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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

High-Resolution Comonomer Sequencing of Blocky Brominated Syndiotactic Polystyrene Copolymers Using 13C NMR Spectroscopy and Computer Simulations

This work demonstrates the first high-resolution comonomer sequencing of Blocky brominated syndiotactic polystyrene (sPS-co-sPS-Br) copolymers based on pentad assign- ments of the quaternary carbon region of the nuclear magnetic resonance spectrum. Copolymers containing p-bromostyrene (Br-Sty) units were prepared in matched sets using postpolymerization bromination methods carried out in the heterogeneous gel state (Blocky) and homogeneous solution state (Random). Quantitative information from the quaternary carbon spectra, heteronuclear multiple bond correlation spectroscopy, electronic structure calculations, and simulated statistically random copolymers was correlated to confirm the carbon resonance assignments for all 20 possible pentad comonomer sequences. Using the experimental pentad sequence prevalences, a computer code was developed to simulate chains with microstructures typical of each sample as a means to visually represent the copolymer blockiness with quantitative precision. Based on the microstructure and distribution of run lengths in these chains, the simulations revealed that the Blocky copolymers contain a high degree of blockiness. By comparing the run lengths in the simulated chains to the average number of styrene units in a crystalline segment of sPS (found by small-angle X-ray scattering), copolymer crystallizability was predicted. For the simulated Blocky B-21% (21 mol % Br-Sty) chain, the probability of randomly selecting a styrene unit in a crystallizable block was 25.8%, while that in the simulated Random R-18% was zero, in excellent agreement with the experimental crystallization behavior measured by differential scanning calorimetry. Additionally, these predictions confirmed that the simulated chains accurately represent the ensemble of chains in their respective copolymer samples. Furthermore, each simulated Blocky chain contained one or more long sPS blocks that paralleled the measured 38-40 styrene units spanning a crystalline segment within the sPS/CCl4 gel. This finding affirmed that the long sPS segments originated from the precise lamellar structure within the heterogeneous gel morphology (i.e., block length is correlated with lamellar thickness). Overall, the ability to tailor the copolymer microstructure through control of the semicrystalline gel morphology opens the door to synthesizing ordered copolymers by postpolymerization functionalization processes with unprecedented levels of compositional control.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Packages of Distributed Energy Technologies Demonstrating Demand Flexibility at Community Scale

The combination of increased electric load growth across all sectors, deferred electrical infrastructure investment, and other factors resulting in variable electric power supply, has created technical challenges to maintaining a resilient and reliable grid. Many federal, regional, and local efforts are in play to modernize the electric grid, including advancing building technologies and distributed energy resources (DERs) that are utilizing smarter controls to become responsive to both occupant and grid needs. This report reviews ten pilot projects demonstrating how groups of buildings combined with behind-the-meter (BTM) DERs such as electric vehicle (EV) charging, battery storage, flexible HVAC and domestic hot water systems, and photovoltaic systems can reliably and cost effectively provide grid services. Each of the ten pilot projects aim to deliver both energy efficiency and demand flexibility (DF) while supporting load growth. The ten demonstration teams are piloting flexible DER packages across diverse communities of residential and commercial buildings to address a variety of regional grid needs. The outcomes of these pilot projects will be used to inform future scaling through utility program development. This paper characterizes the ten teams, showcasing the decision-making process used by each group to develop their packages (Section 2), the grid services they plan to deliver (Section 3), the types of DER packages selected for deployment within building sectors (Section 4) and trends between building sector, DER types, and grid services In order to achieve community scale benefits, the pilot projects must utilize aggregated control mechanisms for coordinating buildings and DERs together. Several types of coordinated control architectures have evolved amongst the teams, influenced by use type, existing market conditions, and integration type. Three coordinated controls architectures have been characterized, highlighting their use cases, benefits, challenges, and tradeoffs in their design. These insights can aid utilities, control vendors, and developers in scaling community-level energy systems (Paul, 2024). Ultimately, the technology packages selected by the ten teams will be coordinated to provide power system services, also known as grid services. Insights from these demonstrations will be useful for grid operators, regulators, aggregators and other stakeholders as they look to deploy demand flexible resources as grid services in the future. The grid services that each team is targeting for demonstration are described in Section 3 and Section 4. Methods for evaluating the grid services have been described in the paper Metrics for Evaluating Grid Service Provision from Communities of Grid-interactive and Efficient Buildings and other DER (MacDonald, 2023). To identify technology packages for demonstration, Section 2 shows that project teams used a range of analysis approaches, including building energy modeling, AMI data analysis, cost-benefit frameworks, and utility pilot data. Some teams emphasized technical modeling to quantify grid impacts and demand reduction potential, while others prioritized economic evaluations, stakeholder input, or exploratory pilots to inform deployment decisions. This diversity reflects the need to tailor selection methods to project goals, available data, and organizational context. Section 5 discusses trends between the DER technologies deployed and the grid service provisions from each team. Residential buildings (multifamily and single family) lean towards technologies that enhance energy efficiency (e.g. weatherization upgrades, smart thermostats) and onsite power generation integration (e.g. solar PV). Commercial building demonstrations prioritize technologies that ensure operational reliability (e.g. battery storage) and centralized energy management systems and optimization solutions. Teams that are deploying controllable storage-based technologies are more likely to provide grid services that require a near real-time response. Teams incorporating load shifting technologies like smart thermostats with HEMs are likely to include energy markets participation and customer bill management offerings. Campus demonstrations are adopting diverse sets of DERs to emphasize renewable generation, paired with centralized control. This section also describes technologies that were considered during project planning but ultimately excluded from final deployment. These demonstrations reveal that effective DER package design should be tailored to building type, customer segment, and construction vintage. Multifamily buildings benefit from centralized HVAC upgrades and supervisory controls, while single-family homes are well-suited for individualized technologies like solar, storage, and smart home energy monitors. Commercial and campus settings prioritize EMIS integration and load optimization. New construction enables cost-effective integration of DER-ready infrastructure, whereas retrofits require deployments aligned with owner and tenant value streams. For utility program planners, early coordination with developers and building owners, paired with segmented and modular program offerings, can improve adoption, scalability, and grid impact.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

