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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Identification of Dynamic Force Coefficients for an Additively Manufactured Hermetic Squeeze Film Bearing Support Damper Utilizing a Pass-Through Channel

Abstract The following paper presents breakthrough experimental results for a new hermetic squeeze film damper (HSFD) concept that is integrally designed within an externally pressurized tilting-pad radial gas bearing support. The flexibly damped gas bearing module was designed for a 7.2″ (183 mm) diameter shaft and fabricated using direct metal laser melting (DMLM); also known as additive manufacturing. The bearing and HSFD were sized based on ongoing studies for oil-free supercritical carbon dioxide (sCO2) power turbines in the 8.5 MW–10 MW power range. The development of the new damper concept was motivated by past dynamic testing on HSFD, which generated frequency-dependent stiffness and damping force coefficients. In efforts to eliminate the frequency dependency, a new HSFD architecture was conceived that adds accumulator volumes and a pass-through channel to previously conceived HSFD flow network designs. The other motivation for the work is the need to develop a cost-effective and reliable oil-free bearing technology that is scalable to large power turbomachinery applications. There were several objectives for the following work. The first objective was to successfully design and fabricate a single piece bearing-damper using additive manufacturing, while dimensionally controlling critical design features. The paper discusses the manufacturing steps and shows cut-ups that reveal adequate clearance control capability with internal damper clearances. The second objective was to perform experimental testing with the new HSFD design in efforts to extract stiffness and damping coefficients for excitation frequencies within 20–160 Hz and peak vibration amplitudes between 0.25 mils (6.35 microns) to 1 mil (25.4 microns). The test results for a single HSFD bearing module indicated that the design modifications to the HSFD architecture were successful in eliminating nearly all the frequency dependencies for the stiffness force coefficient. The dynamic tests yielded a stiffness coefficient that varied between 112 klb/in. (19.6 MN/m) and 96 klb/in. (16.8 MN/m). The damping force coefficient however, exhibited relatively more variation with frequency with values residing between 175 lb-s/in. (31 kN-s/m) to 214 lb-s/in. (37 kN-s/m). Finally, the paper advances a three-dimensional fluid-structure interaction (FSI) model using transient finite element analysis (FEA) coupled to a computational fluid dynamics (CFD) model. The FSI analysis performed between 20 Hz and 80 Hz was used to predict the stiffness and damping of the HSFD using a quarter-section model of the damper. The FSI analysis was able to support test results by showing only a 6–7.4% change in the magnitude of force coefficients. Stiffness predictions agree reasonably well with experiments whereas damping is underpredicted.

Engineering↗
DOE OSTI2024

Energetic Particles - microturbulence interaction thrust (EP SIWG white paper)

This white paper outlines a strategic approach to Energetic Particle (EP) research, aimed at applying the results to the future burning devices such as ITER. Our goal is to deepen the understanding of the interplay between plasma background microturbulence and diverse species of energetic ions in burning plasma devices. This strategy is poised to advance our knowledge in this crucial area of fusion science, paving the way for significant breakthroughs in plasma physics and fusion energy research. We consider the confinement of energetic ions such as auxiliary heating beams, minority ions from ICRH, and fusion product alpha particles. Recent studies have underscored the potential of EP/microturbulence interaction to enhance fusion plasma performance by stabilizing microturbulence. However, it also poses risks, such as exacerbating Alfvénic eigenmode (AE) instabilities, which could compromise the sustainability of plasma discharge by causing EP losses. The interplay between EPs and background microturbulence is evident in the phenomenon of effective pitch angle scattering, a crucial aspect of the quasilinear (QL) theory. This theory is integral to developing numerically-efficient yet comprehensive and self-consistent approaches, recently employed to investigate the relaxation of energetic particle populations in the holistic modeling of fusion-grade plasmas. The primary objectives of this white paper thrust encompass a dual focus. First is that we need to delve into the mechanisms through which microturbulence engenders effective pitch angle scattering. This investigation will entail the examination of the presence of known or self-consistently predicted spectra of modes accountable for microturbulence. In these explorations, electrostatic microturbulence serves as a logical initial stride towards achieving the trust's objectives. This endeavor is anticipated to yield formulations expressing the parametric dependencies of the effective pitch angle scattering frequency on variables such as thermal plasma electron and/or ion temperatures, as well as their respective thermal conductivities. In our second objective, we aim to delve into the intricate formation of zonal flow (ZF) structures amidst the complex interplay of microturbulence and Alfvenic eigenmodes (AEs). This endeavor poses greater challenges as we seek to unravel the macroscopic manifestations influenced by microturbulence, termed as zonal structures (ZS), stemming from microturbulence-induced ZF. Drawing from this understanding, we anticipate employing a QL approach to yield comprehensive insights into the distribution function of energetic particles (EP) within phase space. This method entails resolving the dominant multidimensional phase space diffusion processes while effectively averaging over the rapid ballistic responses. Nevertheless, substantial strides remain imperative to realize a comprehensive whole-device modeling framework. This entails meticulous verification and validation exercises against experimental observations, as well as rigorous benchmarking against theoretical frameworks and numerical simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

SPARC-X: Quantum simulations at extreme scale - reactive dynamics from first principles

