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DOE OSTI2012

Integrated Radiation Transport and Nuclear Fuel Performance for Assembly-Level Simulations

The Advanced Multi-Physics (AMP) Nuclear Fuel Performance code (AMPFuel) is focused on predicting the temperature and strain within a nuclear fuel assembly to evaluate the performance and safety of existing and advanced nuclear fuel bundles within existing and advanced nuclear reactors. AMPFuel was extended to include an integrated nuclear fuel assembly capability for (one-way) coupled radiation transport and nuclear fuel assembly thermo-mechanics. This capability is the initial step toward incorporating an improved predictive nuclear fuel assembly modeling capability to accurately account for source-terms, such as neutron flux distribution, coolant conditions and assembly mechanical stresses, of traditional (single-pin) nuclear fuel performance simulation. A novel scheme is introduced for transferring the power distribution from the Scale/Denovo (Denovo) radiation transport code (structured, Cartesian mesh with smeared materials within each cell) to AMPFuel (unstructured, hexagonal mesh with a single material within each cell), allowing the use of a relatively coarse spatial mesh (10 million elements) for the radiation transport and a fine spatial mesh (3.3 billion elements) for thermo-mechanics with very little loss of accuracy. With this novel capability, AMPFuel was used to model an entire 1717 pressurized water reactor fuel assembly with many of the features resolved in three dimensions (for thermo-mechanics and/or neutronics). A full assembly calculation was executed on Jaguar using 40,000 cores in under 10 hours to model over 160 billion degrees of freedom for 10 loading steps. The single radiation transport calculation required about 50% of the time required to solve the thermo-mechanics with a single loading step, which demonstrates that it is feasible to incorporate, in a single code, a high-fidelity radiation transport capability with a high-fidelity nuclear fuel thermo-mechanics capability and anticipate acceptable computational requirements. The results of the full assembly simulation clearly show the axial, radial, and azimuthal variation of the neutron flux, power, temperature, and deformation of the assembly, highlighting behavior that is neglected in traditional axisymmetric fuel performance codes that do not account for assembly features, such as guide tubes and control rods.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2020

Application of an Embedded Fracture and Borehole Modeling Approach to the Understanding of EGS Collab Experiment 1

EGS Collab is a series of meso-scale experiments and associated numerical simulation activities being funded by the United States Department of Energy, Geothermal Technologies Office (GTO) to investigate enhanced geothermal system processes under in-situ stress and slightly elevated temperature conditions. This project is designed to provide scientists and engineers with immediate access to impermeable rock at scales larger than possible in the laboratory, but generally smaller than those for commercial production. Immediate access to rock is provided via the existing drifts of the former Homestake Gold Mine, now operated as the Sanford Underground Research Facility in Lead, South Dakota. The objectives of the EGS Collab project are to develop well controlled fracture networks between injector and producer boreholes using normal and shear stimulation for permeability enhancement. The first experimental site was located off the West Access drift on the 4850 Level (4850 feet below ground surface) in phyllite of the Precambrian Poorman formation, and involved the creation of a fracture network comprising a combination of hydraulic and natural fractures. A second experimental site is now being considered near the battery alcove on the 4100 Level in amphibolite and rhyolite of the Yates Unit. Data generated during these experiments will be compared against predictions of a suite of computer codes specifically designed to solve problems involving coupled thermal, hydrological, geomechanical, and geochemical processes. Comparisons between experimental and numerical simulation results will provide code developers with direction for improvements and verification of process models, build confidence in the suite of available numerical tools, and ultimately identify critical future development needs for the geothermal modeling community. Moreover, conducting thorough comparisons of models, modelling approaches, measurement approaches and measured data, via the EGS Collab project, will serve to identify techniques that are most likely to succeed at the Frontier Observatory for Research in Geothermal Energy (FORGE), the GTO’s flagship EGS research effort. Experiment 1 has comprised a series of successful tests, including a long-term chilled-water circulation test, but the testbed has two atypical EGS elements. Active ventilation in the adjacent drift over a 50-year period cooled the testbed rock mass and hydraulic fracturing intersected monitoring boreholes making them conduits for fluid flow. Numerical simulations executed in support of the design of the EGS Collab Experiment 1, computed a radial temperature distribution and stress gradient orthogonal to the drift axis, resulting in the forecast of an oblong hydraulic fracture geometry, extended in the direction of the drift from the stimulation borehole. This paper describes the numerical simulation of long-term chilled-water circulation test with an embedded fracture and borehole modeling approach. The principal objective of the simulation work is show agreement between experimental observations in terms of production fluid temperatures, monitoring borehole temperatures, injection and production pressures, and tracer recoveries using a single conceptual model for the fracture network, using characterization and monitoring data generally available for EGS. A secondary objective is to improve the numerical simulation result comparisons with additional data available from the broader monitoring equipment within the testbed versus deeper, hotter, and more remote EGS. One complicating factor for comparisons between the numerical simulation results and experimental observations, is the Joule-Thomson heating associated with large pressure drops across the fracture network.

numerical simulation, embedded fractures and boreh↗

DOE OSTI2014

SU-E-T-146: Effects of Uncertainties of Radiation Sensitivity of Biological Modelling for Treatment Planning

