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DOE OSTI2024

Debunking common myths in coastal circulation modeling

Despite tremendous progress in algorithm development, computational efficiency and transition into operations over the past two decades, coastal modeling still lacks scientific rigor due to proliferation of many ‘gray’ areas related to various modeling choices made by modelers. Here, in this paper, we propose some guiding principles for the modeling community to improve performance, and we also debunk commonly held myths that make the coastal modeling lack rigor. Using our own experience in developing seamless cross-scale unstructured-grid based models for the past two decades, we describe in unprecedented detail the end-to-end modeling process (i.e., from digital elevation models (DEMs) to mesh generation to post analysis), and demonstrate that defensible modeling is within reach for any end user by following three guiding principles: (1) Bathymetry is a first order forcing in coastal domains and thus should be respected in all aspects of modeling; (2) Oceanographic processes are driven across multiple spatial scales and so models should enable appropriate resolution as needed; and (3) Model assessment should focus on physical processes. Through qualitative and quantitative model assessments, we demonstrate the fundamental role played by bathymetry/topography as embedded in DEMs in making the results defensible, which is unfortunately glossed over in many modeling studies. Focusing on process-based assessment simplifies the calibration process. A major conclusion of this work is that model developers and operators should maximize the scientific rigor for in silico oceanography by avoiding some common pitfalls that rely on error compensation at the expense of representation of physical system processes. We present some best practice procedures for defensive and trustworthy numerical modeling.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

SetBERT: the deep learning platform for contextualized embeddings and explainable predictions from high-throughput sequencing

MOTIVATION: High-throughput sequencing (HTS) is a modern sequencing technology used to profile microbiomes by sequencing thousands of short genomic fragments from the microorganisms within a given sample. This technology presents a unique opportunity for artificial intelligence to comprehend the underlying functional relationships of microbial communities. However, due to the unstructured nature of HTS data, nearly all computational models are limited to processing DNA sequences individually. This limitation causes them to miss out on key interactions between microorganisms, significantly hindering our understanding of how these interactions influence the microbial communities as a whole. Furthermore, most computational methods rely on post-processing of samples which could inadvertently introduce unintentional protocol-specific bias. RESULTS: Addressing these concerns, we present SetBERT, a robust pre-training methodology for creating generalized deep learning models for processing HTS data to produce contextualized embeddings and be fine-tuned for downstream tasks with explainable predictions. By leveraging sequence interactions, we show that SetBERT significantly outperforms other models in taxonomic classification with genus-level classification accuracy of 95%. Furthermore, we demonstrate that SetBERT is able to accurately explain its predictions autonomously by confirming the biological-relevance of taxa identified by the model. AVAILABILITY AND IMPLEMENTATION: All source code is available at https://github.com/DLii-Research/setbert. SetBERT may be used through the q2-deepdna QIIME 2 plugin whose source code is available at https://github.com/DLii-Research/q2-deepdna.

Ludwig, David W↗

DOE OSTI2024

Street-level temperature estimation using graph neural networks: Performance, feature embedding and interpretability

Estimating street-level air temperature is a challenging task due to the highly heterogeneous urban surfaces, canyon-like street morphology, and the diverse physical processes in the built environment. Though pioneering studies have embarked on investigations via data-driven approaches, many questions remain to be answered. Here, in this study, we leveraged an innovative framework and redefined the street-level temperature estimation problem using Graph Neural Networks (GNN) with spatial embedding techniques. The results showed that GNN models are more capable and consistent of estimating street-level temperature among tested locations, benefiting from its unique strength in handling extensive data over unstructured graph topology. In addition, we conducted in-depth analysis of feature importance to enhance the model interpretability. Among the urban features analyzed in this study, the time-variant canopy density and meter-level land use data emerge as crucial factors. Our findings highlight GNN 's high potential in capturing the complex dynamics between urban elements and their impacts on microclimate, thus offering valuable insights for comprehensive urban data collection and urban climate modeling in general. Collectively, this study also contributes to urban planning and policy by providing avenues to enhance city resilience against climate change, thereby advancing the agenda for environmental stewardship and urban sustainability.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1991