We have developed the massively parallel electronic structure code SPARC-X: a computational framework for performing Kohn-Sham Density Functional Theory (DFT) calculations that can scale linearly with the number of atoms in the system, while being able to leverage petascale and emerging exascale parallel computers to study chemical phenomena at unprecedented length and time scales. SPARC-X exploits a recent breakthrough in electronic structure methodologies: systematically improvable, strictly local, orthonormal, discontinuous real-space bases that efficiently and systematically capture the local chemistry of the system. With further adaptation using new machine-learning techniques and the use of the massively parallel Spectral Quadrature (SQ) electronic structure method, the algorithmic complexity and prefactor associated with DFT calculations involving semilocal as well as hybrid functionals are dramatically reduced. Using petascale computational resources, SPARC-X enables quantum mechanical simulations at length and time scales previously accessible only by empirical approaches, e.g., 1,000,000 atoms for a few picoseconds using semilocal functionals or 1,000 atoms for a few picoseconds using hybrid functionals. Using exascale resources, the sizes and times targeted are two orders of magnitude larger. Such a capability has applications in a wide variety of chemical sciences, including reactive interfaces where large length- and/or long time-scales are needed and traditional force fields fail. This is particularly important in dynamic catalysis, where bond breaking and formation must be understood in detail. We developed, tested, and employed the SPARC-X framework to understand the photocatalytic properties of TiO 2 nanoparticles, revealing finite size effects that cannot be captured with standard model systems or functionals. This integrated development and application strategy ensures that SPARC-X remains a robust, efficient, and scalable software package for quantum simulations on current petascale and emerging exascale computing resources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Performance Criteria for Capture and/or Immobilization Technologies (Revision 1)

The capture and subsequent immobilization of regulated volatile radionuclides from the off-gas streams of a used nuclear fuel (UNF) reprocessing facility has been a topic of substantial research interest for the US Department of Energy and its international counterparts. Removal of specific radionuclides from the plant effluent streams before discharge to the environment is required to meet regulations set forth by the US Environmental Protection Agency. Upon removal, the radionuclides, as well as associated sorbents that cannot be regenerated in a cost-effective manner, are destined for conversion to a waste form. Research in separation and capture methodologies has included a wide range of technology types, and studies of waste forms are correspondingly diverse. In considering the future development and implementation of both sorbents and waste forms, it is necessary to identify benchmark measures of performance to objectively evaluate each sorbent system or waste form. Sets of performance criteria and associated metrics have been developed for sorbent and waste form evaluation. These criteria address physical, radiological, and chemical characteristics, technical practicality, technical maturity, cost, and, for sorbents, system performance. The criteria and metrics appear to be robust and should be applicable despite the eventual waste classification (as either high- or low-level waste). They are flexible enough to address both aqueous reprocessing and electrochemical reprocessing of UNF. These criteria sets can serve as tools to evaluate performance at multiple stages within the development process, and in this revision (Revision 1) they have been used to assess technologies relating to krypton/xenon separations and iodine capture from off-gas streams arising from UNF reprocessing. Assessment of krypton/xenon separations using engineered forms of two zeolite minerals (silver mordenite and hydrogen mordenite in a polyacrylonitrile-based binder [AgZ-PAN/HZ-PAN]) found that the zeolite-based separation is relatively advanced in its development, but several key issues require resolution. First, desorption processes for both krypton and xenon require refinement to provide an understanding of the product purity that can be achieved. Second, adsorption rate data is needed in order to calculate the bed depth required for effective separation. Finally, it is strongly recommended that a technical review of krypton/xenon separation by AgZ-PAN/HZ-PAN be performed to synergize available data and assess the cost savings and operational benefits that may be realized from implementation of this technology. Assessment of metal organic frameworks (MOFs) for their use in the separation of krypton/xenon found that the ideal separation would be performed using a single-column system with a MOF selective for krypton over xenon. A robust research effort should work to identify a krypton-selective MOF designed to operate at temperatures of approximately 0°C or higher, which could be preferred over cryogenic krypton/xenon capture for used fuel reprocessing off-gas streams. In the case of the CaSDB-MOF (the most well-understood xenon sorbent to date), two issues are judged of high importance. First, xenon breakthrough capacity for the CaSDB-MOF in prototypical conditions should be determined. Preliminary research indicates that breakthrough may be near immediate, presenting a substantial obstacle in separative system design. Second, development of desorption methodology should be performed to determine regeneration time, energy requirements, and the product stream composition. Silver-based sorbents (AgZ and AgAero) for use in iodine capture from the dissolver off-gas were evaluated against the established criteria. These sorbents are significantly better understood for this application as a result of research efforts over the past decade. The potential implementation of AgAero at a large scale is hindered by its physical degradation by components of the dissolver off-gas stream. Less is known about the adsorption of iodine by these sorbents from other off-gas streams in the plant. Initial experimental efforts have been closely coordinated in an effort to understand organic iodine (such as would be found in the vessel off-gas) adsorption by AgZ and AgAero. Future work should expand this experimental program, and analysis of other reprocessing facility off-gas streams such as the vitrification off-gas stream should be conducted to better understand other potential applications for iodine sorbents. A review of iodine waste form development shows that this area is diverse and that multiple promising waste forms have been identified for the immobilization of radioactive iodine. Efforts related to the direct conversion of iodine sorbents (including AgZ and AgAero) should be continued because of the advantages of direct conversion in a waste management strategy and other sorbents should continue to be advanced as merited.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Systems Biology-Based Optimization of Extremely Thermophilic Lignocellulose Conversion to Bioproducts

