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Machine Learning for NASA Advanced Information Systems

NASA's Advanced Information Systems Technology (AIST) Program is one of several Technology programs managed by the Earth Science Technology Office (ESTO) in the Earth Science Division (ESD). The AIST Program focuses on advanced information systems and novel computer science technologies that will be needed by NASA Earth Science in the next 5 to 10 years. The three main thrusts of the AIST Program deal with Novel Observing Strategies (NOS), Analytic Collaborative Frameworks (ACF) and Earth System Digital Twins (ESDT). For all these thrusts, Machine Learning (ML) is increasingly being used in multiple aspects of Earth science systems, e.g., for onboard autonomy and decision making, for the analysis of massive and diverse datasets as well as more recently for developing surrogate models that will represent one of the main components of future Digital Twins of the Earth. Particularly, ESDT technologies developed by the AIST Program will allow to develop integrated Earth Science frameworks that will mirror the Earth with state-of-the-art models (Earth system models and others), timely and relevant observations, and analytic tools. These information systems will be used for supporting near- and long-term science and policy decisions. ESDT frameworks will build on previously developed AIST capabilities and technologies to integrate interconnected models with continuous streams of observations, data analytics, data assimilation, simulations, advanced visualizations and the ability to conduct "what-if" scenarios. This talk will describe the three thrusts of the AIST Program with a special focus on Machine Learning and how it is being used at all steps of the Earth Science data lifecycle.

Mathematical and Computer Sciences (General)↗

DOE OSTI2023

Scalable simulation of coupled adsorption and transport of methane in confined complex porous media with density preconditioning

The growing significance of shales and tight formations in the transition to less carbon-intensive and clean energy drives the research endeavor to understand the physics of gas flow within these systems. However, shales are composed of massively heterogeneous physical and chemical features. Most nano-sized pores connect to millimeter-scale fractures, leading to multiscale transport. These nano-scale pore throats demonstrate non-classical flow behavior, such as non-negligible slip velocities and adsorbed gas layers at the boundary. As a result, classical computational fluid dynamics models do not capture the physics. In this work, we develop a coupling scheme for the multiple-relaxation-time (MRT) lattice Boltzmann (LB) method that integrates the Peng-Robinson equation of state into a pseudo-potential interaction model to capture the physics of methane flow in irregular networks of channels that represent nano-scale porous media. We use atomistic simulations to calibrate and validate our model in slit nano-channels. We propose a preconditioning scheme to initialize the coupled transport and adsorption simulation of methane in complex porous media. The results of this implementation of LB agree with Direct Simulation Monte Carlo (DSMC) and Molecular Dynamics (MD) simulations. We then scale up the LB implementation through vectorization and indirect addressing. We parallelize it using Message Passing Interface (MPI) and OpenMP frameworks to simulate transport and adsorption in complex media with a million lattices. Additionally, we analyze the differences between coupled and transport-only simulations in two case studies and show that considering phase behavior, i.e., adsorption, can significantly change the flow behavior. This work constitutes an important step towards bridging the gap between molecular flow and system-scale behavior of complex disordered porous media.

42 ENGINEERING↗

DOE OSTI2020

PANTHER: A Programmable Architecture for Neural Network Training Harnessing Energy-Efficient ReRAM

The wide adoption of deep neural networks has been accompanied by ever-increasing energy and performance demands due to the expensive nature of training them. Additionally, numerous special-purpose architectures have been proposed to accelerate training: both digital and hybrid digital-analog using resistive RAM (ReRAM) crossbars. ReRAM-based accelerators have demonstrated the effectiveness of ReRAM crossbars at performing matrix-vector multiplication operations that are prevalent in training. However, they still suffer from inefficiency due to the use of serial reads and writes for performing the weight gradient and update step. A few works have demonstrated the possibility of performing outer products in crossbars, which can be used to realize the weight gradient and update step without the use of serial reads and writes. However, these works have been limited to low precision operations which are not sufficient for typical training workloads. Moreover, they have been confined to a limited set of training algorithms for fully-connected layers only. To address these limitations, we propose a bit-slicing technique for enhancing the precision of ReRAM-based outer products, which is substantially different from bit-slicing for matrix-vector multiplication only. We incorporate this technique into a crossbar architecture with three variants catered to different training algorithms. To evaluate our design on different types of layers in neural networks (fully-connected, convolutional, etc.) and training algorithms, we develop PANTHER, an ISA-programmable training accelerator with compiler support. Our design can also be integrated into other accelerators in the literature to enhance their efficiency. Our evaluation shows that PANTHER achieves up to 8.02×, 54.21×, and 103× energy reductions as well as 7.16×, 4.02×, and 16× execution time reductions compared to digital accelerators, ReRAM-based accelerators, and GPUs, respectively.

