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DOE OSTI2020

Empirical correlations for prediction of minimum miscible pressure and near-miscible pressure interval for oil and CO 2 systems

Although near-miscible CO 2 flooding has recently received considerable attention, no criteria are available to predict its applicability to a specific reservoir. Evaluating the viability of near-miscible flooding requires experimental exploration of a specific region in pressure–temperature space. The near-miscible pressure field is bounded on one side in P, T space by the MMP (minimum miscible pressure). This paper provides robust empirical correlations to estimate the MMP for both pure and impure CO 2 and for prediction of the near-miscible pressure region for CO 2 -oil. Many slim tube analyses, lacking high density data points, systematically underestimate the MMP, when the near miscible region is not accounted for. They are based on 147 published data sets that include: slim tube experimental determination of MMP; interfacial tension (IFT) between oil and CO 2 ; concentration of solution gas; and purity of the CO 2 . This paper is the first to begin a systematic exploration of the pressure-temperature space within which near-miscible effects characterize CO 2 floods. Our new correlations provide a basis for identifying and investigating the nature of near miscible effects associated with existing CO 2 floods. For a case study of an offshore field we determined that lower and upper pressure boundaries for effective near-miscible flooding, are 0.87 MMP and 1.07 MMP at reservoir temperatures. The proposed model is the first empirical correlation for the prediction of near-miscible pressure region, it will provide the basis for both screening the relative potential of oil reservoirs for economically viable miscible or near-miscible CO 2 -flooding.

42 ENGINEERING↗

DOE OSTI2022

Impact of coolant temperature on the combustion characteristics and emissions of a stratified-charge direct-injection spark-ignition engine fueled with E30

The direct injection spark ignition (DISI) engine has received considerable attention due to its potential to increase the power density of traditional spark ignition engines while significantly improving fuel economy through lean, unthrottled combustion. However, the market introduction of DISI engines operated in a lean combustion mode is inhibited by their unsatisfactory emissions, especially during cold start conditions that make proper mixture formation more challenging. Ethanol-blended gasoline, now a widely used fuel, makes the cold start of a DISI engine more difficult, leading to higher HC and soot emissions because of the high latent heat of vaporization of ethanol relative to gasoline. This work investigated the impact of coolant temperature on the characteristics of combustion and emissions in a stratified-charge DISI engine fueled with an E30 fuel (i.e. 30% ethanol in gasoline), while the coolant temperature was alternated between four levels (45, 60, 75, and 90 °C) to simulate different conditions throughout the warm-up process. The experiments showed that the coolant temperature affected the post-spark inflammation time, as well as the speed, intensity, and stability of the combustion process in the engine. When the coolant temperature rose, the engine produced more NOX and less CO, PM and HC. In addition, high-speed direct photography was used to obtain crank-angle resolved images of fuel sprays and flames in the cylinder. As the coolant temperature rose, the liquid spray lengths became shorter, reducing the possibility of wall wetting, and reduced irradiance from soot particles also indicated less nonpremixed combustion. The in-cylinder imaging results are consistent with the observed combustion and emission characteristics and shed light on the underlying processes. Finally, some potential solutions to the emissions challenges faced here could be either raising in-cylinder temperatures by using trapped residuals or modifying the injection schedule, for example by increasing the number of injections or to inject later in the cycle into a higher-density environment.

42 ENGINEERING↗

DOE OSTI2022

Multimodal study of the impact of stimulation pH on shale pore structure, with an emphasis on organics behavior in alkaline environments

