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NASA NTRSDate not supplied

A Flat-Panel 8x8 Sequentially Rotated Wideband Microstrip Patch Phased Array Antenna for K/Ka-band 6U CubeSat Communications Applications

Flat-panel electronically-scanned phased arrays have recently become a mainstream technology in the areas of radar and satellite applications, particularly due to their low profile and not having to steer the beams mechanically, both of which helps in realizing a lightweight phased array solution that also occupies a smaller volume. Typically for satellite applications, circularly polarized element radiators are often employed in order to deal with the signal fading problem. In this project, the main objective was to design a flat-panel phased array solution for 6U CubeSat communication applications. As for the required specifications, the array should have two operating bands: 22.55–23.55 GHz and 25.5–27.5 GHz, which span part of the K and Ka bands. For both these bands, the panel should be able to radiate dual circular polarization, in both transmit and receive modes, and with a stable gain performance. Since the individual bands are very closely spaced, it was realized that a single wideband design would be a better solution than a closely spaced dual-band design. Thus, a stacked microstrip patch configuration was selected for the element radiator design, which is known to be wideband. Now, it is rather difficult to realize both wide impedance and axial ratio bandwidths simultaneously, and at high frequencies, without complicating the element stacked patch design.. As a result, a sequential rotation technique wasemployed to significantly improves the axial ratio bandwidth and circular polarization purity of an array, all the while improving the pattern symmetry (P. S. Hall, “Application of Sequential Feeding to Wide Bandwidth, Circularly Polarised Microstrip Patch Arrays,” IEE Proc. H (Microw., Antennas Propag.), vol. 136, no. 5, pp. 390–398, Oct. 1989). The designed element radiator is a circularly-polarized probe-fed stacked patch antenna, which has a 10-dB matching bandwidth of 22.3– 30 GHz, with only a 5.14% axial ratio bandwidth around the center frequency of the required band, i.e., ~25 GHz. However, the resulting array demonstrates a very low axial ratio (≪ 3 dB) over the entire bandwidth, due to the application of the sequential rotation technique. The 8×8 array was able to scan up to ±51° and ±40° at frequencies 23.05 GHz and 26.5 GHz (center frequencies of the lower and upper bands), respectively, while maintaining a ≤ 3 dB axial ratio and a ≤ 3 dB gain drop from the corresponding peak broadside value at both the principle and the diagonal radiation planes. The designed layout for the 8×8 phased array prototype is shown in Fig. 1, which is currently undergoing fabrication and RF assembly. The antenna will be tested in the far-field anechoic chambers of the Antenna and Microwave Laboratory (AML) at San Diego State University and at NASA Glenn Research Center (GRC).

5G↗
NASA NTRSDate not supplied

NASA’s Human Data Repositories: An In Depth Look at the New Data Request Process

As NASA transitions its focus to travel back to the moon and on to new destinations, the need to ensure the capture, analysis, and application of research and medical data is of greater urgency than at any other previous time. In this era of limited resources and challenging schedules, the Human Research Program (HRP), based at NASA’s Johnson Space Center (JSC), recognizes the need to extract the greatest possible amount of information from the data already captured. To this end, the HRP Chief Scientist Office (CSO), HRP Program Planning and Control (PP&C) Office, and the Space Medicine Operations Division have been working together to make reuse of both research data and medical monitoring data more accessible to the user community through the Life Science Data Archive (LSDA) and the Lifetime Surveillance of Astronaut Health (LSAH) Repositories. The task of both LSDA and LSAH repositories is to acquire, preserve, and distribute retrospective research (LSDA) and medical (LSAH) data and information both within the NASA community and to the science community at large, for knowledge discovery, retrospective analysis, and planning of future research studies. An additional goal is to encourage collaboration with non-NASA institutions also faced with enhancing human performance in extreme environments. In September 2022, the LSDA website and its contents transitioned to a new NASA Life Sciences Portal (https://nlsp.nasa.gov/explore/lsdahome). This site continues to feature publicly releasable information such as non-attributable datasets, experiment descriptions (from Project Mercury to ISS, as well as from multiple flight analog missions), descriptions of medical monitoring data, and LSAH newsletters (1992 - 2022). The website also provides an updated portal to request additional research and medical data not accessible from the public website. This presentation will provide an in-depth look at the new system as it relates to finding and requesting retrospective data. We will also detail processes from making a request to delivering data for different types of data requests (i.e., attributable, or non-attributable). This includes descriptions of various approval boards, what information and actions the requestor is responsible for, and key milestones in making data available for reuse.

