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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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All sources NASA NTRS DOE OSTI

DOE OSTI2022

Comparing and contrasting In-Vial and full-scale systems for sparging volatile analytes

Here, in-vial sparging was demonstrated as an effective, practical alternative to a full-scale sparging system for supporting the analysis of volatile constituents. Using elemental mercury (Hg 0 ) and toluene as representative purgeable analytes, the mass removal for various sparge configurations was measured and a reduced order model was developed and validated. In the primary experiments, Hg 0 in the sparge gas was trapped on activated carbon or gold, thermally desorbed, and quantified using atomic absorption or atomic fluorescence spectroscopy. Toluene experiments using the same in-vial sparge apparatus and sparge parameters were performed to demonstrate the applicability of the reduced order model to a broad range of compounds. Toluene removal was tracked by measuring the remaining toluene in sparged aliquots using Ultraviolet-visible (UV–Vis) spectroscopy. For the sparging, flow rates varied from 25 to 75 mL/min for periods from 0 to 30 min. Sparge performance, mass removal as a function of time, and sparge gas volume were measured for both in-vial and full-scale systems. A model based on dimensionless Henry's Law coefficient, normalized sparge gas volume, and fractional extent of equilibrium matched the experimental data for both compounds and provides a practical tool for future applications. For the conditions tested in this study, the calibrated model indicated that the sparge gas in the in-vial system reached approximately 33% of its equilibrium value before exiting the water surface, while a full-scale system reached approximately 100%. The tests validated the quality, reproducibility, and predictability of sparging performance for both full scale and in-vial sparge systems. Related factors such as waste generation, worker risk, and labor were also assessed. Full scale sparge systems provide the advantage of lower detection levels due to larger sample volume, while the in-vial sparge systems provide advantages for most other factors; including automatability, reducing secondary wastes, lessening the need to clean and check the sparge apparatus, and lowering labor and costs. The data and associated reduced order model support continued development and deployment of in-vial sparge platforms as a practical option for analysis of purgeable analytes such as volatile organic compounds and volatile metals/organometallics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Force Fields for High Concentration Aqueous KOH Solutions and Zincate Ions

Motivated by increasing interest in electrochemical devices that include highly alkaline electrolytes, we investigated two force fields for potassium hydroxide (KOH) at high concentrations in water. The “FNB” model uses the SPC/E water model, while the “FHM” model uses the TIP4P/2005 water model. Here, we also developed parameters to describe zincate ions in these solutions. The density and viscosity of KOH using the FHM model are in better agreement with experiment than the values from the FNB model. Comparing the properties of the zincate solutions to the available experimental data, we find that both force fields agree reasonably well, although the FHM parameters give a better prediction of the viscosity. The developed force field parameters can be used in future simulations of zincate/KOH solutions in combination with other species of interest.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Influence of Linker Identity on the Photochemistry of Uranyl-Organic Frameworks

While uranyl-based metal–organic frameworks (MOFs) boast impressive photocatalytic abilities, significant questions remain regarding their excitation pathways and methods to fine-tune their performance due to the lack of information regarding heterogeneous uranyl catalysis. Herein, we investigated how linker identity and photoexcitation impact uranyl photocatalysis when the uranyl coordination environment remains constant. Toward this end, we prepared three uranyl-based MOFs (NU-1301, NU-1307, and ZnTCPP-U2) and then examined the structural and photochemical properties of each through X-ray diffraction, X-ray absorption, and photoluminescence. We then correlated our observations to the photocatalytic performance for fluorination of cyclooctane. The excitation profile from NU-1301 and NU-1307 exhibited spin-forbidden linker transitions and uranyl vibronic progressions, with uranyl excitation and emission being most dominant in NU-1301. Consequently, NU-1301 was a more active photocatalyst than NU-1307. In contrast, the excitation profile from ZnTCPP-U2 contained transitions associated with the porphyrin linker exclusively. Photocatalytic activity from ZnTCPP-U2 significantly underperformed in comparison to that of the other two MOFs. Furthermore, these data suggest that linkers’ photophysical properties can be used to predict the photocatalytic behavior of uranyl-containing MOFs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1983

Analytical prediction of aerothermal environment in a wing-elevon cove

The fluid/thermal environment in the cove between the wing and elevon surfaces was a concern throughout the design and initial operational phases of the space shuttle. Experimenal and analytical investigations provided some understanding of the environmental in the wing-elevon cove. An incompressible finite element analysis of flow through straight parallel walls and curved parallel walls to determine the effects of cove geomtry on the fluid/thermal environment is described. Results from this analysis agree qualitatively with experimental data. The centerline gas temperatures and cold wall heating rates are virtually identical for the two cases indicating that the slight curvature has little effect on the overall thermal environment.

