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At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

From disorganized data to emergent dynamic models: Questionnaires to partial differential equations

Starting with sets of disorganized observations of spatially varying and temporally evolving systems, obtained at different (also disorganized) sets of parameters, we demonstrate the data-driven derivation of parameter dependent, evolutionary partial differential equation (PDE) models capable of generating the data. This tensor type of data is reminiscent of shuffled (multidimensional) puzzle tiles. The independent variables for the evolution equations (their “space” and “time”) as well as their effective parameters are all emergent , i.e. determined in a data-driven way from our disorganized observations of behavior in them. We use a diffusion map based questionnaire approach to build a smooth parametrization of our emergent space/time/parameter space for the data. This approach iteratively processes the data by successively observing them on the “space,” the “time” and the “parameter” axes of a tensor. Once the data become organized, we use machine learning (here, neural networks) to approximate the operators governing the evolution equations in this emergent space. Our illustrative examples are based (i) on a simple advection–diffusion model; (ii) on a previously developed vertex-plus-signaling model of Drosophila embryonic development; and (iii) on two complex dynamic network models (one neuronal and one coupled oscillator model) for which no obvious smooth embedding geometry is known a priori. This allows us to discuss features of the process like symmetry breaking, translational invariance, and autonomousness of the emergent PDE model, as well as its interpretability.

generative models↗
NASA NTRS1980

The chemical abundances of the Cassiopeia A fast-moving knots - Explosive nucleosynthesis on a minicomputer

A simplified nuclear reaction network for explosive nucleosynthesis calculations is described in which only the most abundant nuclear species and the most important reactions linking these species are considered. This scheme permits the exploration of many cases without excessive computational effort. Good agreement with previous calculations employing more complex reaction networks is obtained. This scheme is applied to the observed chemical abundances of the fast-moving knots in the supernova remnant Cassiopeia A and it is found that a wide range of initial conditions could yield the observed abundances. The abundances of four of the knots with significant and different amounts of elements heavier than oxygen are consistent with an origin in material of the same initial composition but processed at different peak temperatures and densities. Despite the observed high oxygen abundances and low abundances of light elements in the knots, they did not necessarily undergo incomplete oxygen burning; in fact, it is not even necessary that oxygen have been present in the initial composition. The agreement between the calculated and observed chemical abundances in Cas A and similar supernova remnants depends primarily upon the relevant nuclear physics and does not provide strong evidence in favor of any particular model of the supernova event.

Johnston, M. D.↗
DOE OSTI2025

Learning the factors controlling mineral dissolution in three-dimensional fracture networks: applications in geologic carbon sequestration

We perform a set of high-fidelity simulations of geochemical reactions within three-dimensional discrete fracture networks (DFN) and use various machine learning techniques to determine the primary factors controlling mineral dissolution. The DFN are partially filled with quartz that gradually dissolves until quasi-steady state conditions are reached. At this point, we measure the quartz remaining in each fracture within the domain as our primary quantity of interest. We observe that a primary sub-network of fractures exists, where the quartz has been fully dissolved out. This reduction in resistance to flow leads to increased flow channelization and reduced solute travel times. However, depending on the DFN topology and the rate of dissolution, we observe substantial variability in the volume of quartz remaining within fractures outside of the primary subnetwork. This variability indicates an interplay between the fracture network structure and geochemical reactions. We characterize the features controlling these processes by developing a machine learning framework to extract their relevant impact. Specifically, we use a combination of high-fidelity simulations with a graph-based approach to study geochemical reactive transport in a complex fracture network to determine the key features that control dissolution. We consider topological, geometric and hydrological features of the fracture network to predict the remaining quartz in quasi-steady state. We found that the dissolution reaction rate constant of quartz and the distance to the primary sub-network in the fracture network are the two most important features controlling the amount of quartz remaining. This study is a first step towards characterizing the parameters that control carbon mineralization using an approach with integrates computational physics and machine learning.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2018

Identifying the Community Structure of the Food-Trade International Multi-Network

