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Visualizations to Aid Decision-Making in the ACCP Value Framework

The Aerosols, Clouds, Convection, and Precipitation Value Framework is used to introduce structure, transparency, and traceability into the decision-making process for a study formulating and assessing potential observing system concepts to respond to the 2017-2027 Decadal Survey for Earth Science and Applications. Within the Value Framework, visualizations play an important role in presenting information and structuring conversations around that information. This paper describes three guiding principles that were applied to the design of the visualizations of the Value Framework, presents examples of visualizations that serve at least one of several functions (structuring communication, facilitating elicitation and aggregation of data, and summarizing complex information), and identifies lessons learned from the development and implementation of those visualizations. The guiding principles and lessons learned have not only contributed to the success of the Value Framework and the ACCP study, but also can be applied in other decision-making activities.

Christopher A Jones↗

DOE OSTI2022

Resilient Observer Design for Cyber-Physical Systems with Data-Driven Measurement Pruning

Resilient observer design for Cyber-Physical Systems (CPS) in the presence of adversarial false data injection attacks (FDIA) is an active area of research. The existing state-of-the-art algorithms tend to break down as more and more knowledge of the system is built into the attack model; also as the percentage of attacked nodes increases. From the view of optimization theory, the problem is often cast as a classical error correction problem for which a theoretical limit of has been established as the maximum percentage attacked nodes for which state recovery is guaranteed. Beyond this limit, the performance of -minimization based schemes, for instance, deteriorates rapidly. Similar performance degradation occurs for other types of resilient observers beyond certain percentages of attacked nodes. In order to increase the corresponding percentage of attacked nodes for which state recoveries can be guaranteed, researchers have begun to incorporate prior information into the underlying resilient observer design framework. For the most pragmatic cases, this prior information is often obtained through a data-driven machine learning process. Existing results have shown a strong positive correlation between the maximum attacked percentages that can be tolerated and the accuracy of the data-driven model. Motivated by these results, this chapter examines the case for pruning algorithms designed to improve the Positive Prediction Value (PPV) of the resulting prior information, given stochastic uncertainty characteristics of the underlying machine learning model. Theoretical quantification of the achievable improvement is given. Simulation results show that the pruning algorithm significantly increases the maximum correctable percentage of attacked nodes, even for machine learning model whose prediction power is comparable to the random flip of a coin.

Resilient Observer, Cyber-physical Systems, Data-D↗

DOE OSTI2025

ORNL National Hydropower Fish Passage Database

Fish passage facilities are used to mitigate impacts of hydropower dams to migratory fish in rivers, but information on the location, types, and characteristics of this infrastructure is incomplete at a national scale. Researchers at Oak Ridge National Laboratory (ORNL) partnered with fish passage engineers and hydropower experts from the US Fish and Wildlife Service (USFWS), the National Oceanic Atmospheric Administration’s National Marine Fisheries Service (NOAA NMFS), and the Low Impact Hydropower Institute (LIHI) to create the first national scale database of fish passage infrastructure at US hydropower developments. This database consists of the ORNL_Fish_Passage_Dataset.zip file with 13 individual .csv files that contain information on fish passage facility engineering characteristics, targeted fish species, operational schedule, and costs. which is of great value to a diverse range of stakeholders. This information was collected between December 2023 and July 2025 from project partners, other hydropower stakeholders, online datasets available for download, and a stakeholder questionnaire Information. This data resource addresses a large gap in knowledge of the deployment of fish passage technology and is freely available to members of the hydropower community, including federal and state regulators and resource agencies, non-governmental organizations (NGOs), industry, and other user groups to support project planning and regulatory (re)licensing activities. This database supports the US Department of Energy Water Power Technologies Office objective to develop decision support tools and data resources that improve environmental performance and ensure hydropower’s long-term value to the American public.

