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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A terminology for scientific workflow systems

The term “scientific workflow” has evolved over the last two decades to encompass a broad range of compositions of interdependent compute tasks and data movements. It has also become an umbrella term for processing in modern scientific applications. Today, many scientific applications can be considered as workflows made of multiple dependent steps, and hundreds of workflow systems have been developed to manage and run these scientific workflows. However, no turnkey solution has emerged from the field to address the diversity of scientific processes and the infrastructure on which they are supposed to be implemented. Instead, new research problems requiring the execution of scientific workflows with some novel feature often lead to the development of an entirely new workflow system. A direct consequence of this situation is that many existing workflow management systems (WMSs) share some salient features, offer similar functionalities, and can manage the same categories of workflows but at the same time also have some distinct capabilities that can be important for specific applications. This situation makes researchers who develop workflows face the complex question of selecting a WMS. This selection can be driven by technical considerations, to find the system that is the most appropriate for their application and for the computing and storage resources available to them, or other factors such as reputation, adoption, strong community support, or long-term sustainability. To address this problem, a group of WMS developers and practitioners joined their efforts to produce a community-based terminology of WMSs. This paper summarizes their findings and introduces this new terminology to characterize WMSs. Furthermore, this terminology is composed of fives axes: workflow structure and characteristics, composition, orchestration, data management, and metadata capture. Each axis comprises several concepts that capture the prominent features of WMSs. Based on this terminology, this paper also presents a classification of 23 existing WMSs according to the proposed axes and terms.

Community-based terminology↗
DOE OSTI2024

Mixed-Salt Based Transformational Solvent Technology for CO 2 Capture

SRI International developed a novel, water-lean, mixed-salt-based transformational solvent technology under a U.S. Department of Energy (DOE)/National Energy Technology Laboratory (NETL)-funded project. This advanced mixed-salt process (A-MSP) can be operated with high cyclic CO 2 loading, and the solution can be regenerated with greater than 10 bar CO 2 pressure at temperatures less than 120°C. The team has created a process model and performed a techno-economic evaluation (TEA) to identify development pathways for technology advancement. The A-MSP will provide a reduction in the CO 2 capture cost and energy penalties and will have the potential for accelerated scale-up to meet the DOE’s new target of <30/tonne-CO 2 by 2030 for coal-fired power plant applications.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2025

Influence of Metal Ion Complexation on the Radiolytic Longevity of Butyramide Extractants under Direct Dissolution Process Conditions

The direct dissolution of voloxidized used nuclear fuel (UNF) into an organic solution–comprised of diluent and specialized extractants–poses a promising alternative to the traditional liquid–liquid solvent extraction approach to reprocessing UNF. However, moving to direct dissolution removes the presence of a concentrated nitric acid aqueous phase, which has been shown to significantly influence the radiolytic longevity of extractants in conventional extraction flowsheets. Given the limited knowledge of radiation effects under direct dissolution conditions, here we present a time-resolved and dose-accumulation study on the impact of direct dissolution conditions on the radiolytic longevity of two candidate butyramide extractants, N,N-di(2-ethylhexyl) butyramide (DEHBA) and N,N-di(2-ethylhexyl)isobutyramide (DEHiBA), in pre-equilibrated n-dodecane solvent in the presence and absence of process-relevant metal ions, specifically, uranium and rhenium. Loss G(DEHBA) and G(DEHiBA) values were found to be comparable to each other, with an average of 0.37 ± 0.02 μmol J –1 , and to previous data from the γ irradiation of DEHBA and DEHiBA under conventional solvent extraction conditions. Rhenium, and by extension technetium, extraction had a modest decrease (~10%) in the overall radiolytic stability of DEHiBA only, despite >2× observed increases in chemical kinetic reactivity of the corresponding complexes with the n-dodecane radical cation. Uranium loading, on the other hand, significantly improved the lifetime of both ligands (>30%) under γ irradiation, with a greater stabilization observed for DEHBA over DEHiBA. The observed radioprotective effect afforded by uranium loading is fortuitous for the longevity of direct dissolution solvent.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2023

Towards Smart Grids Enhanced Situation Awareness: A Bi-Level Quasi-Static State Estimation Model