In vivo evaluation of an adaptive resuscitation controller using whole blood and crystalloid infusates for hemorrhagic shock

Introduction Hemorrhage remains the leading cause of preventable death on the battlefield. The most effective means to increase survivability is early hemorrhage control and fluid resuscitation. Unfortunately, fluid resuscitation requires constant adjustments to ensure casualty is properly managed, which is often not feasible in the pre-hospital setting. In this study, we showed how an adaptive closed-loop controller for hemorrhage resuscitation can be used to automate hemodynamic management using a swine hemorrhagic shock injury model. Methods The adaptive resuscitation controller (ARC) was previously developed to track pressure–volume responsiveness in real time and adjust its infusion rate to reach the target mean arterial pressure (MAP). Swine while maintained under a surgical plane of anesthesia and analgesia underwent a splenectomy, followed by two hemorrhage and resuscitation events. For the first resuscitation event, hemorrhage was induced to reduce the MAP to 35 mmHg until arterial lactate reached 4 mmol/L. The ARC system then infused whole blood (WB) to reach the target MAP and maintained the subject using crystalloids for 120 min. For the second resuscitation event, the subjects were hemorrhaged again but resuscitated using only crystalloid infusion to reach the target MAP and 120-min maintenance. Results The ARC was effective at WB resuscitation, reaching the target MAP in 2.0 ± 1.0 min. The median performance error was 1.1% ± 4.6%, and target overshoot was 14.4% ± 7.0% of the target MAP. The ARC maintained all animals throughout the 120 min maintenance period. For the second crystalloid-based resuscitation, ARC required a longer time to reach the target MAP, at an average rise time of 4.3 ± 4.0 min. However, target overshoot was reduced to 8.4% ± 7.3% of the target MAP. Much higher flow rates were required to maintain the target MAP during the second resuscitation event than during the first resuscitation event. Discussion The ARC was able to rapidly reach and maintain the target MAP effectively. However, this sometimes required large volumes of fluid as the ARC’s only goal was to reach the target MAP. Further clinical insight is needed regarding the preferred aggression level to achieve the target MAP. In conclusion, the ARC was successful in its programmed objective of reaching and maintaining the target MAP for extended periods of time in vivo , a critical next step toward improving hemorrhage treatment in the pre-hospital environment.

Snider, Eric J.↗
DOE OSTI2017

Structural, electronic and optical properties of monoclinic Na{sub 2}Ti{sub 3}O{sub 7} from density functional theory calculations: A comparison with XRD and optical absorption measurements

Powder samples of bulk monoclinic sodium trititanate Na{sub 2}Ti{sub 3}O{sub 7} were prepared carefully by solid state reaction, and its monoclinic P2{sub 1}/m crystal structure and morphology were characterized by X-ray powder diffraction (XRD) and scanning electron microscopy (SEM), respectively. Moreover, the sodium trititanate main energy band gap was estimated as E{sub g}=3.51±0.01 eV employing UV–Vis spectroscopy, which is smaller than the measured 3.70 eV energy gap published previously by other authors. Aiming to achieve a better understanding of the experimental data, density functional theory (DFT) computations were performed within the local density and generalized gradient approximations (LDA and GGA, respectively) taking into account dispersion effects through the scheme of Tkatchenko and Scheffler (GGA+TS). Optimal lattice parameters, with deviations relative to measurements Δa=−0.06 Å, Δb=0.02 Å, and Δc=−0.09 Å, were obtained at the GGA level, which was then used to simulate the sodium trititanate electronic and optical properties. Indirect band transitions have led to a theoretical gap energy value of about 3.25 eV. Our results, however, differ from pioneer DFT results with respect to the specific Brillouin zone vectors for which the indirect transition with smallest energy value occurs. Effective masses for electrons and holes were also estimated along a set of directions in reciprocal space. Lastly, our calculations revealed a relatively large degree of optical isotropy for the Na{sub 2}Ti{sub 3}O{sub 7} optical absorption and complex dielectric function. - Graphical abstract: Monoclinic sodium trititanate Na2Ti3O7 was characterized by experiment and dispersion-corrected DFT calculations. An indirect gap of 3.5 eV is predicted, with heavy electrons and anisotropic holes ruling its conductivity. - Highlights: • Monoclinic Na2Ti3O7 was characterized by experiment (XRD, SEM, UV–Vis spectroscopy). • DFT GGA+TS optimized geometry and optoelectronic properties were obtained. • An experimental (theoretical) indirect gap of 3.5 (3.25) eV is predicted. • Heavy electrons and anisotropic holes rule the conductivity. • Ti-O bond lengths and charge states probably cause oxygen reactivity variations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Characterization of Fresh Nuclear Fuel using Time-Of-Flight Neutrons