Achieving international food security requires improved understanding of how international trade networks connect countries around the world through the import-export flows of food commodities. The properties of international food trade networks are still poorly documented, especially from a multi-network perspective. In particular, nothing is known about the multi-network's community structure. Here we find that the individual crop-specific layers of the multi-network have densely connected trading groups, a consistent characteristic over the period 2001-2011. Further, the multi-network is characterized by low variability over this period but with substantial heterogeneity across layers in each year. In particular, the layers are mostly assortative: more-intensively connected countries tend to import from and export to countries that are themselves more connected. We also fit econometric models to identify social, economic and geographic factors explaining the probability that any two countries are co-present in the same community. Our estimates indicate that the probability of country pairs belonging to the same food trade community depends more on geopolitical and economic factors-such as geographical proximity and trade-agreement co-membership-than on country economic size and/or income. These community-structure findings of the multi-network are especially valuable for efforts to understand past and emerging dynamics in the global food system, especially those that examine potential 'shocks' to global food trade.

complex networks↗
DOE OSTI2024

PYSIMFRAC: A Python library for synthetic fracture generation and analysis

In this paper, we introduce PYSIMFRAC, an open-source python library for generating 3-D synthetic fracture realizations, integrating with fluid simulators, and performing analysis. PYSIMFRAC allows the user to specify one of three fracture generation techniques (Box, Gaussian, or Spectral) and perform statistical analysis including the autocorrelation, moments, and probability density functions of the fracture surfaces and aperture. This analysis and accessibility of a python library allows the user to create realistic fracture realizations and vary properties of interest. In addition, PYSIMFRAC includes integration examples to two different pore-scale simulators and the discrete fracture network simulator, dfnWorks. The capabilities developed in this work provides opportunity for quick and smooth adoption and implementation by the wider scientific community for accurate characterization of fluid transport in geologic media. We present PYSIMFRAC along with integration examples and discuss the ability to extend PYSIMFRAC from a single complex fracture to complex fracture networks.

58 GEOSCIENCES↗
NASA NTRS2007

Using G4FETs as a Data Router for In-Plane Crossing of Signal Paths

Theoretical analysis and some experiments have demonstrated that siliconon- insulator (SOI) 4-gate transistors the type known as G(exp 4)FETs could be efficiently used for in-plane crossing of signal paths. Much of the effort of designing very-large-scale integrated (VLSI) circuits is focused on area-efficient routing of signals. The main source of difficulty in VLSI signal routing is the requirement to prevent crossing, in the same plane, of wires that are meant to be kept electrically insulated from each other. Consequently, it often becomes necessary to design and build VLSI circuits in multiple layers with vias (connections between conductors in different layers at selected locations). Suitable devices that would prevent, or at least sufficiently suppress, undesired electrical coupling (cross-talk) between wires crossing in the same plane would enable compact, simpler implementation complex interconnection networks with in-plane crossings that, heretofore, have not been possible in VLSI circuitry. The use of G4FETs as in-plane signal-crossing devices or routers, in combination with the use of G(exp 4)FETs as universal programmable logic gates, would create opportunities for reducing complexity in VLSI design.

Fijany, Amir↗
DOE OSTI2024

Static and Dynamic Thermomechanical Properties of Phase-Separated Epoxy Networks with Tuned Microstructures

Here, polymerization-induced phase separation is a useful method for the construction of heterogeneous epoxy networks with properties exceeding their homogeneous counterparts. In this work, we examine the static and dynamic thermomechanical properties of phase-separated epoxy networks salient to their application as encapsulants. Three heterogeneous epoxy-amine networks with nano-, meso-, and macro-phase-separated morphologies comprised of hard and soft domains are compared to a rigid, unstructured network. The glass transition profiles of the heterogeneous networks are complex, spanning many decades in the frequency domain. The nanophase-separated morphology leads to higher coefficient of thermal expansion, yet surprisingly is characterized by reduced residual stress. Under both quasi-static and dynamic compression (strain rates of order 10 –3 and 10 3 s –1 , respectively), the nanophase-separated network also exhibits higher modulus and strength. In split-Hopkinson bar experiments, the energy dissipation characteristics of the epoxy networks were nearly identical. Curiously, however, the Hugoniot response of the macro-phase-separated network determined by ballistic shockwave analysis indicates a remarkable ability of this material to mitigate shockwave propagation in comparison to many homogeneous and heterogeneous polymer materials. Collectively, this work reveals several previously unreported phenomena with respect to structure–property relationships in phase-separated epoxy networks, illustrating the potential value of systematically tuned microstructures for optimization of application-specific physical properties.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Integration of Decentralized Graph-Based Multi-Agent Reinforcement Learning with Digital Twin for Traffic Signal Optimization