Smart Grid situational awareness is provided by Energy Management Systems. A core process of these systems is State Estimation. The great majority of state estimators model the Smart Grid through a set of nonlinear algebraic equations, named the measurement model. Problem formulation considers the Gauss solution. Several model improvements have been presented regarding the Gauss solution, aiming between others to provide measurement noise robustness to the state estimation process. While considerable effort has been focused on such developments, state estimation is still constrained by the implicit modelling error, and thus inevitably vulnerable to cyber-threats. In this work, a state estimation bi-level formal model is presented towards Smart Grids enhanced situational awareness considering the concepts of synthetic measurements and innovation. Comparative test results with the state-of-the-art on the IEEE 14-bus system are presented highlighting improved situational awareness to bad data. Easy-to-implement model, without hard-to-derive parameters, built-on the classic weighted least squares solution, highlight potential aspects for real-life implementation.

cyber security↗
DOE OSTI2022

How do spherical black holes grow monopole hair?

Black holes in certain modified gravity theories that contain a scalar field coupled to curvature invariants are known to possess (monopole) scalar hair while non-black-hole spacetimes (like neutron stars) do not. Therefore, as a neutron star collapses to a black hole, scalar hair must grow until it settles to the stationary black hole solution with (monopole) hair. In this paper, we study this process in detail and show that the growth of scalar hair is tied to the appearance and growth of the event horizon (before an apparent horizon forms), which forces scalar modes that would otherwise (in the future) become divergent to be radiated away. We prove this result rigorously in general first for a large class of modified theories, and then we exemplify the results by studying the temporal evolution of the scalar field in scalar Gauss-Bonnet gravity in two backgrounds: (i) a collapsing Oppenheimer-Snyder background, and (ii) a collapsing neutron star background. In case (i), we find an exact scalar field solution analytically, while in case (ii) we solve for the temporal evolution of the scalar field numerically, with both cases supporting the conclusion presented above. Thus, our results suggest that the emission of a burst of scalar field radiation is a necessary condition for black hole formation in a large class of modified theories of gravity.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Unifying Advective and Diffusive Descriptions of Bedform Pumping in the Benthic Biolayer of Streams

Abstract Many water quality and ecosystem functions performed by streams occur in the benthic biolayer, the biologically active upper (~5 cm) layer of the streambed. Solute transport through the benthic biolayer is facilitated by bedform pumping, a physical process in which dynamic and static pressure variations over the surface of stationary bedforms (e.g., ripples and dunes) drive flow across the sediment‐water interface. In this paper we derive two predictive modeling frameworks, one advective and the other diffusive, for solute transport through the benthic biolayer by bedform pumping. Both frameworks closely reproduce patterns and rates of bedform pumping previously measured in the laboratory, provided that the diffusion model's dispersion coefficient declines exponentially with depth. They are also functionally equivalent, such that parameter sets inferred from the 2D advective model can be applied to the 1D diffusive model, and vice versa. The functional equivalence and complementary strengths of these two models expand the range of questions that can be answered, for example, by adopting the 2D advective model to study the effects of geomorphic processes (such as bedform adjustments to land use change) on flow‐dependent processes and the 1D diffusive model to study problems where multiple transport mechanisms combine (such as bedform pumping and turbulent diffusion). By unifying 2D advective and 1D diffusive descriptions of bedform pumping, our analytical results provide a straightforward and computationally efficient approach for predicting, and better understanding, solute transport in the benthic biolayer of streams and coastal sediments.

Grant, Stanley B.↗
DOE OSTI2023

Controlling solute channel formation using magnetic fields

Solute channel formation introduces compositional and microstructural variations in a range of processes, from metallic alloy solidification, to salt fingers in ocean and water reservoir flows. Applying an external magnetic field interacts with thermoelectric currents at solid/liquid interfaces generating additional flow fields. This thermoelectric (TE) magnetohydrodynamic (TEMHD) effect can impact on solute channel formation, via a mechanism recently drawing increasing attention. To investigate this phenomenon, we combined in situ synchrotron X-ray imaging and Parallel-Cellular-Automata-Lattice-Boltzmann based numerical simulations to study the characteristics of flow and solute transport under TEMHD. Observations suggest the macroscopic TEMHD flow appearing ahead of the solidification front, coupled with the microscopic TEMHD flow arising within the mushy zone are the primary mechanisms controlling plume migration and channel bias. Two TE regimes were revealed, each with distinctive mechanisms that dominate the flow. Further, we show that grain orientation modifies solute flow through anisotropic permeability. These insights led to a proposed strategy for producing solute channel-free solidification using a time-modulated magnetic field.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Nemchinov-Dyson Solutions of the Two-Dimensional Axisymmetric Inviscid Compressible Flow Equations [Review]

