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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Small Unmanned Aircraft Systems Integration into the National Airspace System Visual-Line-of-Sight Human-in-the-Loop Experiment

As part of the Unmanned Aircraft Systems (UAS) in the National Airspace System (NAS) project, research on integrating small UAS (sUAS) into the NAS was underway by a human-systems integration (HSI) team at the NASA Langley Research Center. Minimal to no research has been conducted on the safe, effective, and efficient manner in which to integrate these aircraft into the NAS. sUAS are defined as aircraft weighing 55 pounds or less. The objective of this human system integration team was to build a UAS Ground Control Station (GCS) and to develop a research test-bed and database that provides data, proof of concept, and human factors guidelines for GCS operations in the NAS. The objectives of this experiment were to evaluate the effectiveness and safety of flying sUAS in Class D and Class G airspace utilizing manual control inputs and voice radio communications between the pilot, mission control, and air traffic control. The design of the experiment included three sets of GCS display configurations, in addition to a hand-held control unit. The three different display configurations were VLOS, VLOS + Primary Flight Display (PFD), and VLOS + PFD + Moving Map (Map). Test subject pilots had better situation awareness of their vehicle position, altitude, airspeed, location over the ground, and mission track using the Map display configuration. This configuration allowed the pilots to complete the mission objectives with less workload, at the expense of having better situation awareness of other aircraft. The subjects were better able to see other aircraft when using the VLOS display configuration. However, their mission performance, as well as their ability to aviate and navigate, was reduced compared to runs that included the PFD and Map displays.

Trujillo, Anna C.↗
NASA NTRSDate not supplied

The Comet Astrobiology Exploration Sample Return (CAESAR) Mission

The Comet Astrobiology Exploration Sample Return (CAESAR) mission will acquire and return to Earth for laboratory analysis a minimum of 80 grams of surface material from the nucleus of comet 67P/Chur-yumov-Gerasimenko (67P). CAESAR will characterize the surface region sampled, preserve the collected sample in a pristine state, and return evolved volatiles by capturing them in a separate gas reservoir. NASA Goddard Space Flight Center provides project management, systems engineering, safety and mission assurance, contamination control, mission operations, and many other important functions. Northrop Grumman Space Systems will build the spacecraft, based on Dawn mission heritage, which like CAESAR, uses solar electric propulsion. CAESAR was selected by for Phase A study in the New Frontiers 4 Competition and will be proposed to New Frontiers 5.Collection of a sample from the surface of comet 67P is facilitated by a set of cameras that together provide images to support sample site selection, perform optical navigation, and document the sample before, during, and after col-lection. The sample is collected at the end of an arm during a 5-second touch-and-go (TAG) maneuver with the Sample Acquisition System (SAS)designed by Honeybee Robotics for the surface properties of comet 67P observed by the Rosetta mission. After sample collection, and while the sample is still cold (< -80°C), the TAG Arm inserts the sample container into the Sample Containment System (SCS) mounted inside the Sample Return Capsule (SRC). The SCS is sealed, preventing the sample from escaping into space. The sample is slowly warmed inside the SCS to enable sublimation of volatiles, which are collected in the Gas Containment System (GCS), a passively cooled gas reservoir. Separating the volatiles from the solid sample protects the solid sample from alteration. Once all sublimated H2O is transferred to the GCS, the GCS is sealed to capture the volatile sit contains, and the SCS is vented to space to maintain the solid sample under vacuum. The SCS vent is closed before Earth entry to prevent atmospheric contamination. Detailed laboratory analyses of the sample from 67P will trace the history of volatile reservoirs, delineate the chemical pathways that led from simple interstellar species to complex molecules, constrain the evolution of the comet, and evaluate the role of comets in delivering water and prebiotic organics to the early Earth. CAESAR will achieve these goals by carrying out coordinated sample analyses that will link macroscopic properties of the comet with microscale mineralogy, chemistry, and isotopic studies of volatiles and solids. Most of the sample (≥75%) will be set aside for analyses by generations of scientists using continually advancing tools and methods, yielding an enduring scientific treasure that only sample return can provide. This presentation will review development conducted during NF4 Phase A and discuss the NF5 mission concept.

