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NASA NTRS2019

Boreal canopy surfaces from spaceborne stereogrammetry

Surface elevation estimates from high resolution spaceborne image (HRSI) stereogrammetry are used to examine fine-scaled structure of boreal forest canopies. These data can depict detailed spatial patterns of vertical forest structure at remote sites across the circumpolar domain where these estimates would otherwise be unavailable. This work examines where these estimates are most effective at describing vertical forest structure to explain which canopy surfaces they represent. We evaluated the variation in canopy surface estimates captured from four general types of HRSI digital surface models (DSMs) across the full range of boreal canopy cover. These DSMs, classified into 4 types by grouping them according to the acquisition's (1) sun elevation angle (low or high) and (2) seasonality-driven ground surface condition (snow presence/absence), vary with acquisition characteristics and the details of this variation continues to be studied. We explored some of this variation by comparing the distributions of differences in boreal canopy percentile heights derived from reference small footprint lidar in Tanana Valley, Alaska with canopy surface elevations derived from these 4 types of HRSI DSMs. We examined how canopy surface estimates from HRSI DSMs differ according to acquisition characteristics and canopy cover, and ultimately which canopy surfaces are represented in these DSMs. Our results help clarify which boreal canopy surfaces are representative of those captured with HRSI DSMs. They show that in the Tanana Valley (1) DSMs grouped by sun elevation angle and ground surface condition provide different surface estimates of boreal canopies; (2) the two DSM types that appear to most differently capture boreal forest canopy surfaces are DSMs from snow-free images acquired at sun elevation angles <30° (Low sun elev. & snow-free) and those with snow-cover at sun elevation angles ≥30° (High sun elev. & snow-free); (3) DSMs with snow most often do not capture upper canopy surfaces; (4) the “Low sun elev. & snow-free” DSMs resolve surfaces that are most representative of upper canopy surfaces (dense forests >60% cover, 70th–80th percentile heights); and (5) in the most dense forests (>80% cover) where canopy gaps are least likely to bias downward the average surface estimates, the snow-free DSM types are representative of 70th - 80th percentile heights (“Low sun elev. & snow-free”) and 60th–70th percentile heights (“High sun elev. & snow-free”). The combination of horizontal structure (canopy cover) and acquisition characteristics affect the boreal vertical structure (canopy surface height) estimates from spaceborne stereogrammetry. These effects should be considered when analyzing products derived from HRSI DSMs, and as part of a comprehensive approach to spaceborne remote sensing of circumpolar boreal forests.

forest structure↗

NASA NTRSDate not supplied

Quantum-Assisted Variational Segmentation for Image-to-Image Wildfire Detection Using Satellite Data