We investigate the two-dimensional (2D) inviscid compressible flow equations in axisymmetric coordinates, constrained by an ideal gas equation of state (EOS). Beginning with the assumption that the 2D velocity field is space-time separable and linearly variable in each corresponding spatial coordinate, we proceed to derive an in nite family of elliptic or hyperbolic, uniformly expanding or contracting \gas cloud" solutions. Construction of specific example solutions belonging to this family is dependent on the solution of a system of nonlinear, coupled, second-order ordinary differential equations, and the prescription of an additional physical process of interest (e.g., uniform temperature or uniform entropy flow). The physical and computational implications of these solutions as pertaining to quantitative code verification or model qualification studies are discussed in some detail.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Approach for energy efficient building design during early phase of design process

Energy consumption in the building sector is about 40% of total energy consumed globally and is trending upwards, along with its contribution to greenhouse gas (GHG) emissions. Given the adverse impacts of GHG emissions, it is crucial to integrate energy efficiency into building designs. The most significant opportunities for enhancing energy performance are present during the initial phases of building design, when there is less impact of other design constraints. Various tools exist for simulating different design options and providing feedback in terms of energy consumption and comfort parameters. These simulation outputs must then be analyzed to derive design solutions. This paper presents an innovative approach that utilizes user input parameters, processes them through cloud computing, and outputs easily understandable strategies for energy-efficient building design. The methodology employs Asynchronous Distributed Task Queues (DTQ) - a more scalable and reliable alternative to conventional speedup techniques-for conducting parametric energy simulations in the cloud. The goal of this approach is to assist design teams in identifying, visualizing, and prioritizing energy-saving design strategies from a range of possible solutions for each project. Furthermore, a tool ‘eDOT’ has been developed utilizing the discussed methodology. Unlike existing tools, eDOT leverages artificial intelligence to dynamically generate and provide design strategies during the early phases of design process. By simplifying the simulation process, eDOT enables design teams to make informed, data-driven decisions without needing to interpret complex simulation outputs. A case study simulated for two locations is provided in this paper to demonstrate the effectiveness of eDOT, further underscoring its practical impact on energy-efficient building design.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

RAG for FLAG: AI Assistance for a Physics Code

Artificial intelligence (AI) has quickly become an important tool in scientific research, where significant efforts are underway to develop tools that will expedite the research process. One area of particular impact is scientific software, which can be particularly complex, and therefore time consuming to learn and use effectively. AI assistants are increasingly helping to streamline the process by performing tasks such as interactively answering user questions or suggesting solutions. Los Alamos National Laboratory (LANL) develops several advanced scientific codes, such as FLAG, which can be used to run multiphysics simulations. With this study, our goal was to develop an AI assistant for FLAG that could help make the process of understanding the software and running physics simulations more efficient. To develop an AI assistant for FLAG, we used a method called retrieval-augmented generation (RAG), which is a technique that uses information from relevant data sources to enhance the accuracy of large language models (LLMs). We used the FLAG user manual and other FLAG documentation as the knowledge base for the RAG system. When a user provides a query, RAG retrieves relevant sections from the knowledge base in response, then uses those excerpts to generate grounded and contextually rich answers. We found that our AI assistant was able to provide context aware answers and source references to user queries. To evaluate performance, we developed a set of 40 benchmark questions and compared the accuracy of the responses to those of two standard LLMs without retrieval. Our AI assistant significantly outperformed the standard LLMs at answering FLAG-related questions, with an 82.5% accuracy rate, compared to 47.5% for both of the standard LLMs. This has the potential to make the process of learning and using FLAG much easier, especially for new users. Ultimately, it supports LANL’s broader mission by empowering scientists and engineers to focus more on discovery and analysis rather than on navigating complex software systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Versatile acid solvents for pristine carbon nanotube assembly

Chlorosulfonic acid and oleum are ideal solvents for enabling the transformation of disordered carbon nanotubes (CNTs) into precise and highly functional morphologies. Currently, processing these solvents using extrusion techniques presents complications due to chemical compatibility, which constrain equipment and substrate material options. Here, we present a novel acid solvent system based on methanesulfonic or p-toluenesulfonic acids with low corrosivity, which form true solutions of CNTs at concentrations as high as 10 g/liter (≈0.7 volume %). The versatility of this solvent system is demonstrated by drop-in application to conventional manufacturing processes such as slot die coating, solution spinning continuous fibers, and 3D printing aerogels. Through continuous slot coating, we achieve state-of-the-art optoelectronic performance (83.6 %T and 14 ohm/sq) at industrially relevant production speeds. This work establishes practical and efficient means for scalable processing of CNT into advanced materials with properties suitable for a wide range of applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Accumulation of Antifreeze Proteins on Ice Is Determined by Adsorption