A G Hayes↗
NASA NTRSDate not supplied

A Remote, Human-in-the-Loop Evaluation of a Multiple-Drone Delivery Operation

Over time, advances in unmanned aircraft systems (UAS) have enabled a shift in the operational paradigm from one operator managing one aircraft to that of multiple operators working together to manage multiple aircraft. This shift has highlighted the need for effective human-autonomy teaming methods to maintain manageable workload levels for operators as well as high standards of system performance and safety. This paper presents a study aimed at evaluating whether automation can help operators manage workload during small UAS (sUAS) package delivery scenarios featuring contingency situations. These contingency situations, resulting from unplanned UAS Volume Reservations (UVRs), required flight path reroutes for multiple aircraft simultaneously. The study manipulated the number of aircraft affected by the UVRs and the level of automation support. The presence of terrain conflicts was also controlled within each scenario. Due to the COVID-19 pandemic, subjects were not able to gain direct access to the Ground Control System (GCS). Therefore, the study was conducted using a subject-surrogate paradigm that required subjects to relay commands through a verbal protocol from remote locations outside of the lab to a researcher surrogate who had direct control of the GCS interfaces at the lab location. Results show that the automated support condition was associated with faster reroute response times, more efficient reroute maneuvers, and significantly lower levels of perceived workload than the manual reroute condition. However, the automation support level did not significantly impact pilots’ ability to avoid the UVR successfully; pilots were overwhelmingly capable of avoiding the UVR in all conditions. The presence of terrain conflicts primarily impacted pilot performance by leading to multiple uploads per vehicle, which was not typically required when pilots only needed to maneuver laterally. Although subjects did not have direct control over the GCS, subjective ratings indicate that the displays under test provided them with sufficient information to manage their aircraft and promptly respond to the unplanned UVRs. Overall, the objective and subjective data strongly suggest that the verbal protocol and subject-surrogate paradigm were effective methods for collecting data remotely amid the COVID-19 pandemic.

multi-UAS↗
NASA NTRS2022

New Constraints on the Dark Matter Density Profiles of Dwarf Galaxies from Proper Motions of Globular Cluster Streams

The central density profiles in dwarf galaxy halos depend strongly on the nature of dark matter (DM). Recently, in Malhan et al. we employed N-body simulations to show that the cuspy cold DM subhalos predicted by cosmological simulations can be differentiated from cored subhalos using the properties of accreted globular cluster (GC) streams since these GCs experience tidal stripping within their parent halos prior to accretion onto the Milky Way. We previously found that clusters that are accreted within cuspy subhalos produce streams with larger physical widths and higher dispersions in line-of-sight velocity and angular momentum than streams that are accreted within cored subhalos. Here, we use the same suite of simulations to demonstrate that the dispersion in the tangential velocities of streams (σ 𝜈 Tan) is also sensitive to the central DM density profiles of their parent dwarfs and GCs that they were accreted from; cuspy subhalos produce streams with larger σ 𝜈 Tan than those accreted inside cored subhalos. Using Gaia EDR3 observations of multiple GC streams we compare their (σ 𝜈 Tan) values with simulations. The measured (σ 𝜈 Tan) values are consistent with both an “in situ” origin and with accretion inside cored subhalos of M ∼ 10(8–9) Me (or very low-mass cuspy subhalos of mass ∼10(8) Mꙩ ). Despite the large current uncertainties in σ 𝜈 Tan, we find a low probability that any of the progenitor GCs were accreted from cuspy subhalos of M ≳ 10 (9) Mꙩ. The uncertainties on Gaia tangential velocity measurements are expected to decrease in future and will allow for stronger constraints on subhalo DM density profiles.

Khyati Malhan↗
NASA NTRSDate not supplied

sUAS Ground Control Station Capabilities Impact on Fleet Management

Future Small Uncrewed Aerial Systems (sUAS) missions will require multiple operators to collaborate and manage large fleets of highly automated vehicles. It is critical that these operators maintain the necessary situational awareness to modify vehicle missions if/when unexpected situations arise in the operating environment. It has been suggested that highly automated vehicle capabilities can lead to reducing the capabilities given to operators to modify/direct vehicle missions. This paper explores various Ground Control Station (GCS) capabilities configurations and their impact on sUAS fleet management. Results show that a GCS with a combination of manual and automated capabilities allowed participants to make more effective decisions while maintaining workload and response times similar to that of a GCS with only automated capabilities.

