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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Implications of Safety and Operational Features of Small, Advanced Reactors for the Evaluation of Important Human Actions

The design and operational characteristics of non-light water reactors are likely to change the role of human actions in safety function management and the types of human actions that are deemed important. The objectives of this report are to: • Identify the implications of small, advanced reactor design characteristics on human performance and the changing role of human actions in the management of safety functions. • Identify the methods that may be used to identify important human actions. • Identify how HFE safety reviewers can help ensure that the methods adequately model human actions to identify those that are important to safety. We identified the implications of small, advanced reactor characteristics on the role of personnel in safety function management. Then we addressed how designers can identify which human actions are important to safety using both probabilistic risk assessment (PRA) and deterministic analyses. PRA identifies important human actions using risk-importance criteria. Deterministically identified important human actions include those identified by analyses of situations such as transients and accidents and defense in depth. In all cases, the acceptability of the analyses is dependent on the modeling, quantification, and criterion selection to determine which human actions are important. How well the designers address these processes determines the acceptability of their methodology.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Detecting Important Drivers of Gridded Population Modeling With Machine Learning

High-resolution population datasets have been lever-aged across a broad swath of domains, such as climate change, public policy, humanitarian aid, and rescue operations, among others. Machine learning methods were adopted to generate high-resolution or gridded population estimates by using various geospatial input features such as buildings, roads, and nighttime lights. In this study, we evaluate the importance of population features using Random Forest models across three levels of analysis, utilizing permutation measures. Our research aims to address key questions to enhance our understanding of high-resolution population modeling, such as: Are certain features globally (10 countries collectively) more important than others? Do optimal features vary by country? Within each country, do feature importance differ across administrative units? What similarities exist in feature importance at the global, country, and administrative unit levels? To answer these questions, we leverage the Kneedle algorithm to automate the selection of optimum features. We find that there are patterns displayed by features across spatial boundaries, evidenced by the same feature being the most important indicator of population across 7 of the 10 countries modeled. Our findings indicate that while important features may vary across geographies, certain features consistently hold greater importance than others agnostic of geography.

Lebakula, Viswadeep [ORNL] (ORCID:0000000152935914↗
DOE OSTI2025

Emissions leakage and economic losses may undermine deforestation-linked oil crop import restrictions

Import restrictions on deforestation-linked commodities are being considered strategies to reduce global deforestation and emissions. However, limited market share of importers imposing such restrictions and the potential for emissions leakage could reduce their effectiveness. Moreover, they could result in negative economic implications for producers and consumers. We quantify future emissions and economic implications of oil palm and soybean import restrictions. Current EU restrictions will likely have minimal impact due to the EU’s otherwise small and diminishing share of global palm and soy demand. If extended beyond the EU, such import restrictions could drive reductions in cumulative LUC emissions by 2050 in key oil crop exporting regions— up to 0.9% in Indonesia, 1.5% in the rest of Southeast Asia, 3.8% in Argentina and 6.7% in Brazil, relative to a scenario with no import restrictions. However, these key exporters could also face losses ranging $\$$4.1-$\$$61 billion in cumulative agricultural production revenue by 2050.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Identification of Important Phenomena for Light Water Reactors During Heat Transport System Failure Events in Integrated Energy Systems

This work adapts historical literature and existing phenomena identification and ranking tables (PIRT) to be applicable to a novel nuclear power plant (NPP) and chemical or thermal process integrated energy system (IES), particularly focusing on the process heat and heat transport system failure events that are not a concern during normal NPP operation but become vital when an IES is considered. Nuclear energy has been suggested to go beyond base-load applications and be used for hydrogen co-generation systems, amongst other IESs. Prior to the implementation of nuclear IESs, sufficient analysis must be performed on accident events to ensure public safety. The events considered were deemed important because of their potential to damage systems, structures, and components (SSCs). Process thermal events of concern include loss of heat load and temperature transient events. Loss of heat load events were characterized as having high importance and being well understood. Temperature transient events may be further categorized by the cyclic loading and harmonics phenomena. Cyclic loading issues were classified as medium to high importance with knowledge gaps existing regarding fatigue and low power operation, while harmonics phenomena were classified as low importance and are well understood. Heat transport system failure events of concern include intermediate and process heat exchanger failures, mass addition to reactor coolant, ingress of material from thermal manifold/energy storage, and loss of intermediate fluid. Furthermore, these events tended to be of high or medium importance, with some knowledge gaps needing to be filled for individual reactor systems due to unique designs.

