Results for “cleaning performance” — Page 52

DOE OSTI2022

Analysis of Energy Justice and Equity Impacts from Replacing Peaker Plants with Energy Storage

Transitions to low-carbon energy systems are essential to mitigating and adapting to climate change. Energy storage systems are a key component in achieving a viable decarbonized electric grid. However, decarbonization alone does not guarantee a fairer, more inclusive, or socially just energy system. Energy equity and justice should be integrated in energy system transitions to ensure benefits and burdens are shared equitably. In this paper, we discuss the relationship between energy storage and social equity by assessing the use of energy storage to replace natural gas-fired (NG) peaker plants. Peaker plants are disproportionately located near disadvantaged communities and tend to be older and high emitters of health-affecting fine particulate matter and other pollutants. This paper investigates the equity implications of NG peaker plant replacements with battery energy storage in the context of Washington State’s peaker plants to highlight the human-centered values of retiring the plants. The study performed production cost simulations using the latest Western Electric Coordinating Council Anchor Dataset 2030 case and found that total generation cost, locational marginal price, and total annual emissions were reduced with the replacements. These reductions will have equity benefits on local communities including access to clean air, enhanced health outcomes, and energy burden reductions.

Tarekegne, Bethel W.↗

DOE OSTI2025

Moltensaltpropnet

MoltenSaltPropnet is a physics-informed machine learning framework that aims to predict the thermophysical properties of molten fluoride and chloride salt mixtures, which are crucial for the design and safety of Generation IV molten salt reactors. The code processes data from the Molten-Salt Thermal Properties Database (MSTDB-TP) and the Janz compendium, converting critically evaluated correlations into fast, differentiable surrogate models for density, viscosity, thermal conductivity, and heat capacity across 448 distinct salt systems. The implementation consists of several key components: 1. Data Curation: The code parses and cleans the raw data, normalizing elemental mole fractions and extracting relevant regression coefficients for various thermophysical properties. 2. Feature Engineering: It generates fixed-length numerical descriptors that encapsulate the composition and temperature, incorporating polynomial interaction terms and dimensionality-reduction techniques to optimize model performance. 3. Coefficient Learning: Four different machine learning architectures are employed: a deep residual network (ResNet), a Kolmogorov–Arnold network (KAN), a sparsity-inducing neural network (SNN), and classical regression models. Each model learns to predict coefficients that define the temperature-dependent correlations for the thermophysical properties. 4. Property Reconstruction: The predicted coefficients are used to compute temperature-dependent property values, ensuring positivity and monotonic trends through a composite loss function that enforces physical constraints. 5. User Interface: An open-source web application enables users to filter the database, train task-specific models, and visualize the results, allowing for rapid exploration of candidate salt mixtures. MoltenSaltPropnet bridges the gap between limited experimental data and high-fidelity reactor simulations, providing a powerful tool for researchers in the field of molten salt reactors and advanced nuclear energy systems.

Retamales, Mauricio Eduardo Tano [Idaho National L↗

DOE OSTI2021

Machine Learning for Improved Availability of the SNS Klystron High Voltage Converter Modulators

Beam availability has increased at the SNS, however, the targeted availability is greater than 95 %, while the SNS has failed to meet lower targets in the past. The HVCM used to power the linac klystrons have been one source of lost beam time and was chosen to explore using AI/ML techniques to improve reliability. Among the possibilities being explored are automating the tuning of HVCMs and predicting component failures such as capacitor aging, rectifier assemblies containing hundreds of diodes, and insulating oil degradation. The methodology pursued includes data cleaning, de-noising, post-analysis data labeling, and machine learning model development. We explore using Long Short-Term Memory and autoencoders for anomaly detection and prognostication used to schedule maintenance. We evaluate the use of model regularizers and constraints to improve the performance of the model and investigate methods to estimate the uncertainty of the models to provide a robust prediction with statistical interoperability. This paper describes the operational experience and known failures of the HVCMs and the proposed ML methodology and the preliminary results of training the AI/ML algorithms.