sUAS↗
NASA NTRSDate not supplied

Preliminary Development of Multi-Vehicle (m:N) Operations with NASA Langley’s Remote Vehicle Operations Center

To achieve the vision of Advanced Air Mobility (AAM), a transition from localized operations of aircraft to remote operations is being pursued across many use cases. This transition will allow fewer human operators to manage more increasingly autonomous aircraft (i.e., m operators managing N vehicles, or m:N). To study this operational concept, the National Aeronautics and Space Administration (NASA) Langley Research Center (LaRC) has developed a prototype remote vehicle operations center and ground control station (GCS) software to conduct research with simulated and real flight operations. To date, flight operations at LaRC have been limited to one vehicle per operator. However, the current paper describes initial development and considerations for enabling m:N flight operations at LaRC. Further, the research described in this paper provides the foundation for a concept of operations (ConOps) that will be developed to support remote operators managing multiple increasingly autonomous vehicles, with the goal of exploring human-autonomy teaming (HAT) concepts that enable more advanced m:N operations. Two key enablers have been identified to facilitate successful m:N operations: GCS software updates for multi-vehicle management and procedural updates for vehicle handoffs during off-nominal events. Additionally, specific modifications were identified across five key areas: technology and software, team structure, inter-team communication, contingency plans, and operator decision flows. Next steps in forming the LaRC m:N ConOps will include working with subject-matter experts to identify off-nominal scenarios, implementing the recommended GCS functionality for m:N operations, and performing integration testing of facility capabilities and new operational procedures. Although the future m:N ConOps will be tailored to the NASA LaRC remote operations facility and flight range, it is intended to be a transparent, accessible, and reality-based exemplar for external organizations seeking to create or evaluate their own m:N operational concepts.

m:N↗
DOE OSTI2024

Fourier-MIONet: Fourier-enhanced multiple-input neural operators for multiphase modeling of geological carbon sequestration

Geologic carbon sequestration (GCS) is a safety-critical technology that aims to reduce the amount of carbon dioxide in the atmosphere, which also places high demands on reliability. Multiphase flow in porous media is essential to understand CO 2 migration and pressure fields in the subsurface associated with GCS. However, numerical simulation for such problems in 4D is computationally challenging and expensive, due to the multiphysics and multiscale nature of the highly nonlinear governing partial differential equations (PDEs). It prevents us from considering multiple subsurface scenarios and conducting real-time optimization. Here, we develop a Fourier-enhanced multiple-input neural operator (Fourier-MIONet) to learn the solution operator of the problem of multiphase flow in porous media. Fourier-MIONet utilizes the recently developed framework of the multiple-input deep neural operators (MIONet) and incorporates the Fourier neural operator (FNO) in the network architecture. Once Fourier-MIONet is trained, it can predict the evolution of saturation and pressure of the multiphase flow under various reservoir conditions, such as permeability and porosity heterogeneity, anisotropy, injection configurations, and multiphase flow properties. Compared to the enhanced FNO (U-FNO), the proposed Fourier-MIONet has 90% fewer unknown parameters, and it can be trained in significantly less time (about 3.5 times faster) with much lower CPU memory (<15%) and GPU memory (<35%) requirements, to achieve similar prediction accuracy. In addition to the lower computational cost, Fourier-MIONet can be trained with only 6 snapshots of time to predict the PDE solutions for 30 years. Furthermore, we observed that Fourier-MIONet can maintain good accuracy when predicting out-of-distribution (OOD) data. The excellent generalizability of Fourier-MIONet is enabled by its adherence to the physical principle that the solution to a PDE is continuous over time. Furthermore, the developed Fourier-MIONet makes it possible to solve the long-time evolution of geological carbon sequestration in a large-scale three-dimensional space accurately and efficiently.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Sulfate Promotes Compact CaCO 3 Formation and Protects Portland Cement from Supercritical CO 2 Attack