D. M. Thomas↗
NASA NTRS2023

Molecular Identification of Microbial Contaminants

Microorganisms can have significant impacts on the success of NASA’s missions, including the integrity of materials, the reliability of scientific results, and maintenance of crew health. Robust cleaning and sterilization protocols are currently in place in NASA facilities, but agency experts agree that microbial contamination is unavoidable and its impact on NASA’s missions and science must be minimized. Therefore, it is critical to understand: 1) what specific microorganisms are present, 2) how they may impact scientific objectives, and 3) how to select appropriate mitigation strategies. The Marshall Space Flight Center (MSFC) Planetary Protection (PP) microbiology lab historically relied solely upon enumeration of culturable microbial contamination associated with spacecraft materials or cleanrooms. However, this process is time consuming, many microbes cannot be cultured, and very few can be identified with any fidelity using NASA standard microbiological methods. The work described in this white paper includes the establishment of molecular identification capabilities at MSFC, including DNA isolation, amplification, purification, and Sanger sequencing. This capability will not only improve planetary protection efforts at MSFC (i.e. by identifying contaminating microorganisms in cleanrooms or on spacecraft) but also offers a service center-wide for the identification of contaminants that arise in other projects, processing locations, or during set up and roll out of spacecraft. This work also lays the foundation for higher throughput efforts to identify large populations of microbes across the lifetime of a project and serves as the starting point for future work into whole genome sequencing, non-culture based methods, or additional characterization studies. Ultimately, accurate identification informs appropriate mitigation strategies, increasing the chances of success for NASA’s missions and objectives.

C. D. Cassilly↗
NASA NTRSDate not supplied

A Solar Sail Shape Modeling Approach for Attitude Control Design and Analysis

Solar sails operating in the space environment experience deformations in sail shape that result in relatively large disturbance torques which dictate the required performance of the spacecraft attitude control and momentum management systems. These deformations are driven by thermal loads on the booms (due to uneven solar heating), manufacturing and assembly tolerances, and variations in membrane tension. The Solar Cruiser spacecraft utilizes a four-quadrant sail design with four 30-meter length booms and four triangular sail membranes, creating a square sail structure of >1600 m2. Medium-fidelity mesh models were developed based on a characteristic deformed shape. A series of parametric studies were conducted using this shape paradigm to determine worst-case deformed sail shapes which produce bounding disturbance torques. A large database of shapes was produced, and the forces and moments induced by each individual shape were calculated using a Rios-Reyes reduced order generalized sail model [1]. Two were selected as reference worst-case shapes for the Solar Cruiser mission: one which produced the highest pitch/yaw root-sum-squared (RSS) torque, and one which produced the highest roll torque. The results showed that the worst-case shapes at high solar incidence angles induce significantly higher (2-10x) disturbance torques than an ideal, flat-plate sail. Even with considerable safety margins, assuming an ideal sail is unlikely to sufficiently bound the disturbances, which is critical when designing the attitude control system and sizing actuators. Accurate sail shape modeling methodologies should therefore be employed on future solar sail missions. References [1] L. Rios-Reyes and D. J. Scheeres, “Generalized Model for Solar Sails,” Journal of Spacecraft and Rockets, Vol. 42, Jan. 2005, pp. 182–185, 10.2514/1.9054.