42 ENGINEERING↗

NASA NTRS1986

Aerobic Fitness Does Not Contribute to Prediction of Orthostatic Intolerance

Several investigations have suggested that orthostatic tolerance may be inversely related to aerobic fitness (VO (sub 2max)). To test this hypothesis, 18 males (age 29 to 51 yr) underwent both treadmill VO(sub 2max) determination and graded lower body negative pressures (LBNP) exposure to tolerance. VO(2max) was measured during the last minute of a Bruce treadmill protocol. LBNP was terminated based on pre-syncopal symptoms and LBNP tolerance (peak LBNP) was expressed as the cumulative product of LBNP and time (torr-min). Changes in heart rate, stroke volume cardiac output, blood pressure and impedance rheographic indices of mid-thigh-leg initial accumulation were measured at rest and during the final minute of LBNP. For all 18 subjects, mean (plus or minus SE) fluid accumulation index and leg venous compliance index at peak LBNP were 139 plus or minus 3.9 plus or minus 0.4 ml-torr-min(exp -2) x 10(exp 3), respectively. Pearson product-moment correlations and step-wise linear regression were used to investigate relationships with peak LBNP. Variables associated with endurance training, such as VO(sub 2max) and percent body fat were not found to correlate significantly (P is less than 0.05) with peak LBNP and did not add sufficiently to the prediction of peak LBNP to be included in the step-wise regression model. The step-wise regression model included only fluid accumulation index leg venous compliance index, and blood volume and resulted in a squared multiple correlation coefficient of 0.978. These data do not support the hypothesis that orthostatic tolerance as measured by LBNP is lower in individuals with high aerobic fitness.

Convertino, Victor A.↗

NASA NTRS2018

Compensation of Oxygen Transmittance Effects for Proximal Sensing Retrieval of Canopy-Leaving Sun-Induced Chlorophyll Fluorescence

Estimates of Sun-Induced vegetation chlorophyll Fluorescence (SIF) using remote sensing techniques are commonly determined by exploiting solar and/or telluric absorption features. When SIF is retrieved in the strong oxygen (O2) absorption features, atmospheric effects must always be compensated. Whereas correction of atmospheric effects is a standard airborne or satellite data processing step, there is no consensus regarding whether it is required for SIF proximal-sensing measurements nor what is the best strategy to be followed. Thus, by using simulated data, this work provides a comprehensive analysis about how atmospheric effects impact SIF estimations on proximal sensing, regarding: (1) the sensor height above the vegetated canopy; (2) the SIF retrieval technique used, e.g., Fraunhofer Line Discriminator (FLD) family or Spectral Fitting Methods (SFM); and (3) the instrument's spectral resolution. We demonstrate that for proximal-sensing scenarios compensating for atmospheric effects by simply introducing the O2 transmittance function into the FLD or SFM formulations improves SIF estimations. However, these simplistic corrections still lead to inaccurate SIF estimations due to the multiplication of spectrally convolved atmospheric transfer functions with absorption features. Consequently, a more rigorous oxygen compensation strategy is proposed and assessed by following a classic airborne atmospheric correction scheme adapted to proximal sensing. This approach allows compensating for the O2 absorption effects and, at the same time, convolving the high spectral resolution data according to the corresponding Instrumental Spectral Response Function (ISRF) through the use of an atmospheric radiative transfer model. Finally, due to the key role of O2 absorption on the evaluated proximal-sensing SIF retrieval strategies, its dependency on surface pressure (p) and air temperature (T) was also assessed. we combined simulated spectral data with p and T measurements obtained for a one-year period in the Hyytiala Forestry Field Station in Finland. Of importance hereby is that seasonal dynamics in terms of T and p, if not appropriately considered as part of the retrieval strategy, can result in erroneous SIF seasonal trends that mimic those of known dynamics for temperature-dependent physiological responses of vegetation.