Neutrons offer excellent characterization capabilities for nuclear fuels and can provide non-destructive insight on micro structure, chemistry and crystallographic phase. Measurements are possible at reactors or pulsed sources, but the inherent energy resolution implicit at pulsed sources offers unique opportunities. Recent advances in 2D neutron detection enabled new capabilities of material characterization using neutron imaging and tomography for nuclear materials. Moreover, time-of-flight (TOF) neutron measurements can enable isotope specific tomography by deriving isotope densities from transmission spectra exhibiting neutron resonances, which are recorded for each pixel. Tomographic algorithms allow 3D reconstructions of the spatial distribution of isotopes, density and features in typical fuel pellet samples. The ability to select radiographs for different energies allows characterizing samples which are opaque for thermal neutrons, such as enriched fuels or fuels containing plutonium, by selecting epi-thermal neutron energies and by knowledge of the total neutron cross sections of the constituents, isotopic enrichment levels can be determined. Nuclear cross sections are typically not calculated directly and databases such as the Evaluated Nuclear Data File (ENDF) database provide this information with cross sections determined by standardized experimental measurements, such as neutron transmission measurements for individual isotopes. Utilizing the information in the ENDF database, the 'inverse' problem can be solved by assuming cross section parameters to be known parameters and areal densities can be determined. This work presents recent advances at the Los Alamos Neutron Science Center (LANSCE) facility at Los Alamos National Laboratory (LANL), which provides the infrastructure to handle radioactive samples, in the development of fuel characterization with TOF neutrons. Examples include recent work on prototype accident tolerant nuclear fuels (ATF). The potential application of these techniques for nondestructive examination of irradiated fuel is under consideration. Non-destructive access to materials features for specimens embedded in containers, particularly for hazardous samples, such as a radioactive nuclear fuel, is indispensable to characterize and ultimately license new fuel forms. Energy-resolve neutron imaging is a new tool for this research field. The results reported here present a new imaging technique, setting a milestone in the capabilities of measuring non-destructively absolute quantities of physical properties. The enabling physics behind this new technique are nuclear resonances. We have demonstrated how absolute areal densities can be extracted from radiographs utilizing pulsed neutron sources. This technique provides information that is otherwise difficult and costly to obtain. In the case of nuclear fuels, the results from energy-dispersive neutron imaging may guide destructive examination of irradiated fuel. This summery does not describe recent analysis, which is ongoing. Particularly, the implementation of calibration standards has improved the determination of enrichment levels without additional calibration. The analysis of the samples was performed with full tomographic reconstructions for isotope specific data and presents a first approach in absolute isotope specific 3D reconstruction. This work was predominantly funded by and received substantial support from the Fuel Cycle R and D program at Los Alamos National Laboratory. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2017

A cost-benefit analysis of a pellet boiler with electrostatic precipitator versus conventional biomass technology: A case study of an institutional boiler in Syracuse, New York

Background: Biomass facilities have received increasing attention as a strategy to increase the use of renewable fuels and decrease greenhouse gas emissions from the electric generation and heating sectors, but these facilities can potentially increase local air pollution and associated health effects. Comparing the economic costs and public health benefits of alternative biomass fuel, heating technology, and pollution control technology options provides decision-makers with the necessary information to make optimal choices in a given location. Methods: For a case study of a combined heat and power biomass facility in Syracuse, New York, we used stack testing to estimate emissions of fine particulate matter (PM{sub 2.5}) for both the deployed technology (staged combustion pellet boiler with an electrostatic precipitator) and a conventional alternative (wood chip stoker boiler with a multicyclone). We used the atmospheric dispersion model AERMOD to calculate the contribution of either fuel-technology configuration to ambient primary PM{sub 2.5} in a 10 km×10 km region surrounding the facility, and we quantified the incremental contribution to population mortality and morbidity. We assigned economic values to health outcomes and compared the health benefits of the lower-emitting technology with the incremental costs. Results: In total, the incremental annualized cost of the lower-emitting pellet boiler was $190,000 greater, driven by a greater cost of the pellet fuel and pollution control technology, offset in part by reduced fuel storage costs. PM{sub 2.5} emissions were a factor of 23 lower with the pellet boiler with electrostatic precipitator, with corresponding differences in contributions to ambient primary PM{sub 2.5} concentrations. The monetary value of the public health benefits of selecting the pellet-fired boiler technology with electrostatic precipitator was $1.7 million annually, greatly exceeding the differential costs even when accounting for uncertainties. Our analyses also showed complex spatial patterns of health benefits given non-uniform age distributions and air pollution levels. Conclusions: The incremental investment in a lower-emitting staged combustion pellet boiler with an electrostatic precipitator was well justified by the population health improvements over the conventional wood chip technology with a multicyclone, even given the focus on only primary PM{sub 2.5} within a small spatial domain. Our analytical framework could be generalized to other settings to inform optimal strategies for proposed new facilities or populations. - Highlights: • PM{sub 2.5} emissions vary greatly across alternative biomass fuels and technologies. • A pellet boiler had a factor of 23 lower emissions than a conventional alternative. • The incremental annualized cost of the lower-emitting technology was $190,000. • The monetary value of annual health benefits of reduced emissions was $1.7 million. • Our analytical framework provides insight about optimal biomass technologies.