Purpose: The aim of this study was to evaluate the distribution of uncertainty of cell survival by radiation, and assesses the usefulness of stochastic biological model applying for gaussian distribution. Methods: For single cell experiments, exponentially growing cells were harvested from the standard cell culture dishes by trypsinization, and suspended in test tubes containing 1 ml of MEM(2x10{sup 6} cells/ml). The hypoxic cultures were treated with 95% N{sub 2}−5% CO{sub 2} gas for 30 minutes. In vitro radiosensitization was also measured in EMT6/KU single cells to add radiosensitizer under hypoxic conditions. X-ray irradiation was carried out by using an Xray unit (Hitachi X-ray unit, model MBR-1505R3) with 0.5 mm Al/1.0 mm Cu filter, 150 kV, 4 Gy/min). In vitro assay, cells on the dish were irradiated with 1 Gy to 24 Gy, respectively. After irradiation, colony formation assays were performed. Variations of biological parameters were investigated at standard cell culture(n=16), hypoxic cell culture(n=45) and hypoxic cell culture(n=21) with radiosensitizers, respectively. The data were obtained by separate schedule to take account for the variation of radiation sensitivity of cell cycle. Results: At standard cell culture, hypoxic cell culture and hypoxic cell culture with radiosensitizers, median and standard deviation of alpha/beta ratio were 37.1±73.4 Gy, 9.8±23.7 Gy, 20.7±21.9 Gy, respectively. Average and standard deviation of D{sub 50} were 2.5±2.5 Gy, 6.1±2.2 Gy, 3.6±1.3 Gy, respectively. Conclusion: In this study, we have challenged to apply these uncertainties of parameters for the biological model. The variation of alpha values, beta values, D{sub 50} as well as cell culture might have highly affected by probability of cell death. Further research is in progress for precise prediction of the cell death as well as tumor control probability for treatment planning.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs Results

Geothermal power plants typically show decreasing heat and power production rates over time. Mitigation strategies include optimizing the management of existing wells - increasing or decreasing the fluid flow rates across the wells - and drilling new wells at appropriate locations. The latter is expensive, time-consuming, and subject to many engineering constraints, but the former is a viable mechanism for periodic adjustment of the available fluid allocations. Data and supporting literature from a study describing a new approach combining reservoir modeling and machine learning to produce models that enable strategies for the mitigation of decreased heat and power production rates over time for geothermal power plants. The computational approach used enables translation of sets of potential flow rates for the active wells into reservoir-wide estimates of produced energy and discovery of optimal flow allocations among the studied sets. In our computational experiments, we utilize collections of simulations for a specific reservoir (which capture subsurface characterization and realize history matching) along with machine learning models that predict temperature and pressure timeseries for production wells. We evaluate this approach using an "open-source" reservoir we have constructed that captures many of the characteristics of Brady Hot Springs, a commercially operational geothermal field in Nevada, USA. Selected results from a reservoir model of Brady Hot Springs itself are presented to show successful application to an existing system. In both cases, energy predictions prove to be highly accurate: all observed prediction errors do not exceed 3.68% for temperatures and 4.75% for pressures. In a cumulative energy estimation, we observe prediction errors that are less than 4.04%. A typical reservoir simulation for Brady Hot Springs completes in approximately 4 hours, whereas our machine learning models yield accurate 20-year predictions for temperatures, pressures, and produced energy in 0.9 seconds. This paper aims to demonstrate how the models and techniques from our study can be applied to achieve rapid exploration of controlled parameters and optimization of other geothermal reservoirs. Includes a synthetic, yet realistic, model of a geothermal reservoir, referred to as open-source reservoir (OSR). OSR is a 10-well (4 injection wells and 6 production wells) system that resembles Brady Hot Springs (a commercially operational geothermal field in Nevada, USA) at a high level but has a number of sufficiently modified characteristics (which renders any possible similarity between specific characteristics like temperatures and pressures as purely random). We study OSR through CMG simulations with a wide range of flow allocation scenarios. Includes a dataset with 101 simulated scenarios that cover the period of time between 2020 and 2040 and a link to the published paper about this project, where we focus on the Machine Learning work for predicting OSR's energy production based on the simulation data, as well as a link to the GitHub repository where we have published the code we have developed (please refer to the repository's readme file to see instructions on how to run the code). Additional links are included to associated work led by the USGS to identify geologic factors associated with well productivity in geothermal fields. Below are the high-level steps for applying the same modeling + ML process to other geothermal reservoirs: 1. Develop a geologic model of the geothermal field. The location of faults, upflow zones, aquifers, etc. need to be accounted for as accurately as possible 2. The geologic model needs to be converted to a reservoir model that can be used in a reservoir simulator, such as, for instance, CMG STARS, TETRAD, or FALCON 3. Using native state modeling, the initial temperature and pressure distributions are evaluated, and they become the initial conditions for dynamic reservoir simulations 4....

