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NASA NTRS2024

NASA Engineering and Safety Center Technical Bulletin No. 24-04: 6DOF Check Cases

In 2015, the NESC released benchmark Earth-based check-cases for well specified, rigid-body, six-degree-of-freedom (6DOF) aero/spacecraft models to promote consistent and accurate flight simulations across multiple Agency tools and facilities. Recently, the NESC expanded upon that effort to add Lunar-based check-cases to support new lunar exploration initiatives. This study produced a smaller, focused set of cases that exercise new and unique features of missions in the lunar environment in comparison with 8 high-fidelity NASA simulation tools and provides a measure of validation for simulations supporting Human Landing Systems.

Flight Mechanics↗

DOE OSTI2022

INGENIOUS - Great Basin Regional Dataset Compilation

This is the regional dataset compilation for the INnovative Geothermal Exploration through Novel Investigations Of Undiscovered Systems (INGENIOUS) project. The primary goal of this project is to accelerate discoveries of new, commercially viable hidden geothermal systems while reducing the exploration and development risks for all geothermal resources. These datasets will be used in INGENIOUS as input features for predicting geothermal favorability throughout the Great Basin study area. Datasets consist of shapefiles, geotiffs, tabular spreadsheets, and metadata that describe: 2-meter temperature probe surveys, quaternary faults and volcanic features, geodetic shear and dilation models, heat flow, magnetotellurics (conductance), magnetics, gravity, paleogeothermal features (such as sinter and tufa deposits), seismicity, spring and well temperatures, spring and well aqueous geochemistry analyses, thermal conductivity, and fault slip and dilation tendency. For additional project information, see the INGENIOUS project site linked in the submission. Terms of use: These datasets are provided "as is", and the contributors assume no responsibility for any errors or omissions. The user assumes the entire risk associated with their use of these data and bears all responsibility in determining whether these data are fit for their intended use. These datasets may be redistributed with attribution (see citation information below). Please refer to the license information on this page for full licensing terms and conditions.

15 GEOTHERMAL ENERGY↗

DOE OSTI2024

Exploring the synergy of kinematics and dynamics for collider physics

In collider experiments, an event is characterized by two distinct yet mutually complementary features: the “global features” and the “local features.” Kinematic information such as the event topology of a hard process, masses, and spins of particles comprises global features spanning the entire phase space. This global feature can be inferred from reconstructed objects. In contrast, representations of particles in gauge groups, such as quantum chromodynamics (QCD), offer localized features revealing the dynamics of an underlying theory. These local features, particularly observed in the patterns of radiation as raw data in various detector components, complement the global kinematic features. We propose a simple but effective neural network architecture that seamlessly integrates information from both kinematics and QCD to enhance the signal sensitivity at colliders. Published by the American Physical Society 2024

Ban, Kayoung (ORCID:000000019691877X)↗

NASA NTRS2018

The Europa Lander Mission: A Space Exploration Challenge for Autonomous Operations

NASA has proposed a Europa Lander Mission, with a notional launch date in the 2024-2025 timeframe. Key features of the mission are: Take 5 samples from 10 cm under the Europa surface; Mission duration is expected to be 20 (Earth) days; Data will be telemetered data back to Earth via a dedicated Carrier Relay Orbiter (CRO) spacecraft. Some information about the Europa surface is available from Cassini, indicating the presence of mountains, ridges, and other complex features, and simulations of planet-wide lighting can be performed, but this information is available only at 6m per pixel resolution (Galileo imagery). While surface temperatures are exceedingly cold (average of 114 degrees Kelvin, or minus 160 degrees Centigrade at the equator), this need not translate to solid (and hard) ice. The possible presence of small grain features (snow-like), penitentes (inverse-icicles), plumes, and other complex terrain features, will complicate operations. The time delay for such a mission is 30-60 minutes. The CRO is in view of the Lander for roughly 8 hours, and is in view of Earth for roughly 10 hours, meaning significant autonomy will be needed. A compelling case can be made for the use of aggressive autonomous operations of the *entire* mission. In this talk we will describe the Europa mission and its objectives, as well as similar Icy Worlds missions of the future. We will then describe the autonomy challenges of such a mission, using examples of similar missions and specific autonomy technologies that have been used in the past to address these challenges. We will make the case that despite some technologies that may be suitable, Europa lander presents a unique, and unaddressed, challenge for autonomy.

