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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

NASA DEVELOP’s Communication Strategy for Sharing Applied Science Outcomes

NASA’s DEVELOP Program conducts rapid feasibility projects that enable the future workforce and current decision makers to collaborate and build capacity to use Earth science data to enhance environmental management. The program communicates its results and applications to a broad audience through multiple methods: social media, website blog posts, print materials, short project videos, an “Ambassador Corps,” scientific and policy conference presentations, community engagement activities, end-of-project presentations, project “hand-offs,” traditional publications (both gray literature and peer-reviewed), and a project archive gallery. This presentation will describe the various methods employed by DEVELOP to communicate the program’s scientific outputs, target audiences, general statistics, community response, and lessons learned.

Capacity Building↗
DOE OSTI2021

Durable Mn-Based PGM-Free Catalysts for Polymer Electrolyte Membrane Fuel Cells

This proposed project aims to develop and evaluate novel manganese based, nitrogen-derived, PGM-free electrocatalysts (denoted as Mn-N-C) to fully address the membrane electrolyte assemblies (MEA)’s ionomer degradation issue resulting from iron. Four thrusts will be pursed in this proposed project. First, advanced first-principles computation methods will be employed to accelerate the rational catalyst design and synthesis. Second, an effective hydro-gel method will be used to maximize atomic Mn active sites embedded in carbon matrix. Next, state-of-the art methods in fuel cell companies will be used to fabricate MEAs containing the Mn-N-C catalysts. Finally, industry standards will be rigorously followed to evaluate fuel cell performance and durability of the Mn-N-C catalysts. With successful completion of the project, it is expected that the following outcomes will be achieved. (1) A set of MEAs containing the Mn-N-C catalysts and with active area large than 50 cm 2 for independent testing, (2) testing results demonstrating that the MEAs of Mn-N-C catalysts have mass activity of 0.044 A/cm 2 at 0.9 VIR-free and H 2 /air performance of 0.50 V at 1.0 A/cm 2 ; (3) fundamental understanding of the composition-structure-property relation of the PGM-free Mn- N-C catalysts, and (4) computational data, measurement data, and publications deposited into the database of ElectroCat Consortium.

08 HYDROGEN↗
NASA NTRS2016

Ultrasonic NDE Simulation for Composite Manufacturing Defects

The increased use of composites in aerospace components is expected to continue into the future. The large scale use of composites in aerospace necessitates the development of composite-appropriate nondestructive evaluation (NDE) methods to quantitatively characterize defects in as-manufactured parts and damage incurred during or post manufacturing. Ultrasonic techniques are one of the most common approaches for defect/damage detection in composite materials. One key technical challenge area included in NASA's Advanced Composite's Project is to develop optimized rapid inspection methods for composite materials. Common manufacturing defects in carbon fiber reinforced polymer (CFRP) composites include fiber waviness (in-plane and out-of-plane), porosity, and disbonds; among others. This paper is an overview of ongoing work to develop ultrasonic wavefield based methods for characterizing manufacturing waviness defects. The paper describes the development and implementation of a custom ultrasound simulation tool that is used to model ultrasonic wave interaction with in-plane fiber waviness (also known as marcelling). Wavefield data processing methods are applied to the simulation data to explore possible routes for quantitative defect characterization.

Leckey, Cara A. C.↗
DOE OSTI2022

Operational Probabilistic Tools for Solar Uncertainty (OPTSUN) (Final Project Report for DOE Solar Forecasting II Project)

