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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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33 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

AGR-5/6/7 Thermal Model with Non-uniform Gas Gaps

Fuel compact temperatures are a crucial factor in assessing the irradiation performance of tri-structural isotropic fuel particles. In the absence of direct measurement, fuel compact temperatures were calculated using a three-dimensional finite element thermal model, which is subject to simulation uncertainty. The most dominant factor in the uncertainty of calculated fuel temperatures is the gas gap uncertainty due to the nub-to-shell clearance caused by a design error of AGR-5/6/7 capsules. The thermal model was revised to examine the most probable graphite offset position for six different days during the irradiation for Capsules 1 and 2. The analysis varied the offset distance and azimuthal direction at both the top and bottom of the holder. The best-fit offset was estimated based on the minimum root mean square error of the residuals (measured minus calculated) for the operational thermocouples (TCs). From these results, the following conclusions were made: (1) The holder offsets led to slightly lower average temperatures but wider temperature variations (lower minimum and higher peak fuel temperatures) for both Capsule 1 and Capsule 2. (2) During earlier cycles (162A–164B), when numerous TCs were still operational, the best-fit offset distance varied over a specific range for both the top and bottom ([0.002–0.0035 in.] for Capsule 1 and [0.003-0.004 in] for Capsule 2). In contrast, the offset azimuthal direction varied widely, especially for the offset at the bottom of the Capsule 1 holder. This is because holder movement was somewhat constrained at the top of Capsule 1 by the TC leads running through the capsule head and into the holder and by the through tubes in Capsule 2, but the Capsule 1 bottom did not have this type of constraint. (3) During later cycles, when all TCs failed, applying the maximum possible offset of 0.006 in. to the northwest direction for both the top and bottom resulted in a calculated peak fuel temperature of 1557? in Capsule 1 (i.e., a 135? increase from 1422? with zero offset on September 20, 2019 (166A)); the maximum offset of 0.0068 in. to the south for both top and bottom resulted in a calculated peak fuel temperature of 1110°C in Capsule 2 on April 20, 2020 (i.e., a 116? increase from 994? with zero offset (168A)). High peak fuel temperatures in Capsule 1 during Cycle 166A could be the cause of massive particle failure near the end of this cycle. (4) Even though the highest temperature at the tip of Type-N TCs, such as TC-1-7, slightly exceeded 1000?, the temperature along the TC wire reached as high as 1335? assuming an offset of 0.006 in. in the northwest of Capsule 1 holder near the end of Cycle 166A. This temperature significantly exceeds the temperature threshold at which TC degradation is expected to occur, ultimately contributing to considerable particle failures in Capsule 1. For eight Type-N TCs in Capsule 2, the peak TC line temperature was much lower (i.e., 1029°C for TC-2-5), assuming maximum offset of 00068 in. to the south during cycle 168A.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Modular Processing of Flare Gas for Carbon Nanoproducts