A model for the infrared emission from an OB star cluster environment

A model for the infrared emission from the neighborhood of an OB star cluster is described. The distribution of gas and dust around the stars, properties of the dust, and the cluster and interstellar radiation fields are variable. The model can be applied to regions around clusters embedded to various degrees in their parental molecular clouds (i.e., compact H II regions, blister-type H II regions, and the tenuous H II regions ionized by naked O stars). The model is used to simulate IRAS observations of a typical blister H II region. Infrared surface brightness and spectral energy distributions are predicted and the impact of limited spatial resolution is illustrated. The model results are shown to be consistent with observations of the exemplary outer Galaxy OB cluster NGC 7380. It is planned to use the model as a diagnostic tool to probe the physical conditions and dust properties in star-formation regions and, ultimately, in an interpretation of the spectral energy distributions of spiral galaxies.

Leisawitz, D.↗

NASA NTRS2015

Connecting Requirements to Architecture and Analysis via Model-Based Systems Engineering

In traditional systems engineering practice, architecture, concept development, and requirements development are related but still separate activities. Concepts for operation, key technical approaches, and related proofs of concept are developed. These inform the formulation of an architecture at multiple levels, starting with the overall system composition and functionality and progressing into more detail. As this formulation is done, a parallel activity develops a set of English statements that constrain solutions. These requirements are often called "shall statements" since they are formulated to use "shall." The separation of requirements from design is exacerbated by well-meaning tools like the Dynamic Object-Oriented Requirements System (DOORS) that remained separated from engineering design tools. With the Europa Clipper project, efforts are being taken to change the requirements development approach from a separate activity to one intimately embedded in formulation effort. This paper presents a modeling approach and related tooling to generate English requirement statements from constraints embedded in architecture definition.

Cole, Bjorn F.↗

NASA NTRS2012

Development of Field Excavator with Embedded Force Measurement

A semi-intelligent excavation mechanism was developed for use with the NASA-built Centaur 2 rover prototype. The excavator features a continuously rotatable large bucket supported between two parallel arms, both of which share a single pivot axis near the excavator base attached to the rover. The excavator is designed to simulate the collection of regolith, such as on the Moon, and to dump the collected soil into a hopper up to one meter tall for processing to extract oxygen. Because the vehicle can be autonomous and the terrain is generally unknown, there is risk of damaging equipment or using excessive power when attempting to extract soil from dense or rocky terrain. To minimize these risks, it is critical for the rover to sense the digging forces and adjust accordingly. It is also important to understand the digging capabilities and limitations of the excavator. This paper discusses the implementation of multiple strain gages as an embedded force measurement system in the excavator's arms. These strain gages can accurately measure and resolve multi-axial forces on the excavator. In order to validate these sensors and characterize the load capabilities, a series of controlled excavation tests were performed at Glenn Research Center with the excavator at various depths and cut angles while supported by a six axis load cell. The results of these tests are both compared to a force estimation model and used for calibration of the embedded strain gages. In addition, excavation forces generated using two different types of bucket edge (straight vs. with teeth) were compared.

Soils↗

NASA NTRS2008

New Developments in the Embedded Statistical Coupling Method: Atomistic/Continuum Crack Propagation

A concurrent multiscale modeling methodology that embeds a molecular dynamics (MD) region within a finite element (FEM) domain has been enhanced. The concurrent MD-FEM coupling methodology uses statistical averaging of the deformation of the atomistic MD domain to provide interface displacement boundary conditions to the surrounding continuum FEM region, which, in turn, generates interface reaction forces that are applied as piecewise constant traction boundary conditions to the MD domain. The enhancement is based on the addition of molecular dynamics-based cohesive zone model (CZM) elements near the MD-FEM interface. The CZM elements are a continuum interpretation of the traction-displacement relationships taken from MD simulations using Cohesive Zone Volume Elements (CZVE). The addition of CZM elements to the concurrent MD-FEM analysis provides a consistent set of atomistically-based cohesive properties within the finite element region near the growing crack. Another set of CZVEs are then used to extract revised CZM relationships from the enhanced embedded statistical coupling method (ESCM) simulation of an edge crack under uniaxial loading.