"sunâ€“induced chlorophyll fluorescence (SIF); pro↗

DOE OSTI2022

Revenue prediction for integrated renewable energy and energy storage system using machine learning techniques

Revenue estimation for integrated renewable energy and energy storage systems is important to support plant owners or operators’ decisions in battery sizing selection that leads to maximized financial performances. A common approach to optimizing revenues of a hybrid hydro and energy storage system is using mixed-integer linear programming (MILP). Although MILP models can provide accurate production cost estimations, they are typically very computationally expensive. To provide a fast yet accurate first-step information to hydropower plant owners or operators who consider integrating energy storage systems, we propose an innovative approach to predicting optimal revenues of an integrated energy generation and storage system. In this study, we examined the performance of two prediction techniques: Generalized Additive Models (GAMs) and machine learning (ML) models developed based on artificial neural networks (ANN). Predictive equations and models are generated based on optimized solutions from a market participation optimization model, the Conventional Hydropower Energy and Environmental Resource System (CHEERS) model. The two predicting techniques reduce the computational time to evaluate annual revenue for one set of battery configurations from 3 h to 1 to 4 min per run while also being implementable with significantly less data. The model validation prediction errors of developed GAMs and ML models are generally below 5%; for model testing predictions, the ML models consistently outperform the regression equations in terms of root mean square errors. This new approach allows plant owners, operators, or potential investors to quickly access multiple battery configurations under different energy generation and market scenarios. This new revenue prediction method will therefore help reduce the barriers, and thereby promoting the deployment of battery hybridization with existing renewable energy sources.

13 HYDRO ENERGY↗

NASA NTRS2004

Science Opportunity Analyzer (SOA): Science Planning Made Simple

.For the first time at JPL, the Cassini mission to Saturn is using distributed science operations for developing their experiments. Remote scientists needed the ability to: a) Identify observation opportunities; b) Create accurate, detailed designs for their observations; c) Verify that their designs meet their objectives; d) Check their observations against project flight rules and constraints; e) Communicate their observations to other scientists. Many existing tools provide one or more of these functions, but Science Opportunity Analyzer (SOA) has been built to unify these tasks into a single application. Accurate: Utilizes JPL Navigation and Ancillary Information Facility (NAIF) SPICE* software tool kit - Provides high fidelity modeling. - Facilitates rapid adaptation to other flight projects. Portable: Available in Unix, Windows and Linux. Adaptable: Designed to be a multi-mission tool so it can be readily adapted to other flight projects. Implemented in Java, Java 3D and other innovative technologies. Conclusion: SOA is easy to use. It only requires 6 simple steps. SOA's ability to show the same accurate information in multiple ways (multiple visualization formats, data plots, listings and file output) is essential to meet the needs of a diverse, distributed science operations environment.