Increasing levels of solar PV can challenge system operations and may require novel methods to operate the power system reliably and efficiently. Power system operating plans generally use deterministic forecasts, in which the variable energy resources are represented by the expected value for each interval of the decision horizon. Probabilistic forecasts are relatively new but have the potential to address the shortfalls of deterministic forecasts. However, understanding how best to use such forecasts is still a key gap in industry and was the focus of this project. The project had three workstreams. In a forecasting workstream, improvements were made to baseline probabilistic forecasts using a number of new approaches such as machine learning methods and improved input data. In a design workstream, advanced simulation tools used these forecasts to investigate newly proposed reserve determination methods. Lastly, in a demonstration workstream a scheduling management platform (SMP) was developed to leverage probabilistic forecasts in a modular and customizable manner. In order to study the benefits that could be accrued, the project team collaborated with three utility partners (Duke Energy, Southern Company and Hawaiian Electric) to deliver improved probabilistic forecasts for each region and to model each region in case studies using advanced production cost modeling tools. Different methods to determine operating reserve requirements from probabilistic forecasts were developed, simulated, and tested across each region. The benefits of using these newly proposed methods varied by utility, but, in general, using probabilistic forecasts as well as historical data to set the reserve requirements seems to improve reliability related results, with less risk of reserve or supply shortfalls. The cost implications were not always straightforward; in some cases the new methods could show a reduction in expected operating costs, but often the increase in reserves associated with better risk mitigation using probabilistic forecasts could result in an increase in operating costs in the simulations. The SMP tool was developed to process probabilistic forecasts from their initial receipt through to scheduling decisions. This open-source tool consists of several modules for scenario development, reserve requirements calculation, and visualization. The SMP tool was demonstrated to a wide range of operators and stakeholders at all three utilities and further improved based on their feedback. The tool will be available on www.epri.com/optsun. The proposed probabilistic information-based reserve determination approaches have the potential to be implemented by different regions to ensure an economic and reliable power system operation on power systems integrating increasing levels of variable renewable resources. The innovative yet practical methods developed in this project demonstrated tangible benefits from using probabilistic forecasts beyond just study-based assessments to include three unique balancing areas. The demonstrated benefits across the multiple utility environments, are expected to provide system operators in all regions the confidence required and a platform to adopt the new forecasting and operating methods.

14 SOLAR ENERGY↗
NASA NTRS1993

Applying formal methods and object-oriented analysis to existing flight software

Correctness is paramount for safety-critical software control systems. Critical software failures in medical radiation treatment, communications, and defense are familiar to the public. The significant quantity of software malfunctions regularly reported to the software engineering community, the laws concerning liability, and a recent NRC Aeronautics and Space Engineering Board report additionally motivate the use of error-reducing and defect detection software development techniques. The benefits of formal methods in requirements driven software development ('forward engineering') is well documented. One advantage of rigorously engineering software is that formal notations are precise, verifiable, and facilitate automated processing. This paper describes the application of formal methods to reverse engineering, where formal specifications are developed for a portion of the shuttle on-orbit digital autopilot (DAP). Three objectives of the project were to: demonstrate the use of formal methods on a shuttle application, facilitate the incorporation and validation of new requirements for the system, and verify the safety-critical properties to be exhibited by the software.

Cheng, Betty H. C.↗
NASA NTRS2007

Workshop on Jet Exhaust Noise Reduction for Tactical Aircraft - NASA Perspective

Jet noise from supersonic, high performance aircraft is a significant problem for takeoff and landing operations near air bases and aircraft carriers. As newer aircraft with higher thrust and performance are introduced, the noise tends to increase due to higher jet exhaust velocities. Jet noise has been a subject of research for over 55 years. Commercial subsonic aircraft benefit from changes to the engine cycle that reduce the exhaust velocities and result in significant noise reduction. Most of the research programs over the past few decades have concentrated on commercial aircraft. Progress has been made by introducing new engines with design features that reduce the noise. NASA has recently started a new program called "Fundamental Aeronautics" where three projects (subsonic fixed wing, subsonic rotary wing, and supersonics) address aircraft noise. For the supersonics project, a primary goal is to understand the underlying physics associated with jet noise so that improved noise prediction tools and noise reduction methods can be developed for a wide range of applications. Highlights from the supersonics project are presented including prediction methods for broadband shock noise, flow measurement methods, and noise reduction methods. Realistic expectations are presented based on past history that indicates significant jet noise reduction cannot be achieved without major changes to the engine cycle. NASA s past experience shows a few EPNdB (effective perceived noise level in decibels) can be achieved using low noise design features such as chevron nozzles. Minimal thrust loss can be expected with these nozzles (< 0.5%) and they may be retrofitted on existing engines. In the long term, it is desirable to use variable cycle engines that can be optimized for lower jet noise during takeoff operations and higher thrust for operational performance. It is also suggested that noise experts be included early in the design process for engine nozzle systems to participate in decisions that may impact the jet noise.

