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

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At least 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Toward a more realistic representation of surface albedo in NASA CERES-derived surface radiative fluxes

Accurate multidecadal radiative flux records are vital to understand Arctic amplification and constrain climate model uncertainties. Uncertainty in the NASA Clouds and the Earth’s Radiant Energy System (CERES)-derived irradiances is larger over sea ice than any other surface type and comes from several sources. The year-long Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in the central Arctic provides a rare opportunity to explore uncertainty in CERES-derived radiative fluxes. First, a systematic and statistically robust assessment of surface shortwave and longwave fluxes was conducted using in situ measurements from MOSAiC flux stations. The CERES Synoptic 1degree (SYN1deg) product overestimates the downwelling shortwave flux by +11.40 Wm –2 and underestimates the upwelling shortwave flux by –15.70 Wm –2 and downwelling longwave fluxes by –12.58 Wm –2 at the surface during summer. In addition, large differences are found in the upwelling longwave flux when the surface approaches the melting point (approximately 0°C). The biases in downwelling shortwave and longwave fluxes suggest that the atmosphere represented in CERES is too optically thin. The large negative bias in upwelling shortwave flux can be attributed in large part to lower surface albedo (–0.15) in satellite footprint relative to surface sensors. Additionally, the results show that the spectral surface albedo used in SYN1deg overestimates albedo in visible and mid-infrared bands. A series of radiative transfer model perturbation experiments are performed to quantify the factors contributing to the differences. The CERES-MOSAiC broadband albedo differences (approximately 20 Wm –2 ) explain a larger portion of the upwelling shortwave flux difference than the spectral albedo shape differences (approximately 3 Wm –2 ). In addition, the differences between perturbation experiments using hourly and monthly MOSAiC surface albedo suggest that approximately 25% of the sea ice surface albedo variability is explained by factors not correlated with daily sea ice concentration variability. Biases in net shortwave and longwave flux can be reduced to less than half by adjusting both albedo and cloud inputs toward observed values. The results indicate that improvements in the surface albedo and cloud data would substantially reduce the uncertainty in the Arctic surface radiation budget derived from CERES data products.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Fungal and bacterial growth variation due to drought and nitrogen addition experimental treatments. Loma Ridge Experimental Project. 2010-2012

Terrestrial ecosystem models assume that microbial communities respond instantaneously, or are immediately resilient, to environmental change. Here we tested this assumption by quantifying the resilience of a leaf litter community to changes in precipitation or nitrogen availability. By manipulating composition within a global change experiment, we decoupled the legacies of abiotic parameters versus that of the microbial community itself. After one rainy season, more variation in fungal composition could be explained by the original microbial inoculum than the litterbag environment (18% versus 5.5% of total variation). This compositional legacy persisted for 3 years, when 6% of the variability in fungal composition was still explained by the microbial origin. In contrast, bacterial composition was generally more resilient than fungal composition. Microbial functioning (measured as decomposition rate) was not immediately resilient to the global change manipulations; decomposition depended on both the contemporary environment and rainfall the year prior. Finally, using metagenomic sequencing, we showed that changes in precipitation, but not nitrogen availability, altered the potential for bacterial carbohydrate degradation, suggesting why the functional consequences of the two experiments may have differed. Predictions of how terrestrial ecosystem processes respond to environmental change may thus be improved by considering the legacies of microbial communities. This data package includes ten csv files (five data files and their corresponding data dictionaries) and one file-level metadata excel file. Data files contains information about which plots were exposed to treatments related to drought and nitrogen, information about litter bags reciprocal transplants manipulation for water input and nitrogen, detail information about water addition, precipitation records, and litter variables collected. Data dictionary files include detail explanation for each column in the data files. The file-level metadata file describes each file mentioned above. All the analyses were done using the R software.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Reproductive and leaf litterfall fluxes in forest ecosystem sites globally (1950-2022)