Integrated Energy System (IES)↗
DOE OSTI2025

Using feature importance as an exploratory data analysis tool on Earth system models

Abstract. Machine learning (ML) models are commonly used to generate predictions, but these models can also support the discovery of new science. Generating accurate predictions necessitates that a model captures the structure of the underlying data. If the structure is properly extracted, ML could be a useful exploratory and evidential tool. In this paper, we present a case study that demonstrates the use of ML for exploratory data analysis (EDA) in the climate space. We apply the ML explainability method of spatiotemporal zeroed feature importance (stZFI) to understand how climate-variable associations evolve over space and time. Our analyses focus on data from ensembles of Earth system models (ESMs) which provide data on different climate states and conditions. We elect to work with ESM ensembles since they allow us to compare feature importance across alternative scenarios not available with observed data. The ensembles also account for natural variability so that we can distinguish between signal and noise due to natural climate variability when computing feature importance. The use of perturbed initial condition ensembles introduces variability mimicking the natural variability in the atmosphere; thus the signals emerging using feature importance (FI) can be evaluated against the natural variability in the climate system. For our analyses, we consider the 1991 volcanic eruption of Mount Pinatubo, which was a large stratospheric aerosol injection. We explore the climate pathway associated with the eruption from aerosols to radiation to temperature at both the near-surface and stratospheric levels. In addition to applying the method to data generated from two different ESMs, we apply stZFI to reanalysis data to compare the associations identified by stZFI. We show how stZFI tracks the importance of aerosol optical depth over time on forecasting temperatures. This case study illustrates usefulness of an ML tool (stZFI) for EDA on a well-studied climate exemplar.

Ries, Daniel (ORCID:0000000250294647)↗
DOE OSTI2025

Global Simulations Suggest Biomass Burning Aerosol Emissions From Grassland Fires Could Be Important Ice Nucleating Particles

Ice nucleating particles (INP) capable of nucleating ice crystals via immersion freezing at temperatures above approximately −35°C may strongly influence cloud glaciation, with implications for global precipitation and climate feedback. In addition to mineral dust, soil dust, and marine organics, laboratory and field measurements suggest biomass burning aerosols (BBA) can act as immersion-mode INP between around −30°C and −15°C. However, the contribution of BBA to the global INP budget remains poorly understood due to poor knowledge of which fuels yield INPs, uncertainties in global coverage of those fuels, and unknown size distributions of the INPs in the BBA. Nonetheless, with some understanding of these uncertainties from sensitivity studies, the relative importance of ice nucleation activity of BBA compared to other INP sources can be quantified. In this work, we investigate the potential global importance of BBA as INP using a global aerosol-climate model, specifically the UK Met Office Unified Model (UM). We evaluate the model using field campaign data sets. We examine potential uncertainties in fuel types and particle sizes on BBA-based INP concentrations. Averaged over June–September between 15°S and 50°S, BBA is a more important INP than dust and marine INP about 30% of the time at altitudes with temperatures between −30°C and −20°C. Our simulations therefore suggest BBA INPs may be at least as important as mineral dust and marine INP over the atmospheric regions and seasons where grassland fires are frequent.