Solar Sail↗
NASA NTRSDate not supplied

Multiple Probe Measurements at Uranus Motivated By Spatial Variability

Motivation: Spatial variations in the temperature field and composition of Uranus' atmosphere demonstrate a need for multiple entry probes to characterize vertical profiles in multiple locations. We will review variation of composition and temperature, which are produced by dynamical processes on a range of scales from global (polar anomalies, zonal bands) to regional (vortices, storms) [1–2]. In particular, the spatial variation of convective activity is not well understood based on existing remote sensing observations [3]. Understanding how these processes operate, and how they modulate variable composition, is key to constraining bulk atmospheric abundances. Abundances in turn provide cosmochemical constraints on planetary origins. Secondary probes at Uranus: Key measurements for secondary probes are temperature-pressure profiles, along with compositional profiles. Radio occultations are limited to shallow levels less than ~2 bar [4]. In situ temperature-pressure measurements (or atmospheric structure measurements) can unambiguously extend these results to deeper levels. Measurements with vertical resolution of at least 2 km are needed to characterize anomalies like the 1.2-bar feature from the Voyager 2 occultation at 2–6° S [4], which supports a range of temperature gradients depending on assumptions of composition. Simultaneous measurements of temperature, pressure, and composition are key to understanding this class of features. Species such as methane, hydrogen sulfide, and ammonia must be measured along a probe descent profile because they vary over several orders of magnitude due to the strong temperature dependence of their saturation vapor pressures [e.g., 5]. Results of these measurements can be interpreted to understand the potential for moist convective activity in the atmosphere. Lessons from the other giant planets: On Jupiter, the Galileo Probe's entry into a meteorologically distinct five-micron hot spot led many to interpret the local composition as column-stretched, so that well-mixed abundances were reached at deeper levels than in surrounding, unperturbed regions [6–8]. Ground-based microwave measurements and Juno data now indicate that the deep depletion of ammonia is a very widespread atmospheric characteristic not limited to 5-µm hot spots [9–11]. But in the absence of multiple probes, we do not know if the other volatiles H2S and H2O behave in the same way. The open questions for the Jupiter case strongly motivate sending multiple probes to Uranus. On Saturn, retrievals of NH3 and PH3 abundances at shallow levels vary with latitude [12], but it is unknown how deep these differences extend, which is why a Saturn probe (or probes) was a mission theme for NASA's New Frontiers 4 and 5 opportunities [13]. Challenges for secondary probes: Cost is an issue due to the perception that it involves sacrifices to other mission elements. Spacecraft trajectories may be constrained by needs for orbit insertion that limit probe deliveries to different latitudes, and additional limitations may be placed on communication windows for probe descent phases (particularly if multiple probes are released from the orbiter simultaneously) [14]. Finally, composition sensors for miniature secondary probes are not at the required technological maturity [15]. Mass spectrometers are typically too large, massive, and powerhungry, while smaller nanosensors are only beginning to be developed for planetary missions. Finally, the need for probe survival heating is most easily met by radioisotope heat sources, but these require regulatory approvals that are even more difficult to satisfy compared to standard environmental reviews [16], unless the secondary probe is designed as a core element of a mission. References: [1] Molter E.M., et al. (2021) PSJ, 2, 3. [2] Rowe-Gurney N., et al. (2021) Icar, 365, 114506. [3] Hueso R., Sánchez-Lavega A. (2019) SSRv, 215, 52. [4] Lindal G.F., et al. (1987) JGR, 92, 14987– 15001. [5] Simon A.A., et al. (2022) RemS, 14, 1518. [6] Atreya, S.K., et al. (1997) in The Three Galileos: The Man, the Spacecraft, the Telescope, pp. 249–260 (C. Barbieri et al., eds.). [7] Showman A.P., Ingersoll A.P. (1998) Icar, 132, 205–220. [8] Friedson A.J. (2005) Icar, 177, 1–17. [9] de Pater I., et al. (2001) Icar, 149, 66–78. [10] Li C., et al. (2017) GeoRL, 44, 5317–5325. [11] de Pater I., et al. (2019) Icar, 322, 168–191. [12] Fletcher L.N., et al. (2009) Icar, 202, 543–564. [13] National Research Council (2011) Vision and Voyages. [14] Sayanagi K.M., et al. (2020) SSRv, 216, 72. [15] Wong M.H. et al. (2021) BAAS, 53, 486. [16] Zide A., Mendoza-Hill A., Cheney D. (2022) COSPAR Abstracts H0.6-0012-22.

Michael H Wong↗
NASA NTRSDate not supplied

Open-source Numerical Modeling of Solidification Cracking Susceptibility: Application to Refractory Alloy Systems