Forest allocation of net primary productivity (NPP) to reproduction is poorly quantified globally, despite its critical role in forest regeneration and a well-supported trade-off with allocation to growth. Although field measurements of total NPP are rare, our work finds that a proxy for reproductive carbon allocation constructed from leaf (L) and reproductive (R) litterfall fluxes, R/(R+L), is strongly correlated with R/NPP, facilitating analysis across a wide range of sites where biometric estimates of NPP are not available (R² = 0.85; Hanbury-Brown et al., 2022, Ward et al., in prep). To investigate relationships between ecosystem-scale reproductive allocation (RA) and climate, soil fertility, and stand age gradients, we conducted a literature search and synthesized 824 observations of annual average leaf and reproductive litterfall fluxes across forest sites globally. The zip file includes 1) a folder Data/ containing the litterfall data ("GlobalForestRA_data.csv") and metadata ("GlobalForestRA_metadata.doc") files. The data file includes geographic coordinates, long-term mean annual temperature and precipitation (1970-2000, extracted from WorldClim2.1), leaf and reproductive litterfall fluxes, sampling interval and protocols, forest characteristics (dominant leaf morphology, information pertaining to forest age and successional stage, and disturbance history) and soil properties (% sand, %silt, %clay, total phosphorus (P), nitrogen (N), cation exchange capacity (CEC) and pH) extracted from SoilGrids250 and from on-site measurements, where available. The metadata file contains information about each variable reported in the data file, including data sources, processing methods, and all references. The Data folder contains two additional files used to create Figure 1; these are described in greater detail in the README.2) R scripts GloalForestRA_analysis.r and GlobalForestRA_SI.r and a folder /Functions used to produce results, figures, and tables in the manuscript Ward et al. (in press)3) a README file describing how the data and R scripts can be used to reproduce statistical results, figures, and tables found in the manuscript. Ward et al. (in press)This repository can also be found at: https://github.com/r-ward/Global_Analysis_ForestRA.Ward, R.E., Zhang-Zheng, H. Aernethy, K., Adu-Bredu, S., Arroyo, L., Bailey, A. et al. (in press). Forest age rivals climate to explain reproductive allocation patterns in forest ecosystems globally. Ecology Letters. Hanbury-Brown, A.R., Ward, R.E. & Kueppers, L.M. (2022). Forest regeneration within Earth system models: current process representations and ways forward. New Phytol., 235, 20–40.Ward et al. (2025), Forest age rivals climate to explain reproductive allocation patterns in forest ecosystems globally, in prep.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Compressive properties of silicone Bouligand structures

Abstract This article presents an examination of silicone, Bouligand lattices in compression. Appearing frequently in biological organisms and manufacturing design, Bouligand structures comprise layers of parallel strands or fibers oriented in a helical fashion. They can exhibit exceptional fracture resistance when composed of rigid or composite materials. The behavior of elastomeric Bouligand structures, however, is less well understood. Additively manufactured (AM) elastomeric lattices have applications in stress mitigation, medical devices, and soft robotics. This article demonstrates that Bouligand structures are a useful addition to the design space of AM elastomers. By adjusting the layer-rotation parameters, lattice stress can increase by more than 300% without altering the porosity. Additionally, we introduce path length metrics that help explain the observed relationship between layer rotation and compression response. Impact statement Additive manufacturing (AM) continues to push the boundary of manufacturable structures and enhance the ability to robustly design for specific properties and behaviors. The more we understand the design space of a novel AM microstructure, the greater its application range. In this article, we describe the mechanical behavior of helicoidal, elastomeric lattices and introduce path length metrics to help explain their stress response. We show that these structures can exhibit a large range of mechanical behaviors in compression, making them well suited for applications such as stress mitigation and impact absorption. Additionally, the path length metrics could become useful design tools and may be applicable to a larger set of cellular structures. These findings expand our ability to rapidly design materials with highly specific and customizable properties to meet the needs of modern engineering challenges. Graphical abstract

Van Meerbeek, I. M. (ORCID:000000021258132X)↗
DOE OSTI2021

Quantitative insights into the dislocation source behavior of twin boundaries suggest a new dislocation source mechanism

Pop-in statistics from nanoindentation with spherical indenters are used to determine the stress required to activate dislocation sources in twin boundaries (TBs) in copper and its alloys. The TB source activation stress is smaller than that needed for bulk single crystals, irrespective of the indenter size, dislocation density and stacking fault energy. Because an array of pre-existing Frank partial dislocations is present at a TB, we propose that dislocation emission from the TB occurs by the Frank partials splitting into Shockley partials moving along the TB plane and perfect lattice dislocations, both of which are mobile. The proposed mechanism is supported by recent high resolution transmission electron microscopy images in deformed nanotwinned (NT) metals and may help to explain some of the superior properties of nanotwinned metals (e.g. high strength and good ductility), as well as the process of detwinning by the collective formation and motion of Shockley partial dislocations along TBs.

