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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,495 records · Page 83
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Pull Force Evaluation of CCS Insulated End Caps

Insulated end caps installed on DC pins of Combined Charging System (CCS) inlets have been identified as potential debris sources within electric vehicle supply equipment (EVSE) connectors, increasing the risk of electric shock and fire hazards. Although standards such as IEC 62196-1:2022 specify mechanical pull-force requirements for these end caps, it remains unclear whether these requirements provide adequate robustness under real-world conditions. To address this concern, the National Charging Experience (ChargeX) Consortium's Hardware Task Force conducted evaluations of insulated end caps used in OEM CCS inlets. This study assesses the performance of insulated end caps used in OEM CCS inlets, specifically those installed on DC pins, using the procedures defined in IEC 62196-1:2022 (Section Sign) 26.7 and SAE J3400, supplemented by additional experimental conditioning. To ensure relevance to real-world conditions, NLR collaborated with charging network operators (CNOs) to guide the selection of charging inlet samples. Material analyses of the insulated end cap samples were conducted to confirm that the tested materials reflected the types and properties commonly observed in the field. The selected samples underwent temperature and humidity conditioning followed by pull-force testing to evaluate the end-cap integrity. Although SAE J3400 inlets were not directly tested due to limited diversity in available field data, the underlying end-cap principles between CCS and SAE J3400 are comparable, allowing the study's insights to be relevant to both technologies. Overall, this study provides a structured evaluation framework to inform potential refinements to mechanical pull-force requirements in charging standards.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

OC6 project Phase III: validation of the aerodynamic loading on a wind turbine rotor undergoing large motion caused by a floating support structure

Abstract. This paper provides a summary of the work done within Phase III of the Offshore Code Comparison Collaboration, Continued, with Correlation and unCertainty (OC6) project, under the International Energy Agency Wind Technology Collaboration Programme Task 30. This phase focused on validating the aerodynamic loading on a wind turbine rotor undergoing large motion caused by a floating support structure. Numerical models of the Technical University of Denmark 10 MW reference wind turbine were validated using measurement data from a 1:75 scale test performed during the UNsteady Aerodynamics for FLOating Wind (UNAFLOW) project and a follow-on experimental campaign, both performed at the Politecnico di Milano wind tunnel. Validation of the models was performed by comparing the loads for steady (fixed platform) and unsteady (harmonic motion of the platform) wind conditions. For the unsteady wind conditions, the platform was forced to oscillate in the surge and pitch directions under several frequencies and amplitudes. These oscillations result in a wind variation that impacts the rotor loads (e.g., thrust and torque). For the conditions studied in these tests, the system aerodynamic response was almost steady. Only a small hysteresis in airfoil performance undergoing angle of attack variations in attached flow was observed. During the experiments, the rotor speed and blade pitch angle were held constant. However, in real wind turbine operating conditions, the surge and pitch variations would result in rotor speed variations and/or blade pitch actuations, depending on the wind turbine controller region that the system is operating. Additional simulations with these control parameters were conducted to verify the fidelity of different models. Participant results showed, in general, a good agreement with the experimental measurements and the need to account for dynamic inflow when there are changes in the flow conditions due to the rotor speed variations or blade pitch actuations in response to surge and pitch motion. Numerical models not accounting for dynamic inflow effects predicted rotor loads that were 9 % lower in amplitude during rotor speed variations and 18 % higher in amplitude during blade pitch actuations.

17 WIND ENERGY↗
DOE OSTI2022

A Universal Refrigerant Charge Fault Detection and Diagnostics Method Based on Pump Down Operation

The performance of the heat pump system varies greatly depending on the refrigerant charge amount. Improving the refrigerant charge fault detection and diagnostics (FDD) method of vapor compression systems have the potential for increasing energy efficiency and reducing service cost. Previous studies to predict refrigerant charge amount are mostly empirical methods which require significant amount of experimental data for high accuracy. The primary goal of this research is to develop a universal charge fault detection method which requires only a few experimental data with high prediction accuracy.Currently, pump down operations are typical practices by HVAC technicians when they need to open the refrigerant circuit to make a repairment. In addition, compressors have a built-in low-pressure cut-off protection function, and the compressor performance maps are commonly available from manufacturers. The proposed method innovatively utilizes the typical pump down operation, the compressor low-pressure cut-off protection, and the compressor performance map. It does not require any geometry information of heat exchangers, refrigerant lines, or charge buffers.The new charge prediction method is firstly formulated through theoretical analysis, then verified and calibrated by a quasi-steady-state simulation of the pump down process for a residential heat pump system. The quasi steady-state simulation uses an HVAC system simulation framework driven by DOE/ORNL Heat Pump Design Model (HPDM). Preliminary experiment validations with heat pump refrigerant leakage tests demonstrate the deviation of the proposed charge prediction method compared with measurement is within 8%. This technology makes refrigerant charge amount available at the technician’s fingertips and leads to shorter maintenance time and fewer site visits.

