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At least 199 records · Page 11

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DOE OSTI2025

Nonadiabatic Force Matching for Alchemical Free-Energy Estimation

We propose a method to compute free-energy differences from nonadiabatic alchemical transformations by using flow-based generative models. The method, nonadiabatic force matching, hinges on estimating the dissipation along an alchemical switching process in terms of a nonadiabatic force field that can be learned through stochastic flow matching. The learned field can be used in conjunction with short-time trajectory data to evaluate upper and lower bounds on the alchemical free energy that variationally converge to the exact value if the field is optimal. Applying the method to evaluate the alchemical free energy of atomistic models shows that it can substantially reduce the simulation cost of a free-energy estimate at a negligible loss of accuracy when compared with thermodynamic integration.

Computational chemistry↗

DOE OSTI2024

Fiber Sorbents – A Versatile Platform for Sorption-Based Gas Separations

Increasing demand for high-purity fine chemicals and a drive for process intensification of large-scale separations have driven significant work on the development of highly engineered porous materials with promise for sorption-based separations. While sorptive separations in porous materials offer energy-efficient alternatives to longstanding thermal-based methods, the particulate nature of many of these sorbents has sometimes limited their large-scale deployment in high-throughput applications such as gas separations, for which the necessary high feed flow rates and gas velocities accrue prohibitive operational costs. These processability limitations have been historically addressed through powder shaping methods aimed at the fabrication of structured sorbent contactors based on pellets, beads or monoliths, commonly obtained as extrudates. These structures overcome limitations such as elevated pressure drops commonly recorded across powder adsorption beds but often accrue thermal limitations arising from elevated particle density and aggregation, which ultimately cap their maximum separation performance. Furthermore, the harsh mechanical strain to which powder particles are subjected during contactor fabrication, in the form of extrusion/compression forces, can result in partial pore occlusion and framework degradation, further limiting their performance. Here, we present the development of porous fiber sorbents as an alternative sorbent contactor design capable of addressing sorbent processability limitations while enabling an array of performance-maximizing heat integration capabilities. This new sorbent form factor leverages pre-existing know-how from hollow fiber spinning to produce fiber-shaped sorbent contactors through the phase inversion of known polymers in a process known as dry-jet/wet quenching. The process of phase inversion allows microporous sorbent particles to be latched onto a macroporous polymer matrix under mild processing conditions, thus making it compatible with soft porous materials prone to amorphization under traditional pelletization conditions. Sorbent fibers can be created with different geometries through control of the spinning apparatus and process, offering the possibility to produce monolithic and hollow fibers alike, the latter of which can be integrated with thermalization fluid flows. In this Account, we summarize our progress in the field of fiber sorbents from both design and application standpoints. We further guide the reader through the evolution of this field from the early inceptive work on zeolite hollow fibers to recent developments on MOF fibers. We highlight the versatile nature of fiber sorbents, both from the composition, fabrication and structure points of view, and further demonstrate how fiber sorbents offer alternative paths in tackling new and challenging chemical separation challenges like direct air capture (DAC), with a final perspective on the future of the field.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Fluid Mechanics Challenges in Direct-Ink-Writing Additive Manufacturing

Direct-ink writing (DIW) has rapidly become a versatile 3D fabrication method due to its ability to deposit a wide range of complex fluids into customizable 3D geometries. This review highlights key fundamental fluid mechanics and soft matter challenges across the different stages of the DIW printing process. The rheology of fluids and suspensions governs the flow behavior through narrow nozzles, posing questions about extrudability, confined flow dynamics, and clogging mechanisms. Downstream, the formation and deposition of extruded filaments involve extensional flows and potential instabilities, while postdeposition dynamics introduces complexities related to yield stress and structural stability. These stages are inherently interdependent, as optimizing material composition without considering filament stability risks compromising the final structure. As DIW applications expand through advanced ink formulations, developing fundamental fluid mechanics frameworks is essential to replace trial-and-error approaches with predictive design methodologies to enable more precise control and improved reliability of the printing process.