Supercritical (sc) CO 2 in geologic carbon sequestration (GCS) can chemically and mechanically deteriorate wellbore cement, raising concerns for long-term operations. In contrast to the conventional view of “sulfate attack” on cement, we found that adding 0.15 M sulfate to the acidic brine can significantly reduce the impact of scCO 2 attack on Portland cement, resulting in stronger cement than that found in a sulfate-free system. Scanning electron microscopy revealed a decreased total attack depth in reacted cement in the presence of sulfate. With a newly defined minimum porosity term in reactive transport modeling, our model suggests that sulfate caused CaCO 3 to fill more nanopore spaces in the cement. Small angle X-ray scattering experiments also showed that sulfate can decrease the pore sizes of the carbonate layer. The results suggest that the interactions between sulfate and cement can generate a less porous CaCO 3 layer, which better resists acidic brine. Using this mechanism as a proof-of-concept, we tested the incorporation of sodium sulfate into Portland cement and synthesized new cement composites that show stronger resistance against scCO 2 attacks. Finally, these newly discovered interfacial interactions between CaCO 3 and sulfate provide new insights into engineering mechanically strong and green materials for safer GCS.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

The [4Fe-4S] Cluster of HydF Is Essential for [FeFe]-Hydrogenase Maturation

The organometallic H-cluster of the [FeFe]-hydrogenase is assembled in vivo through a complex process requiring the action of three dedicated maturation enzymes, HydG, HydE, and HydF, as well as the aminomethyl-lipoyl-H-protein (H met ) of the glycine cleavage system (GCS). Here we probe the role of HydF and its [4Fe-4S] cluster in [FeFe]-hydrogenase maturation by using a defined semisynthetic approach in which [Fe I 2 (μ-SH) 2 (CO) 4 (CN) 2 ] 2– ([2Fe] E ) is used to bypass HydE and HydG, and GCS components are used in place of cell lysate. We show that inclusion of the iron–sulfur carrier protein NfuA and the high-CO-affinity myoglobin variant Mb H64L provides dramatically improved hydrogenase activities up to 828 μmol/min/mg, equivalent to the best reported activities for Chlamydomonas reinhardtii [FeFe]-hydrogenase isolated from the native organism. Apo-HydF lacking a [4Fe-4S] cluster provides very little hydrogenase activity; however, full maturation is restored with the addition of NfuA, which we demonstrate reconstitutes the [4Fe-4S] cluster of HydF. In addition, a HydF variant lacking a [4Fe-4S] cluster by changing two cysteine ligands to alanine is completely unable to support either semisynthetic maturation using [2Fe] E , or full maturation using HydG and HydE, even in the presence of NfuA, demonstrating that the HydF [4Fe-4S] cluster is absolutely essential for [FeFe]-hydrogenase maturation. The possibility that the HydF [4Fe-4S] cluster plays a role in direct binding of [2Fe] E is negated by our results with the HydF D311C variant, which demonstrate that the labile Asp311 cluster ligand is not essential for [2Fe] E binding and HydA maturation. We therefore conclude that [2Fe]E binds HydF adjacent to, but not directly coordinated to, the [4Fe-4S] cluster. The HydF [4Fe-4S] cluster is proposed to be essential due to its impact on the [2Fe] E binding orientation and the ability of the HydF/[2Fe] E complex to form productive interactions with H met or the H met /T-protein complex during DTMA ligand biosynthesis.

cluster chemistry↗
DOE OSTI2024

Boosting Barlow Twins Reduced Order Modeling for Machine Learning‐Based Surrogate Models in Multiphase Flow Problems

Abstract We present an innovative approach called boosting Barlow Twins reduced order modeling (BBT‐ROM) to enhance the reliability of machine learning surrogate models for multiphase flow problems. BBT‐ROM builds upon Barlow Twins reduced order modeling that leverages self‐supervised learning to effectively handle linear and nonlinear manifolds by constructing well‐structured latent spaces of input parameters and output quantities. To address the challenge of high contrast data in multiphase flow problems due to injection wells and faults, we employ a boosting algorithm within BBT‐ROM. This algorithm sequentially trains a set of weak models (i.e., inaccurate models), improving prediction accuracy through ensemble learning. To evaluate the performance of BBT‐ROM, we conduct three three‐dimensional multiphase flow problems, including waterflooding and geologic carbon storage (GCS), with varying numbers of input parameter cases and model domain features. The results demonstrate that BBT‐ROM excels at predicting non‐wetting phase saturation (e.g., oil or saturation) and fluid pressure, with average relative errors ranging from 0.5% to 3%. Importantly, BBT‐ROM showcases robustness when faced with limited input parameter space during GCS testing.