The quantum computing community has been searching for suitable applications to demonstrate the potential of near-term quantum devices. Quantum machine learning is a potential candidate, particularly using models that cannot be efficiently simulated with classical computers [1, 2]. This work focuses on a transition phase of quantum computers where the quantum machine learning model is still simulable classically but projected not to be simulable as the size of the model grows. Ultimately quantum computers may have advantages for high-dimensional real-world problems. Due to the limited number of qubits in current noisy intermediate-scale quantum (NISQ) devices, the direct application of quantum computers in high dimensional data is not feasible. To remedy this problem, an encoder-decoder architecture can be utilized. The encoder model would transform the high-dimensional data into a compact representation, to a level that small quantum computers can be used today (or in the near future), and the decoder would take the quantum processed outputs back to the high-dimensional space. Addressing the two challenges of quantum machine learning, this work investigates a hybrid supervised generative model with a quantum Ising Born machine embedded as the latent distribution. The model contains four main parts (Figure 1.a.): (1) a U-NET architecture responsible for learning segmentation flow, (2) a Prior network responsible for learning an encoded latent distribution of the input data, (3) a Born machine which represents the latent distribution, and (4) a Posterior network in charge of learning the joint encoded latent distribution of inputs and target data. The initial model, proposed by [3], is optimized by (1) maximizing the overlap of the prior and posterior latent distributions, and (2) minimizing the segmentation loss. The proposed model is designed to be investigated in a simulation environment applied to the real-world application of wildfire segmentation. Specifically, the model is designed to solve the patchy wildfire segmentations of Moderate Resolution Imaging Spectroradiometer (MODIS) by taking the MODIS observations and using Visible Infrared Imaging Radiometer Suite’s (VIIRS) consistent wildfire product as the target. The model solves patchy wildfire segmentations and provides insight into the epistemic errors sourced from model variation. The model utilizes the Born machine as a QUBO solver to represent the latent space as a Bernoulli distribution. The proposed configuration allows the variational segmentation model to leverage the true quantum probabilistic nature and derive a more expressive latent configuration, increasing the model performance in describing wildfire segmentations. The quantum probabilistic information of the Born machine is directly incorporated in the Kullback-Leibler divergence loss in the prior and posterior distributions, forcing the Bernoulli latent distribution to maximize the overlap of input and joint input-target distributions. The proposed model is then trained and compared with a baseline only consisting of direct Bernoulli latent distribution with no Born machine representing the latent space. The models are evaluated based on the segmentation metrics, such as precision, recall, intersect of union, with uncertainty boundaries accounting for the stochastic nature of the model. Our findings show that even in low latent-dimensional space (due to the limit in computational power of the classical quantum simulator), we are able to effectively capture the latent representation and hence the model performs better than the baseline. The findings are a projection for scaling the model into higher dimensional latent space with the Born machine surpassing the baseline performance. Figure 1. Sub-figure (a) demonstrates the architecture for the training phase. The model consists of a Prior and Posterior network that encode inputs and joint input-target data into compact representations, respectively. The Born machine represents the latent distribution, and the U-NET branch learns the segmentation patterns of the data. The stochasticity is introduced to the U-NET through its last layer to create meaningful but stochastic segmentations. Sub-figure (b) represents the inference phase where the model takes the stochastic behavior from the prior network and injects that into the U-NET. Each attempt of inference will generate different but similar segmentations from the same distribution of the wildfire event. REFERENCES [1] Coyle, B., Mills, D., Danos, V., & Kashefi, E. (2020). The Born supremacy: quantum advantage and training of an Ising Born machine. npj Quantum Information, 6(1), 1-11. [2] Liu, J. G., & Wang, L. (2018). Differentiable learning of quantum circuit born machines. Physical Review A, 98(6), 062324. [3] Kohl, S., Romera-Paredes, B., Meyer, C., De Fauw, J., Ledsam, J. R., Maier-Hein, K., ... & Ronneberger, O. (2018). A probabilistic u-net for segmentation of ambiguous images. Advances in neural information processing systems, 31.

quantum machine learning↗

DOE OSTI2017

Additions to the ICSBEP AND IRPHEP Handbooks Since NCSD 2013

Innumerable experiments have been performed worldwide to support criticality safety activities. Those experiments represent significant investments in cost, expertise, and infrastructure; as such, they represent valuable assets as the basis for recording, development, and validation of methods and nuclear data vital for modern and future criticality safety needs. Preservation and evaluation of these experimental data as benchmarks serves to provide for the best interest of the nuclear criticality safety community. High-quality integral benchmark experiments have always been a priority. There is ever growing interest in developing integral benchmark data to increasingly quantify and reduce calculational uncertainties to meet the demands of future criticality safety needs supporting next generation reactors and advanced fuel cycle concepts. The importance of drawing upon existing benchmark data is becoming more apparent be-cause of dwindling availability of critical facilities worldwide and the high cost of per-forming new experiments. Integral benchmark data from the International Handbook of Evaluated Criticality Safety Benchmark Experiments (ICSBEP Handbook) and the International Handbook of Reactor Physics Benchmark Experiments (IRPhEP Handbook) are widely utilized. Significant benchmark data have been added to those two handbooks since the last Nuclear Criticality Safety Division Topical Meeting in Wilmingt on, North Carolina (September-October 2013). This paper highlights those additions. The 2016 edition of the ICSBEP Handbook now includes data for 570 evaluations containing benchmark specifications for 4,913 critical, subcritical, or near-critical configurations, representing contributions from over 20 countries. There are a total of 7 criticality-alarm-placement/shielding evaluations containing a total of 45 benchmark configurations, and 8 fundamental physics benchmark evaluations containing a total of 215 measurements relevant to criticality safety applications. The IRPhEP Handbook now includes data from 151 experimental series (representing 50 reactor facilities) and also represents contributions from over 20 countries. Of the 151 benchmarks, five are draft contributions. Both the ICSBEP and IRPhEP are collaborative efforts that involve numerous scientists, engineers, and administrative support personnel worldwide. Continued contribution to these handbooks is expected to further provide the high-quality benchmark data necessary to compliment criticality safety activities going forward.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2018