Introduction. Alloys such as aluminum, nickel-base, and austenitic stainless steels are susceptible to solidification cracking during welding and 3D printing. Compositional optimization is one method used to effectively mitigate solidification cracking of those alloy systems. With the surge in hypersonic and in-space propulsion activities, refractory metals (Nb, Mo, Ta, W, and Re) and their alloy derivatives are increasing in importance due to their extreme high melting point and retention of high-temperature strength; however, their chemistry was most typically optimized to promote ductility during mechanical operations such as drawing and forming. Welding of such alloys has been a challenge due to a number of issues including solidification cracking, atmospheric contamination (O, C, and N), as well as a shift in ductile-to-brittle transition to higher temperature following grain growth induced by welding. Compositional optimization of refractory alloys for solidification cracking resistance in particular is desirable as their usage increases with the advent of advanced manufacturing methods such as 3D printing. This work evaluates the effect of compositional variation in refractory metal systems on the solidification cracking susceptibility with the goals of optimizing existing alloys and joining process techniques, and formulating new alloys with increased solidification cracking resistance. Experimental Procedures. A python code was developed in a Jupyter notebook environment (Michael and Sowards, 2023) to facilitate the calculation of crack susceptibility index proposed by Kou (2015). Composition is entered as a single point, or as a 1-D or 2-D array. The notebook calls pycalphad (Otis and Liu, 2017 and Bocklund et al, 2020) to calculate the evolution of fraction solid as a function of temperature (under either Scheil or equilibrium assumptions) and then evaluates steepness of the fraction solid curve near the terminal stage of solidification to predict solidification cracking resistance. Open source thermodynamic databases available at online repositories are used (van de Walle). The process is setup in an automated fashion to generate plots that show variation in solidification cracking susceptibility according to composition on 1-D line plots or 2-D contour plots. The Jupyter notebook and crack susceptibility algorithm was also integrated with a widely used commercial CALPHAD code for validation and alloy exploration. Results and Discussion. The crack susceptibility model was first validated against a series of refractory alloy compositions evaluated in past work which utilized a specialized Varestraint test built inside a vacuum chamber environment (Lessman and Gold, 1971). The alloys tested in the Varestraint apparatus included T-111 (Ta-8W-2Hf), ASTAR-811C (Ta-8W-1Re-0.7Hf-0.025C), FS-85 (Nb-27Ta-10W-1Zr), T-222 (Ta-9.6W-2.4Hf-0.01C), Ta-10W, B-66 (Nb-5Mo-5V-1Zr), and SCb-291 (Nb-10W-10Ta). The initial test of the model showed a strong correlation with empirical Varestraint data, i.e., a Spearman rank correlation between model predictions and hot cracking measurements was observed to be greater than 0.8. Following the validation, a set of refractory metal binary mixtures was investigated to evaluate sensitivity of Nb, Mo, W, and Ta to C, N, and O content. A series of plots were produced that suggest ppmw ranges of C, N, and O where solidification cracking increases significantly and reaches a maximum. Also comparative ranking of each primary refractory metal to each interstitial was produced. For example C produces greater cracking response in Mo whereas O produces greater cracking response in Ta and Nb. Such compositional values have utility in setting limits on pickup of these interstitial elements during welding and printing rather than using a one-size-fits-all approach. Furthermore, the results have use in determining additive powder recycling requirements, which is especially pertinent for refractory metal powders due to their high cost compared to conventional alloys. Another application created thousands of hypothetical alloys within the nominal specified composition range of two widely used refractory alloys C103 (Nb-10Hf-1Ti) and TZM (Mo-0.5Ti-0.1Zr). The cracking index was calculated for the alloys and results were fed into machine learning regression techniques including Multiple Linear Regression, Ridge Regression, and Lasso Regression to determine relative potency each alloying element had on computed solidification cracking index. A series of linear equations were produced that relate composition of C103 and TZM to solidification cracking index. The crack susceptibility of C103 for example is described by an equation of the form: cracking index ~ O + 0.667*C + 0.635*N + 0.00037*Ta – 0.0008*Hf (in wt.%) From that equation, it is clear that O has strong propensity to induce solidification cracking. Interestingly, Hf is shown to reduce calculated cracking response. Finally, realizing the potential of this method to discover new refractory alloy formulations across the period table that have low solidification cracking sensitivity, the code was applied to new untested alloy systems including W-Zr-C, W-Ta-C, and others. Conclusions. In summary, an open source numerical method has been developed using Python code to calculate Kou’s crack susceptibility index. The method was applied to refractory metals which are inherently difficult to study from a weldability testing standpoint since inert shielding gas is not sufficient and welding is typically done in vacuum, especially in light of findings presented here where oxygen has profound influence on solidification cracking. This work revealed the effect of compositional variations on a series of refractory metals and showed the framework defined here will be useful in 1) the development of new alloys that have improved weldability and 3D printability, 2) placing compositional limits on existing alloys, and 3) ensuring adequate controls of manufacturing processes such as 3D printing where powder reuse is critical. Keywords. pycalphad; Python; refractory metals; solidification cracking. References. B. Bocklund et. al. (2020) http://doi.org/10.5281/zenodo.3630657. S. Kou. (2015) https://doi.org/10.1016/j.actamat.2015.01.034. G.G. Lessmann and R.E. Gold. Welding Journal, issue 1, pp. 1-s – 8-s (1971). F.N. Michael and J.W. Sowards. NASA/TM-20230002218 (2023). R. Otis and Z.-K. Liu. (2017) http://doi.org/10.5334/jors.140. A. Van de Wallle et. al. (2018) https://doi.org/10.1016/j.calphad.2018.04.003.

pycalphad↗
NASA NTRS2023

Breaking Wave Field Statistics With A Multi-Layer Model

The statistics of breaking wave fields are characterised within a novel multi-layer framework, which generalises the single-layer Saint-Venant system into a multi-layer and non-hydrostatic formulation of the Navier–Stokes equations. We simulate an ensemble of phase-resolved surface wave fields in physical space, where strong nonlinearities, including directional wave breaking and the subsequent highly rotational flow motion, are modelled, without surface overturning. We extract the kinematics of wave breaking by identifying breaking fronts and their speed, for freely evolving wave fields initialised with typical wind wave spectra. The Λ(c) distribution, defined as the length of breaking fronts (per unit area) moving with speed c to c + dc following Phillips (J. Fluid Mech., vol. 156, 1985, pp. 505–531), is reported for a broad range of conditions. We recover the Λ(c) ∝ c−6 scaling without wind forcing for sufficiently steep wave fields. A scaling of Λ(c) based solely on the root-mean-square slope and peak wave phase speed is shown to describe the modelled breaking distributions well. The modelled breaking distributions are in good agreement with field measurements and the proposed scaling can be applied successfully to the observational data sets. The present work paves the way for simulations of the turbulent upper ocean directly coupled to a realistic breaking wave dynamics, including Langmuir turbulence, and other sub-mesoscale processes.