36 MATERIALS SCIENCE↗
DOE OSTI2024

The insufficiency of the charge transfer model for resonant $\text{XES}$ in $\text{UF}$ 4

Recently, it was suggested that the single-electron charge transfer model (ligand 2p to Uranium 5f transition) could be used to explain the U M 4,5 Resonant X-ray Emission Spectroscopy (XES) of uranium dioxide (UO 2 ) and uranium tetrafluoride (UF 4 ). Here in this report, it is shown that this model produces results inconsistent with a joint consideration of both of the bandgaps of UO 2 and UF 4 and is thus insufficient to explain the observed behavior. An alternative Raman-based model is proposed.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Materials scarcity during the clean energy transition: Myths, challenges, and opportunities

Abstract Efforts to reach net zero targets by the second half of the century will have profound materials supply implications. The anticipated scale and speed of the energy transition in both transportation and energy storage raises the question of whether we risk running out of the essential critical materials needed to enable this transition. Early projections suggest that disruptions are likely to occur in the short term for select critical materials, but at the same time these shortages provide a powerful incentive for the market to respond in a variety of ways before supply-level stress becomes dire. In April 2023, the MRS Focus on Sustainability subcommittee sponsored a panel discussion on the role of innovation in materials science and engineering in supporting supply chains for clean energy technologies. Drawing on examples from the panel discussion, this perspective examines the myth of materials scarcity, explains the compelling need for innovation in materials in helping supply chains dynamically adapt over time, and illustrates how the Materials Research Society is facilitating engagement with industry to support materials innovation, now and in the future. Graphical Abstract Highlights In this commentary, we examine the myth of materials scarcity, explain the compelling need for innovation in materials in helping supply chains dynamically adapt over time, and show how the materials research community can effectively engage with industry, policymakers, and funding agencies to drive the needed innovation in critical areas. Discussion Demand for certain materials used in clean energy technologies is forecasted to increase by multiples of current production over the next decades. This has drawn attention to supply chain risks and has created a myth that we will “run out” out of certain materials during the energy transition. The reality is that markets have multiple mechanisms to adapt over the long-term, and near-term shortages or expectations of shortages provide a powerful incentive for action. In this commentary, we highlight different ways materials innovation can help solve these issues in the near term and long term, and how the materials research community can effectively engage with industry and policymakers.

Ku, Anthony Y. (ORCID:0000000337622914)↗
DOE OSTI2022

Variable Daily Autocorrelation Functions of High-Frequency Seismic Data on Mars

Abstract High-frequency seismic data on Mars are dominated by wind-generated lander vibrations, which are radiated partially to the subsurface. Autocorrelation functions (ACFs) of seismic data on Mars filtered between 1 and 5 Hz show clear phases at ∼1.3, ∼2.6, and ∼3.9 s. Daily temporal changes of their arrival times (dt/t) correlate well with the daily changes of ground temperature, with ∼5% daily variation and ∼50 min apparent phase delay. The following two mechanisms could explain the observations: (1) the interference of two predominant spectral peaks at ∼3.3 and ∼4.1 Hz, assumed to be both lander resonance modes, generate the apparent arrivals in the ACFs; (2) the interference of the lander vibration and its reflection from an interface ∼200 m below the lander generate the 3.3 Hz spectral peak and ∼1.3 s arrival in the ACFs. The driving mechanism of the resolved dt/t that most likely explains the ∼50 min delay is thermoelastic strain at a near-surface layer, affecting the lander–ground coupling and subsurface structures. The two outlined mechanisms suggest, respectively, up to ∼10% changes in ground stiffness at 1–5 Hz and ∼15% velocity changes in the top ∼20 m layer. These are upper bound values considering also other possible contributions. The presented methodology and results contribute to analysis of ACFs with limited data and the understanding of subsurface materials on Mars.

