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DOE OSTI2019

Stress-Induced Anomalous Transport in Natural Fracture Networks

In this work we investigate the effects of geological stress on fluid flow and tracer transport in natural fracture networks. We show the emergence of non-Fickian (anomalous) transport from the interplay among fracture network geometry, aperture heterogeneity, and geological stress. In this study, we extract the fracture network geometry from the geological map of an actual rock outcrop, and we simulate the geomechanical behavior of fractured rock using a hybrid finite-discrete element method. We analyze the impact of stress on the aperture distribution, fluid flow field, and tracer transport properties. Both stress magnitude and orientation have strong effects on the fracture aperture field, which in turn affects fluid flow and tracer transport through the system. We observe that stress anisotropy may cause significant shear dilation along long, curved fractures that are preferentially oriented to the stress loading. This, in turn, induces preferential flow paths and anomalous early arrival of tracers. An increase in stress magnitude enhances aperture heterogeneity by introducing more small apertures, which exacerbates late-time tailing. This effect is stronger when there is higher heterogeneity in the initial aperture field. To honor the flow field with strong preferential flow paths, we extend the Bernoulli Continuous Time Random Walk model to incorporate dual velocity correlation length scales. The proposed upscaled transport model captures anomalous transport through stressed fracture networks and agrees quantitatively with the high-fidelity numerical simulations.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2016

Top-of-the-Atmosphere Shortwave Flux Estimation from Satellite Observations: An Empirical Neural Network Approach Applied with Data from the A-Train Constellation

Estimates of top-of-the-atmosphere (TOA) radiative flux are essential for the understanding of Earth's energy budget and climate system. Clouds, aerosols, water vapor, and ozone (O3) are among the most important atmospheric agents impacting the Earth's shortwave (SW) radiation budget. There are several sensors in orbit that provide independent information related to these parameters. Having coincident information from these sensors is important for understanding their potential contributions. The A-train constellation of satellites provides a unique opportunity to analyze data from several of these sensors. In this paper, retrievals of cloud/aerosol parameters and total column ozone (TCO) from the Aura Ozone Monitoring Instrument (OMI) have been collocated with the Aqua Clouds and Earth's Radiant Energy System (CERES) estimates of total reflected TOA outgoing SW flux (SWF). We use these data to develop a variety of neural networks that estimate TOA SWF globally over ocean and land using only OMI data and other ancillary information as inputs and CERES TOA SWF as the output for training purposes. OMI-estimated TOA SWF from the trained neural networks reproduces independent CERES data with high fidelity. The global mean daily TOA SWF calculated from OMI is consistently within 1% of CERES throughout the year 2007. Application of our neural network method to other sensors that provide similar retrieved parameters, both past and future, can produce similar estimates TOA SWF. For example, the well-calibrated Total Ozone Mapping Spectrometer (TOMS) series could provide estimates of TOA SWF dating back to late 1978.

CERES data with high fidelity↗

NASA NTRS2007

Neural Network Burst Pressure Prediction in Composite Overwrapped Pressure Vessels

Acoustic emission data were collected during the hydroburst testing of eleven 15 inch diameter filament wound composite overwrapped pressure vessels. A neural network burst pressure prediction was generated from the resulting AE amplitude data. The bottles shared commonality of graphite fiber, epoxy resin, and cure time. Individual bottles varied by cure mode (rotisserie versus static oven curing), types of inflicted damage, temperature of the pressurant, and pressurization scheme. Three categorical variables were selected to represent undamaged bottles, impact damaged bottles, and bottles with lacerated hoop fibers. This categorization along with the removal of the AE data from the disbonding noise between the aluminum liner and the composite overwrap allowed the prediction of burst pressures in all three sets of bottles using a single backpropagation neural network. Here the worst case error was 3.38 percent.