Huff, Dennis L.↗
DOE OSTI2024

Data Visualization and Analytics for Optimal Process Parameter Selection in Turning

The objective of this project is to research physics-guided machine learning methods to recommend optimal tools and machining process parameters for turning applications using the MSC test database. For a given turning application, the MSC metalworking specialist needs to make decisions on tools and the associated process parameters for the MSC customer. For a given material, there are many alternatives for tools and a wide range of process parameters to consider. MSC has built a database of tools and parameters for different applications from the historical turning tests completed at various customer sites. The research project aims to use machine learning methods to predict optimal tools and process parameters for the MSC metalworking specialists using the MSC test database. This enables continuous learning of optimal tool and process parameters for different applications as new information is collected from testing. Through MSC, this information can be shared with machining shops across the US leading to improved productivity and efficiency.

42 ENGINEERING↗
NASA NTRS2004

Hybrid Particle-Element Simulation of Impact on Composite Orbital Debris Shields

This report describes the development of new numerical methods and new constitutive models for the simulation of hypervelocity impact effects on spacecraft. The research has included parallel implementation of the numerical methods and material models developed under the project. Validation work has included both one dimensional simulations, for comparison with exact solutions, and three dimensional simulations of published hypervelocity impact experiments. The validated formulations have been applied to simulate impact effects in a velocity and kinetic energy regime outside the capabilities of current experimental methods. The research results presented here allow for the expanded use of numerical simulation, as a complement to experimental work, in future design of spacecraft for hypervelocity impact effects.

Fahrenthold, Eric P.↗
DOE OSTI2024

Spin-Controllable Dynamics in Defect-Engineered Carbon Nanotubes as Single Photon Emitters: Data-Driven Modeling and Computations

Quantum technologies, such as quantum computing and sensing, require efficient single-photon emission (SPE) sources that operate at room temperature in telecom wavelengths. While several materials can serve as SPE sources, no single platform meets all the criteria for efficiency, ambient operation, and scalability. Single-walled carbon nanotubes (SWCNTs) with covalently attached molecules offer a promising solution. Their SPE can be easily tuned via modifications of the SWCNT's diameter, chirality, and bonded molecules, enabling emission across near-IR to telecom wavelengths at ambient conditions. However, to fully realize the potential of SWCNTs and unlock their quantum capabilities, a deeper understanding of how structural defects from molecular adducts affect their emission and competing photoexcited processes is essential. To address this gap in our knowledge, this project combined quantum chemistry calculations with data-driven methods of cheminformatics (QSAR) and machine learning (ML). The developed computational approaches have provided several design strategies for covalent functionalization of SWCNTs to improve their optical response. The collaboration with Los Alamos National Lab (LANL) enabled direct comparison of computational and experimental data, facilitating method validation. This partnership was enhanced through access to LANL's Center for Integrated Nanotechnologies (CINT) utilizing User Facility Program and summer internships, which provided three NDSU graduate students with hands-on experience at LANL. The outcomes of this project included (1) Advancing the current stage of computational methods in accurate modeling of non-adiabatic spin-dependent photoexcited dynamics and its applicability to nanosystems consisting of thousands of atoms, realized as open-access codes linked to existing DFT-based software; (2) Establishing the relationship between the structure of adducts and SWCNTs and intrinsic excitonic and spin properties of defect states for guiding novel synthetic strategies and experimental probes of chemically functionalized SWCNTs as near-IR emitting materials; (3) Generating virtual libraries of hypothetical functionalized SWCNTs for virtual screening of their chemical structures and optical properties, leveraging new functionalities of SWCNTs; (4) Offering a unique experience for NDSU graduate students that prepared them for future scientific careers related to materials modeling and big data processing. These results were summarized in 12 published journal papers and 3 recently submitted papers. One of a key finding is that the position of defect sites on the SWCNT surface primarily drives the emission redshift (up to 100 meV), while the polarity of the defect-inducing molecules has a much smaller effect (~10 meV). However, the electron-donating or withdrawing properties of a molecule influence selecting reactivity of defect sites. These insights important for optimizing synthetic protocols for desired emissions in SWCNTs. We also revealed that the interaction between two defects at various positions on the SWCNT enhances the redshift and optical activity of states, favoring strong near-IR emission. This suggests that manipulations in defect concentrations is a promising strategy for controlling efficient emission. Mostly important, the defect position was found controllable by the spin states of photoexcited intermediates: Excited aromatic molecules form ortho defects with SWCNTs at their singlet states in the presence of oxygen, while oxygen-free conditions favor para defects via the triplet-state mechanism. Additionally, a heat-activated [2+2] cycloaddition reaction facilitates divalent defect formation with fewer bonding positions that narrows emission bands. These groundbreaking findings have been experimentally validated and significantly advance our understanding of defect chemistry in SWCNTs. Using a novel encoding technique and 3D-MoRSE descriptors, we developed highly accurate ML/QSAR models to predict both the 3D structure and optical properties of SWCNTs with chemical defects. This model enabled the creation of a virtual library of 125,556 structures, providing new insights into the relationship between SWCNT-defect structure and emission.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

