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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

A Multi-Objective, Hub-and-Spoke Supply Chain Design Model for Densified Biomass

In this paper we propose a model to design the supply chain for densified biomass. Rail is typically used for longhaul, high-volume shipment of densified biomass. This is the reason why a hub-and-spoke network structure is used to model this supply chain. The model is formulated as a multi-objective, mixed-integer programing problem under economic, environmental, and social criteria. The goal is to identify the feasibility of meeting the Renewable Fuel Standard (RFS) by using biomass for production of cellulosic ethanol. The focus is not just on the costs associated with meeting these standards, but also exploring the social and environmental benefits that biomass production and processing offers by creating new jobs and reducing greenhouse gas (GHG) emissions. We develop an augmented ?-constraint method to find the exact Pareto solution to this optimization problem. We develop a case study using data from the Mid-West. The model identifies the number, capacity and location of biorefineries needed to make use of the biomass available in the region. The model estimates the delivery cost of cellulosic ethanol under different scenario, the number new jobs created and the GHG emission reductions in the supply chain.

09 BIOMASS FUELS↗
DOE OSTI2019

Making inertial confinement fusion models more predictive

Computer models of inertial confinement fusion (ICF) implosions play an essential role in experimental design and interpretation as well as our understanding of fundamental physics under the most extreme conditions that can be reached in the laboratory. Building truly predictive models is a significant challenge, with the potential to greatly accelerate progress to high yield and ignition. One path to more predictive models is to use experimental data to update the underlying physics in a way that can be extrapolated to new experiments and regimes. We describe a statistical framework for the calibration of ICF simulations using data collected at the National Ignition Facility (NIF). We perform Bayesian inferences for a series of laser shots using an approach that is designed to respect the physics simulation as much as possible and then build a second model that links the individual-shot inferences together. We show that this approach is able to match multiple X-ray and neutron diagnostics for a whole series of NIF “BigFoot” shots. Within the context of 2D radiation hydrodynamic simulations, our inference strongly favors a significant reduction in fuel compression over other known degradation mechanisms (namely, hohlraum issues and engineering perturbations). This analysis is expanded using a multifidelity technique to pick fuel-ablator mix from several candidate causes of the degraded fuel compression (including X-ray preheat and shock timing errors). Finally, we use our globally calibrated model to investigate the extra laser drive energy that would be required to overcome the inferred fuel compression issues in NIF BigFoot implosions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2023

DPOD2020: A DORIS Extension of the ITRF2020 for Precise Orbit Determination

As one of the tracking systems used to determine orbits of the altimeter mission satellites (such as TOPEX/Poseidon, Envisat, Jason-1/-2/-3, CryoSat-2, Saral/Altika, Sentinel-3A/-3B, HY-2A/C/D, Jason-CS/Sentinel-6A, SWOT), DORIS (Doppler Orbitography Radiopositionning Integrated by Satellite) allows to determine positions and velocities of tracking stations that define a stable reference for the estimation of the precise orbits and thus are fundamental for the quality of the altimeter data and derived mean sea level products. Due to the time evolution of the DORIS ground network, some stations included in the 2020 realization of the International Terrestrial Reference Frame (ITRF2020) have been decommissioned and since 2021.0 a few new stations were added to the tracking network. Therefore, to satisfy operational requirements for POD (Precise Orbit Determination) and routine delivery of geodetic products, the International DORIS Service (IDS) regularly updates the DPOD (DORIS terrestrial reference frame for Precise Orbit Determination). The DPOD solutions include mean positions and velocities of all the DORIS stations since 1993.0 derived from the stacking of the latest IDS weekly combined series aligned to the current ITRF. In this paper, we first present the stacking process of the DPOD2020 version 1.0. Then, we address the validation procedure of the DPO2020 including comparison with ITRF2020 and POD tests. For eighty percent of all the time segments of all the DORIS stations, the station position differences between DPOD2020 version 1.0 and ITRF2020 are smaller than ten millimeters. The major position differences between these two solutions are associated with the DORIS sites either localized in the South Atlantic Anomaly region or with time spans smaller than one year. Compared to DPOD2014, the DPOD2020 shows reduction of the main statistics of the DORIS-to-DORIS tie residuals (differences between the estimated and measured ties). The POD tests showed similar results for DPOD2020 and DPOD2014 for most of the altimetric satellites (TOPEX/Poseidon, Jason-1, CryoSat-2, Jason-3). In addition, we observed better POD results with DPOD2020 for the latest altimetric satellite Sentinel-3A as well as a slight degradation for Jason-2. That degradation was fully explained by slightly worst results for the stations localized in the South Atlantic Anomaly region.