Machine learning (ML) methods, particularly Reinforcement Learning (RL), have gained widespread attention for optimizing traffic signal control in intelligent transportation systems. However, existing ML approaches often exhibit limitations in scalability and adaptability, particularly within large traffic networks. This paper introduces an innovative solution by integrating decentralized graph-based multi-agent reinforcement learning (DGMARL) with a Digital Twin to enhance traffic signal optimization, targeting the reduction of traffic congestion and network-wide fuel consumption associated with vehicle stops and stop delays. In this approach, DGMARL agents are employed to learn traffic state patterns and make informed decisions regarding traffic signal control. The integration with a Digital Twin module further facilitates this process by simulating and replicating the real-time asymmetric traffic behaviors of a complex traffic network. The evaluation of this proposed methodology utilized PTV-Vissim, a traffic simulation software, which also serves as the simulation engine for the Digital Twin. The study focused on the Martin Luther King (MLK) Smart Corridor in Chattanooga, Tennessee, USA, by considering symmetric and asymmetric road layouts and traffic conditions. Comparative analysis against an actuated signal control baseline approach revealed significant improvements. Experiment results demonstrate a remarkable 55.38% reduction in Eco_PI, a developed performance measure capturing the cumulative impact of stops and penalized stop delays on fuel consumption, over a 24 h scenario. In a PM-peak-hour scenario, the average reduction in Eco_PI reached 38.94%, indicating the substantial improvement achieved in optimizing traffic flow and reducing fuel consumption during high-demand periods. These findings underscore the effectiveness of the integrated DGMARL and Digital Twin approach in optimizing traffic signals, contributing to a more sustainable and efficient traffic management system.

42 ENGINEERING↗
DOE OSTI2024

Integration of Decentralized Graph-Based Multi-Agent Reinforcement Learning with Digital Twin for Traffic Signal Optimization

Machine learning (ML) methods, particularly Reinforcement Learning (RL), have gained widespread attention for optimizing traffic signal control in intelligent transportation systems. However, existing ML approaches often exhibit limitations in scalability and adaptability, particularly within large traffic networks. This paper introduces an innovative solution by integrating decentralized graph-based multi-agent reinforcement learning (DGMARL) with a Digital Twin to enhance traffic signal optimization, targeting the reduction of traffic congestion and network-wide fuel consumption associated with vehicle stops and stop delays. In this approach, DGMARL agents are employed to learn traffic state patterns and make informed decisions regarding traffic signal control. The integration with a Digital Twin module further facilitates this process by simulating and replicating the real-time asymmetric traffic behaviors of a complex traffic network. The evaluation of this proposed methodology utilized PTV-Vissim, a traffic simulation software, which also serves as the simulation engine for the Digital Twin. The study focused on the Martin Luther King (MLK) Smart Corridor in Chattanooga, Tennessee, USA, by considering symmetric and asymmetric road layouts and traffic conditions. Comparative analysis against an actuated signal control baseline approach revealed significant improvements. Experiment results demonstrate a remarkable 55.38% reduction in Eco_PI, a developed performance measure capturing the cumulative impact of stops and penalized stop delays on fuel consumption, over a 24 h scenario. In a PM-peak-hour scenario, the average reduction in Eco_PI reached 38.94%, indicating the substantial improvement achieved in optimizing traffic flow and reducing fuel consumption during high-demand periods. These findings underscore the effectiveness of the integrated DGMARL and Digital Twin approach in optimizing traffic signals, contributing to a more sustainable and efficient traffic management system.