Gohil, Kanishk [Carnegie Mellon University, Pittsb↗
DOE OSTI2025

Spatial Replication Is Important for Developing Landscape Genetic Inferences for a Wetland Salamander

Habitat fragmentation is a pressing threat to wildlife populations, and maintenance of gene flow between populations is an essential goal of conservation. Resistance surfaces have emerged as an important tool for modelling connectivity and developing management strategies to mitigate effects of habitat fragmentation. However, recent studies have noted inconsistencies in the factors most strongly associated with connectivity across different landscapes. Thus, replication of genetic-based resistance surface optimisation across landscapes may be necessary for making robust conclusions about the influence of environmental variables. Accordingly, replication represents a substantive challenge and opportunity in the field of landscape genetics. In this study, we conducted replicated landscape genetic analyses across five landscapes in Tennessee and Kentucky for a threatened wetland amphibian, the four-toed salamander (Hemidactylium scutatum). We tested multiple hypotheses of how different landscape features that could directly affect small, desiccation-intolerant amphibians (e.g., canopy cover) influenced gene flow and assessed the appropriate scale at which to model different features. We found some concordance in the landscape features that influenced gene flow (e.g., a common importance of forest cover and topography), but also some differences—potentially owing to the difference in variability of predictors across landscapes. We also found discordance in the scale of effect of different features across landscapes. In conclusion, our work emphasises that flat areas of moist forest not bisected by roads may be important for H. scutatum conservation, and our replicated design allows us to identify relationships that would have been missed if only using one study site.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Review of Technical Photovoltaic Key Performance Indicators and the Importance of Data Quality Routines

Technical key performance indicators (KPIs) are important metrics used to assess and quantitatively summarize various aspects of photovoltaic (PV) systems, including long-term performance, economic viability, and carbon footprint. Herein, a group of experts of the International Energy Agency's Photovoltaic Power Systems Programme Task 13 collect and describ the most important technical KPIs used in the industry. Thereby, a set of best practices for reliably handling PV system data is presented and the impact of data quality and climatic variability on KPI calculation is investigated. Further, the effective use of technical KPIs allows triggering data-driven and informed decisions to optimize PV systems and providing a comprehensive overview of how PV systems operate across different conditions and climates. With the worldwide growth of the PV industry, more companies operate/own PV systems in different regions, where the climatic and seasonal profiles differ. This requires context-aware evaluation of KPIs, or the judicious application of multiple KPIs, to ensure that each asset is evaluated correctly. Beyond that, there is untapped potential in the utilization of KPIs through geospatial mapping and extrapolation of fleet KPIs. This study demonstrates that the uncertainty in KPI estimation is not well understood and depends on data quality, climatic variability, and system configuration.

14 SOLAR ENERGY↗
DOE OSTI2024

On the importance of species immiscibility in mixing-layer dynamics at supercritical pressures

The mixing dynamics of injected propellants is a key factor in determining the ignition performance and combustion-instability response of rocket engines, internal combustion engines, and gas turbines. A key source of uncertainty in the prediction of phase-exchange dynamics is the immiscibility of the injected propellants at supercritical pressure conditions, which induces liquid/vapor phase separation and surface-tension dynamics. While experimental observations indicate the presence of liquid/vapor interfacial structures, this interfacial dynamics is typically neglected in numerical analyses. To address this issue, the objective of the present study is to systematically evaluate the importance of species immiscibility on the phase-exchange dynamics of cryogenic LOX/GH2 mixing layers at typical rocket engine injection conditions. This is accomplished by comparing simulations of (i) a recently developed interface-capturing Regularized-Interface Method (RIM) formulation, and (ii) the commonly employed Diffuse-Interface Method (DIM) formulation. Analysis shows that the interfacial dynamics significantly impact the atomization and mixing of the propellants in the near-injector region, which is not captured by the DIM formulation. The findings of this study extend to other immiscible injection systems, such as LOX/kerosene and hydrocarbon-fuel/air, thereby highlighting the importance of resolving species immiscibility in simulating high-pressure combustion engines.