The tight nature of shale formations calls for hydraulic fracturing techniques able of altering the pore architecture to facilitate the production of hydrocarbon resources. The composition, pH, salinity, density and chemistry of Hydraulic Fracturing Fluids (HFF) vary substantially depending on reservoir characteristics. pH is among the most important stimulation fluid properties, ranging from very acidic, for carbonate reservoirs, to basic, in the case of clay-rich formations. pH regulates what geochemical reactions take place between the stimulation fluid and shale, as well as pore architecture alteration. The dissolution of carbonates and pyrite, and the precipitation of common minerals such as barite, gypsum and iron oxides in acidic environments have been extensively documented. In contrast, alkali stimulation environments and the role of organic components have received less attention. This research provides insight into the role of both minerals and organic components during alkaline stimulation, and the resulting pore architecture alterations. Here, a set of reactive experiments are performed using three different shales, with varying organic matter (OM) content, at different pH (3, 6, 8, 10, 12). The analysis of pore architecture alteration induced by the reaction was performed via nitrogen (N2) gas adsorption and Time-Domain Nuclear Magnetic Resonance (TD-NMR). Inductively Coupled Plasma - Optical Emission Spectrometry (ICP-OES) was used to measure the composition of the stimulation fluid post-reaction. The system with a larger impact on the pore architecture (pH 12) underwent a more extensive analysis through the application of Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) and x-ray Diffraction (XRD). Results show that mineral dissolution contributed to the creation of secondary porosity, mainly through dissolution of clays and silicates. We show that the abundance and distribution of organic matter both play a significant role in changes in pore architecture. Most samples showed an increase in inter-organic porosity, likely due to the organic acids within kerogen reacting with the stimulation fluid. Samples with a larger amount and more widespread distribution of organic matter show the most significant alterations. Finally, we also highlight the dissolution process of iron-rich minerals, including framboidal pyrite, as another important source of secondary porosity.

04 OIL SHALES AND TAR SANDS↗

DOE OSTI2023

Numerical modeling of hydrogen mixing in a direct-injection engine fueled with gaseous hydrogen

Hydrogen is considered as one of the most promising options to achieve effective decarbonization of the energy and transportation sectors. As such, it has recently been receiving increasing attention because of its promising potential as an energy carrier for advanced energy and propulsion systems. With a focus on internal combustion engines, direct injection (DI) of gaseous hydrogen during the compression stroke offers great potential for high engine efficiency and specific power while reducing the risk of backfiring and pre-ignition issues. Therefore, many experimental and numerical efforts have recently been dedicated to understanding the physical and chemical behaviors of hydrogen in engine during mixing and combustion. This study focuses on computational fluid dynamics (CFD) modeling of the hydrogen DI process in a hydrogen optical research engine. Under the conditions studied, gaseous hydrogen is injected into the combustion chamber via a centrally located single-hole injector at a pressure of 100 bar. Two configurations, namely low-and high-tumble, are investigated to understand the impact of different in-cylinder flow patterns on the fuel-air mixture preparation. Simulations are carried out using the commercial CFD software CONVERGE. Here, the in-cylinder turbulence is modeled with an unsteady Reynolds-averaged Navier-Stokes (URANS) formulation closed by the renormalization group (RNG) k-ε model. Several numerical methods and model constants, including but not limited to turbulent Schmidt number, are evaluated. The numerical results are systematically compared against experimental measurements of velocity and hydrogen concentration fields on the vertical center plane to assess the performance of the CFD model, unveil the physics of hydrogen mixing, and establish best practices for modeling hydrogen DI under relatively high injection pressure conditions.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2022

High-performance of CrOx/HZSM-5 catalyst on non-oxidative dehydrogenation of C 2 H 6 to C 2 H 4 : Effect of supporting materials and associated mechanism

Ethane (C 2 H 6 ) is an important inexpensive and widely available fuel resource. High-value use of C 2 H 6 has become increasingly important. Catalytic dehydrogenation of C 2 H 6 to ethylene (C 2 H 4 ) has attracted much attention in recent years due to its high energy efficiency. The direct non-oxidative ethane dehydrogenation (EDH) to ethylene is a promising strategy to produce ethylene and hydrogen at the same time. In this research, Cr/HZSM-5 catalyst with superior stability was synthesized and exhibited an C 2 H 6 converting activity of 1.47 µmol/(g·s) with the corresponding C 2 H 6 conversion and C 2 H 4 selectivity of 37.3% and 90%, respectively. Herein, the synergistic effects of Si and Al in supporting materials were investigated by comparing Cr/HZSM-5 with SBA-15, SiO 2 and Al 2 O 3 supported ones, which contains either Al or Si with different structures. Characterization results indicated that the intimate interactions between Cr and support significantly improved the catalytic performance. The presence of Al in the support promoted the formation of more active Cr 6+ species by forming the aluminum-chromium-chromate (Cr-O-Al) structures which were more efficient to active C-H bond and form (Cr, Al)-OH groups during the reaction. Meanwhile, the formation of internal silanol group with the dissociative adsorbed H* could stabilized the active Cr phase to achieve a stable dehydrogenation activity.