Hill, Eric v. K.↗

NASA NTRS1993

Determination of cosmic bodies size-velocity distribution by observation of current impacts on Mars

Collisions of cosmic bodies with terrestrial planets involve many physical processes such as deceleration and ablation during their flight through an atmosphere, the impact at a surface accompanied by cratering, melting and evaporation of surface material, generation of shock waves, etc. If body velocity is high enough then a thermal radiation is very important. All these processes on Mars proceed differently than on the other planets because of the low density of its atmosphere. In particular, this leads to the fact that smaller bodies of sizes of the order of 0.1-10 m strike the planet surface without being decelerated and perform some effects which may be detected by equipment placed on a board of artificial satellites, by a network of stations at the surface of Mars and even from the Earth. These observations can be used to determine size-velocity distribution of such bodies in the Solar System. Numerical simulation of the impacts at the surface of Mars have been carried out using two-dimensional gas dynamic code with detailed consideration of the thermal radiative transfer. This work is an extension of our previous paper. We have expanded a range of projectile sizes up to r sub 0 = 100 m. For such a large-scale body, the initial stage of the impact, involving crate ring and ejection of surface material, is very important. Thus, these effects have been taken into account.

Nemchinov, I. V.↗

DOE OSTI2016

Charge transport network dynamics in molecular aggregates

Due to the nonperiodic nature of charge transport in disordered systems, generating insight into static charge transport networks, as well as analyzing the network dynamics, can be challenging. Here, we apply time-dependent network analysis to scrutinize the charge transport networks of two representative molecular semiconductors: a rigid n-type molecule, perylenediimide, and a flexible p-type molecule, bBDT(TDPP)2. Simulations reveal the relevant timescale for local transfer integral decorrelation to be ~100 fs, which is shown to be faster than that of a crystalline morphology of the same molecule. Using a simple graph metric, global network changes are observed over timescales competitive with charge carrier lifetimes. These insights demonstrate that static charge transport networks are qualitatively inadequate, whereas average networks often overestimate network connectivity. Finally, a simple methodology for tracking dynamic charge transport properties is proposed.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2023

Traffic Signal Optimization by Integrating Reinforcement Learning and Digital Twins

Machine learning (ML) methods, especially reinforcement learning (RL), have been widely considered for traffic signal optimization in intelligent transportation systems. Most of these ML methods are centralized, lacking in scalability and adaptability in large traffic networks. Further, it is challenging to train such ML models due to the lack of training platforms and/or the cost of deploying and training in a real traffic networks. This paper presents an approach for the integration of decentralized graph-based multi-agent reinforcement learning (DGMARL) with a Digital Twin (DT) to optimize traffic signals for the reduction of traffic congestion and network-wide fuel consumption related to stopping. Specifically, the DGMARL agents learn traffic state patterns and make decisions regarding traffic signal control with assistance from a Digital Twin module, which simulates and replicates the traffic behaviors of a real traffic network. The proposed approach was evaluated using PTV-Vissim [1], a microscopic traffic simulation platform. PTV-Vissim is also the simulation engine of the DT, enabling emulation and optimization of the traffic signals on the MLK Smart Corridor in Chattanooga, Tennessee. Compared to an actuated signal control baseline approach, experiment results show that Eco_PI, a developed performance measure capturing the impact of stops on fuel consumption, was reduced by 44.27% in a 24-hour and an average of 29.88% in a PM-peak-hour scenario.

Multi-Agent Reinforcement Learning, Digital Twin, ↗

DOE OSTI2020

Biomass feedstock transport using fuel cell and battery electric trucks improves lifecycle metrics of biofuel sustainability and economy

