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At least 91 records · Page 5

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DOE OSTI2025

Electrochemical Direct Air Capture of CO 2 Using Redox-Active Textiles

We have used vapor phase chemistry to construct a textile electrode that comprises an immobilized PCET agent, poly(1-aminoanthraquinone) (PAAQ) encapsulated within a conductive polymer, and incorporated it into CO 2 -separating electrochemical flow cells. This system facilitates pH-swing-driven carbon capture from a 1% CO 2 feed and air, with the former at a competitively low energetic cost of 202 kJ/molCO 2 .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Invertible Neural Networks for Airfoil Design

We report the airfoil design problem, in which an engineer seeks a shape with desired performance characteristics, is fundamental to aerodynamics. Design workflows traditionally rely on iterative optimization methods using low-fidelity integral boundary-layer methods as higher-fidelity adjoint-based computational fluid dynamics methods are computationally expensive. Surrogate-based approaches can accelerate the design process but still rely on some iterative inverse design procedure. In this work, we leverage emerging invertible neural network (INN) tools to enable the rapid inverse design of airfoil shapes for wind turbines. INNs are specialized deep-learning models with well-defined inverse mappings. When trained appropriately, INN surrogate models are capable of forward prediction of aerodynamic and structural quantities for a given airfoil shape as well as inverse recovery of airfoil shapes with specified aerodynamic and structural characteristics. The INN approach offers a roughly 100 times speed-up compared to adjoint-based methods for inverse design. We demonstrate the INN tool for inverse design on three test cases of 100 airfoils each that satisfy the performance characteristics close to those of airfoils used in wind-turbine blades. All generated shapes satisfy the desired aerodynamic characteristics, demonstrating the success of the INN approach for inverse design of airfoils.

17 WIND ENERGY↗

DOE OSTI2021

Arctic observations and numerical simulations of surface wind effects on Multi-Angle Snowflake Camera measurements

Abstract. Ground-based measurements of frozen precipitation are heavily influenced by interactions of surface winds with gauge-shield geometry. The Multi-Angle Snowflake Camera (MASC), which photographs hydrometeors in free-fall from three different angles while simultaneously measuring their fall speed, has been used in the field at multiple midlatitude and polar locations both with and without wind shielding. Here, we present an analysis of Arctic field observations – with and without a Belfort double Alter shield – and compare the results to computational fluid dynamics (CFD) simulations of the airflow and corresponding particle trajectories around the unshielded MASC. MASC-measured fall speeds compare well with Ka-band Atmospheric Radiation Measurement (ARM) Zenith Radar (KAZR) mean Doppler velocities only when winds are light (≤5ms-1) and the MASC is shielded. MASC-measured fall speeds that do not match KAZR-measured velocities tend to fall below a threshold value that increases approximately linearly with wind speed but is generally <0.5ms-1. For those events with wind speeds ≤1.5ms-1, hydrometeors fall with an orientation angle mode of 12∘ from the horizontal plane, and large, low-density aggregates are as much as 5 times more likely to be observed. Simulations in the absence of a wind shield show a separation of flow at the upstream side of the instrument, with an upward velocity component just above the aperture, which decreases the mean particle fall speed by 55 % (74 %) for a wind speed of 5 m s−1 (10 m s−1). We conclude that accurate MASC observations of the microphysical, orientation, and fall speed characteristics of snow particles require shielding by a double wind fence and restriction of analysis to events where winds are light (≤5ms-1). Hydrometeors do not generally fall in still air, so adjustments to these properties' distributions within natural turbulence remain to be determined.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Systems and methods of decoupled hydrogen generation using energy-bearing redox pairs

Described herein are systems and methods of hydrogen generation and electrolyte regeneration as independent operations in separate redox flow cells. The operations can be decoupled by using an energy-bearing redox pair that electrochemically bears energy facilitating flexible, efficient hydrogen generation. In one example, the hydrogen generation redox flow cell can include a liquid, energy-bearing electrolyte solution in which at least one species of an energy-bearing redox pair is dissolved, to decouple the hydrogen evolution reaction from the reaction at the opposite electrode (e.g., the oxygen evolution reaction of conventional direct water electrolysis). Each species of the energy-bearing redox pair is associated with a standard electrode potential within the water electrolysis window.