actuated signal control↗
DOE OSTI2023

Assessing pore network heterogeneity across multiple scales to inform CO2 injection models

Geologic heterogeneity is a key feature that must be considered when translations of scaled data are performed. This paper presents the assessment of geologic heterogeneity using a multiscale workflow that includes image analysis-based methods coupled with well log analysis to provide data in which fractals and machine learning methods estimate the carbon dioxide (CO 2 ) storage resource potential of a reservoir. The heterogeneity of rock properties of the complex Bell Creek reservoir in Montana, USA, was explored at the pore scale (~nm to mm), core scale (~mm to m), and well scale (~cm to m). The data used in this study included advanced image analysis of micro-CT (computed tomography) images (pore scale), thin sections (pore scale), plugs and core images (core scale) and well logs (well scale). The micro-CT images were segmented using a U-net segmentation approach into objects of pores and grains. Further, the segmented images were reconstructed into subvolumes of different sizes. Physical properties (porosity and permeability) and fractal dimensions were calculated for the various subvolumes, and Lorenz coefficient (Lc) values, a single parameter to describe the degree of heterogeneity within a pay zone section, were calculated from thin-section images and well logs. Porosity and fractal dimension values were used to estimate the 188-µm threshold of representative elementary volume (REV) in this study. Both the Lc and fractal dimension values were found to be negatively correlated. When these two parameters are combined, it is possible to discern differences in the complex porous networks of the samples analyzed in this study.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Understanding Contaminant Migration within a Dynamic River Corridor through Field Experiments and Reactive Transport Modeling

The behavior of a persistent uranium plume within an extended river corridor at the DOE Hanford site is dominantly controlled by river stage fluctuations in the adjacent Columbia River. The plume behavior is further complicated by substantial heterogeneity in physical and geochemical properties of the host aquifer sediments. Multi-scale field and laboratory experiments and reactive transport modeling were integrated to understand the complex plume behavior influenced by highly variable hydrologic and geochemical conditions in time and space. In this paper, we (1) describe multiple data sets from field-scale uranium adsorption and desorption experiments performed at our experimental well-field, (2) develop a reactive transport model that incorporates hydrologic and geochemical heterogeneities characterized from multi-scale and multi-type datasets and a surface complexation reaction network based on laboratory studies, and (3) compare the modeling and observation results to provide insights on how to refine the conceptual model and reduce prediction uncertainties. The experimental results revealed significant spatial variability in uranium adsorption/desorption behavior, while modeling demonstrated that ambient hydrologic and geochemical conditions and heterogeneities in sediment physical and chemical properties both contributed to complex plume behavior and its persistence. This research underscores the great challenges in adequately characterizing this type of site to model the reactive transport processes over scales of 10 m or more. Our analysis provides important insights into the characterization, understanding, modeling, and remediation of groundwater contaminant plumes influenced by dynamic surface water and groundwater interactions.

Chen, Xingyuan↗
DOE OSTI2021

Developing Multi-Gene CRISPRa/I Programs to Accelerate DBTL Cycles in ABF Hosts Engineered for Chemical Production (CRADA 468)

