and measuring progress over time. One problem faced by industry is unstandardized metrics for quantifying waste performance that may not be well suited to demonstrate progress. This paper studies traditional methods used to measure waste performance and highlights advantages, disadvantages, and limitations of each method. It also examines the suitability of applying the methods in different manufacturing circumstances. Finally, the paper presents a case study of a large manufacturer that faced inconsistencies in its tracked measurement metric. A solution was proposed and implemented to alter the methodology to enable more accurate waste performance tracking against a baseline.

circular economy↗

Counter Data Paucity through Adversarial Invariance Encoding: A Case Study on Modeling Battery Thermal Runaway

Lithium-ion batteries, widely used for their durability and high energy storage, face the risk of internal short circuits leading to catastrophic thermal runaway events. These events, triggered by external stimuli like mechanical loads, pose safety concerns in applications such as electric vehicles. Detecting and understanding thermal runaway events is crucial, but physics-driven models struggle to explain the non-linear evolution of battery temperature during these events, considering factors like material composition and state-of-charge. Due to the rarity of these events and the cost of data collection, we propose a deep learning (DL) model to predict battery temperature responses during thermal runaway. The challenge lies in the scarcity of data, making traditional DL models prone to overfitting and learning low-quality representations of the complex process.Our approach introduces a novel few-shot architecture that incorporates an adversarially governed invariant encoding process. This architecture aims to distill "invariant" relationships by addressing distributional shifts in data across various battery properties, facilitating the detection of thermal runaway events. Specifically, our results demonstrate that deep learning models conditioned on these "invariant" representations outperform state-of-the-art baselines, achieving a remarkable 96.8% performance improvement in terms of the popular metric MAPE. This framework presents a promising direction for enhancing battery safety modeling, particularly in the context of rare and complex events like thermal runaway. Our code and code and dataset used for the paper are public1.

Tabassum, Anika [ORNL] (ORCID:0000000254600955)↗

Development of response models for the Earth Radiation Budget Experiment (ERBE) sensors. Part 1: Dynamic models and computer simulations for the ERBE nonscanner, scanner and solar monitor sensors

Dynamic models and computer simulations were developed for the radiometric sensors utilized in the Earth Radiation Budget Experiment (ERBE). The models were developed to understand performance, improve measurement accuracy by updating model parameters and provide the constants needed for the count conversion algorithms. Model simulations were compared with the sensor's actual responses demonstrated in the ground and inflight calibrations. The models consider thermal and radiative exchange effects, surface specularity, spectral dependence of a filter, radiative interactions among an enclosure's nodes, partial specular and diffuse enclosure surface characteristics and steady-state and transient sensor responses. Relatively few sensor nodes were chosen for the models since there is an accuracy tradeoff between increasing the number of nodes and approximating parameters such as the sensor's size, material properties, geometry, and enclosure surface characteristics. Given that the temperature gradients within a node and between nodes are small enough, approximating with only a few nodes does not jeopardize the accuracy required to perform the parameter estimates and error analyses.

Halyo, Nesim↗