Representation of activity in images using geospatial temporal graphs

Various technologies pertaining to modeling patterns of activity observed in remote sensing images using geospatial-temporal graphs are described herein. Graphs are constructed by representing objects in remote sensing images as nodes, and connecting nodes with undirected edges representing either distance or adjacency relationships between objects and directed edges representing changes in time. Activity patterns may be discerned from the graphs by coding nodes representing persistent objects like buildings differently from nodes representing ephemeral objects like vehicles, and examining the geospatial-temporal relationships of ephemeral nodes within the graph.

47 OTHER INSTRUMENTATION↗

DOE OSTI2021

Block graft copolymer binders and their use in silicon-containing anodes of lithium-ion batteries

A graft copolymer composition comprising the following structure: wherein: Ax represents a polymer backbone having a number of polymerized monomer units x; [By] represents a multiplicity of a graft polymer side chain having a number of polymerized monomer units y, and at least a portion of the monomer units in By contains a group —C(O)OM, with M independently selected from H and alkali metals; [C] represents a multiplicity of positions on the polymer backbone Ax where the graft polymer side chain B or any other graft polymer side chain is not attached; the subscript w represents a grafting density of the group By, wherein w is an integer within a range of 10-50%; and the subscript z represents a density of the group C, wherein z=(100−w) %. The invention is also directed to lithium-ion batteries in which the above-described composition is incorporated in an anode of the battery.

Saito, Tomonori↗

DOE OSTI2021

Universal image representation based on a multimodal graph

A system for classifying a target image with segments having attributes is provided. The system generates a graph for the target image that includes vertices representing segments of the image and edges representing relationships between the connected vertices. For each vertex, the system generates a subgraph that includes the vertex as a home vertex and neighboring vertices representing segments of the target image within a neighborhood of the segment represented by the home vertex. The system applies an autoencoder to each subgraph to generate latent variables to represent the subgraph. The system applies a machine learning algorithm to a feature vector comprising a universal image representation of the target image that is derived from the generated latent variables of the subgraphs to generate a classification for the target image.

Bremer, Peer-Timo↗

DOE OSTI2023

Universal image representation based on a multimodal graph

Bremer, Peer-Timo↗

DOE OSTI2017

Additions to the ICSBEP and IRPHEP handbooks since NCSD 2013

Innumerable experiments have been performed worldwide to support criticality safety activities. Those experiments represent significant investments in cost, expertise, and infrastructure; as such, they represent valuable assets as the basis for recording, development, and validation of methods and nuclear data vital for modern and future criticality safety needs. Preservation and evaluation of these experimental data as benchmarks serves to provide for the best interest of the nuclear criticality safety community. High-quality integral benchmark experiments have always been a priority. There is ever growing interest in developing integral benchmark data to increasingly quantify and reduce calculational uncertainties to meet the demands of future criticality safety needs supporting next generation reactors and advanced fuel cycle concepts. The importance of drawing upon existing benchmark data is becoming more apparent because of dwindling availability of critical facilities worldwide and the high cost of performing new experiments. Integral benchmark data from the International Handbook of Evaluated Criticality Safety Benchmark Experiments (ICSBEP Handbook) and the International Handbook of Reactor Physics Benchmark Experiments (IRPhEP Handbook) are widely utilized. Significant benchmark data have been added to those two handbooks since the last Nuclear Criticality Safety Division Topical Meeting in Wilmington, North Carolina (September-October 2013). This paper highlights those additions. The 2016 edition of the ICSBEP Handbook now includes data for 570 evaluations containing benchmark specifications for 4913 critical, subcritical, or near-critical configurations, representing contributions from over 20 countries. There are a total of seven criticality-alarm-placement/shielding evaluations containing a total of 45 benchmark configurations, and eight fundamental physics benchmark evaluations containing a total of 215 measurements relevant to criticality safety applications. The IRPhEP Handbook now includes data from 151 experimental series (representing 50 reactor facilities) and also represents contributions from over 20 countries. Of the 151 benchmarks, five are draft contributions. Both the ICSBEP and IRPhEP are collaborative efforts that involve numerous scientists, engineers, and administrative support personnel worldwide. Continued contribution to these handbooks is expected to further provide the high-quality benchmark data necessary to compliment criticality safety activities going forward. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2019