Li, Zhenning↗
DOE OSTI2022

Machine learning molecular dynamics simulations toward exploration of high-temperature properties of nuclear fuel materials: case study of thorium dioxide

Predicting materials properties of nuclear fuel compounds is a challenging task in materials science. Their thermodynamical behaviors around and above the operational temperature are essential for the design of nuclear reactors. However, they are not easy to measure, because the target temperature range is too high to perform various standard experiments safely and accurately. Moreover, theoretical methods such as first-principles calculations also suffer from the computational limitations in calculating thermodynamical properties due to their high calculation-costs and complicated electronic structures stemming from f-orbital occupations of valence electrons in actinide elements. Here, we demonstrate, for the first time, machine-learning molecular-dynamics to theoretically explore high-temperature thermodynamical properties of a nuclear fuel material, thorium dioxide. The target compound satisfies first-principles calculation accuracy because f-electron occupation coincidentally diminishes and the scheme meets sampling sufficiency because it works at the computational cost of classical molecular-dynamics levels. We prepare a set of training data using first-principles molecular dynamics with small number of atoms, which cannot directly evaluate thermodynamical properties but captures essential atomistic dynamics at the high temperature range. Then, we construct a machine-learning molecular-dynamics potential and carry out large-scale molecular-dynamics calculations. Consequently, we successfully access two kinds of thermodynamic phase transitions, namely the melting and the anomalous λ transition induced by large diffusions of oxygen atoms. Furthermore, we quantitatively reproduce various experimental data in the best agreement manner by selecting a density functional scheme known as SCAN. Our results suggest that the present scale-up simulation-scheme using machine-learning techniques opens up a new pathway on theoretical studies of not only nuclear fuel compounds, but also a variety of similar materials that contain both heavy and light elements, like thorium dioxide.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

Fusion Energy Sciences Network Requirements Review. Final Report, April - October 2021

The Energy Sciences Network (ESnet) is the high-performance network user facility for the US Department of Energy (DOE) Office of Science (SC) and delivers highly reliable data transport capabilities optimized for the requirements of data-intensive science. In essence, ESnet is the circulatory system that enables the DOE science mission by connecting all of its laboratories and facilities in the US and abroad. ESnet is funded and stewarded by the Advanced Scientific Computing Research (ASCR) program and managed and operated by the Scientific Networking Division at Lawrence Berkeley National Laboratory (LBNL). ESnet is widely regarded as a global leader in the research and education networking community. Throughout 2021, ESnet and the Office of Fusion Energy Sciences (FES) of the DOE SC organized an ESnet requirements review of FES-supported activities. Preparation for these events included identification of key stakeholders: program and facility management, research groups, and technology providers. Each stakeholder group was asked to prepare formal case study documents about their relationship to the FES program to build a complete understanding of the current, near-term, and long-term status, expectations, and processes that will support the science going forward.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

An ASME-Compliant Helium-4 Evaporation Refrigerator for the SpinQuest Experiment

This paper presents the design, safety basis, and commissioning results of a 1 K liquid helium-4 (4He) evaporation refrigerator developed for the Fermilab SpinQuest Experiment (E1039). The system represents the first high power helium evaporation refrigerator operated in a fixed target scattering experiment at Fermilab and was engineered to comply with the Fermilab ES&H Manual (FESHM) requirements governing pressure vessels, piping, cryogenic systems, and vacuum vessels. The design is mapped to ASME B31.3 (Process Piping) and the ASME Boiler and Pressure Vessel Code (BPVC) for pressure boundary integrity and overpressure protection, with documented compliance to FESHM Chapters 5031 (Pressure Vessels), 5031.1 (Piping Systems), and 5033 (Vacuum Vessels). This work documents the methodology used to reach compliance and approval for the 4He evaporation refrigerator at Fermilab which the field lacks. Design considerations specific to the high radiation target-cave environment including remotely located instrumentation approximately 20 m from the cryostat are summarized, together with the relief-system sizing methodology used to accommodate transient heat loads from dynamic nuclear polarization microwaves and the high-intensity proton beam. Commissioning data from July 2024 confirms that the system satisfies all thermal performance and safety objectives.