Pappas, G. C.↗

DOE OSTI2026

Toward $\nu_{\mu}$CC $\eta$ Meson Cross Sections on Argon in SBND

The modeling of resonant neutrino interactions on argon is critical for achieving precision neutrino oscillation measurements and searching for physics beyond the Standard Model with both DUNE and the Short-Baseline Neutrino Program. Higher mass resonances beyond the $\Delta$(1232) baryon are particularly poorly constrained experimentally, and yet contribute a significant rate in multi-GeV interactions. The measurement of $\eta$(548) meson production from the decay of these resonances provides a unique probe, as well as the opportunity to measure observables sensitive to final state interactions with the nucleus. This poster will present progress towards the measurement of meson production with the Short-Baseline Near Detector (SBND) at Fermilab, leveraging the clean $\eta \rightarrow \gamma \gamma$ decay channel. The large statistics of the SBND dataset will provide high-precision measurements of this channel capable of constraining uncertainties in the modeling of higher order resonant interactions. This analysis further provides important handles on electromagnetic shower reconstruction performance through evaluation of the 548 MeV $\eta$ invariant mass peak.

Antonakis, Alexander [UC, Santa Barbara]↗

NASA NTRS1974

Development of a filter regeneration system for advanced spacecraft fluid systems

The development of a filter regeneration system for efficiently cleaning fluid particulate filters is presented. Based on a backflush/jet impingement technique, the regeneration system demonstrated a cleaning efficiency of 98.7 to 100%. The operating principles and design features are discussed with emphasis on the primary system components that include a regenerable filter, vortex particle separator, and zero-g particle trap. Techniques and equipment used for ground and zero-g performance tests are described. Test results and conclusions, as well as possible areas for commercial application, are included.

Behrend, A. F., Jr.↗

NASA NTRS1974

Prediction of compressor stall for distorted and undistorted flow by use of a multistage compressor simulation on the digital computer

A simulation technique is presented for the prediction of compressor stall for axial-flow compressors for clean and distorted inlet flow. The simulation is implemented on the digital computer and uses stage stacking and lumped-volume gas dynamics. The resulting nonlinear differential equations are linearized about a steady-state operating point, and a Routh-Hurwitz stability test is performed on the linear system matrix. Parallel compressor theory is utilized to extend the technique to the distorted inlet flow problem. The method is applied to the eight-stage J85-13 compressor.

Daniele, C. J.↗

NASA NTRS1975

Prediction of compressor stall for distorted and undistorted flow by use of a multistage compressor simulation on the digital computer

A simulation technique is presented for the prediction of compressor stall for axial-flow compressors for clean and distorted inlet flow. The simulation is implemented on the digital computer and uses stage stacking and lumped-volume gas dynamics. The resulting nonlinear differential equations are linearized about a steady-state operating point, and a Routh-Hurwitz stability test is performed on the linear system matrix. Parallel compressor theory is utilized to extend the technique to the distorted inlet flow problem. The method is applied to the eight-stage J85-13 compressor. Analytical stall prediction for the undistorted stall line shows good agreement with experimental results. The predicted stall line for distorted inlet flow is in agreement with experimental results only for large distortion extents and/or low distortion levels. Results for low distortion extents and high distortion level do not agree with experimental results.

Daniele, C. J.↗

NASA NTRS1987

Support of the eight-foot high temperature tunnel modification project

In order to meet the need for propulsion testing in the high supersonic range from Mach 4 to Mach 7, NASA has undertaken the modification of the Langley Eight Foot High Temperature Tunnel to add alternate Mach number capability and oxygen enrichment to allow the testing of operating engines at these Mach numbers and at true temperature tunnel. The transfer of liquid oxygen (LOX) from a storage vessel to a rocket engine generally requires the use of a pressurizing gas at high pressures. Although nitrogen is preferred, unfortunately, when gaseous nitrogen (GN2) is used as the pressurant to transfer liquid oxygen from a storage tank to the tunnel combustor, it contaminates the liquid oxygen and effects a loss of performance in the engine. The contamination of the LOX by the pressurizing GN2 is described, which may prove to be an important operational constraint. It is desirable to have reliable data concerning the penetration of GN2 into LOX during pressurization and the subsequent of self cleaning after blowdown.