This was a collaborative project involving researchers at the University of Georgia, North Carolina State University, Sanford‐Burnham‐Prebys Med. Discovery Institute and the University of Rhode Island. The over-arching goal was to demonstrate that non-model microorganisms, specifically extreme thermophiles, can be a strategic metabolic engineering platform for industrial biotechnology. We engineered the most thermophilic lignocellulose-degrading organism known, Caldicellulosiruptor bescii (Cbes) , which grows optimally near 80°C, and the most thermophilic fermentative organism known, Pyrococcus furiosus (Pfu) , which grows optimally at 100°C, to produce several key industrial chemicals. This work leveraged recent breakthrough advances in the development of molecular genetic tools for these organisms, complemented by a deep understanding of its metabolism and physiology gained over the past decade of study in the PIs’ laboratories. We applied the latest metabolic reconstruction and modeling approaches to optimize biomass to product conversion. Bio-processing above 70°C can have important advantages over near-ambient operations. Highly genetically-modified microorganisms usually have a fitness disadvantage and can be easily overtaken in culture when contaminating microbes are present. The high growth temperature of extreme thermophiles precludes growth or survival of virtually any contaminating organism or phage. This reduces operating costs associated with reactor sterilization and maintaining a sterile facility. In addition, at industrial scales, heat production from microbial metabolic activity vastly outweighs heat loss through bioreactor walls such that cooling is required. Extreme thermophiles have the advantage that non-refrigerated cooling water can be used if needed, and heating requirements can be met with low-grade steam typically in excess capacity on plant sites. We assembled a highly interdisciplinary team that brought together all of the expertise for the project to have successful outcomes. This project also built upon and utilized extensive information already available in the PIs’ labs for both Cbes and Pfu to develop models that provide a comprehensive description of these organisms’ physiology and metabolism that were utilized to inform metabolic engineering strategies. The models were validated with experimental data and the results demonstrated that unpretreated lignocellulose has the potential to be converted into value-added industrial chemicals at high loading at bioreactor scale. The data generated from this research were published in twenty-one peer-reviewed papers in international journals with online access.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Protein–Protein Interaction Networks Derived from Classical and Machine Learning-Based Natural Language Processing Tools

The study of protein-protein interactions (PPIs) provides insight into various biological mechanisms, including the binding of antibodies to antigens, enzymes to inhibitors or promoters, and receptors to ligands. Recent studies of PPIs have led to significant biological breakthroughs. For example, the study of PPIs involved in the human:SARS-CoV-2 viral infection mechanism aided in the development of the SARS-CoV-2 vaccines. Though several databases exist for the manual curation of PPI networks, text mining methods have been routinely demonstrated as useful alternatives for newly studied or understudied species where databases are incomplete. Here, the relationship extraction (RE) performance of several open-source classical text processing, machine learning (ML)-based natural language processing (NLP), and large language model (LLM)-based NLP tools were compared. Overall, our results indicated that networks derived from classical methods tend to have high true positive rates at the expense of having overconnected-networks, ML-based NLP methods have lower true positive rates but networks with the closest structures to the target network, and LLM-based NLP methods tend to exist in-between the two other approaches, with variable performances. Finally, the selection of a specific NLP approach should be tied to the needs of a study and text availability, as models varied in performance due to the amount of text provided.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Results and lessons learned from accelerating radio frequency modeling using machine learning [slides]

The “advanced tokamak” reactor concept is a leading candidate for a steady state fusion pilot plant. An advanced tokamak (AT) sustains a majority of the required plasma current with effects resulting from maintenance of the peaked pressure at the device center. This current is augmented by auxiliary current drive sources. These auxiliary actuators may consist of neutral particle beams and/or radio frequency (RF) systems such as lower hybrid current drive (LHCD) and high harmonic fast wave (HHFW) current drive using radio and microwaves from antennas. The primary focus of this work is to develop models of RF current profile control suitable for use in integrated modeling frameworks and for real-time control in experiments. Direct physics models of RF current drive can be computationally intensive. In order to achieve predictive times appropriate for the thousands of calls needed in real-time control of experiments and for use in integrated models, we will apply modern machine learning (ML) techniques to accelerate these models and interpolate their results. To generate the fast and accurate models for use in control level algorithms and integrated modeling we need to replace present models with high dimensional interpolation of their results. We will perform additional simulations across a broader parameter range for EAST and other tokamaks in different physics regimes (Alcator C-Mod, DIII-D, WEST, CFETR, ARC, ITER) and combine them into a larger database for training and testing of the ML models. Further testing of the control level models with experimental current profile data from EAST and C-Mod tokamaks will provide additional confirmation of the control level model before integration in a tokamak control system or integrated modeling suite. ML will be used to optimize the selection of training data consisting of RF current driven at different values of density profile, temperature profile, plasma current, and wavenumber. ML will also be used to facilitate classification of current drive from these input data. The output of this effort will be a validated classifier capable of determining the current drive profiles for HHFW CD and LHCD on a mille-second timescale. This will provide a breakthrough capability enabling real-time control of RF driven current profiles in experiments including ITER ICRF and use integrated modeling frameworks requiring thousands of current profile calculations in discharge simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

State of Innovation 2025: Progress in Accelerating Next-Generation Cement and Concrete Technologies