We report the use of new vehicle technologies such as fuel cell hybrid electric and fully electric powertrains for biomass feedstock supply is an unexplored solution to reducing biofuel production cost, greenhouse gas emissions, and health impacts. These technologies have found success in light-duty vehicle applications and are in development for heavy-duty trucks. This study presents the first detailed stochastic techno-economic analysis and life-cycle assessment of biomass feedstock supply systems with diesel, fuel cell hybrid electric, and fully electric trucks and determines their impacts on biofuel production considering butanol as a representative biofuel. This study finds that fuel cell hybrid electric and fully electric trucks consume less energy relative to the diesel-powered truck regardless of the evaluated circumstances, including payloads of truck (loaded and empty), pavement types (gravel and paved), road conditions (normal and damaged), and road networks (local and highways). The use of fuel cell hybrid and fully electric trucks powered by H 2 -fuel and renewable sources of electricity, respectively, results in a large reduction in cost and carbon footprint, specifically for a long-distance hauling, and minimize other economic and environmental impacts. While the economic advantage of fuel cell hybrid electric vehicle is dependent on the price of H 2 -fuel and road conditions, use reduces the GHG emissions of biobutanol per 100 km-trucking-distance by 0.98-10.9 gCO 2e /MJ. Results show that converting to fully electric truck transport decreases the biobutanol production cost and GHG emissions per 100 km-trucking-distance by 0.4-7.3 cents/L and 0.78 to 9.1 gCO 2e /MJ, respectively. This study establishes the foundation for future investigations that will guide the development of economically, socially, and environmentally sustainable biomass feedstock supply system for cellulosic biorefineries or other goods transportation systems.

09 BIOMASS FUELS↗

DOE OSTI2018

Let the Good Times Roll – Energy Code Savings into the Future

Long term benefits of energy codes provide valuable information for setting policies, for state adoption, and in evaluating the cost-benefits of energy codes. Pacific Northwest National Laboratory (PNNL) evaluated the long-term impacts associated with energy codes in residential and commercial buildings from 2010 through 2040. The analysis developed and implemented a new methodology that relied on available data and market trends. The study was heavily vetted with key stakeholders including, DOE, Regional Energy Efficiency Organization network (REEOs), ACEEE and others. Several complex streams of information were combined in the analysis, including results from hundreds of simulation models, codes field study data from DOE, floor space data from EIA's Annual Energy Outlook, and data on projected technology improvements from DOE’s Technology Roadmaps. This paper describes the methodology used for the assessment and presents the energy and economic impacts in terms of site energy savings, consumer energy cost savings, and reduced CO2 emissions at the state level and at aggregated national level. The assessment concluded that model energy codes will save $126 billion for consumers on their utility bills, reduce carbon emissions by 841 metric-million tons, and save 12.82 quads of primary energy on a cumulative basis from 2010 through 2040. The results are being used by DOE to underline their role in improving building energy efficiency, by REEOs and states for promoting state code adoption, and by ACEEE and others to perform their own analyses related to energy codes.

buidling energy codes, Impacts of Energy Codes↗

DOE OSTI2019

Sensitivity of global soil carbon stocks to combined nutrient enrichment

Soil stores approximately twice as much carbon as the atmosphere and fluctuations in the size of the soil carbon pool directly influence climate conditions. We used the Nutrient Network global change experiment to examine how anthropogenic nutrient enrichment might influence grassland soil carbon storage at a global scale. In isolation, enrichment of nitrogen and phosphorous had minimal impacts on soil carbon storage. However, when these nutrients were added in combination with potassium and micronutrients, soil carbon stocks changed considerably, with an average increase of 0.04 KgCm -2 year -1 (standard deviation 0.18 KgCm -2 year -1). These effects did not correlate with changes in primary productivity, suggesting that soil carbon decomposition may have been restricted. Although nutrient enrichment caused soil carbon gains most dry, sandy regions, considerable absolute losses of soil carbon may occur in high-latitude regions that store the majority of the world’s soil carbon. These mechanistic insights into the sensitivity of grassland carbon stocks to nutrient enrichment can facilitate biochemical modelling efforts to project carbon cycling under future climate scenarios.