3D printing↗

DOE OSTI2025

From clutter to clarity: Emergent neural operators via questionnaire metrics

Real-world datasets in chemical engineering and bioengineering processes—such as those from catalytic reactors, multiphase flows, polymerization reactors, bioreactors, and clinical trials—can often be unlabeled or disorganized, rendering the training of existing supervised learning models ineffective at learning the underlying dynamics. To salvage these datasets for decision-making, we first seek to obtain clarity from the cluttered data. Here, we present a framework for developing “structural” generative models, discovering emergent equations, and constructing efficient emulators from scrambled datasets by integrating unsupervised organizational learning techniques (Questionnaires) with advanced deep learning architectures (Deep Hidden Physics Models and Deep Operator Networks). Our approach is demonstrated on two illustrative model systems: (a) a 1D advection–diffusion partial differential equation representing a winding underground pipe and (b) an ensemble of Stuart–Landau oscillators, an agent-based system of coupled ordinary differential equations. In both cases, we successfully reconstruct meaningful spatial, temporal, and parameter embeddings from scrambled data, enabling good predictions of system dynamics. As a result, we highlight the framework’s potential for broader applications, enabling data-driven system identification in fields with inherently disorganized or hidden parameter spaces.

42 ENGINEERING↗

DOE OSTI2026

Probing the atomic dynamics of ultrafast melting with femtosecond electron diffraction

Melting is an every-day phase transition that is determined by thermodynamic parameters like temperature and pressure. In contrast, ultra-fast melting is governed by the microscopic response to a rapid energy input and, thus, can reveal the strength and dynamics of atomic bonds as well as the energy flow rate to the lattice. Accurately describing these processes remains challenging and requires detailed insights into transient states encountered. Here, we present data from femtosecond electron diffraction measurements that capture the structural evolution of copper during the ultrafast solid-to-liquid phase transformations. At absorbed energy densities 2-4 times the melting threshold, melting begins at the surface slightly below the nominal melting point followed by rapid homogeneous melting throughout the volume. Molecular dynamics simulations reproduce these observations and reveal a weak electron-lattice energy transfer rate for the given experimental conditions. Both simulations and experiments show no indications of rapid lattice collapse when its temperature surpasses proposed limits of superheating, providing evidence that the inherent dynamics limits the speed of disordering in ultrafast melting of metals.

FOS: Physical sciences↗

DOE OSTI2024

Selective ion transport through hydrated micropores in polymer membranes

Abstract Ion-conducting polymer membranes are essential in many separation processes and electrochemical devices, including electrodialysis 1 , redox flow batteries 2 , fuel cells 3 and electrolysers 4,5 . Controlling ion transport and selectivity in these membranes largely hinges on the manipulation of pore size. Although membrane pore structures can be designed in the dry state 6 , they are redefined upon hydration owing to swelling in electrolyte solutions. Strategies to control pore hydration and a deeper understanding of pore structure evolution are vital for accurate pore size tuning. Here we report polymer membranes containing pendant groups of varying hydrophobicity, strategically positioned near charged groups to regulate their hydration capacity and pore swelling. Modulation of the hydrated micropore size (less than two nanometres) enables direct control over water and ion transport across broad length scales, as quantified by spectroscopic and computational methods. Ion selectivity improves in hydration-restrained pores created by more hydrophobic pendant groups. These highly interconnected ion transport channels, with tuned pore gate sizes, show higher ionic conductivity and orders-of-magnitude lower permeation rates of redox-active species compared with conventional membranes, enabling stable cycling of energy-dense aqueous organic redox flow batteries. This pore size tailoring approach provides a promising avenue to membranes with precisely controlled ionic and molecular transport functions.

Science & Technology - Other Topics↗

DOE OSTI2026

Complexity analysis of a CT injection experiment on BRB

In this work, we use Jensen–Shannon complexity and permutation entropy to analyze the magnetic field fluctuations of an astrophysically scaled plasma experiment. The experiment was intended to emulate an interplanetary coronal mass ejection event in the lab, recreating the major sections seen in satellite data. We also use a technique called “delay,” in which we use select elements, skipping one or more data points at a time, in our time series data to obtain Jensen–Shannon complexity as a function of frequency and investigate the frequency of maximized complexity. We then compare the delay frequencies to other frequencies in the plasma. We found that the frequencies for maximum complexity do not correspond to the frequencies investigated, implying that other physical mechanisms lead to an increase in complexity at these frequencies.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Comparison of Fuel Cycles for Lead-Lithium and Pure Lithium Liquid Metal Walls in a Magnetized Target Fusion Power Plant