Bey, K. S.↗

NASA NTRS1998

Small Crack Growth and Fatigue Life Predictions for High-Strength Aluminium Alloys: Experimental and Fracture Mechanics Analysis - Part 1

The small crack effect was investigated in two high-strength aluminium alloys: 7075-T6 bare and LC9cs clad alloy. Both experimental and analytical investigations were conducted to study crack initiation and growth of small cracks. In the experimental program, fatigue tests, small crack and large crack tests A,ere conducted under constant amplitude and Mini-TWIST spectrum loading conditions. A pronounced small crack effect was observed in both materials, especially for the negative stress ratios. For all loading conditions, most of the fatigue life of the SENT specimens was shown to be crack propagation from initial material defects or from the cladding layer. In the analysis program, three-dimensional finite element and A weight function methods were used to determine stress intensity factors and to develop SIF equations for surface and corner cracks at the notch in the SENT specimens. A plastisity-induced crack-closure model was used to correlate small and large crack data, and to make fatigue life predictions, Predicted crack-growth rates and fatigue lives agreed well with experiments. A total fatigue life prediction method for the aluminum alloys was developed and demonstrated using the crack-closure model.

Wu, X. R.↗

DOE OSTI2023

Advances in Computational Approaches for Estimating Passive Permeability in Drug Discovery

Passive permeation of cellular membranes is a key feature of many therapeutics. The relevance of passive permeability spans all biological systems as they all employ biomembranes for compartmentalization. A variety of computational techniques are currently utilized and under active development to facilitate the characterization of passive permeability. These methods include lipophilicity relations, molecular dynamics simulations, and machine learning, which vary in accuracy, complexity, and computational cost. This review briefly introduces the underlying theories, such as the prominent inhomogeneous solubility diffusion model, and covers a number of recent applications. Various machine-learning applications, which have demonstrated good potential for high-volume, data-driven permeability predictions, are also discussed. Due to the confluence of novel computational methods and next-generation exascale computers, we anticipate an exciting future for computationally driven permeability predictions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1981

Comments on Reynolds number effects and the role of NTF in the development of air vehicles

The National Transonic Facility (NTF) capability to match the full scale Reynolds numbers of all but the largest airplanes is discussed. Conversion factors to enable calculation of Sl-unit equivalents for all U.S. units are listed. Using data from several facilities, analytic methods, and flight test data, a competetive aircraft in the relatively low Reynolds number was developed. The NTF offers the capability to obtain data at full scale Reynolds numbers in the cruise condition for most of the products, and will be much closer than previous tunnels to full scale Reynolds number for the operating envelopes. It is primarily on the operating envelope that Reynolds number effects are most important and least predictable.

Nagel, A. L.↗

NASA NTRS1983

Coolant passage heat transfer with rotation. A progress report on the computational aspects

Turbine airfoils are subjected to increasingly higher heat loads which escalate the cooling requirements in order to satisfy life goals for the component materials. If turbine efficiency is to be maintained, however, cooling requirements should be as low as possible. To keep the quantity of cooling air bounded, a more efficient internal cooling scheme must be developed. One approach is to employ airfoils with multipass cooling passages that contain devices to augment internal heat transfer while limiting pressure drop. Design experience with multipass cooling passage airfoils has shown that a surplus of cooling air must be provided as a margin of safety. This increased cooling air leads to a performance penalty. Reliable methods for predicting the internal thermal and aerodynamic performance of multipass cooling passage airfoils would reduce or eliminate the need for the safety margin of surplus cooling air. The objective of the program is to develop and verify improved analytical methods that will form the basis for design technology which will result in efficient turbine components with improved durability without sacrificing performance. The objective will be met by: (1) establishing a comprehensive experimental data base that can form the basis of an empirical design system; (2) developing computational fluid dynamic techniques; and (3) analyzing the information in the data base with both phenomenological modeling and mathematical modeling to derive a suitable design and analysis procedure.