On the specificity between mapping of initial and final states of the magneto Rayleigh-Taylor instability

In this LDRD we investigated the application of machine learning methods to understand dimensionality reduction and evolution of the Rayleigh-Taylor instability (RTI). As part of the project, we undertook a significant literature review to understand current analytical theory and machine learning based methods to treat evolution of this instability. We note that we chose to refocus on assessing the hydrodynamic RTI as opposed to the magneto-Rayleigh-Taylor instability originally proposed. This choice enabled utilizing a wealth of analytic test cases and working with relatively fast running open-source simulations of single-mode RTI. This greatly facilitated external collaboration with URA summer fellowship student, Theodore Broeren. In this project we studied the application of methods from dynamical systems learning and traditional regression methods to recover behavior of RTI ranging from the fully nonlinear to weakly nonlinear (wNL) regimes. Here we report on two of the tested methods SINDy and a more traditional regression-based approach inspired by analytic wNL theory with which we had the most success. We conclude with a discussion of potential future extensions to this work that may improve our understanding from both theoretical and phenomenological perspectives.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2006

Human Factors Engineering as a System in the Vision for Exploration

In order to accomplish NASA's Vision for Exploration, while assuring crew safety and productivity, human performance issues must be well integrated into system design from mission conception. To that end, a two-year Technology Development Project (TDP) was funded by NASA Headquarters to develop a systematic method for including the human as a system in NASA's Vision for Exploration. The specific goals of this project are to review current Human Systems Integration (HSI) standards (i.e., industry, military, NASA) and tailor them to selected NASA Exploration activities. Once the methods are proven in the selected domains, a plan will be developed to expand the effort to a wider scope of Exploration activities. The methods will be documented for inclusion in NASA-specific documents (such as the Human Systems Integration Standards, NASA-STD-3000) to be used in future space systems. The current project builds on a previous TDP dealing with Human Factors Engineering processes. That project identified the key phases of the current NASA design lifecycle, and outlined the recommended HFE activities that should be incorporated at each phase. The project also resulted in a prototype of a webbased HFE process tool that could be used to support an ideal HFE development process at NASA. This will help to augment the limited human factors resources available by providing a web-based tool that explains the importance of human factors, teaches a recommended process, and then provides the instructions, templates and examples to carry out the process steps. The HFE activities identified by the previous TDP are being tested in situ for the current effort through support to a specific NASA Exploration activity. Currently, HFE personnel are working with systems engineering personnel to identify HSI impacts for lunar exploration by facilitating the generation of system-level Concepts of Operations (ConOps). For example, medical operations scenarios have been generated for lunar habitation in order to identify HSI requirements for the lunar communications architecture. Throughout these ConOps exercises, HFE personnel are testing various tools and methodologies that have been identified in the literature. A key part of the effort is the identification of optimal processes, methods, and tools for these early development phase activities, such as ConOps, requirements development, and early conceptual design. An overview of the activities completed thus far, as well as the tools and methods investigated will be presented.