Geochemistry & Geophysics↗
DOE OSTI2021

History Matching and Performance Prediction of a Polymer Flood Pilot in Heavy Oil Reservoir on Alaska North Slope

The first-ever polymer flood pilot to enhance heavy oil recovery on Alaska North Slope (ANS) is ongoing. After more than 2.5 years of polymer injection, significant benefit has been observed from the decrease in water cut from 65% to less than 15% in the project producers. The primary objective of this study is to develop a robust history-matched reservoir simulation model capable of predicting future polymer flood performance. In this work, the reservoir simulation model has been developed based on the geological model and available reservoir and fluid data. In particular, four high transmissibility strips were introduced to connect the injector-producer well pairs, simulating short-circuiting flow behavior that can be explained by viscous fingering and reproducing the water cut history. The strip transmissibilities were manually tuned to improve the history matching results during the waterflooding and polymer flooding periods, respectively. It has been found that higher strip transmissibilities match the sharp water cut increase very well in the waterflooding period. Then the strip transmissibilities need to be reduced with time to match the significant water cut reduction. The viscous fingering effect in the reservoir during waterflooding and the restoration of injection conformance during polymer flooding have been effectively represented. Based on the validated simulation model, numerical simulation tests have been conducted to investigate the oil recovery performance under different development strategies, with consideration for sensitivity to polymer parameter uncertainties. The oil recovery factor with polymer flooding can reach about 39% in 30 years, twice as much as forecasted with continued waterflooding. Besides, the updated reservoir model has been successfully employed to forecast polymer utilization, a valuable parameter to evaluate the pilot test’s economic efficiency. All the investigated development strategies indicate polymer utilization lower than 3.5 lbs/bbl in 30 years, which is economically attractive.

Wang, Xindan↗
DOE OSTI2022

Fluid – Fluid Interfacial Area and Its Impact on Relative Permeability - A Pore Network Modeling Study

Abstract Relative permeability (kr) is commonly modeled as an empirical function of phase saturation. Although current empirical models can provide a good match of one or two measured relative permeabilities using saturation alone, they are unable to predict relative permeabilities well when there is hysteresis or when physical properties such as wettability change. Further, current models often result in relative permeability discontinuities that can cause convergence and accuracy problems in simulation. To overcome these problems, recent research has modeled relative permeability as a state function of both saturation (S) and phase connectivity (X). Pore network modeling (PNM) data, however, shows small differences in relative permeability for the same S-X value when approached from a different flow direction. This paper examines the impact of one additional Minkowski parameter (Mecke and Arns, 2005), the fluid-fluid interfacial area, on relative permeability to identify if that satisfactorily explains this discrepancy. We calculate the total fluid-fluid interfacial areas (IA) during two-phase (oil/water) flow in porous media using pore network modeling. The area is calculated from PNM simulations using the areas associated with corners and throats in pore elements of different shapes. The pore network is modeled after a Bentheimer sandstone, using square, triangular, and circular pore shapes. Simulations were conducted for numerous primary drainage and imbibition cycles at a constant contact angle of 0° for the wetting phase. Simultaneous measurements of capillary pressure, relative permeability, saturation, and phase connectivity are made for each displacement. Fluid-fluid interfacial area is calculated from the PNM capillary pressure, the fluid location in the pore elements, and the pore element dimensional data. The results show that differences in the relative permeability at the same (S,X) point is explained well by differences in the fluid-fluid interfacial area (IA). That is, for a larger change in IA at these intersection points, the permeability difference is greater. That difference in relative permeability approaches zero as the difference in IA approaches zero. This confirms that relative permeability can be modeled better as a unique function of S, X, and IA. The results also show that an increase in IA restricts flow decreasing the nonwetting (oil) phase permeability. This decrease is caused by an increase in the throat area fraction compared to the corner area as the total area IA increases. The wetting phase relative permeability, however, shows the inverse trend, in that its relative permeability is greater when IA becomes larger owing to a greater fraction of the total area associated with the corners. The area IA, however, impacts the nonwetting phase relative permeability more than the wetting phase relative permeability. Corner flow improves the wetting phase relative permeability because the wetting phase is continuous there. Finally, a sensitivity analysis shows that relative permeability a is more sensitive to change in S than they are for IA for the case studied implying that if only two parameters are used to model relative permeability it is better to choose S and X.

