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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

The carbon challenge: Design, synthesis, and chemisorption behavior of solid sorbents in direct air capture of carbon dioxide

Direct air capture (DAC) of CO 2 is a promising solution for reducing the carbon footprint through "negative emission" technology. However, the low CO 2 concentration (~400 ppm) and the dynamic nature of DAC processes present challenges in designing effective sorbent systems. Recent advancements in material design and structural engineering have led to the development of high-performance solid sorbents, offering a more stable, safe, and energy-efficient alternative to traditional liquid CO 2 capture methods. This review highlights progress in solid sorbent-based DAC, focusing on amine-modified materials, hydrogen-bonded frameworks, and ionic liquid-engineered scaffolds. The discussion covers design principles, synthesis methodologies, and their impact on CO 2 chemisorption, comparing the advantages and limitations of each approach. Characterization techniques, especially operando methods and computational tools, are reviewed to understand sorbent behavior during CO 2 integration and release. The reaction pathways and interaction mechanisms of these sorbents with CO 2 are analyzed to guide future design. Additionally, the CO 2 chemisorption behaviors, including capacity, sorption kinetics, recyclability, and durability in the presence of gaseous impurities and under humid conditions will be evaluated and compared. Further, the review offers unique insights into the physical properties, chemical structures, and surface engineering effects of these sorbents, based on comprehensive characterization and evaluation techniques.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Hybrid Additive Manufacturing and Electric Field-Assisted Sintering of High-temperature Heat Exchangers via Sacrificial Channel Molds

A hybrid manufacturing approach, integrating additive manufacturing (AM) with powder methodology via electric field-assisted sintering (EFAS), was developed for the fabrication of high-temperature compact heat exchangers (CHX) from refractory metals. The methodology employed additively manufactured sacrificial channel molds (SCMs) as shapeholders for CHX channels, which were embedded in metal powders using EFAS. Following embedding, the SCMs were chemically dissolved to form the internal channel network. SCMs were fabricated using both digital light processing (DLP) and direct ink writing (DIW) from chemically reactive, calcium-based ceramic feedstocks with varying ratios of Al2O3 reinforcement. The microstructure, phase composition, and dissolution behavior of both as-printed and embedded SCMs were investigated. The shrinkage behavior of the SCMs embedded in refractory metals, as well as the interfacial characteristics between the SCMs and metal matrix, were studied. The results showed that the SCMs containing sufficient chemical reactive ceramics dissolved effectively before and after embedding. The as-printed SCMs retained the phase composition of their feedstocks, but the embedded SCMs containing calcium-based ceramics and Al2O3 exhibited the formation of calcium aluminates due to high temperature exposure during embedding. Most SCMs exhibited a cellular Al2O3 network filled with Ca-rich ceramics. Shrinkage after embedding was strongly dependent on SCM density, with lower density SCMs exhibiting greater shrinkage. A thin SCM-affected zone was observed at the metal matrix surface, characterized by increased porosity compared to the bulk matrix. This effect was attributed to infiltration of the SCM materials into powder particle boundaries under pressure, followed by their removal during dissolution. This study demonstrates the feasibility of manufacturing CHXs from hard-to-process refractory metals for use in harsh environments.

36 - MATERIALS SCIENCE↗
DOE OSTI2024

Enhancing Short-Term Flexibility through Characterization of HVDC Transmission Systems

Efficient power grid operation hinges on maintaining safety and reliability through adaptable system flexibility standards. However, in a future power grid characterized by extensive renewable energy integration, conventional system flexibility and predetermined requirements may falter under extreme conditions. This paper addresses the formidable challenge of sustaining secure and dependable power grid operations under these circumstances, emphasizing the role of controllable high voltage direct current (HVdc) transmission systems in amplifying overall system flexibility and tackling operational complexities. To quantitatively assess short-term flexibility and its associated economic benefits from HVdc within specific regions, we propose a Power Flow Distribution Factors (PTDFs) based approach. This methodology takes into account both dispatchable generators and HVdc systems, pinpointing their contributions. Through an analysis of the Western Electricity Coordinating Council (WECC) system, we demonstrate the practical application of this approach to scrutinize the impacts of existing and future HVdc lines.

