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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Bacterial nitrite production oxidizes Fe(II) bioremediating acidic abandoned coal mine drainage

Passive remediation systems (PRSs) treating either acidic or neutral abandoned coal mine drainage (AMD) are colonized by bacteria that can bioremediate iron (Fe) through chemical cycling. Due to the low pH in acidic AMD, iron oxidation from soluble Fe(II) to precipitated Fe(III) is mainly directed by microbial oxidation. Less well described are biotic reactions that lead to iron remediation through abiotic secondary reactions. We describe here iron oxidation in acidic AMD that is mediated by the bacterial reduction of nitrate to nitrite followed by the geochemical oxidation of Fe(II). Within an acidic PRS, 4,560 bacteria cultured from the microbial community were screened for their ability to oxidize iron and to perform nitrate-dependent iron oxidation (NDFO). Iron oxidation in the culturable community was observed in every pond of the system, ranging from 2.1% to 11.4%, and NDFO was observed in every pond, ranging from 1.4% to 6.0% of the culturable bacteria. Five NDFO isolates were purified and identified as Paraburkholderia spp. One of our isolates, Paraburkholderia sp. AV18 was shown to drive NDFO through the bacterial production of nitrite that in turn chemically oxidizes Fe(II) (nitrate reduction-iron oxidation; NRIO). AV18 expressed nitrate reductase, napA, concurrent to nitrite production. Burkholderiales are found by 16S rRNA gene sequencing in every pond of the PRS. The frequency of NDFO metabolism in the culturable microbial community and abundance of Burkholderiales in the PRS suggest nitrite producers contribute to the bioremediation of iron in acidic AMD and may be an unharnessed opportunity to increase iron bioremediation in acidic conditions.

(NDFO)↗
DOE OSTI2021

Reoptimization of Quantum Circuits via Hierarchical Synthesis

The current phase of quantum computing is in the Noisy Intermediate-Scale Quantum (NISQ) era. On NISQ devices, two-qubit gates such as CNOTs are much noisier than single-qubit gates, so it is essential to minimize their count. Quantum circuit synthesis is a process of decomposing an arbitrary unitary into a sequence of quantum gates, and can be used as an optimization tool to produce shorter circuits to improve overall circuit fidelity. However, the time-to-solution of synthesis grows exponentially with the number of qubits. As a result, synthesis is intractable for circuits on a large qubit scale. In this paper, we propose a hierarchical, block-by-block opti-mization framework, QGo, for quantum circuit optimization. Our approach allows an exponential cost optimization to scale to large circuits. QGo uses a combination of partitioning and synthesis: 1) partition the circuit into a sequence of independent circuit blocks; 2) re-generate and optimize each block using quantum synthesis; and 3) re-compose the final circuit by stitching all the blocks together. We perform our analysis and show the fidelity improvements in three different regimes: small-size circuits on real devices, medium-size circuits on noisy simulations, and large-size circuits on analytical models. Our technique can be applied after existing optimizations to achieve higher circuit fidelity. Further, using a set of NISQ benchmarks, we show that QGo can reduce the number of CNOT gates by 29.9% on average and up to 50% when compared with industrial compiler optimizations such as t|ket). When executed on the IBM Athens system, shorter depth leads to higher circuit fidelity. We also demonstrate the scalability of our QGo technique to optimize circuits of 60+ qubits, Our technique is the first demonstration of successfully employing and scaling synthesis in the compilation tool chain for large circuits. Overall, our approach is robust for direct incorporation in production compiler toolchains to further improve the circuit fidelity.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Single-cell and spatial omics in plants: from cellular atlases to regulatory mechanisms

Single-cell RNA sequencing (scRNA-seq) has transformed transcriptomic studies by enabling gene expression profiling at the resolution of individual cells within and across a broad range of tissue types, revealing cellular heterogeneity that is obscured in bulk tissue transcriptomes. Over the past decade, improvements in microfluidics and library preparation have drastically increased throughput, allowing tens of thousands of cells to be assayed in a single experiment. Although initially developed in animal systems, scRNA-seq has rapidly emerged as a powerful and widely adopted approach in plant biology. Beyond transcriptomics, the integration of single-cell data with chromatin accessibility, proteomics, metabolomics, and spatial omics is enabling a system-level understanding of plant gene regulation and cellular organization. Network-based analytical frameworks further support the reconstruction of gene regulatory networks and the interpretation of complex single-cell data. In this review, we summarize the current technological landscape of plant single-cell studies, discuss key experimental and analytical challenges, and review emerging strategies for validating single-cell discoveries. We also discuss future directions in applying single-cell technologies to woody perennials plants and bioenergy-relevant crops, emphasizing their potential to accelerate the discovery of cell type-specific regulatory mechanisms underlying growth, stress resilience, and biomass production.