Saether, E.↗

NASA NTRSDate not supplied

Out-of-plane behavior of selectively stitched curved beams subjected to four-point bending

Through-the-thickness stitching can be used to provide a pathway to reduce interlaminar damage in laminated polymer composites. To decrease the amount of stitching, a selective stitching process is necessary to reduce the total manufacturing time in stitched polymer composites. In this study, L-shaped laminates are stitched in the radius section to arrest out-of-plane damage that can occur when subjected to four-point bending loads. A cohesive zone finite element model of the L-shaped laminate was developed. An embedded spring element approach was used to represent the through-the-thickness reinforcements within the finite element model. Specimens with stitches in the radius region of the L-shaped laminate were found to have greater maximum loads before failure. The through-the-thickness stitches are observed to locally reduce the radial stresses in the radius region of the L-shaped laminate, which results in delamination growth away from the radius centerline and greater load-carrying capability.

Stitched Composites↗

NASA NTRS2018

Metabrain for Embedded Cognition (MBEC)

This study presents the application of Hidden Markov Models (HMM) to determine specialized features without expert input. Specifically, the application of such a method for classification of high multi-path fading is targeted, for demonstrating the feasibility of such an approach. This is the first step in the development of a meta-brain for embedded cognition (M-BEC) suite that can be used to apply machine learning to various communication systems at NASA GRC. The project explores the concept of fading and how it affects communication systems in a negative way. Currently, supervised learning methods are used to study the effects of fading on space links. However, such models rely on expert features to make predictions as to the state of a link and whether fading is present. This project offers the possibility of having the HMM learn what characteristics are important and make predictions based on those characteristics. This project explores Hidden Markov Models, their theory and applications to various problems, as well as the underlying equations and assumptions. A preliminary result is presented and recommendations are made as to the use of such an approach for communications systems.

Propagation↗

NASA NTRS2005

Managed Development Environment Successes for MSFC's VIPA Team

This paper outlines the best practices of the Vehicle Design Team for VIPA. VIPA is the Vehicle Integrated Performance Analysis Team at MSFC. This team was created to reconnect the individual engineering disciplines to be able to perform system level technical assessments in support of program decisions. The functions of the VIPA Vehicle Design (VVD) discipline team are to maintain the controlled reference geometry and provide linked, simplified geometry for each of the other discipline analyses. The core of the VVD work, and the approach for VVD s first task of controlling the reference geometry, involves systems engineering, top-down, layout-based CAD modeling within a Product Data Manager (PDM) development environment. The topdown approach allcws for simple contrnl of very large, integrated assemblies and greatly enhances the ability to generate trade configurations and reuse data. The second W D task, model simplification for analysis, is handled within the managed environment through application of the master model concept. In this approach, there is a single controlling, or master, product definition dataset. Connected to this master model are reference datasets with live geometric and expression links. The referenced models can be for drawings, manufacturing, visualization, embedded analysis, or analysis simplification. A discussion of web based interaction, including visualization, between the design and other disciplines is included. Demonstrated examples are cited, including the Space Launch Initiative development cycle, the Saturn V systems integration and verification cycle, an Orbital Space Plane study, and NASA Exploration Office studies of Shuttle derived and clean sheet launch vehicles. The VIPA Team has brought an immense amount of detailed data to bear on program issues. A central piece of that success has been the Managed Development Environment and the VVD Team approach to modeling.