observation planning operations↗

NASA NTRS2014

3D Printing In Zero-G ISS Technology Demonstration

The National Aeronautics and Space Administration (NASA) has a long term strategy to fabricate components and equipment on‐demand for manned missions to the Moon, Mars, and beyond. To support this strategy, NASA and Made in Space, Inc. are developing the 3D Printing In Zero‐G payload as a Technology Demonstration for the International Space Station (ISS). The 3D Printing In Zero‐G experiment ('3D Print') will be the first machine to perform 3D printing in space. The greater the distance from Earth and the longer the mission duration, the more difficult resupply becomes; this requires a change from the current spares, maintenance, repair, and hardware design model that has been used on the International Space Station (ISS) up until now. Given the extension of the ISS Program, which will inevitably result in replacement parts being required, the ISS is an ideal platform to begin changing the current model for resupply and repair to one that is more suitable for all exploration missions. 3D Printing, more formally known as Additive Manufacturing, is the method of building parts/objects/tools layer‐by‐layer. The 3D Print experiment will use extrusion‐based additive manufacturing, which involves building an object out of plastic deposited by a wire‐feed via an extruder head. Parts can be printed from data files loaded on the device at launch, as well as additional files uplinked to the device while on‐orbit. The plastic extrusion additive manufacturing process is a low‐energy, low‐mass solution to many common needs on board the ISS. The 3D Print payload will serve as the ideal first step to proving that process in space. It is unreasonable to expect NASA to launch large blocks of material from which parts or tools can be traditionally machined, and even more unreasonable to fly up multiple drill bits that would be required to machine parts from aerospace‐grade materials such as titanium 6‐4 alloy and Inconel. The technology to produce parts on demand, in space, offers unique design options that are not possible through traditional manufacturing methods while offering cost-effective, high‐precision, low‐unit on‐demand manufacturing. Thus, Additive Manufacturing capabilities are the foundation of an advanced manufacturing in space roadmap. The 3D Printing In Zero‐G experiment will demonstrate the capability of utilizing Additive Manufacturing technology in space. This will serve as the enabling first step to realizing an additive manufacturing, print‐on‐demand "machine shop" for long‐duration missions and sustaining human exploration of other planets, where there is extremely limited ability and availability of Earth‐based logistics support. Simply put, Additive Manufacturing in space is a critical enabling technology for NASA. It will provide the capability to produce hardware on‐demand, directly lowering cost and decreasing risk by having the exact part or tool needed in the time it takes to print. This capability will also provide the much‐needed solution to the cost, volume, and up‐mass constraints that prohibit launching everything needed for long‐duration or long‐distance missions from Earth, including spare parts and replacement systems. A successful mission for the 3D Printing In Zero‐G payload is the first step to demonstrate the capability of printing on orbit. The data gathered and lessons learned from this demonstration will be applied to the next generation of additive manufacturing technology on orbit. It is expected that Additive Manufacturing technology will quickly become a critical part of any mission's infrastructure.

Werkheiser, Niki↗

NASA NTRS2018

Demonstration of Probabilistic Sensitivity Analyses Tools on the Structural Response of a Representative Inflatable Space Structure

This work provides an initial step toward demonstrating a probabilistic numerical simulation capability to support trade studies and the development of a certification plan for inflatable space habitats. This study concentrates on interpreting the results from probabilistic analysis and numerical simulation tools to identify parameter sensitivities for a novel inflatable airlock concept, specifically the Non‐Axisymmetric Inflatable Pressure Structure (NAIPS) that was designed and tested under NASA's Minimalistic Advanced Softgoods Hatch (MASH) Program. A brief overview of the finite element model is provided along with the probabilistic sensitivity analysis approach. The sensitivity studies required a model that was numerically stable and efficient enough that hundreds of simulations could be completed in the allotted time. Therefore, the existing full model was simplified by: extracting a quarter symmetry section of the dome; focusing on a single inflation pressure; and replacing the non‐linear material stress‐strain curves with linear, isotropic materials defined by elastic moduli. Responses of interest include the sensitivity of various structural component loads to material properties, cord lengths, inflation pressure and friction. Multiple sensitivity studies were completed and three are reported here. The first study focused on utilizing wide input parameter ranges to provide an opportunity to assess numerical robustness. The next two studies narrowed the parameter ranges to enable focus on understanding uncertainty at a fixed operating condition. The completion of the sensitivity studies improved understanding of the interdependence of multiple inputs on the responses. In addition, numerical stability of the simulations over wide parameter ranges, shows the feasibility of incorporating uncertainty‐based methods in the design and certification of inflatable space habitats. With the experience and trust gained, it is anticipated that these same methods will be applied to nonlinear, orthotropic models in the future.

Lyle, Karen H.↗

NASA NTRS2001

Advanced Hall Electric Propulsion for Future In-space Transportation

The Hall thruster is an electric propulsion device used for multiple in-space applications including orbit raising, on-orbit maneuvers, and de-orbit functions. These in-space propulsion functions are currently performed by toxic hydrazine monopropellant or hydrazine derivative/nitrogen tetroxide bi-propellant thrusters. The Hall thruster operates nominally in the 1500 sec specific impulse regime. It provides greater thrust to power than conventional gridded ion engines, thus reducing trip times and operational life when compared to that technology in Earth orbit applications. The technology in the far term, by adding a second acceleration stage, has shown promise of providing over 4000s Isp, the regime of the gridded ion engine and necessary for deep space applications. The Hall thruster system consists of three parts, the thruster, the power processor, and the propellant system. The technology is operational and commercially available at the 1.5 kW power level and 5 kW application is underway. NASA is looking toward 10 kW and eventually 50 kW-class engines for ambitious space transportation applications. The former allows launch vehicle step-down for GEO missions and demanding planetary missions such as Europa Lander, while the latter allows quick all-electric propulsion LEO to GEO transfers and non-nuclear transportation human Mars missions.