Aceto, L. D.↗

NASA NTRS1986

Development of a ROT22 - DATAMAP interface

This report (Contract NAS2-10331- Mod 10), outlines the development and validation of an interface between the three-dimensional transonic analysis program ROT22 and the Data from Aeromechanics Test and Analytics-Management and Analysis Package (DATAMAP). After development of the interface, the validation is carried out as follows. First, the DATAMAP program is used to analyze a portion of the Tip Aerodynamics and Acoustics Test (TAAT) data. Specifically, records 2872 and 2873 are analyzed at an azimuth of 90 deg, and record 2806 is analyzed at 60 deg. Trim conditions for these flight conditions are then calculated using the Bell performance prediction program ARAM45. Equivalent shaft, pitch, and twist angles are calculated from ARAM45 results and used as input to the ROT22 program. The interface uses the ROT22 results and creates DATAMAP information files from which the surface pressure contours and sectional pressure coefficients are plotted. Twist angles input to ROT22 program are then iteratively modified in the tip region until the computed pressure coefficients closely match the measurements. In all cases studied, the location of the shock is well predicted. However, the negative pressure coefficients were underpredicted. This could be accounted for by blade vortex interaction effects.

Shenoy, K. R.↗

NASA NTRS1994

Testing and analysis of flat and curved panels with multiple cracks

An experimental and analytical investigation of multiple cracking in various types of test specimens is described in this paper. The testing phase is comprised of a flat unstiffened panel series and curved stiffened and unstiffened panel series. The test specimens contained various configurations for initial damage. Static loading was applied to these specimens until ultimate failure, while loads and crack propagation were recorded. This data provides the basis for developing and validating methodologies for predicting linkup of multiple cracks, progression to failure, and overall residual strength. The results from twelve flat coupon and ten full scale curved panel tests are presented. In addition, an engineering analysis procedure was developed to predict multiple crack linkup. Reasonable agreement was found between predictions and actual test results for linkup and residual strength for both flat and curved panels. The results indicate that an engineering analysis approach has the potential to quantitatively assess the effect of multiple cracks in the arrest capability of an aircraft fuselage structure.

Broek, David↗

NASA NTRS2015

Exploring Life Support Architectures for Evolution of Deep Space Human Exploration

Life support system architectures for long duration space missions are often explored analytically in the human spaceflight community to find optimum solutions for mass, performance, and reliability. But in reality, many other constraints can guide the design when the life support system is examined within the context of an overall vehicle, as well as specific programmatic goals and needs. Between the end of the Constellation program and the development of the "Evolvable Mars Campaign", NASA explored a broad range of mission possibilities. Most of these missions will never be implemented but the lessons learned during these concept development phases may color and guide future analytical studies and eventual life support system architectures. This paper discusses several iterations of design studies from the life support system perspective to examine which requirements and assumptions, programmatic needs, or interfaces drive design. When doing early concept studies, many assumptions have to be made about technology and operations. Data can be pulled from a variety of sources depending on the study needs, including parametric models, historical data, new technologies, and even predictive analysis. In the end, assumptions must be made in the face of uncertainty. Some of these may introduce more risk as to whether the solution for the conceptual design study will still work when designs mature and data becomes available.

Anderson, Molly↗

NASA NTRS2015

Exploring Life Support Architectures for Evolution of Deep Space Human Exploration

Anderson, Molly S.↗

NASA NTRSDate not supplied

ML/AI Applications to the Atmosphere Science Data and Simulations (Demonstration and Vision)

Artificial Intelligence has been recognized as one of the most powerful tools for scientific research. It has a wide range of applications in atmospheric science and plays a significant role in advancing our understanding of the Earth-Atmosphere system, as well as improving our ability to monitor, predict, and respond to atmospheric observations and simulations. Different AI architectures can help us handle various problems according to our research goals. In this presentation, we will introduce several use cases and models for applying computer vision to active remote sensing data from CALIOP, analyzing cloud dynamical relationships for climate model parameterizations, and building surrogate models for lidar simulations. The presentation will highlight the advantages that AI offers compared to traditional analytical methods. By assembling pieces of the multidimensional atmospheric science puzzle, we can integrate our knowledge from observations to simulations.

Shan Zeng↗

DOE OSTI2021

Towards data-driven constitutive modelling for granular materials via micromechanics-informed deep learning

The analytical description of path-dependent elastic-plastic responses of a granular system is highly complicated because of continuously evolving microstructures and strain localisation within the system undergoing deformation. This study offers an alternative to the current analytical paradigm by developing micromechanics-informed machine-learning based constitutive modelling approaches for granular materials. A set of critical variables associated with the constitutive behaviour of granular materials are identified through an incremental stress-strain relationship analysis. Depending on the strategy to exploit the priori micromechanical knowledge, three different training strategies are explored. The first model uses only the measurable external variables to make stress predictions; the second model utilises a directed graph to link all the external strain sequences and internal microstructural evolution variables into a single prediction model comprised of a series of sub-mappings, and the third model explicitly integrates the physically important non-temporal properties with external strain paths into training through an enhanced Gated Recurrent Unit (GRU). These three models show satisfactory agreement with unseen test specimens based on multi-directional loading cases. The basic features and potential applications of each model are explained. Lastly, the key factors for constitutive training and limitations of the current work are also discussed in detail.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Investigating the relation between instantaneous driving decisions and safety critical events in naturalistic driving environment