58 GEOSCIENCES↗
DOE OSTI2024

Efficient and generalizable nested Fourier-DeepONet for three-dimensional geological carbon sequestration

Geological carbon sequestration (GCS) involves injecting CO2 into subsurface geological formationsfor permanent storage. Numerical simulations could guide decisions in GCS projects by predictingCO 2 migration pathways and the pressure distribution in storage formation. However, these simula-tions are often computationally expensive due to highly coupled physics and large spatial-temporalsimulation domains. Surrogate modelling with data-driven machine learning has become a promis-ing alternative to accelerate physics-based simulations. Among these, the Fourier neural operator(FNO) has been applied to three-dimensional synthetic subsurface models. Despite its good accuracyin simulating CO 2 plume migration, it requires large computational resources in training and alsolacks generalizability. Here, to further improve performance, we have developed a nested Fourier-DeepONet by combining the expressiveness of the FNO with the modularity of a deep operatornetwork (DeepONet). This new framework is twice as efficient as a nested FNO for training and has atleast 80% lower GPU memory requirement due to its flexibility to treat temporal coordinates sepa-rately. These performance improvements are achieved without compromising prediction accuracy.In addition, the generalization and extrapolation ability of nested Fourier-DeepONet beyond thetraining range has been thoroughly evaluated. Nested Fourier-DeepONet outperformed the nestedFNO for extrapolation in time with more than 50% reduced error. It also exhibited good extrapolationaccuracy beyond the training range in terms of reservoir properties, number of wells, and injectionrate.

Lee, Jonathan E. [Department of Chemical and Envir↗
DOE OSTI2024

Carbon Transport and Storage Planning and Viability Support Tools

The EDX disCO2ver Carbon Transport and Storage Planning and Viability Support Tools are made up of the Carbon Storage Planning Inquiry Tool (CS PlanIT, Justman et al. 2024) and the Carbon Storage Technical Viability Approach Support Tool (CS TVA). Together, these tools support data access to support understanding data availability to support planning efforts for carbon transport and storage. The Carbon Storage Planning Inquiry Tool (CS PlanIT) is an online web mapping application designed to help users explore, query, and evaluate multiple data layers to support and accelerate carbon storage resource and feasibility assessments and planning efforts. CS PlanIT currently contains a range of datasets associated with geologic, technical, and infrastructure factors. The data sets can be filtered geographically for an area of interest to update statistics and charts within the dashboard. The dashboard is divided into different sections called widgets, relating to different steps in the carbon storage planning process. The resources in this submission include a link to PlanIT, as well as a data catalog and link to user documentation. The original citation for the CS PlanIT tool, which has now been integrated into the toolset here, was: - Devin Justman, Scott Pantaleone, Maneesh Sharma, Lucy Romeo, Paige Morkner, CS PlanIT (Carbon Storage Planning Inquiry Tool) , 6/28/2024, https://edx.netl.doe.gov/dataset/cs-planit-carbon-storage-planning-inquiry-tool, DOI: 10.18141/2377953 The Carbon Storage Technical Viability Approach Support (CS TVA) Tool displays spatial data availability for the many components of Geologic Carbon Storage (GCS) technical viability assessment (Creason al 2025). Identifying sites suitable for GCS requires evaluating the intersection of myriad factors, including reservoir conditions, subsurface and surface hazards, infrastructure requirements, and energy community metrics. The technical viability of a site can only be confirmed for instances where all these factors have data available, and where those data support viability. Additional Resources related to the Technical Viability Assessment Tool: - Julia Mulhern, Casey White, Araceli Lara, Neyda Cordero Rodriguez, Zachary Jackson, Jacob Shay, Gabriel Creason, MacKenzie Mark-Moser, Paige Morkner, Kelly Rose, Carbon Storage Technical Viability Approach (CS TVA) Database, 3/26/2025, https://edx.netl.doe.gov/dataset/edx4ccs-carbon-storage-technical-viability-approach-database , DOI:10.18141/1984655 - Julia Mulhern, MacKenzie Mark-Moser, Gabriel Creason, Casey White, Araceli Lara, Neyda Cordero Rodriguez, Zach Jackson, Paige Morkner, Kelly Rose, Carbon Storage Technical Viability Approach (CS TVA) Matrix, 3/27/2025, https://edx.netl.doe.gov/dataset/carbon-storage-technical-viability-approach-cs-tva-matrix , DOI: 10.18141/2539979 - Gabriel Creason, Zach Jackson, Neyda Cordero Rodriguez, Julia Mulhern, Casey White, Araceli Lara, MacKenzie Mark-Moser, Paige Morkner, Kelly Rose, Carbon Storage Technical Viability Approach (CS TVA) Data Availability Results Database, 3/27/2025, https://edx.netl.doe.gov/dataset/carbon-storage-technical-viability-approach-cs-tva-data-availability-results-database, DOI:10.18141/2538557