Matson, Paul [Oak Ridge National Laboratory (ORNL)↗

DOE OSTI2021

Binary Complex Neural Network Acceleration on FPGA

Being able to learn from complex data with phase information is imperative for many signal processing applications. Today’s real-valued deep neural networks (DNNs) have shown efficiency in latent information analysis but fall short when applied to the complex domain. Deep complex networks (DCN) , in contrast, can learn from complex data, but have high computational costs; therefore, they cannot satisfy the instant decision making requirements of many deployable systems dealing with short observations or short signal bursts. Recent, Binarized Complex Neural Network (BCNN), which integrates DCNs with binarized neural networks (BNN), shows great potential in classifying complex data in real-time. In this paper, we propose a structural pruning based accelerator of BCNN, which is able to provide more than 5000 frames/s inference throughput on edge devices. The high performance comes from both the algorithm and hardware sides. On the algorithm side, we conduct structural pruning to the original BCNN models and obtain 20 × pruning rates with negligible accuracy loss; on the hardware side, we propose a novel 2D convolution operation accelerator for the binary complex neural network. Experimental results show that the proposed design works with over 90% utilization and is able to achieve the inference throughput of 5882 frames/s and 4938 frames/s for complex NIN-Net and ResNet-18 using CIFAR-10 dataset and Alveo U280 Board.

Peng, Hongwu↗

NASA NTRS1995

Visualizing turbulent mixing of gases and particles

A physical model and interactive computer graphics techniques have been developed for the visualization of the basic physical process of stochastic dispersion and mixing from steady-state CFD calculations. The mixing of massless particles and inertial particles is visualized by transforming the vector field from a traditionally Eulerian reference frame into a Lagrangian reference frame. Groups of particles are traced through the vector field for the mean path as well as their statistical dispersion about the mean position by using added scalar information about the root mean square value of the vector field and its Lagrangian time scale. In this way, clouds of particles in a turbulent environment are traced, not just mean paths. In combustion simulations of many industrial processes, good mixing is required to achieve a sufficient degree of combustion efficiency. The ability to visualize this multiphase mixing can not only help identify poor mixing but also explain the mechanism for poor mixing. The information gained from the visualization can be used to improve the overall combustion efficiency in utility boilers or propulsion devices. We have used this technique to visualize steady-state simulations of the combustion performance in several furnace designs.

Ma, Kwan-Liu↗

DOE OSTI2024

A Principled Framework to Assess the Information-Theoretic Fitness of Brain Functional Sub-Circuits

In systems and network neuroscience, many common practices in brain connectomic analysis are often not properly scrutinized. One such practice is mapping a predetermined set of sub-circuits, like functional networks (FNs), onto subjects’ functional connectomes (FCs) without adequately assessing the information-theoretic appropriateness of the partition. Another practice that goes unchallenged is thresholding weighted FCs to remove spurious connections without justifying the chosen threshold. This paper leverages recent theoretical advances in Stochastic Block Models (SBMs) to formally define and quantify the information-theoretic fitness (e.g., prominence) of a predetermined set of FNs when mapped to individual FCs under different fMRI task conditions. Our framework allows for evaluating any combination of FC granularity, FN partition, and thresholding strategy, thereby optimizing these choices to preserve the important topological features of the human brain connectomes. By applying to the Human Connectome Project with Schaefer parcellations at multiple levels of granularity, the framework showed that the common thresholding value of 0.25 was indeed information-theoretically valid for group-average FCs, despite its previous lack of justification. Our results pave the way for the proper use of FNs and thresholding methods, and provide insights for future research in individualized parcellations.

Duong-Tran, Duy (ORCID:0009000944967575)↗

DOE OSTI2022

Improving growth of Cupriavidus necator H16 on formate using adaptive laboratory evolution-informed engineering

Conversion of CO 2 to value-added products presents an opportunity to reduce GHG emissions while generating revenue. Formate, which can be generated by the electrochemical reduction of CO 2 , has been proposed as a promising intermediate compound for microbial upgrading. Here we present progress towards improving the soil bacterium Cupriavidus necator H16, which is capable of growing on formate as its sole source of carbon and energy using the Calvin-Benson-Bassham (CBB) cycle, as a host for formate utilization. Using adaptive laboratory evolution, we generated several isolates that exhibited faster growth rates on formate. The genomes of these isolates were sequenced, and resulting mutations were systematically reintroduced by metabolic engineering, to identify those that improved growth. Further, the metabolic impact of several mutations was investigated further using RNA-seq transcriptomics. We found that deletion of a transcriptional regulator implicated in quorum sensing, PhcA, reduced expression of several operons and led to improved growth on formate. Growth was also improved by deleting large genomic regions present on the extrachromosomal megaplasmid pHG1, particularly two hydrogenase operons and the megaplasmid CBB operon, one of two copies present in the genome. Based on these findings, we generated a rationally engineered ..delta..phcA and megaplasmid-deficient strain that exhibited a 24% faster maximum growth rate on formate. Moreover, this strain achieved a 7% growth rate improvement on succinate and a 19% increase on fructose, demonstrating the broad utility of microbial genome reduction. This strain has the potential to serve as an improved microbial chassis for biological conversion of formate to value-added products.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2013