Engineering↗
DOE OSTI2023

Multipole groups and fracton phenomena on arbitrary crystalline lattices

Multipole symmetries are of interest in multiple contexts, from the study of fracton phases, to nonergodic quantum dynamics, to the exploration of new hydrodynamic universality classes. However, prior explorations have focused on continuum systems or hypercubic lattices. In this work, we systematically explore multipole symmetries on arbitrary crystal lattices. We explain how, given a crystal structure (specified by a space group and the occupied Wyckoff positions), one may systematically construct all consistent multipole groups. We focus on two-dimensional crystal structures for simplicity, although our methods are general and extend straightforwardly to three dimensions. We classify the possible multipole groups on all two-dimensional Bravais lattices, and on the Kagome and breathing Kagome crystal structures to illustrate the procedure on general crystal lattices. Using Wyckoff positions, we provide an in-principle classification of all possible multipole groups in any space group. We explain how, given a valid multipole group, one may construct a consistent lattice Hamiltonian and a low-energy field theory. We then explore the physical consequences, beginning by generalizing certain results originally obtained on hypercubic lattices to arbitrary crystal structures. Next, we identify two apparently novel phenomena: an emergent, robust subsystem symmetry on the triangular lattice, and an exact multipolar symmetry on the breathing Kagome lattice that does not include conservation of charge (monopole), but instead conserves a vector charge. This makes clear that there is new physics to be found by exploring the consequences of multipolar symmetries on arbitrary lattices, and this work provides the map for the exploration thereof, as well as guiding the search for emergent multipolar symmetries and the attendant exotic phenomena in real materials based on nonhypercubic lattices.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Analytical methods for online data quality assessment

This chapter provides a comprehensive overview of the main steps for algorithmic sensor signal quality assessment, which can enhance the decision-making process for water resource recovery facility (WRRF) operation and optimization. It introduces the concept of redundancy as the basis for data quality assessment. It also explains the typical data processing pipeline, which consists of preliminary analysis, data pre-processing, and specific algorithmic approaches. Each of these processes is presented and discussed in three separate sections. Importantly, this chapter introduces the main approaches for data quality assessment, provides guidelines for selecting the most suitable one and the key performance indicators to evaluate them and explains how to collect metadata through such an algorithmic approach.

Aguado, Daniel↗
DOE OSTI2020

XXVIIth International Conference on Supersymmetry and Unification of Fundamental Interactions (SUSY 2019) (Final Report)

Supersymmetry (SUSY) is one of most elegant extensions of the Standard Model (SM) and explains the puzzles of the SM by providing a candidate to explain the dark matter content of the universe, allowing scientists to understand the origin of the electroweak scale requiring the top mass to be around 170 GeV and leading to the unification of forces at a grand unified scale. Further the minimal supersymmetric standard model (MSSM) predicts the Higgs boson mass to be less than 135 GeV. The discovery of the Higgs Boson with mass around 125 GeV at the LHC has provided a major support to SUSY ideas. Searches for SUSY are ongoing at the Large Hadron Collider (LHC). Direct and indirect dark matter experiments are searching for a particle dark matter candidate which arises most naturally in SUSY models. Proton decay predicted by SUSY grand unified theories is being searched for at deep underground experiments. In addition, recent advances in neutrino and dark matter physics, observational astrophysics, precision cosmology and the promising new window into the cosmos opened by the direct detection of gravitational waves, have brought new ideas on the potential connections between new fundamental particles and our understanding of their impact on the early universe and its evolution. At present, the major questions include: Is SUSY still the best candidate for models beyond the SM? Do we have any well motivated alternative to SUSY? Have we exhausted all possibilities to search for new physics at high and low energy scales? XXVIIth International Conference on Supersymmetry and Unification of Fundamental Interactions (SUSY 2019), hosted by Texas A&M University – Corpus Christi during May 20-24, 2019, provided a unique venue to discus and understand the status of SUSY, connection between particle physics and cosmology, supersymmetry and its alternative, Higgs sector, neutrino sector, flavor sector, dark matter, electroweak phase transition, astroparticle physics, gravitational waves and string theory. Discussion of results from the LHC, recent neutrino experiments and observations, direct and indirect dark matter detection experiments, detection of gravitational waves, data from particle colliders, as well as measurements of the CMB and Large Scale Structure were an integral part of SUSY 2019. To ensure the younger participants will benefit from the conference the most, the conference was preceded by the 4 day long pre-SUSY summer school for graduate students and postdocs. The invited speakers were leading scientists in the fields of SUSY interest. The school took place on Texas A&M University – Corpus Christi campus during the week prior the SUSY 2019 conference (May 15 – 18, 2019). Since its inception in 1993, SUSY has become one of the most important and widely attended international meetings in high energy physics, devoted to new ideas in fundamental particle physics. SUSY 2019 brought together approximately 250 scientists, theorists, phenomenologists, experimentalists and cosmologists, (including over 60 graduate students and 70 postdocs) representing 22 nations: Australia, Belgium, Canada, Chile, China, Colombia, France, Germany, India, Italy, Japan, Mexico, Peru, Portugal, Romania, South Korea, Spain, Sweden, Switzerland, Taiwan, United Kingdom and United States. SUSY 2019 provided a stimulating venue for the exchange of scientific ideas among experts in dark matter, neutrino physics, particle physics, astrophysics and cosmology. The following scientific topics were delivered during SUSY 2019 in form of 44 plenary talks and over 200 parallel talks: Unification of Forces; Electroweak, Top and Higgs Physics; Precision Calculations and MC tools; BSM in Flavor Physics; Neutrino Masses: Models and Phenomenology; Cosmology and Gravitational Waves; Dark Matter, Astroparticle Physics; Formal Field Theory and Strings; Alternatives to Supersymmetry; Quantum Information: Machine Learning/Big Data. 28 talks were given during the pre-SUSY program related to the following topics: Neutrino Physics; Big Data; Collider Physics & SUSY; String Phenomenology; Cosmology; Dark Matter; SUSY Models and Phenomenology