DORIS↗
DOE OSTI2016

Modular Homogeneous Chromophore–Catalyst Assemblies

Photosynthetic reaction center (RC) proteins convert incident solar energy to chemical energy through a network of molecular cofactors which have been evolutionarily tuned to couple efficient light-harvesting, directional electron transfer, and long-lived charge separation with secondary reaction sequences. These molecular cofactors are embedded within a complex protein environment which precisely positions each cofactor in optimal geometries along efficient electron transfer pathways with localized protein environments facilitating sequential and accumulative charge transfer. By contrast, it is difficult to approach a similar level of structural complexity in synthetic architectures for solar energy conversion. However, by using appropriate self-assembly strategies, we anticipate that molecular modules, which are independently synthesized and optimized for either light-harvesting or redox catalysis, can be organized into spatial arrangements that functionally mimic natural photosynthesis. In this Account, we describe a modular approach to new structural designs for artificial photosynthesis which is largely inspired by photosynthetic RC proteins. We focus on recent work from our lab which uses molecular modules for light-harvesting or proton reduction catalysis in different coordination geometries and different platforms, spanning from discrete supramolecular assemblies to molecule–nanoparticle hybrids to protein-based biohybrids. Molecular modules are particularly amenable to high-resolution characterization of the ground and excited state of each module using a variety of physical techniques; such spectroscopic interrogation helps our understanding of primary artificial photosynthetic mechanisms. In particular, we discuss the use of transient optical spectroscopy, EPR, and X-ray scattering techniques to elucidate dynamic structural behavior and light-induced kinetics and the impact on photocatalytic mechanism. Two different coordination geometries of supramolecular photocatalyst based on the [Ru(bpy)3]2+ (bpy = 2,2'-bipyridine) light-harvesting module with cobaloxime-based catalyst module are compared, with progress in stabilizing photoinduced charge separation identified. These same modules embedded in the small electron transfer protein ferredoxin exhibit much longer charge-separation, enabled by stepwise electron transfer through the native [2Fe-2S] cofactor. We anticipate that the use of interchangeable, molecular modules which can interact in different coordination geometries or within entirely different structural platforms will provide important fundamental insights into the effect of environment on parameters such as electron transfer and charge separation, and ultimately drive more efficient designs for artificial photosynthesis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2010

Cruise-Efficient Short Takeoff and Landing (CESTOL): Potential Impact on Air Traffic Operations