Bacterial metabolism is comprised of large and complex gene networks that can produce valuable chemical products. Sophisticated organism engineering efforts are required to optimize production of high-value compounds from these networks. In principle, synthetic multi-gene transcriptional programs could be constructed to reengineer these networks for efficient industrial chemical production. In practice, however, our incomplete ability to understand and model the underlying networks, combined with our limited ability to predictably control the expression of multiple genes makes achieving this goal difficult. To overcome these challenges, we will combine new CRISPR-Cas multi-gene expression programs with computational modeling, machine learning, and multi-omics data to enhance the efficacy of design-build-test-learn (DBTL) cycles. For industrially promising microorganisms in early stages of development, creating technologies for rapidly engineering complex multi-gene programs could be transformative for accelerating data- and model-driven strain design. New CRISPR-Cas tools allow programmable gene activation (CRISPRa) or repression (CRISPRi) at multiple genes simultaneously, using the catalytically inactive Cas9 protein (dCas9) with guide RNAs that recognize DNA targets through predictable Watson-Crick base pairing. To enable accelerated DBTL cycles, we will combine these technologies with advanced Agile BioFoundry (ABF) capabilities for multi-omics data collection and machine learning. We will demonstrate the immediate applicability of these tools by rapidly improving the production of an industrial aromatic in multiple ABF organisms. We recently identified and optimized new transcriptional activators that can be linked to programmable CRISPR-Cas DNA binding domains to activate gene expression in E. coli. We can now use these CRISPRa tools as generalizable trans-acting regulators for combinatorial multi-gene expression tuning that can be easily transferred to new pathways and networks without additional genome engineering. We anticipate these tools will also transfer to new hosts. We have recently found that CRISPRa systems developed in E. coli can be readily ported to Pseudomonas putida, suggesting that multi-gene CRISPRa/i programs for diverse ABF organisms may be within reach.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1989

Distributed neural control of a hexapod walking vehicle

There has been a long standing interest in the design of controllers for multilegged vehicles. The approach is to apply distributed control to this problem, rather than using parallel computing of a centralized algorithm. Researchers describe a distributed neural network controller for hexapod locomotion which is based on the neural control of locomotion in insects. The model considers the simplified kinematics with two degrees of freedom per leg, but the model includes the static stability constraint. Through simulation, it is demonstrated that this controller can generate a continuous range of statically stable gaits at different speeds by varying a single control parameter. In addition, the controller is extremely robust, and can continue the function even after several of its elements have been disabled. Researchers are building a small hexapod robot whose locomotion will be controlled by this network. Researchers intend to extend their model to the dynamic control of legs with more than two degrees of freedom by using data on the control of multisegmented insect legs. Another immediate application of this neural control approach is also exhibited in biology: the escape reflex. Advanced robots are being equipped with tactile sensing and machine vision so that the sensory inputs to the robot controller are vast and complex. Neural networks are ideal for a lower level safety reflex controller because of their extremely fast response time. The combination of robotics, computer modeling, and neurobiology has been remarkably fruitful, and is likely to lead to deeper insights into the problems of real time sensorimotor control.

Beer, R. D.↗
DOE OSTI2022

Disk Evolution Study through Imaging of Nearby Young Stars (DESTINYS): A Panchromatic View of DO Tau’s Complex Kilo-astronomical-unit Environment

While protoplanetary disks are often treated as isolated systems in planet formation models, observations increasingly suggest that vigorous interactions between Class II disks and their environments are not rare. DO Tau is a T Tauri star that has previously been hypothesized to have undergone a close encounter with the HV Tau system. As part of the DESTINYS ESO Large Programme, we present new Very Large Telescope (VLT)/SPHERE polarimetric observations of DO Tau and combine them with archival Hubble Space Telescope (HST) scattered-light images and Atacama Large Millimeter/submillimeter Array (ALMA) observations of CO isotopologues and CS to map a network of complex structures. The SPHERE and ALMA observations show that the circumstellar disk is connected to arms extending out to several hundred astronomical units. HST and ALMA also reveal stream-like structures northeast of DO Tau, some of which are at least several thousand astronomical units long. These streams appear not to be gravitationally bound to DO Tau, and comparisons with previous Herschel far-IR observations suggest that the streams are part of a bridge-like structure connecting DO Tau and HV Tau. We also detect a fainter redshifted counterpart to a previously known blueshifted CO outflow. While some of DO Tau's complex structures could be attributed to a recent disk–disk encounter, they might be explained alternatively by interactions with remnant material from the star formation process. These panchromatic observations of DO Tau highlight the need to contextualize the evolution of Class II disks by examining processes occurring over a wide range of size scales.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

WigglyRivers: A tool to characterize the multiscale nature of meandering channels