This project demonstrated the technical viability and economic promise of a modular system for converting flared natural gas into valuable carbon nanoproducts (CNPs) through catalytic chemical vapor deposition (CVD). All major project milestones were successfully completed, including reactor design and commissioning, catalyst development, process optimization, technoeconomic analysis, and application testing in concrete systems. The overarching goal was to create a scalable, field-deployable process that valorizes stranded methane by producing high-value carbon materials for use in cementitious composites. At the lab scale, the team designed and built a fluidized bed reactor optimized for use with silica fume-supported nickel catalysts synthesized via atomic layer deposition (ALD). A statistically designed sintering study enabled precise tuning of nickel nanoparticle size, identifying the influence of oxygen partial pressure, time, and temperature on catalyst morphology and performance. These insights allowed the team to target catalyst conditions that maximize carbon nanofilament growth. Subsequent CVD experiments achieved up to 31.8 wt% carbon deposition under optimized conditions, with TEM confirming the presence of nanofilament structures and sustained hydrogen evolution during reaction. Reactor upgrades and empirical fluidization studies supported the development of reliable, repeatable experimental protocols. The modular pilot-scale skid reactor was fully constructed, instrumented, and commissioned. Capable of operating at 675–800°C and pressures up to 290 psig, the system was designed for continuous operation at a carbon production rate of 1 kg/hr. Initial demonstration runs confirmed solids handling, thermal control, and system leak-tightness, although a critical reactor component (the downfeed tube) was inadvertently omitted during final assembly. This omission limited gas–solid contact and prevented meaningful carbon deposition during pilot-scale CVD runs. Nonetheless, the system operated safely under design conditions, and the root cause of performance limitations was clearly identified. Complementary work on UHPC formulations demonstrated that small additions of carbon nanoproducts, including those derived from flare gas, can significantly enhance mechanical performance while preserving workability. A comprehensive study of CNF dispersion techniques and mix design optimization led to a clear protocol for integrating these nanomaterials into concrete. Incorporation of CNPs improved flexural toughness and reduced porosity, supporting their use in high-performance infrastructure applications. A technoeconomic analysis (TEA) confirmed that this process can produce CNP-loaded catalyst material at a levelized cost below $\$$7/kg across a range of catalyst loadings and reaction yields. With estimated market values for the carbon composite product ranging from $\$$14 to over $\$$60/kg, and the ability to blend CNPs into concrete at sub-percent levels with less than 10% added cost, the system presents a compelling economic case. While additional engineering work is needed to optimize fluidization and heat transfer at scale, this project establishes a strong foundation for commercial development. The process is not only technically sound but also economically promising, representing a viable pathway for flare gas mitigation through modular carbon nanomaterial production.

03 NATURAL GAS↗
DOE OSTI2025

Reconstruction of current profiles from external magnetic and MSE measurements on DIII-D

This work introduces a new current model into the EFIT code for plasma current profile reconstruction. Using this model, edge-localized current density profiles are reconstructed solely from external magnetic measurements. Quantitative comparisons show that edge current profiles obtained with the new current model align closely with those from kinetic equilibrium reconstruction. The new model enables current density reconstruction with fewer diagnostics than kinetic equilibrium reconstruction (Lao et al 1990 Nucl. Fusion 30 1035–49), demonstrating that edge-localized current density information can be obtained from external magnetic measurements only, without the help of kinetic profiles and internal diagnostics, and without using the bootstrap current model. Furthermore, with the constraints of external magnetic and MSE measurements, current profiles in full radius obtained from the new model are in good agreement with the results of kinetic equilibrium reconstruction, which not only improves the efficiency of current reconstruction but also helps avoid possible errors caused by different kinds of diagnoses. Possible applications are also discussed, and a brief analysis of the evolution of the full-radius current density is performed. Noticeable decreases in both the magnitude and gradient of the edge current around the time of ELM bursts are observed. Moreover, analysis of the full radius current density profiles ( ρ N = 0 − 1.0, ρN denotes to square root of the normalized toroidal flux) reveals that as the edge current changes, the inner current also changes following a sinusoidal-like pattern at almost all time slices during four type-I ELMs in discharge 161493. The current evolution in the inner region ( ρ N = 0.2 − 0.6) is synchronized with the boundary region ( ρ N = 0.89 − 1.0), while the intermediate region ( ρ N = 0.6 − 0.89) shows opposite behavior. This sinusoidal-like variations in full-radius current density profile indicates strong core-pedestal coupling, suggesting ELMs significantly affect core plasmas, though current changes manifest differently across radial regions.