The cement and concrete sectors are entering a decisive period as next-generation technologies advance from laboratory research to demonstration, early deployment, and first-of-a-kind commercial plants. Building on the 2024 State of Innovation report, the 2025 outlook highlights both the rapid acceleration of innovation and the urgent need for coordinated action across the value chain. Venture capital activity into the cement and concrete space stabilized following the record surge of 2022-2023, yet landmark financings, such as Sublime Systems' $200 million round and Terra CO2's $124 million Series B, signal continued investor confidence in companies approaching commercialization. Corporate procurement has become a powerful new catalyst, with Microsoft, Amazon, and CRH (Cement Roadstone Holdings) Ventures providing long-term commitments that underpin the first wave of next-generation cementitious products. The sector is shifting from early-stage experimentation toward the scaling of well-capitalized leaders capable of bridging the critical "capitalization gap." Early innovators continue to expand the toolkit through novel binders, electrochemical cements, biogenic limestone, and carbonate mineralization pathways. Going into 2026, cost competitiveness, durability validation, and scalability enabled by resilient supply chains remain the decisive factors for market adoption. At the 2025 Next Generation Cement and Concrete Critical Technologies Meeting, attendees emphasized dual-track funding strategies that integrate federal grants with private capital as key to enabling market breakthrough. State programs and corporate demand are sustaining momentum, while successful companies increasingly demonstrate both economic value and reduced dependence on imported materials. The National Concrete Pavement Technology Center and others underscored that broad integration of next-generation materials will hinge on standards compatibility, verified field performance, and workforce readiness. Colorado continues to serve as a proving ground through pilot programs that combine supplier training, phased implementation, and real-world data to de-risk innovation and provide replicable models for other regions. The 2025 Cement and Concrete Critical Technologies Workshop reinforced that scaling next-generation materials will require alignment among technology innovation, performance validation, and market demand. Stakeholders must move beyond siloed efforts toward collaborative frameworks that coordinate standards, funding, and infrastructure deployment. As a neutral convener and technical validator, the National Laboratory of the Rockies (NLR) plays a pivotal role in bridging innovation and market adoption through collaborative research, technology validation, and entrepreneurship programs. By uniting innovators, incumbents, policymakers, contractors, and investors, NLR and its partners are helping chart a credible pathway toward widespread commercialization in the decade ahead.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Adaptive Multirate Mass Transfer (aMMT) Model: A New Approach to Upscale Regional‐Scale Transport Under Transient Flow Conditions

Abstract The long‐term evaluation of regional‐scale groundwater quality needs efficient upscaling methods for transient flow. Upscaling techniques, such as the Multirate Mass Transfer (MRMT) method with constant upscaling parameters, have been used for transport with steady‐state flow, yet the upscaling parameters (i.e., rate coefficients) may be time dependent. This study proposed and validated an adaptive MRMT (aMMT) method by allowing the mass transfer coefficients in MRMT to change with the flow field. Advective‐dispersive contaminant transport simulated in a 3‐D heterogeneous medium was used as a reference solution. Equivalent transport under homogeneous flow conditions was evaluated by applying the MRMT and aMMT models for upscaling. The relationship between mass transfer coefficients and flow rates was fitted under steady‐state flow driven by various hydraulic gradients. A power law relationship was obtained, which was then used to update the mass transfer coefficients in each stress period under transient flow conditions in the aMMT method. Results indicated that for advection‐dominated transport, both the MRMT and aMMT methods can upscale the anomalous transport dynamics affected by subgrid heterogeneity under transient flow conditions. Whereas for diffusion‐dominated systems, the MRMT model failed to capture the tails of tracer breakthrough curves after the boundary condition changed, but the results from the aMMT model were significantly improved. However, if the overall flow direction changed, both MRMT and aMMT failed to represent the breakthrough curve tail generated by the heterogeneous system. The results point toward a promising path for upscaling transport in complex aquifers with transient flow.

Guo, Zhilin↗
DOE OSTI2021

DOE Energy Frontier Research Centers Center for Direct Catalytic Conversion of Biomass to Biofuels (C3Bio)