Carbon storage↗
DOE OSTI2024

Application of quantitative risk assessment to address stakeholder questions in geologic carbon storage

Ambitious international greenhouse gas emissions reduction targets demand a rapid transformation to a low-carbon economy. This transformation includes the accelerated adoption of carbon dioxide (CO2) capture and storage (CCS) technology. However, as with any large-scale engineering enterprise, the widespread commercial-scale deployment of geologic carbon storage (GCS) raises important questions about technology and cost-effectiveness, safety, environmental risk, and long-term liability. Effectively assessing and managing risks and liability associated with GCS projects is a key technical need throughout the project life cycle-from site selection and permitting to monitoring design, operational risk management, and post-operational site closure. This presentation highlights recent advancements in tools for quantitative risk assessment, being developed by the National Risk Assessment Partnership (NRAP). NRAP is a multi-year, multinational laboratory research collaboration sponsored by the U.S. Department of Energy's Office of Fossil Energy and Carbon Management. Our focus will be on these tools' applications in addressing critical stakeholder questions related to supporting permitting to ensure secure and environmentally protective storage; designing effective and efficient monitoring plans; evaluating the effectiveness of remedial actions and risk management alternatives; and informing liability assessment and investment decisions. This paper will detail the key functionality of NRAP’s Open-Source Integrated Assessment Model (NRAP-Open-IAM), a computational framework for assessing leakage risk and containment assurance. This model features streamlined workflows for calculating leakage risk profiles, delineating risk-based area of review, and assessing contingency plans and post-injection site care requirements. ORION is an open-source, observation-based ensemble forecasting toolkit to help operators assess the seismic hazard at a carbon storage site. The State of Stress Analysis Tool (SOSAT), designed to assess subsurface stress conditions and evaluate geomechanical risk resulting from CO2 injection in an area of interest will also be presented. We will also introduce a prototype model to evaluate storage project costs and liability associated with risk management. The Technoeconomic and Liability Evaluation for Storage (TALES) model uses results from forecasts of leakage and induced seismicity risk to estimate the lifecycle cost of managing risk. Finally, a preliminary example of how the NRAP Risk-based Adaptive Monitoring Plan (RAMP) tool can be used to design efficient and effective site monitoring plans and estimate the detectability of fluid leakage will be provided. The relevance of these tools for addressing key stakeholder questions amidst uncertainty will be emphasized.

decision support↗
DOE OSTI2024

Curating Carbon Storage Data for Reuse: Enabling Research and Modeling from Earth’s Surface to Subsurface

The volume of public geologic carbon storage (GCS) data resources has continued to increase in recent years as the result of an increase in funding from government, industry, and academia towards national, basin, regional and field scale studies to ensure carbon capture and storage becomes a commercially viable operation. Despite the increasing volume of data, GCS data applied towards analyses such as geologic, cost, and risk modeling continues to be multi-sourced and often disparate in nature, published across government agencies, websites, data repositories and buried in derivative reports and documents. Much of the time preparing for an analysis and derivative product development is spent collecting, aggregating, transforming and preparing input data. There have been significant efforts within the DOE National Energy Technology Laboratory’s Carbon Storage Program to optimize multi-source, multi-scale subsurface geologic data curation and aggregation to support data discovery, interoperability, and reuse. Methods include the use of artificial intelligence, machine learning, and data science techniques. This talk will discuss the workflows, best practices, and processes developed to support the aggregation and curation of data through the whole system – surface to subsurface data - that support multi-scale, multi-purpose analysis for carbon storage research.