diffused-interface method↗
DOE OSTI2026

Advancing Porous Carbons: Understanding the Importance of Surface Chemistry for the Energy–Environment Nexus

This review intends, in a critical way, the comprehensive view of the importance of porous carbons surface chemistry for their applications in an energy− environment nexus. Surface chemistry is presented as a combination of functional heteroatom-containing groups, dopants, and structural defects. First, we briefly address carbon surface chemical environment and the methods of its modification and characterization, indicating their practical limitations. Then, the effects of surface chemistry on separation, catalysis, energy storage, sensing and microwave absorption are introduced. Besides a critical analysis of published findings on these topics, we also include our views on the advancement in the processes which rely on porous carbons surface chemistry, and identify strategic areas and directions that should deserve further attention. We focus on new findings and important original contributions to the field. Since the community of carbon researchers grows following the strategic application of these materials, the role of functional groups, dopants and structural defects in various cutting-edge applications is emphasized, showing the progress in the field and the evolution of findings. A clear determination of the effects of carbon surface is often a challenge since carbons porosity and the locations of specific bonds/sites/ defects in the carbon texture provide nanoconfinement effects.

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

Importance of Electron Correlation on the Geometry and Electronic Structure of [2Fe–2S] Systems: A Benchmark Study of the [Fe 2 S 2 (SCH 3 ) 4 ] 2–,3–,4– , [Fe 2 S 2 (SCys) 4 ] 2– , [Fe 2 S 2 (S- p -tol) 4 ] 2– , and [Fe 2 S 2 (S- o -xyl) 4 ] 2– Complexes

Iron–sulfur clusters are crucial for biological electron transport and catalysis. Obtaining accurate geometries, energetics, manifolds of their excited electronic states, and reduction energies is important to understand their role in these processes. Here, using a [2Fe–2S] model complex with Fe II and Fe III oxidation states, which leads to different charges, i.e., [Fe 2 S 2 (SMe) 4 ] 2–,3–,4– , we benchmarked a variety of computational methodologies ranging from density functional theory (DFT) to post-Hartree–Fock methods, including complete active space self-consistent field (CASSCF), multireference configuration interaction, the second-order N-electron valence state perturbation theory (NEVPT2), and the linearized integrand approximation of adiabatic connection (AC0) approaches. Additionally, we studied three experimentally well-characterized complexes, [Fe 2 S 2 (SCys) 4 ] 2– , [Fe 2 S 2 (S-o-tol) 4 ] 2– , and [Fe 2 S 2 (S-o-xyl) 4 ] 2– , via DFT methods. We conclude that the dynamic electron correlation is important for accurately predicting the geometry of these complexes. Broken symmetry (BS) DFT correctly predicts experimental geometries of low-spin multiplicity, while CASSCF does not. However, BS-DFT significantly overestimates the difference between the low- and high-spin electronic states for a given oxidation state. At the same time, CASSCF underestimates it but provides relative energies closer to the reference NEVPT2 results. Finally, AC0 provides energetics of NEVPT2 quality with the additional advantage of being able to use large CASSCF sizes. NEVPT2 gives the best estimates of the Fe III /Fe III → Fe II /Fe III (4.27 eV) and Fe II /F III → Fe II /F II (7.72 eV) reduction energies. The results provide insight into the electronic structure of these complexes and assist in the understanding of their physical properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Importance Sampling Model-Based Diffusion for Trajectory Optimization

Trajectory optimization for robotic systems remains a challenging problem. This is especially true for robotic systems featuring nonlinear dynamics and many degrees of freedom. Data-based or model-free diffusion has recently been popularized in the fields of artificial intelligence and trajectory optimization. Model-Based Diffusion provides a data-free method of trajectory optimization, trained at runtime on a system dynamics model, suitable for high-dimensional models. This paper examines how importance sampling can enhance the performance of Model-Based Diffusion for trajectory optimization. Here, we quantify the benefits of importance sampling across three long horizon planning tasks. These results show as much as a 13x improvement in sample efficiency depending on environment and optimization parameters.