SIM_EXPLORE: Software for Directed Exploration of Complex Systems

Physics-based numerical simulation codes are widely used in science and engineering to model complex systems that would be infeasible to study otherwise. While such codes may provide the highest- fidelity representation of system behavior, they are often so slow to run that insight into the system is limited. Trying to understand the effects of inputs on outputs by conducting an exhaustive grid-based sweep over the input parameter space is simply too time-consuming. An alternative approach called "directed exploration" has been developed to harvest information from numerical simulators more efficiently. The basic idea is to employ active learning and supervised machine learning to choose cleverly at each step which simulation trials to run next based on the results of previous trials. SIM_EXPLORE is a new computer program that uses directed exploration to explore efficiently complex systems represented by numerical simulations. The software sequentially identifies and runs simulation trials that it believes will be most informative given the results of previous trials. The results of new trials are incorporated into the software's model of the system behavior. The updated model is then used to pick the next round of new trials. This process, implemented as a closed-loop system wrapped around existing simulation code, provides a means to improve the speed and efficiency with which a set of simulations can yield scientifically useful results. The software focuses on the case in which the feedback from the simulation trials is binary-valued, i.e., the learner is only informed of the success or failure of the simulation trial to produce a desired output. The software offers a number of choices for the supervised learning algorithm (the method used to model the system behavior given the results so far) and a number of choices for the active learning strategy (the method used to choose which new simulation trials to run given the current behavior model). The software also makes use of the LEGION distributed computing framework to leverage the power of a set of compute nodes. The approach has been demonstrated on a planetary science application in which numerical simulations are used to study the formation of asteroid families.

Burl, Michael↗

DOE OSTI2021

Unmanned aircraft system (UAS) detection and assessment via temporal intensity aliasing

A method and system for temporal frequency analysis for identification of unmanned aircraft systems. The method includes obtaining a sequence of video image frames and providing a pixel from an output frame of the video; generating a fluctuating pixel value vector; examining the fluctuating pixel value vector over a period of time; obtaining the frequency information present in the pixel fluctuations; summing the frequency coefficients for the vectorized pixel values from the fluctuating pixel value vector; obtaining an image representing a two dimensional space based on the summed center frequency coefficients; generating a series of still frames equal to a summation of the center frequency coefficients for pixel variations; and combining the temporal information into spatial locations in a matrix to provide a single image containing the spatial and temporal information present in the sequence of video image frame.

Woo, Bryana Lynn↗

DOE OSTI2023

Unmanned aircraft system (UAS) detection and assessment via temporal intensity aliasing

Woo, Bryana Lynn↗

DOE OSTI2021

Evaluation of nature and intensity of fire concrete spalling by frequency analysis of sound records

Highlights: • Original sound-based NDT method to assess the spalling form and intensity within the time of the test is proposed. • Frequency analysis of recorded sound provides a quantitative and qualitative assessment of concrete propensity to spalling. • Nature of fire concrete spalling can be categorized by a well-defined frequency value. • Waveform and spectrogram analysis delivers information on the exact number of spalling events within the performed test. The paper presents a new method for identifying the form and intensity of spalling using an analysis of the acoustic signal emitted by concrete. To assess the intensity and monitor the course of concrete spalling during fire exposure, the analysis of the recorded sound from the furnace chamber is used. The spalling intensity was determined based on signal amplitude analysis. Investigation of sound records showed that the character of spalling can be categorized by a well-defined frequency value. Based on the analysis of the Fast Fourier Transform (FFT) spectrum, the authors have distinguished the following phenomena that are the source of the signal by assigning the corresponding frequency of the sound wave vibration: explosive spalling, aggregate spalling and destructive spalling.