09 BIOMASS FUELS↗
DOE OSTI2026

Semi-Analytical Hierarchical Bayesian Inference of Nonlinear Model Structure in Stochastic Dynamics: Applied to Compartmental Models of Infectious Diseases

A Bayesian computational framework for parsimonious inference in stochastic nonlinear dynamical systems is presented. This framework enables the concurrent estimation of system states, time-varying parameters, time-invariant parameters, and the optimal sparsity structure of the model parameters. Because differential equation-based models are often simplified mechanistic or phenomenological representations, robust inference from noisy measurement data requires explicit treatment of model error and uncertainty. Model error and time-varying parameters can be represented as random processes, enabling inference while making minimal assumptions about the underlying sources of discrepancy and variability. Adopting stochastic differential equation representations affords the model significant flexibility, but can also render it susceptible to overfitting during statistical inversion, where the inferred model may track noise rather than the underlying signal. To alleviate the effects of overfitting and to enable the discovery of the optimal sparse representation of the time-invariant parameters, a Bayesian sparse learning algorithm is embedded within the framework. This sparse learning framework adopts an approximate hierarchical Bayesian setting defined by a series of semi-analytical expressions. The model structure inference framework is validated using a stochastic compartmental model for tracking and forecasting active cases of an infectious disease. Compartmental models describe population-level infectious disease dynamics through interactions among population fractions grouped by disease state. Mathematically, such models consist of a system of coupled ordinary differential equations. This example adopts an expressive compartmental model that includes multiple possible interactions between disease states, motivated by early uncertainty surrounding COVID-19 reinfection dynamics and their implications for long-term epidemic forecasting. The sparse learning exercise permits the inference of a priori unknown epidemiological dynamics from simulated public health data, discovering the nested compartmental model that optimizes the trade-off between average data-fit and model complexity. It is shown that inducing sparsity among the model parameters eliminates redundant interactions between compartments, equivalently revealing the optimal coupling structure between differential equations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

POROSITY AND BAND-STRENGTH MEASUREMENTS OF MULTI-PHASE COMPOSITE ICES

We use experimental mid-infrared optical constants and extended effective medium approximations to determine the porosity and the band strengths of multi-phase composite ices grown at 30 K. A set of porous H{sub 2}O:CH{sub 4} ices are taken as a prototypical example. As a benchmark and proof of concept, the stoichiometry of the ice constituents is retreived with good accuracy from the refractive indices and the extinction coefficients of the reference binary ice mixtures with known compositions. Accurate band strengths are then calculated from experimental mid-infrared spectra of complex ices. We notice that the presence of pores has only a small effect on the overall band strengths, whereas a water dilution can considerably alter them. Different levels of porosity are observed depending on the abundance of methane used as a gas contaminant premixed with water prior to background deposition. The absorption profiles are also found to vary with deposition rate. To explain this, we use Monte Carlo simulations and we observe that the deposition rate strongly affects the pore size distribution as well as the ice morphology through reorganization processes. Extrapolated to genuine interstellar ices, the methodology presented in this paper can be used to evaluate the porosity and to quantify the relative abundances from observational data.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2017

Solution of Heliospheric Propagation: Unveiling the Local Interstellar Spectra of Cosmic-ray Species

Local interstellar spectra (LIS) for protons, helium, and antiprotons are built using the most recent experimental results combined with state-of-the-art models for propagation in the Galaxy and heliosphere. Two propagation packages, GALPROP and HelMod, are combined to provide a single framework that is run to reproduce direct measurements of cosmic-ray (CR) species at different modulation levels and at both polarities of the solar magnetic field. To do so in a self-consistent way, an iterative procedure was developed, where the GALPROP LIS output is fed into HelMod, providing modulated spectra for specific time periods of selected experiments to compare with the data; the HelMod parameter optimization is performed at this stage and looped back to adjust the LIS using the new GALPROP run. The parameters were tuned with the maximum likelihood procedure using an extensive data set of proton spectra from 1997 to 2015. The proposed LIS accommodate both the low-energy interstellar CR spectra measured by Voyager 1 and the high-energy observations by BESS, Pamela, AMS-01, and AMS-02 made from the balloons and near-Earth payloads; it also accounts for Ulysses counting rate features measured out of the ecliptic plane. The found solution is in a good agreement with proton, helium, and antiproton data by AMS-02, BESS, and PAMELA in the whole energy range.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Development of Methane Emissions Model to Assess Fuel Recovery Potential at Gas Well Sites Using On Site Compression