Plan Execution↗

DOE OSTI2025

Multi-technique characterization of iron reduction by an Antarctic Shewanella : an analog system for putative Martian biosignature identification

ABSTRACT Microbes from terrestrial extreme environments enable testing of biosignature production in conditions relevant to astrobiological targets. Mars, which was likely more conducive to life during early warmer and wetter epochs, has inspired missions that search for signs of early life in the surficial rock record, including mineral or organic biosignatures. Microbial iron reduction is a common and ancient metabolism that may have also operated on other rocky celestial bodies. To investigate biosignature production during iron reduction, aShewanellasp. (strain BF02_Schw) isolated from a subglacial discharge known as Blood Falls, Antarctica, was incubated with the electron acceptor ferrihydrite (Fh). Biosignatures associated with Fh reduction were identified using a suite of techniques currently utilized or proposed for Mars missions, including X-ray diffraction and infrared, Mössbauer, and Raman spectroscopy. The biotic origin of features was validated by transcriptional changes observed between treatments with and without Fh and comparison to killed controls. In live treatments, Fh was reduced to magnetite and goethite, both detected in Martian lacustrine basins. Several soluble and volatile metabolites were also detected, including riboflavin and dimethyl sulfide (DMS), which could be astrobiological indicators of active microbial processes. While none of the identified biosignatures individually would serve as definitive proof of life (past or present), detecting concomitant features associated with known terrestrial biotic processes would provide compelling rationale for more targeted life detection missions. Terrestrial extremophiles can support the exploration of astrobiologically relevant microbial processes, validation of life detection instrumentation, and potentially the discovery of new biomarkers. IMPORTANCE Culture-based experiments with terrestrial extremophiles can elucidate biosignatures that may be analogous to those produced under extraterrestrial conditions, and thus inform sampling and technology strategies for future missions. Here, we demonstrate the production of several biosignatures under iron-reducing conditions byShewanellasp. BF02_Schw, originally isolated from an Antarctic analog feature. These biosignatures could be detectable using flight-ready instrumentation. Growth experiments with terrestrial extremophiles can identify biosignatures measurable by current methodologies and inform the development and optimization of techniques for detecting extant or extinct life on other worlds.

Biotechnology & Applied Microbiology↗

NASA NTRS1997

A Three-Line Stereo Camera Concept for Planetary Exploration

This paper presents a low-weight stereo camera concept for planetary exploration. The camera uses three CCD lines within the image plane of one single objective. Some of the main features of the camera include: focal length-90 mm, FOV-18.5 deg, IFOV-78 (mu)rad, convergence angles-(+/-)10 deg, radiometric dynamics-14 bit, weight-2 kg, and power consumption-12.5 Watts. From an orbit altitude of 250 km the ground pixel size is 20m x 20m and the swath width is 82 km. The CCD line data is buffered in the camera internal mass memory of 1 Gbit. After performing radiometric correction and application-dependent preprocessing the data is compressed and ready for downlink. Due to the aggressive application of advanced technologies in the area of microelectronics and innovative optics, the low mass and power budgets of 2 kg and 12.5 Watts is achieved, while still maintaining high performance. The design of the proposed light-weight camera is also general purpose enough to be applicable to other planetary missions such as the exploration of Mars, Mercury, and the Moon. Moreover, it is an example of excellent international collaboration on advanced technology concepts developed at DLR, Germany, and NASA's Jet Propulsion Laboratory, USA.