EFIT↗
DOE OSTI2025

All-Atom Modeling and Simulation of Biopolymer Interface: Dual Role of Antifouling Polymer Brushes

Antifouling polymer brushes are well-known for their exceptional resistance to unwanted protein adsorption. While experimental studies have extensively characterized protein–polymer brush interactions, computational investigations remain limited, largely due to the challenges in accurately modeling and integrating these complex, multicomponent systems without resorting to oversimplification. To address this challenge, this study presents one of the most comprehensive and realistic model systems to date, comprising the substrate, grafted polymers, and proteins. Our work interprets the interactions between polymer brush and protein based on realistic modeling without simplification. In particular, this study utilizes molecular modeling and simulation of polycationic and polyzwitterionic brushes─poly(dimethylaminoethyl methacrylate) (PDMAEMA), poly(2-(N-oxide-N,N-dimethylamino)ethyl methacrylate) (PNOMA), and poly(2-(N-3-sulfopropyl-N,N-dimethylammonium)ethyl methacrylate) (PSBMA)─grafted onto α-quartz substrates via polymerization initiator linkers. The brush models were developed to closely replicate experimentally synthesized samples and to provide detailed insights into structural and dynamical changes at the molecular level during protein adsorption. Using steered molecular dynamics simulations, we show that the PSBMA brush, due to its high local density, exhibits the greatest resistance to protein insertion. Cα root-mean-square deviation and interaction pattern analyses further reveal that the PSBMA brush also induces the most significant destabilization of lysozyme, while the PDMAEMA brush enhances protein stability through ion-mediated interactions. The PNOMA brush, while requiring the lowest force for protein adsorption, induces greater protein destabilization than the PDMAEMA brush, primarily due to electrostatic repulsion caused by a short carbon spacer length. Hydration analysis reveals that both the PSBMA brush and the lysozyme interacting with it exhibit the most rapid dehydration, attributed to the brush’s high local chain density, which results in the greatest lysozyme destabilization and the highest adsorption force. These findings highlight the dual role of antifouling polymer brushes: resisting protein adsorption and modulating protein structural dynamics. In conclusion, this study provides valuable insights for the rational design of next-generation antifouling materials and offers a framework for realistic model development in complex multicomponent systems.

Adsorption↗
DOE OSTI2025

Stabilize High-IBR Power Systems with Grid-Forming Inverters

The increasing share of IBRs challenges the stable operation of our power systems. Leveraging an oscillation event in Kaua`i Island as an example, we showcase the root cause of this event and highlight the potential of grid-forming inverters to reduce the stability risks in high-IBR power systems.

grid-forming inverters↗
DOE OSTI2024

Wideband Impedance Passivation of MMCs for Suppressing Harmonic Oscillations

Harmonic instability and associated oscillation events have become one of the main concerns in MMC-based HVdc systems. These oscillations can appear in the range from a few hundred hertz to several kilohertz, and the root cause is identified as delay-induced negative damping of MMC impedance appearing at the system resonance frequency. Here, this paper introduces a wideband impedance reshaping method through MMC control to eliminate such negative damping for both grid-following (GFL) and grid-forming (GFM) MMCs, which makes the MMC impedance completely passive from the second harmonic frequency upward, thus preventing all harmonic oscillations. First, simplified impedance models of MMCs are derived for the harmonic stability analysis and the impedance reshaping control design. Next, a passivity-based impedance reshaping method is presented, as well as practical considerations for its implementation. In addition, to maintain the MMC's disturbance ride-through capability, an adaptive activation scheme is developed, which enables the wideband impedance reshaping control only in the presence of harmonic oscillation events. The effectiveness of the proposed method is validated by frequency domain analysis and by electromagnetic transient (EMT) simulations of two typical MMC-based power systems.