Jiarong Wu↗
NASA NTRSDate not supplied

Benchmarking Computational Tools for Calling SNPs and Indels in Complex Microbial Populations

The NASA BioNutrients missions seek to understand the suitability of microorganisms for bioproduction during space flight. One topic of interest is the stability of microbial genomes during long-term ambient storage and subsequent rehydration and growth. To address these questions, samples from 8 species were flown to ISS for 5 years of desiccated storage at ambient temperature (Stasis Packs) and 2 species were packaged along with powdered media inside a bioreactor system to allow hydration and growth in microgravity (Production Packs). For both systems, Whole Genome Sequencing (WGS) of the DNA extracted from the returned samples and paired ground controls will be conducted to identify changes in genome stability due to time, storage conditions and growth in space. Across the technical replicates, ground controls, 10 timepoints, and multiple experimental conditions, ~300 samples have been selected for initial analysis with WGS sequencing to 100x coverage. A flexible and resource efficient mutation calling pipeline is needed to process this large dataset and allow for comparisons between species. Many bioinformatics tools for calling Indels and Single Nucleotide Variants (SNVs) are designed for use with pure isolates, where true variations from the reference genome are expected to dominate the reads aligning to the location of mutation. In contrast, DNA from the Stasis Pack (SP) samples was collected directly after recovery from desiccated storage and the Production Pack (PP) samples were collected after fermentation. In this context, reads with mutations are expected to be less frequent than reads that align with the reference genome, as each sample will include multiple lines of cells. Thus, BioNutrients samples are expected to be similar to samples from cancer cell or “pooled” sequencing approaches. In preparation for the analysis of the BioNutrients samples, we have tested three mutation calling tools (GATK for Microbes, BreSeq and DiscoSNP) designed for complex samples. A challenge of validating mutation identification pipelines is a lack of “Ground Truth” datasets, especially for complex samples. To compare these three tools, we sought to identify mutations in pre-existing WGS data collected from populations of Chlamydomonas reinhardtii that were exposed to UV mutagenesis and growth in LEO as part of the Space Algae-1 mission. Here we present a summary of these tools against the analysis originally conducted using the CRISP tool. Critical metrics are compared such as runtime, the number of SNPs, the number and size of Indels, and patterns of transversion and transitions identified by each tool are reported. By sharing these benchmarking results collected in support of the BioNutrients mission, we aim to guide others seeking to identify SNVs in similarly complex microbial samples.

Biology↗
NASA NTRSDate not supplied

NICS (NASA Instrument Capabilities Study) Instrument Schedule and Cost Study

This paper summarizes work performed on the Flight Projects Directorate Planetary Science Projects Division (PSPD, Code 430) NICS (NASA Instrument Capabilities study) instrument schedule and cost study. Included are a short summary of the original NICS (NASA, 2008), and the design and approach, data collection, analysis, preliminary findings and recommendations from select areas of the current study. The NICS (2008) was chartered by then NASA Chief Engineer Michael Ryschkewitsch and chaired by Goddard Space Flight Center (GSFC) engineer, John Leon. The focus was to identify problem areas in instrument development and, if possible, to offer solutions. In the area of instrument developments, the NICS (2008) identified a lack of resources and authority to successfully manage to instrument cost and schedule requirements; and a lack of critical skills, expertise, and leadership to successfully implement unique (one-of-a-kind) high technology developments (NASA, 2008, pp. 51, 52). Additionally, the NICS (2008) found problems in requirements formulation, reviews and management; unrealistic caps and overly optimistic estimates; and externally directed changes which increased the likelihood of overrunning cost and schedule (NASA, 2008, pp.53, 54). It is noteworthy that NICS findings are consistent with previous studies at the mission level (Robbins, Schmidt & White, 2020). Five years later in 2013, the Instrument Projects Division (IPD) was established to implement and manage instrument projects greater than $20M. The IPD was known as Code 490. Its structure incorporated several of the NICS (2008) recommendations. To see if these incorporated recommendations made a difference, and to identify other potential challenges in instrument developments, two parallel studies were initiated. Originally led by the IPD, now led by the PSPD, and the Instrument and Payload Systems Engineering Branch (IPSE, Code 592), respectively, the instrument schedule and cost study and the instrument technical complexity study began in 2017. Data collection was initiated in 2020 and is on-going. This paper is limited to the IPD/PSPD study. Among other findings, preliminary data indicate IPD/PSPD project management support positively influenced instrument development as related to providing a dedicated level of support staff, including a deputy Instrument Project Manager (dIPM), reducing IPM leadership changes, and providing other project support. Next steps include continued data collection and analysis, and mapping to technical complexity data.

NICS implementation↗
DOE OSTI2020

Eurycea lucifuga (Cave Salamander). Diet.