Operational Flexibility, Multi-terminal HVdc, tran↗
DOE OSTI2025

Raptor

Raptor is an efficient Python-based tool for predicting the formation and morphology of stochastic lack of fusion defects in metal AM processes. A major obstacle for the qualification and certification of additively manufactured parts in critical applications continues to be performance variability caused in part by porosity-related defects. High-fidelity process models that could predict these defect features are currently too computationally expensive for component-level analysis. To address this, Raptor employs a high-performance geometric method to model the dynamic melt pool rather than relying on computationally intensive thermal fluid dynamics. This allows Raptor to rapidly identify regions of unmelted material that correspond to lack of fusion pores. The efficiency of this approach significantly reduces the time and resources needed for generating 3D defect predictions, which enables users to conduct large-scale parameter studies and evaluate how process variations affect part quality. The framework offers operational flexibility; users can execute simulations through a simple command line interface or integrate core functions as a library within larger computational workflows. Simulation outputs include 3D porosity maps for visualization and tools for quantitative morphological analysis. These results are suitable for direct comparison with experimental characterization data from methods such as X-ray computed tomography and can be used for statistical process optimization.

Subraveti, Vamsi [Vanderbilt Univ., Nashville, TN ↗
DOE OSTI2025

Combining Observations and Models: A Review of the CARDAMOM Framework for Data‐Constrained Terrestrial Ecosystem Modeling

The rapid increase in the volume and variety of terrestrial biosphere observations (i.e., remote sensing data and in situ measurements) offers a unique opportunity to derive ecological insights, refine process‐based models, and improve forecasting for decision support. However, despite their potential, ecological observations have primarily been used to benchmark process‐based models, as many past and current models lack the capability to directly integrate observations and their associated uncertainties for parameterization. In contrast, data assimilation frameworks such as the CARbon DAta MOdel fraMework (CARDAMOM) and its suite of process‐based models, known as the Data Assimilation Linked Ecosystem Carbon Model (DALEC), are specifically designed for model‐data fusion. This review, motivated by a recent CARDAMOM community workshop, examines the development and applications of CARDAMOM, with an emphasis on its role in advancing ecosystem process understanding. CARDAMOM employs a Bayesian approach, using a Markov Chain Monte Carlo algorithm to enable data‐driven calibration of DALEC parameters and initial states (i.e., carbon pool sizes) through observation operators. CARDAMOM's unique ability to retrieve localized model process parameters from diverse datasets—ranging from in situ measurements to global satellite observations—makes it a highly flexible tool for analyzing spatially variable ecosystem responses to environmental change. However, assimilating these data also presents challenges, including data quality issues that propagate into model skill, as well as trade‐offs between model complexity, parameter equifinality, and predictive performance. We discuss potential solutions to these challenges, such as reducing parameter equifinality by incorporating new observations. This review also offers community recommendations for incorporating emerging datasets, integrating machine learning techniques, strengthening collaboration with remote sensing, field, and modeling communities, and expanding CARDAMOM's relevance for localized ecosystem monitoring and decision‐making. CARDAMOM enables a deep, mechanistic understanding of terrestrial ecosystem dynamics that cannot be achieved through empirical analyses of observational datasets or weakly constrained models alone.

Bayesian inference↗
DOE OSTI2025

Basic Research Needs for Inverse Methods for Complex Systems under Uncertainty [Brochure]

The four priority research directions outlined in this brochure represent a cohesive vision for advancing the science of inverse problems for complex systems under uncertainty. Together, they address the critical challenges of: discovering, exploiting, and preserving physical and problem structure; overcoming model limitations; integrating disparate, multimodal, and/or dynamic data; and tailoring the solution of inverse problems to downstream tasks. While each PRD focuses on a distinct aspect of inverse-problem research, their interconnected nature highlights the importance of a holistic approach that leverages progress across all areas to achieve transformative solutions. This agenda calls for research across mathematics, statistics, and computer science disciplines, which are guided and complemented by rapid advances in artificial intelligence, high-performance computing, and experimental facilities, to unlock new capabilities, maximize scientific impact, and meet the growing demands of inverse problems that arise across applications that are critical to DOE's mission.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Computationally Guided Design of Multiple Impurities Tolerant Cathode for Solid Oxide Fuel Cell Applications