17 WIND ENERGY↗
DOE OSTI2025

Hydraulic constraints to stomatal conductance in flooded trees

Stomatal closure is a pervasive response among trees exposed to flooded soil. We tested whether this response is caused by reduced hydraulic conductance in the soil-to-leaf hydraulic continuum (k total ), and particularly by reduced root hydraulic conductance (k root ), which has been widely hypothesized. We tracked stomatal conductance at the leaf level (g s ) and canopy scale (G s ) along with physiological conditions in two temperate tree species, Magnolia grandiflora and Quercus virginiana, that were subjected to flood and control conditions in a greenhouse experiment. Flooding reduced g s , G s , k root and k total . Path analysis showed strong support for direct effects of k total on g s and for flood duration on k total , but not k root on k total . A process-based model that accounted for the k total reduction predicted the timeseries of G s in flood and control treatment trees reasonably well (predicted versus observed G s R 2 = 0.80 and 0.51 for M. grandiflora and Q. virginiana, respectively). However, accounting only for k root reduction in flooded trees was insufficient for predicting observed G s reduction. Together, these results suggest that hydraulic constraints were not limited to roots and highlight the need to account for flooding effects on k total when projecting forest ecosystem function using process-based models.

Plant stress↗
DOE OSTI2024

Process–Property–Performance Mapping of Additively Manufactured 316H Stainless Steel Components

The Advanced Materials and Manufacturing Technologies Program is focused on accelerating the development of advanced materials and components fabricated via additive manufacturing, and is using laser powder bed fusion (LPBF) of 316H stainless steel as an initial case study. In the previous fiscal year, miniature high-throughput specimens were printed on multiple LPBF systems to provide initial processing windows to minimize porosity and limit epitaxial grain growth during prints. This fiscal year, scaled builds were completed on three different LPBF systems at ORNL: a GE Concept Laser M2, a Renishaw AM400, and an EOS M290. Builds on the Concept Laser were conducted on multiple powder lots and processing parameter ranges to provide microstructure effects on time-independent and time-dependent mechanical properties. Builds on the Renishaw were produced using Oak Ridge National Laboratory (ORNL)-optimized printing parameters and Argonne National Laboratory (ANL)-optimized printing parameters to compare outcomes of parallel process optimization efforts at different national laboratories on the same LPBF system. Similarly, the build completed on the EOS M290 replicated the processing parameters of builds completed at Los Alamos National Laboratory (LANL). Optical microscopy and electron backscatter diffraction characterization was completed on all builds. In addition to the general round robin characterization, this work-package generated time-independent data, including tensile and fracture toughness test data on scaled Concept Laser builds as a function of processing parameters and post-build heat treatment. This analysis is complimentary to work in parallel work packages aiming to establish heat treatment and processing effects on time-dependent properties. It was found that although the stress-relief heat treatment provides the highest strength at lower-temperatures, tensile strength begins to converge at higher temperatures regardless of heat treatment condition. In addition, the more rigorous solution annealing and hot-isostatic pressing post-build heat treatments result in higher fracture toughness than the stress-relieved condition. The root-causes of the lower fracture toughness of the stress-relieved LPBF 316H material was informed via a stress-relief optimization study on a scaled concept laser print, where it was found that although dislocation recovery was largely complete after only a couple hours at 650°C, the extended hold of the current 24h heat treatment employed on scaled builds likely caused increased carbide volume fractions along the LPBF 316H grain boundaries, thereby deteriorating crack propagation resistance. This trend was seen to become more deleterious with additional increases of stress-relief temperature to 750°C or 850°C. These results have helped inform a new optimal stress-relief annealing condition for LPBF 316H for future campaign testing (650°C for 2h).

36 MATERIALS SCIENCE↗
DOE OSTI2024

Process–Property–Performance Mapping of Additively Manufactured 316H Stainless Steel Components