Morkner, Paige↗
DOE OSTI2025

Deep Learning-based Surrogate Model for Efficient Reservoir Simulation in Large-scale Geological Carbon Storage: Application in IBDP Dataset

This project introduces an advanced deep learning (DL)-based surrogate modeling approach to enhance the efficiency and accuracy of large-scale geological carbon storage (GCS) simulations. Using the Illinois Basin Decatur Project (IBDP) dataset as training data, the study employs a residual U-Net architecture to predict critical state variables such as pressure and CO₂ saturation, as well as CO₂ plume migration. By incorporating key geological parameters (e.g., porosity, permeability, and rock facies) and physics-informed inputs like the diffusive time of flight and time step, the DL model effectively reduces computational complexity while maintaining robust physical constraints. Compared to traditional simulators like Eclipse, the DL model achieves remarkable accuracy, with a root mean square error (RMSE) of 1.57 psi for pressure and 0.007 for saturation, and dramatically reduces computational time from hours to just 69.9 seconds for 50-step simulations. These results demonstrate the potential of innovative DL methodologies to improve the predictivity and operational efficiency of GCS simulations, providing a reliable foundation for decision-making in CCS operations. Supported by the SMART initiative, this project underscores the success of leveraging computational innovations to advance CCS technologies.

advanced deep learning↗
DOE OSTI2025

DELVE-ing into the Milky Way’s Globular Clusters: Assessing Extratidal Features in NGC 5897, NGC 7492, and Testing Detectability with Deeper Photometry

Extratidal features around globular clusters (GCs) are tracers of their disruption, stellar stream formation, and their host’s gravitational potential. However, these features remain challenging to detect due to their low surface brightness. We conduct a systematic search for such features around 19 GCs in the DECam Local Volume Exploration (DELVE) survey Data Release 2, discovering a new extra-tidal envelope around NGC 5897 and find tentative evidence for an extended envelope surrounding NGC 7492. Through a combination of dynamical modeling and analyzing synthetic stellar populations, we demonstrate these envelopes may have formed through tidal disruption. We use these models to explore the detectability of these features in the upcoming Legacy Survey of Space and Time (LSST), finding that while LSST’s deeper photometry will enhance detection significance, additional methods for foreground removal like proper motions or metallicities may be important for robust stream detection. Our results both add to the sample of globular clusters with extratidal features and provide insights on interpreting similar features in current and upcoming data.

Chiti, A. [Univ. of Chicago, IL (United States); S↗
DOE OSTI2026

Modeling supercritical CO 2 flow and mineralization in reactive host rocks with PFLOTRAN v7.0

Understanding the flow and reactivity of CO 2 injected into geological reservoirs is important for many subsurface applications including secure geologic carbon storage (GCS), critical mineral extraction, enhanced geothermal systems (EGS), and enhanced oil recovery (EOR). Traditionally, subsurface CO 2 injection for GCS applications has focused on geologic formations with favorable subsurface configurations for CO 2 migration and trapping through non-reactive mechanisms such as structural, solubility, and petrophysical trapping. Recently, CO 2 -reactive rocks such as mafic and ultramafic basalts have been investigated for their potential to react with injected CO 2 in situ to simultaneously dissolve host rock minerals and mineralize CO 2 as carbonates. Engineering rapid CO 2 mineralization in the subsurface is attractive because of the increased density of stored CO 2 , the additional safety factors associated with solidification, and the potential to extract valuable critical minerals. Here we present recent developments in the parallel flow and reactive transport simulator PFLOTRAN to model coupled CO 2 -brine flow and reactive transport for a wide range of injection and production applications involving reactive CO 2 -brine systems. These developments are based on the well established and trusted CO 2 flow capabilities in the STOMP-CO 2 simulator. New capabilities added to PFLOTRAN include new CO 2 -brine equations of state with optional thermal coupling, several new constitutive relationships like capillary pressure smoothing and scanning path hysteresis, a fully implicit well model, and native linkage with PFLOTRAN's well-established reactive transport libraries. A series of benchmarks between PFLOTRAN and STOMP-CO 2 verify the newly developed CO 2 -brine flow capabilities. Demonstrations of coupled CO 2 -brine flow modeling and reactive transport show how CO 2 mineralization can be engineered in reactive host rocks. Finally, an example use case involving copper leaching by CO 2 and critical mineral extraction is presented to showcase the strengths of this new implementation. Several limitations still remain, including limited availability of field data to parameterize models. Future work should constrain the evolution of mineral surface area during mineralization and the temperature and/or pH dependence of geochemical reactions for specific systems of interest.

Critical Minerals↗