Preliminary Evaluation of Removing Spent Nuclear Fuel from Shutdown Nuclear Power Plant Sites - 2018 - 19101

This is a technical paper that does not take into account the contractual limitations under the Standard Contract for Disposal of Spent Nuclear Fuel and/or High-Level Radioactive Waste (Standard Contract) (10 CFR Part 961). Under the provisions of the Standard Contract, DOE does not consider spent nuclear fuel in multi-assembly canisters to be an acceptable waste form, absent a mutually agreed to contract amendment. To the extent discussions or recommendations in this paper conflict with the provisions of the Standard Contract, the Standard Contract provisions prevail. The U.S. Department of Energy Office of Integrated Waste Management (DOE-IWM) continues to conduct evaluations of removing spent nuclear fuel (SNF) from shutdown nuclear power plant sites. The 14 shutdown sites included in the evaluation to date have nuclear power reactors that have been permanently shut down and the sites have been decommissioned or are undergoing decommissioning. The 14 shutdown sites are Maine Yankee, Yankee Rowe, Connecticut Yankee, Humboldt Bay, Big Rock Point, Rancho Seco, Trojan, La Crosse, Zion, Crystal River, Kewaunee, San Onofre, Vermont Yankee, and Fort Calhoun. The Oyster Creek Nuclear Generating Station ceased operations in September 2018 and will be included in future evaluations. The evaluations of the shutdown sites contain four components: - A characterization of the SNF and greater-than-Class C low-level radioactive waste (GTCC waste) inventory; - A description of the on-site infrastructure at the shutdown sites; - An evaluation of the near-site transportation infrastructure and experience at the shutdown sites - An evaluation of the actions necessary to prepare for and remove SNF and GTCC waste. The primary sources for the inventory of SNF were the DOE GC-859 and RW-859 spent nuclear fuel inventory databases. This inventory has been augmented with data obtained from the shutdown sites for SNF discharges that occurred after June 2013. The primary sources for information on the conditions of site and near-site transportation infrastructure and experience included field observations and information collected during visits to the 14 shutdown sites, information provided by managers and staff at the shutdown sites, and Google Earth imagery. Extensive photographs taken during the shutdown site visits have proven to be particularly useful in documenting the current conditions at and near the shutdown sites. Tribal and state representatives, State and Regional Group representatives, and Federal Railroad Administration representatives have participated in 11 of the shutdown site visits. In addition to visiting the shutdown site, site visits often include activities such as meeting with local railroads to discuss the near-site railroad infrastructure and meeting with community engagement panels. Oyster Creek permanently ceased power operations on September 17, 2018 and fuel was removed from the reactor vessel on September 25, 2018. Planning for the Oyster Creek site visit has begun and is conducted in three stages. In the first stage, data and documents that are applicable to Oyster Creek are collected. In the second stage, questions are developed based on the data and documents collected during the first stage. These questions are submitted to the Oyster Creek site approximately one month before the site visit. The third stage involves establishing specific schedules and participants. Participants will likely include tribal and state representatives, the Council of State Governments - Eastern Regional Conference, and the Federal Railroad Administration. State participation will likely include a representative of the State Rail Safety Participation Program, the New Jersey Department of Transportation, and the New Jersey Bureau of Nuclear Engineering. The site visit is tentatively expected to occur in April or May of 2019. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

Distribution Development for the RDX Regional Model at Los Alamos National Laboratory - 20385