03 NATURAL GAS↗

DOE OSTI2024

Optimal control of the electron temperature profile in DIII-D using machine learning surrogate models

The viability of the tokamak as a potential fusion reactor depends on the ability to keep the plasma in a stable regime while achieving temperatures, densities, and confinement times that are as high as possible. Tokamak scenario development attempts to find plasma regimes that achieve all of these conditions and are accessible with a given set of hardware constraints. This requires the ability to control plasma properties such as the normalized beta, the internal inductance, safety factor, rotation, etc. One property that has received less attention than some of the others, but is no less critical to achieving high performance, is the electron temperature (T e ) profile. In this work, Linear Quadratic Integral (LQI) control is used to develop a controller for the electron temperature profile in DIII-D. The controller is based on a linearized model derived from the transport equation that describes the evolution of the electron temperature, and includes contributions from the neural network surrogate models NubeamNet and MMMnet. Furthermore, the controller is tested in simulation using COTSIM, and is proven capable of tracking a target T e profile.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2026

The role of quantum computing in advancing scientific high-performance computing: A perspective from the ADAC institute

Quantum computing (QC) has gained significant attention over the past two decades due to its potential for speeding up classically demanding tasks. This transition from an academic focus to a thriving commercial sector is reflected in substantial global investments. While advancements in qubit counts and functionalities continue at a rapid pace, current quantum systems still lack the scalability for practical applications, facing challenges such as too high error rates and limited coherence times. Here, this perspective paper examines the relationship between QC and high-performance computing (HPC), highlighting their complementary roles in enhancing computational efficiency. It is widely acknowledged that even fully error-corrected QC will not be suited for all computational tasks. Rather, future compute infrastructures are anticipated to employ quantum acceleration within hybrid systems that integrate HPC and QC. While QC can enhance classical computing, traditional HPC remains essential for maximizing quantum acceleration. This integration is a priority for supercomputing centers and companies, sparking innovation to address the challenges of merging these technologies. The novelty of this work lies in its unique perspective, reflecting the collective insights of the Accelerated Data Analytics and Computing (ADAC) Institute, a global consortium of over 20 leading HPC centers. Recognizing the growing importance of QC, ADAC established a Quantum Computing Working Group in 2023 to foster collaboration and knowledge-sharing among its members. This paper synthesizes insights from the group’s collaborative efforts and incorporates findings from a member survey that captures shared experiences, ongoing projects, and strategic directions. By outlining the current landscape and challenges of QC integration into HPC ecosystems, this work offers HPC specialists practical and forward-looking guidance on the opportunities and implications of QC in computationally intensive endeavors.

Accelerated Data Analytics and↗

DOE OSTI2020

Influence of manganese abundances on iron and arsenic solubility in rice paddy soils

Arsenic (As) mobilization in rice paddy soils under fluctuating redox conditions is influenced by the biogeochemical cycling of redox sensitive elements such as iron (Fe) and manganese (Mn). Arsenic mobility in paddy soils is highly variable, and the influence of Mn abundances and Mn/Fe ratios on As mobility in these soils have received little attention. In this contribution, we developed a complementary set of field and laboratory experiments designed to evaluate the impact of Mn on interconnected Fe and As solubilization in rice paddy soils experiencing wetting-drying cycles through controlled irrigation. Porewater monitoring and synchrotron-based imaging and spectroscopy of thin sections prepared from an Arkansas paddy soil confirmed that As release was primarily governed by reductive dissolution of Fe (oxy)hydroxide phases. Experiments with laboratory soil microcosms amended with the synthetic nanocrystalline Mn oxide, δ-MnO 2 , showed that higher initial Mn/Fe inhibited Fe and As mobilization into porewater relative to unamended soil by up to 95% and 45%, respectively. Geochemical modeling suggests that pH increases driven by microbial MnO 2 reduction, in conjunction with microbial Fe- and sulfate-reduction in carbonate-rich porewater, enhanced the precipitation of siderite (FeCO 3(s) ), mackinawite (FeS (s) ), and potentially a Mn(II) arsenate phase. These secondary mineral phases likely played a greater role in controlling As solubilization than the role of Mn as a redox buffer regulating the redox conditions in the flooded soils. Field and laboratory experiments showed that alternate wetting and drying approaches with a single dry-down can be effective at reducing dissolved As concentrations in porewater through the oxidation of Fe. Differences in soil Mn/Fe ratios had no clear impact on the effectiveness of dry-downs as a strategy to reduce As mobilization.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