The National Aeronautics and Space Administration (NASA) is investigating technological and operational concepts for introducing Cruise-Efficient Short Takeoff and Landing (CESTOL) aircraft into a future US National Airspace System (NAS) civil aviation environment. CESTOL is an aircraft design concept for future use to increase capacity and reduce emissions. CESTOL provides very flexible takeoff, climb, descent and landing performance capabilities and a high-speed cruise capability. In support of NASA, this study is a preliminary examination of the potential operational impact of CESTOL on airport and airspace capacity and delay. The study examines operational impacts at a subject site, Newark Liberty Intemational Airport (KEWR), New Jersey. The study extends these KEWR results to estimate potential impacts on NAS-wide network traffic operations due to the introduction of CESTOL at selected major airports. These are the 34 domestic airports identified in the Federal Aviation Administration's Operational Evolution Plan (OEP). The analysis process uses two fast-time simulation tools to separately model local and NAS-wide air traffic operations using predicted flight schedules for a 24-hour study period in 2016. These tools are the Sen sis AvTerminal model and NASA's Airspace Concept Evaluation System (ACES). We use both to simulate conventional-aircraft-only and CESTOL-mixed-with-conventional-aircraft operations. Both tools apply 4-dimension trajectory modeling to simulate individual flight movement. The study applies AvTerminal to model traffic operations and procedures for en route and terminal arrival and departures to and from KEWR. These AvTerminal applications model existing arrival and departure routes and profiles and runway use configurations, with the assumption jet-powered, large-sized civil CESTOL aircraft use a short runway and standard turboprop arrival and departure procedures. With these rules, the conventional jet and CESTOL aircraft are procedurally separated from each other geographically and in altitude during tenninal airspace approach and departure operations, and each use a different arrival runway. AvTeminal implements its unique Focal-point Scheduling Process to sequence, space and delay aircraft to resolve spacing and overtake conflicts among flights in the airspace and airport system serving KEWR. This Process effectively models integrated arrival and departure operations. AvTerminal assesses acceptance rates and delay magnitude and causality at selected locations, including en route outer boundary fixes, tenninal airspace arrival and departure boundary fixes, terminal airspace arrival merge and departure diverge fixes, and runway landing and takeoff runways. The analysis compares the resulting capacity impacts, flight delays and delay sources between CESTOL and conventional KEWR operations. AvTerminal quantitative results showed that CESTOL has significant capability to increase airport arrival acceptance rates (35-40% at KEWR) by taking advantage of otherwise underused airspace and runways where available. The study extrapolates the AvTerminal-derived KEWR peak arrival and departure acceptance rates to estimate capacity parameter values for each of the OEP airports in the ACES modeling of traffic through the entire NAS network. The extrapolations of acceptance rates allow full, partial or no achievement of CESTOL capacity gains at an OEP airport as determined by assessments of the degree to which local procedures allow leveraging of CESTOL capabilities. These assessments consider each OEP airport's runway geometries, runway system configurations, airport and airspace operations, and potential CESTOL traffic loadings. The ACES modeling, simulates airport and airspace spacing constraints imposed by airport runway system, terminal and en route air traffic control and traffic flow management operations using airport acceptance rates representing conventional-aircraft-only and CESTOL-mixed operations. CEOL aircraft are assumed to have Mach 0.8, and alternatively Mach 0.7, cruise speeds to examine compatibility with conventional aircraft operations in common airspace. The ACES results provides estimates of CESTOL delay impact NAS-wide and at OEP airports due to changes in OEP airport acceptance rates and changes in en route airspace potential conflict rates. Preliminary results show meaningful nationwide delay reductions (20%) due to CESTOL operations at 34 major domestic airports.

Couluris, G. J.↗
DOE OSTI2022

A comparison of neural network architectures for data-driven reduced-order modeling

The popularity of deep convolutional autoencoders (CAEs) has engendered new and effective reduced-order models (ROMs) for the simulation of large-scale dynamical systems. Despite this, it is still unknown whether deep CAEs provide superior performance over established linear techniques or other network-based methods in all modeling scenarios. To elucidate this, the effect of autoencoder architecture on its associated ROM is studied through the comparison of deep CAEs against two alternatives: a simple fully connected autoencoder, and a novel graph convolutional autoencoder. Through benchmark experiments, it is shown that the superior autoencoder architecture for a given ROM application is highly dependent on the size of the latent space and the structure of the snapshot data, with the proposed architecture demonstrating benefits on data with irregular connectivity when the latent space is sufficiently large.