15 GEOTHERMAL ENERGY↗

DOE OSTI2025

Testing the thermal Sunyaev-Zel’dovich power spectrum of a halo model using hydrodynamical simulations

Statistical properties of large-scale cosmological structures serve as powerful tools for constraining the cosmological properties of our Universe. Tracing the gas pressure, the thermal Sunyaev-Zel’dovich (tSZ) effect is a biased probe of mass distribution and, hence, can be used to test the physics of feedback or cosmological models. Therefore, it is crucial to develop robust modelling of hot gas pressure for applications to tSZ surveys. Since gas collapses into bound structures, it is expected that most of the tSZ signal is within halos produced by cosmic accretion shocks. Hence, simple empirical halo models can be used to predict the tSZ power spectra. In this study, we employed the HMx halo model to compare the tSZ power spectra with those of several hydrodynamical simulations: the Horizon suite and the Magneticum simulation. We examine various contributions to the tSZ power spectrum across different redshifts, including the one- and two-halo term decomposition, the amount of bound gas, the importance of different masses, and the electron pressure profiles. Our comparison of the tSZ power spectrum reveals discrepancies between the halo model and cosmological simulations that increase with redshift. We find a 20% to 50% difference between the measured and predicted tSZ angular power spectrum over the multipole range ℓ = 10 3 − 10 4 . Our analysis reveals that these differences are driven by the excess of power in the predicted two-halo term at low k and in the one-halo term at high k . At higher redshifts ( z ∼ 3), simulations indicate that more power comes from outside the virial radius than from inside, suggesting a limitation in the applicability of the halo model. We also observe differences in the pressure profiles, despite the fair level of agreement on the tSZ power spectrum at low redshift with the default calibration of the halo model. In conclusion, our study suggests that the properties of the halo model need to be carefully controlled against real or mock data to be proven useful for cosmological purposes.

Ayçoberry, Emma (ORCID:0000000292351195)↗

DOE OSTI2019

Large-Signal Impedance-Based Modeling and Mitigation of Resonance of Converter-Grid Systems

Large-signal impedance of grid-connected converters can be used to predict resonance-generated distortions in converter-grid systems. Note that the large-signal impedance of a network represents its impedance response for different magnitudes of perturbation injected at its terminals. This paper presents large-signal impedance-based modeling and mitigation of resonance of grid-connected voltage source converters (VSCs). Challenges of large-signal modeling because of the inapplicability of the small-signal approximation are addressed by leveraging the dominating influence of hard nonlinearities (such as pulse-width modulation saturation and limiters) over soft nonlinearities (such as Park's transformations and PLL) in shaping the large-signal behavior of the converter. The paper develops large-signal gains of hard nonlinearities using different types of describing functions. The paper shows that the large-signal impedance of a VSC can be shaped to reduce resonance-generated distortions by inserting limiters in the control system of the VSC. Developed large-signal impedance models are validated using numerical simulations of a VSC with dq current control and PLL. In conclusion, a large-signal impedance measurements of a commercial 1 MW VSC-based inverter and a medium-voltage doubly-fed induction generator with approximate 4 MW rating are presented to experimentally demonstrate the influence of the injected perturbation magnitude on the impedance response.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2019

Unsupervised machine learning based on non-negative tensor factorization for analyzing reactive-mixing

Analysis of reactive-diffusion simulations representing complex mixing processes requires a large number of independent model runs. For each high-fidelity model simulation, the model inputs are varied and the predicted mixing behavior is represented by temporal and spatial changes in species concentration. It is then required to discern how the model inputs (such as diffusivity, dispersion, anisotropy, and velocity field properties) impact the mixing process. This task is challenging and typically involves interpretation of large model outputs representing temporal and spatial changes of species concentration within the model domain. Yet, the task can be automated and substantially simplified by applying Machine Learning (ML) methods. Here, we present an application of an unsupervised ML method (called NTF k ) using Non-negative Tensor Factorization (NTF) coupled with a custom clustering procedure based on k-means to reveal the temporal and spatial features in product concentrations. An attractive and unique aspect of the proposed ML method is that it ensures the extracted features are non-negative, which is important to obtain a meaningful deconstruction of the mixing processes. The ML methodology is applied to a large set of high-resolution finite-element model simulations representing anisotropic reaction-diffusion processes in perturbed vortex-based velocity fields. The applied finite-element method ensures that spatial and temporal species concentration are always non-negative, even in the case of high anisotropic contrasts. The simulated reaction is a fast irreversible bimolecular reaction A+B→C , where species A and B react to form species C. The reactive-diffusion model input parameters that control mixing include properties of the velocity field (such as vortex structures), anisotropic dispersion, and molecular diffusion. We demonstrate the applicability of the ML feature extraction method to produce a meaningful deconstruction of model outputs to discriminate between different physical processes impacting the reactants, their mixing, and the spatial distribution of the product C. The introduced ML analysis allowed us to identify additive temporal and spatial features that characterize mixing behavior. The application of the proposed NTF k approach is not limited to reactive-mixing. NTFk can be readily applied to any observed or simulated datasets that can be represented as tensors (multi-dimensional arrays) and have separable latent signatures or features.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2013