General Fusion (GF) is developing an adaptable, commercial fusion power plant based on magnetized target fusion (MTF). The GF approach involves forming a spherical torus of deuterium-tritium plasma in a large (~4 m diameter) cavity formed in liquid metal, and then collapsing that cavity with an array of pneumatic piston drivers. The liquid metal is constantly flowing through the fusion chamber and out to processing systems where tritium and heat will be extracted using tritium extraction technologies and heat exchangers, respectively. Here, this study focuses on two candidate designs for the liquid metal blanket and first wall material for the General Fusion Magnetized Target Fusion (GF MTF) power plant and assesses their impact on the tritium fuel cycle. The first candidate is the lead lithium eutectic (LLE) and the second candidate is pure lithium (Li). It was found that the main differences between LLE and Li designs are the extraction technologies required to remove tritium from the blanket and the amount of tritium and its distribution within the facility. More than 80% of the in-process tritium inventory for the LLE design is contained in the isotope separation system, while for the Li design, over 60% of the in-process tritium inventory is contained within the blanket material. This is due to significant tritium retention by Li. For the Li blanket, the burden of tritium processing rests on the blanket extraction technology rather than the traditional exhaust processing route. Thus, the blanket extraction technology is a main driver of tritium inventory in the Li system and determines the subsequent interface with the tritium processing plant.

General Fusion↗

DOE OSTI2024

A Vision–based Robust $\mathcal{H}$ ∞ Gain Scheduling Longitudinal and Lateral Following Controller for Autonomous Vehicles on Urban Curved Roads

Implementing advanced driver assistance systems (ADAS) in congested and intricate urban traffic scenarios poses significant challenges. To address the frequent stop–and–go motions exhibited by autonomous vehicles (AVs) navigating urban roads with changes in curvature, we propose a vision–based robust $\mathcal{H}$ ∞ adaptive cruise control system (ACC) for longitudinal control, plus a lane keeping assist system (LKAS) for lateral control. For the vision-based ACC, a weighted probability objective function for the vehicle following behavior is formulated. We incorporate $\mathcal{H}$ ∞ performance and gain scheduling techniques to mitigate the impact of uncertainty in visual sensor measurements. Furthermore, the optimal time headway is scheduled based on the velocity to ensure traffic flow efficiency and safety during the vehicle following process. For the LKAS, we introduce a road curvature estimation method that integrates lane and vehicle dynamics information to obtain the lateral and heading offsets. Next, the design criterion of the observer–based robust gain scheduling lateral motion controller is established by linear matrix inequality (LMI). Here, a series of experiments conducted within a camera–in–loop platform validate the proposed method.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2024

ML Classifier Fusion for Three Data Streams with Quality Inversely Proportional to Time Resolution

We consider a monitoring scenario of phenomenon using three different streams of measurements whose quality is proportional to their constant inter-arrival times. Each measurement of a stream needs to be binary-classified to reflect the state of interest of the phenomenon. A set of classifiers is separately trained and fused for each stream at its time resolution using measurements collected under known states. We present a machine learning method to fuse the outputs of these fusers to provide a final classification at the finest time resolution. We show that this fused-fusers method provides decisions with likely superior classification probability compared to the best individual classifiers and fused-classifiers. We derive generalization equations that guarantee a superior classification probability of fused-fusers with a confidence probability specified by the classifiers’ generalization equations. We apply these results to study a practical problem of classifying Pu/Np target dissolution events at a radiochemical processing facility using gamma spectral measurements of effluent flows.