The U.S. natural gas production and consumption has increased 85.5% since 2005 primarily due to the unconventional production methods of horizontal drilling and hydraulic fracturing. Natural gas used as a fuel has a lower greenhouse gas (GHG) footprint than coal and petroleum due to lower Carbon Dioxide (CO2) emissions when combusted. However, the “greener” benefit to natural gas may be negated by leaks in production and transmission systems. Methane (CH4), the primary hydrocarbon in natural gas, has an estimated Global Warming Potential (GWP) of 28-36 over 100 years, meaning it can absorb 28-36 more energy than CO2 which has a GWP of 1.0. Natural gas well sites are prone to methane emissions, or leaks and irregular gas releases, vented to atmosphere throughout production and transmission. The U.S. Department of Energy (DOE) and the National Energy Technology Laboratory (NETL) has recently granted West Virginia University (WVU) funding under agreement DE-FOA-0002005, to “Advance technologies to mitigate methane emissions and increase the efficiency of the natural gas transportation infrastructure”. As part of this funding WVU was tasked with identifying and quantifying sources of methane emissions at unconventional well sites, processing this data, and developing a system to recapture these emissions. A 0-D Simulink model was developed, utilizing standardized methodologies, data from previously conducted studies, as well as collected data from well sites in the Marcellus shale play region. The model was developed to predict emission rates from various components at natural gas well sites as well as the potential to utilize these emissions as fuel for the natural gas powered compressor engines on-site. This model was utilized to run high, medium, and low cases for four identified emission sources, engine size, pneumatic controller count, liquid level production which dictates tank emissions, and compressor packing vent emissions. Due to discrepancies in transient tank emission data, a high and low emission factor for tanks was used, resulting in two sets of 81 executed cases, and 162 unique cases of total site emissions and potential for fuel consumption. Each of the cases were run over 86,400 seconds at a 1 Hz, representative of a full 24 hour day of operation. The fuel consumption offset an average of 557% of fuel consumption on an energy density basis across all 81 cases with the high tank emission factor with a maximum offset of 2334%. The fuel consumption offset was an average of 82.9% for all 81 cases with the low tank emission factor with a maximum offset of 337%. This study highlights flaws in the use of publicly available methane number calculations to determine natural gas’s suitability as an engine fuel as well as the lack of public data for transient liquid storage tank emissions.

03 NATURAL GAS↗
DOE OSTI2019

SDN-NGenIA, a Software Defined Next Generation Integrated Architecture for HEP and Data Intensive Science

This project aims to enable the LHC and other leading programs in high energy physics and other global science domains funded by the DOE to operate with a new level of efficiency and control, through the development of a Next Generation Integrated Architecture (NGenIA) based on intelligent software defined network (SDN)-driven network systems coupled to high throughput applications. While the initial focus is on the challenging LHC use case, the systems and products being developed are general, and apply to many fields of data intensive science ranging from astrophysical sky surveys to bioinformatics and earth observation, as well as other organizations facing the challenges of extracting knowledge from distributed multi-petabyte data stores. A central concept in this development program is a new paradigm ‘consistent network operations’ among widely distributed computing and storage facilities, where stable high throughput flows, at set rates, cross load-balanced network paths, up to flexible high water marks that are adjusted in real time to accommodate other network traffic. The large smooth flows are launched and managed by SDN services that act in concert with the experiments’ site-resident data distribution and management systems, to meet the expanding needs of the science programs. The technical goals include the construction of autonomous, intelligent site-resident services that interact dynamically with network-resident services, and with the science programs’ principal data distribution and management tools, to request or command network resources in support of high throughput petascale to exascale workflow Specific work items include: 1. Developing compact Data Transfer Nodes (DTNs) with auto-tuning functions that support data transfer rates in the 100 Gbps to the 1 Tbps range when used with high throughput data transfer applications 2. Deep site orchestration among virtualized clusters, storage subsystems and subnets to successfully coschedule CPU, storage and network resources 3. Science-program designed site architectures, operational modes, and policy and resource usage priorities, adjudicated across multiple network domains and virtual organizations, using the orchestration functions and methods 4. Seamlessly extending end-to-end operation across both extra-site and intra-site boundaries through the use of Open vSwitch (OVS), FireQoS + next generation Science DMZs 5. Novel methods of system integration that enable granular control of extreme scale long distance transfers through flow matching of scattered source-destination address pairs to multi-domain dynamic circuits 6. Funneling massive sets of streams to DTNs at the site edge hosting petascale buffer pools configured for flows of 100 Gbps and up, exploiting state of the art data transfers 7. Adaptive scheduling based on pervasive end-to-end monitoring, including DTN or compute-node resident agents providing comprehensive end-system profiling 8. Developing unsupervised and supervised machine learning and modeling methods to optimize the workflow involving terabyte to multi-petabyte datasets.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Deliver Signal Phase and Timing (SPAT) for Energy Optimization of Vehicle Cohort Via Cloud-Computing and LTE Communications