Eurycea lucifuga is a common and widespread facultative inhabitant of caves and springs in the southeastern and central United States (Powell et al. 2016. Peterson Field Guide to Reptiles and Amphibians of Eastern and Central North America. Houghton Mifflin Harcourt, Boston, Massachusetts. 512 pp.). This species is known to feed on a variety of invertebrates, such as snails, crustaceans, beetles, and flies (Peck 1974. Nat. Speleol. Soc. Bull. 36:7–10). Although several millipede species also can serve as prey for E. lucifuga, some are presumably avoided due to the presence of defensive secretions (e.g., Abacionidae, Paradoxosomatidae). Captive feeding trials and field study show that E. lucifuga do not actively prey upon cave-obligate Tetracion millipedes, which produce defensive secretions comprised primarily of the compound p-cresol (Peck and Richardson 1976. Annales de Speleologie 31:175–182). On the other hand, Pseudotremia millipedes, which do not possess chemical defenses, are a significant component of the diet of cave-dwelling E. lucifuga (Peck and Richardson 1976, op. cit.)

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Neural Network-Enhanced Reproducing Kernel Particle Method for Image-Based Multiphysics Damage Modeling of Energy Storage Materials

Energy storage materials undergo significant stresses during charge/discharge cycling, which makes understanding their reliability and durability fundamental in predicting performance and service life. Strong electrochemical-mechanical coupling and highly anisotropic material properties contribute to the formation and propagation of micro-cracking, largely along material interfaces and grain boundaries. With microstructural images supplied by the National Renewable Energy Laboratory (NREL), image-based modeling techniques are used to represent the complex material microstructures that dictate the coupled physics of these systems. Traditional electrochemical-mechanical models rely on mesh-based finite element methods, which can lead to difficulties in capturing crack propagation due to mesh dependency. Additionally, commonly used damage models, such as the continuous damage model and the cohesive zone model, often have steep tradeoffs between discontinuous field accuracy and computational expense. In this work, a neural network-enhanced reproducing kernel particle method (NN-RKPM) [1] is leveraged to accurately capture damage and crack propagation throughout the material by learning the location, orientation, and sharpness of discontinuity while allowing for a coarser nodal distribution than that necessary for capturing sharp solution transitions using traditional mesh-based methods. NN-RKPM is used to inform how crack opening and closure in turn affect the coupled chemical equations and material microstructure. Reference: [1] Baek, J., Chen, J. S., Susuki, K., "Neural Network enhanced Reproducing Kernel Particle Method for Modeling Localizations," International Journal for Numerical Methods in Engineering, Vol. 123, pp 4422-4454, https://doi.org/10.1002/nme.7040, 2022.

damage modeling↗
DOE OSTI2022

NECTURUS MACULOSUS (Common Mudpuppy). NESTING

Common Mudpuppies (Necturus maculosus maculosus) are the most widespread and wellstudied of the Proteidae, ranging from northern Georgia into eastern Canada and west to the Great Plains. Despite this extensive range, most of our knowledge of life history traits for mudpuppies is based on studies occurring in the northern half of its range, specifically the Great Lakes region. Little is known regarding life history traits in the southern portion of its range. In Tennessee, N. maculosus is thought to occur statewide in permanent streams, rivers, and likely in reservoirs and lakes. Literature suggests that parturition and nest-guarding in N. maculosus maculosus occurs in late winter through spring, but there are no published observations to verify this in Tennessee (Pasachnik and Niemiller 2011. In Niemiller and Reynolds [eds.], The Amphibians of Tennessee, pp. 231-233. University of Tennessee Press, Knoxville, Tennessee). Here, we report four observations of N. maculosus maculosus nests that were discovered while conducting snorkel surveys and lifting cover objects opportunistically for aquatic salamanders from 2011 to 2017, and approximate timing of laying in three river tributaries to the Tennessee River.

Nelson, Stephen K.↗
DOE OSTI2024

Multiphysics Degradation Modeling of Energy Storage Materials via RKPM with a Neural Network-Enhancement

In energy storage materials, strong electrochemical-mechanical coupling and highly anisotropic material properties contribute to the formation and propagation of micro-cracking during charge/discharge cycling, resulting in reduced performance and service life. A coupled electro-chemo-mechanical reproducing kernel particle method (RKPM) formulation is developed, and a patch-test is formulated to certify optimal convergence of the proposed RKPM method for the coupled physics system. With microstructural images supplied by the National Renewable Energy Laboratory (NREL), pixel-based model construction by RKPM is then used to represent the complex material microstructures for modeling the coupled physics of these systems. Further, a neural network-enhanced reproducing kernel particle method (NN-RKPM) [1, 2] is introduced to effectively model damage and crack propagation in the material microstructures; the location, orientation, and solution transition near a localization are automatically captured by superimposed block-level NN optimizations. This NN enrichment approach allows for effective modeling of localizations via a fixed background discretization, relieving tedious efforts for adaptive refinement in traditional mesh-based methods. Applications to the heterogeneous microstructures of Li-ion battery cathodes will be presented to demonstrate the effectiveness of the proposed methods. Reference: [1] Baek, J., Chen, J. S., Susuki, K., "Neural Network enhanced Reproducing Kernel Particle Method for Modeling Localizations," International Journal for Numerical Methods in Engineering, Vol. 123, pp 4422-4454, https://doi.org/10.1002/nme.7040, 2022. [2] Baek, J., Chen, J. S., "A Neural Network-Based Enrichment of Reproducing Kernel Approximation for Modeling Brittle Fracture", Computer Methods in Applied Mechanics and Engineering Vol. 410, 116590, 2024.