Li, Miaomiao [ORNL] (ORCID:0000000321326168)↗
DOE OSTI2021

Practical choices for space-filling designs

Space-filling designs are now commonly used as a flexible model-free strategy for providing good coverage throughout an input space of interest for a variety of computer and physical experiment design scenarios. Some of the preliminary choices about how to frame the problem and which type of design to use can have a substantial impact on the success or failure of the experiment, and yet how to make these critical choices is often under-emphasized in the literature. In this paper, we explore several of the practical choices required by the experimenter and describe a sequence of steps to help create an ideal design that matches the goals and constraints of the experiment. These choices include the specification of the input space, the scaling of the variables, the degree of uniformity of the design points across the input space, and the space-filling characteristics. In addition, some new tools for defining the weights to implement non-uniform space-filling designs are provided. Finally, the methods are demonstrated with several illustrative examples and a real-world chemical engineering experiment for carbon capture.

42 ENGINEERING↗
DOE OSTI2026

Xanthos-Lake Model Source Code

This repository contains the source code for Xanthos-Lake, a lake-modeling extension of the Xanthos framework that introduces a coupled lake component comprising the Xanthos-Lake Snow and Ice Model (xLSIM) and the Xanthos-Lake Water Balance Model (xLWBM). xLSIM is a basin-aware machine-learning model for lake snow, ice, and thermal conditions. It predicts monthly lake ice thickness, snow depth, snow-cover fraction, mixing-layer temperature, and lake ice fraction from meteorological forcing and lake surface-area information. It uses sequence-based deep-learning architectures, including Transformer and hybrid Long Short-Term Memory–Transformer (LSTM–Transformer) models, together with seasonal encoding, multi-lake learning, physical masking, and basin-level cryospheric and non-cryospheric classification. The training workflow uses Ray for scalable execution and includes optional Ray Tune hyperparameter optimization. Model predictions, observations, diagnostics, and feature-importance outputs are written in NetCDF. xLWBM is the water-balance component of the new lake framework. It simulates monthly lake storage, surface area, evaporation, inflow, outflow, and lake–groundwater exchange. It combines physical water-balance equations with calibrated bathymetric relationships, weir-based outlet flow, modified Penman open-water evaporation, groundwater head relaxation, Penman–Monteith snow and ice sublimation, and snow, ice, and thermal conditions supplied by xLSIM. The model calibrates lake parameters against satellite-derived surface-area data, using evaporation-based calibration where surface-area data are unavailable, and supports small, medium, and large lake classes. For large lakes, xLWBM is integrated with the managed-routing workflow so that lake storage and outflow interact directly with downstream river routing and reservoir operations. Together, xLSIM and xLWBM provide Xanthos with a coupled lake-modeling capability. xLSIM supplies the snow, ice, and thermal conditions that affect lake evaporation and snow- and ice-related water exchanges, while xLWBM translates those conditions into dynamic lake storage, surface area, evaporation, and discharge. In return, xLWBM supplies evolving lake surface area to xLSIM. This coupling enables Xanthos to represent lakes as active hydrologic components within basin-scale water-availability and routing simulations.

Machine Learning↗
DOE OSTI2025

Advancing Multi-Hazard Risk and Safety Considerations for Aging Nuclear Facilities (Revision 1)