New capabilities to predict, design and control the chemistries of carbon could answer a global imperative to transition from fossil-based to sustainable transportation fuels. While the use of inexpensive hydrocarbons has been an unparalleled achievement and enabler of economic prosperity for many nations, singular dependence upon crude oil has given rise to systemic vulnerabilities in climate, energy, economic, and national security. Lignocellulosic biomass, a renewable and carbon-neutral resource, has the potential to displace an estimated annual equivalent of three billion barrels of oil in the U.S. alone (National Research Council 2009, U. S. Department of Energy, 2011). However, biomass has only one-third the energy density of crude oil (Agrawal and Singh 2009, Richard 2010) and lacks petroleum’s versatility as a feedstock for fuels and chemicals. These limitations keep biomass conversion below the efficiency level needed for strategic impact while the scientific challenge of routing carbon from one molecular context to another remains unmet. In 2009, the Center for Direct Catalytic Conversion of Biomass to Biofuels (C3Bio) recognized the potential of chemical catalysis and fast pyrolysis to overcome such limitations by transforming the main components of biomass (cellulose, xylan, and lignin) from grasses and trees directly to liquid hydrocarbons and aromatic co-products. Enabled by the EFRC high-risk, high-reward approach to grand challenge science, C3Bio researchers have been key national players in disrupting the conventional paradigm of the cellulosic biorefinery into a new future of “no carbon left behind”—the full utilization of carbon from plant cell walls in energy-dense fuels (Fig. 1). We identified catalytic and fast-pyrolytic pathways that utilize cellulose, xylan and, most significantly, lignin. We developed catalytic processes that deoxygenate and transform monomers and isolated polymers into useful products and tested their use with intact biomass. We gained control of lignin synthesis within plants and initiated tailoring biomass to its end-use through the tools of plant molecular biology and genetic engineering. C3Bio breakthroughs have increased the energy density of biomass-derived substrates via catalytic and pyrolytic conversions into products such as benzoquinones, furfural and hydroxymethylfurfural, levoglucosan and levulinic acid, methoxypropylphenols and propylbenzene. Such advances in biomass conversion would not have been possible without simultaneous advances in analytical instrumentation and methodologies, and imaging technologies and applications. The legacy science developed by C3Bio enables design and control strategies for achieving a targeted product portfolio of fuel and chemical feedstocks from a diverse range of native and tailored biomass. Our research provides the knowledge base required for a bio-economy with product streams as diverse in functionality as those of the petrochemical industry. Coupling targeted computational modeling with experimentation, we achieved: (1) fundamental understanding of biopolymers and cell wall architecture assembly, (2) discovery of new chemistries that allow the development of highly selective pathways to fuels and desirable chemicals, and (3) an integrated systems-level understanding to control catalytic and pyrolytic pathways.

09 BIOMASS FUELS↗
DOE OSTI2023

Ultrasonic waveguide reflectometry for quench detection (CRADA Final Report)

Early detection of thermal runaway (quench) is critical for safely operating high-temperature superconductor (HTS) magnets. Voltage-based techniques provide late detection of quenching in HTS, which can lead to magnet burnout and system damage. Various non-voltage quench-detection approaches are therefore investigated nowadays that are based on magnetic, acoustic, and optical sensing. LBNL has been actively developing the quench detection principle based on diffuse ultrasonic wave propagation since 2017 and has demonstrated the approach's viability with several peer-reviewed publications and a patent application. Over the last decade, Etegent, LBNL partner for this proposal and effort, has been developing a sensing technology that utilizes the propagation of stress waves through the solid metallic and nonmetallic waveguides (WG). The method is based on the dependence of materials' elastic properties and the speed of sound on temperature. Compared with optical analog, acoustic WG operates at much lower frequencies, eliminating the need for expensive, complex, highly sensitive receivers and data processing. Etegent proved this technology in many harsh environments, mainly concentrated high-temperature measurements. The initial contact between LBNL and Etegent started in 2020, aiming at combining our knowledge and effort toward developing a viable ultrasound-based quench detection technique for use with HTS magnets. A preliminary study conducted jointly in 2022 found no principle restrictions for expanding the WG technology toward cryogenic temperature operation. The present CRADA work aimed at designing, building, and testing ultrasonic waveguide temperature sensors at relevant cryogenic conditions, and demonstrating this technology's applicability to detecting heating and quenching in High Temperature Superconductor (HTS)-based cables and magnets. The technical objectives of the project included: (1) Design and fabricate ultrasonic WG sensors suitable for cryogenic operation, (2) Test ultrasonic WGs under cryogenic conditions, (3) Determining a suitable way of insulating WGs to prevent ultrasonic leakage while maintaining good thermal contact with the surrounding medium, (4) WG integration and cryogenic test with HTS conductors and HTS magnets, and (5) Design and basic prototyping of a standalone WG-based quench detection system. This project adds value to the present LBNL magnet diagnostics portfolio and enables a breakthrough in addressing the HTS magnet quench detection challenge.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Evaluating Polymer Properties with Different Additives for Carbon Capture and Other Applications

Anthropogenic climate change is one of this generation’s most pressing concerns, with the potential to completely alter the delicate balance we’ve struck with nature. Already, global temperatures have risen 1.29°C, leading to disrupted weather systems, extinctions, increased risks of wildfires, and sea level rise, to name a few effects. Carbon dioxide emission from the combustion of fossil fuels and other industrial activity is a large driver of this phenomenon, as it absorbs heat before it can be radiated away from Earth, trapping it. Carbon dioxide has reached unprecedented levels in our atmosphere, showing a 50% increase from preindustrial averages to a whopping 430 ppm. Thus, reducing the amount of carbon dioxide via carbon capture technology is an important endeavor that serves to benefit everyone. The Microencapsulated CO 2 Sorbent (MECS) team at Lawrence Livermore National Laboratory (LLNL) has turned to microencapsulation to approach this endeavor. Microcapsules provide an attractive approach to carbon capture, combining large surface areas for more efficient mass transfer, regenerative abilities, reduced solvent loss, and improved handling. Additionally, while existing carbon capture technology relies on industrial plants, capsules could present a modular approach to carbon capture, reducing the need for extensive physical infrastructure. The MECS team’s design consists of a polymer membrane that contains a liquid carbon sequestering sorbent, aqueous sodium carbonate. The carbon capturing reaction occurs in three distinct steps, the first of which is the dissolution of carbon dioxide into the sorbent solution and its conversion into carbonic acid (H 2 CO 3 ), shown in equations 1 and 2 respectively. Because this step hinges upon the ability of carbon dioxide to reach the solution inside the capsule, it is necessary that the microcapsule shell is permeable to carbon dioxide gas. The MECS team produces these microcapsules using the in-air droplet encapsulation apparatus (IDEA) shown in figure 1, which can produce uniform micron-scale droplets at speeds much faster than traditional single-dispersal microfluidic-based techniques. The IDEA Is 100 times faster than these current techniques and can reach up to 1000 times their speed when incorporating a multi-nozzle design. Additionally, because droplets are produced in-air via vibration, IDEA can decrease post-processing times and material waste by 99% and can fabricate microgels that are 10 to 100 times more viscous than can be produced via traditional microfluidics. While this design represents a breakthrough in the throughput, efficiency, and tunability of microcapsule production, it imposes a major constraint on the microcapsule curing process. Because microcapsule shells are crosslinked with UV light while falling 30 cm through the air, this gives them a reaction window of approximately 0.2 seconds. Thus, the system and shell formulations must be optimized such that the shells can be fully crosslinked within this very narrow window, prompting investigations into curing behavior.