36 MATERIALS SCIENCE↗

NASA NTRS2000

Use of Computed Tomography for Characterizing Materials Grown Terrestrially and in Microgravity

The purpose behind this work is to provide NASA Principal Investigators (PI) rapid information, non-destructively, about their samples. This information will be in the form of density values throughout the samples, especially within slices 1 mm high. With correct interpretation and good calibration, these values will enable the PI to obtain macro chemical compositional analysis for his/her samples. Alternatively, the technique will provide information about the porosity level and its distribution within the sample. Experience gained with a NASA MRD-sponsored Advanced Technology Development (ATD) project on this topic has brought the technique to a level of maturity at which it has become a viable characterization tool for many of the Materials Science PIs, but with equipment that could never be supported within their own facilities. The existing computed tomography (CT) facility at NASA's Kennedy Space Center (KSC) is ideally situated to furnish information rapidly and conveniently to PIs, particularly immediately before and after flight missions.

Gillies, Donald C.↗

NASA NTRS2001

Use of Computed Tomography for Characterizing Materials Grown Terrestrially and in Microgravity

The purpose behind this work is to provide NASA Principal Investigators (PIs) rapid information, nondestructively, about their samples. This information will be in the form of density values throughout the samples, especially within slices 1 mm high. With correct interpretation and good calibration, these values will enable the PI to obtain macro chemical compositional analysis for his/her samples. Alternatively, the technique will provide information about the porosity level and its distribution within the sample. Experience gained with a NASA Microgravity Research Division-sponsored Advanced Technology Development (ATD) project on this topic has brought the technique to a level of maturity at which it has become a viable characterization tool for many of the Materials Science Pls, but with equipment that could never be supported within their own facilities. The existing computed tomography (CT) facility at NASA's Kennedy Space Center (KSC) is ideally situated to furnish information rapidly and conveniently to PIs, particularly immediately before and after flight missions.

Gillies, Donald C.↗

DOE OSTI2022

Integrating Reanalysis and Satellite Cloud Information to Estimate Surface Downward Long-Wave Radiation

The estimation of downward long-wave radiation (DLR) at the surface is very important for the understanding of the Earth’s radiative budget with implications in surface–atmosphere exchanges, climate variability, and global warming. Theoretical radiative transfer and observationally based studies identify the crucial role of clouds in modulating the temporal and spatial variability of DLR. In this study, a new machine learning algorithm that uses multivariate adaptive regression splines (MARS) and the combination of near-surface meteorological data with satellite cloud information is proposed. The new algorithm is compared with the current operational formulation used by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Land Surface Analysis (LSA-SAF). Both algorithms use near-surface temperature and dewpoint temperature along with total column water vapor from the latest European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis ERA5 and satellite cloud information from the Meteosat Second Generation. The algorithms are trained and validated using both ECMWF-ERA5 and DLR acquired from 23 ground stations as part of the Baseline Surface Radiation Network (BSRN) and the Atmospheric Radiation Measurement (ARM) user facility. Results show that the MARS algorithm generally improves DLR estimation in comparison with other model estimates, particularly when trained with observations. When considering all the validation data, root mean square errors (RMSEs) of 18.76, 23.55, and 22.08 W·m –2 are obtained for MARS, operational LSA-SAF, and ERA5, respectively. The added value of using the satellite cloud information is accessed by comparing with estimates driven by ERA5 total cloud cover, showing an increase of 17% of the RMSE. The consistency of MARS estimate is also tested against an independent dataset of 52 ground stations (from FLUXNET2015), further supporting the good performance of the proposed model.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Ambient Synchrophasor Measurement Based System Inertia Estimation

This paper develops an algorithm to estimate the system inertia value based on ambient synchrophasor measurement. Informative features are extracted from ambient synchrophasor measurements for machine-learning-based inertia estimation. Besides ambient synchrophasor measurements of FNET/GridEye, other available data relevant to inertia (such as weather and system load data) are also used to improve the inertia estimation accuracy. Then a machine learning algorithm to estimate system inertia is developed. A test dataset including ambient synchrophasor data from FNET/GridEye measurements and the WECC system inertia data from NERC is used to evaluate the performance of the developed inertia estimation method. The average and maximum estimation errors of the developed inertia estimation method is lower than 5% and 10%, respectively. This accuracy is higher than reported accuracy values in existing literature.