This project will demonstrate a multi-hazard time-dependent probabilistic risk assessment (PRA) approach for nuclear facilities considering aging-related deterioration of structures. A generic pressurized water reactor (PWR) reactor subjected to seismic mainshock-aftershock sequences considering the aging of the containment structure will be used as a case study to demonstrate the multi-hazard PRA approach. Using advanced modeling and simulation, seismic mainshock-aftershock fragility functions will be simulated for the containment structure considering aging effects. A multi-hazard PRA model for a generic PWR reactor will be built to quantify the multi-hazard core damage frequency (CDF) and large early release frequency (LERF) with explicit time-dependent modeling of event sequences. To date, the cascading impacts of multi-hazards are not adequately accounted for in the PRA models for nuclear facilities. In addition, for both initial and periodic evaluation of facilities to withstand natural phenomena hazards (NPH), deterioration of the SSCs due to aging and other effects is not adequately considered. By advancing multi-hazard considerations accounting for aging effects, this project will contribute to improved understanding of the safety of aging nuclear facilities. Project outcomes such as the multi-hazard CDF and LERF will also allow facility owners to optimize upgrade and retrofit protocols. This project is divided into two components: (1) advanced modeling and simulations; and (2) multi-hazard PRA. For the first component, Idaho National Laboratory’s (INL) Multi-hazard Analysis for STOchastic time-DOmaiN phenomena (MASTODON) and BlackBear codes will be used to simulate the seismic response and damage of a containment structure under cascading mainshock-aftershock sequences with aging effects. Uncertainties related to the seismic inputs, material parameters, and environmental factors such as temperature and humidity will be identified and propagated to the fragility functions. These fragility functions, which consider aging effects, will be time dependent. The second component will take the fragility information from the first component to build a multi-hazard time-dependent PRA model starting from a generic PWR model to evaluate the multi-hazard CDF and LERF and characterize the associated consequences. For this component, OpenPRA Web Application and SAPHIRE code will be used. Events such as the Fukushima Daiichi accident have highlighted the importance of considering the cascading impacts of multi-hazards for PRA. Moreover, many of the reactors in the current nuclear fleet in the United States (US) are already operating well beyond their initially planned design life, and applications for further extensions to operating licenses are being considered. Therefore, considering multi-hazard effects and aging deterioration in the NPH risk assessment process will contribute to the safety of both existing and future nuclear facilities. Outcomes of this project will thus directly benefit the standards DOE-STD-1020-16 and DOE-HDBK-1224-18.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Deciphering enhancer sequence using thermodynamics-based models and convolutional neural networks

Abstract Deciphering the sequence-function relationship encoded in enhancers holds the key to interpreting non-coding variants and understanding mechanisms of transcriptomic variation. Several quantitative models exist for predicting enhancer function and underlying mechanisms; however, there has been no systematic comparison of these models characterizing their relative strengths and shortcomings. Here, we interrogated a rich data set of neuroectodermal enhancers in Drosophila, representing cis- and trans- sources of expression variation, with a suite of biophysical and machine learning models. We performed rigorous comparisons of thermodynamics-based models implementing different mechanisms of activation, repression and cooperativity. Moreover, we developed a convolutional neural network (CNN) model, called CoNSEPT, that learns enhancer ‘grammar’ in an unbiased manner. CoNSEPT is the first general-purpose CNN tool for predicting enhancer function in varying conditions, such as different cell types and experimental conditions, and we show that such complex models can suggest interpretable mechanisms. We found model-based evidence for mechanisms previously established for the studied system, including cooperative activation and short-range repression. The data also favored one hypothesized activation mechanism over another and suggested an intriguing role for a direct, distance-independent repression mechanism. Our modeling shows that while fundamentally different models can yield similar fits to data, they vary in their utility for mechanistic inference. CoNSEPT is freely available at: https://github.com/PayamDiba/CoNSEPT.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

PayamDiba/CoNSEPT

Deciphering the sequence-function relationship encoded in enhancers holds the key to interpreting non-coding variants and understanding mechanisms of transcriptomic variation. Several quantitative models exist for predicting enhancer function and underlying mechanisms; however, there has been no systematic comparison of these models characterizing their relative strengths and shortcomings. Here, we interrogated a rich data set of neuroectodermal enhancers in Drosophila, representing cis- and trans- sources of expression variation, with a suite of biophysical and machine learning models. We performed rigorous comparisons of thermodynamics-based models implementing different mechanisms of activation, repression and cooperativity. Moreover, we developed a convolutional neural network (CNN) model, called CoNSEPT, that learns enhancer ‘grammar’ in an unbiased manner. CoNSEPT is the first general-purpose CNN tool for predicting enhancer function in varying conditions, such as different cell types and experimental conditions, and we show that such complex models can suggest interpretable mechanisms. We found model-based evidence for mechanisms previously established for the studied system, including cooperative activation and short-range repression. The data also favored one hypothesized activation mechanism over another and suggested an intriguing role for a direct, distance-independent repression mechanism. Our modeling shows that while fundamentally different models can yield similar fits to data, they vary in their utility for mechanistic inference.

