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Low-Cost, High-Performance Carbon Fiber for Compressed Natural Gas Storage Tanks (Final Technical Report – Down Select Report)

The aim of this project is to reduce the cost of Type IV, carbon fiber (CF) composite overwrap compressed gas storage tanks by reducing the cost of CF and CF composites. The project team worked to reduce the cost of CF by exploring and testing opportunities for a low-cost alternative precursor material for CF production to supplant market-dominant and costly polyacrylonitrile (PAN). Concurrently, the team aimed to reduce the cost of the tanks at the composite level by improving the interfacial adhesion between the fibers and the matrix resin through the incorporation of low-cost nanoparticles recycled from waste materials, which would reduce the volume of costly CF required to achieve the same tank performance. At the end of the first year, the project team selected mesophase pitch as the primary precursor candidate from a field of materials based on the superior mechanical performance and cost-saving potential. During the second year, the team produced CFs derived from mesophase pitch achieving an average tensile strength of 365.6 ksi and average tensile modulus of 40.74 Msi. Facility availability for spinning and converting these fibers at greater scale has hindered scale-up demonstration, but the team has identified opportunities to conduct this work in the near term. Cost modeling shows that these mesophase pitch-derived CFs can be up to 40% less expensive than PAN-derived CFs due to the lower cost of the feedstock material, higher throughput, greater conversion yield, and lower cost spinning method and compared to PAN. Additionally, the team has demonstrated at lab-scale that nanoparticle coating CFs can significantly increase the interfacial shear strength and load transfer efficiency of CFs in a matrix. Single filament pull-out testing showed a 27% average increase in max interfacial shear strength due to this coating. A continuous method of applying these coatings to a tow of CF has been developed for scale-up. 26 m tows of coated CFs were produced using this system and formed into composite ring samples for ASTM ring burst testing. Issues with the testing protocol have limited assessment of these results. A prototype Type IV tank was designed to meet ANSI HGV2 standards, and the design criteria set out by DOE, using the CF properties developed by the team paired with a proprietary resin matrix, a polyamide liner, and aluminum end bosses. The tank weighs 153.1 kg and with a total capacity of 5.8 kg H2 (5.6 kg usable), which yields a gravimetric capacity of 1.17 kWh/kg. Cost modeling predicts that the tank will have a projected cost of $15.73/kWh. Tank performance modeling does not include considerations for fiber-matrix load transfer efficiency improvements offered by nanoparticle coating method.

08 HYDROGEN↗

Terra-Populus v0.1: A Python Library for LandScan High-Definition Population Analysis and Modeling

The terra-populus library is designed for use by the LandScan HD technical team, offering a streamlined set of tools for generating and updating LandScan HD datasets from foundational building-level data, referred to as 'molecules,' provided by the building-level attribution team. This document serves as the primary technical documentation for terra-populus. Version 0.1 of the library includes the core modeling components necessary for LandScan HD production. It enables the generation of the LandScan HD Baseline dataset as well as corresponding confidence measures for the occupancy rates used. Parameters have been included for incorporating damaged building indicators and changes in population, to faciliate the creation of rapid updates for LandScan HD. Future iterations of terra-populus will introduce tools for creating a confidence index, and quantifying and propagating uncertainty, facilitating the creation of probabilistic LandScan HD outputs. This report provides an overview of the tools available in the library and the corresponding code implementations. One of the key advancements implemented in terra-populus is a redefinition of the atomic modeling unit for LandScan HD. Traditionally, the LandScan HD vector analytical framework has generated population estimates at the building sub-component (molecule) level. However, terra-populus adopts a building-level modeling approach. This shift is an operational decision aimed at aligning LandScan HD outputs with confidence measures, which are computed and validated at the building level (confidence measures are not included in this version of terra-populus, aside from those associated with the occupancy rates). Additional advancements to the LandScan HD modeling, as implemented by terra-populus, include a minimum population value parameter and an auto assignment of building floor counts. The population minimum value was implemented to prevent buildings and subsequent LandScan HD pixels that contained small values that may not rasterize in production. An 'auto' value has been included as a method for dealing with buildings lacking floor count information, where it is the average floor count of all other buildings with a residential building use type tag. The logic behind this is to remain consistent with the current logic employed for dealing with building use type null instances, where a null use type is defaulted to residential since it is the most common building type. The auto logic is intended to apply the most common building floor count of the most common type of buildings. The tools provided in terra-populus represent a significant step forward in improving the efficiency, reproducibility, and transparency of the LandScan HD modeling process. As the library evolves, it will continue to serve as a foundational resource for high-resolution population modeling.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

Solvent Hold Tank Sample Results for MCU-19-2-3-4 (January 2019), MCU-19-83-84-85 (February 2019), MCU-19-208-209-210 (March 2019) and MCU-19-138-139-140 (March special trim): Quarterly Report

A trend summary of three sets of Solvent Hold Tank (SHT) monthly samples (MCU-19-2-3-4, MCU-19-83-84-85, MCU-19-208-209-210); and a set from a special trim (MCU-19-138-139-140) are reported. Most of the conclusions are based on the March SHT sample (MCU-18-208-209-210). Analyses of the March SHT sample (MCU-19-208-209-210) indicated that the Modifier (Cs-7SB) and the Extractant (MaxCalix) concentrations were below their nominal recommended concentrations (169,000 mg/L and 46,900 mg/L, respectively) by 3% and 6%, respectively. The Suppressor (N,N’,N”–tris(3,7-dimethyloctyl)guanidine or TiDG) concentration has increased since the January 2019 measurement to 1141 mg/L, which is above the minimum recommended concentration (479 mg/L). These new concentrations are consistent with the concentrations observed when MCU was fully operational in 2016. The Semi-Volatile Organic Analysis (SVOA) did not detect any impurities. However, the Fourier Transform Hydrogen Nuclear Magnetic Resonance (FT-HNMR) analysis detected presence of phthalates at a few ppm. The impurity concentration was highest in the January sample and consistently detected in the February and March samples (but always registered as a few ppm or less). Another impurity observed in the samples was mercury. Based on the March SHT sample, up to 25 ± 5 micrograms of mercury per mL of solvent was detected (as the average of the Direct Mercury Analysis (DMA) and X-Ray Fluorescence (XRF) methods). The higher mercury concentration in the solvent (as determined in the last three-monthly samples) is possibly due to the higher mercury concentration in Salt Batches 9 and 10. The gamma concentration (~2.55E5 dpm/mL) measured in the March 2019 SHT samples was consistent with previous values observed when MCU was operating continuously (for example, between December 2016 and January 2017). Statistical analysis of the different analytical methods detected a bias (a statistical difference at 5% level) between the FT-HNMR and HPLC methods when measuring the MaxCalix and Modifier concentration; the FT-HNMR gave a higher concentration possibly due to peak overlapping in the measured data. A bias was also detected when measuring total mercury by the DMA method versus the XRF method with the XRF method reported higher mercury concentrations. The laboratory will continue to monitor the quality of the solvent for any new impurities or degradation of the solvent components.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

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↗

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↗

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.↗

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↗

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↗

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↗

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↗

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↗

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↗

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↗

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↗

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↗

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↗

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↗

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↗