Improved Biomass Cooking Stoves and Improved Stove Emission Equipment

In developing countries, there is an urgent need for access to safe, efficient, and more affordable cooking technologies. Nearly 2.5 billion people currently use an open fire or traditional cookstove to prepare their meals, and recent models predict that use of biomass for cooking will continue to be the dominant energy use in rural, resource-poor households through 2030. For these families, cooking poses serious risks to health, safety, and income. An alarming 4 million people, primarily women and children, die prematurely each year from indoor and outdoor exposure to the harmful emissions released by solid fuel combustion. Use of traditional stoves can also have a significant impact on deforestation and climate change. This dire situation creates a critical need for cookstoves that significantly and verifiably reduce fuel use and emissions in order to reach protective levels for human health and the environment. Additionally, advances in the scientific equipment needed to measure and monitor stove fuel use and emissions have not kept pace with the significant need within the industry. While several testing centers in the developed world may have hundred thousand-dollar emissions testing systems, organizations in the field have had little more than a thermometer, a scale, and subjective observations to quantify the performance of stove designs. There is an urgent need for easy-to-use, inexpensive, accurate, and robust stove testing equipment for use by laboratory and field researchers around the world. ASAT and their research partner, Aprovecho Research Center (ARC), have over thirty years of experience addressing these two needs, improved cookstoves and emissions monitoring equipment, with expertise spanning the full spectrum of development from conceptual design to product manufacturing and dissemination. This includes: 1) research, design, and verification of clean biomass cookstove technology and emissions monitoring equipment; 2) mass production of quality-controlled stove and emissions equipment at levels scalable to meet global demand; and 3) global distribution through a variety of channels and partners. ARC has been instrumental in designing and improving more than 100 stove designs over the past thirty years. In the last four years, ASAT and ARC have played a key role in the production and sales of over 200,000 improved stoves in the developed and developing world. The ARC-designed emissions equipment is currently used by researchers in laboratories and field studies on five continents. During Phase I of the DOE STTR grant, ASAT and ARC worked together to apply their wealth of product development experience towards creating the next generation of improved cookstoves and emissions monitoring equipment. Highlights of Phase I for the biomass cookstove project include 1) the development of several new stove technologies that reached the DOE 50/90 benchmark; 2) fabrication of new stove prototypes by ASAT’s manufacturing partner, Shengzhou Stove Manufacturing (SSM); 3) field testing of prototype stoves with consumers in Puerto Rico and the US; and 4) the selection of three stove prototypes for further development and commercialization during Phase II. Highlights of Phase I for the emissions monitoring equipment project include: 1) creation of a new emissions monitoring equipment product, the Laboratory Emissions Monitoring System (LEMS 2) the addition of gravimetric PM measurements to the stove testing systems to meet International Standards Organization criteria; 3) the addition of a CO{sub 2} sensor and wireless 3G capability to the IAP Meter; and 4) and the improvement of sensors and signal quality on all systems. Twelve Regional Testing and Knowledge Centers purchased this equipment during the Phase I project period.

09 BIOMASS FUELS↗

DOE OSTI2023

Detailed study of quark-hadron duality in spin structure functions of the proton and neutron

Background: The response of hadrons, the bound states of the strong force (QCD), to external probes can be described in two different, complementary frameworks: as direct interactions with their fundamental constituents, quarks and gluons, or alternatively as elastic or inelastic coherent scattering that leaves the hadrons in their ground state or in one of their excited (resonance) states. The former picture emerges most clearly in hard processes with high momentum transfer, where the hadron response can be described by the perturbative expansion of QCD, while at lower energy and momentum transfers, the resonant excitations of the hadrons dominate the cross section. The overlap region between these two pictures, where both yield similar predictions, is referred to as quark-hadron duality and has been extensively studied in reactions involving unpolarized hadrons. Some limited information on this phenomenon also exists for polarized protons, deuterons, and 3 He nuclei, but not yet for neutrons. Purpose: In this paper, we present comprehensive and detailed results on the correspondence between the extrapolated deep inelastic structure function g 1 of both the proton and the neutron with the same quantity measured in the nucleon resonance region. Thanks to the fine binning and high precision of our data, and using a well-controlled perturbative QCD (pQCD) fit for the partonic prediction, we can make quantitative statements about the kinematic range of applicability of both local duality and global duality. Method: We use the most updated QCD global analysis results at high x from the Jefferson Lab Angular Momentum Collaboration to extrapolate the spin structure function g 1 into the nucleon resonance region and then integrate over various intervals in the scaling variable x. We compare the results with the large data set collected in the quark-hadron transition region by the CLAS Collaboration, including, for the first time, deconvoluted neutron data, integrated over the same intervals. We present this comparison as a function of the momentum transfer Q 2 . Results: We find that, depending on the integration interval and the minimum momentum transfer chosen, a clear transition to quark-hadron duality can be observed in both nucleon species. Furthermore, we show, for the first time, the approach to scaling behavior for g 1 measured in the resonance region at sufficiently high momentum transfer. Conclusions: Here, our results can be used to quantify the deviations from the applicability of pQCD for data taken at moderate energies and can help with extraction of quark distribution functions from such data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2016

Geophysical estimation of shallow permafrost distribution and properties in an ice-wedge polygon-dominated Arctic tundra region

Shallow permafrost distribution and characteristics are important for predicting ecosystem feedbacks to a changing climate over decadal to century timescales because they can drive active layer deepening and land surface deformation, which in turn can significantly affect hydrologic and biogeochemical responses, including greenhouse gas dynamics. As part of the U.S. Department of Energy Next-Generation Ecosystem Experiments- Arctic, we have investigated shallow Arctic permafrost characteristics at a site in Barrow, Alaska, with the objective of improving our understanding of the spatial distribution of shallow permafrost, its associated properties, and its links with landscape microtopography. To meet this objective, we have acquired and integrated a variety of information, including electric resistance tomography data, frequency-domain electromagnetic induction data, laboratory core analysis, petrophysical studies, high-resolution digital surface models, and color mosaics inferred from kite-based landscape imaging. The results of our study provide a comprehensive and high-resolution examination of the distribution and nature of shallow permafrost in the Arctic tundra, including the estimation of ice content, porosity, and salinity. Among other results, porosity in the top 2 m varied between 85% (besides ice wedges) and 40%, and was negatively correlated with fluid salinity. Salinity directly influenced ice and unfrozen water content and indirectly influenced the soil organic matter content. A relatively continuous but depth-variable increase in salinity led to a partially unfrozen saline layer (cryopeg) located below the top of the permafrost. The cryopeg environment could lead to year-round microbial production of greenhouse gases. Results also indicated a covariability between topography and permafrost characteristics including icewedge and salinity distribution. In addition to providing insight about the Arctic ecosystem, through integration of lab-based petrophysical results with field data, this study also quantified the key controls on electric resistivity at this Arctic permafrost site, including salinity, porosity, water content, ice content, soil organic matter content, and lithologic properties.