42 ENGINEERING↗
NASA NTRSDate not supplied

Framework for National Airspace System (NAS) Level Sustainability Assessment of New Aircraft

This paper introduces a framework for investigating the future sustainability benefits of conceptual vehicles. The framework considers the complex interactions between such vehicles and how it can be flown in the National Airspace System. Aggregate benefits for reduction in fuel use and emissions need to consider fleet wide operations, demand distributions, NAS Infrastructure, and differing business models. To extract true system level benefits multi-objective function optimization for operational energy use, acoustics, emissions and NAS Wide efficiency and interoperability incorporating physic-based system-wide flight simulations in the National Airspace system is required. The framework marries traditional vehicle synthesis processes with a NAS Simulation environment to simulate how a new synthesized vehicle will behave in the NAS.

Sustainability↗
DOE OSTI2019

Impacts of Travel Demand Information Diffusion on Reducing Empty Vehicle Miles Traveled by Ridesourcing Vehicles

Ride-hailing or ride-sourcing services provide alternative mobility options in several cities and have seen exponential growth in market share owing to the convenience they provide. Even though usage of such services may be associated with car-light and car-free lifestyles, there are growing concerns around their impact on urban transportation network operations due to empty miles driven without a passenger (deadheading). This study is motivated by the potential for reducing deadhead mileage by providing information on future trip demand to ride-sourcing drivers (at the destination end of their current trip). Future trip demand information enables the driver to wait in place for the next rider without cruising around and contributing to congestion. A machine learning model is applied using historical data to predict hourly and 10-minute future interval demand for ride-sourcing. Two heuristic algorithms have been developed to i) determine drivers that act on demand information received by waiting in place for the next rider, and ii) match drivers who wait to their next trip by minimizing deadheading distance. Data from ride-sourcing providers in Austin, Texas (RideAustin) and in Chengdu, China (DiDi) are leveraged to apply the methodology. Results show that the process results in 68%-82% and 53%-60% trip-level deadheading miles reduction for RideAustin and DiDi sample operations, respectively. As the maximum tolerable driver's waiting time increases, deadheading savings slightly increase. Even if the maximum percentage of drivers waiting is reduced, significant average deadheading savings by trip can be achieved for that portion.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Evaluation of E3SM land model snow simulations over the western United States

Abstract. Seasonal snow has crucial impacts on climate, ecosystems, and humans, but it is vulnerable to global warming. The land component (ELM) of the Energy Exascale Earth System Model (E3SM) mechanistically simulates snow processes from accumulation, canopy interception, compaction, and snow aging to melt. Although high-quality field measurements, remote sensing snow products, and data assimilation products with high spatio-temporal resolution are available, there has been no systematic evaluation of the snow properties and phenology in ELM. This study comprehensively evaluates ELM snow simulations over the western United States at 0.125∘ resolution during 2001–2019 using the Snow Telemetry (SNOTEL) in situ networks, MODIS remote sensing products (i.e., MCD43 surface albedo product), the spatially and temporally complete (STC) snow-covered area and grain size (MODSCAG) and MODIS dust and radiative forcing in snow (MODDRFS) products (STC-MODSCAG/STC-MODDRFS), and the snow property inversion from remote sensing (SPIReS) product and two data assimilation products of snow water equivalent and snow depth – i.e., University of Arizona (UA) and SNOw Data Assimilation System (SNODAS). Overall the ELM simulations are consistent with the benchmarking datasets and reproduce the spatio-temporal patterns, interannual variability, and elevation gradients for different snow properties including snow cover fraction (fsno), surface albedo (αsur) over snow cover regions, snow water equivalent (SWE), and snow depth (Dsno). However, there are large biases of fsno with dense forest cover and αsur in the Rocky Mountains and Sierra Nevada in winter, compared to the MODIS products. There are large discrepancies of snow albedo, snow grain size, and light-absorbing particle-induced snow albedo reduction between ELM and the MODIS products, attributed to uncertainties in the aerosol forcing data, snow aging processes in ELM, and remote sensing retrievals. Against UA and SNODAS, ELM has a mean bias of −20.7 mm (−35.9 %) and −20.4 mm (−35.5 %), respectively, for spring, and −13.8 mm (−27.8 %) and −10.2 mm (−22.2 %), respectively, for winter. ELM shows a relatively high correlation with SNOTEL SWE, with mean correlation coefficients of 0.69 but negative mean biases of −122.7 mm. Compared to the snow phenology of STC-MODSCAG and SPIReS, ELM shows delayed snow accumulation onset dates by 17.3 and 12.4 d, earlier snow end dates by 35.5 and 26.8 d, and shorter snow durations by 52.9 and 39.5 d, respectively. This study underscores the need for diagnosing model biases and improving ELM representations of snow properties and snow phenology in mountainous areas for more credible simulation and future projection of mountain snowpack.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Towards multi-fidelity deep learning of wind turbine wakes