The Advanced Materials and Manufacturing Technologies Program is focused on accelerating the development of advanced materials and components fabricated via additive manufacturing, and is using laser powder bed fusion (LPBF) of 316H stainless steel as an initial case study. In the previous fiscal year, miniature high-throughput specimens were printed on multiple LPBF systems to provide initial processing windows to minimize porosity and limit epitaxial grain growth during prints. This fiscal year, scaled builds were completed on three different LPBF systems at ORNL: a GE Concept Laser M2, a Renishaw AM400, and an EOS M290. Builds on the Concept Laser were conducted on multiple powder lots and processing parameter ranges to provide microstructure effects on time-independent and time-dependent mechanical properties. Builds on the Renishaw were produced using Oak Ridge National Laboratory (ORNL)-optimized printing parameters and Argonne National Laboratory (ANL)-optimized printing parameters to compare outcomes of parallel process optimization efforts at different national laboratories on the same LPBF system. Similarly, the build completed on the EOS M290 replicated the processing parameters of builds completed at Los Alamos National Laboratory (LANL). Optical microscopy and electron backscatter diffraction characterization was completed on all builds. In addition to the general round robin characterization, this work-package generated time-independent data, including tensile and fracture toughness test data on scaled Concept Laser builds as a function of processing parameters and post-build heat treatment. This analysis is complimentary to work in parallel work packages aiming to establish heat treatment and processing effects on time-dependent properties. It was found that although the stress-relief heat treatment provides the highest strength at lower-temperatures, tensile strength begins to converge at higher temperatures regardless of heat treatment condition. In addition, the more rigorous solution annealing and hot-isostatic pressing post-build heat treatments result in higher fracture toughness than the stress-relieved condition. The root-causes of the lower fracture toughness of the stress-relieved LPBF 316H material was informed via a stress-relief optimization study on a scaled concept laser print, where it was found that although dislocation recovery was largely complete after only a couple hours at 650°C, the extended hold of the current 24h heat treatment employed on scaled builds likely caused increased carbide volume fractions along the LPBF 316H grain boundaries, thereby deteriorating crack propagation resistance. This trend was seen to become more deleterious with additional increases of stress-relief temperature to 750°C or 850°C. These results have helped inform a new optimal stress-relief annealing condition for LPBF 316H for future campaign testing (650°C for 2h).

36 MATERIALS SCIENCE↗
DOE OSTI2025

Quantitative insights for diagnosing performance bottlenecks in lithium–sulfur batteries

Lithium–sulfur (Li–S) batteries hold significant promise for electric vehicles and aviation due to their high energy density and cost-effectiveness. However, understanding the root causes of performance degradation remains a formidable challenge, as the interplay of multiple factors obscures key failure mechanisms. A major limitation has been the inability to quantify soluble sulfur species within practical detection limits accurately and to correlate electrochemical processes with associated physical inventory changes. Here, we introduce the high-performance liquid chromatography-ultraviolet spectroscopy and gas chromatography sequential characterization (HUGS) toolkit, capable of precisely quantifying seven distinct sulfur and polysulfide species at concentrations as low as 40 ppb. HUGS has been successfully applied to practical coin and pouch cells without requiring cell modification. Furthermore, our self-developed software, Dr HUGS, enhanced the data analysis speed by over 30 times, enabling multi-source data integration and delivering comprehensive analysis results within minutes. Using HUGS, we identify significant capacity losses from inactive lithium and sulfur during initial cycles and sulfide-rich solid–electrolyte interphase (SEI) formation on the anode during later cycles. Notably, our findings reveal that soluble polysulfides have minimal contributions to capacity loss, challenging long-standing assumptions. Moreover, HUGS demonstrates that constant-pressure setups in Li–S pouch cells improve compositional uniformity compared to constant-gap configurations. For sulfurized polyacrylonitrile (SPAN) cathodes, unique issues such as non-sulfide SEI formation and lithium pulverization are observed, which can be mitigated through localized high-concentration electrolytes to enhance lithium inventory retention. By enabling precise quantification of critical inventory components, HUGS provides transformative insights into failure mechanisms across various electrolytes and cathode chemistries, guiding rational design strategies for next-generation energy storage systems.