Solid oxide fuel cells (SOFCs) have been recognized as promising candidates in the demand of reducing the dependence on fossil fuels, which is one of the solutions to the threat of global warming. It was reported that the direct chemical-to-electrical energy conversion in SOFCs comes with negligible emission of greenhouse gases and up to 60% working efficiency. SOFC has unique advantages over traditional power generation technologies and other types of fuel cells due to the use of relatively inexpensive materials, low sensitivity to poisoning impurities in fuels, relatively high conversion efficiency, and fuel flexibility (like carbon-based fuels). However, during the SOFC operation, the cathode is exposed to intrinsic and extrinsic airborne contaminants at high temperatures (600–900°C), leading to their reaction with electrode materials, irreversible chemical and structural changes, and electrochemical performance degradation. of which CO2, SO2, Cr6+ and H2O are known to be concerns. Thus, developing a comprehensive understanding of the multiple impurities poisoning of SOFC cathodes in the presence of single or multiple impurities is necessary to address the long-term degradation of the SOFC cathode systems at operating conditions. An integrated computational and experimental approach has been utilized in the current work aiming at the comprehensive investigation of the cathode materials under single or multiple impurities poisoning effects, understanding the multiple impurities poisoning mechanism(s), and improving the long-term durability of the SOFC cathodes to develop better performing impurity-tolerant cathode materials. We first examined the agreement between the simulation approach and the experimental observations at various atmospheric and temperature conditions to confirm the reliability of CALPHAD for poisoning simulations in these cathodes. Then, using this approach, we investigated the accelerated testing protocol, which has become the standard method for conducting impurity poisoning experiments involving these cathode materials. We found our simulation approach is able to predict in which systems, i.e., cathode material and treatment environment, the accelerated testing protocols reflect actual operating conditions. Finally, in-depth simulations were done for these three candidate cathodes under different experimental conditions and suggested directions toward alternative cathode materials that have superior impurity tolerance.

36 MATERIALS SCIENCE↗
DOE OSTI2025

In situ high-temperature Raman spectroscopy for online EAF slag analysis

Real-time monitoring of slag chemistry is critical for optimizing Electric Arc Furnace (EAF) steelmaking operations, where dynamic variations in slag composition directly influence slag foaming, refractory degradation, and thermal efficiency. Conventional techniques such as X-ray fluorescence (XRF), Fourier-transform infrared (FTIR), and scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM-EDS) are commonly used to analyze slag composition, but their offline nature and equipment constraints limit their applicability for online monitoring in harsh industrial environments. To address this challenge, we present an in situ, high-temperature analytical approach that integrates Raman spectroscopy with a custom designed fiber-optic probe for real-time slag characterization at 1550 °C. The system enables non destructive spectral acquisition from molten slags, providing molecular-level insights into silicate polymerization and iron oxidation states. Eight synthetic slag samples were evaluated, and key Raman features—such as Q n silicate units and FeO₄/FeO₆ coordination environments—were identified and quantitatively correlated with slag basicity and Fe₂O₃ content. The results demonstrate agreement between Raman spectral ratios and bulk slag chemistry, validating the method’s capability to track compositional and structural changes under molten temperature. This work establishes the feasibility of deploying fiber-optic Raman probe for online EAF slag monitoring and highlights their potential to support closed-loop control strategies, thereby enhancing process stability, refractory protection, and steel quality in industrial steelmaking applications.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