Antifreeze proteins (AFPs) enable the survival of organisms in freezing environments by binding to ice crystals and suppressing their growth. The rate at which AFPs accumulate on ice is determined by an interplay between their diffusion from the aqueous solution and their adsorption to the ice crystal. To interrogate the relative importance of these two processes, we combined fluorescence experiments with reaction–diffusion modeling. We found that as AFPs accumulate on ice, their concentration in the aqueous solution does not develop a gradient but remains equal to its bulk concentration throughout our experiments. Furthermore, these findings led us to conclude that AFP accumulation on ice crystals, which are smaller than 100 μm in radius, is not limited by the diffusion of AFPs, but by the kinetics of AFP adsorption.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

A reaction mechanism for plasma electrolysis of AgNO 3 forming silver nanoclusters and nanoparticles

In plasma-driven solution electrolysis (PDSE), gas-phase plasma-produced species interact with an electrolytic solution to produce, for example, nanoparticles. An atmospheric pressure plasma jet (APPJ) directed onto a liquid solution containing a metallic salt will promote reduction of metallic ions in solution, generating metallic clusters that nucleate to form nanoparticles. In this article, results from a computational investigation are discussed of a PDSE process in which a radio-frequency APPJ sustained in helium impinges on a silver nitrate solution, resulting in growth of silver nanoparticles. A reaction mechanism was developed and implemented in a global plasma chemistry model to predict nanoparticle growth. To develop the reaction mechanism, density functional theory was used to generate probable silver growth pathways up to Ag 9 . Neutral clusters larger than Ag 9 were classified as nanoparticles. Kinetic reaction rate coefficients for thermodynamically favorable growth pathways were estimated based on an existing, empirically determined base reaction mechanism for smaller Ag particle interactions. These rates were used in conjunction with diffusion-controlled reaction rate coefficients that were calculated for other Ag species. The role of anions in reduction of Agn ions in forming nanoparticles is also discussed. Oxygen containing impurities or admixtures to the helium, air entrainment into the APPJ, and dissociation of saturated water vapor above the solution can produce additional reactive oxygen species in solution, resulting in the production of anions and O 2 – in particular. For a given molarity, delivering a sufficient fluence of reducing species will produce similar nanoparticle densities and sizes for all applied power levels. In conclusion, comparisons are made to alternate models for nanoparticle formation, including charged nanoparticles and use of direct current plasmas.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Wickability-optimized textured liquid-desiccant air dehumidifiers for independent moisture management in energy-efficient buildings

Liquid-desiccant-based air conditioning systems are envisioned to enable independent humidity management, thereby improving the energy efficiency of future buildings. Existing liquid-desiccant-based air conditioning concepts, however, suffer from a poor liquid flow distribution deteriorating moisture removal rate. They are consequently flooded with the liquid-desiccant solution, which significantly degrades the energy efficiency of the dehumidification process. Here, in this work, the capillary forces and wickability effect of textured air dehumidifier surfaces are altered to minimize the liquid-desiccant flow rate of the fully wetted state, thereby transforming the physics of interfacial desiccant flow distribution. Consequently, the wickability-optimized air dehumidifier surface maximizes both moisture removal rate and dehumidification energy efficiency. It was interestingly found that the length scale of a textured air dehumidifier surface concept is optimized at an intermediate pattern density. Dry solid-air menisci appear at length scales exceeding the optimum pattern distance while the effective liquid–air interfacial area is reduced at smaller length scales, both of which degrade the moisture removal rate. At the optimum pattern density, the effective liquid–air interfacial area increases with the solution flow rate, thereby increasing the dehumidification rate. At a water vapor pressure potential of 3 kPa and a solution flow rate of 2.8 g/s, experimental results indicated a moisture removal rate of 0.16 g/m2-s for a textured surface concept with a capillary length scale of 3 mm, a 28% improvement compared with that of smooth-plate dehumidifier surfaces. A high moisture removal rate of the textured surface at a low desiccant flow rate led to a high thermal efficiency of 0.75 at a water vapor pressure potential of 5.6 kPa and a LiBr flow rate of 2.8 g/s. The insights gained from the present study accelerate the development of advanced textured surface concepts for next-generation liquid-desiccant-based air dehumidification systems offering independent humidity management for future energy-efficient buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Effect of water models on structure and dynamics of lignin in solution