Mihriban Whitmore↗
DOE OSTI2022

Flood hazard model calibration using multiresolution model output

Riverine floods pose a considerable risk to many communities. Improving flood hazard projections has the potential to inform the design and implementation of flood risk management strategies. Current flood hazard projections are uncertain, especially due to uncertain model parameters. Calibration methods use observations to quantify model parameter uncertainty. With limited computational resources, researchers typically calibrate models using either relatively few expensive model runs at high spatial resolutions or many cheaper runs at lower spatial resolutions. This leads to an open question: is it possible to effectively combine information from the high and low resolution model runs? We propose a Bayesian emulation–calibration approach that assimilates model outputs and observations at multiple resolutions. As a case study for a riverine community in Pennsylvania, we demonstrate our approach using the LISFLOOD-FP flood hazard model. Here, the multiresolution approach results in improved parameter inference over the single resolution approach in multiple scenarios. Results vary based on the parameter values and the number of available models runs. Our method is general and can be used to calibrate other high dimensional computer models to improve projections.

multiresolution↗
DOE OSTI2026

Measurement of the 252 Cf ⁢(sf) prompt fission neutron spectrum utilizing 12 C ⁡(𝑛, 𝑛) and 9 Be ⁢(𝑛, 𝑛) neutron scattering reference measurements

The 252 Cf spontaneous fission (sf), prompt fission neutron spectrum (PFNS) is a fundamental quantity for nuclear physics measurements of neutron-emitting reactions. This energy distribution of neutrons emitted from fission has been considered a neutron data standard for decades and has been utilized as a reference for neutron detection efficiency, validation of Monte Carlo simulations, benchmarking of dosimetry standards, and more. A significant portion of the global collection of nuclear data on neutron-induced reactions is correlated with the 252 Cf ⁢(sf) PFNS. Despite the reliance on this quantity by the nuclear physics community, the historical collection of 252 Cf PFNS measurements display systematic disagreements that are not understood or easily explained. These experimental discrepancies could potentially bias the 252 Cf PFNS Standard evaluation. On top of this, these past experiments frequently employed correlated experimental measurement or analysis methods. The artificial intelligence (AI)/machine learning (ML)-informed californium chi-nuclear data experiment (AIACHNE) project was formed to (a) investigate these discrepancies utilizing AI/ML methods to identify outlying regions of literature data, assign these regions to features of the experiment itself, and perform an improved evaluation of the 252 Cf PFNS and (b) perform a new experimental measurement of this quantity designed to improve upon the existing literature database. Here, in this work, we report on the AIACHNE 252 Cf PFNS experiment utilizing a new analysis method uncorrelated with all previous measurements: neutron efficiency determinations based on elastic neutron scattering on 12 C and 9 Be . This new method provides an independent test of the existing literature data and evaluation of the 252 Cf ⁢(sf) PFNS. The method is described with detailed covariance quantification procedures, as well as a direct discussion of the sources of uncertainty described as requirements in the “Templates” series of papers. The 252 Cf ⁢(sf) PFNS reported in this work agrees well with the overall shape of the existing standard PFNS evaluation as well as many literature measurements, thus verifying the current evaluation utilizing new techniques. However, the results suggest that there are deficiencies in the angle-differential 12 C and 9 Be ⁢(𝑛, 𝑛) evaluated nuclear data, which produce unphysical structures in the reported result. While these structures are relatively minor, they become obvious because of the high statistical precision of the data and the expected smooth continuity of the 252 Cf ⁢(sf) PFNS.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1996

New Approaches to HSCT Multidisciplinary Design and Optimization

The successful development of a capable and economically viable high speed civil transport (HSCT) is perhaps one of the most challenging tasks in aeronautics for the next two decades. At its heart it is fundamentally the design of a complex engineered system that has significant societal, environmental and political impacts. As such it presents a formidable challenge to all areas of aeronautics, and it is therefore a particularly appropriate subject for research in multidisciplinary design and optimization (MDO). In fact, it is starkly clear that without the availability of powerful and versatile multidisciplinary design, analysis and optimization methods, the design, construction and operation of im HSCT simply cannot be achieved. The present research project is focused on the development and evaluation of MDO methods that, while broader and more general in scope, are particularly appropriate to the HSCT design problem. The research aims to not only develop the basic methods but also to apply them to relevant examples from the NASA HSCT R&D effort. The research involves a three year effort aimed first at the HSCT MDO problem description, next the development of the problem, and finally a solution to a significant portion of the problem.