58 GEOSCIENCES↗

DOE OSTI2016

Extracting Baseline Electricity Usage Using Gradient Tree Boosting

To understand how specific interventions affect a process observed over time, we need to control for the other factors that influence outcomes. Such a model that captures all factors other than the one of interest is generally known as a baseline. In our study of how different pricing schemes affect residential electricity consumption, the baseline would need to capture the impact of outdoor temperature along with many other factors. In this work, we examine a number of different data mining techniques and demonstrate Gradient Tree Boosting (GTB) to be an effective method to build the baseline. We train GTB on data prior to the introduction of new pricing schemes, and apply the known temperature following the introduction of new pricing schemes to predict electricity usage with the expected temperature correction. Our experiments and analyses show that the baseline models generated by GTB capture the core characteristics over the two years with the new pricing schemes. In contrast to the majority of regression based techniques which fail to capture the lag between the peak of daily temperature and the peak of electricity usage, the GTB generated baselines are able to correctly capture the delay between the temperature peak and the electricity peak. Furthermore, subtracting this temperature-adjusted baseline from the observed electricity usage, we find that the resulting values are more amenable to interpretation, which demonstrates that the temperature-adjusted baseline is indeed effective.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Forest reorganisation effects on fuel moisture content can exceed changes due to climate warming in wet temperate forests

Abstract The distributions of vegetation and fire activity are changing rapidly in response to climate warming. In many regions, climate effects on dead fuel moisture content (FMC) are expected to increase future wildfire activity. However, forest FMC is largely driven by microclimate conditions, which are moderated from open weather by vegetation canopies. As shifts in vegetation increase under climate warming, the extent to which future fire activity will be driven by climate directly or associated vegetation shifts remains unresolved. Here, we present a study aimed at quantifying the relative magnitudes of (i) direct climate warming, and (ii) vegetation change, on FMC. Field sites to evaluate these effects were established in a natural laboratory of altered forest states to mature wet temperate forest in south‐eastern Australia. FMC was estimated using a process‐based model and 48 years of reconstructed climate data. Canopy effects on microclimate were captured by transferring inputs from climate to microclimate using models parameterised with field observations. To evaluate the relative magnitude of climate and vegetation effects, we calculated the maximum difference in mean annual FMC across annual climate replicates and compared this to FMC differences across reorganising forest sites. Our results show vegetation effects on FMC can exceed those related to expected climate change. Changes to forest structure and composition increased (+15.7%) and decreased (−12.3%) mean annual FMC, with a larger negative effect when forest cover was completely removed (−18.5%). In contrast, the largest climate effect on FMC was −6.6% across 48‐years of data. Our study demonstrates that the magnitude of vegetation effects on FMC can exceed expected climate change effects. Models of future fire activity that do not account for changing vegetation effects on microclimate are omitting a key biophysical control on FMC and therefore may not be accurately predicting future fire activity.

Brown, Tegan P.↗

DOE OSTI2015

Investigation of particle and vapor wall-loss effects on controlled wood-smoke smog-chamber experiments

Here, smog chambers are extensively used to study processes that drive gas and particle evolution in the atmosphere. A limitation of these experiments is that particles and gas-phase species may be lost to chamber walls on shorter timescales than the timescales of the atmospheric processes being studied in the chamber experiments. These particle and vapor wall losses have been investigated in recent studies of secondary organic aerosol (SOA) formation, but they have not been systematically investigated in experiments of primary emissions from combustion. The semi-volatile nature of combustion emissions (e.g. from wood smoke) may complicate the behavior of particle and vapor wall deposition in the chamber over the course of the experiments due to the competition between gas/particle and gas/wall partitioning. Losses of vapors to the walls may impact particle evaporation in these experiments, and potential precursors for SOA formation from combustion may be lost to the walls, causing underestimations of aerosol yields. Here, we conduct simulations to determine how particle and gas-phase wall losses contributed to the observed evolution of the aerosol during experiments in the third Fire Lab At Missoula Experiment (FLAME III). We use the TwO-Moment Aerosol Sectional (TOMAS) microphysics algorithm coupled with the organic volatility basis set (VBS) and wall-loss formulations to examine the predicted extent of particle and vapor wall losses. We limit the scope of our study to the dark periods in the chamber before photo-oxidation to simplify the aerosol system for this initial study. Our model simulations suggest that over one-third of the initial particle-phase organic mass (41 %) was lost during the experiments, and over half of this particle-organic mass loss was from direct particle wall loss (65 % of the loss) with the remainder from evaporation of the particles driven by vapor losses to the walls (35 % of the loss). We perform a series of sensitivity tests to understand uncertainties in our simulations. Uncertainty in the initial wood-smoke volatility distribution contributes 18 % uncertainty to the final particle-organic mass remaining in the chamber (relative to base-assumption simulation). We show that the total mass loss may depend on the effective saturation concentration of vapor with respect to the walls as these values currently vary widely in the literature. The details of smoke dilution during the filling of smog chambers may influence the mass loss to the walls, and a dilution of ~ 25:1 during the experiments increased particle-organic mass loss by 33 % compared to a simulation where we assume the particles and vapors are initially in equilibrium in the chamber. Finally, we discuss how our findings may influence interpretations of emission factors and SOA production in wood-smoke smog-chamber experiments.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2002