The availability of large-scale naturalistic driving data provides enormous opportunities for studying relationships between instantaneous driving decisions prior to involvement in safety critical events (SCEs). This study investigates the role of driving instability prior to involvement in SCEs. While past research has studied crash types and their contributing factors, the role of pre-crash behavior in such events has not been explored as extensively. The research demonstrates how measures and analysis of driving volatility can be leading indicators of crashes and contribute to enhancing safety. Highly detailed microscopic data from naturalistic driving are used to provide the analytic framework to rigorously analyze the behavioral dimensions and driving instability that can lead to different types of SCEs such as roadway departures, rear end collisions, and sideswipes. Modeling results reveal a positive association between volatility and involvement in SCEs. Specifically, increases in both lateral and longitudinal volatilities represented by Bollinger bands and vehicular jerk lead to higher likelihoods of involvement in SCEs. Further, driver behavior related factors such as aggressive driving and lane changing also increases the likelihood of involvement in SCEs. Driver distraction, as represented by the duration of secondary tasks, also increases the risk of SCEs. Likewise, traffic flow parameters play a critical role in safety risk. The risk of involvement in SCEs decreases under free flow traffic conditions and increases under unstable traffic flow. Further, the model shows prediction accuracy of 88.1 % and 85.7 % for training and validation data. These results have implications for proactive safety and providing in-vehicle warnings and alerts to prevent the occurrence of such SCEs.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2025

Computationally efficient models for aqueous organic redox flow batteries

The rising usage of intermittent energy has garnered the need for large scale energy storage systems. Redox flow batteries (RFB) based energy storage system shows promising potential. Numerical simulations and machine learning approaches have been widely used to study RFB performance. The development of autonomous material discovery framework and digital twin of energy storage system usually needs to query cell performance through fast response models. In this study, two computationally efficient models are introduced: a physics-based analytical flow battery model (EZBattery), and a machine learning operator model (Deep Operator Network, denoted by DeepONet). Both models can provide cell performance near instantly, and prediction accuracy was systematically examined on an application of evaluating the performances of a 780 cm 2 aqueous organic redox flow battery (AORFB), using potential anolyte candidates in dihydroxyphenazine (DHP)-based family of organic materials. A validated computationally expansive 3-dimensional multi-physics finite element model by COMSOL was used as the ground truth and provided the training data set for the DeepONet. 1280 samples were generated with 10 properties to mimic the different possible anolyte candidates, and the cell performances were evaluated under 10 different combined operating conditions. The accuracy comparisons for the two computationally efficient models show that both models can provide comparable accuracy in predicting cell charging/discharging voltage curves. DeepONet can provide slightly higher overall accuracy than EZBattery with faster calculation speed, but highly relies on the training dataset. EZBattery does not need a training dataset and can provide interpretable physics-based explanations of the results, while being more flexible to adjust to adapt any different cell designs, flow battery architectures, and electrolyte materials.

Analytical model↗

DOE OSTI2023

Navigating the Expansive Landscapes of Soft Materials: A User Guide for High-Throughput Workflows

Synthetic polymers are highly customizable with tailored structures and functionality, yet this versatility generates challenges in the design of advanced materials due to the size and complexity of the design space. Thus, exploration and optimization of polymer properties using combinatorial libraries has become increasingly common, which requires careful selection of synthetic strategies, characterization techniques, and rapid processing workflows to obtain fundamental principles from these large data sets. Herein, we provide guidelines for strategic design of macromolecule libraries and workflows to efficiently navigate these high-dimensional design spaces. We describe synthetic methods for multiple library sizes and structures as well as characterization methods to rapidly generate data sets, including tools that can be adapted from biological workflows. We further highlight relevant insights from statistics and machine learning to aid in data featurization, representation, and analysis. This Perspective acts as a “user guide” for researchers interested in leveraging high-throughput screening toward the design of multifunctional polymers and predictive modeling of structure–property relationships in soft materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Tris(isodecyl)guanidine Degradation in the MCU System

The current solvent blend consists of four components; an extractant, the modifier, a suppressor, and the diluent. Of the four components, only the suppressor – tris(isodecyl)guanidine (TiDG) has exhibited an appreciable depletion rate during facility operations. Using data derived from Modular Caustic-Side Solvent Extraction Unit (MCU) process samples, Savannah River National Lab (SRNL) derived a method to predict the TiDG depletion based upon time and volume of feed processed.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