Roberts, Jordan D. [Virginia U.]↗
DOE OSTI2023

Quantum machine learning with differential privacy

Abstract Quantum machine learning (QML) can complement the growing trend of using learned models for a myriad of classification tasks, from image recognition to natural speech processing. There exists the potential for a quantum advantage due to the intractability of quantum operations on a classical computer. Many datasets used in machine learning are crowd sourced or contain some private information, but to the best of our knowledge, no current QML models are equipped with privacy-preserving features. This raises concerns as it is paramount that models do not expose sensitive information. Thus, privacy-preserving algorithms need to be implemented with QML. One solution is to make the machine learning algorithm differentially private, meaning the effect of a single data point on the training dataset is minimized. Differentially private machine learning models have been investigated, but differential privacy has not been thoroughly studied in the context of QML. In this study, we develop a hybrid quantum-classical model that is trained to preserve privacy using differentially private optimization algorithm. This marks the first proof-of-principle demonstration of privacy-preserving QML. The experiments demonstrate that differentially private QML can protect user-sensitive information without signficiantly diminishing model accuracy. Although the quantum model is simulated and tested on a classical computer, it demonstrates potential to be efficiently implemented on near-term quantum devices [noisy intermediate-scale quantum (NISQ)]. The approach’s success is illustrated via the classification of spatially classed two-dimensional datasets and a binary MNIST classification. This implementation of privacy-preserving QML will ensure confidentiality and accurate learning on NISQ technology.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Drone flight visible images, digital elevation maps, and geotiffs, Council, Seward Peninsula, Alaska, USA, July 2017

Remote sensing data collected from Lawrence Berkeley National Laboratory’s (LBNL) unmanned aerial system (UAS) quadcopter platform – Inspire-1 – operated by the Ameriflux Management Project (ameriflux.lbl.gov) Technical Team. The Insprire-1 is a visible imagery UAS platform that simultaneously measures very high spatial resolution optical red/green/blue (RGB) at regular intervals along each flight path. This package includes data from four flight paths over the NGEE Arctic Council Road Site near mile marker 71 in the Seward Peninsula, Alaska in July 2017. Derived image products for each flight include ortho-mosaiced RGB, an RGB-based digital surface model (DSM) using the structure from motion (SfM) technique, and a digital terrain model (DTM). Ancillary aircraft data, flight mission parameters, and general flight conditions are also included. Unprocessed and processed data products are included in this package (processing levels 0-2). This metadata document contains flight campaign, instrument and file metadata, along with a description of data processing levels, data products and file naming scheme.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic) was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy’s Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy’s Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Drone flight visible images, digital elevation maps, and geotiffs, Teller, Seward Peninsula, Alaska, USA, July 2017

Remote sensing data collected from Lawrence Berkeley National Laboratory’s (LBNL) unmanned aerial system (UAS) quadcopter platform – Inspire-1 – operated by the Ameriflux Management Project (ameriflux.lbl.gov) Technical Team. The Insprire-1 is a visible imagery UAS platform that simultaneously measures very high spatial resolution optical red/green/blue (RGB) at regular intervals along the flight path. This package includes data from a flight path over the NGEE Arctic Teller Site, west of mile marker 27 Bob Blodgett Highway in the Seward Peninsula, Alaska in July 2017. Derived image products include ortho-mosaiced RGB, an RGB-based digital surface model (DSM) using the structure from motion (SfM) technique, and a digital terrain model (DTM). Ancillary aircraft data, flight mission parameters, and general flight conditions are also included. Unprocessed and processed data products are included in this package (processing levels 0-2). This metadata document contains flight campaign, instrument and file metadata, along with a description of data processing levels, data products and file naming scheme.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic) was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy’s Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy’s Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Drone flight visible images, digital elevation maps, and geotiffs, Kougarok, Seward Peninsula, Alaska, USA, July 2017