Sandau, Rainer↗

DOE OSTI2026

Visualization for Insight and Data Analysis in Energy Research

This talk explores how advanced visualization technologies are transforming analytical reasoning and knowledge discovery in energy research, drawing on recent work at the National Laboratory of the Rockies' Computational Science Center. Through a series of scientific case studies, we demonstrate how immersive and high-resolution visualization environments enable scientists and engineers to identify previously unseen patterns and features - insights that often remain hidden in traditional desktop-based analysis. By embedding richer information into interactive analytics tools, these approaches support the exploration of complex, multivariate parameter spaces, where interaction itself catalyzes understanding. Beyond capability, we emphasize the critical role of visualization design grounded in perception and cognition, showing how visual encodings directly influence analytical outcomes. Spanning applications from materials science to integrated energy systems, these visualization approaches accelerate innovation and improve decision-making by enabling deeper, more reliable insight into increasingly complex energy data.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1981

Earthlike planets: Surfaces of Mercury, Venus, earth, moon, Mars

The surfaces of the earth and the other terrestrial planets of the inner solar system are reviewed in light of the results of recent planetary explorations. Past and current views of the origin of the earth, moon, Mercury, Venus and Mars are discussed, and the surface features characteristic of the moon, Mercury, Mars and Venus are outlined. Mechanisms for the modification of planetary surfaces by external factors and from within the planet are examined, including surface cycles, meteoritic impact, gravity, wind, plate tectonics, volcanism and crustal deformation. The origin and evolution of the moon are discussed on the basis of the Apollo results, and current knowledge of Mercury and Mars is examined in detail. Finally, the middle periods in the history of the terrestrial planets are compared, and future prospects for the exploration of the inner planets as well as other rocky bodies in the solar system are discussed.

Murray, B.↗

NASA NTRS2020

Marshall Space Flight Center Research and Technology Report 2019

Today, our calling to explore is greater than ever before, and here at Marshall Space Flight Center—we make human deep space exploration possible. A key goal for Artemis is demonstrating and perfecting capabilities on the Moon for technologies needed for humans to get to Mars. This year’s report features 10 of the Agency’s 16 Technology Areas, and I am proud of Marshall’s role in creating solutions for so many of these daunting technical challenges. Many of these projects will lead to sustainable in-space architecture for human space exploration that will allow us to travel to the Moon, on to Mars, and beyond. Others are developing new scientific instruments capable of providing an unprecedented glimpse into our universe. NASA has led the charge in space exploration for more than six decades, and through the Artemis program we will help build on our work in low Earth orbit and pave the way to the Moon and Mars. At Marshall, we leverage the skills and interest of the international community to conduct scientific research, develop and demonstrate technology, and train international crews to operate further from Earth for longer periods of time than ever before— first at the lunar surface, then on to our next giant leap, human exploration of Mars. While each project in this report seeks to advance new technology and challenge conventions, it is important to recognize the diversity of activities and people supporting our mission. This report not only showcases the Center’s capabilities and our partnerships, it also highlights the progress our people have achieved in the past year. These scientists, researchers and innovators are why Marshall and NASA will continue to be a leader in innovation, exploration, and discovery for years to come.

Dankanich, John W.↗

NASA NTRS2018

TPSAS-NF1676L-32014-DND

The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), on-board the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) is a satellite-borne polarization sensitive lidar. It has been providing the vertical distributions of clouds and aerosols along with their microphysical and optical properties since 2006. One of its important Level 2 products, feature classification, has been determined using the lidar information from 532 nm parallel and perpendicular channels, and 1064 nm channel measurements of layer integrated backscatter. Deep machine learning methods which combine both the channel and texture information to recognize feature patterns is uniquely beneficial when applied to this data. In this study, we will use Convolutional Neural Network (CNN), a deep machine learning method, to classify lidar aerosol subtypes by using the lidar profile observations. This method uses additional information from the vertical texture of the feature instead of using only the layer information. Note that in the integrated layer properties, the texture information has been masked due to averaging. Our results will show how the texture information plays a role in the classification. This preliminary work explores the benefits and potential of deep machine learning methods for lidar retrievals and focuses on the aerosol subtype classification. The broader application extends to the classification of other feature types. Future applications include the developing deep machine learning methods with neural networks to retrieve properties of the features, and studies of indirect effect of cloud-aerosol interaction from lidar measurements.