Rao, Nageswara↗

DOE OSTI2024

Adaptive Power Flow Approximations With Second-Order Sensitivity Insights

The power flow equations are fundamental to power system planning, analysis, and control. However, the inherent non-linearity and non-convexity of these equations present formidable obstacles in problem-solving processes. To mitigate these challenges, recent research has proposed adaptive power flow linearizations that aim to achieve accuracy over wide operating ranges. The accuracy of these approximations inherently depends on the curvature of the power flow equations within these ranges, which necessitates considering second-order sensitivities. In this paper, we leverage second-order sensitivities to both analyze and improve power flow approximations. We evaluate the curvature across broad operational ranges and subsequently utilize this information to inform the computation of various sample-based power flow approximation techniques. Additionally, we leverage second-order sensitivities to guide the development of rational approximations that yield linear constraints in optimization problems. In conclusion, this approach is extended to enhance accuracy beyond the limitations of linear functions across varied operational scenarios.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Valorization of waste polyolefins to butene, unsaturated fatty alcohols, and branched alkenes using CO 2 and plasma catalyst

Butene, branched alkenes, and short-chain unsaturated fatty alcohols are among the chemicals that have a wide range of industrial applications in the production of fuels, chemicals, and polymers. In this work, we produced these valuable commodity chemicals from waste plastics using a single-step plasma-catalytic process at atmospheric pressure CO 2 . The study shows that combining non-thermal plasma and zeolite could convert polyolefins at a temperature of 200 °C within 15 minutes, producing liquids rich in C 5 and C 6 branched alkenes and C 6 -C 8 unsaturated fatty alcohols. Additionally, gaseous products include a high yield of butene. Comparative studies indicate that combining CO 2 plasma with zeolite synergistically increases reaction rates and alters product compositions. Product selectivity was strongly dependent on reaction conditions, including plasma power, gas flow rates, reactor temperature, and catalyst loading. Furthermore, this process was applicable to common polyolefins and post-consumer polyethylene, indicating that the plasma catalytic approach has promising potential to valorize waste plastics and greenhouse gas CO 2 into versatile chemicals.

42 ENGINEERING↗

DOE OSTI2024

Virtual Resource Management Framework (CRADA Final Report)

There is a need for advanced and widespread business automation within the nuclear industry to drive down operational costs while sustaining or improving safe operations. While there are many business process automation platforms commercially available, the difficulty is that business processes typically rely on a mixture of resource types to accomplish the desired activities and there is no universal software framework virtualizing diverse resource types for the purpose of automation. In this context, we are referring to any capability, physical or intangible, that can be used by an organization to achieve its objectives as a resource. To deploy business automation broadly and enable integrated operations for nuclear (ION), a framework is needed to represent all resource types and their associated disparate data within a plant and to enable seamless flow of resource information to the technologies used for process automation and resource optimization.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

Numerical analysis of air dehumidification through electrospray-enhanced vortical flow cyclone separator

The dehumidification of air is a highly energy-intensive process, typically requiring vapor compression systems to cool the air significantly below its dew point temperature. Here, this study numerically investigates air dehumidification achieved by electrostatic and dielectrophoresis forces in a converging duct and centrifugal principles in cyclone separators. An electrospray was employed to energize water droplets, which then captured water vapor via dielectrophoresis. A novelty of the present study is the inclusion of a cyclone separator positioned downstream of the electrospray. This combination increased the dehumidification efficiency and controlled the flow of the condensate. By integrating electrospray technology and applying an electric field, this study examines the impact of particle separation efficiency, pressure drops, and cyclone performance on air dehumidification. The control parameters for air dehumidification were investigated. Three cyclones were modelled for airflow rates of 5 cubic feet per minute [cfm] (0.0024 m 3 /s), 38 cfm (0.0179 m 3 /s), and 200 cfm (0.094 m 3 /s). The results indicated air dehumidification was up to 5.7 % under environmental conditions, similar to those used to validate the CFD model. The degree of air dehumidification was primarily influenced by the electric charge acquired by the water droplets, their flight trajectory, and the vorticity intensity within the cyclone. Air dehumidification could increase by up to 13 % by increasing the number of droplets injected into the air stream, the flight time, and the electrical charge. The breakdown electric field strength threshold near the electrospray was a limiting factor for further enhancing dehumidification performance. This study examines the optimization of an electro-assisted dehumidification system that includes cyclone-based droplet separation through numerical analysis. The results provide insights into the design of energy-efficient air treatment technologies.