electro-chemo-mechanical coupling↗
DOE OSTI2024

Search For Low-Mass Quark-Antiquark Resonances Produced With an Initial State Photon at 13 TeV Using the CMS Detector

We present a search for low-mass narrow qq̅ resonances. This search uses data from LHC pp collisions at a center of mass of 13 TeV in Run 2, and corresponds to an integrated luminosity of 137 fb^{-1}, currently using 10\% of data. Utilizing full Run 2 data allows the use of a lower photon pT threshold trigger than a previous analysis performed with only 2016 data, allowing this analysis to be more sensitive to resonances in the low mass region. We require an initial state photon recoiling against the narrow resonance, leading to the resonance having a high transverse momentum. The high pT decay products of the resonance collimate and are reconstructed as a single large jet with an internal two-pronged substructure. A two-pronged dijet score based on the ParticleNet tagger is used to select jets with two-pronged substructure. The background is estimated via a data-driven method using a transfer factor between the distributions which fail and pass the two-pronged substructure requirement. The new physics signal is searched for as a narrow peak excess above the Standard Model backgrounds in the jet mass spectrum.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Leveraging a Neural Network-Enhanced Reproducing Kernel Particle Method for Multiphysics Degradation Modeling of Energy Storage Materials

Energy storage materials exhibit strong electro-chemo-mechanical coupling and highly anisotropic material properties, contributing to the formation and propagation of micro-cracking during charge/discharge cycling and resulting in reduced performance and service life. A coupled electro-chemo-mechanical reproducing kernel particle method (RKPM) formulation has been developed to analyze this system. With microstructural images supplied by the National Renewable Energy Laboratory (NREL), pixel-based model construction by RKPM is used to represent the complex material microstructures that dictate the coupled physics of these systems. Traditional electro-chemo-mechanical models rely on mesh-based finite element methods, which can lead to difficulties in meshing such complex geometries and capturing crack propagation due to mesh dependency. Here, a neural network-enhanced reproducing kernel particle method (NN-RKPM) [1, 2] is introduced to effectively model damage and crack propagation in the material microstructures; the location, orientation, and solution transition near a localization are automatically captured by superimposed block-level NN optimizations. This NN enrichment approach allows for effective modeling of localizations via a fixed background discretization, relieving tedious efforts for adaptive refinement in traditional mesh-based methods. Applications to the heterogeneous microstructures of Li-ion battery cathodes will be presented to demonstrate the effectiveness of the proposed methods. NN-RKPM is additionally used to inform how crack opening and closure in turn affect the coupled chemical equations and material microstructure. Reference: [1] Baek, J., Chen, J. S., Susuki, K., "Neural Network enhanced Reproducing Kernel Particle Method for Modeling Localizations," International Journal for Numerical Methods in Engineering, Vol. 123, pp 4422-4454, https://doi.org/10.1002/nme.7040, 2022. [2] Baek, J., Chen, J. S., "A Neural Network-Based Enrichment of Reproducing Kernel Approximation for Modeling Brittle Fracture", Computer Methods in Applied Mechanics and Engineering Vol. 410, 116590, 2024.