We report engineering wake models that accurately predict wake in a computationally efficient manner are very important for tasks such as layout optimization and control of wind farms. In this paper, we explore an application of deep learning (DL) to learn the wake model from hierarchies of physics-based approaches ranging from analytical models to an approximate form of the Reynolds-averaged Navier-Stokes equations. We first illustrate the application of principal component analysis to obtain a lower-dimensional representation that allows a computationally tractable training and deployment of DL models. Then, the DL model is trained to learn the mapping from input parameter space to the principal components, which are then used to reconstruct the three-dimensional flow field. Additionally, we investigate a composite framework consisting of two neural networks to learn the correlation between low- and high-fidelity data with Gauss and curl models treated as proxies for low- and high-fidelity models, respectively. The prediction from both DL models matches well with the high-fidelity data with a maximum relative percentage error for the kinetic energy flux of <1%. This work opens up possibilities for data-efficient construction of surrogate models for wake prediction that can be used to study the influence of wind speed and yaw angles on wind farm power production.

17 WIND ENERGY↗
DOE OSTI2024

Sparsified time-dependent Fourier neural operators for fusion simulations

This paper presents a sparsified Fourier neural operator for coupled time-dependent partial differential equations (ST-FNO) as an efficient machine learning surrogate for fluid and particle-based fusion codes such as NIMROD (Non-Ideal Magnetohydrodynamics with Rotation - Open Discussion) and GTC (Gyrokinetic Toroidal Code). ST-FNO leverages the structures in the governing equations and utilizes neural operators to represent Green's function-like numerical operators in the corresponding numerical solvers. Once trained, ST-FNO can rapidly and accurately predict dynamics in fusion devices compared with first-principle numerical algorithms. In general, ST-FNO represents an efficient and accurate machine learning surrogate for numerical simulators for multi-variable nonlinear time-dependent partial differential equations, with the proposed architectures and loss functions. The efficacy of ST-FNO has been demonstrated using quiescent H-mode simulation data from NIMROD and kink-mode simulation data from GTC. The ST-FNO H-mode results show orders of magnitude reduction in memory and central processing unit usage in comparison with the numerical solvers in NIMROD when computing fields over a selected poloidal plane. The ST-FNO kink-mode results achieve a factor of 2 reduction in the number of parameters compared to baseline FNO models without accuracy loss.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Fluorinated polythiophenes with ester side chains for boosting the V OC and efficiency in non-fullerene polymer solar cells