Predictive Signal Phase and Timing (SPAT) message set is one fundamental building block for vehicle-to-infrastructure (V2I) applications such as Eco-Approach and Departure (EAD) at traffic signal controlled urban intersections. Among the two complementary communication methods namely short-range sidelink (PC5) and long-range cellular radio link (Uu), this paper documents the work with long-range link: the complete data chain includes connecting to the traffic signals via existing backhaul communication network, collecting the raw signal phase state data, predicting the signal state changes and delivering the SPAT data via a geofenced service to requests over HTTP protocols. An Application Programming Interface (API) library is developed to support various cellular data transmission reduction and latency improvement techniques. An emulation-based algorithm is applied to predict the traffic signal state changes to provide adequate prediction horizon (e.g., at minimum 2 minutes) for the cohort energy optimization. In fact, the same connectivity and SPAT delivery methodology has been applied to traffic signalized intersections nationwide in the United States upon public agency approvals for access to their firewalled traffic control network and signal control systems or directly to individual controllers. This methodology proves its effectiveness and potential for rapid growth of such SPAT deliveries at mass production scale without needing infrastructure hardware retrofit or excessive communication means. To support the energy optimization of light and heavy-duty vehicle cohorts of mixed automation and propulsion systems (EV, ICE and hybrid), the connection and SPAT deliveries at two sites were completed, including public roads in Washtenaw County, Michigan and closed track test sites at American Center for Mobility (ACM) in Ypsilanti, Michigan. However, only closed test track results at ACM will be presented in this paper. A neuroevolution based optimizer is developed and implemented to control the speed of a vehicle cohort with different propulsion systems and automation levels. Closed track tests showed significant energy savings of the cohort operation.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2018

Valley Stoner instability of the composite Fermi sea

We study two-component electrons in the lowest Landau level at total filling factor ν T = 1/2 with anisotropic mass tensors and principal axes rotated by π/2 as realized in aluminum arsenide (AlAs) quantum wells. Combining exact diagonalization and the density matrix renormalization group we demonstrate that the system undergoes a quantum phase transition from a gapless state in which both flavors are equally populated to another gapless state in which all the electrons spontaneously polarize into a single flavor beyond a critical mass anisotropy of m x /m y ~7. We propose that this phase transition is a form of itinerant Stoner transition between a two-component and a single-component composite Fermi sea states and describe a set of trial wave functions which successfully capture the quantum numbers and shell filling effects in finite size systems as well as providing a physical picture for the energetics of these states. Our estimates indicate that the composite Fermi sea of AlAs is the analog of an itinerant Stoner magnet with a finite spontaneous valley polarization. Here, we pinpoint experimental evidence indicating the presence of Stoner magnetism in the Jain states surrounding ν = 1/2.

2-dimensional systems↗
DOE OSTI2023

Evaluation of Uncertainties in the Anthropogenic SO 2 Emissions in the USA from the OMI Point Source Catalog

Satellite remote sensing is a promising method of monitoring emissions that may be missing in inventories, but the accuracy of these estimates is often not clear. We demonstrate here a comprehensive evaluation of errors in anthropogenic sulfur dioxide (SO 2 ) emission estimates from NASA’s OMI point source catalog for the contiguous US by comparing emissions from the catalog with high-quality emission inventory data over different dimensions including size of individual sources, aggregate vs individual source errors, and potential bias in individual source estimates over time. For sources that are included in the catalog, we find that errors in aggregate (sum of error for all included sources) are relatively low. Errors for individual sources in any given year can be substantial, however, with over- or underestimates in terms of total error ranging from –80 to 110 kt (roughly 10–90th percentile). In this study, we find that these errors are not necessarily random over time and that there can be consistently positive or negative biases for individual sources. We did not find any overall statistical relationship between the degree of isolation of a source and bias, either at a 40 or 70 km scales. For a sub-set of sources where inventory emissions over a radius of 70 km around an OMI detection are larger than twice the emissions within 40 km, the OMI value is consistently overestimated. We find, as expected, that emission sources not included in the catalog are the largest aggregate source of difference between the satellite estimates and inventories, especially in more recent years where source emission magnitudes have been decreasing and note that trends in satellite detections do not necessarily track trends in total emissions. We find that the OMI-based SO 2 emissions are accurate in aggregate, when summed over a number of sources, but must be interpreted more cautiously at the individual source level. Similar analyses would be valuable for other satellite emission estimates; however, in many cases, the appropriate high-quality reference data may need to be generated.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Neutronics of Advanced-Fuel Fusion Experiments with DT Triggering