pyGWBSE: a high throughput workflow package for GW-BSE calculations

Abstract We develop an open-source python workflow package, py GWBSE to perform automated first-principles calculations within the GW-BSE (Bethe-Salpeter) framework. GW-BSE is a many body perturbation theory based approach to explore the quasiparticle (QP) and excitonic properties of materials. GW approximation accurately predicts bandgaps of materials by overcoming the bandgap underestimation issue of the more widely used density functional theory (DFT). BSE formalism produces absorption spectra directly comparable with experimental observations. py GWBSE package achieves complete automation of the entire multi-step GW-BSE computation, including the convergence tests of several parameters that are crucial for the accuracy of these calculations. py GWBSE is integrated with Wannier90 , to generate QP bandstructures, interpolated using the maximally-localized wannier functions. py GWBSE also enables the automated creation of databases of metadata and data, including QP and excitonic properties, which can be extremely useful for future material discovery studies in the field of ultra-wide bandgap semiconductors, electronics, photovoltaics, and photocatalysis.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Absolute stimulated Raman side scatter in direct-drive laser-produced plasmas

Production of suprathermal electrons by stimulated Raman scattering (SRS) is a principal concern for contemporary direct-drive inertial confinement fusion experiments at the National Ignition Facility and similar systems since such electrons penetrate and preheat the target core, preventing efficient implosion. The higher temperatures and longer scale lengths in these experiments favor SRS over two-plasmon decay, which predominated in earlier experiments. In particular, current experiments are expected to exceed the threshold for absolute Raman side scatter, which would then dominate the interaction since it grows temporally until saturated by nonlinear mechanisms such as hot-electron production. Until recently, analyses of SRS side scatter have treated the case of a single laser beam incident on a plasma, but the direct-drive approach to laser fusion employs a multitude of beams to drive the implosion. Here in this paper, we present an analysis that can be applied to an arbitrary number of beams with varied angles of incidence and polarizations. In the case of a single beam, it allows a physically motivated derivation and verification of an analytic threshold formula. In the general case of multiple beams and arbitrary orientation and polarizations, the threshold is found by numerical integration of a set of first-order linear partial differential equations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Novel architectures for stabilization of Mn-rich cathodes: a high-valent approach to interfaces

Lithium- and manganese-rich (LMR) layered oxides continue to generate significant interest as promising, earth-abundant cathode materials for next-generation Li-ion batteries. In spite of their attractive capacity and cost advantages, a few long-standing challenges still hamper their widespread adoption, with manganese dissolution being one of the most persistent and vexing issues. In the present study, we explore the incorporation of Sb5+ as a high-valent cation and exploit its ability to form unique lithium-rich surface and grain-boundary structures that can integrate directly with the LMR lattice. When synthesized under appropriate conditions, Sb5+ orders strongly with Li+ to form localized Li+–Sb5+ motifs, which play a key role in restructuring the surface and grain-boundary regions. These restructured regions act as protective, stabilizing entities that substantially suppress electrolyte-driven side reactions, reduce impedance growth, limit manganese dissolution, and help retain cyclable lithium during long-term electrochemical cycling. Further improvements of the electrochemical performance of Sb-treated LMR were achieved using a well-known additive to mitigate Mn dissolution and highlight the synergistic effects of combined strategies. Overall, this work showcases how high-valent elements such as Sb5+ can help tailor the surface and intergranular regions and work in synergy with other modifiers (e.g. electrolyte additives), enhancing the cycle life and practical viability of LMR cathodes for use in graphite-based full cells.

Mallick, Subhadip [Argonne National Laboratory (AN↗
DOE OSTI2024

Improvement of Coal Power Plant Dry Cooling Technology through Application of Cold Thermal Energy Storage