As the power conversion efficiency (PCE) of organic photovoltaics (OPVs) approximates the 19 % threshold, wide bandgap (WBG) polythiophenes (PTs) have gained increasing attention due to their simple chemical structures and ease of synthesis, making them promising candidates for large-scale production. However, the benchmark polymer poly(3-hexylthiophene) (P3HT) is limited by its high-lying HOMO energy level, which restricts the open-circuit voltage (V OC ) in solar cells. Here, in this study, we introduce a novel series of PT derivatives (PDC8-T, PDC8-3T, PDC16-3T, and PDC16-3T-2F) featuring ester side chains designed to fine-tune electronic properties through a streamlined three-step synthesis. Additionally, we incorporated a π-spacer to reduce steric hindrance and elongated alkyl side chains to improve solubility and processability. Compared to P3HT, these PT derivatives demonstrate a significant reduction in HOMO energy levels, lowering by approximately 0.3–0.4 eV. Among them, PDC16-3T-2F—with fluorine atom substitution—achieves the lowest HOMO energy level, induces a coplanar molecular conformation, and enhances polymer aggregation behavior. We evaluated these PT derivatives in inverted non-fullerene bulk-heterojunction (NFA BHJ) OPVs. The PDC8-3T device showed a relatively low PCE of 0.69 %, with a V OC of 0.76 V, a short-circuit current density (J SC ) of 3.32 mA/cm 2 , and a fill factor (FF) of 27.3 %. In contrast, the PDC16-3T-2F device achieved an impressive PCE of 7.21 %, with a V OC of 0.85 V, a J SC of 14.60 mA/cm 2 , and an FF of 58.4 %. This remarkable improvement is attributed to the fluorine substitution, which not only enhances molecular orientation but also downshifts the HOMO energy level and further boosts the V OC . Hence, these molecular design strategies have led to a fibrillar bicontinuous interpenetrating network with optimal nanoscale phase separation within the active layer.

14 SOLAR ENERGY↗
DOE OSTI2017

Spin order and dynamics in the diamond-lattice Heisenberg antiferromagnets CuRh 2 O 4 and CoRh 2 O 4

Here, antiferromagnetic insulators on a diamond lattice are candidate materials to host exotic magnetic phenomena ranging from spin-orbital entanglement to degenerate spiral ground states and topological paramagnetism. Compared to other three-dimensional networks of magnetic ions, such as the geometrically frustrated pyrochlore lattice, the investigation of diamond-lattice magnetism in real materials is less mature. In this work, we characterize the magnetic properties of model A-site spinels CoRh 2 O 4 (cobalt rhodite) and CuRh 2 O 4 (copper rhodite) by means of thermomagnetic and neutron-scattering measurements, and we perform group theory analysis, Rietveld refinement, mean-field theory, and spin-wave theory calculations to analyze the experimental results. Our investigation reveals that cubic CoRh 2 O 4 is a canonical S = 3/2 diamond-lattice Heisenberg antiferromagnet with a nearest-neighbor exchange J = 0.63 meV and a Néel ordered ground state below a temperature of 25 K. In tetragonally distorted CuRh 2 O 4 , competing exchange interactions between up to third-nearest-neighbor spins lead to the development of an incommensurate spin helix at 24 K with a magnetic propagation vector k m = (0,0,0.79). Strong reduction of the ordered moment is observed for the S = 1/2 spins in CuRh 2 O 4 and captured by our 1/ S corrections to the staggered magnetization. Our work identifies CoRh 2 O 4 and CuRh 2 O 4 as reference materials to guide future work searching for exotic quantum behavior in diamond-lattice antiferromagnets.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Optimal Power Dispatch of DGs in Radial and Mesh AC Grids: A Hybrid Solution Methodology between the Salps Swarm Algorithm and Successive Approximation Power Flow Method

In this paper, we address the problem of the optimal power dispatch of Distributed Generators (DGs) in Alternating Current (AC) networks, better known as the Optimal Power Flow (OPF) problem. We used, as the objective function, the minimization of power losses (P loss ) associated with energy transport, which are subject to the set of constraints that compose AC networks in an environment of distributed generation. To validate the effectiveness of the proposed methodology in solving the OPF problem in any network topology, we employed one 10-node mesh test system and three radial text systems: 10, 33, and 69 nodes. In each test system, DGs were allowed to inject 20%, 40%, and 60% of the power supplied by the slack generator in the base case. To solve the OPF problem, we used a master–slave methodology that integrates the optimization method Salps Swarm Algorithm (SSA) and the load flow technique based on the Successive Approximation (SA) method. Moreover, for comparison purposes, we employed some of the algorithms reported in the specialized literature to solve the OPF problem (the continuous genetic algorithm, the particle swarm optimization algorithm, the black hole algorithm, the antlion optimization algorithm, and the Multi-Verse Optimizer algorithm), which were selected because of their excellent results in solving such problems. The results obtained by the proposed solution methodology demonstrate its superiority and convergence capacity in terms of minimization of P loss in both radial and mesh systems. It provided the best reduction in minimum P loss in short processing times and showed excellent repeatability in each test system and scenario under analysis.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Evaluating Opportunities to Improve Material and Energy Impacts in Commodity Supply Chains.