CUI, YI↗

NASA NTRS1983

El Chichon eruption cloud - Comparison of lidar and optical thickness measurements for October 1982

Sun photometer and lidar backscatter measurements of the El Chichon volcanic cloud were obtained during an airborne latitude survey. The observations were collected between 46 deg N and 46 deg S from Oct. 19-Nov. 7, 1982. Comparisons between these data sets have been performed. An aerosol optical model was developed for the conversion of the lidar measurements to optical thickness values using numerical aerosol size distribution data and index of refraction information collected with coordinated dustsonde balloon flights. The derived lidar optical thickness values were found to agree with the sun photometer optical thickness values within measurement uncertainties. The lidar derived values ranged from 0.16 at the equator to 0.03 at 30 deg S latitude. Peak values were concentrated between 35 deg N and 10 deg S.

Swissler, T. J.↗

DOE OSTI2022

Vibrational Entropy of Crystalline Solids from Covariance of Atomic Displacements

The vibrational entropy of a solid at finite temperature is investigated from the perspective of information theory. Ab initio molecular dynamics (AIMD) simulations generate ensembles of atomic configurations at finite temperature from which we obtain the N-body distribution of atomic displacements, ρN. We calculate the information-theoretic entropy from the expectation value of lnρN. At a first level of approximation, treating individual atomic displacements independently, our method may be applied using Debye–Waller B-factors, allowing diffraction experiments to obtain an upper bound on the thermodynamic entropy. At the next level of approximation we correct the overestimation through inclusion of displacement covariances. We apply this approach to elemental body-centered cubic sodium and face-centered cubic aluminum, showing good agreement with experimental values above the Debye temperatures of the metals. Below the Debye temperatures, we extract an effective vibrational density of states from eigenvalues of the covariance matrix, and then evaluate the entropy quantum mechanically, again yielding good agreement with experiment down to low temperatures. Our method readily generalizes to complex solids, as we demonstrate for a high entropy alloy. Further, our method applies in cases where the quasiharmonic approximation fails, as we demonstrate by calculating the HCP/BCC transition in Ti.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

NASA NTRS2021

NASA Strategy for Technology Development

The National Aeronautics and Space Administration (NASA) Office of the Chief Technologist (OCT) provides the strategy and leadership that integrates NASA’s technology development and open innovation activities to advance American aeronautic, science, and space exploration objectives. OCT performs an agency-level technology coordination role, coordinating with the NASA Mission Directorates, field centers, and other government agencies to align the agency’s technology investments to meet mission requirements while filling gaps, anticipating future needs, and minimizing duplication of effort. OCT provides the strategy and coordination that guide NASA's technology and innovation activities through Strategic Technology Integration, Digital Transformation, the Science and Technology Partnership Forum, and Innovation. The Strategic Technology Integration team informs policy, requirements, and strategy for NASA’s technology development activities in support of the Chief Technologist by coordinating with NASA mission directorates, other government agencies, and external organizations. As part of these efforts, OCT develops the NASA Technology Taxonomy and the NASA Strategic Space Technology Investment Plan. Digital Transformation will increasingly change the way NASA operates and will enable the agency’s missions to be completed more efficiently and effectively. It will allow current and future employees to use their talents in more innovative ways. The Science and Technology Partnership Forum is a strategic forum, with principal partners at the United States Space Force and the National Reconnaissance Office. It was established to identify synergistic efforts and technologies in order to leverage those synergies and influence portfolios across space agencies in areas deemed pervasive and ready for collaboration. NASA's Innovation efforts are designed to transform diverse ideas into value and are guided by a framework that is focused on the agency’s people, processes, partnerships and portfolio. The innovation framework is used to connect the workforce to tools, strategies and activities to increase the pace of innovation. OCT translates National space policies into NASA technology strategies and coordination across the agency and interagency communities to guide NASA's technology and innovation activities to achieve capabilities needed for future missions. This chapter provides insight into the value of analyzing future states to inform technology developments to plot the strategic direction of NASA’s space technology program.

Erica Rodgers↗