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

National Geographic Interview -- Pit Manufacturing

Los Alamos has been contacted by a freelance reporter working on assignment for National Geographic, Sarah Scoles. Scoles is working on a story about plutonium pit production. She says the story will be an explainer -- detailing the pit production process from start to finish, in context with the planned increase in production. Scoles is seeking to interview a LANL expert or experts to help explain the process, the Lab's history in pit production, and future role.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Elucidating Hydrogen Reaction-Induced Water Desorption from Oxide-Passivated Metal Surfaces for Plasma Applications

Elucidating the mechanisms responsible for sub-microsecond desorption of water and other impurities from electrode surfaces at high heating rates is crucial for understanding pulsed power behavior. Ionization of desorbed impurities in the vacuum regions causes power or current loss; devising methods to limit such desorption during the short time scale of pulsed power is needed to improve corresponding applications. Previous molecular modeling studies have strongly suggested that, under high vacuum conditions, the amount of water impurity desorbing from oxide surfaces on metal electrodes is at a sub-monolayer level at room temperature, which appears insufficient to explain observed pulsed power energy losses at high current densities. In this work, we apply Density Functional Theory (DFT) techniques to show that hydrogen trapped inside iron metal can diffuse into hematite (α-Fe 2 O 3 ) on the metal surface, ultimately reacting with the oxide to form Fe(II) and H 2 O. The latter desorbs at elevated temperature and may explain the anomalous amount of desorbed impurity inferred from pulsed-power experiments. We also apply a suite of characterization techniques to demonstrate that when iron metal is heated to 650 °C, the dominant surface oxide component becomes α-Fe 2 O 3 . The oxide facets exposed are found to be a mixture of (0001), (10-10), and others, in agreement with the DFT models used.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

Hybridizing Machine Learning and Physically-based Earth System Models to Improve Prediction of Multivariate Extreme Events (AI Exploration of Wildland Fire Prediction)