Wang, Wei↗

DOE OSTI2024

Dean vortex-enhanced blood plasma separation in self-driven spiral microchannel flow with cross-flow microfilters

Point-of-care (POC) diagnostic devices have been developing rapidly in recent years, but they are mainly using saliva instead of blood as a test sample. A highly efficient self-separation during the self-driven flow without power systems is desired for expanding the point-of-care diagnostic devices. Microfiltration stands out as a promising technique for blood plasma separation but faces limitations due to blood cell clogging, resulting in reduced separation speed and efficiency. These limitations are mainly caused by the high viscosity and hematocrit in the blood flow. A small increment in the hematocrit of the blood significantly increases the pressure needed for the blood plasma separation in the micro-filters and decreases the separation speed and efficiency. Addressing this challenge, this study explores the feasibility of diluting whole blood within a microfluidic device without external power systems. This study implemented a spiral microchannel utilizing the inertial focusing and Dean vortex effects to focus the red blood cells and extract the blood with lower hematocrit. The inertial migration of the particles during the capillary flow was first investigated experimentally; a maximum of 88% of the particles migrated to the bottom and top equilibrium positions in the optimized 350 × 60 μm (cross-sectional area, 5.8 aspect ratio) microchannel. With the optimized dimension of the microchannel, the whole blood samples within the physiological hematocrit range were tested in the experiments, and more than 10% of the hematocrit reduction was compared between the outer branch outlet and inner branch outlet in the 350 × 60 μm microchannel.

Biochemistry & Molecular Biology↗

DOE OSTI2021

Blood Plasma Self-Separation Technologies during the Self-Driven Flow in Microfluidic Platforms

Blood plasma is the most commonly used biofluid in disease diagnostic and biomedical analysis due to it contains various biomarkers. The majority of the blood plasma separation is still handled with centrifugation, which is off-chip and time-consuming. Therefore, in the Lab-on-a-chip (LOC) field, an effective microfluidic blood plasma separation platform attracts researchers’ attention globally. Blood plasma self-separation technologies are usually divided into two categories: active self-separation and passive self-separation. Passive self-separation technologies, in contrast with active self-separation, only rely on microchannel geometry, microfluidic phenomena and hydrodynamic forces. Passive self-separation devices are driven by the capillary flow, which is generated due to the characteristics of the surface of the channel and its interaction with the fluid. Comparing to the active plasma separation techniques, passive plasma separation methods are more considered in the microfluidic platform, owing to their ease of fabrication, portable, user-friendly features. We propose an extensive review of mechanisms of passive self-separation technologies and enumerate some experimental details and devices to exploit these effects. The performances, limitations and challenges of these technologies and devices are also compared and discussed.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Adsorption-based membranes for air separation using transition metal oxides

In this work, we use computational modeling to examine the viability of adsorption-based pore-flow membranes for separating gases when a purely size-based separation strategy is ineffective. Using molecular dynamics simulations of O 2 and N 2 , we model permeation through a nanoporous graphene membrane. Permeation is assumed to follow a five-step adsorption-based pathway, with desorption being the rate-limiting step. Using this model, we observe increased selectivity between O 2 and N 2 , resulting from increased adsorption energy differences. We explore the limits of this strategy, providing an initial set of constraints that need to be satisfied to allow for selectivity. Finally, we provide a preliminary exploration of some transition metal oxides that appear to satisfy those conditions. Using density functional theory calculations, we confirm that these oxides possess adsorption energies needed to operate as adsorption-based pore-flow membranes. These adsorption energies provide a suitable motivation to examine adsorption-based pore-flow membranes as a viable option for air separation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Gradient flow based phase-field modeling using separable neural networks

Allen–Cahn equation is a reaction–diffusion equation and is widely used for modeling phase separation. Machine learning methods for solving the Allen–Cahn equation in its strong form suffer from inaccuracies in collocation techniques, errors in computing higher-order spatial derivatives, and the large system size required by the space–time approach. To overcome these challenges, we propose solving the gradient flow of the Ginzburg–Landau free energy functional, which is equivalent to the Allen–Cahn equation, thereby avoiding the second-order spatial derivatives associated with the Allen–Cahn equation. A minimizing movement scheme is employed to solve the gradient flow problem, eliminating the complexities of a space–time approach. We utilize a separable neural network that efficiently represents the phase field through low-rank tensor decomposition. As we use the minimizing movement scheme to numerically solve the gradient flow problem, we thus, refer to the proposed method as the Separable Deep Minimizing Movement (SDMM) method. The evaluation of the functional in the minimizing movement scheme using the Gauss quadrature technique bypasses the inaccuracies associated with collocation techniques traditionally used to solve partial differential equations. A hyperbolic tangent transformation is introduced on the phase field prior to the evaluation of the functional to ensure that it remains strictly bounded within the values of the two phases. For this transformation, theoretical guarantee for energy stability of the minimizing movement scheme is established. Our results suggest that this transformation helps to improve the accuracy and efficiency significantly. The proposed method resolves the challenges faced by state-of-the-art machine learning techniques, outperforming them in both accuracy and efficiency. It is also the first machine learning method to achieve an order of magnitude speed improvement over the finite element method. In addition to its formulation and computational implementation, several case studies illustrate the applicability of the proposed method.