36 MATERIALS SCIENCE↗
DOE OSTI2022

The Anthropology of Aquaculture

Aquaculture is nothing new. It has a long, fascinating history that stretches from antiquity at least 8,000 years ago. What is new is the evolution of aquaculture in modern times into highly intensive monocultures which arose in the 1970–1980's. Modern aquaculture production has grown worldwide but remains concentrated in Asia due to the: (1) increased demands for aquatic foods as explosive population growth occurred in coastal cities with increasing affluence, (2) expansion of scientific and engineering breakthroughs, (3) high export values of aquatic foods, and (4) sharp decline of costs of global to local transport/shipping. The pioneering anthropologist Claude Levi-Strauss brought the idea of “structuralism” to anthropology: the concept that societies throughout history followed universal patterns of behavior. A qualitative document analysis of the key anthropological literature to assess aquaculture developments from antiquity to the beginning of the modern era was conducted to evaluate if there was adequate evidence to support a theory of anthropological “structuralism” for aquaculture in human history. Seven case studies of the cultural/environmental history of aquaculture were reviewed in diverse parts of the world (China, Australia, Egypt, Europe, South America, Canada/USA, Hawai'i). Analysis supports the structural theory that whenever the demands of aquatic/seafood-eating peoples exceeded the abilities of their indigenous fishery ecosystems to provide for them, they developed aquaculture. Modern aquaculture concepts and new communities of practice in “restoration aquaculture” have beginnings in Indigenous anthropology and archeology in aquaculture and point the way for Indigenous nations to engage as leaders of the United Nations Food and Agriculture Organization (FAO) ecosystem approach to aquaculture worldwide. Bringing ancient knowledge of Indigenous aquaculture into the modern context is an essential part of an alternative, “radical transformation” of modern aquaculture. There is an urgent need to develop and promote locally designed and culturally appropriate aquaculture systems that fit into the livelihoods of communities as part of a larger, diverse portfolio of food security.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Time delay lens modelling challenge

ABSTRACT In recent years, breakthroughs in methods and data have enabled gravitational time delays to emerge as a very powerful tool to measure the Hubble constant H0. However, published state-of-the-art analyses require of order 1 yr of expert investigator time and up to a million hours of computing time per system. Furthermore, as precision improves, it is crucial to identify and mitigate systematic uncertainties. With this time delay lens modelling challenge, we aim to assess the level of precision and accuracy of the modelling techniques that are currently fast enough to handle of order 50 lenses, via the blind analysis of simulated data sets. The results in Rungs 1 and 2 show that methods that use only the point source positions tend to have lower precision ($10\!-\!20{{\ \rm per\ cent}}$) while remaining accurate. In Rung 2, the methods that exploit the full information of the imaging and kinematic data sets can recover H0 within the target accuracy (|A| < 2 per cent) and precision (<6 per cent per system), even in the presence of a poorly known point spread function and complex source morphology. A post-unblinding analysis of Rung 3 showed the numerical precision of the ray-traced cosmological simulations to be insufficient to test lens modelling methodology at the percent level, making the results difficult to interpret. A new challenge with improved simulations is needed to make further progress in the investigation of systematic uncertainties. For completeness, we present the Rung 3 results in an appendix and use them to discuss various approaches to mitigating against similar subtle data generation effects in future blind challenges.