Remote sensing data collected from Lawrence Berkeley National Laboratory’s (LBNL) unmanned aerial system (UAS) quadcopter platform – Inspire-1 – operated by the Ameriflux Management Project (ameriflux.lbl.gov) Technical Team. The Insprire-1 is a visible imagery UAS platform that simultaneously measures very high spatial resolution optical red/green/blue (RGB) at regular intervals along each flight path. This package includes data from four flight paths over the NGEE Arctic Kougarok Site, west of mile marker 64 on Kuzitrin Road in the Seward Peninsula, Alaska in July 2017. Derived image products include ortho-mosaiced RGB, an RGB-based digital surface model (DSM) using the structure from motion (SfM) technique, and a digital terrain model (DTM). Ancillary aircraft data, flight mission parameters, and general flight conditions are also included. Unprocessed and processed data products are included in this package (processing levels 0-2). This metadata document contains flight campaign, instrument and file metadata, along with a description of data processing levels, data products and file naming scheme.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic) was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy’s Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy’s Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

UPF and Y-12 Criticality Accident Alarm System (CAAS) Radiation Testing Report

This report documents the experimental conditions and results for the testing of the Mirion Technologies CAAS-3S Criticality Accident Alarm System (CAAS) for the Y-12 National Security Complex (Y-12) and Uranium Processing Facility (UPF). The testing was conducted at the Godiva IV Burst Reactor at the National Criticality Experiments Research Center (NCERC) at the Nevada National Security Site (NNSS) during the week of January 11, 2021. The testing was conducted in accordance with 25774-QL-POA-JR00-00002-VDE-27.0, CAAS-3S Radiation Testing of Cabinet Components Test Plan. The tests subjected the CAAS-3S system to intense and short duration mixed neutron and gamma radiation fields and to high integrated radiation doses. The purpose of the test was to environmentally qualify various CAAS-3S components in a radiation environment. Successful completion of the tests was predicated on the collaborative efforts of several organizations. Sandia National Laboratory (SNL) and Lawrence Livermore National Laboratory (LLNL) provided dosimetry to estimate photon and neutron dose. Los Alamos National Laboratory (LANL) manages the NCERC facility, operated the Godiva IV reactor, and provided field support for CAAS-3S placement and communications interface for data acquisition systems supplied by Mirion Technologies. Throughout the test campaign, Y-12 and UPF personnel provided technical support in determining changes to the test schedule, as needed, interpretation of the results, and comparison between the measured dosimetry data and predicted results. Throughout the test campaign, Mirion personnel provided technical support in the operation of the CAAS-3S system itself.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

NREL’s Wind Turbine Drivetrain Condition Monitoring and Wind Plant Operation and Maintenance Research During the 2010s: A US Land-Based Perspective

The wind industry has seen tremendous growth during the past two decades, with the global cumulative installation capacity reaching more than 650 gigawatts by the end of 2019. Despite performance and reliability improvements of utility-scale wind turbines over the years, the industry still experiences premature component failures, leading to increased operation and maintenance (O&M) costs. Among various turbine components, gearboxes—and, more broadly, drivetrains—have shown to be costly to maintain throughout the design life of a wind turbine. The problem of premature component failure is industry wide. As early as 2007, the US Department of Energy (DOE) started to address this challenge through the National Renewable Energy Laboratory’s (NREL’s) reliability initiative that first focused on gearboxes and more recently expanded to entire drivetrains. The wind turbine drivetrain condition monitoring and wind plant O&M research that is the subject of this paper is part of the NREL initiative and includes a few research and development (R&D) activities conducted during the 2010s. These activities included technology evaluation during the first few years; novel monitoring technique investigation (specifically, compact filter analysis) during the middle years; and data and physics domain modeling for fault detection and prediction in recent years. A high-level summary of these activities is provided in this paper along with some key observations from each activity. Most of the work discussed has been published and can be referred to for more information. They reflect the expected evolution of wind turbine condition monitoring and O&M in the US market—primarily, a land-based perspective. In addition, we have identified several R&D opportunities that can be picked up by the research community to help industry advance in related areas, making wind power more cost competitive in the future.