Golembeski, Seth [Georgia Institute of Technology,↗
DOE OSTI2026

Make the Fastest Faster: Importance Mask Synthesis for Interactive Volume Visualization using Reconstruction Neural Networks

Visualizing a large-scale volumetric dataset with high resolution is challenging due to the substantial computational time and space complexity. Recent deep learning-based image inpainting methods significantly improve rendering latency by reconstructing a high-resolution image for visualization in constant time on GPU from a partially rendered image where only a portion of pixels go through the expensive rendering pipeline. However, existing solutions need to render every pixel of either a predefined regular sampling pattern or an irregular sample pattern predicted from a low-resolution image rendering. Both methods require a significant amount of expensive pixel-level rendering. In this work, we provide Importance Mask Learning (IML) and Synthesis (IMS) networks, which are the first attempts to directly synthesize important regions of the regular sampling pattern from the user’s view parameters, to further minimize the number of pixels to render by jointly considering the dataset, user behavior, and the downstream reconstruction neural network. Our solution is a unified framework to handle various types of inpainting methods through the proposed differentiable compaction/decompaction layers. Experiments show our method can further improve the overall rendering latency of state-of-the-art volume visualization methods using reconstruction neural network for free when rendering scientific volumetric datasets. Our method can also directly optimize the off-the-shelf pre-trained reconstruction neural networks without elongated retraining.

Large-scale data↗
DOE OSTI2025

Storms Are an Important Driver of Change in Tropical Forests

Tropical forest dynamics and composition have changed over recent decades, but the proximate drivers of these changes remain unclear. Investigations into these trends have focused on increasing drought stress, CO 2 , temperature, and fires, whereas convective storms are generally overlooked. We argue that existing literature provides clear support for the importance of storms as drivers of forest change. We reanalyze the largest plot-based study of tropical forest carbon dynamics to show that lightning frequency—an indicator of storm activity—strongly predicts forest carbon storage and residence time, and its inclusion improves model fit and weakens evidence for the effects of high temperatures. Convective storm activity has increased 5%–25% per decade over the past half century. Extrapolating from historic trends, we estimate that storms likely contribute ca. 50% of the reported increases in biomass mortality across Amazonia, with all realistic combinations of assumptions indicating a possible range of 12%–118%. Spatial variation in storm activity shows weak relationships with drought, demonstrating that forests can experience high drought stress, high storm activity, or both. Accordingly, we hypothesise that convective storms are among the most important drivers of tropical forest change, and as such, they require significant research investment to avoid misguiding science, policy, and management.

biomass carbon↗
DOE OSTI2024

Estimating the Importance of Viral Contributions to Soil Carbon Dynamics

ABSTRACT Biogeochemical models for predicting carbon dynamics increasingly include microbial processes, reflecting the importance of microorganisms in regulating the movement of carbon between soils and the atmosphere. Soil viruses can redirect carbon among various chemical pools, indicating a need for quantification and development soil carbon models that explicitly represent viral dynamics. In this opinion, we derive a global estimate of carbon potentially released from microbial biomass by viral infections in soils and synthesize a quantitative soil carbon budget from existing literature that explicitly includes viral impacts. We then adapt known mechanisms by which viruses influence carbon cycles in marine ecosystems into a soil‐explicit framework. Finally, we explore the diversity of virus–host interactions during infection and conceptualize how infection mode may impact soil carbon fate. Our synthesis highlights key knowledge gaps hindering the incorporation of viruses into soil carbon cycling research and generates specific hypotheses to test in the pursuit of better quantifying microbial dynamics that explain ecosystem‐scale carbon fluxes. The importance of identifying critical drivers behind soil carbon dynamics, including these elusive but likely pervasive viral mechanisms of carbon redistribution, becomes more pressing with climate change.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