Ding, X.↗
DOE OSTI2023

Wind Energy Accomplishments and Year-End Performance Report: Fiscal Year 2023

As the United States' largest source of renewable energy, wind is already playing a vital role in the nation's shift to electricity generated from sustainable resources. Scientists, engineers, and analysts at the U.S. Department of Energy's (DOE's) National Renewable Energy Laboratory (NREL) are propelling our country closer to this clean energy future with new efficient, reliable, and cost-competitive wind technologies and deployment strategies. In Fiscal Year 2023 (FY 2023), NREL received national acclaim for recent wind technology breakthroughs, applauded sustained decades of progress in wind energy research, and heralded new initiatives to support the next generation of wind development. This report and NREL's achievements of the last year set the stage for innovative research to come. Two NREL software innovations used to assess wind technology options were recognized with R&D 100 Awards. The Renewable Energy Potential (reV) model helps planners, developers, and researchers calculate wind energy capacity, generation, and cost for specific facilities, fleets and scenarios. NREL's Simulation and Emulation for Advanced Systems validates transmission and distribution solutions to minimize implementation risks. Both of these tools can be applied to a range of renewable solutions to cut costs and improve the reliability of systems in even the most remote locations. The laboratory commemorated landmark anniversaries for wind energy research facilities and programs managed by NREL. The National Wind Technology Center (NWTC) celebrated 30 years as a world-class hub for renewable energy research. An important component of the NWTC on NREL's Flatirons Campus, the one-of-a-kind 7-megawatt (MW) Controllable Grid Interface wind energy research apparatus, turned 10 this year. The Collegiate Wind Competition completed its tenth annual competition in May 2023. Six years after the grand challenges in wind energy science were first compiled, NREL brought together more than 100 wind energy experts from 15 countries to revisit the field's most pressing research needs. Other key new research initiatives examined ways to simultaneously address the needs for skilled wind energy workers and equitable job opportunities. NREL released the first national-level report evaluating offshore wind industry workforce gaps and ways to build a large, diverse pool of job candidates. The lab also co-hosted the International Partnering Forum's Offshore Wind Workforce Summit as part of its ongoing efforts to address the special challenges of that industry sector. NREL research and development (R&D) for the DOE Wind Energy Technologies Office (WETO) continues to build momentum in its efforts to combat climate change, create clean energy jobs, and promote energy justice. This report provides more detail on these top achievements and other accomplishments made by NREL and its partners during FY 2023 (between Oct. 1, 2022, and Sept. 30, 2023).

accomplishments↗
DOE OSTI2025

Single-shot in-line x-ray phase-contrast imaging of void-shockwave interactions in fusion energy materials

Recent breakthroughs in nuclear fusion, specifically the report of reactions exceeding scientific breakeven at the National Ignition Facility (NIF), highlight the potential of inertial fusion energy (IFE) as a sustainable and virtually limitless energy source. However, further progress in IFE requires characterization of defects in ablator materials and how they affect fuel capsule compression. Voids within the ablator can degrade energy yield, but their impact on the density distribution has primarily been studied through simulations, with limited high-resolution experimental validation. To address this, we used the x-ray free-electron laser (XFEL) at the matter in extreme conditions (MECs) instrument at the Linac coherent light source (LCLS) to capture 2D x-ray phase-contrast (XPC) images of a void-bearing sample with a composition similar to inertial confinement fusion (ICF) ablators. By driving a compressive shockwave through the sample using MEC's long-pulse laser system, we analyzed how voids influence shockwave propagation and density distribution during compression. To quantify this impact, we extracted phase information using two phase retrieval algorithms. First, we applied the contrast transfer function (CTF) method, paired with Tikhonov regularization and a fast optimization approach to generate an initial phase estimate. We then refined the result using a projected gradient descent (PGD) method that works directly with the sample's refractive index. Comparing these results with radiation adaptive grid Eulerian (xRAGE) radiation hydrodynamic simulations enables identification of model validation needs or improvements. By calculating phase maps in situ, it becomes possible to reconstruct areal density maps, improving understanding of laser-capsule interactions and advancing IFE research.