Lignin, a major biomass component, can be an excellent source for different monomers in the polymer industry. However, the complex and heterogeneous structure of lignin poses a significant challenge for designing energy-efficient processes for depolymerization. As many proposed depolymerization processes are solvothermal, it is essential to understand the structure and dynamics of lignin in solution, in particular aqueous solution. Here, we utilize molecular dynamics simulations to understand the effect of water models on the structure and dynamics of different model lignin oligomers (softwood and hardwood) as a function of temperature. We have examined three different water models: TIP3P, TIP4P/Ew, and flexible SPC/Fw. We find that the diffusion constant of lignin oligomers in an aqueous solution differs significantly depending on the water model used. The diffusion constant of lignin in the TIP3P water model is almost twice as large as that in SPC/Fw and TIP4P/Ew. The softwood and hardwood oligomers adopt an extended structure in TIP3P water compared to SPC/Fw and TIP4P/Ew. Given the different levels of sensitivity of transport and structural properties of aqueous lignin on water models, it is important to take these into account when discussing results from a specific water model.

09 BIOMASS FUELS↗
DOE OSTI2021

Cardinality-Regularized Hawkes-Granger Model

We propose a new sparse Granger-causal learning framework for temporal event data. We focus on a specific class of point processes called the Hawkes process. We begin by pointing out that most of the existing sparse causal learning algorithms for the Hawkes process suffer from a singularity in maximum likelihood estimation. As a result, their sparse solutions can appear only as numerical artifacts. In this paper, we propose a mathematically well-defined sparse causal learning framework based on a cardinality-regularized Hawkes process, which remedies the pathological issues of existing approaches. We leverage the proposed algorithm for the task of instance-wise causal event analysis, where sparsity plays a critical role. We validate the proposed framework with two real use-cases, one from the power grid and the other from the cloud data center management domain.

Ide, Tsuyoshi↗
DOE OSTI2025

Addressing Inherent Challenges to Chemical Relithiation of Cycled End‐of‐Life Cathode Materials

Recycling end‐of‐life (EOL) lithium‐ion batteries (LIBs) is important to retain valuable resources from critical materials present in EOL battery waste. Direct recycling methods offer an opportunity to recover intact valuable cathode materials with minimal re‐processing. An important step of the direct recycling process is relithiation which is used to restore lithium content to EOL cathode materials. However, little has been done to study how preprocessing steps such as washing or binder removal may affect relithiation methods in the direct recycling process. Here, the evolution of fluorine byproducts left over from preprocessing steps during a low‐temperature chemical redox mediator relithiation process is tracked. A facile washing step is presented as a solution for mediating adverse effects of surface contamination on the chemical relithiation performance. The structure, lithium content, and electrochemical performance of relithiated EOL NMC 622 material that underwent a pre‐relithiation washing step to remove fluorine byproducts is shown to match that of pristine NMC 622. In this work, it is showed that redox mediator relithiation as a part of a direct recycling process is a promising low energy method that can be applied to EOL material with inherent surface impurities if the proper pre‐relithiation processing steps are implemented.

25 ENERGY STORAGE↗
DOE OSTI2021

Achieving strong and stable nanocrystalline Al alloys through compositional design

Abstract Al alloys often suffer from low mechanical strength and lack high-temperature microstructural and mechanical robustness. A series of binary and ternary nanocrystalline (NC) Al transition metal alloys with supersaturated solid solution and columnar nanograins have been recently developed by using magnetron sputtering, manifesting a new realm of mechanical properties and thermal stability. Distinct solutes cause evident differences in the phase transformations and efficiencies for grain refinement and crystalline-to-amorphous transition. Certain sputtered Al-TM alloys have shown room-temperature mechanical strengths greater than 2 GPa and outstanding thermal stability up to 400 °C. In addition, the NC Al alloys show mechanical anisotropy and tension–compression asymmetry, revealed by micromechanical tests. Through the process encapsulating various compositionally distinct systems, we attempt to illuminate the solute effects on grain refinement and properties and more importantly, tentatively unravel the design criteria for high-strength and yet thermally stable NC Al alloys. Graphic Abstract

36 MATERIALS SCIENCE↗