The U.S. power infrastructure is currently heavily reliant on water cooling. The power plants in the U.S. account for approximately 40% of freshwater withdrawals, with 90% of it used in condenser cooling. The most used cooling technology in coal-fired power plants is a once-through condenser; however, this cooling method requires high water withdrawal rates and results in thermal pollution of the water source. Wet WCTs offer an alternative to once-through condensers due to much lower water withdrawals. However, these systems suffer from water consumption through evaporation making them undesirable options in areas subject to droughts and in arid areas. The direct dry cooled condensers (ACCs) and dry cooling towers (DCT) account for 1.8%, hybrid cooling (ACC + WCT) accounts for 0.5%, while other cooling technologies represent the rest (0.7%). The ACC/DCTs represent an attractive alternative for power plants; however, this technology has not been widely adopted in the US (less than 2% of power plants) due to its negative impact on plant performance. As a rule of thumb, dry cooling results in performance penalty equivalent to approximately 2%-point efficiency loss compared to wet cooling, although the actual magnitude varies with ambient dry bulb temperature, DBT which may vary considerably during the day. As DBT increases, the plant power output decreases, reaching a minimum at the hottest period of the day, which usually coincides with the highest electricity demand for air condition load. Therefore, performance of power plants using DCT/ACC cooling technology is the lowest during the summer mid-day when ambient temperature is the highest. For example, the decrease of the inlet air temperature to the DCT/ACC by 2 Deg C could generate up to 5% additional power at peak demand. It is, therefore, important to improve dry cooling technology to maintain the viability of coal-fired power plants in a carbon constrained future. The method for reducing the cooling air temperature and keeping it constant would mitigate this problem significantly. Objectives of this project were to develop, design, evaluate, and demonstrate a cost-effective system for improving performance of a DCT or ACC for thermal (coal-fired) power plant applications using a low-cost heat storage materials, such as pervious concrete (PC) and phase change material (PCM). Thus, the study focused on development of the system(s) that could be used to alleviate the difficulties in operating DCT/ACC during the summer months by storing cold energy during the nighttime in inexpensive materials PC and PCM and using it during the hottest period(s) of the day. Since very large quantities of cold energy need to be stored to make an impact on performance of a large power plant, it is essential that the storage materials and associated cold energy storage design(s) are inexpensive and the system is simple to build, maintain and operate. To achieve the project objectives, a comprehensive approach, including material development and characterization, component and system modeling, and laboratory- and prototype-scale experiments, was employed including modeling of the system components and of the entire system, development (engineering) of the materials for the heat storage modules of the Cold Thermal Energy Storage System (CTESS) and determination of their properties, design, manufacturing and setup of the laboratory- and prototype-scale test facility and testing, design, manufacturing and setup of the prototype-scale test facility. A modular design of CTESS was employed, where representative modules were designed as the integrated direct contact heat exchanger and thermal energy storage (TES) system. CTESS modules were manufactured and tested. Two prototype-scale designs of the CTESS modules were developed and tested. The use of CTESS increases plant generation increases since it lowers air temperature entering ACC/DCT and keeps it constant during the hottest time of the day. For a PCM-based CTESS, the ambient air temperature is lowered close to the PCM phase change temperature. The duration of the cooling effect depends on the latent heat and mass of PCM in the CTESS. For this project, commercial grade CaCl2 hexahydrate (CaCl2·6H2O or CC6) PCM with phase change temperature of 25 Deg. C was used due to its low cost. For practical reasons, the CTESS was designed to maintain the cooling effect for four hours. The low phase change temperature associated with the commercial grade PCM used in CTESS results in considerably higher improvement in net generation compared to the laboratory (pure) grade. The resistance to heat transfer results in lower net generation compared to the ideal case where resistance to heat transfer is zero. The results demonstrate that CTESS is effective in improving the performance of a dry cooling system. However, its effectiveness depends on the relationship between the ambient air conditions and PCM phase change temperature. As is the case with the heat rejection system, for the best performance, the PCM used in CTESS would need to be matched to the ambient air conditions. The results obtained in this report for selected geographical locations are valid for CC6 and demonstrate that the level of performance to be achieved by the technology will be location-dependent, as is the case with the air cooled condensers. The methodology for engineering of PC-PCM-based heat storage medium is applicable to other PCMs that may need to be used for other ambient air conditions and geographical locations.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2023

Integration of Nuclear Material Accounting Data and Process Monitoring Data for Improvement on Detection Probability in Safeguarding Electrochemical Processing Facilities (Final Technical Report)