Nuclear fusion studies are currently devoted to the Deuterium-Tritium (DT) fuel cycle, the easiest way to reach ignition. The recent stress on safety by the world community has stimulated the research on other fuel cycles, based on advanced reactions, such as Deuterium-Helium-3 (DHe). With DHe, it is not necessary to breed and fuel tritium. The DHe cycle has a very low presence of energetic fusion neutrons, due to DD and DT side reactions, and it has the possibility to obtain electrical power by direct energy conversion of the protons produced by the fusion reactions. DHe fusion has its own set of problems, such as the availability of {sup 3}He and the attainment of the higher plasma parameters. To explore the possibilities of DHe plasmas, a burning plasma experiment at high magnetic field and high plasma densities is particularly attractive. Ignitor is a proposed compact high-magnetic field tokamak, aimed at studying plasma-burning conditions in Deuterium-Tritium plasmas. The Ignitor experiment is designed to reach ignition under controlled DT burning conditions. The machine parameters have been established, on the basis of existing knowledge of the confinement properties of high density plasmas. The scientific goal of the Ignitor experiment is to approach, for the first time, the ignition conditions of a magnetically confined D-T plasma. However, the plasma density limit in Ignitor being well above the optimal density for DT ignition, is potentially suitable to the higher densities required for DHe burning. In fact, Ignitor has been designed also to explore conditions where 14.7-MeV protons and 3.6-MeV alpha particles produced by the DHe reactions can supply a significant amount of thermal energy to a well-confined plasma. In particular, Ignitor can sustain plasma currents exceeding those required to confine proton orbits at birth, and has more than sufficiently high densities so that the slowing-down time of both the protons and the DT alpha particles is shorter than the electron energy replacement time of the thermal plasma in which they are produced. Preliminary analyses show that a fusion power PF around 2 MW, may be reached. As a start, Ignitor can perform initial studies at the level of approximately 1 MW of power in charged particles from the DHe reaction in a mostly DT plasma. CANDOR, a design evolution of Ignitor in the direction of a reactor using a DHe fuel cycle, has been proposed; it is a feasibility study of a high-field DHe experiment of larger dimensions and higher fusion power than Ignitor, however still based on the core Ignitor technologies. The main characteristics of the CANDOR machine are the following: the major radius Ro is about double than Ignitor, plasma currents up to 25 MA with toroidal magnetic fields B{sub T} = 13 T can be produced. Unlike Ignitor, CANDOR would operate with higher values of poloidal beta around unity and the central part of the plasma column in the Second Stability region. The characteristic times over which the plasma discharge can be sustained are longer by more than a factor of 4 than those of Ignitor. To produce such a strong magnetic field in a larger machine, the toroidal field coils have been divided into two sets of coils and the central solenoid (air core transformer) is placed between them in the inboard part. In CANDOR, the DHe burning regime can be reached, by a combination of ICRF heating and alpha particle heating due to DT fusion reactions, which take the role of a sort of trigger. Thanks to this fact, and unlike other proposed DHe fusion experiments, CANDOR is capable of reaching DHe ignition using the existing technologies, and present knowledge of plasma confinement phenomena. With the initial use of DT, where tritium comprises some 50% of an initial lower density DT plasma, but is not added thereafter, the need for an intense auxiliary heating, which is one of the main technological drawbacks of DHe ignition, would be considerably alleviated, becoming feasible with the present technology. However, this method has the disadvantage of using tritium and of presenting a higher neutron flux (due to DT reactions) than 'pure' DHe plasmas, generating a neutron flux transient when passing from the initial DT trigger reaction to the final DHe burning plasma. However, for an experimental machine one-of-a-kind of relatively small dimensions, this is not an issue. (authors)

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Coarse-graining Hamiltonian systems using WSINDy

Abstract Weak form equation learning and surrogate modeling has proven to be computationally efficient and robust to measurement noise in a wide range of applications including ODE, PDE, and SDE discovery, as well as in coarse-graining applications, such as homogenization and mean-field descriptions of interacting particle systems. In this work we extend this coarse-graining capability to the setting of Hamiltonian dynamics which possess approximate symmetries associated with timescale separation. A smooth $$\varepsilon$$ ε -dependent Hamiltonian vector field $$X_\varepsilon$$ X ε possesses an approximate symmetry if the limiting vector field $$X_0=\lim _{\varepsilon \rightarrow 0}X_\varepsilon$$ X 0 = lim ε → 0 X ε possesses an exact symmetry. Such approximate symmetries often lead to the existence of a Hamiltonian system of reduced dimension that may be used to efficiently capture the dynamics of the symmetry-invariant dependent variables. Deriving such reduced systems, or approximating them numerically, is an ongoing challenge. We demonstrate that WSINDy can successfully identify this reduced Hamiltonian system in the presence of large perturbations imparted in the $$\varepsilon >0$$ ε > 0 regime, while remaining robust to extrinsic noise. This is significant in part due to the nontrivial means by which such systems are derived analytically. WSINDy naturally preserves the Hamiltonian structure by restricting to a trial basis of Hamiltonian vector fields. The methodology is computationally efficient, often requiring only a single trajectory to learn the global reduced Hamiltonian, and avoiding forward solves in the learning process. In this way, we argue that weak-form equation learning is particularly well-suited for Hamiltonian coarse-graining. Using nearly-periodic Hamiltonian systems as a prototypical class of systems with approximate symmetries, we show that WSINDy robustly identifies the correct leading-order system, with dimension reduced by at least two, upon observation of the relevant degrees of freedom. While our main contribution is computational, we also provide a contribution to the literature on averaging theory by proving that first-order averaging at the level of vector fields preserves Hamiltonian structure in nearly-periodic Hamiltonian systems. This provides theoretical justification for our approach as WSINDy’s computations occur at the level of Hamiltonian vector fields. We illustrate the efficacy of our proposed method using physically relevant examples, including coupled oscillator dynamics, the Hénon–Heiles system for stellar motion within a galaxy, and the dynamics of charged particles.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Background based Gaussian mixture model lesion segmentation in PET