Representing uncertainty in model inputs often means finding a balance between uncertainty and physical reality. Developing wide distributions may seem conservative in principle, but this approach may lead to unrealistic model results. Characterizing the current state of knowledge of stochastic inputs presents many challenges, especially if the data available are limited or have limited relevance to the site. Relationships among these inputs may also be important to represent but are typically complex or difficult to define. Often special adjustments must be made to account for reduced credibility in particular data. If parameters are strongly related to one another, a correlation structure may be developed for input to the model. Other techniques such as regression models may be used to incorporate relationships between the information available and the desired parameters. The process of developing distributions must consider details of the model in terms of what the distribution is meant to represent. This paper uses the example of a probabilistic fate and transport model for hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) in the regional aquifer at Los Alamos National Laboratory (LANL). For many parameters, a single draw is applied to all space and time over which the model is run, for a single iteration. This simplification is often made for many reasons, and can often be beneficial, but also adds additional complexity in the distribution development process. Defining the distributional goals as they relate to the modeling process is an important step, which should take place prior to evaluation of the data. Usually, the distributions developed are meant to characterize the average value of the parameter over the spatial and temporal domain of the model. Distribution development requires consideration of many sources of information on the parameter where available, ideally from multiple references. Examples of different sources include data from different references but also from different conditions, measurement methods, or experimental types. Depending on these conditions and the reliability or relevance of particular references, different sources of data may each contribute valuable information but have varying relevance to the site. In these cases, weighting data unequally is a useful way to incorporate this information. As an example, aqueous dispersivity data are available for a variety of rock types. Only a few values are available for the desired rock type, and this is not enough to develop a distribution. Therefore, dispersivity values from other rock types are included in distribution development but are down-weighted such that the best data have the most influence on the distribution developed. In another case, K{sub d} distributions in the model are meant to represent a known composition of multiple soil types. Data from these materials are weighted accordingly to develop a distribution for the weighted average K{sub d} across soil types. Other cases include varying reliability of different sources, and weighting data according to the confidence in these sources. In some cases, input parameters are correlated with one another. An example is advective porosity, which is positively correlated with total porosity and must be less than total porosity. Paired data with both parameters must exist to discern the relationship between the two parameters and if it is necessary to build a correlation structure into the model. In general, correlated parameters may be represented in the model by a multivariate distribution, or perhaps more desirably, capturing the correlation within the developed distributions which may be treated as independent from one another. In the example of porosity, this can be done by transforming advective porosity into a proportion of total porosity which may be drawn independently from the distribution of total porosity. This paper explores how complex data and correlations can be incorporated to meet the distributional goals of the model, using the RDX regional model as a detailed example. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2022

Discrete dislocation dynamics for crystal RVEs. Part 1: Periodic network kinematics

A novel implementation of the dislocation flux boundary condition in discrete dislocation dynamics is presented. The continuity of the individual dislocation loops in a periodic representative crystal volume (RVE) is enforced across the boundary of the RVE with the help of a dual topological description for representing dislocation line kinematics in two equivalent spaces representing the deforming crystal, the RVE and the unbounded crystal spaces. The former describes the motion of the dislocations in the simulated crystal RVE whereas the latter represents the motion of dislocations in an infinite space containing all replicas of the RVE. A mapping between the two spaces forms the basis of the implementation of flux boundary condition. The implementation details are discussed in the context of statistical homogeneity of bulk crystals undergoing macroscopically homogeneous plastic deformation. In this case, the boundary nodes associated with dislocation segments bear no relevance in the motion of the dislocations. Finally, some test cases are presented and discussed to establish the proposed approach.

42 ENGINEERING↗

DOE OSTI2023

High temporal resolution generation expansion planning for the clean energy transition

As power systems integrate increasing quantities of wind, solar and energy storage resources, it is important to revisit power system capacity expansion modeling methods and assumptions that have been utilized in thermal- dominated systems. We conduct a series of case study analyses using a simplified representation of the Electric Reliability Council of Texas (ERCOT) system to demonstrate how least-cost capacity expansion outcomes are impacted by changes in model resolution across two temporal dimensions: 1) the number of considered representative periods, and 2) the system dispatch interval. First, we find that the least-cost generation portfolio can differ significantly for small changes in the number of representative days, but largely converges to the 365-day result once 104 representative days are considered. Furthermore, systems with wind, solar and storage resources were more sensitive to changes in the number of representative days than a thermal-dominated system. Second, we find that considering five-minute dispatch resolution consistently results in least-cost generation portfolios with less solar capacity and more energy storage capacity than corresponding scenarios with hourly dispatch intervals. This suggests that hourly dispatch representation fails to capture the intra-hour volatility of solar generation, and therefore also overlooks opportunities for storage resources to provide system value by balancing this volatility. Collectively these results indicate that capacity expansion modelers should revisit conventional approaches to temporal representation when conducting analyses of deeply decarbonized power systems to ensure that such analyses are robust and actionable. To our knowledge, this is the first study to analyze capacity expansion outcomes with five-minute dispatch resolution in this manner.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2026