degradation↗
DOE OSTI2025

Measurement of $\nu_\mu$ CC Interactions With Two-Proton Final State in MINERvA

This dissertation presents a measurement of charged–current (CC) muon–neutrino interactions with exactly two protons and no pions in the final state (CC~$2p\,0\pi$), using data collected by the MINERvA detector in the NuMI medium–energy beam at Fermilab. Such two–proton topologies are a sensitive probe of nuclear dynamics in the few–GeV regime, including multi–nucleon correlations (npnh, notably $2p2h$) and intranuclear final–state interactions (FSI) such as pion absorption and nucleon rescattering. A precise experimental characterization of these processes is essential both for neutrino–interaction theory and for reducing systematic uncertainties in oscillation experiments that rely on accurate modeling of neutrino–nucleus interactions. Events are selected by requiring a $\nu_\mu$ CC interaction with a reconstructed $\mu^-$ and two proton tracks originating from a common vertex in MINERvA’s finely segmented scintillator tracker, with no reconstructed mesons. Muon charge and momentum are constrained by matching to the MINOS Near Detector, while proton identification exploits energy–loss profiles and stopping–proton features. Backgrounds from pion–producing channels that enter the signal region through FSI or reconstruction effects are constrained with data–driven sidebands (Michel–electron and isolated–cluster “blob” samples) and tuned via a simultaneous fit across signal and sideband regions. To correct detector resolution and acceptance effects, the analysis employs iterative Bayesian unfolding with extensive validation: statistical pseudo–experiments, and robustness checks against generator systematic “universes” and additional strong shape warps. Single–differential cross sections are reported for three observables tailored to the two–proton final state: the opening–angle cosine $\cos\!\left(\theta_{pp}\right)$, the leading–proton momentum, and the subleading–proton momentum. Systematic uncertainties include contributions from neutrino flux, interaction modeling (e.g., npnh and resonance parameters, pion FSI), and detector response (calibration, reconstruction efficiencies). The resulting distributions provide targeted constraints on the interplay of multi–nucleon dynamics and FSI that shape CC~$2p\,0\pi$ final states on hydrocarbon. Comparisons to modern GENIE–based simulations highlight kinematic regions where model components require refinement. These measurements thus inform generator tuning and improve the reliability of neutrino–energy reconstruction strategies for current and future long–baseline oscillation programs.

Syrotenko, Vladyslav S. [Tufts U.]↗
DOE OSTI2024

Evaluating Nationwide Supply Chain for Circularity of PET and Olefin Plastics

PET (Polyethylene Terephthalate, #1) and olefin plastics including HDPE (High Density Polyethylene, #2), LDPE/LLDPE (Low Density/Linear Low-Density Polyethylene, #4) and PP (Polypropylene, #5) together comprise nearly 80% of the U.S. plastic market. Due to their high market share and extensive application in packaging these polymers have better potential for circularity than other polymer types. To understand the potential of future scenarios with higher recycling rates, supply chain scenarios for PET and olefin plastic packaging need to be analyzed with increased availability and collection of plastics. Currently there exists a knowledge gap to understand the circular supply chain on a national level. Performing a nationwide analysis introduces certain challenges such as variability in the plastic mix of the recycling stream in different municipalities, lack of collection of some types of plastics, regional differences in cost and estimating a national supply curve based on state level access rate and participation rate. Currently, a limited number of Plastic Reclaimers recycle the nation’s collected plastic involving transportation over long distances surpassing state boundaries. Modeling a nationwide scenario instead of regional/state based scenario will facilitate transportation beyond state boundaries for the development of a circular economy. A Mixed-integer Linear Programming model was developed, to identify the optimal location and capacity of the Material Recovery Facilities (MRFs) nationwide subject to maximizing the profit margin of the industrial entities within the model. We considered three different scenarios including the base case scenario (S1) collecting 2.27 million metric tons/year as well as two additional scenarios with available plastic supply of 1.8 times (S2) and 2.2 times (S3) of the base case scenario. The model identified 166, 275 and 319 counties as potential MRF locations for scenarios S1, S2 and S3 respectively. Compared to the existing number of counties having MRFs in the US, the model results indicated a reduction of 38% of MRFs for S1. For S2 and S3, the number of counties with MRFs increased 3% and 20% respectively compared to counties currently with MRFs. The average profit remains constant between $180-$181/ton regardless of the increased plastic collection.

54 - ENVIRONMENTAL SCIENCES/GLOBAL CLIMATE CHANGE ↗
DOE OSTI2026

Beyond Point Estimates: Benchmarking Uncertainty Quantification Methods on the AION-1 Astronomical Foundation Model

Foundation models for astronomical surveys offer powerful learned representations that can be transferred to downstream regression tasks such as galaxy property estimation. However, point predictions alone are insufficient for scientific inference; reliable uncertainty quantification (UQ) is essential. We compare seven UQ methods on galaxy property regression using frozen AION-1 foundation-model embeddings, predicting redshift, stellar mass, stellar-population age, gas-phase metallicity, and specific star-formation rate, from Legacy Survey photometry/imaging and DESI spectra, with PROVABGS-derived labels. Distribution-free conformal methods achieve marginal coverage within $\sim$1 pp of the nominal 90% across all properties, while non-conformal baselines (Deep Ensembles, MC~Dropout) fail to calibrate reliably. Among conformal approaches, Conformalized Quantile Regression (CQR) delivers the best coverage in the bin with the poorest model predictions. More importantly, only the Locally Valid and Discriminative (LVD) framework -- particularly when operating on AION-1 embeddings -- also provides finite-sample \emph{local validity}, producing intervals that adapt to each galaxy's local prediction difficulty rather than relying on marginal guarantees alone. These results establish conformal prediction, and LVD in particular, as the preferred UQ framework for uncertainty-aware inference on foundation-model embeddings in astrophysics.

Tame-Narvaez, Karla [Fermilab] (ORCID:000000022249↗