Oleson, Steven R.↗

NASA NTRS2003

Exploration of Advanced Probabilistic and Stochastic Design Methods

The primary objective of the three year research effort was to explore advanced, non-deterministic aerospace system design methods that may have relevance to designers and analysts. The research pursued emerging areas in design methodology and leverage current fundamental research in the area of design decision-making, probabilistic modeling, and optimization. The specific focus of the three year investigation was oriented toward methods to identify and analyze emerging aircraft technologies in a consistent and complete manner, and to explore means to make optimal decisions based on this knowledge in a probabilistic environment. The research efforts were classified into two main areas. First, Task A of the grant has had the objective of conducting research into the relative merits of possible approaches that account for both multiple criteria and uncertainty in design decision-making. In particular, in the final year of research, the focus was on the comparison and contrasting between three methods researched. Specifically, these three are the Joint Probabilistic Decision-Making (JPDM) technique, Physical Programming, and Dempster-Shafer (D-S) theory. The next element of the research, as contained in Task B, was focused upon exploration of the Technology Identification, Evaluation, and Selection (TIES) methodology developed at ASDL, especially with regards to identification of research needs in the baseline method through implementation exercises. The end result of Task B was the documentation of the evolution of the method with time and a technology transfer to the sponsor regarding the method, such that an initial capability for execution could be obtained by the sponsor. Specifically, the results of year 3 efforts were the creation of a detailed tutorial for implementing the TIES method. Within the tutorial package, templates and detailed examples were created for learning and understanding the details of each step. For both research tasks, sample files and tutorials are attached in electronic form with the enclosed CD.

Marvis, Dimitri N.↗

NASA NTRS2003

Exploration of Advanced Probabilistic and Stochastic Design Methods

Mavris, Dimitri N.↗

NASA NTRS2013

Dual-Polarization, Multi-Frequency Antenna Array for use with Hurricane Imaging Radiometer

Advancements in common aperture antenna technology were employed to utilize its proprietary genetic algorithmbased modeling tools in an effort to develop, build, and test a dual-polarization array for Hurricane Imaging Radiometer (HIRAD) applications. Final program results demonstrate the ability to achieve a lightweight, thin, higher-gain aperture that covers the desired spectral band. NASA employs various passive microwave and millimeter-wave instruments, such as spectral radiometers, for a range of remote sensing applications, from measurements of the Earth's surface and atmosphere, to cosmic background emission. These instruments such as the HIRAD, SFMR (Stepped Frequency Microwave Radiometer), and LRR (Lightweight Rainfall Radiometer), provide unique data accumulation capabilities for observing sea surface wind, temperature, and rainfall, and significantly enhance the understanding and predictability of hurricane intensity. These microwave instruments require extremely efficient wideband or multiband antennas in order to conserve space on the airborne platform. In addition, the thickness and weight of the antenna arrays is of paramount importance in reducing platform drag, permitting greater time on station. Current sensors are often heavy, single- polarization, or limited in frequency coverage. The ideal wideband antenna will have reduced size, weight, and profile (a conformal construct) without sacrificing optimum performance. The technology applied to this new HIRAD array will allow NASA, NOAA, and other users to gather information related to hurricanes and other tropical storms more cost effectively without sacrificing sensor performance or the aircraft time on station. The results of the initial analysis and numerical design indicated strong potential for an antenna array that would satisfy all of the design requirements for a replacement HIRAD array. Multiple common aperture antenna methodologies were employed to achieve exceptional gain over the entire spectral frequency band while exhibiting superb VSWR (voltage standing wave ratio) values. Element size and spacing requirements were addressed for a direct replacement of the thicker, lower-performance, stack ed patch antenna array currently employed for the HIRAD application. Several variants to the multiband arrays were developed that exhibited four, equally spaced, high efficiency, "sweet spot" frequency bands, as well as the option for a high-performance wideband array. The 0.25-in. (≈6.4- mm) thickness of the antenna stack-up itself was achieved through the application of specialized antenna techniques and meta-materials to accomplish all design objectives.