42 ENGINEERING↗

DOE OSTI2022

Separating Oil-Water Mixtures Using Bump Arrays

Particle separation is an important process step across many fields. One technique being applied for separating solids such as blood components or sand particles from carrier fluids is the use of arrays of aligned posts called deterministic lateral arrays to bump particles to one side in the flow stream to enhance separation. This technique may be useful for separation of deformable particles. The ability to efficiently separate two-phase industrial (oil/water) mixtures is key for future use of valuable resources. Over 1 trillion gallons of petroleum production water could be reclaimed annually for reuse in the drought-ridden western US states. The ability to reclaim this petroleum production water may be critical for the Central High Plains (Colorado, Kansas, Oklahoma, Texas, and New Mexico). Trends in just the High Plains area already lost 20 to 25% of the irrigated farming area due to insufficient ground water storage to irrigate, and farmland losses are expected to grow to 40%. Proving this technology is key to reuse of petroleum production water for crop irrigation or to replace water from currently failing aquifers in rich agricultural lands of the Central High Plains. We conducted experiments applying mesofluidic separation for flowing two-phase (oil/water) mixtures. Experiments were conducted using oils of differing viscosities with water as the carrier fluid; separation was achieved over a range of oil-water concentrations. We describe the results of these experiments in this paper.

oil-water separation, micelle, droplet separation,↗

DOE OSTI2024

Water separator for hydrogen steam injected turbine engine

A propulsion system for an aircraft includes a core engine that includes a core flow path where air is compressed in a compressor section, communicated to a combustor section, mixed with a hydrogen based fuel and ignited to generate a core gas flow that is expanded through a turbine section. The propulsion system includes a hydrogen fuel system that is configured to supply hydrogen fuel to the combustor through a fuel flow path, a condenser that is arranged along the core flow path and configured to condense water within the core gas flow, and a separator that includes an inlet, a primary water outlet and an exit that includes a plurality of exit guide vanes for straightening the core gas flow that proceeds through the exit. The exit guide vanes include a secondary water outlet for removing condensed water in the core gas flow that proceeds through the exit.

Terwilliger, Neil J.↗

DOE OSTI2024

Multiscale Flow for robust and optimal cosmological analysis

We propose Multiscale Flow, a generative Normalizing Flow that creates samples and models the field-level likelihood of two-dimensional cosmological data such as weak lensing. Multiscale Flow uses hierarchical decomposition of cosmological fields via a wavelet basis and then models different wavelet components separately as Normalizing Flows. The log-likelihood of the original cosmological field can be recovered by summing over the log-likelihood of each wavelet term. This decomposition allows us to separate the information from different scales and identify distribution shifts in the data such as unknown scale-dependent systematics. The resulting likelihood analysis can not only identify these types of systematics, but can also be made optimal, in the sense that the Multiscale Flow can learn the full likelihood at the field without any dimensionality reduction. We apply Multiscale Flow to weak lensing mock datasets for cosmological inference and show that it significantly outperforms traditional summary statistics such as power spectrum and peak counts, as well as machine learning–based summary statistics such as scattering transform and convolutional neural networks. We further show that Multiscale Flow is able to identify distribution shifts not in the training data such as baryonic effects. Finally, we demonstrate that Multiscale Flow can be used to generate realistic samples of weak lensing data.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

NorthStar Helium Cooling System. Final Report on LANL Contribution

From the beginning of NNSA support to the NorthStar development of a production facility for the medical isotope Molybdenum-99 (Mo99), LANL has been responsible for the design and testing of a target and its cooling. The target is comprised of a stack of Mo100 disks separated by coolant flow gaps between the disks and between the first disk and a housing window, which separates the target and its coolant from the vacuum of the beam line. The target, and therefore its cooling requirements, evolved steadily over the years of system development

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2022

U.S. Cobalt: A Cycle of Diverse and Important Uses

We present a material flow and stock analysis for cobalt use and waste in the United States from 1996 to 2020, including the separation of cobalt flows into six product groups and five end-use sectors. The results demonstrate the importance of U.S. manufacturing for air travel, energy storage, cemented carbides, and numerous other applications. Battery use in the United States has become the primary factor in cobalt demand, but the batteries are manufactured mainly elsewhere. The correlation of cobalt products to principal end-use sectors provides an informed view of the location of cobalt in in-use stocks. Furthermore, two approaches could primarily address these stocks: enhanced recycling of batteries in consumer/business electronics and implementing cobalt-free substitutes for chemical uses and future battery technologies. Overall, cobalt's uses are as diverse and societally crucial as any element in the periodic table but have heretofore not received the levels of attention they deserve.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Cooperative Research and Development Agreement between National Energy Technology Laboratory and Korea Institute of Energy Research (KIER) [Abstract]