Ngo, Kim Chi↗

NASA NTRS1994

Water tunnel flow visualization study of a 4.4 percent scale X-31 forebody

A water-tunnel test of a 4.4 percent-scale, forebody-only model of the X-31 aircraft with different forebody strakes and nosebooms has been performed in the Flow Visualization Facility at the NASA Dryden Flight Research Center. The focus of the study was to determine the relative effects of the different configurations on the stability and symmetry of the high-angle-of-attack forebody vortex flow field. The clean, noseboom-off configuration resisted the development of asymmetries in the primary vortices through 70 deg angle of attack. The wake of the X-31 flight test noseboom configuration significantly degraded the steadiness of the primary vortex cores and promoted asymmetries. An alternate L-shaped noseboom mounted underneath the forebody had results similar to those seen with the configuration, enabling stable, symmetrical vortices up to 70 deg angle of attack. The addition of strakes near the radome tip along the waterline increased the primary vortex strength while it simultaneously caused the vortex breakdown location co move forward. Forebody strakes did not appear to significantly reduce the asymmetries in the forebody vortex field in the presence of the flight test noseboom.

Cobleigh, Brent R.↗

NASA NTRS2006

NASA Tech Briefs, March 2006

Topics covered include: Medical Signal-Conditioning and Data-Interface System; Instruments for Reading Direct-Marked Data-Matrix Symbols; Processing EOS MLS Level-2 Data; Ground Processing of Data From the Mars Exploration Rovers; Estimating Total Electron Content Using 1,000+ GPS Receivers; NASA Solar Array Demonstrates Commercial Potential; Improved Control of Charging Voltage for Li-Ion Battery; Programmable Pulse-Position-Modulation Encoder; Wavelength-Agile External-Cavity Diode Laser for DWDM; Pattern-Recognition Processor Using Holographic Photopolymer; Submicrosecond Power-Switching Test Circuit; Three-Function Logic Gate Controlled by Analog Voltage; Integrated System for Autonomous Science; Montage Version 3.0; Utilizing AI in Temporal, Spatial, and Resource Scheduling; Satellite Image Mosaic Engine; Architecture for Control of the K9 Rover; HFGMC Enhancement of MAC/GMC; Automated Activation and Deactivation of a System Under Test; Cleaning Carbon Nanotubes by Use of Mild Oxygen Plasmas; Generating Aromatics From CO2 on Mars or Natural Gas on Earth; Attaching Thermocouples by Peening or Crimping; Heat Treatment of Friction-Stir-Welded 7050 Aluminum Plates; Generating Breathable Air Through Dissociation of N2O; High-Performance Scanning Acousto-Ultrasonic System; Correction for Thermal EMFs in Thermocouple Feedthroughs; Using Quasiparticle Poisoning To Detect Photons; Estimating Resolution Lengths of Hybrid Turbulence Models; Education and Training Module in Alertness Management; Cargo-Positioning System for Next-Generation Spacecraft; Micro-Imagers for Spaceborne Cell-Growth Experiments; Holographic Solar Photon Thrusters; Plasma-Based Detector of Outer-Space Dust Particles; and Generation of Data-Rate Profiles of Ka-Band Deep-Space Links.

Source record↗

NASA NTRS2008

Reliability of Semiconductor Laser Packaging in Space Applications

A typical set up used to perform lifetime tests of packaged, fiber pigtailed semiconductor lasers is described, as well as tests performed on a set of four pump lasers. It was found that two lasers failed after 3200, and 6100 hours under device specified bias conditions at elevated temperatures. Failure analysis of the lasers indicates imperfections and carbon contamination of the laser metallization, possibly from improperly cleaned photo resist. SEM imaging of the front facet of one of the lasers, although of poor quality due to the optical fiber charging effects, shows evidence of catastrophic damage at the facet. More stringent manufacturing controls with 100% visual inspection of laser chips are needed to prevent imperfect lasers from proceeding to packaging and ending up in space applications, where failure can result in the loss of a space flight mission.

Gontijo, Ivair↗

NASA NTRS2024

Cost and Benefit Analysis of Mitigating, Tracking, and Remediating Orbital Debris