When evaluated at the process level, next-generation technologies may be more energy and emissions intensive than current technology. However, many advanced technologies have the potential to reduce material and energy consumption in upstream or downstream processing stages. In order to fully understand the benefits and consequences of technology deployment, next-generation technologies should be evaluated in context, as part of a supply chain. This work presents the Material Flows through Industry (MFI) scenario modeling tool. The MFI tool is a cradle-to-gate linear network model of the U.S. industrial sector that can model a wide range of manufacturing scenarios, including changes in production technology, increases in industrial energy efficiency, and substitution between functionally equivalent materials. The MFI tool was developed to perform supply chain scale analyses in order to quantify the impacts and benefits of next-generation technologies and materials at that scale. For the analysis presented in this paper, the MFI tool is utilized to explore a case study comparing a steel supply chain to the supply chains of several functionally equivalent materials. Several of the alternatives to the baseline steel supply chain include next-generation production technologies and materials. Results of the case study show that aluminum production scenarios can out-perform the steel supply chain by using either an advanced smelting technology or an increased aluminum recycling rate. The next-generation material supply chains do not perform as well as either aluminum or steel, but may offer additional use phase reductions in energy and emissions that are outside the scope of the MFI tool. Future work will combine results from the MFI tool with a use phase analysis.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Cognitive IoT and Edge Computing for Intrusion Detection with Federated TinyML

Internet of Things (IoT) and Edge Computing (EC) are rapidly becoming an integral part of the modern society. By 2030, there is estimated to be over 40 billion active and connected IoT devices [1]. This rapid progress also comes with a significant implication on cybersecurity. Back-end infrastructure and systems have a much broader attack than they did previously due to vulnerable IoT/EC devices being connected to wireless networks. This expanding attack surface is a growing concern because IoT/EC are increasingly being used in critical systems such as power grids, health care, and smart homes. To effectively address a problem of this scale, cognitive cyber methods—which can autonomously detect and react to cyber attacks as they develop—are needed. To address this, we bring Artificial Intelligence (AI) and Machine Learning (ML) to IoT/EC devices, using tinyML to monitor voluminous IoT data against cyber threats, and using Federated Learning (FL) to share local detection knowledge across the system while preserving privacy. We propose a novel three-layer architecture: (1) an IoT layer for tinyML-based inference, (2) an edge layer for ML model training, and (3) a cloud layer for FL operations. Using the publicly available 11-class N-BaIoT dataset [2], we demonstrate that this architecture mitigates resource constraints at the IoT layer while improving detection accuracy over standard two-layer designs. An outlier-resistant scaler, feature reduction, and quantization enable the tinyML model to maintain detection accuracy with a reduced model size. Additionally, federated learning that only utilizes the intersection (across heterogenous devices) of the reduced feature set achieves superior detection accuracy compared to locally trained models.

Li, Mingyan [ORNL] (ORCID:0009000569532640)↗
DOE OSTI2024

Application of Modified Meshgraphnets for Subsurface Prediction during CO2 Sequestration