Dibaeinia, Payam↗
DOE OSTI2021

Milestone M6 Report: Reducing Excess Data Movement Part 1

This is the second in a sequence of three Hardware Evaluation milestones that provide insight into the following questions: What are the sources of excess data movement across all levels of the memory hierarchy, going out to the network fabric? What can be done at various levels of the hardware/software hierarchy to reduce excess data movement? How does reduced data movement track application performance? The results of this study can be used to suggest where the DOE supercomputing facilities, working with their hardware vendors, can optimize aspects of the system to reduce excess data movement. Quantitative analysis will also benefit systems software and applications to optimize caching and data layout strategies. Another potential avenue is to answer cost-benefit questions, such as those involving memory capacity versus latency and bandwidth. This milestone focuses on techniques to reduce data movement, quantitatively evaluates the efficacy of the techniques in accomplishing that goal, and measures how performance tracks data movement reduction. We study a small collection of benchmarks and proxy mini-apps that run on pre-exascale GPUs and on the Accelsim GPU simulator. Our approach has two thrusts: to measure advanced data movement reduction directives and techniques on the newest available GPUs, and to evaluate our benchmark set on simulated GPUs configured with architectural refinements to reduce data movement.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

O-GlcNAc transferase regulates collagen deposition and fibrosis resolution in idiopathic pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is a chronic pulmonary disease that is characterized by an excessive accumulation of extracellular matrix (ECM) proteins (e.g. collagens) in the parenchyma, which ultimately leads to respiratory failure and death. While current therapies exist to slow the progression, no therapies are available to resolve fibrosis. We characterized the O-linked N-Acetylglucosamine (O-GlcNAc) transferase (OGT)/O-GlcNAc axis in IPF using single-cell RNA-sequencing (scRNA-seq) data and human lung sections and isolated fibroblasts from IPF and non-IPF donors. The underlying mechanism(s) of IPF were further investigated using multiple experimental models to modulate collagen expression and accumulation by genetically and pharmacologically targeting OGT. Furthermore, we hone in on the transforming growth factor-beta (TGF-β) effector molecule, Smad3, by co-expressing it with OGT to determine if it is modified and its subsequent effect on Smad3 activation. We found that OGT and O-GlcNAc levels are upregulated in patients with IPF compared to non-IPF. We report that the OGT regulates collagen deposition and fibrosis resolution, which is an evolutionarily conserved process demonstrated across multiple species. Co-expression of OGT and Smad3 showed that Smad3 is O-GlcNAc modified. Blocking OGT activity resulted in decreased phosphorylation at Ser-423/425 of Smad3 attenuating the effects of TGF-β1 induced collagen expression/deposition. OGT inhibition or knockdown successfully blocked and reversed collagen expression and accumulation, respectively. Smad3 is discovered to be a substrate of OGT and its O-GlcNAc modification(s) directly affects its phosphorylation state. These data identify OGT as a potential target in pulmonary fibrosis resolution, as well as other diseases that might have aberrant ECM/collagen accumulation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Uncovering the truth about M101, NGC 3938, and their significant others through radiative transfer

ABSTRACT Solving the inverse problem in spiral galaxies, that allows the derivation of the spatial distribution of dust, gas, and stars, together with their associated physical properties, directly from panchromatic imaging observations, is one of the main goals of this work. To this end, we used radiative transfer models to decode the spatial and spectral distributions of the nearby face-on galaxies M101 and NGC 3938. In both cases, we provide excellent fits to the surface-brightness distributions derived from GALEX, SDSS, 2MASS, Spitzer, and Herschel imaging observations. Together with previous results from M33, NGC 628, M51, and the Milky Way, we obtain a small statistical sample of modelled nearby galaxies that we analyse in this work. We find that in all cases Milky Way-type dust with Draine-like optical properties provide consistent and successful solutions. We do not find any ‘submm excess’, and no need for modified dust-grain properties. Intrinsic fundamental quantities like star-formation rates (SFR), specific SFR (sSFR), dust opacities, and attenuations are derived as a function of position in the galaxy and overall trends are discussed. In the SFR surface density versus stellar mass surface density space, we find a structurally resolved relation (SRR) for the morphological components of our galaxies, that is steeper than the main sequence (MS). Exception to this is for NGC 628, where the SRR is parallel to the MS.