Focal Areas: This project responds to two focal areas identified in the DOE Call for AI4ESP White Papers: 1) Predictive modeling through the use of artificial intelligence (AI) techniques, and 2) insights gleaned from complex data using explainable AI and big data analytics. Science Challenge: Large wildland fires (hereafter wildfires) appearing as high-impact compound climate extreme events are closely related to hydroclimate and water cycle extremes that modulate surface fuel supply and combustibility. These compound events have multivariate climatic features (e.g., temperature, precipitation, relative humidity, wind, lightning) and societal drivers (e.g., forest management, land use change, human caused ignitions). Meanwhile, they induce strong feedbacks to the coupled atmosphere, biosphere, and hydrosphere by perturbing regional and global radiation budget as well as ecological, biogeochemical, and water cycles across multiple spatiotemporal scales. The nonlinear interactions between these natural and anthropogenic components of the Earth system are too complex to be completely and adequately represented in today’s Earth system models (ESMs). The inherent stochastic nature of fire activity at all scales further increases the difficulty of its prediction using ESMs that are usually developed from deterministic equations and parameterizations. Besides, concurrence of long-term (decadal to interdecadal) global climate change and fire regime shifts overlapping with short-term (intraseasonal to interannual) variations of regional fire weather and burning activity confound predictability of these compound extreme events. We propose to address the above scientific challenges by using machine learning (ML)-based data-driven modeling techniques to integrate observations and physically-based ESMs’ simulations in a computationally efficient hybrid prediction system. This prediction system is supposed to characterize the wildfire’s sensitivity to climate and exogenous drivers at high resolution (~ 0.25°) on subseasonal to seasonal (S2S) timescales providing improved predictability and explainability. We will use the system to help identify: (1) What are the computational elements of a hybrid system needed to predict compound climate extreme events such as global wildfires? (2) What are the key drivers (either natural or anthropogenic) that modulate short-term variations of multivariate fire weather and burning activity over different regions? How can one take advantage of those driver-response relationships to improve the predictability of large wildfires on S2S time scales? (3) What are the underlying physical mechanisms and sources of improved predictability? Which ML techniques are optimal in revealing and adapting these mechanisms?

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Interpretable Deep Learning for the Earth System with Fractal Nets

Focal Area 3: Explainable AI Our confidence in the projections made by Earth System Models (ESMs) depends on understanding them to be, in some important respects, faithful representations of the Earth system. Here we present an “explainable Artificial Intelligence (AI)” method that allows us to uncover the dynamical structure of the observed and modeled Earth system, discover hidden links across wide spatiotemporal scales, target model development efforts at poorly-represented dynamics, and optimize observed or modeled data collection to maximize predictive information. Science Challenge: Dynamical system science for the Earth system poses unique challenges given the large degree of internal climate variability. Thus, tools that help us understand how ESMs succeed and fail at representing these dynamics are crucial, particularly in relation to the observed system. Furthermore, the computational and memory constraints on ESM data output motivate in situ analysis of ESM dynamics, including automatic detection of dynamical shifts. Also, of key importance are procedures that leverage ESMs to optimize observational campaigns for improving process representation, reducing structural uncertainty and improving model skill.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Performance of solar leasing for low- and middle-income customers in Connecticut

Policymakers are increasingly interested in expanding access to rooftop photovoltaic systems. This study analyzes the financial performance of a Connecticut Green Bank (CGB) solar leasing program, run in partnership with PosiGen, that targets low- and moderate-income customers. We show that this program has successfully reached underserved customers and has reasonable repayment rates given the credit characteristics of the participants. The CGB/PosiGen program reaches many more underserved customers than other PV financing programs in Connecticut.For example, the majority of CGB/PosiGen participants (58%) live in census tracts that have a median income of less than 80% of the area median income (AMI). In contrast, only 9% of participants in the other CGB solar financing programs live in these census tracts. Furthermore, the majority of CGB/PosiGen participants (56%) have FICO scores that would generally be considered non-prime (<670), whereas only 2% of participants in the other programs have similarly low scores. Credit, not income, is the primary factor that explains participants’ financial performance. Overall, we find that delinquency and annualized losses are higher for PosiGen (2.3% and 0.9%) than for other CGB programs (1.4% and 0.1%). Across the CGB programs, lower credit scores are associated with higher rates of delinquency and loss. Therefore, participants’ lower credit scores explain much of the program’s higher rates of delinquency and annualized losses. PosiGen leases perform competitively with market-rate solar and non-solar leases and loans. When compared to securities backed by market-rate PV loans and leases with similar amounts of seasoning, we find that the PosiGen leases have higher delinquency rates but comparable gross loss rates. The similarity in losses is notable given that loss rates for PosiGen leases were higher than those of the other CGB leases and loans. Rather than the PosiGen leases having high loss rates, other CGB leases and loans have unusually low loss rates. We also compare PosiGen to non-solar benchmarks, including indices of auto and consumer loans. We find that the PosiGen leases have significantly less delinquency than non-prime auto loans and have performance comparable to many consumer loans. The U.S. Department of Energy’s Solar Energy Technologies Office supported this research.

14 SOLAR ENERGY↗