Orbital debris may collide with crewed and robotic spacecraft, placing them at risk. The wide range of debris, from 9,000-kilogram rocket bodies to millions of millimeter-size debris, has led to a similarly wide range of proposed actions for addressing the risks posed by debris. However, the costs and benefits of these actions have historically been unknown. This is a challenge for decision makers who are choosing which actions to support through technology development or policy changes. NASA’s Office of Technology, Policy, and Strategy is addressing these technical and economic uncertainties by building a capability to (1) complete rigorous calculations of the net present value of each action, (2) identify an optimal portfolio of actions to reduce risk, and (3) quantitatively analyze policies related to space sustainability. This report describes our progress toward that capability and to solicit feedback from the space and economic communities. Our previous work, referred to here as Phase 1, assessed the costs and benefits of performing debris remediation on operationally relevant timescales. The current analysis contains major updates to the risk model used in Phase 1 and expands the breadth of actions considered to include mitigating the creation of debris, improving the ability to track debris, and more methods for cleaning up existing debris. We demonstrate that our approach of measuring risks in dollars allows for the effectiveness of seemingly incommensurate actions to be compared and generates insights that other approaches to measuring risk have missed.

Jericho Locke↗

NASA NTRS2024

Cost and Benefit Analysis of Mitigating, Tracking, and Remediating Orbital Debris

Presentation accompanying the technical report with the same name. The abstract for the report and the full study is repeated below. Orbital debris may collide with crewed and robotic spacecraft, placing them at risk. The wide range of debris, from 9,000-kilogram rocket bodies to millions of millimeter-size debris, has led to a similarly wide range of proposed actions for addressing the risks posed by debris. However, the costs and benefits of these actions have historically been unknown. This is a challenge for decision makers who are choosing which actions to support through technology development or policy changes. NASA’s Office of Technology, Policy, and Strategy is addressing these technical and economic uncertainties by building a capability to (1) complete rigorous calculations of the net present value of each action, (2) identify an optimal portfolio of actions to reduce risk, and (3) quantitatively analyze policies related to space sustainability. This report describes our progress toward that capability and to solicit feedback from the space and economic communities. Our previous work, referred to here as Phase 1, assessed the costs and benefits of performing debris remediation on operationally relevant timescales. The current analysis contains major updates to the risk model used in Phase 1 and expands the breadth of actions considered to include mitigating the creation of debris, improving the ability to track debris, and more methods for cleaning up existing debris. We demonstrate that our approach of measuring risks in dollars allows for the effectiveness of seemingly incommensurate actions to be compared and generates insights that other approaches to measuring risk have missed.

Jericho Locke↗

DOE OSTI2021

Machine Learning for Improved Availability of the SNS Klystron High Voltage Converter Modulators

Beam availability has increased at the SNS, however, the targeted availability is greater than 95 %, while the SNS has failed to meet lower targets in the past. The HVCM used to power the linac klystrons have been one source of lost beam time and was chosen to explore using AI/ML techniques to improve reliability. Among the possibilities being explored are automating the tuning of HVCMs and predicting component failures such as capacitor aging, rectifier assemblies containing hundreds of diodes, and insulating oil degradation. The methodology pursued includes data cleaning, de-noising, post-analysis data labeling, and machine learning model development. We explore using Long Short-Term Memory and autoencoders for anomaly detection and prognostication used to schedule maintenance. We evaluate the use of model regularizers and constraints to improve the performance of the model and investigate methods to estimate the uncertainty of the models to provide a robust prediction with statistical interoperability. This paper describes the operational experience and known failures of the HVCMs and the proposed ML methodology and the preliminary results of training the AI/ML algorithms.

Pappas, Chris↗

DOE OSTI2021

Machine Learning for Improved Availability of the SNS Klystron High Voltage Converter Modulators

Beam availability has increased at the SNS, however, the targeted availability is greater than 95 %, while the SNS has failed to meet lower targets in the past. The HVCM used to power the linac klystrons have been one source of lost beam time and was chosen to explore using AI/ML techniques to improve reliability. Among the possibilities being explored are automating the tuning of HVCMs and predicting component failures such as capacitor aging, rectifier assemblies containing hundreds of diodes, and insulating oil degradation. The methodology pursued includes data cleaning, de-noising, post-analysis data labeling, and machine learning model development. We explore using Long Short-Term Memory and autoencoders for anomaly detection and prognostication used to schedule maintenance. We evaluate the use of model regularizers and constraints to improve the performance of the model and investigate methods to estimate the uncertainty of the models to provide a robust prediction with statistical interoperability. This paper describes the operational experience and known failures of the HVCMs and the proposed ML methodology and the preliminary results of training the AI/ML algorithms.