Shan Zeng Kowalski↗

DOE OSTI2020

Real-time biomass feedstock particle quality detection using image analysis and machine vision

Abstract A common and costly challenge in the nascent biorefinery industry is the consistent handling and conveyance of biomass feedstock materials, which can vary widely in their chemical, physical, and mechanical properties. Solutions to cope with varying feedstock qualities will be required, including advanced process controls to adjust equipment and reject feedstocks that do not meet a quality standard. In this work, we present and evaluate methods to autonomously assess corn stover feedstock quality in real time and provide data to process controls with low-cost camera hardware. We explore the use of neural networks to classify feedstocks based on actual processing behavior and pixel matrix feature parameterization to further assess particle attributes that may explain the variable processing behavior. We used the pretrained ResNet neural network coupled with a gated recurrent unit (GRU) time-series classifier trained on our image data, resulting in binary classification of feedstock anomalies with favorable performance. The textural aspects of the image data were statistically analyzed to determine if the textural features were predictive of operational disruptions. The significant textural features were angular second moment, prominence, mean height of surface profile, mean resultant vector, shade, skewness, variation of the polar facet orientation, and direction of azimuthal facets. Expansion of these models is recommended across a wider variety of labeled feedstock images of different qualities and species to develop a more robust tool that may be deployed using low-cost cameras within biorefineries.

09 BIOMASS FUELS↗

DOE OSTI2021

Influence of QD photosensitizers in the photocatalytic production of hydrogen with biomimetic [FeFe]-hydrogenase. Comparative performance of CdSe and CdTe

Photocatalytic systems comprising a hydrogenase-type catalyst and CdX (X = S, Se, Te) chalcogenide quantum dot (QD) photosensitizers show extraordinary hydrogen production rates under visible light excitation. What remains unknown is the mechanism of energy conversion in these systems. In this work, we have explored this question by comparing the performance of two QD sensitizers, CdSe and CdTe, in photocatalytic systems featuring aqueous suspensions of a [Fe 2 (μ-1,2-benzenedithiolate) CO 6 ] catalyst and an ascorbic acid sacrificial agent. Overall, the hydrogen production yield for CdSe-sensitized reactions QDs was found to be 13 times greater than that of CdTe counterparts. According to emission quenching experiments, an enhanced performance of CdSe sensitizers reflected a greater rate of electron transfer from the ascorbic acid (k Asc ). The observed difference in the QD-ascorbic acid charge transfer rates between the two QD materials was consistent with respective driving forces for these systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Identifying common stored product insects using automated deep learning methods

Monitoring stored product insect pests is a common practice for post-harvest management of stored grain and grain-based commodities, which helps ensure product quality from harvest to final consumer. Current methods of sampling and monitoring can be time-consuming, labor-intensive, expensive and require expertise in insect identification. Therefore, this study aims to develop an image-based automated identification system for common stored product insect species using deep-learning methods. Top-down images of the common stored product adult insect species of Rhyzopertha dominica, Cryptolestes ferrugineus, Tribolium castaneum, Sitophilus oryzae, and Oryzaephilus surinamensis were acquired and analyzed. Deep learning-based, state-of-the-art Convolutional Neural Networks (CNN) models (ResNet-50, MobileNet-v2, DarkNet-53, and EfficientNet-b0) were fine-tuned with a transfer learning approach to classify the insect species. All models were able to correctly identify the insect species with at least 96% accuracy and with few misclassifications. One issue with trained CNNs is that they do not explain the reasoning for the classification and are often called a “black box”. Therefore, visualization methods called Gradient-weighted Class Activation Mapping (Grad-CAM) were implemented to explore the black box network. The Grad-CAM uses heat maps to highlight the major image features that the network focused on to make insect species predictions. The Grad-CAM verifies the network's prediction and also helps improve network performance. This study contributes to the overall goal of developing a camera-based system for monitoring stored grain insects. As a result, the developed system would empower warehouse, flour mills, and other food facilities with a tool to quickly and accurately identify insect species in stored product environments and could be implemented as part of a close to real-time monitoring system.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Active interlocking metasurfaces enabled by shape memory alloys