Study of Fluid Flow Control in Protein Crystallization using Strong Magnetic Fields

An important component in biotechnology, particularly in the area of protein engineering and rational drug design is the knowledge of the precise three-dimensional molecular structure of proteins. The quality of structural information obtained from X-ray diffraction methods is directly dependent on the degree of perfection of the protein crystals. As a consequence, the growth of high quality macromolecular crystals for diffraction analyses has been the central focus for biochemists, biologists, and bioengineers. Macromolecular crystals are obtained from solutions that contain the crystallizing species in equilibrium with higher aggregates, ions, precipitants, other possible phases of the protein, foreign particles, the walls of the container, and a likely host of other impurities. By changing transport modes in general, i.e., reduction of convection and sedimentation, as is achieved in "microgravity", researchers have been able to dramatically affect the movement and distribution of macromolecules in the fluid, and thus their transport, formation of crystal nuclei, and adsorption to the crystal surface. While a limited number of high quality crystals from space flights have been obtained, as the recent National Research Council (NRC) review of the NASA microgravity crystallization program pointed out, the scientific approach and research in crystallization of proteins has been mainly empirical yielding inconclusive results. We postulate that we can reduce convection in ground-based experiments and we can understand the different aspects of convection control through the use of strong magnetic fields and field gradients. Whether this limited convection in a magnetic field will provide the environment for the growth of high quality crystals is still a matter of conjecture that our research will address. The approach exploits the variation of fluid magnetic susceptibility with concentration for this purpose and the convective damping is realized by appropriately positioning the crystal growth cell so that the magnetic susceptibility force counteracts terrestrial gravity. The general objective is to test the hypothesis of convective control using a strong magnetic field and magnetic field gradient and to understand the nature of the various forces that come into play. Specifically we aim to delineate causative factors and to quantify them through experiments, analysis and numerical modeling. Once the basic understanding is obtained, the study will focus on testing the hypothesis on proteins of pyruvate dehydrogenase complex (PDC), proteins E1 and E3. Obtaining high crystal quality of these proteins is of great importance to structural biologists since their structures need to be determined. Specific goals for the investigation are: 1. To develop an understanding of convection control in diamagnetic fluids with concentration gradients through experimentation and numerical modeling. Specifically solutal buoyancy driven convection due to crystal growth will be considered. 2. To develop predictive measures for successful crystallization in a magnetic field using analyses and numerical modeling for use in future protein crystal growth experiments. This will establish criteria that can be used to estimate the efficacy of magnetic field flow damping on crystallization of candidate proteins. 3. To demonstrate the understanding of convection damping by high magnetic fields to a class of proteins that is of interest and whose structure is as yet not determined. 4. To compare quantitatively, the quality of the grown crystals with and without a magnetic field. X-ray diffraction techniques will be used for the comparative studies. In a preliminary set of experiments, we studied crystal dissolution effects in a 5 Tesla magnet available at NASA Marshall Space Flight Center (MSFC). Using a Schlieren setup, a 1mm crystal of Alum (Aluminum-Potassium Sulfate) was introduced in a 75% saturated solution and the resulting dissolution plume was observed. The experiment was conducted both in the presence and absence of a magnetic field gradient. The magnet produces a gradient field of approx. 1 Tesla2/cm. Image analysis of the recorded images indicated an enhanced plume velocity that was of the order of the measurement limit. For this experiment, both the gradient and gravity fields are in the same direction resulting in an enhanced effective gravity that tends to accelerate the observed plume velocity. While the results are not conclusive, pending further tests, it clearly points out the inadequacy of the MSFC magnet for conducting protein crystallization experiments and the need for a stronger magnet. In spacebased experiments, however, where the gravitational effects are small, only a weak magnetic field will be required to control or mitigate the effects of convective contamination.

Ramachandran, Narayanan↗

NASA NTRSDate not supplied

Implementing Atmospheric Infrared Sounder (AIRS) and Cross-Track Infrared Sounder (CrIS) Cloud-Clearing Algorithm into the NASA GEOS: Focus on the 2017 Atlantic Tropical Cyclone Season