The KAERI advanced spent fuel conditioning process (ACP) process is a critical component of the US- South Korean nuclear cooperation and the following “123 Agreement.” Its development has received considerable attention in both countries. The ACP is an electrochemical processing (pyroprocessing) that recycles over 96% of the used nuclear fuel (UNF). It is also intrinsically proliferation-resistant in theory. In normal operation, the U/TRU product is very hot radiologically. In addition, the Cm provides a high level of spontaneous neutrons, making the product unsuitable for weapon use. However, as pointed in some study, “the need for safeguards to protect against the diversion and misuse of separated plutonium applies essentially equally to all grades of plutonium.” As pointed by many studies, the well-established traditional Nuclear Material Accounting (NMA) approach cannot be directly applied to electrochemical processing because of the lack of an input accountability tank, the non-continuous material flow, and the unsatisfactory level of confidence in sampling methods. Therefore, nuclear safeguards remain a grand challenge in the developing of commercial electrochemical separations facilities, especially around the heart of such facilities, the electrorefiner (ER) systems. In contrast to NMA data, process monitoring (PM) data is normally an indirect measurement of the SNM and is acquired much more frequently. In a broad sense, PM includes monitoring by various types of equipment, e.g. radiation detectors, cameras, voltage, current sensors. Because it is already being collected by the operator, the additional cost to safeguards is low. It has long been believed that PM data can supplement NMA data and help improve safeguards, although the benefits are hard to quantify. The U.S. DOE’s Material Protection, Accounting, and Control Technology (MPACT) campaign has made substantial investments into innovative PM sensor technology and predictive model development for real- or near real-time measurement and prediction of molten salt density and level, salt composition and actinide concentration especially Pu, the cell voltage, and the cell current to supplement traditional NMA. For aqueous-based reprocessing facilities, it is reported that PM, integrated with traditional NMA, have a high detection probability for specific diversions. For electrochemical reprocessing, preliminary studies have shown that PM data can support traditional NMA in various ways by providing a basis to estimate some of the in-processing nuclear material inventories. Despite early success, further studies on fusion of PM data and NMA data are still needed, which is the goal of this proposed work.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Unsupervised probabilistic models for sequential Electronic Health Records

We develop an unsupervised probabilistic model for heterogeneous Electronic Health Record (EHR) data. Utilizing a mixture model formulation, our approach directly models sequences of arbitrary length, such as medications and laboratory results. This allows for subgrouping and incorporation of the dynamics underlying heterogeneous data types. The model consists of a layered set of latent variables that encode underlying structure in the data. These variables represent subject subgroups at the top layer, and unobserved states for sequences in the second layer. We train this model on episodic data from subjects receiving medical care in the Kaiser Permanente Northern California integrated healthcare delivery system. The resulting properties of the trained model generate novel insight from these complex and multifaceted data. In addition, we show how the model can be used to analyze sequences that contribute to assessment of mortality likelihood.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

A Bayesian inferencing framework for ultrasound wave speed measurements in metal additive manufacturing

Process-related changes during metal additive manufacturing introduce microstructural variability in the material properties of printed parts, directly affecting component reliability. Accurate estimation of these property variations with part performance are essential for quality assurance. Ultrasound testing offers a non-destructive means to estimate mechanical properties and detect defects; however, conventional analysis methods often neglect the influence of microstructural variability, limiting their effectiveness. Here, this research presents a Bayesian inference technique for quantifying wave speed uncertainty from ultrasound measurements of metal additive manufactured parts. By integrating prior ultrasound data with a Bayesian model, the proposed approach generates posterior density estimates of wave speed that systematically account for manufacturing-induced variability and uncertainty. The novelty of this research lies in applying a Bayesian framework to analyze experimental ultrasound measurements within the context of metal additive manufacturing variability. The method enhances the accuracy of wave speed estimation by 64%, defect position by 50% and increases confidence associated with wave speed variance by 30% across different porosity levels, thereby providing a robust foundation for improved decision-making and increased reliability in additively manufactured components.