KIER and NETL will collaborate to study hydrate-based technologies to enhance desalination, gas separation, and hydrocarbon flow through pipelines and wellbores. With optimized selections of hydrate formers and reactors, gas hydrates can be effective and economic mechanisms of separation for solid-water and gas mixtures, and gas hydrate formation or dissociation can be significantly delayed or expedited with suitably selected additives. Comprehensive laboratory studies, including identifying novel hydrate formers or inhibitors to control temperature and pressure requirements and understanding their working mechanism or developing reactor systems for production of large quantities of hydrates, will be performed at both KIER and NETL either parallelly or serially.

03 NATURAL GAS↗

DOE OSTI2020

Cooperative Research and Development Agreement between National Energy Technology Laboratory and Raytheon Technologies Corporation [Abstract]

02 PETROLEUM↗

DOE OSTI2021

Aeroacoustic Assessment of Wind Plant Controls

The Aeroacoustic Assessment project aims to quantify changes in aeroacoustic noise generation by a utility scale wind turbine operating under imposed yaw offsets common for wake steering and wind plant control strategies. Active plant control utilizing wake deflection control strategies have been shown experimentally and computationally to reliably produce 1-2% of additional annual energy production (AEP) without significant changes in turbine structural loads, the potential impact on aeroacoustic emissions has yet to be quantified or completely understood. Yawed operation of a wind turbine changes the three-dimensional aerodynamic interaction between the rotor blades and the incoming atmospheric flow, leading to changes in noise generation. This work quantifies the extent to which active control induces additional aeroacoustic emissions from additional separation and other flow interaction dynamic effects. Given public concerns about wind turbine noise and the need for observational data required for regulators to establish noise restrictions, we must understand potential acoustic emissions resulting from active control prior to commercial deployment and the development of practical noise reduction methods and technologies. The work outlined in this report details the new aeroacoustic measurement capability developed at NREL and its application to making full-field observations of noise from a utility-scale wind turbine. Preliminary noise modeling with the aeroacoustics module in OpenFAST helped to establish the design of the experiment, specifically targeting the noise observed at observers distributed around the DOE-owned GE 1.5 MW wind turbine. Low-frequency and infrasound measurement capability implemented in this project establish an operational baseline for the wind turbine, setting the stage for future control, rotor blade, and turbine design research.

17 WIND ENERGY↗

DOE OSTI2024

Multiscale Normalizing Flows for Gauge Theories

Scale separation is an important physical principle that has previously enabled algorithmic advances such as multigrid solvers. Previous work on normalizing flows has been able to utilize scale separation in the context of scalar field theories, but the principle has been largely unexploited in the context of gauge theories. This work gives an overview of a new method for generating gauge fields using hierarchical normalizing flow models. This method builds gauge fields from the outside in, allowing different parts of the model to focus on different scales of the problem. Numerical results are presented for $U(1)$ and $SU(3)$ gauge theories in 2, 3, and 4 spacetime dimensions.

Abbott, Ryan↗

DOE OSTI2022

Monitoring of Temperature Measurements for Different Flow Regimes in Water and Galinstan with Long Short-Term Memory Networks and Transfer Learning of Sensors

Temperature sensing is one of the most common measurements of a nuclear reactor monitoring system. The coolant fluid flow in a reactor core depends on the reactor power state. We investigated the monitoring and estimation of the thermocouple time series using machine learning for a range of flow regimes. Measurement data were obtained, in two separate experiments, in a flow loop filled with water and with liquid metal Galinstan. We developed long short-term memory (LSTM) recurrent neural networks (RNNs) for sensor predictions by training on the sensor’s own prior history, and transfer learning LSTM (TL-LSTM) by training on a correlated sensor’s prior history. Sensor cross-correlations were identified by calculating the Pearson correlation coefficient of the time series. The accuracy of LSTM and TL-LSTM predictions of temperature was studied as a function of Reynolds number (Re). The root-mean-square error (RMSE) for the test segment of time series of each sensor was shown to linearly increase with Re for both water and Galinstan fluids. Using linear correlations, we estimated the range of values of Re for which RMSE is smaller than the thermocouple measurement uncertainty. For both water and Galinstan fluids, we showed that both LSTM and TL-LSTM provide reliable estimations of temperature for typical flow regimes in a nuclear reactor. The LSTM runtime was shown to be substantially smaller than the data acquisition rate, which allows for performing estimation and validation of sensor measurements in real time.

Pantopoulou, Stella↗