Numerical Weather Prediction (NWP) centers assimilate cloud-free infrared (IR) radiances because the assimilation of all-sky IR radiances is not yet operationally achievable. The cloud-clearing procedure offers a simpler, but effective strategy that produces cloud-affected radiances suitable for assimilation in partially cloudy regions. Several studies conducted by this team have demonstrated that IR Cloud-Cleared Radiances (CCRs), if thinned more aggressively than clear-sky radiances, can improve analysis and forecasts, particularly in meteorologically active areas. However, CCRs are not used by operational centers due partly to the thought that the process of cloud-clearing may affect latency and introduce difficult-to-control external dependencies. This study presents the results of implementing an Atmospheric Infrared Sounder (AIRS) and Cross-Track Infrared Sounder (CrIS) cloud-clearing procedure into the NASA Goddard Earth Observing System (GEOS) to demonstrate the portability of the procedure. The AIRS and CrIS cloud-clearing algorithms have been deprived of external dependencies, made customizable to any specific model, and the computational efficiency has been improved via parallelization. The revised AIRS and CrIS cloud-clearing algorithms allow a customized choice of channel selection, the use of a user-specified model's fields as first guess, and can perform in real time. Data assimilation experiments with the hybrid 4DEnVar GEOS system were successfully performed for the 2017 tropical cyclones (TC) season with a focus on three major hurricanes (Harvey, Irma, and Maria). This study shows that assimilation of locally-generated CCRs have a positive impact on both global skill and TC representation, compared to the assimilation of AIRS and CrIS clear-sky radiances, and a comparable or slightly improved impact compared to assimilation of CCRs produced by external sources, such as NASA's Distributed Active Archive Centers and NOAA’s Comprehensive Large Array-data Stewardship System. The customization and computational efficiency of the revised procedure would enable its usability in a real-time forecast context.

Niama Boukachaba↗

NASA NTRS2000

GCSS Cirrus Parcel Model Comparison Project

The Cirrus Parcel Model Comparison Project, a project of GCSS Working Group on Cirrus Cloud Systems (WG2), involves the systematic comparison of current models of ice crystal nucleation and growth for specified, typical, cirrus cloud environments. The goal of this project is to document and understand the factors resulting in significant inter-model differences. The intent is to foment research leading to model improvement and validation. In Phase 1 of the project reported here, simulated cirrus cloud microphysical properties are compared for situations of "warm" (-40 C) and "cold" (-60 C) cirrus subject to updrafts of 4, 20 and 100 cm/s, respectively. Five models participated. These models employ explicit microphysical schemes wherein the size distribution of each class of particles (aerosols and ice crystals) is resolved into bins. Simulations are made including both homogeneous and heterogeneous ice nucleation mechanisms. A single initial aerosol population of sulfuric acid particles is prescribed for all simulations. To isolate the treatment of the homogeneous freezing (of haze drops) nucleation process, the heterogeneous nucleation mechanism is disabled for a second parallel set of simulations. Qualitative agreement is found for the homogeneous-nucleation-only simulations, e.g., the number density of nucleated ice crystals increases with the strength of the prescribed updraft. However, non-negligible quantitative differences are found. Detailed analysis reveals that the homogeneous nucleation formulation, aerosol size, ice crystal growth rate (particularly the deposition coefficient), and water vapor uptake rate are critical components that lead to differences in predicted microphysics. Systematic bias exists between results based on a modified classical theory approach and models using an effective freezing temperature approach to the treatment of nucleation. Each approach is constrained by critical freezing data from laboratory studies, but each includes assumptions that can only be justified by further laboratory data. Consequently, it is not yet clear if the two approaches can be made consistent. Large haze particles may deviate considerably from equilibrium size in moderate to strong updrafts (20-100 cm/s) at -60 C when the commonly invoked equilibrium assumption is lifted. The resulting difference in particle-size-dependent solution concentration of haze particles may significantly affect the ice nucleation rate during the initial nucleation interval. The uptake rate for water vapor excess by ice crystals is another key component regulating the total number of nucleated ice crystals. This rate, the product of ice number concentration and ice crystal diffusional growth rate, which is sensitive to the deposition coefficient when ice particles are small, partially controls the peak nucleation rate achieved in an air parcel and the duration of the active nucleation time period. The effects of heterogeneous nucleation are most pronounced in weak updraft situations. Vapor competition by the nucleated (heterogeneous) ice crystals limits the achieved ice supersaturation and thus suppresses the contribution of homogeneous nucleation. Correspondingly, ice crystal number density is markedly reduced. Definitive laboratory and atmospheric benchmark data are needed for the heterogeneous nucleation process. Inter-model differences are correspondingly greater than in the case of the homogeneous nucleation process acting alone.

Lin, Ruei-Fong↗

NASA NTRSDate not supplied

Single and Multi-Node Modeling of Direct, Submerged, and Self-Pressurization of A Cryogenic Propellant Tank Using Nodal Tools