Little, John↗

DOE OSTI2024

Multicriteria-Based Selection of Microalgae Biorefineries: Biomass Composition Defines the Most Suitable Product Portfolios

The supply of microalgae-derived biofuels and bioproducts will be vital in a global-scale bioeconomy. As the diversity of microalgae strains and their compositional plasticity may yield a wide array of products of market interest, the design of effective biorefineries is a central aspect in the path to making microalgae-based products available in the market. In this way, the conversion of microalgae biomass should be planned to employ mature technologies, while maximizing economic and environmental benefits obtained from a diverse product portfolio. This study applied sequential, hybrid Multicriteria Decision Analyses (MCDA) to aid the decision-making process of outlining the most suitable biorefining pathways for specific compositional profiles. For this, the methodology simultaneously considered multiple technical, economic, and environmental criteria, such as market aspects for the main algae-derived products, potential reduction in greenhouse gas (GHG) emissions provided by the biobased alternatives, and technology readiness level of conversion routes, among others. The framework was tested using productivity and compositional data for 13 high-productivity summer strains cultivated under varying nutrient availability. The analysis pointed to compositional profile being a key driver in defining the core biorefining strategy of algae biomass, with lipid-to-carbohydrate ratios higher than roughly 1 warranting the preferential processing of lipids into hydrocarbon fuels with the remainder of the algae biomass compounds being sent to higher-value applications, such as carboxylic acids and renewable thermoplastic substitutes. An in-depth process simulation, techno-economic assessment (TEA), and life-cycle analysis (LCA) effort was carried out as a closing step to corroborate the results stemming from the proposed framework. This study validates the use of MCDAs as a screening method prior to implementing more time-consuming analysis techniques and makes a compelling case for this approach as a product selection tool on a wide range of algae species, thus helping establish species-agnostic (but composition-driven) biorefineries.

biofuels↗

DOE OSTI2020

Detection of Cyclopropenylidene on Titan with ALMA

We report the first detection on Titan of the small cyclic molecule cyclopropenylidene (c-C{sub 3}H{sub 2}) from high-sensitivity spectroscopic observations made with the Atacama Large Millimeter/submillimeter Array. Multiple lines of cyclopropenylidene were detected in two separate data sets: ∼251 GHz in 2016 (Band 6) and ∼352 GHz in 2017 (Band 7). Modeling of these emissions indicates abundances of 0.50 ± 0.14 ppb (2016) and 0.28 ± 0.08 (2017) for a 350 km step model, which may either signify a decrease in abundance, or a mean value of 0.33 ± 0.07 ppb. Inferred column abundances are (3–5) × 10{sup 12} cm{sup −2} in 2016 and (1–2) × 10{sup 12} cm{sup −2} in 2017, similar to photochemical model predictions. Previously the C{sub 3}H{sub 3}{sup +} ion has been measured in Titan’s ionosphere by Cassini’s Ion and Neutral Mass Spectrometer (INMS), but the neutral (unprotonated) species has not been detected until now, and aromatic versus aliphatic structure could not be determined by the INMS. Our work therefore represents the first unambiguous detection of cyclopropenylidene, the second known cyclic molecule in Titan’s atmosphere along with benzene (C{sub 6}H{sub 6}) and the first time this molecule has been detected in a planetary atmosphere. We also searched for the N-heterocycle molecules pyridine and pyrimidine finding nondetections in both cases, and determining 2σ upper limits of 1.15 ppb (c-C{sub 5}H{sub 5}N) and 0.85 ppb (c-C{sub 4}H{sub 4}N{sub 2}) for uniform abundances above 300 km. These new results on cyclic molecules provide fresh constraints on photochemical pathways in Titan’s atmosphere, and will require new modeling and experimental work to fully understand the implications for complex molecule formation.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS2005