Additive manufacturing↗
DOE OSTI2022

Scaling the Microbial Ecology of Soil Carbon (Final Report)

This work developed new techniques and discoveries in quantitative microbial ecology, focusing on soil carbon. The work advanced a new approach to stable isotope probing (SIP), adding a quantitative way to infer taxon-specific rates of growth, mortality, and associated carbon (C) fluxes in soil microbial communities, a framework that the work demonstrated can scale from individual microbial taxa to the integrated soil system. Among the “-omics” techniques in microbial ecology, those based on stable isotope probing (SIP) hold particular promise for addressing the challenge of scaling from molecules to the major biogeochemical element cycles. Because SIP measures directly the fluxes of elements into nucleic acids, it physically connects element flux to genetic information. The work explored new ways to quantify taxon-specific C-use and growth efficiency and tested hypotheses about responses of the soil microbial community to experimental warming and to latitudinal variation in temperature. This work pushed the frontier of –omics enabled techniques by demonstrating their applicability at the ecosystem scale, and by relating taxon-specific isotope assimilation to dissimilatory processes in the C cycle, thereby enabling the identification of organisms especially responsible for soil C loss, in other words, attributing ecosystem-scale element fluxes to individual microbial taxa.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Reliability modeling in a predictive maintenance context: A margin-based approach

Current system reliability methods (typically based on fault trees or reliability block diagrams) can effectively propagate reliability data from the asset to the system level in order to identify system critical points. However, employed asset reliability data are an approximated integral representation of the past industrywide operational experience, and they neglect the present asset health status (available, for example, from online monitoring data and diagnostic assessments) and forecasted health projection (when available from prognostic models). Asset health should be informed solely by that specific asset’s current and historical performance data and should not be an approximated integral representation of the past industrywide operational experience (as currently performed by system reliability models through Bayesian updating processes). Sensor data, diagnostic assessments, and prognostic assessments are in fact not considered in plant reliability models used to inform system engineers on the most critical assets. In addition, the propagation of quantitative health data from the asset to the system level is a challenge given the diverse nature and structure of health data elements (e.g., vibration spectra, temperature readings, expected failure time). Ideally, in a predictive maintenance context, system reliability models should support decision making by propagating available health information from the asset to the system level in order to provide a quantitative snapshot of system health and identify the most critical assets. Here, this paper is directly addressing these two goals by proposing a different approach for reliability modeling that relies on asset diagnostic and prognostic assessments, along with monitoring data to measure asset health. The propagation of health data from the asset to the system level is performed through fault tree models not in probability terms, but in terms of margin where margin is the “distance” between the present status and an undesired event (e.g., failure or unacceptable performance). Through a cause-effect lens, while classical reliability models target the effect associated with asset performance, a margin-based approach focuses on the cause of an undesired asset performance (i.e., its health). Hence, thinking of reliability in terms of margins implies decision-making based on causal reasoning. We will show how fault tree models can be solved using a margin language and how this process can effectively assist system engineers to identify the most critical assets.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Spatiotemporal Adaptive Passive Direct Air Capture

Carbon Collect Inc., along with Arizona State University, the Electric Power Research Institute (EPRI), PM Group, and Trimeric Corporation, completed an initial design of a commercial-scale, passive direct air capture (DAC) system termed “carbon trees” that will capture, separate, and store at least 100,000 tonnes/year of carbon dioxide (CO2) from air (net basis). Passive DAC is unique among DAC technologies in that passive air delivery by wind avoids the energy penalty of forced convection. Carbon Collect Inc.’s sorbent-agnostic approach offers the flexibility to choose sorbents for a wide range of climates. A combination of steam, low-grade heat, and vacuum releases the CO2 from the sorbent, which is extracted from the chamber and purified and compressed for geological storage. A commercial carbon tree forest combines the output of several thousand trees for compression and purification with high heat and energy integration. The project team prepared an initial engineering design package for each of three geographically diverse host sites throughout the United States to better understand the effect of local/regional ambient conditions on DAC system performance and project costs. A techno-economic analysis, life cycle analysis, business case analysis, and an environmental, health, and safety risks assessment were also completed for each of the three geographically diverse host sites.

14 SOLAR ENERGY↗