Interlocking metasurfaces (ILMs) are a newly developed joining technology that relies on arrays of interlocking features that transmit force and constrain motion between adjoining bodies in one or more directions. This study explores harnessing the shape memory effect (SME) in Nickel-Titanium shape memory alloys (NiTi SMAs) in structures fabricated using additive manufacturing (AM) to advance the development of active ILMs by creating unit cells that open or close at specific temperatures. The study encompasses designing and fabricating two distinct interlocking array configurations using near-equiatomic NiTi powder and the laser powder bed fusion (L-PBF) AM technique, following a previously developed AM process optimization framework to manufacture defect-free parts. To guide the design process, finite element analysis (FEA) was employed to predict strain values during engage-disengage cycles. The martensitic transformation characteristics of the ILMs were characterized. Thermomechanical testing revealed that the ILMs demonstrate high locking force once engaged, coupled with complete shape recovery and good cyclic stability. Digital image correlation (DIC) was also employed to validate the FEA predictions during the engage-disengage cycles. The results indicate that NiTi SMA-based ILMs can be designed and fabricated into complex shapes using L-PBF. By leveraging the SME, the functionality of an ILM can be improved upon. The combination of computational modeling, additive manufacturing, and thermomechanical and physical property characterization provides a framework for designing future ILMs out of active materials.

Additive manufacturing↗

DOE OSTI2020

Tunable Magnetic Exchange between Rare-Earth Metal 5d and Iron 3d States: A Case Study of the Multiple Magnetic Transitions in Gd 6 FeBi 2 and the Solid Solutions Dy 6– x Gd x FeBi 2 (1 ≤ x ≤ 5) with Curie Temperatures in the Range 120–350 K

In this paper, the room-temperature magnet Gd 6 FeBi 2 was comprehensively characterized by means of temperature-dependent single-crystal X-ray diffraction, magnetization measurements, and experimental electron density and electronic structure computations. This work explores the electron-spin effects of Fe on this structure and suggests that Gd6FeBi2 shows structural features and exchange interactions, which are clearly distinguishable from other analogues, including the solid solutions Dy 6–x Gd x FeBi 2 (0 ≤ x ≤ 5). The unique traits of Gd 6 FeBi 2 and its derivatives encompass abnormal variations of lattice parameters with the temperature, as well as the presence of multiple magnetic transitions of complex nature. Based on the comprehensive analyses, it can be suggested that the unique magnetic response in this material is the result from tunable Fe spin states, which are coupled with strong Gd 5d–Fe 3d interactions, which differ from other members of this large, structural family. In the ground state, according to density functional theory calculations, the Fe atom carries two net electrons, whose spins are coupled in an antiparallel fashion to the spins of the Gd electrons. Near the Curie temperature, the Fe net moment is reduced intermittently, coupled with a multiple of magnetic transitions. The magnetic correlations are manifested in unexpected variations of the lattice parameters as a function of temperature, suggesting spin–lattice interactions. Furthermore, this study emphasizes the important role of Gd 5d electrons in tuning Fe 3d-based magnetic contribution, which enables better understanding on the spin coupling in other related compounds and sheds light on the development of new magnetic and spintronic compounds.

36 MATERIALS SCIENCE↗

DOE OSTI2024

A Copper-Binding Peptide with Therapeutic Potential against Alzheimer′s Disease: From the Blood–Brain Barrier to Metal Competition