25 ENERGY STORAGE↗
DOE OSTI2025

Disruption of the endogenous indole glucosinolate pathway impacts the Arabidopsis thaliana root exudation profile and rhizobacterial community

Root exudates are composed of primary and secondary metabolites known to modulate the rhizosphere microbiota. Glucosinolates are defense compounds present in the Brassicaceae family capable of deterring pathogens, herbivores and biotic stressors in the phyllosphere. In addition, traces of glucosinolates and their hydrolyzed byproducts have been found in the soil, suggesting that these secondary metabolites could play a role in the modulation and establishment of the rhizosphere microbial community associated with this family. Here, we used Arabidopsis thaliana mutant lines, including the cyp79B2cyp79B3 double mutant line with a disruption in the indole glucosinolate pathway and atr1D, which overexpresses ATR1 and increases glucosinolate production. These lines were analyzed using liquid chromatography-tandem mass spectrometry (LC-MS/MS) and 16S rRNA amplicon sequencing to evaluate how genetic modifications to the indole glucosinolate pathway affects the root exudate profile of Arabidopsis thaliana, and, in turn, impacts the rhizosphere microbial community. Metabolic analysis of root exudates from the wild-type Columbia (Col-0), along with the mutant lines, confirmed that alterations to the indole glucosinolate biosynthetic pathway result in shifts in the root exudate profile of the plant. We observed changes in the relative abundance of exuded metabolites. Moreover, 16S rRNA amplicon sequencing results provided evidence that the rhizobacterial communities associated with the plant lines used were directly impacted in diversity and community composition. Here, this work provides further information on the involvement of secondary metabolites and their role in modulating the rhizobacterial community. Root metabolites dictate the presence of different bacterial species, including plant growth-promoting rhizobacteria (PGPR). Our results suggest that genetic alterations in the indole glucosinolate pathway cause disruptions beyond the endogenous levels of the plant, significantly changing the abundance and presence of different metabolites in the root exudates of the plants as well as the microbial rhizosphere community.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Symbiosis Under Stress: How Arbuscular Mycorrhizal Fungi and Sorghum Metabolism Shift Under Drought

As drought becomes more prevalent across the globe, causing billions of dollars in agricultural loss, the need to maintain crop health and productivity grows increasingly important. Out of the most important cereal crops, sorghum shows the greatest drought tolerance, and plant-microbiome interactions at the root region play a crucial role in this. A key microbial player is arbuscular mycorrhizal fungi (AMF), which deliver water and nutrients to plants in exchange for nutrients they cannot produce. Using sorghum as a model plant, we combine mass-spectrometry based proteomics and metabolomics to examine metabolic interactions between sorghum roots and AMF under drought stress. With AMF, sorghum downregulated lipid-related biological processes involving fatty acid biosynthesis and metabolism. Similarly, amino acid biosynthesis pathways were also suppressed; however, metabolite analysis revealed increased amino acid abundance related to the phenylpropanoid pathway. AMF also enhanced the upregulation of drought-protective osmolytes, such as mannitol and sorbitol, suggesting their key role in mediating sorghum’s response to drought stress. AMF hyphal biomass also had an increased abundance of key osmoprotectant amino acids, indicating similar mechanisms of drought tolerance between sorghum and AMF. Metabolomic evidence also suggested that carbohydrate exchange between sorghum and AMF shifted under stress, indicating an altered exudation pattern likely driven by drought response. Our results demonstrate the molecular mechanisms through which AMF modulate sorghum metabolism under drought conditions, highlighting their promising role in improving crop resilience. By identifying the molecular targets that can improve drought tolerance, we can begin engineering drought resistant agricultural biosystems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Causal relationships of vegetation productivity with root zone water availability and atmospheric dryness at the catchment scale