In the face of the increasingly dire consequences of anthropogenic climate change, capturing and storing carbon dioxide is paramount. However, several impediments exist to the safe and effective subsurface storage of CO2, such as cost of transport, identification of suitable sites for subsurface storage, and assessment of long-term risk from storage in subsurface aquifers. Accurate subsurface modeling is necessary to ensure that CO2 storage is both safe and effective. Still, such modeling has traditionally required either substantial time and computational power (numerical simulation) or a substantial amount of pre-existing data for training (machine learning models). Additionally, these models lack flexibility in dealing with both changes in discretization of the input data and generalizability beyond the data on which they are trained. In order to address these issues, this research applies graph neural networks (GNNs) to predict subsurface saturation and pressure during CO₂ injection in a model of the Illinois Basin-Decatur Project (IBDP). GNNs provide a flexible, intuitive method for representing and manipulating complex unstructured data, which is often found in many practical domain problems such as fluid flow and subsurface characterization. These unstructured grids are easily represented in GNNs by representing spatially-localized features such as permeability, porosity, saturation, and pressure as nodes in a graph and relationships between these properties as edges connecting these nodes. This research applies a specific GNN model called MeshGraphNets (MGN) to model the change in CO2 saturation and pressure over a 50-month time period (36 months of injection, 14 months post-injection). The MGN model leverages a message passing process that allows the network to learn both the spatial and temporal dynamics of this system simultaneously. Additionally, training on a limited dataset (64 realizations, 20 time points each) resulted in a high degree of accuracy in saturation prediction both within the same timeframe as the training (20 months, 0.039 average RMSE) and when projecting out to the end of injection (36 months, 0.053 average RMSE). Temporal predictions such as those generated by MGNs and other similar models are prone to accumulated error over time; in order to address this, a multi-step rollout (MSR) training process was applied to calculate training loss. This method mimics the forward prediction during inference by “rolling out” multiple time points in a single training step using the previous prediction as input to the MGN model. By calculating the loss several time steps forward from the current prediction, the model is forced to find a more stable state over time. Application of MSR to the MGN model resulted in an average 15% reduction in inference error over time during forward prediction. This study showcases the immense potential of GNNs as a game-changing methodology for predicting pressure and saturation evolution in CCS projects, ultimately paving the way for more sustainable and effective carbon storage solutions. Presentation prepared for the 2024 AiChE Annual Meeting, October 27 to November 1 2024, San Diego, CA.

Holcomb, Paul↗
DOE OSTI2016

Continuous Structural Transition in Glass-Forming Molten Titanate BaTi 2 O 5

The structure of the model titanate glass former BaTi2O5 has been studied over a wide temperature (T) range in the molten, supercooled, and glassy states under conditions of aerodynamic levitation. Both high-energy X-ray diffraction and Ti K-edge X-ray absorption spectroscopy reveal a continuous structural transition involving reduction of the cation-oxygen (and oxygen-cation) average coordination numbers and bond lengths with increasing T. Ti-0 coordination in the moderately supercooled and equilibrium melt follows a linear trend n(Tio) = 5.4(1)- [3.5(7) x 10(-4)]T [K] (1300 <= T <= 1830 K, T-g = 960 K, T-m = 1660 K). Comparison to the melt-quenched glass implies an increase in partial derivative n(Tio)/partial derivative T at lower T, as T-g is approached from above. Both Ba-0 coordination and bond length also decrease at higher T, and the role of Ba addition is to reduce n(Tio) below its value in pure molten TiO2, which is related to the presence of density maxima in molten BaO-TiO2. Density measurements made by imaging of the levitated melt yielded rho(T) = 4.82(55)- 0.0004(3)T in units of K and g cm(-3). While BaTi2O5 glass likely consists of a fully connected Ti-0 network, free of nonbridging oxygen (NBO) [OTi1 and with at least 13(4)% [OTi3] triclusters, the 1835(40) K equilibrium melt contains at least 10(4)% NBO along with 90(4)% bridging oxygen [OTi2]. The results highlight the fact that glasses can be considered as structural analogues of melts only for those melts deeply supercooled into the glass transition region. The results imply possible fictive T dependence of titanate glass structure, suggesting applications as, e.g., laser written waveguides with large refractive indices and refractive index contrasts. The temperature-dependent structure further implies a super-Arrhenian melt viscosity with consequences for glass manufacture, titanate-rich slags produced in iron smelting, TiO2-bearing magmas, and by analogy silicate melts at high pressures, as found in planetary interiors.

36 MATERIALS SCIENCE↗