The pressurization of cryogenic propellant tanks will always be an important process so long as cryogenic liquids are being considered as fuel sources or used for other in-space applications. Pressure control of the tank ullage is necessary for managing propellant flowrates to an engine or a receiver tank, and modeling of the process is used to predict the pressurant requirements and the amount of propellant boiloff. Direct ullage pressurization is the more traditional approach to tank pressurization, as the physics are straight-forward, and ample test (flight) data have been collected and analyzed over the past several decades. Submerged injection pressurization is an alternate method for tank pressurization and has been shown to reduce pressurant requirements, subcool the propellant, and reduce the risk of ullage collapse. Additionally, the pressurant gas entering the ullage is usually much colder when using the submerged pressurization approach, resulting in reduced propellant boiloff. These benefits are at the expense of vaporizing a small percentage of the propellent. Both tank pressurization methods are viable options for current and future space missions, and it is important to have the capability of analyzing the tank ullage conditions for both approaches. Our previous work has demonstrated the development of a Generalized Fluid System Simulation Program (GFSSP) model, which contains a thermodynamic equilibrium heat and mass transfer subroutine capable of effectively analyzing both direct and submerged pressurization systems [1-2]. This subroutine has most recently been enhanced to include the non-equilibrium effect of pressurant dissolution into the propellant. To date the ullage has always been represented as a single node, and although the simulated single-node temperatures have good comparison with the volume-averaged ullage temperatures computed from test data, the physics of the thermal stratification in the ullage were never captured, and adjustment factors in the model were required. The purpose of this paper is to introduce the development of a multi-node ullage model using GFSSP and to discuss the improvements of the simulated ullage temperature distribution and its resulting effects on ullage heat transfer processes. Test data from the Cryogenic Propellant Storage and Transfer Engineering Developmental Unit (CPST EDU) was used for model validation. For additional comparison, a Thermal Desktop (TD) model was also developed to analyze the CPST EDU direct ullage pressurization tests using both a single node and multi-node approach. The model includes the direct pressurant line, vent line, fill/drain line, and a TD FloCAD Compartment. The TD FloCAD Compartment is employed to represent the liquid and ullage as single volumes inside the tank, to include a liquid/vapor interface, and to generate network level objects such as lumps (analogous to nodes in GFSSP), paths, and ties between the fluid and thermal elements. An established heat load on the model tank was leveraged from a pre-existing higher-fidelity model correlated to CPST EDU test data.

pressurization↗

DOE OSTI2015

Aircraft measurements of BrO, IO, glyoxal, NO 2 , H 2 O, O 2 –O 2 and aerosol extinction profiles in the tropics: comparison with aircraft-/ship-based in situ and lidar measurements

Here, tropospheric chemistry of halogens and organic carbon over tropical oceans modifies ozone and atmospheric aerosols, yet atmospheric models remain largely untested for lack of vertically resolved measurements of bromine monoxide (BrO), iodine monoxide (IO) and small oxygenated hydrocarbons like glyoxal (CHOCHO) in the tropical troposphere. BrO, IO, glyoxal, nitrogen dioxide (NO 2 ), water vapor (H 2 O) and O 2 –O 2 collision complexes (O 4 ) were measured by the University of Colorado Airborne Multi-AXis Differential Optical Absorption Spectroscopy (CU AMAX-DOAS) instrument, aerosol extinction by high spectral resolution lidar (HSRL), in situ aerosol size distributions by an ultra high sensitivity aerosol spectrometer (UHSAS) and in situ H 2 O by vertical-cavity surface-emitting laser (VCSEL) hygrometer. Data are presented from two research flights (RF12, RF17) aboard the National Science Foundation/National Center for Atmospheric Research Gulfstream V aircraft over the tropical Eastern Pacific Ocean (tEPO) as part of the "Tropical Ocean tRoposphere Exchange of Reactive halogens and Oxygenated hydrocarbons" (TORERO) project (January/February 2012). We assess the accuracy of O 4 slant column density (SCD) measurements in the presence and absence of aerosols. Our O 4 -inferred aerosol extinction profiles at 477 nm agree within 6% with HSRL in the boundary layer and closely resemble the renormalized profile shape of Mie calculations constrained by UHSAS at low (sub-Rayleigh) aerosol extinction in the free troposphere. CU AMAX-DOAS provides a flexible choice of geometry, which we exploit to minimize the SCD in the reference spectrum (SCD REF , maximize signal-to-noise ratio) and to test the robustness of BrO, IO and glyoxal differential SCDs. The RF12 case study was conducted in pristine marine and free tropospheric air. The RF17 case study was conducted above the NOAA RV Ka'imimoana (TORERO cruise, KA-12-01) and provides independent validation data from ship-based in situ cavity-enhanced DOAS and MAX-DOAS. Inside the marine boundary layer (MBL) no BrO was detected (smaller than 0.5 pptv), and 0.2–0.55 pptv IO and 32–36 pptv glyoxal were observed. The near-surface concentrations agree within 30% (IO) and 10% (glyoxal) between ship and aircraft. The BrO concentration strongly increased with altitude to 3.0 pptv at 14.5 km (RF12, 9.1 to 8.6° N; 101.2 to 97.4° W). At 14.5 km, 5–10 pptv NO 2 agree with model predictions and demonstrate good control over separating tropospheric from stratospheric absorbers (NO 2 and BrO). Our profile retrievals have 12–20 degrees of freedom (DoF) and up to 500 m vertical resolution. The tropospheric BrO vertical column density (VCD) was 1.5 × 10 13 molec cm –2 (RF12) and at least 0.5 × 10 13 molec cm –2 (RF17, 0–10 km, lower limit). Tropospheric IO VCDs correspond to 2.1 × 10 12 molec cm –2 (RF12) and 2.5 × 10 12 molec cm –2 (RF17) and glyoxal VCDs of 2.6 × 10 14 molec cm –2 (RF12) and 2.7 × 10 14 molec cm –2 (RF17). Surprisingly, essentially all BrO as well as the dominant IO and glyoxal VCD fraction was located above 2 km (IO: 58 ± 5%, 0.1–0.2 pptv; glyoxal: 52 ± 5%, 3–20 pptv). To our knowledge there are no previous vertically resolved measurements of BrO and glyoxal from aircraft in the tropical free troposphere. The atmospheric implications are briefly discussed. Future studies are necessary to better understand the sources and impacts of free tropospheric halogens and oxygenated hydrocarbons on tropospheric ozone, aerosols, mercury oxidation and the oxidation capacity of the atmosphere.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