Finckenor, Jeff↗

NASA NTRS2005

Managed Development Environment Successes for MSFC's VIPA Team

This paper outlines the best practices of the Vehicle Design Team for VIPA. The functions of the VIPA Vehicle Design (VVD) discipline team are to maintain the controlled reference geometry and provide linked, simplified geometry for each of the other discipline analyses. The core of the VVD work, and the approach for VVD s first task of controlling the reference geometry, involves systems engineering, top-down, layout-based CAD modeling within a Product Data Manager (PDM) development environment. The top- down approach allows for simple control of very large, integrated assemblies and greatly enhances the ability to generate trade configurations and reuse data. The second VVD task, model simplification for analysis, is handled within the managed environment through application of the master model concept. In this approach, there is a single controlling, or master, product definition dataset. Connected to this master model are reference datasets with live geometric and expression links. The referenced models can be for drawings, manufacturing, visualization, embedded analysis, or analysis simplification. A discussion of web based interaction, including visualization, between the design and other disciplines is included. Demonstrated examples are cited, including the Space Launch Initiative development cycle, the Saturn V systems integration and verification cycle, an Orbital Space Plane study, and NASA Exploration Office studies of Shuttle derived and clean sheet launch vehicles. The VIPA Team has brought an immense amount of detailed data to bear on program issues. A central piece of that success has been the Managed Development Environment and the VVD Team approach to modeling.

Finckenor, Jeff↗

DOE OSTI2026

Machine learning for seismic low-frequency extrapolation

The cycle-skipping problem that plagues full waveform inversion (FWI) can be at least partially mitigated if low frequencies (which encode the kinematics of wave propagation in seismic data) are recorded. However, seismic sources and receivers are band-limited, so seismic data does not generally include signals down to 0 Hz. To improve our ability to solve the seismic inverse problem, one can synthesize this missing low-frequency (LF) content from the recorded high-frequency (HF) data using machine learning (ML) models. Deep learning models such as convolutional neural networks (CNNs) demonstrate impressive ability to perform low frequency extrapolation. However, such models require powerful hardware (GPU machines) and careful training. We assess the extrapolation capabilities of three different ML models that do not require GPU machines, namely, random forest, Gaussian process regression and gradient boosting, on both synthetic and real data. Experimental results on two synthetic data sets (generated from a low velocity lens embedded in a homogeneous medium, and the Marmousi model) demonstrate that FWI applied to the extrapolated data consistently improves inversion accuracy relative to FWI applied to the original data sets that do not contain low frequencies. Application of low-frequency extrapolation to real data from the Northwest Shelf of Australia demonstrates that tree-based ML models such as gradient boosting can outperform CNNs in terms of both accuracy and computational cost on non-GPU architectures.

58 GEOSCIENCES↗

NASA NTRS2012

Designing Fault-Injection Experiments for the Reliability of Embedded Systems

This paper considers the long-standing problem of conducting fault-injections experiments to establish the ultra-reliability of embedded systems. There have been extensive efforts in fault injection, and this paper offers a partial summary of the efforts, but these previous efforts have focused on realism and efficiency. Fault injections have been used to examine diagnostics and to test algorithms, but the literature does not contain any framework that says how to conduct fault-injection experiments to establish ultra-reliability. A solution to this problem integrates field-data, arguments-from-design, and fault-injection into a seamless whole. The solution in this paper is to derive a model reduction theorem for a class of semi-Markov models suitable for describing ultra-reliable embedded systems. The derivation shows that a tight upper bound on the probability of system failure can be obtained using only the means of system-recovery times, thus reducing the experimental effort to estimating a reasonable number of easily-observed parameters. The paper includes an example of a system subject to both permanent and transient faults. There is a discussion of integrating fault-injection with field-data and arguments-from-design.

White, Allan L.↗

DOE OSTI2026

Stakeholder-guided holistic, Adaptive Framework for enhancing community Energy Resilience (SAFER) (Final Technical Report)