Abstract. This study explores the causal relationships between catchment water availability, vapor pressure deficit, and gross primary productivity (GPP) across 341 catchments in the contiguous US. Seasonal climatic, hydrological, and vegetation characteristics were represented using the Horton index, ecological aridity index, evaporative fraction index, and carbon uptake efficiency. Statistical methods, including circularity statistics, correlation analysis, and causality tests, were employed to determine the complex interactions between catchment wetness, atmospheric dryness, and vegetation carbon uptake. The results revealed a maximum lag of 2 months in the intra-annual variability of catchment water supply–productivity and atmospheric water demand–productivity relationships, with hysteresis patterns varying with the catchment's hydrological characteristics. In catchments not permanently under water-limited or energy-limited conditions, vegetation experiences hydrological stress during the peak growing period, coinciding with the highest gross primary productivity and carbon uptake efficiency being out of phase with the Horton index and in phase with the evaporative fraction index. Causality analysis highlights strong temporal continuity in GPP seasonal characteristics, with a cause–effect relationship between catchment water supply, atmospheric demand, and vegetation productivity spanning a maximum of 2 months. These findings underscore the need for a comprehensive functional framework that integrates catchment water supply, atmospheric demand, and vegetation productivity to enhance our understanding and predictive capabilities with regard to ecosystem responses to climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A framework for testing soil carbon dynamics post land-use transition in a multisector dynamics model

Soil carbon plays a crucial role in the global carbon cycle. Changes in land use can determine whether carbon is stored or is emitted into the atmosphere as carbon dioxide, which has broad implications for the human and Earth systems. These feedbacks to the carbon cycle and their socio-economic drivers are modelled by many global multisector dynamics models to project future possibilities for the human-Earth system. One notable model of this class is the Global Change Analysis Model (GCAM), which uses a simplified process to model soil organic carbon (SOC) content after land-use transition across 384 land units. While the current GCAM soil carbon framework is based on scientific principles, it has not been tested against experimental data. This work examines rates of SOC change from GCAM input data. Specifically, first order rate constants derived from model inputs were compared to values from two syntheses to assess GCAM’s accuracy. Welch’s t-tests and linear models were used to determine if rate constants were consistent across all tested geographical areas and land-use transition types. While we found that there was general agreement on the direction and magnitude (i.e., rate) of SOC change, the rate constant derived from GCAM and empirical values differed strongly in a subset of specific instances. These results indicate that GCAM’s current SOC dynamics during land use transition successfully capture broad patterns of change in this critical carbon pool, but should be interpreted with caution at finer spatial scales. One potential cause of these discrepancies is our highly aggregated variable, soil timescale, which could be made more granular to improve accuracy. When using economically rooted multisector dynamics models, such as GCAM, it is critical to understand such model limitations for representing specific Earth system processes.

carbon↗
DOE OSTI2025

Artificial Intelligence and Machine Learning Applications in Modern Power Systems

Machine learning (ML) and artificial intelligence (AI) algorithms offer valuable tools for the analysis and interpretation of large datasets. These tools have the capability to uncover insights that may not be readily apparent within these datasets. In recent years, the integration of ML and AI has become increasingly prevalent in various applications within the power system domain. One of the earliest instances of machine learning in power systems can be traced back to demand forecasting, where artificial neural networks were employed for short-term load forecasting. In contemporary power systems, an abundance of high-resolution geospatial and temporal data is generated at various time intervals, ranging from sub-seconds (Phasor Measurement Units or PMUs) to seconds (Supervisory Control and Data Acquisition or SCADA), minutes (Process Information or PI), and extending to days, months, and years. These datasets contain valuable information concerning system reliability and performance. This information holds the potential to offer critical insights into system operations, as well as solutions for predicting and mitigating contingencies to prevent cascading outages. Despite the immense power of machine learning tools, system operators, planners, and utilities often exhibit hesitancy in fully embracing AI-enabled system operations and planning. This cautious approach persists, even as numerous diverse applications of machine learning continue to emerge in the realm of power systems. In this chapter, our focus will delve deep into ML and AI applications tailored for power systems. These applications aim to furnish system operators with enhanced situational awareness and augment their decision-making capabilities, especially during challenging operating conditions. Specific areas of interest encompass root cause analyses of electricity market datasets and the strategic selection of representative samples from vast power system databases for training ML/AI models. Finally, the chapter will conclude with a short discussion on the future of ML/AI in power systems and possible directions that the industry is moving towards.

power system applications, machine learning (ML), ↗