Global change, nutrient enrichment, nutrient Netwo↗

DOE OSTI2019

Spallation-altered Accreted Compositions for X-Ray Bursts: Impact on Ignition Conditions and Burst Ashes

Model predictions of X-ray burst ashes and light curves depend on the composition of the material accreted from the companion star, in particular the abundance of CNO elements. It has previously been pointed out that spallation in the atmosphere of the accreting neutron star can destroy heavy elements efficiently. In this work we study this spallation using a realistic reaction network that follows the complete spallation cascade and takes into account not only destruction, but also production of elements by the spallation of heavier species. We find an increased survival probability of heavier elements compared to previous studies, resulting in significantly higher CNO abundances.We provide resulting compositions as a function of accretion rate, and explore their impact on 1D multi-zone X-ray burst models. Finally, we find significant changes in the composition of the burst ashes, which will affect the thermal and compositional structure of accreted neutron star crusts.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

Parallel Evolutionary Optimization for Neuromorphic Network Training

One of the key impediments to the success of current neuromorphic computing architectures is the issue of how best to program them. Evolutionary optimization (EO) is one promising programming technique; in particular, its wide applicability makes it especially attractive for neuromorphic architectures, which can have many different characteristics. In this paper, we explore different facets of EO on a spiking neuromorphic computing model called DANNA. We focus on the performance of EO in the design of our DANNA simulator, and on how to structure EO on both multicore and massively parallel computing systems. We evaluate how our parallel methods impact the performance of EO on Titan, the U.S.'s largest open science supercomputer, and BOB, a Beowulf-style cluster of Raspberry Pi's. We also focus on how to improve the EO by evaluating commonality in higher performing neural networks, and present the result of a study that evaluates the EO performed by Titan.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Parallel Evolutionary Optimization for Neuromorphic Network Training

Schuman, Catherine D↗

DOE OSTI2026

Understanding Adsorption and Reactions at Aqueous Oxide Interfaces with Neural Network Potential Molecular Dynamics

Chemical processes at metal oxide−water interfaces are of central importance in geochemistry, biology, and energy technologies. A better understanding of these processes would allow us to make a significant step toward optimizing and controlling them, which could in turn lead to broader impacts. Computational modeling is indispensable to accomplishing this task because complexity and disorder often make it difficult to extract atomistic information from experiments. Balancing computational cost and accuracy, simulation schemes based on efficient machine learning representations of the potential energy surface (PES) predicted by ab initio calculations have become increasingly popular over the past decade. In particular, several studies have demonstrated the ability of machine learning models to accurately reproduce the complex ab initio PESs of aqueous oxide interfaces, allowing simulations of systems and processes that are not accessible with ab initio methods. In this Account, we review our recent efforts to understand adsorption processes and reactions at aqueous oxide interfaces using deep potential molecular dynamics (DPMD), a simulation scheme employing deep neural networks (DNNs), which has proven to be quite successful in accurately describing many different systems in the condensed phase. After summarizing the DPMD methodology, we first review our work on the acid−base chemistry of oxide surfaces in contact with water, a fundamental characteristic that controls proton transfer and surface charge at the interface. We focus on the aqueous interface of rutile IrO 2 , an oxide material thus far considered the best catalyst for the oxygen evolution reaction (OER). We show that this interface is characterized by a large fraction of dissociated water and a strong Brønsted acidity of the surface sites, in good agreement with the experimentally measured value of the point of zero proton charge. In our second example, we investigate how the adsorption of organic species from ambient air or water affects the structure and wettability of the aqueous interfaces of TiO 2 , a prototypical photocatalytic material. This is a question that is relevant to understanding the UV-induced hydrophilicity of TiO 2 surfaces, a property at the basis of self-cleaning windows and related applications. Specifically focusing on formic and acetic acids, the two most common atmospheric organic acids, our simulations reveal that these acids control the wettability of TiO 2 largely through acid−base chemistry at the interface rather than chemisorption on the oxide surface, a finding that could help improve the design of self-cleaning surfaces and photocatalytic devices. Finally, we review our recent study of methanol at TiO 2 −water interfaces, a system whose interest is largely motivated by the role of methanol in enhancing photocatalytic hydrogen evolution on TiO 2 . Our simulations provide mechanistic insights into the coupled roles of the organic adsorbate and water at the TiO 2 interface, with implications for how methanol enhances the activity of H 2 evolution.