The Relative Importance of Forced and Unforced Temperature Patterns in Driving the Time Variation of Low-Cloud Feedback

Abstract Atmospheric models forced with observed sea surface temperatures (SSTs) suggest a trend toward a more-stabilizing cloud feedback in recent decades, partly due to the surface cooling trend in the eastern Pacific (EP) and the warming trend in the western Pacific (WP). Here, we show model evidence that the low-cloud feedback has contributions from both forced and unforced feedback components and that its time variation arises in large part through changes in the relative importance of the two over time, rather than through variations in forced or unforced feedbacks themselves. Initial-condition large ensembles (LEs) suggest that the SST patterns are dominated by unforced variations for 30-yr windows ending prior to the 1980s. In general, unforced SSTs are representative of an ENSO-like pattern, which corresponds to weak low-level stability in the tropics and less-stabilizing low-cloud feedback. Since the 1980s, the forced signals have become stronger, outweighing the unforced signals for the 30-yr windows ending after the 2010s. Forced SSTs are characterized by relatively uniform warming with an enhancement in the WP, corresponding to a more-stabilizing low-cloud feedback in most cases. The time-evolving SST pattern due to this increasing importance of forced signals is the dominant contributor to the recent stabilizing shift of low-cloud feedback in the LEs. Using single-forcing LEs, we further find that if only greenhouse gases evolve with time, the transition to the domination of forced signals occurs 10–20 years earlier compared to the LEs with full forcings, which can be understood through the compensating effect between aerosols and greenhouse gases.

58 GEOSCIENCES↗
DOE OSTI2025

Seed coat transcriptomic profiling of 5-593, a genotype important for genetic studies of seed coat color and patterning in common bean ( Phaseolus vulgaris L.)

Common bean (Phaseolus vulgaris L.) market classes have distinct seed coat colors, which are directly related to the diverse flavonoids found in the mature seed coat. To understand and elucidate the molecular mechanisms underlying the regulation of seed coat color, RNA-Seq data was collected from the black bean 5-593 and used for a differential gene expression and enrichment analysis from four different seed coat color development stages. 5-593 carries dominant alleles for 10 of the 11 major genes that control seed coat color and expression and has historically been used to develop introgression lines used for seed coat genetic analysis. Pairwise comparison among the four stages identified 6,294 differentially expressed genes (DEGs) varying from 508 to 5,780 DEGs depending on the compared stages. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed that phenylpropanoid biosynthesis, flavonoid biosynthesis, and plant hormone signal transduction comprised the principal pathways expressed during bean seed coat pigment development. Transcriptome analysis suggested that most structural genes for flavonoid biosynthesis and some potential regulatory genes were significantly differentially expressed. Further studies detected 29 DEGs as important candidate genes governing the key enzymatic flavonoid biosynthetic pathways for common bean seed coat color development. Additionally, four gene models, Pv5-593.02G016100, 593.02G078700, Pv5-593.02G090900, and Pv5-593.06G121300, encode MYB-like transcription factor family protein were identified as strong candidate regulatory genes in anthocyanin biosynthesis which could regulate the expression levels of some important structural genes in flavonoid biosynthesis pathway. These findings provide a framework to draw new insights into the molecular networks underlying common bean seed coat pigment development.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Resonance Self-Shielding: Why it is so Important

This paper is one of a series that I am writing to document my 58 years of experience with ENDF and Neutron Transport calculations, beginning when I worked at the National Nuclear Data Center (NNDC), Brookhaven National Laboratory (BNL), from 1967 to 1972. During those years I was the head of the computer unit of NNDC, assigned to develop computer codes to pre-process, view and test ENDF/B data. Since then, I have continued to support the ENDF effort without any official position or monetary compensation, because I realized how important accurate nuclear data is for use in use in our Engineering applications. It is so important to realize that regardless of how accurate or even perfect our application codes may be to transport particles, without accurate nuclear data we are in a “Garbage In = Garbage Out” situation.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