Channel sinuosity is ubiquitous along river networks, producing complex patterns that encapsulate and influence morphodynamic processes and ecosystem services. Accurately characterizing these patterns is challenging with traditional curvature-based algorithms. Here, in this study, we present WigglyRivers, a Python package that builds on existing wavelet-based methods to create an unsupervised meander identification and characterization tool. The package uses planimetric information the user provides or from the USGS’s High-Resolution National Hydrography Dataset to characterize individual reaches or entire river networks. WigglyRivers also includes a supervised river identification tool for manually selecting individual meandering features. Here, we provide examples of idealized river transects and show the capabilities of WigglyRivers. We also use the supervised identification tool to validate the unsupervised identification on river transects across the continental US. WigglyRivers is a tool to understand better the multiscale characteristics of river networks and the link between river geomorphology and river corridor connectivity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

The dynamics and stratigraphy of distributary channel networks (Final Technical Report)

Sedimentary reservoirs are frequently composed of the deposits of distributary channel networks. These networks are associated with coarse sediments (and high permeability) within the channel network, and relatively fine sediments (and low permeability) outside the channel network. The shape of the network is complex, and a product of the environmental boundary conditions under which it grows (Figure 1). These systems control heterogeneity at small and large scales within sedimentary basins, and understanding this heterogeneity and the links between scales represents the important scientific frontier addressed by this project.

58 GEOSCIENCES↗
DOE OSTI2024

Carbon Capture, Transport, And Storage (CTS) Cost Modeling Of The Onshore Gulf Coast

The onshore Gulf of Mexico region presents significant opportunities for CO2 capture, transport, and storage due to its numerous CO2 sources, such as power plants, refineries, and its substantial CO2 storage potential. However, operators face critical decisions in designing an efficient and cost-effective CO2 pipeline network. This study examines the economic implications of two primary strategies: constructing a trunkline with excess initial capacity versus developing dedicated pipelines incrementally as new CO2 sources come online. Building a trunkline first offers the advantage of future-proofing the network, allowing for the accommodation of increased CO2 volumes from various sources over time. However, this approach incurs higher upfront costs and risks underutilizing the transport capacity in the initial stages, potentially resulting in economic inefficiencies. Conversely, constructing dedicated pipelines for each new CO2 source as it becomes operational may avoid the initial overcapacity issue but fails to capitalize on the economies of scale. This could lead to higher overall costs due to the duplication of infrastructure and increased complexity in network management. This research employs a comprehensive cost-benefit analysis, integrating factors such as capital expenditure, operational costs, projected CO2 volumes, and potential economies of scale. Through this analysis, we aim to provide operators with insights into the most economically viable strategy for CO2 pipeline network design in the region. The findings underscore the importance of strategic planning and highlight the trade-offs between immediate capacity utilization and long-term cost savings, ultimately guiding stakeholders towards informed decision-making in the development of CO2 transport infrastructure. Presented at the 41st USAEE/IAEE North American Conference, 3-6 November 2024, Baton Rouge, LA, United States.

Shih, Chung Yan↗
DOE OSTI2024

Enhancing Docking Accuracy with PECAN2, a 3D Atomic Neural Network Trained without Co-Complex Crystal Structures

Decades of drug development research have explored a vast chemical space for highly active compounds. The exponential growth of virtual libraries enables easy access to billions of synthesizable molecules. Computational modeling, particularly molecular docking, utilizes physics-based calculations to prioritize molecules for synthesis and testing. Nevertheless, the molecular docking process often yields docking poses with favorable scores that prove to be inaccurate with experimental testing. To address these issues, several approaches using machine learning (ML) have been proposed to filter incorrect poses based on the crystal structures. However, most of the methods are limited by the availability of structure data. Here, we propose a new pose classification approach, PECAN2 (Pose Classification with 3D Atomic Network 2), without the need for crystal structures, based on a 3D atomic neural network with Point Cloud Network (PCN). The new approach uses the correlation between docking scores and experimental data to assign labels, instead of relying on the crystal structures. We validate the proposed classifier on multiple datasets including human mu, delta, and kappa opioid receptors and SARS-CoV-2 Mpro. Our results demonstrate that leveraging the correlation between docking scores and experimental data alone enhances molecular docking performance by filtering out false positives and false negatives.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