The need to consider regional supply chains and water usage in H 2 –steel transition

The iron and steel industry is one of the largest contributors to U.S. and global greenhouse gas emissions. Hydrogen can act as a promising reducing agent and clean energy carrier to decarbonize this sector, and has received significant attention in terms of process modelling, techno-economic analysis, and life cycle assessment in recent years. Policy incentives, hydrogen storage and transportation, and water stress levels are key factors that require significantly more consideration in order to realize hydrogen’s potential to decarbonize this industry. This review demonstrates the need for a systematic understanding and critical assessment of these areas, and their profound impacts on the decarbonization of the iron and steel sector. Furthermore, hydrogen and water supply face competition from other hard-to-decarbonize sectors, which should be considered on national and regional levels. Lastly, future research should also consider the impact of other environmental factors and hydrogen leak when deploying hydrogen at scale for industrial decarbonization.

Green steel↗

DOE OSTI2022

Soil temperature mitigation due to vegetation biophysical feedbacks

Understanding the interactions between temperature regimes and vegetation cover is one of the key scientific topics in global environmental change and land surface process research. Recently, the prominent global greening trend has inspired considerable interest in examining climate feedbacks to vegetation changes. However, the impacts of recent widespread vegetation cover changes on soil temperature have been less documented and have received insufficient attention. Here, a high-resolution regional climate model was used to examine the potential impact of vegetation cover changes over the past 37 years, obtained as satellite observational data, on regional air and soil temperatures across the Heilong-Amur River Basin. Our sensitivity experiments indicated that the 1.85% increase in the regional fractional vegetation cover (FVC) from 1982 to 2018 cooled the air temperature by 0.045°C and the soil temperature by 0.19°C and that this cooling effect would continue in the near future, from 2016-2018 to 2051-2053. We found that soil temperatures were more sensitive to vegetation cover changes than air temperatures, particularly in mid-high latitude forest ecosystems; this finding helps to explain the nonsignificant decrease in soil temperatures in forest ecosystems over the past 40 years detected in the ERA5-Land reanalysis. Further, our results also suggest that vegetation restoration in forest ecosystems could mitigate the effects of climate change on permafrost environments by increasing the number of soil freezing days.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Review of allanite: Properties, occurrence and mineral processing technologies

Allanite is commonly encountered as an accessory rare-earth silicate mineral in association with minerals such as garnet, biotite, and feldspar. It is distributed globally and occurs in igneous formations such as granites, pegmatites, and syenites, as well as in various metamorphic rocks such as schist, gneiss, and amphibolite. Moreover, it can be found in mineral veins formed through hydrothermal activity. While allanite has not yet been extensively utilized for the production of rare-earth elements, recent discoveries of high-grade rare-earth-rich allanite deposits in Wyoming, USA, highlight its economic potential. However, despite ongoing research on the mineralogy and processing of rare-earth minerals, allanite has not received widespread attention in mineral processing. To achieve economical extraction of rare-earth elements from allanite in the future, systematic studies on processing techniques (e.g., density separation, magnetic separation, flotation, leaching) are imperative to fully unlock the potential of allanite as a rare-earth element source. To pave the way for future investigation of allanite and address the unique processing challenges, this review article aims to comprehensively summarize previous studies, encompassing properties, occurrences, and processing technologies of allanite.