adsorption↗

DOE OSTI2015

Cd(II) complexes with different nuclearity and dimensionality based on 3-hydrazino-4-amino-1,2,4-triazole

A series of zero- to two-dimensional Cd(II) coordination compounds have been synthesized by the reaction of Cd(II) salts and 3-hydrazino-4-amino-1,2,4-triazole di-hydrochloride (HATr·2HCl). [CdCl{sub 2}(HATr){sub 2}] (1) and [Cd{sub 2}Cl{sub 4}(HATr){sub 2}(H{sub 2}O){sub 2}] (2) have discrete mononuclear and binuclear structures, respectively. [Cd(HATr){sub 2}(ClO{sub 4}){sub 2}]{sub n} (3) presents polymeric 1-D chain and [Cd{sub 2}(NO{sub 3}){sub 2}Cl{sub 2}(HATr){sub 2}]{sub n} (4) shows 2-D frameworks. All Cd(II) ions exhibit distorted octahedral configurations in 1–3, whilst both hexa and heptacoordinated Cd(II) are formed in 4. The HATr ligands adopt chelating coordinated mode in 1, while tri-dentate bridging–chelating mode in 2–4. The chloride ion is a mono-coordinated ligand in 1 and 2, but it bridges two adjacent metal ions in 4. Furthermore, thermal behaviors have been investigated and the results reveal that all complexes have good thermal stability. The impact sensitivity test indicates that complex 3 is sensitive to impact stimuli. - Graphical abstract: Four Cd(II) complexes based on 3-hydrazino-4-amino-1,2,4-triazole ligands exhibit diverse structures from mononuclear to 2D networks. - Highlights: • Cd(II) complexes containing 3-hydrazino-4-amino-1,2,4-triazole ligands. • Mononuclear, binuclear, 1-D and 2-D structures. • Good thermal stability. • Thermal decomposition kinetics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

A Multiyear Constraint on Ammonia Emissions and Deposition Within the US Corn Belt

Abstract The US Corn Belt is a global hotspot of atmospheric ammonia (NH 3 ), a gas known to adversely impact the environment and human health. We combine hourly tall tower (100 m) measurements and bi‐weekly, spatially distributed, ground‐based observations from the Ammonia Monitoring Network with the US National Emissions Inventory (NEI) and WRF‐Chem simulations to constrain NH 3 emissions from April to September 2017–2019. We show that: (1) NH 3 emissions peaked from May to July and were 1.6–1.7 times the annual NEI average; (2) average growing season NH 3 emissions from agricultural lands were remarkably similar across years (3.27–3.64 nmol m −2 s −1 ), yet showed substantial episodic variability driven by meteorology and land management; (3) dry deposition was 40% of gross emissions from agricultural lands and exceeded 100% of gross emissions in natural lands. Our findings provide an important benchmark for evaluating future NH 3 emissions and mitigation efforts.

Hu, Cheng↗

DOE OSTI2021

Porosity-Permeability Evolution During Simultaneous Mineral Dissolution and Precipitation