Alzheimer’s disease (AD) is the most common form of dementia worldwide. AD brains are characterized by the accumulation of amyloid-β peptides (Aβ) that bind Cu 2+ and have been associated with several neurotoxic mechanisms. Although the use of copper chelators to prevent the formation of Cu 2+ -Aβ complexes has been proposed as a therapeutic strategy, recent studies show that copper is an important neuromodulator that is essential for a neuroprotective mechanism mediated by Cu 2+ binding to the cellular prion protein (PrPC). Therefore, in addition to metal selectivity and blood–brain barrier (BBB) permeability, an emerging challenge for copper chelators is to prevent the formation of neurotoxic Cu 2+ -Aβ species without perturbing the neuroprotective Cu 2+ -PrPC interaction. Previously, we reported the design of a tetrapeptide (TP) that withdraws Cu 2+ from Aβ(1–16) and impacts the Cu 2+ -induced aggregation of Aβ(1–40). In this study, we improved the drug-like properties of TP in a BBB model, evaluated the metal selectivity of the optimized peptide (TP*), and tested its effect on Cu 2+ coordination to PrPC and proteins involved in copper trafficking, such as copper transporter 1 and albumin. Our results show that changing the stereochemistry of the first residue prevents TP degradation in the BBB model and coadministration of TP with a peptide that increases BBB permeability allows its passage through the BBB model. TP* is highly selective toward Cu 2+ in the presence of Zn 2+ ions, transfers Cu 2+ to copper-trafficking proteins, and forms a ternary TP*-Cu 2+ -PrP species that does not perturb the physiological conformation of PrP and displays only a minor impact in the neuroprotective Cu 2+ -dependent interaction of PrPC with the N-methyl-d-aspartate receptor. Overall, these results show that TP* displays desirable features for a copper chelator with therapeutic potential against AD. Moreover, this is the first study that explores the effect of a Cu 2+ chelator with therapeutic potential for AD on Cu 2+ coordination to PrPC (an emerging key player in AD pathology), integrating recent knowledge about metalloproteins involved in AD with the design of copper chelators against AD.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

ERF: Energy Research and Forecasting Model

High performance computing (HPC) architectures have undergone rapid development in recent years. As a result, established software suites face an ever increasing challenge to remain performant on and portable across modern systems. Many of the widely adopted atmospheric modeling codes cannot fully (or in some cases, at all) leverage the acceleration provided by General-Purpose Graphics Processing Units, leaving users of those codes constrained to increasingly limited HPC resources. Energy Research and Forecasting (ERF) is a regional atmospheric modeling code that leverages the latest HPC architectures, whether composed of only Central Processing Units (CPUs) or incorporating GPUs. ERF contains many of the standard discretizations and basic features needed to model general atmospheric dynamics. The modular design of ERF provides a flexible platform for exploring different physics parameterizations and numerical strategies. ERF is built on a state-of-the-art, well-supported, software framework (AMReX) that provides a performance portable interface and ensures ERF's long-term sustainability on next generation computing systems. This paper details the numerical methodology of ERF, presents results for a series of verification/validation cases, and documents ERF's performance on current HPC systems. The roughly 5× speed up of ERF (using GPUs) over Weather Research and Forecasting (CPUs only) for a 3D squall line test case highlights the significance of leveraging GPU acceleration.

17 WIND ENERGY↗

DOE OSTI2025

In vivo mapping of mutagenesis sensitivity of human enhancers

Distant-acting enhancers are central to human development1. However, our limited understanding of their functional sequence features prevents the interpretation of enhancer mutations in disease2. Here we determined the functional sensitivity to mutagenesis of human developmental enhancers in vivo. Focusing on seven enhancers that are active in the developing brain, heart, limb and face, we created over 1,700 transgenic mice for over 260 mutagenized enhancer alleles. Systematic mutation of 12-base-pair blocks collectively altered each sequence feature in each enhancer at least once. We show that 69% of all blocks are required for normal in vivo activity, with mutations more commonly resulting in loss (60%) than in gain (9%) of function. Using predictive modelling, we annotated critical nucleotides at the base-pair resolution. The vast majority of motifs predicted by these machine learning models (88%) coincided with changes in in vivo function, and the models showed considerable sensitivity, identifying 59% of all functional blocks. Taken together, our results reveal that human enhancers contain a high density of sequence features that are required for their normal in vivo function and provide a rich resource for further exploration of human enhancer logic.

Kosicki, Michael↗