The Stakeholder-guided holistic, Adaptive Framework for enhancing community Energy Resilience (SAFER) project advances resilience science and engineering by addressing challenges in rural Kansas communities where aging infrastructure, extreme weather, and socioeconomic disparities heighten vulnerability to energy disruptions. Traditional approaches often focus on technical performance while overlooking community concerns and priorities. SAFER responds by integrating community perspectives with advanced analytical frameworks to create a holistic model for measuring and improving resilience. Project objectives included developing novel resilience metrics, advancing modeling frameworks that capture interdependencies across infrastructures, and embedding community-centric indicators directly into planning processes for distributed energy resources. The key technical innovations included the creation of self-organizing map (SOM)-based indices for objective resilience quantification, hetero-functional graph theory (HFGT) models linking power, water, transportation, and community assets, and graph neural network (GNN) tools for identifying critical nodes in complex systems. Community-centric energy planning was demonstrated through optimal siting and sizing of (photovoltaic) PV and battery storage, ensuring resilience enhancements also addressed energy burden and energy insecurity. SAFER engaged community partners in Dodge City and Ford County through surveys, focus groups, and workshops, generating more than 600 responses that established baseline measures of energy burden, financial insecurity, and willingness-to-pay to avoid outages. This data, organized in terms of a community capitals framework, informed the development of weighted reliability indices that better reflect community costs than traditional utility metrics. SAFER’s GNN-based critical node identification framework identified expert-labelled critical nodes with over 99% accuracy, while also uncovering additional functionalities essential for proactive resilience planning. The project’s models demonstrated that optimal PV and storage deployment could improve resilience indices by over 11 percent, with dispatch strategies further enhancing outcomes, confirming both the technical effectiveness and economic feasibility of these approaches. Through its combined emphasis on rigorous modeling, community-focused planning, and community engagement, SAFER advances the state of resilience research while delivering direct benefits to rural communities. The project provides tools, guidelines, and resilience heatmaps that help utilities, local governments, and residents better anticipate disruptions, prioritize investments, and strengthen the capacity to withstand and recover from energy-related hazards. Furthermore, the developed HFG and GNN frameworks are designed for transferability, allowing them to be adapted for resilience planning in other communities with minimal retraining. This inductive learning capability provides a scalable pathway to extend the SAFER project’s impact. Thus, creating a foundation for a nationally applicable model of infrastructure resilience. Additionally, the HFG can also be extended to include other FEMA community lifelines.

14 SOLAR ENERGY↗

NASA NTRS1994

Design of a cooperative problem-solving system for en-route flight planning: An empirical evaluation

Both optimization techniques and expert systems technologies are popular approaches for developing tools to assist in complex problem-solving tasks. Because of the underlying complexity of many such tasks, however, the models of the world implicitly or explicitly embedded in such tools are often incomplete and the problem-solving methods fallible. The result can be 'brittleness' in situations that were not anticipated by the system designers. To deal with this weakness, it has been suggested that 'cooperative' rather than 'automated' problem-solving systems be designed. Such cooperative systems are proposed to explicitly enhance the collaboration of the person (or a group of people) and the computer system. This study evaluates the impact of alternative design concepts on the performance of 30 airline pilots interacting with such a cooperative system designed to support enroute flight planning. The results clearly demonstrate that different system design concepts can strongly influence the cognitive processes and resultant performances of users. Based on think-aloud protocols, cognitive models are proposed to account for how features of the computer system interacted with specific types of scenarios to influence exploration and decision making by the pilots. The results are then used to develop recommendations for guiding the design of cooperative systems.

Layton, Charles↗

NASA NTRS1994

Design of a cooperative problem-solving system for en-route flight planning: An empirical evaluation

Both optimization techniques and expert systems technologies are popular approaches for developing tools to assist in complex problem-solving tasks. Because of the underlying complexity of many such tasks, however, the models of the world implicitly or explicitly embedded in such tools are often incomplete and the problem-solving methods fallible. The result can be 'brittleness' in situations that were not anticipated by the system designers. To deal with this weakness, it has been suggested that 'cooperative' rather than 'automated' problem-solving systems be designed. Such cooperative systems are proposed to explicitly enhance the collaboration of the person (or a group of people) and the computer system. This study evaluates the impact of alternative design concepts on the performance of 30 airline pilots interacting with such a cooperative system designed to support en-route flight planning. The results clearly demonstrate that different system design concepts can strongly influence the cognitive processes and resultant performances of users. Based on think-aloud protocols, cognitive models are proposed to account for how features of the computer system interacted with specific types of scenarios to influence exploration and decision making by the pilots. The results are then used to develop recommendations for guiding the design of cooperative systems.