Pricopi, D.↗
DOE OSTI2023

ATCUN-like Copper Site in βB2-Crystallin Plays a Protective Role in Cataract-Associated Aggregation

Cataract is the leading cause of blindness worldwide, and it is caused by crystallin damage and aggregation. Senile cataractous lenses have relatively high levels of metals, while some metal ions can directly induce the aggregation of human γ-crystallins. Here, for this work, we evaluated the impact of divalent metal ions in the aggregation of human βB2-crystallin, one of the most abundant crystallins in the lens. Turbidity assays showed that Pb 2+ , Hg 2+ , Cu 2+ , and Zn 2+ ions induce the aggregation of βB2-crystallin. Metal-induced aggregation is partially reverted by a chelating agent, indicating the formation of metal-bridged species. Our study focused on the mechanism of copper-induced aggregation of βB2-crystallin, finding that it involves metal-bridging, disulfide-bridging, and loss of protein stability. Circular dichroism and electron paramagnetic resonance (EPR) revealed the presence of at least three Cu 2+ binding sites in βB2-crystallin, one of them with spectroscopic features typical for Cu 2+ bound to an amino-terminal copper and nickel (ATCUN) binding motif, which is found in Cu transport proteins. The ATCUN-like Cu binding site is located at the unstructured N-terminus of βB2-crystallin, and it could be modeled by a peptide with the first six residues in the protein sequence (NH 2 -ASDHQF-). Isothermal titration calorimetry indicates a nanomolar Cu 2+ binding affinity for the ATCUN-like site. An N-truncated form of βB2-crystallin is more susceptible to Cu-induced aggregation and is less thermally stable, indicating a protective role for the ATCUN-like site. EPR and X-ray absorption spectroscopy studies reveal the presence of a copper redox active site in βB2-crystallin that is associated with metal-induced aggregation and formation of disulfide-bridged oligomers. Our study demonstrates metal-induced aggregation of βB2-crystallin and the presence of putative copper binding sites in the protein. Whether the copper-transport ATCUN-like site in βB2-crystallin plays a functional/protective role or constitutes a vestige from its evolution as a lens structural protein remains to be elucidated.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Continuous cropping of potato changed the metabolic pathway of root exudates to drive rhizosphere microflora

For potato production, continuous cropping (CC) could lead to autotoxicity buildup and microflora imbalance in the field soil, which may result in failure of crops and reduction in yield. In this study, non-targeted metabolomics (via liquid chromatography with tandem mass spectrometry (LC–MS/MS)) combined with metagenomic profiling (via high-throughput amplicon sequencing) were used to evaluate correlations between metabolomics of potato root exudates and communities of bacteria and fungi around potato plants to illustrate the impacts of CC. Potato plants were grown in soil collected from fields with various CC years (0, 1, 4, and 7 years). Metabolomic analysis showed that the contents and types of potential autotoxins in potato root exudates increased significantly in CC4 and CC7 plants (i.e., grown in soils with 4 and 7 years of CC). The differentially expressed metabolites were mainly produced via alpha-linolenic acid metabolism in plant groups CC0 and CC1 (i.e., no CC or 1 year CC). The metabolomics of the groups CC4 and CC7 became dominated by styrene degradation, biosynthesis of siderophore group non-ribosomal peptides, phenylpropanoid biosynthesis, and biosynthesis of various plant secondary metabolites. Continuous cropping beyond 4 years significantly changed the bacterial and fungal communities in the soil around the potato crops, with significant reduction of beneficial bacteria and accumulation of harmful fungi. Correlations between DEMs and microflora biomarkers were established with strong significances. These results suggested that continuous cropping of potato crops changed their metabolism as reflected in the plant root exudates and drove rhizosphere microflora to directions less favorable to plant growth, and it needs to be well managed to assure potato yield.