42 ENGINEERING↗

DOE OSTI2026

Emerging anomaly detection techniques for electronic health records: A survey

Background Anomaly detection in electronic health records (EHRs) is a cornerstone of biomedical informatics, with direct implications for patient safety, clinical decision-making, and the prevention of healthcare fraud. Once guided primarily by simple rule-based methods, the field has advanced rapidly, driven by increased computing power, richer and more detailed health data, and the rise of machine learning and deep learning techniques. The objective of this paper is to provide a comprehensive overview of modern approaches to detecting anomalies in EHRs, outlining their strengths, limitations, and relevance to key healthcare challenges. We review traditional statistical methods alongside newer ML- and DL-based strategies and hybrid models, with particular attention to how these techniques support transparency and build clinical trust. Methods This paper presents a thorough and critical survey through systematic review (PRISMA-based) of the latest anomaly detection strategies in time-sequence data domains within electronic health record systems. Results We explore a broad spectrum of methodologies, including statistical models, supervised and unsupervised learning approaches, hybrid frameworks, and state-of-the-art ML-based techniques that collectively advance the precision and scalability of detecting anomalies in complex clinical datasets. In addition to mapping current capabilities, we address the enduring challenges that hinder widespread implementation and provide a forward-looking perspective on the future of anomaly detection in the data-rich landscape of modern healthcare. Summary The advancement in AI-based approaches is reported along with the basic principles of the individual approaches and their applicability. The increased availability of high-quality data, advancements in DL approaches, and enhanced computation power are leading to more frequent adaptation of DL-based approaches. Emerging DL-based approaches that have been adapted in other domains or recently applied in the EHR domain are also discussed in detail. Although DL-based approaches can improve model predictions by incorporating comorbidities, their application is limited in low-frequency data domains (e.g., when the total available data remains in the single digits). Therefore, the user must carefully consider the application based on data availability.

Anomaly detection↗

DOE OSTI2024

A multiphase flow model of water droplets dielectrophoretic-induced air dehumidification phenomena

Air humidity in indoor spaces plays a critical role in human comfort and health. Dehumidification systems are used for building humidity controls, but they can take significant energy consumption, especially in geographic locations with high outdoor humidity and warm climates. Consequently, there is a growing demand for innovative dehumidification processes that consume minimal energy. Dielectrophoretic air dehumidification represents one such promising approach. However, it has not garnered significant attention due to the absence of engineering models and simulation tools capable of evaluating its performance and limitations at large-scale airflows. A new numerical multiphase CFD model, which is also experimentally validated, is developed in a customized Reacting Foam solver based on OpenFOAM® version 9. The newly developed model seeks to decrease substantial energy consumption and lower costs by leveraging the dielectrophoretic phenomenon to regulate moisture levels in the air. The solver integrates a hybrid Eulerian-Lagrangian framework to track the droplet's trajectory and growth rate while solving the continuum equations for the moist air. An electrospray produces electrically charged droplets, which grow during their in-flight trajectories as water vapor condenses onto their surfaces. The role of electrostatic forces in promoting vapor condensation within a high-gradient electrical field is investigated, and the dielectrophoretic vapor nucleation process on charged water droplets is discussed. The CFD model was validated against results from the literature and from proof-of-concept experiments conducted by the authors, which showed a 2 % air dehumidification with a single electrospray and airflow rate of 5 cubic feet per minute. The simulation results indicated that augmenting the number of electrically charged spray droplets increased the dehumidification of the air to 25 %. The initial mean droplet diameter, the orientation of the injector and relative humidity significantly influence the assessment of dehumidification. As a result, scaling up this approach to larger airflow volumes is identified as a potential future research direction.

42 ENGINEERING↗

DOE OSTI2024

A Review: Indirect Optimal Control of Wave Energy Converters

Wave energy conversion has been the subject of interest in the past several years. While there are several concepts for converting wave power into electric power, the cost of the electric power harvested from ocean waves remains high. One of the main challenges, though it receives less attention, is the control of the wave energy converter (WEC). This paper presents a treatment for the WEC control problem within the context of optimal control theory. The result is systematic development for an explicit expression for a control that maximizes the harvested energy while meeting operational constraints such as the maximum device stroke and the maximum control force. The control presented here can also be adjusted to meet device design constraints such as a limitation on the amount of reactive power available from the power take-off (PTO) unit; this feature enables a control co-design for the PTO unit. Numerical simulations are presented in this paper for demonstration.