Hodge, D. S. [Colorado State Univ., Fort Collins, ↗
DOE OSTI2022

Findings on Subtask 3.1 - Bakken Rich Gas Enhanced Oil Recovery Project

Total in-place oil for the Bakken petroleum system (BPS) (which includes the Bakken and Three Forks Formations) has been estimated to be 600 billion barrels (bbl). However, BPS wells have decline rates as high as 85% over the first 3 years of their lives, and primary recovery factors typically range from 3% to 10% of original oil in place. Given the low initial recovery rates, even small incremental productivity improvements could dramatically increase technically recoverable oil in the BPS. One potential solution is enhanced oil recovery (EOR) using gas injection, such as carbon dioxide (CO2) or hydrocarbon (HC) gases. While commonly used in conventional reservoirs, CO2 EOR in unconventional tight oil reservoirs has been limited to pilot tests. EOR using rich gas (mixture of methane, ethane, and propane) has also been employed in numerous pilots in several unconventional plays and has recently been successfully applied in the Eagle Ford play. If successful, large-scale gas-based EOR in the BPS could dramatically increase oil productivity and recovery factors and extend the life of the play for decades. While CO2 may be a technically suitable working fluid for EOR in the BPS, supplies are limited and costs for using CO2 in EOR pilots are prohibitively high. Meanwhile, produced gas flaring has presented challenges for BPS operators in North Dakota. Analysis conducted by the North Dakota Pipeline Authority indicates that the current gas-gathering infrastructure in North Dakota is insufficient to accommodate all of the associated gas that is produced from the BPS. The geographically isolated location of North Dakota relative to large natural gas markets, combined with suppressed natural gas prices, has made it economically challenging for industry to invest capital in expanding gas-gathering infrastructure in the state. These circumstances led to a research program conducted by the Energy & Environmental Research Center (EERC) in partnership with Liberty Resources Management Company LLC (LR) to examine the potential to use rich gas injection for EOR and mitigate flaring. A rich gas EOR pilot test was designed and executed by LR at its Stomping Horse development area in Williams County, North Dakota. From July 2018 through May 2019, a total of 160 million standard cubic feet (MMscf) of rich produced gas was injected into the BPS using five different wells in a sequential injection strategy. LR’s Leon–Gohrick drill spacing unit (DSU) was used as the test site. Regulatory oversight was provided by the North Dakota Industrial Commission (NDIC). Technical support was provided by the EERC through a series of laboratory, modeling, and field-based activities, and additional post-pilot research activities incorporated learnings from the test, developed new laboratory data, improved fracture modeling methods, and developed machine learning and big data analytics. The results from the Stomping Horse rich gas EOR pilot activities indicate that developing an effective, economical EOR approach for the BPS will require more field tests. Another key lesson learned from the Stomping Horse tests is that detailed pre- and posttest data on reservoir conditions and fluids production are essential. Robust reservoir characterization provides information that is crucial to creating realistic geomodels and conducting valid dynamic simulations of potential EOR scenarios. A detailed understanding of the completions and production history of offset wells is also necessary for valid test result interpretations. This knowledge is essential to designing the operational parameters of injectivity tests and interpreting the results. A conformance control strategy is also essential to success. Laboratory-based examinations of rich gas interactions with reservoir fluids and rocks were conducted, with an emphasis on determining the ability to mobilize oil in the tight reservoir rocks and shales of the BPS. Injection fluid composition was shown to have a positive impact on reducing reservoir oil minimum miscibility pressure (MMP), reducing interfacial tension (IFT), and altering wettability. IFT and contact angle measurements demonstrated that wettability can be altered in the presence of rich gas, suggesting the potential to improve oil recovery. Iterative modeling of surface infrastructure and reservoir performance using data generated by the various project activities was conducted. A geologic model of the Stomping Horse area was built; history-matched oil, gas, and water production was used in simulations of various EOR scenarios. Early programmatic modeling results were used to support LR’s design and operation of the EOR pilot and to provide insight regarding optimization of future commercial-scale BPS EOR design and operations. Post-pilot modeling focused on alternative methods of understanding complex fracture networks and accelerating simulation time. These led to improved simulation run times and provide excellent history-matching results. Several of these iterative models were used as the bases for developing algorithms into machine learning and big data analytics. History matching in reservoir simulation is time-consuming and computer processing-intensive. Machine learning algorithms were created, and an automated history-matching tool was developed. A large set of synthetic reservoir simulations were created to generate well responses (oil, gas, and water production, well bottomhole pressure [BHP], and tracer or propane breakthrough) for a set of EOR operating parameters that included offset well status (open or closed), injectate (rich gas or propane), injection rate, and injection well BHP. A user interface was developed to provide real-time visualization. Machine learning-based models were developed to provide rapid forecasting of well performance given a set of user-defined EOR operating parameters. These predictive models allow the user to modify the offset well status, injection rate, and injection well BHP and rapidly forecast future production performance. The combination of real-time visualization tools with real-time forecasting tools provides a framework for real-time control—operational changes that the EOR site operator can enact (e.g., changing gas injection rates) to affect the observed performance and potentially improve the EOR outcome. There is great reason to be optimistic about the future of EOR in the Bakken. The results of the laboratory studies suggest significant potential for high rates of oil mobilization using produced field gas injection under the right conditions. The results of the lab studies, combined with rigorous statistical analysis of well production data and associated modeling efforts, confirm the notion that fluid mobility within the reservoir is controlled by fractures. As more knowledge is gained about the nature and distribution of fracture networks in the Bakken, the industry will be in a better position to predict and, ultimately, influence fluid mobility. New field tests are necessary to develop a more complete understanding of those conditions. Thoughtful and creatively engineered field tests within a well-characterized geologic setting will yield the fundamental knowledge needed to take Bakken oil production to the next level. This subtask was cofunded through the EERC–U.S. Department of Energy Joint Program on Research and Development for Fossil Energy-Related Resources Cooperative Agreement No. DE-FE0024233. Nonfederal funding was provided by the North Dakota Industrial Commission’s Oil and Gas Research Program and Computer Modelling Group.

04 OIL SHALES AND TAR SANDS↗
DOE OSTI2024

Using Generative AI to implement the discrepancy checker for a Nearly Autonomous Management and Control System for Advanced Reactors

Developments related to generative artificial intelligence (AI) have brought a major breakthrough in AI. These developments are rapidly accelerating developments in different science and engineering applications. Nearly Autonomous Management and Control (NAMAC) system provides recommendations to the operator for maintaining the safety and performance of the reactor. The discrepancy checker (DC) is an important component of the NAMAC) system, whose goal is to determine if the plant is moving towards the expected system state after the control actions are injected. In this work, we explore generative AI methods, particularly, a generative pretrained transformer (GPT) for implementing the DC function in NAMAC. The GPT-based DC aims to alert the operator in situations outside NAMAC’s scope and act as a chatbot the operator can use to retrieve relevant information. This study involves two versions of GPT developed by OpenAI: GPT-3.5 and GPT-4. These GPTs are trained on huge amounts of undisclosed general domain datasets. We explored two methods to adapt GPTs for DC implementation in NAMAC: fine-tuning and retrieval augmented generation. A small knowledge base (information file) that encompasses rules for DC implementation and some general information related to NAMAC has been created to support DC implementation using GPT. In this work, the GPT-based DC implementations have been tested for their reasoning abilities, comprehension, information retrieval, and extraction abilities. It should be noted that this paper only presents a preliminary study to test the feasibility of DC implementation using generative AI technology. Given the potential risks and severe consequences associated with nuclear reactor applications, combined with the black-box nature of AI, extensive offline and online testing and reliability analyses of GPT-based DCs are needed for further developing such capabilities.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