Schrage, D. P.↗
NASA NTRSDate not supplied

Contamination, Transportation or Transmutation in LENR Material Analyses

LENR experiments using various loading and triggering mechanisms rely on a variety of elemental and isotopic assays to determine LENR effects. These assays include optical microscopy, Scanning Electron Microscopy with Energy Dispersive X-Ray Analysis (SEM/EDX), X-Ray Photoelectron Spectroscopy (XPS) and Transmission Electron Microscopy (TEM). All of these methods observe the surface of a material. By dissolving or vaporizing the sample, Inductively Coupled Plasma Optical Element Spectroscopy (ICP-OES) can determine elemental composition (ppb) or with mass spectroscopy, isotopes (ICP/MS). One can use a focused ion beam (FIB) to cut open a sample and observe the “cut” with either SEM/EDX, or Time-of-Flight Secondary Ion Mass Spectroscopy (TOF-SIMS). High Purity Gamma Ray Spectroscopy (HPGe) and alpha/ beta Liquid Scintillator spectroscopy can be used. We’ve employed these assay methods in our NASA GRC research under both the Advanced Energy Conversion and Lattice Confinement Fusion Projects. However, there are limitations associated with each of these methods ranging from handling contamination, instrumental limitations including sensitivity and field of view, and sample preparation.

Low Energy Nuclear Reactions↗
DOE OSTI2024

“Production of High Specific Activity 155 Tb, 161 Tb and 203 Pb for Research and Clinical Applications: Effective Target Design, Target Material Recycling and Radioisotope Separation”

Radioisotopes are essential for the development and application of radiopharmaceuticals that target specific diseases, such as cancer, offering unique potential for precision medicine. The growing demand for theranostic radioisotopes underscores their critical role in personalized medicine, where they enhance diagnostic imaging, minimize patient radiation exposure, and improve targeted tissue uptake, particularly in receptor- and antigen-directed therapies. The theranostic pair terbium-155 (diagnostic) and terbium-161 (therapeutic) holds significant promise for advancing individualized, targeted, and dosimetry-driven radiotherapies. However, the United States currently lacks routine and reliable production of these isotopes. This project made significant progress toward addressing this supply issue by developing production and separation methods for terbium-155 and terbium-161 while also training the next generation of the nuclear and radiochemistry workforce. This grant also strengthened collaboration between scientists at the University of Washington, the University of Missouri and Brookhaven National Laboratory. The research effort focused on evaluating target preparation methods, optimizing irradiation parameters, and refining isolation processes. In addition, the project provided extensive hands-on training to graduate students and postdoctoral fellows, equipping them with expertise in radioisotope production technologies and fostering the growth of the nuclear science workforce.

07 ISOTOPE AND RADIATION SOURCES↗
NASA NTRSDate not supplied

Comparisons of Performance Metrics and Machine Learning Methods on an Entry Descent and Landing Database

This work focuses on evaluating machine learning methods and their applicability to the generation of an aerodynamic database, particularly for trajectory analysis of a capsule during entry, descent, and landing with a focus on uncertainty quantification. The source data to be used is the wind tunnel and computational data for the Integrated Design Assessment Team (IDAT) configuration of the Orion project, which has been publicly released. The methods used to generate the proposed databases are designed to naturally include a prediction interval, which will be evaluated both for their mean response as well as how well the prediction interval performs. These machine learning methods are compared to a traditionally generated database used by the Orion team as a baseline. It is found that while these machine learning methods perform well, the Orion database tends to still outperform them showing that engineering experience is still needed to make the best database possible. However, these methods still provide comparable results with significantly less effort.

Orion↗