Learning from the Mars Rover Mission: Scientific Discovery, Learning and Memory

Purpose: Knowledge management for space exploration is part of a multi-generational effort. Each mission builds on knowledge from prior missions, and learning is the first step in knowledge production. This paper uses the Mars Exploration Rover mission as a site to explore this process. Approach: Observational study and analysis of the work of the MER science and engineering team during rover operations, to investigate how learning occurs, how it is recorded, and how these representations might be made available for subsequent missions. Findings: Learning occurred in many areas: planning science strategy, using instrumen?s within the constraints of the martian environment, the Deep Space Network, and the mission requirements; using software tools effectively; and running two teams on Mars time for three months. This learning is preserved in many ways. Primarily it resides in individual s memories. It is also encoded in stories, procedures, programming sequences, published reports, and lessons learned databases. Research implications: Shows the earliest stages of knowledge creation in a scientific mission, and demonstrates that knowledge management must begin with an understanding of knowledge creation. Practical implications: Shows that studying learning and knowledge creation suggests proactive ways to capture and use knowledge across multiple missions and generations. Value: This paper provides a unique analysis of the learning process of a scientific space mission, relevant for knowledge management researchers and designers, as well as demonstrating in detail how new learning occurs in a learning organization.

Linde, Charlotte↗

DOE OSTI2025

Development of a River Dynamical Core for E3SM to simulate compound flooding on Exascale-class heterogeneous supercomputers

Flooding events pose significant risk to human life, property, and infrastructure. Physically-consistent quantification of altered flood risks in global models requires hyper-resolution (~1 km) or fine flood simulations using two-dimensional (2D) physics schemes, both of which are unavailable in the current generation Earth System Models. Here, in this work, we have developed the River Dynamical Core (RDycore), which is an open-source, 2D shallow water equation (SWE) library for the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM). RDycore uses PETSc and libCEED libraries that allows it to run efficiently on CPUs and GPUs, as well as select a time-integration algorithm at runtime without requiring any code modifications. RDycore achieves spatial error convergence rates for problems with analytical and manufactured solutions similar to those reported previously in the literature, or consistent with the implemented first-order spatial discretization scheme. RDycore's accuracy in predicting flooding for a well-studied dam break problem is comparable to existing SWE models. For a problem with 471 million grid cells, RDycore achieves a speedup of 6.6x and 7.6x on GPUs compared to CPUs when using 320 compute nodes on DOE's Perlmutter and Frontier supercomputers, respectively. The one-way coupling of the RDycore library within E3SM is demonstrated by performing multiple 5-day flooding simulations during Hurricane Harvey driven by five precipitation datasets. The E3SM--RDycore simulations at 30 m spatial resolution accurately simulate maximum water height during the hurricane when benchmarked against a previously published study and achieve a speedup of 15x (Perlmutter) and 21x (Frontier) on GPUs relative to CPUs. The work presented here is the foundational step in providing hardware and algorithmic portability framework for simulating kilometer-scale river dynamics within E3SM.

Flood Simulation↗

DOE OSTI2026

A Cryogenic Muon Tagging System Integrated with a Superconducting Qubit Device for Radiation-Induced Error Mitigation

Superconducting qubits are highly sensitive to ionizing radiation, which can induce correlated errors and limit scalable fault-tolerant quantum computing. In particular, cosmic-ray muons can deposit energy in the substrate, generating phonon bursts that break Cooper pairs and produce quasiparticles, leading to correlated decoherence events across multiple qubits. We present the development of a cryogenic muon tagging system based on Kinetic Inductance Detectors (KIDs) and its integration with superconducting quantum hardware. Originally developed within the ACE-SuperQ project and validated as a standalone detector, the system demonstrated a muon tagging efficiency of approximately 90% and excellent agreement with Monte Carlo simulations. Building on this validation, the tagging system has been integrated with a multi-qubit superconducting chip operated in a dilution refrigerator. The detector configuration consists of a multi-layer KID stack arranged above and below the quantum device, enabling time-coincident identification of muon-induced events within the same cryogenic environment. The integrated setup has been successfully commissioned, enabling simultaneous operation of the qubit chip and the muon tagging system. A first measurement campaign has been carried out, and preliminary data show time-correlated events between the muon tagging detectors and the qubit readout. A quantitative analysis of radiation-induced effects on qubit performance is currently ongoing. This work represents a step toward the implementation of event-level radiation tagging as a tool for characterizing and potentially mitigating correlated errors in superconducting quantum processors, while establishing a modular platform for future studies at the interface between particle physics and quantum information science.

Roy, Tanay [Fermilab] (ORCID:000000019442862X)↗