17 WIND ENERGY↗
DOE OSTI2023

Performance assessment of a real water source heat pump within a hardware-in-the-loop (HIL) testing environment

Over the last decade, the global fight against climate change through electrification has led to an increase in research on building heating, ventilation, and air conditioning (HVAC) systems that utilize intelligent control algorithms to provide demand-side grid service while also maintaining the thermal comfort of building occupants. As the pivotal point between building electricity consumption and indoor thermal comfort, high-efficiency electrical vapor-compression heat pumps are at the center of these emerging studies, and various grid-interactive and occupant-comfort control algorithms have been developed for them. The impact of these algorithms on heat pump operation and performance when subjected to different weather conditions, building loads, and grid requests calls for investigation and verification via experimental testing with actual heat pumps integrated with real-time building and grid responses. This study introduces a Water-Source Heat Pump (WSHP) Hardware-in-The-Loop (HIL) Test Facility that is the first of its kind. This testbed utilizes a 2-ton variable speed water-to-air heat pump that is capable of interacting with a virtual environment currently comprised of an EnergyPlus (E+) building simulation, an agent-based occupant behavioral model, and a single U-tube ground-loop heat exchanger (GLHE) model. Detailed descriptions of the testbed’s physical design and operation, virtual environment, as well as their mutual communication is provided. An uncertainty analysis is also performed under manufacturer specified heating and cooling design conditions. This analysis shows that the total load across the WSHP’s demand side heat exchanger, i.e., the sum of its latent and sensible components, can be measured with a relative uncertainty of ± 10.4% and ± 3.6% in cooling and heating mode respectively. The WSHP’s coefficient of performance (COP) can be measured with relative uncertainties of ± 10.4% in cooling mode, and ± 3.7% in heating mode. A preliminary 24-h experimental demonstration is then performed utilizing the DOE prototype small commercial office building model in E+. The simulation takes place in Atlanta, GA on the date of 08/26/15 from 12:00 AM to 11:59 PM using TMY3 weather data. Here, the results from this demonstration show that over the course of this experiment the simulated outputs of zone dry-bulb temperature, zone humidity ratio, and WSHP inlet water temperature can be tracked by testbed emulators up to a root mean squared error (RMSE) of ± 0.27 °C, ± 0.376 g/kg, and ± 0.85 °C respectively. The WSHP’s dynamic behavioral characteristics and performance are also captured, and correspond well with the authors’ previous understanding of heat pump efficiency as a function of evaporator and condenser fluid inlet conditions respectively.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Development of Predictive Models for Advanced Reactor Autonomous Control

Advanced reactor designs including microreactors and small modular reactors will contribute to the clean production of cheap energy, and autonomous control for advanced reactors is an appealing option for reducing cost. However, there is a lack of industry experience applying autonomous control for advanced nuclear reactors. To accelerate the development and industry acceptance of autonomous control software for nuclear reactors, we aim to demonstrate autonomous control of the Purdue University research reactor (PUR-1) using INL-developed model predictive control (MPC) methods. To prepare for this demonstration, data-driven predictive models based on process data collected from PUR-1 have been developed and integrated with MPC and used to control a physics-based model of PUR-1. A data-driven dynamics model and a gated recurrent unit (GRU) network were both trained on process data from PUR-1. The dynamics model was shown to effectively control the reactor model with MPC when provided reactivity as a control variable but failed to control the model through the control rod positions. The GRU network produced more accurate predictions than the dynamics model when evaluated on operational data, and future work will include the evaluation of the GRU network in the controller.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Anticipating gelation and vitrification with medium amplitude parallel superposition (MAPS) rheology and artificial neural networks

Abstract Anticipating qualitative changes in the rheological response of complex fluids (e.g., a gelation or vitrification transition) is an important capability for processing operations that utilize such materials in real-world environments. One class of complex fluids that exhibits distinct rheological states are soft glassy materials such as colloidal gels and clay dispersions, which can be well characterized by the soft glassy rheology (SGR) model. We first solve the model equations for the time-dependent, weakly nonlinear response of the SGR model. With this analytical solution, we show that the weak nonlinearities measured via medium amplitude parallel superposition (MAPS) rheology can be used to anticipate the rheological aging transitions in the linear response of soft glassy materials. This is a rheological version of a technique called structural health monitoring used widely in civil and aerospace engineering. We design and train artificial neural networks (ANNs) that are capable of quickly inferring the parameters of the SGR model from the results of sequential MAPS experiments. The combination of these data-rich experiments and machine learning tools to provide a surrogate for computationally expensive viscoelastic constitutive equations allows for rapid experimental characterization of the rheological state of soft glassy materials. We apply this technique to an aging dispersion of Laponite ® clay particles approaching the gel point and demonstrate that a trained ANN can provide real-time detection of transitions in the nonlinear response well in advance of incipient changes in the linear viscoelastic response of the system.