Pappas, Chris↗

DOE OSTI2024

Experimental investigation of surface roughness/wettability pattern effect on crystallization fouling over falling-film flow

In many industries, including desalination, scaling in heat exchangers is a persistent issue, and most methods of scaling control involve environmentally hazardous chemical additives. Exploiting the effects of surface features and flow-imposed shear on salt deposition might introduce a new approach to scale management, opening the possibility of self-cleaning surfaces. In this work, we experimentally explore surface roughness and wettability patterns interaction with salt deposition and flow shear. Heat transfer and scale mass deposition on flat surfaces are measured during thin-film evaporation of calcium sulfate solution. A wide range of surface roughness (0.027–2.66 μm) is studied at fixed flow and heat flux conditions. Due to improved mixing, heat transfer is enhanced with rougher surfaces initially; however, degrades more quickly than smoother surfaces. Following a period of initial mass deposition fluctuation, a steady scaling stage is observed, with rough surfaces scaling more than smooth surfaces. Wettability patterned surfaces can suppress crystal nucleation without sacrificing overall surface wetting, resulting in antifouling performance and heat transfer enhancement. Scale mass deposition on a patterned surface is reduced by 60 %, and 27 % heat transfer improvement over the baseline surface is found. Furthermore, this study provides guidance to surface manipulation for heat transfer improvement and fouling mitigation.

42 ENGINEERING↗

DOE OSTI2025

Machine-learning and first-principles investigation of lightweight medium-entropy alloys for hydrogen-storage applications

The transition to a low-carbon economy demands efficient and sustainable energy-storage solutions, with hydrogen emerging as a promising clean-energy carrier and with metal hydrides recognized for their hydrogen-storage capacity. Here, we leverage machine learning (ML) to predict hydrogen-to-metal (H/M) ratios and solution energy by incorporating thermodynamic parameters and local lattice distortion (LLD) as key features. Our best-performing ML model provides improvements to H/M ratios and solution energies over a broad class of medium-entripy alloys (easily extendable to multi-principal-element alloys), such as Ti–Nb-X (X = Mo, Cr, Hf, Ta, V, Zr) and Co–Ni-X (X = Al, Mg, V). Ti–Nb–Mo alloys reveal compositional effects in H-storage behavior, in particular Ti, Nb, and V enhance H-storage capacity, while Mo reduces H/M and hydrogen weight percent by 40–50 %. We attributed results in molybdenum-rich alloys to slow hydrogen kinetics, as validated by our pressure-composition-temperature (PCT) isotherm experiments on pure Ti and Ti 5 Mo 95 alloys. Density functional theory (DFT) and molecular dynamics (MD) simulations also confirm that Ti and Nb promote H diffusion, whereas Mo hinders it, highlighting the interplay between electronic structure, lattice distortions, and hydrogen uptake. Notably, our Gradient Boosting Regression model identifies LLD as a critical factor in H/M predictions. Here, to aid material selection, we present two periodic tables illustrating elemental effects on (a) H 2 wt% and (b) solution energy, derived from ML, and provide a reference for identifying alloying elements that enhance hydrogen solubility and storage.

08 HYDROGEN↗

DOE OSTI2026

PARTICULATE STUDY OF NEG PUMPS IRRADIATED IN THE CEBAF TUNNEL

Non-evaporable getter (NEG) pumps are being used to maintain ultra-high vacuum in the beamline of superconducting radio-frequency (SRF) accelerators, such as the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. Because of the sensitivity of the performance of SRF cavities to particulate contamination, it is important to evaluate the integrity of the NEG material after exposure to high radiation during beam operation. The particulate from two NEG pumps based on ZAO® getter alloy was measured with a particle counter in a clean-room. The pumps were assembled onto a hermetically sealed setup which was placed in the CEBAF tunnel. The setup was exposed to 24 h beam operation for ~ 230 days. The total gamma-rays dose measured on the test setup was ~ 1.6 Mrad. The total neutron dose measured at ~ 75 cm of the test setup was ~ 11 krad. The particulate count from the two pumps was measured again in the clean-room after irradiation. Whereas an increase of particulate counts was measured, compared to before irradiation, subsequent measurements indicate the absence of systematically loose particulate. The pumping speed of one of the irradiated pumps was also measured to be consistent with that of a non-irradiated one, corroborating the absence of significant damage to the ZAO® NEG material due to irradiation.

Ciovati, G. [Thomas Jefferson National Accelerator↗