Xing, Yanhong↗
DOE OSTI2022

Persistent memory as an effective alternative to random access memory in metagenome assembly

Abstract Background The assembly of metagenomes decomposes members of complex microbe communities and allows the characterization of these genomes without laborious cultivation or single-cell metagenomics. Metagenome assembly is a process that is memory intensive and time consuming. Multi-terabyte sequences can become too large to be assembled on a single computer node, and there is no reliable method to predict the memory requirement due to data-specific memory consumption pattern. Currently, out-of-memory (OOM) is one of the most prevalent factors that causes metagenome assembly failures. Results In this study, we explored the possibility of using Persistent Memory (PMem) as a less expensive substitute for dynamic random access memory (DRAM) to reduce OOM and increase the scalability of metagenome assemblers. We evaluated the execution time and memory usage of three popular metagenome assemblers (MetaSPAdes, MEGAHIT, and MetaHipMer2) in datasets up to one terabase. We found that PMem can enable metagenome assemblers on terabyte-sized datasets by partially or fully substituting DRAM. Depending on the configured DRAM/PMEM ratio, running metagenome assemblies with PMem can achieve a similar speed as DRAM, while in the worst case it showed a roughly two-fold slowdown. In addition, different assemblers displayed distinct memory/speed trade-offs in the same hardware/software environment. Conclusions We demonstrated that PMem is capable of expanding the capacity of DRAM to allow larger metagenome assembly with a potential tradeoff in speed. Because PMem can be used directly without any application-specific code modification, these findings are likely to be generalized to other memory-intensive bioinformatics applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Central star formation in double-peak, gas-rich radio galaxies

The respective contributions of gas accretion, galaxy interactions, and mergers to the mass assembly of galaxies, as well as the evolution of their molecular gas and star-formation activity are still not fully understood. In a recent work, a large sample of double-peak (DP) emission-line galaxies have been identified from the SDSS. While the two peaks could represent two kinematic components, they may be linked to the large bulges that their host galaxies tend to have. Star-forming DP galaxies display a central star-formation enhancement and have been discussed as compatible with a sequence of recent minor mergers. In order to probe merger-induced star-formation mechanisms, we conducted observations of the molecular-gas content of 35 star-forming DP galaxies in the upper part of the main sequence (MS) of star formation (SF) with the IRAM 30 m telescope. Including similar galaxies 0.3 dex above the MS and with existing molecular-gas observations from the literature, we finally obtained a sample of 52 such galaxies. We succeeded in fitting the same kinematic parameters to the optical ionised- and molecular-gas emission lines for ten (19%) galaxies. We find a central star-formation enhancement resulting most likely from a galaxy merger or galaxy interaction, which is indicated by an excess of gas extinction found in the centre. This SF is traced by radio continuum emissions at 150 MHz, 1.4 GHz, and 3 GHz, all three of which are linearly correlated in log with the CO luminosity with the same slope. The 52 DP galaxies are found to have a significantly larger amount of molecular gas and longer depletion times, and hence a lower star-formation efficiency, than the expected values at their distance of the MS. The large bulges in these galaxies might be stabilising the gas, hence reducing the SF efficiency. This is consistent with a scenario of minor mergers increasing the mass of bulges and driving gas to the centre. We also excluded the inwards-directed gas migration and central star-formation enhancement as the origin of a bar morphology. Hence, these 52 DP galaxies could be the result of recent minor mergers that funnelled molecular gas towards their centre, triggering SF, but with moderate efficiency.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Integration of equitable resilience metrics into climate-informed electric utility planning processes: phase one

Working together, Sandia National Laboratories, Southern California Edison (SCE) - an Investor-Owned Utility (IOU) - and the California Public Utilities Commission (CPUC) are studying how electric utilities can use equity and resilience metrics to help inform the prioritization and sequencing of resilience-driven infrastructure investments. To this end, this project evaluated “Social Burden,” an equitable resilience metric which measures the potential impact of disruptions in access to non-electric critical services on people and estimates community resilience to these disruptions. The Social Burden was expanded to incorporate SCE’s existing equity metric and applied to evaluate the potential impacts from a range of climate-informed hypothetical outage scenarios developed under SCE’s 2022 Climate Adaptation Vulnerability Assessment. One baseline (“blue-sky”) state and eight different outage scenarios were evaluated to measure the potential impacts of the outages on non-electric infrastructure, critical services, and people. Key findings include: 1) the Social Burden framework is flexible enough to adapt to and build upon existing utility equity and/or resilience metrics, 2) Social Burden results highlight the high degree of non-electric service redundancy within the SCE service area with most (6/8) hypothetical outage scenarios predicted to increase people’s Social Burden by less than 10%; however, 3) access to critical services and people’s ability to obtain them is unequal and spatially clustered, meaning that there are some hypothetical outage scenarios (2/8) that will exert a higher toll on communities directly experiencing the outage as well as some nearby communities with pre-existing vulnerabilities. The report concludes with recommendations for potential use cases of the expanded Social Burden metric and identifies priority follow-on work. Potential use cases may include incorporating equity into IOU’s prioritization of climate resilience investments. Additionally, Social Burden analysis may provide additional data and insights to augment grid planning, potentially by identifying additional needs and/or prioritizing previously identified needs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