Comprehensive framework for assessing and optimizing existing research networks

Conservation, monitoring, and research networks, or collections of ecological research sites unified under a common mission of data collection or a research mission, are essential infrastructure for understanding large landscapes. However, most networks developed opportunistically over decades rather than through systematic design, creating potential limitations in the ability to address conservation challenges across entire regions. We developed a framework to evaluate how well an existing research network represents the environmental conditions its members study and devised an approach to rank sites of priority for strategic expansion. Our approach measures performance through environmental representativeness, geographic coverage, and adequacy for scientific inference and thus optimizes limited monitoring resources to maximize scientific impact. We demonstrated this approach with the U.S. Department of Agriculture (USDA) Forest Service Experimental Forests and Ranges Network (EFRN), a 79‐site network across the United States that grew opportunistically over a century. At the national scale, the network effectively captured high‐biomass forests important for carbon cycle research; 82% of forest biomass was in well‐represented areas. Some areas in Texas, Florida, the Rocky Mountains, and the West Coast had no relevant EFRN sites, which limits the ability to make regional inferences. A fundamental challenge for the EFRN was that sites improving regional extent coverage sometimes provided minimal national benefits, which can create conflicts between local and global priorities. Adding the highest‐ranked candidate site provided a relevant site for 17% of currently poorly represented 1‐km pixel cells nationally, but regional and national site rankings varied considerably due to nested spatial inference. This framework provides quantitative tools for strategic infrastructure decision‐making, ensures that limited monitoring resources maximize conservation impact, and can be applied broadly to address the widespread challenge of optimizing conservation and monitoring networks worldwide.

additional site↗

DOE OSTI2014

Late Quaternary glacier sensitivity to temperature and precipitation distribution in the Southern Alps of New Zealand

Glaciers respond to climate variations and leave geomorphic evidence that represents an important terrestrial paleoclimate record. However, the accuracy of paleoclimate reconstructions from glacial geology is limited by the challenge of representing mountain meteorology in numerical models. Precipitation is usually treated in a simple manner and yet represents difficult-to-characterize variables such as amount, distribution, and phase. Furthermore, precipitation distributions during a glacial probably differed from present-day interglacial patterns. We applied two models to investigate glacier sensitivity to temperature and precipitation in the eastern Southern Alps of New Zealand. A 2-D model was used to quantify variations in the length of the reconstructed glaciers resulting from plausible precipitation distributions compared to variations in length resulting from change in mean annual air temperature and precipitation amount. A 1-D model was used to quantify variations in length resulting from interannual climate variability. Assuming that present-day interglacial values represent precipitation distributions during the last glacial, a range of plausible present-day precipitation distributions resulted in uncertainty in the Last Glacial Maximum length of the Pukaki Glacier of 17.1?km (24%) and the Rakaia Glacier of 9.3?km (25%), corresponding to a 0.5°C difference in temperature. Smaller changes in glacier length resulted from a 50% decrease in precipitation amount from present-day values (-14% and -18%) and from a 50% increase in precipitation amount (5% and 9%). Our results demonstrate that precipitation distribution can produce considerable variation in simulated glacier extents and that reconstructions of paleoglaciers should include this uncertainty.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2017

Recent advances in understanding secondary organic aerosol: Implications for global climate forcing: Advances in Secondary Organic Aerosol

Anthropogenic emissions and land use changes have modified atmospheric aerosol concentrations and size distributions over time. Understanding preindustrial conditions and changes in organic aerosol due to anthropogenic activities is important because these features (1) influence estimates of aerosol radiative forcing and (2) can confound estimates of the historical response of climate to increases in greenhouse gases. Secondary organic aerosol (SOA), formed in the atmosphere by oxidation of organic gases, represents a major fraction of global submicron-sized atmospheric organic aerosol. Over the past decade, significant advances in understanding SOA properties and formation mechanisms have occurred through measurements, yet current climate models typically do not comprehensively include all important processes. Our review summarizes some of the important developments during the past decade in understanding SOA formation. We also highlight the importance of some processes that influence the growth of SOA particles to sizes relevant for clouds and radiative forcing, including formation of extremely low volatility organics in the gas phase, acid-catalyzed multiphase chemistry of isoprene epoxydiols, particle-phase oligomerization, and physical properties such as volatility and viscosity. Several SOA processes highlighted in this review are complex and interdependent and have nonlinear effects on the properties, formation, and evolution of SOA. Current global models neglect this complexity and nonlinearity and thus are less likely to accurately predict the climate forcing of SOA and project future climate sensitivity to greenhouse gases. Efforts are also needed to rank the most influential processes and nonlinear process-related interactions, so that these processes can be accurately represented in atmospheric chemistry-climate models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