control design↗

DOE OSTI2025

Research progress on organic acid pretreatment of lignocellulose

Lignocellulosic biomass is a naturally occurring, renewable resource that is utilized to produce a variety of high–value-added products, such as fuels, acids, and building block chemicals. The pretreatment of lignocellulosic biomass is a crucial step in the deconstruction and fractionation of its components. Organic acids, such as formic, acetic, lactic, and maleic acids, have been widely studied for their effectiveness in lignocellulose pretreatment. Organic acid–based pretreatment techniques are gaining increased attention due to their ability to selectively separate hemicellulose and cellulose, promote oligomer formation, and minimize byproducts. This paper presents a comprehensive review of the various advancements in the science and application of organic acids for the pretreatment of lignocellulose. Furthermore, the significant challenges of organic acid recovery after pretreatment are highlighted, and different recovery methods are discussed. In conclusion, the future challenges related to utilizing organic acids for lignocellulose pretreatment are summarized, with a strong emphasis on adopting a sustainable approach to converting valuable bioresources into renewable products.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

A review of active probing-based system identification techniques with applications in power systems

System identification is becoming a relevant research area for numerous applications in power grids due to the increasing complexity of the system. A paradigm shift in power system infrastructure driven by renewable energy resources, controllable loads, and new power electronics technologies have given rise to new challenges in power systems operation and control. This increased complexity of power systems and unavailability of physics-based models of most inverter-based resources requires that traditional modeling of power systems approaches be complemented by system identification-based black or grey box modeling techniques. In light of the importance of system identification and little attention paid to the applications of these techniques in transforming power systems, this paper provides a comprehensive review of active probing-based system identification methods in the context of power system applications. It reviews applications of both linear and nonlinear system identification with discussion on their potential and key takeaways. To motivate a further practical use in power systems, the paper provides an example of system identification, to develop a state space model of an unknown plant with step-by-step details. The paper highlights the advantages of using modern power electronics-based sources in the identification process and discusses the emerging research directions for future.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Numerical assessment of fault impact on caprock seals during CO 2 sequestration

Coupled fluid-flow and geomechanical analysis of caprock integrity has gained a lot of attention among scientists and researchers investigating the long-term performance of geologic carbon storage systems. Reactivation of pre-existing fractures within the caprock or re-opening of faults can create permeable pathways which can influence the seal integrity. Stability of the caprock during and after injection of super-critical CO 2 , and the impact of pre-existing fractures in the presence or absence of one or multiple faults have been investigated in this study. The impact of the wellbore orientation and the injection rate are among other key factors in understanding the structural trapping mechanisms within such geological formations. In this study, we numerically investigated the impact of each of these factors. This study revealed the interplay between joints and faults and how different leakage pathways are formed and under which scenario they play a dominant role in terms of CO 2 leakage. Finally, this study also highlights the role of one versus multiple faults in the domain and the importance of the fault hydrological property in forming leakage pathway.

58 GEOSCIENCES↗

DOE OSTI2019

Ballistic-diffusive phonon transport in cellulose nanocrystals by ReaxFF molecular dynamics simulations

Cellulose nanocrystals (CNCs), a ubiquitous nano-sized natural biopolymer, have drawn considerable attention in recent years due to their excellent mechanical and chemical properties. However, thermal properties of CNCs, which are critical for their potential applications as structural or functional materials, are less investigated. In this work, their thermal properties were systematically studied by molecular dynamics (MD) simulations in terms of the effects of polymorphs, size, temperature, and strain. It was found that the thermal conductivities depend on the polymorphs of CNCs, but the thermal conductance of unit chain of each polymorph is similar. Moreover, the strong dependence of the thermal conductivities of CNCs on their length and cross-sectional area indicates the existence of remarkable ballistic-diffusive phonon transport. Single chain CNCs can achieve a thermal conductivity of ~6 W/(m∙K), implying their potential as high thermal conductive polymer. The mechanical strain can enhance the alignment of cellulose skeleton, which reduces phonon scattering and thus increases thermal conductivity. Possible underlying mechanism of the varying thermal conductivity was further discussed and attributed to the shift of phonon frequencies and varied degree of orientation. This study reveals the sophisticated ballistic-diffusive phonon transport in CNCs, and pave path to future design of CNC-based materials with desirable thermal properties.

42 ENGINEERING↗