Lennon, Kyle R. (ORCID:0000000212515461)↗
DOE OSTI2023

Novel method for accurately estimating membrane transport properties and mass transfer coefficients in reverse osmosis

Here, we present a simple and robust method to simultaneously characterize the water and salt permeability (A, B) of reverse osmosis (RO) membranes and mass transfer coefficient (k) in membrane modules. The proposed methodology comprises a set of RO experiments performed at different operating pressures or stages. The measured water and salt fluxes in each stage are simultaneously fitted to the RO transport equations by performing a non-linear regression, using A, B, and k as regression parameters. We first perform a systematic accuracy analysis of the proposed method across the full operational range of RO. The assessment shows that the method accuracy is substantially higher than current methods and increases with number of experimental stages and driving forces. This assessment is used to inform the design of an experimental protocol that minimizes errors in estimated A, B, and k. We then evaluate two commercial RO membranes following the new protocol. For both membranes, A and B parameters decrease by 17% and 15% from the dilute solution to seawater concentrations, whereas the k parameter remains constant. Our study demonstrates that the proposed method, informed by data-driven experimental designs, provides a new approach for accurately characterizing transport phenomenon in membrane processes with feeds of less than 100 g/L total dissolved solids.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Investigating Fast Scanning Calorimetry and Differential Scanning Calorimetry as Screening Tools for Thermoset Polymer Material Compatibility with Laser-Based Powder Bed Fusion

As additive manufacturing (AM) technology has developed and progressed, a constant topic of research in the area is expanding the library of materials to be used with these techniques. Among AM methods that utilize polymers, laser-based powder bed fusion (PBF-LB) has preferentially used thermoplastic polymers as its starting materials, but the deposition and material joining method employed in PBF-LB may also be compatible with powdered thermoset polymer precursors as feedstocks. To assess the compatibility of candidate thermosetting polymers and PBF-LB, characterization techniques and protocols that link fundamental material behavior to material behavior in the processing environment are needed. Therefore, the objectives of this work are to compare the curing behavior measured with two different calorimetry techniques that can operate in different heating rate regimes, differential scanning calorimetry (DSC) and fast scanning calorimetry (FSC), and to assess the capabilities of these techniques to act as materials screening tools for PBF-LB. A commercial polyester powder coating is used as a model material to evaluate the potential of obtaining complimentary information for material screening through a combination of calorimetry methods, and its non-isothermal curing behavior is measured at heating rates between 5 °C/min and 7500 °C/min. Curing exotherms are observed with both calorimetry techniques, and comparing the enthalpy associated with curing shows that incomplete curing occurs at higher heating rates, with relative conversion values of approximately 30%. The curing data are fit with two isoconversional models, Friedman and Starink, which show a reduced activation energy at higher heating rates as well, signifying a lower barrier to curing at the conditions used in the FSC experiments. Overall, the results of this work indicate that using these two calorimetry techniques as tiered screening tools can provide valuable information about how curing may proceed in PBF-LB and inform materials selection and design activities for additive manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Low latency optical-based mode tracking with machine learning deployed on FPGAs on a tokamak

Active feedback control in magnetic confinement fusion devices is desirable to mitigate plasma instabilities and enable robust operation. Optical high-speed cameras provide a powerful, non-invasive diagnostic and can be suitable for these applications. Here, in this study, we process high-speed camera data, at rates exceeding 100 kfps, on in situ field-programmable gate array (FPGA) hardware to track magnetohydrodynamic (MHD) mode evolution and generate control signals in real time. Our system utilizes a convolutional neural network (CNN) model, which predicts the n = 1 MHD mode amplitude and phase using camera images with better accuracy than other tested non-deep-learning-based methods. By implementing this model directly within the standard FPGA readout hardware of the high-speed camera diagnostic, our mode tracking system achieves a total trigger-to-output latency of 17.6 μs and a throughput of up to 120 kfps. This study at the High Beta Tokamak-Extended Pulse (HBT-EP) experiment demonstrates an FPGA-based high-speed camera data acquisition and processing system, enabling application in real-time machine-learning-based tokamak diagnostic and control as well as potential applications in other scientific domains.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