Purpose: Quantitative {sup 18}F-fluorodeoxyglucose positron emission tomography is limited by the uncertainty in lesion delineation due to poor SNR, low resolution, and partial volume effects, subsequently impacting oncological assessment, treatment planning, and follow-up. The present work develops and validates a segmentation algorithm based on statistical clustering. The introduction of constraints based on background features and contiguity priors is expected to improve robustness vs clinical image characteristics such as lesion dimension, noise, and contrast level. Methods: An eight-class Gaussian mixture model (GMM) clustering algorithm was modified by constraining the mean and variance parameters of four background classes according to the previous analysis of a lesion-free background volume of interest (background modeling). Hence, expectation maximization operated only on the four classes dedicated to lesion detection. To favor the segmentation of connected objects, a further variant was introduced by inserting priors relevant to the classification of neighbors. The algorithm was applied to simulated datasets and acquired phantom data. Feasibility and robustness toward initialization were assessed on a clinical dataset manually contoured by two expert clinicians. Comparisons were performed with respect to a standard eight-class GMM algorithm and to four different state-of-the-art methods in terms of volume error (VE), Dice index, classification error (CE), and Hausdorff distance (HD). Results: The proposed GMM segmentation with background modeling outperformed standard GMM and all the other tested methods. Medians of accuracy indexes were VE <3%, Dice >0.88, CE <0.25, and HD <1.2 in simulations; VE <23%, Dice >0.74, CE <0.43, and HD <1.77 in phantom data. Robustness toward image statistic changes (±15%) was shown by the low index changes: <26% for VE, <17% for Dice, and <15% for CE. Finally, robustness toward the user-dependent volume initialization was demonstrated. The inclusion of the spatial prior improved segmentation accuracy only for lesions surrounded by heterogeneous background: in the relevant simulation subset, the median VE significantly decreased from 13% to 7%. Results on clinical data were found in accordance with simulations, with absolute VE <7%, Dice >0.85, CE <0.30, and HD <0.81. Conclusions: The sole introduction of constraints based on background modeling outperformed standard GMM and the other tested algorithms. Insertion of a spatial prior improved the accuracy for realistic cases of objects in heterogeneous backgrounds. Moreover, robustness against initialization supports the applicability in a clinical setting. In conclusion, application-driven constraints can generally improve the capabilities of GMM and statistical clustering algorithms.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Cesium Removal from Surrogate Pyroprocessing Salt by Electrodeposition

Active metals in used nuclear fuel dissolve into the salt during pyroprocessing and are not removed by electrorefining or drawdown operations. The buildup of 137 Cs over time increases the heat load and ionizing radiation level of the salt such that it must be replaced frequently, resulting in a significant amount of salt waste. An effective means of managing cesium in the molten salt electrolyte would increase the efficiency of pyroprocessing and decrease the volume of salt waste requiring disposal. A previous report summarized issues that must be addressed when developing a removal strategy and assessed the suitability of existing methods and remaining technological gaps to their application (Rose and Thomas 2023). Cesium is extremely stable in molten salt as a chloride—even more stable than the LiCl-KCl eutectic base salt used for pyroprocessing fuel—which makes removing cesium a challenge. However, sufficiently strong atomic interactions occur between active metal species and liquid metals that make the electrodeposition of active metal fission products into liquid metal electrodes energetically favorable. The feasibility of recovering cesium from LiCl-KCl pyroprocessing salt through electrodeposition into liquid metals is eing assessed by identifying potentially effective liquid metals and performing tests to determine the effectiveness of electrodepositing cesium from a LiCl/KCl salt into these liquid metals. Previous studies investigating the electrodeposition of Sr 2+ , and Ba 2+ into zinc, cadmium, bismuth, lead, tin and antimony have shown that alkali and alkaline earth metals can be electrodeposited at liquid metal cathodes (Kim et al., 2018). The removal of Ba 2+ and Sr 2+ was measured to be more efficient than the removal of monovalent cations due to the greater thermochemical driving force for alloying those elements with the liquid metal (Jang et al. 2022). Because the equilibrium potentials are dependent on the interactions of the active metal in the liquid metal, it is likely that the other alkali metals Li + , and K + , will deposit from LiCl/KCl salt with the Cs + . Therefore, application of this method to recover active metals from pyroprocessing salt will benefit from the use of a liquid metal and set of operating conditions that sufficiently increase the reduction potential of cesium to remove cesium from the waste salt with an acceptable amount of co-deposited lithium and potassium.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