Permeability in reactive porous media evolves in complex ways that are hard to predict. Macroscopic empirical equations are often used to estimate permeability evolution but fail to reflect the impact of pore scale reactions on permeability. This work aims to understand the evolution of permeability in systems where mineral dissolution and precipitation occur simultaneously. Pore network models are used for permeability simulation of a Paluxy sandstone using pore and pore-throat size distributions from the analysis of X-ray CT images. Pore and pore-throat radii are increased and decreased to reflect the effects of dissolution and precipitation reactions. Varying spatial distributions of reactions are simulated and the resulting porosity and permeability values compared with commonly used macroscopic equations. It is observed that when dissolution occurs at the inlet and precipitation at the outlet, porosity increases while permeability decreases. Similar results are also observed in the respective opposite scenario where precipitation occurs at the inlet and dissolution at the outlet. When dissolution and precipitation reactions are randomly distributed throughout the network, porosity increases with little change in permeability that is not captured by the empirical equations. When reactions are controlled by pore and pore-throat sizes, such as dissolution occurring in pores and pore-throats of small size and precipitation in pores and pore-throats of larger size, porosity and permeability decrease. When precipitation occurs in pores and pore-throats of smaller size and dissolution in pores and pore-throats of larger size, porosity and permeability increase and is described relatively well by the Verma-Pruess porosity-permeability equation.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Fracture Intensity Impacts on Reaction Front Propagation and Mineral Weathering in Three‐Dimensional Fractured Media

Abstract Studying reaction front propagation in heterogeneous natural settings is challenging, but numerical simulations can provide insight into the varying spatial and temporal scales of reaction front propagation. Here, the impact of increasing fracture intensity on mineral dissolution rates, and the extent of reaction front propagation is investigated using reactive transport simulations in upscaled discrete fracture network domains with varied fracture intensity. Domain‐averaged dissolution rates vary less than 0.5 log units regardless of the fracture intensity, but the spatial distribution of reactions is controlled by the location and number of dead‐end fractures and the number of connected flowpaths through the domain. Higher fracture intensities lead to more weathering in the domain because of more available mineral for water‐rock interactions. We find that reaction fronts propagate through the primary flowpaths in the first 10,000 years of the simulation for a 10‐m length domain, then propagate into secondary flowpaths and dead‐end fractures between 10,000 and 100,000 years, and finally into the matrix over timescales of hundreds of thousands of years. The domain‐averaged reaction rates decrease through time corresponding to a transition from dissolution in advection‐dominated, fast‐flowing pathways, to dissolution in transport‐limited zones of disconnected fractures and matrix. Matrix dissolution, or dissolution under transport‐limited conditions, is the dominant process at late times in these simulations. The results of these simulations recreate the observed paradox found in nature where highly fractured hillslopes tend to be more weathered but have slower weathering rates, while hillslopes with fewer fractures, are less weathered but have higher dissolution rates.

58 GEOSCIENCES↗

DOE OSTI2024

Model-Based Detection of Coordinated Attacks (DCA) in Distribution Systems

The fast-paced growth in digitization of smart grid components enhances system observability and remote-control capabilities through efficient communication. However, enhanced connectivity results in heightened system vulnerability towards cybersecurity risks in the cyber-physical power system. Coordinated cyber-attacks (CCA), when undetected, lead to system-wide impact in terms of large disturbances or widespread outages. Detecting CCA in the cyber layer is critical to thwart cyber-attacks in real-time before the attack impacts the physical system. The challenge of locating CCA stems from the complex grid dynamics, making it difficult to distinguish between normal operational variations and cyber-attack impact. CCA often employs multiple attack vectors targeting geographically distributed components, further complicating CCA identification. Existing research in intrusion detection is primarily focused on the transmission network and limited to detecting individual attacks. In this paper, a novel proactive DCA strategy is proposed for early detection of CCA by establishing correlations among distinct attack events through model-based reinforcement learning that utilizes abductive reasoning to conclude the attacker goal. The solution includes understanding the system model, learning the system dynamics, and correlating individual cyber-attacks to extract the attacker’s objective. The developed learning algorithm identifies the most probable attack path to reach the attacker’s objective by predicting the next attack steps. A DNP3-based cyber-physical co-simulation testbed is developed to test the proposed algorithm using the IEEE 13-node test feeder.

24 POWER TRANSMISSION AND DISTRIBUTION↗