High-order partitioned spectral deferred correction solvers for multiphysics problems

We present an arbitrarily high-order, conditionally stable, partitioned spectral deferred correction (SDC) method for solving multiphysics problems using a sequence of pre-existing single-physics solvers. This method extends the work in [1], [2], which used implicit-explicit Runge-Kutta methods (IMEX) to build high-order, partitioned multiphysics solvers. We consider a generic multiphysics problem modeled as a system of coupled ordinary differential equations (ODEs), coupled through coupling terms that can depend on the state of each subsystem; therefore the method applies to both a semi-discretized system of partial differential equations (PDEs) or problems naturally modeled as coupled systems of ODEs. The sufficient conditions to build arbitrarily high-order partitioned SDC schemes are derived. Based on these conditions, various of partitioned SDC schemes are designed. The stability of the first-order partitioned SDC scheme is analyzed in detail on a coupled, linear model problem. We show that the scheme is conditionally stable, and under conditions on the coupling strength, the scheme can be unconditionally stable. We demonstrate the performance of the proposed partitioned solvers on several classes of multiphysics problems with moderate coupling strength. They include a stiff linear system of ODEs, advection-diffusion-reaction systems, and fluid-structure interaction problems with both incompressible and compressible flows, where we verify the design order of the SDC schemes and study various stability properties. We also directly compare the accuracy, stability, and cost of the proposed partitioned SDC solver with the partitioned IMEX method in [1], [2] on this suite of test problems. The results suggest that the high-order partitioned SDC solvers are more robust than the partitioned IMEX solvers for the numerical examples considered in this work, while the IMEX methods require fewer implicit solves.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Spatial top-down proteomics for the functional characterization of human kidney

Background: The Human Proteome Project has credibly detected nearly 93% of the roughly 20,000 proteins which are predicted by the human genome. However, the proteome is enigmatic, where alterations in amino acid sequences from polymorphisms and alternative splicing, errors in translation, and post-translational modifications result in a proteome depth estimated at several million unique proteoforms. Recently mass spectrometry has been demonstrated in several landmark efforts mapping the human proteoform landscape in bulk analyses. Herein, we developed an integrated workflow for characterizing proteoforms from human tissue in a spatially resolved manner by coupling laser capture microdissection, nanoliter-scale sample preparation, and mass spectrometry imaging. Results: Using healthy human kidney sections as the case study, we focused our analyses on the major functional tissue units including glomeruli, tubules, and medullary rays. After laser capture microdissection, these isolated functional tissue units were processed with microPOTS (microdroplet processing in one-pot for trace samples) for sensitive top-down proteomics measurement. This provided a quantitative database of 616 proteoforms that was further leveraged as a library for mass spectrometry imaging with near-cellular spatial resolution over the entire section. Notably, several mitochondrial proteoforms were found to be differentially abundant between glomeruli and convoluted tubules, and further spatial contextualization was provided by mass spectrometry imaging confirming unique differences identified by microPOTS, and further expanding the field-of-view for unique distributions such as enhanced abundance of a truncated form (1-74) of ubiquitin within cortical regions. Conclusions: We developed an integrated workflow to directly identify proteoforms and reveal their spatial distributions. Where of the 20 differentially abundant proteoforms identified as discriminate between tubules and glomeruli by microPOTS, the vast majority of tubular proteoforms were of mitochondrial origin (8 of 10) where discriminate proteoforms in glomeruli were primarily hemoglobin subunits (9 of 10). These trends were also identified within ion images demonstrating spatially resolved characterization of proteoforms that has the potential to reshape discovery-based proteomics because the proteoforms are the ultimate effector of cellular functions. Applications of this technology have the potential to unravel etiology and pathophysiology of disease states, informing on biologically active proteoforms, which remodel the proteomic landscape in chronic and acute disorders.

59 BASIC BIOLOGICAL SCIENCES↗