42 ENGINEERING↗

DOE OSTI2024

Editorial: Resolving atmospheric flow in complex environments: recent experiments in terrain and forest canopies

The characterization of atmospheric flows in complex environments, which may include steep terrain slopes and heterogeneous vegetation and/or forest cover, is a long-standing challenge in boundary-layer meteorology. Atmospheric observations are complicated by the presence of transient, terrain-induced flow features, forest-canopy-atmosphere interactions, and atmospheric stability effects, not to mention the logistical hurdles involved with instrument deployment, data analysis, and quality control. Furthermore, challenges in atmospheric modeling arise due to numerical errors associated with complex terrain flows, as well as reliance on simplified parameterizations for unresolved processes such as turbulent mixing and land-surface or forest-canopy-atmosphere interactions. These modeling challenges are exacerbated in the so-called “gray zone,” wherein features of interest have length scales that are similar to the model grid spacing, or when the principal flow layer is smaller than the grid spacing (e.g., slope flows).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Monte Carlo Event Generation with Continuous Normalizing Flows

We apply continuous normalizing flows trained with the flow matching method to the problem of phase-space sampling in Monte Carlo event generation for high-energy collider physics. Focusing on lepton-pair and top-quark pair production with multiple jets, the two computationally most expensive processes at the Large Hadron Collider, we train helicity-conditioned continuous normalizing flows to remap the random numbers used in matrix element evaluation. Compared to standard methods, we achieve unweighting efficiency improvements by factors of up to 184 and 25 for the two processes at their respective highest jet number, at the cost of an increased evaluation time. When combining the advantages of continuous normalizing flows with the fast evaluation times of coupling-layer-based flows, using the RegFlow approach, we find parton-level unweighted event generation walltime gains of about a factor of 10 at the highest jet numbers. These substantial gains highlight the promise of samplers based on machine learning for next-generation collider experiments.

Bothmann, Enrico [CERN; Gottingen U.] (ORCID:00000↗

DOE OSTI2024

Verification and Validation of Spectral Element Code for Supercritical CO2 Flow in Vertical Heated Tubes

The investigation of heat transfer in supercritical CO2 (sCO2) has garnered considerable attention in recent decades, given sCO2's potential as a promising working fluid for advanced power conversion cycles. Despite previous research efforts, there are still gaps in our understanding of sCO2 heat transfer, particularly in conditions associated with heat transfer deterioration. To delve into sCO2 heat transfer more comprehensively, we propose employing the high-fidelity computational fluid dynamics code NekRS to simulate sCO2 flow using the large eddy simulation technique. Through graphics processing unit acceleration, NekRS achieves a higher computational speed than traditional CPU-based systems. However, before using NekRS in practical applications involving sCO2, it is imperative to perform verification and validation. Here, this paper presents our efforts to verify and validate the NekRS code's capability for simulating sCO2 using heated vertical tubes, where heat transfer deterioration usually happens. To accommodate the unique properties of sCO2, we have modified the NekRS code by integrating third-party property modules, such as REFPROP and PROPATH. Our simulations are compared with experimental and numerical data from the literature, instilling confidence in leveraging NekRS for future engineering applications. Our simulations also reveal that the accuracy of the property module significantly impacts the results, with REFPROP outperforming PROPATH for sCO2 properties. Additionally, we observed that, depending on the flow direction, buoyancy can either enhance or suppress turbulence in sCO2 flow. In upward flow, under certain conditions, the suppressed turbulence leads to heat transfer deterioration, resulting in elevated wall temperatures.

NekRS↗

DOE OSTI2024

Discrete element modeling of irregular-shaped soft pine particle flow in an FT4 powder rheometer

Pine residues are a commonly used biomass feedstock that consists of different anatomical fractions, each with distinct particle characteristics. Here, in this work, an experiment-informed discrete element model is developed to investigate the flowability of pine residues in an FT4 rheometer. Multi-sphere particles with distinct particle attributes are created to model each anatomical fraction type. A systematic analysis of specimens with varying particle characteristics (e.g., anatomical fraction type, particle shape, and size) is conducted to elucidate the relationship between particle attributes and flowability. The results show that stems recorded the highest axial force and torque and, correspondingly, the highest flow energy, which is attributed to their high stiffness and interlocking effect. Increasing their percentage in the mixture increases the flow energy while increasing needles tends to decrease flow energy. Knowledge gained in this study on the flow of anatomical fractions is important for the efficient and robust processing of pine residues.

09 BIOMASS FUELS↗