Process Intensification of Distillation using a Microwick Technology to Demonstrate Separation of Propane and Propylene

Process intensification (PI) of distillation using a microchannel distillation (MCD) device successfully reduced the height of a theoretical plate (HETP) in separating propane and propylene to 1 cm, representing 10 theoretical stages. Mass transfer is enhanced by using thin wicking structures that are 0.17 mm thick in counter-flow with vapor. Liquid is segregated in the wicks by applying a siphon, which also enables the device to operate horizontally. A scalable device containing 11 wicks was operated cryogenically in total reflux. The HETP varied between 1.0 and 3.7 cm, representing a dramatic reduction over commercial structured packings and comparable to other PI approaches. Significant improvements are expected with further development. Potential application for intensified distillation processes include distributed manufacturing and difficult separations involving close boiling compounds and meeting high purity specifications. The ultimate application is isotopic enrichment, where the number of stages required is typically multiple thousands.

Distillation, Process Intensification, Microchanne↗

DOE OSTI2015

A Comprehensive Study of Formic Acid Oxidation on Palladium Nanocrystals with Different Types of Facets and Twin Defects

Palladium has been recognized as the best anodic, monometallic electrocatalyst for the formic acid oxidation (FAO) reaction in a direct formic acid fuel cell. Here we report a systematic study of FAO on a variety of Pd nanocrystals, including cubes, right bipyramids, octahedra, tetrahedra, decahedra, and icosahedra. These nanocrystals were synthesized with approximately the same size, but different types of facets and twin defects on their surfaces. Our measurements indicate that the Pd nanocrystals enclosed by {1 0 0} facets have higher specific activities than those enclosed by {1 1 1} facets, in agreement with prior observations for Pd single-crystal substrates. If comparing nanocrystals predominantly enclosed by a specific type of facet, {1 0 0} or {1 1 1}, those with twin defects displayed greatly enhanced FAO activities compared to their single-crystal counterparts. To rationalize these experimental results, we performed periodic, self-consistent DFT calculations on model single-crystal substrates of Pd, representing the active sites present in the nanocrystals used in the experiments. The calculation results suggest that the enhancement of FAO activity on defect regions, represented by Pd(2 1 1) sites, compared to the activity of both Pd(1 0 0) and Pd(1 1 1) surfaces, could be attributed to an increased flux through the HCOO-mediated pathway rather than the COOH-mediated pathway on Pd(2 1 1). Since COOH has been identified as a precursor to CO, a site-poisoning species, a lower coverage of CO at the defect regions will lead to a higher activity for the corresponding nanocrystal catalysts, containing those defect regions.

Environmental Molecular Sciences Laboratory↗

DOE OSTI2022

Process-oriented stochastic perturbations applied to the parametrization of turbulence and shallow convection for ensemble prediction

Weather forecasting nowadays often requires some estimation of uncertainties associated with the output of meteorological models, in order to better inform decision making, especially in the context of intense weather events. Ensemble prediction systems provide such information through sets of possible scenarios which are designed to represent various uncertainty sources, including model uncertainties. A wide variety of methods have been proposed to estimate model uncertainties, among which perturbation methods targeting uncertain processes are a promising research field. Here, we focus on the representation of small-scale variability by process-oriented perturbation schemes applied to two key physical processes, namely turbulence and shallow convection. The perturbations are applied to a single-column version of the convection-permitting AROME model, in three idealized boundary-layer cases. Large-eddy simulations (LESs) of the same cases serve as a reference for the subgrid variability that has to be represented, and the results are also compared to those given by the Stochastically Perturbed Parametrization Tendencies (SPPT) method, which is a method commonly used by weather forecast centres to represent model uncertainty. The spread produced by our process-oriented perturbations of turbulence and shallow convection does not represent all the small-scale variability implied by the LESs for temperature and humidity. However, it is of a similar order of magnitude for the wind, thanks to perturbations generated by the stochastic turbulence scheme. The dispersion is structurally different from what is obtained with SPPT. It is non-negligible in the lower levels, where SPPT perturbations are usually suppressed because of numerical instabilities, indicating a possible complementarity between the schemes.

54 ENVIRONMENTAL SCIENCES↗