Layton, Charles↗

NASA NTRS2004

O VI Emission from the Interstellar Medium

This project's primary goal was to examine the Local Bubble, a large hot bubble surrounding the solar neighborhood. In order to do this, we observed the 1032 and 1038 A resonance line emission from O VI in the bubble and used the,results to comment on models for the Local Bubble and its embedded clouds. In order to maximize the signal to noise of our spectrum, we combined the awarded guest investigator observation with unpublished FUSE In Orbit Checkout observations. The resulting spectrum was sufficiently good as to enable us to place tight 2 sigma upper limits on the intensities of the 1032 and the 1038 A resonance lines. We also measured or placed upper limits on the other cosmic lines in the bandpass, including C III and C II. These are the first known ultraviolet emission line measurements and/or upper limits for the gas in the Local Bubble (as opposed to gas anywhere along long lines of sight). With the O VI upper limits, we were able to quantitatively evaluate competing theories for the origins of the Local Bubble. The upper limits are well below those expected in the Breitschwerdt model (which proposes that during its its early development, the Local Bubble rapidly expanded beyond its nascent cloud and, as a result, is now vastly underionized). The upper limits on the O VI resonance line doublet intensity and the measurement of the C III intensity, garnered from this project, combined with measurements of the O VI column density, garnered from another project, are so far below the predictions, that they make a good case for eliminating the Breitschwerdt model from the field of possibilities. Thus, instead of being vastly underionized, the Local Bubble is near ionizational equilibrium. In addition, the upper limits challenge the other well-known model for the Local Bubble. In that model, the Local Bubble was blown by a series of supernova explosions and winds and contains a myriad of evaporating clouds. The intensity of the O VI resonance line doublet predicted from models exceeds the observationally determined 2 sigma upper limits. The discrepancy prompts the exciting suggestion that there may be some astrophysics yet to be considered in standard hydrodynamic models. These measurements performed a second useful function. By subtract- ing them from the O VI intensity observed on long sight-lines through the Galactic halo and Local Bubble, we were able to estimate the intensity originating the Galactic halo.

Shelton, Robin L.↗

NASA NTRS1994

Physical processes in eclipsing pulsars: Eclipse mechanisms and diagnostics

We investigate how the radio emission of a pulsar interacts with plasma derived from a stellar companion. Various physical mechanisms that can cause radio pulse eclipse are discussed, and predictions are made for the polarization properties of the emergent radio wave. We consider eclipses by a wind from the stellar companion, by a stellar magnetosphere, or by material entrained in the pulsar wind. Eclipses due to refraction require either a relatively high plasma density or a sharp edge to the plasma distribution. The conditions that must prevail for free-free absorption to be effective in eclipsing a radio beam are also outlined. Pulse smearing may be important at higher frequencies; related eclipse mechanisms include pulse spreading due to a rapidly changing electron column, and scattering by Langmuir turbulence. The high brightness temperature radio beam can generate its own plasma turbulence via a number of nonlinear parametric instabilities, such as the instability associated with stimulated Raman scattering. When the plasma turbulence is heavily damped, the radio bean can still undergo induced Compton scattering. Stimulated scattering effects such as these are very sensitive to the presence of narrow-band substructure in the pulsar radio emission. Finally, we consider the possibility that plasma derived from a stellar companion may mix with the relativistic pulsar wind and cause cyclotron absorption at low radio frequencies. Even if the cyclotron optical depth is small, fluctuations in the emergent polarization of the radio beam on the timescale of a few seconds are a very sensitive probe of the spatial structure of the magnetic field in the pulsar wind. The current observational properties of two known eclipsing pulsar systems, PSR 1957+20 and PSR 1744-24A, are used to construct tentative eclipse models. The favored model for PSR 1957+20 is cyclotron or synchrotron absorption by plasma embedded in the pulsar wind combined with pulse smearing at high frequency, and the favored model for PSR 1744-24A is backscattering off plasma turbulence generated by the stimulated Raman scattering parametric instability. Pulsar eclipses promise to provide a good diagnostic of pulsar winds and possible of the pulse emission mechanism.

Thompson, C.↗