Results for “enhanced sampling” — Page 30

NASA NTRSDate not supplied

Extraterrestrial Mining Via Two Coupled Thermal-Driven Phenomena

Two-coupled thermal-driven phenomena, Marangoni effect and thermal fractional decomposition under high vacuum, previously observed by the authors could lead to an extraterrestrial mining operation that would significantly reduce mechanical operation and allow in-situ product extraction directly from the mineral without the necessity of neither mineral beneficiation nor use of terrestrial precursors. Thermal Marangoni effect alone and coupled with fractional decomposition have been corroborated through paths of 10 and 13 inches respectively on molten JSC-1A lunar regolith simulant presenting a novel and valuable potential for extraterrestrial mining as the observed outcome (self-transportation via Marangoni effect and fractional decomposition at higher temperature) will be more prominent on extraterrestrial surfaces under higher vacuum and reduced gravity. A comprehensive 3D model built by the authors demonstrated to be a crucial tool to determine the right location of the sample to optimize the gradient temperature along the wall of a long tubular crucible enhancing the Marangoni effect as surface tension (the driving force for the thermal Marangoni effect) depends on the temperature gradient.

Jesus A Dominguez↗

DOE OSTI2025

A Convolution Neural Network for Voltage Event Classification at a Photovoltaic Inverter

This paper presents a convolutional neural network (CNN) developed to identify voltage events in photovoltaic (PV) inverters. The CNN is trained on synthetic data generated using the IEEE 13-bus distribution feeder model and evaluated on field measured data collected from Energy Northwest’s Horn Rapids Solar, Storage, and Training (HRSST) facility. The study focuses on two common voltage events: faults and voltage sags. The CNN is configured to analyze voltage and current waveforms from three-phase PV systems, demonstrating excellent accuracy during training. Field data from the HRSST facility is employed to assess its real-world performance, where the CNN achieves perfect identification of faults and voltage sags in a sample of nine events. This work highlights the potential of the proposed method to enhance PV protection schemes, providing a robust foundation for improved voltage event detection and grid reliability.

Cornachione, Matthew A.↗

NASA NTRS2003

Using Fractional Clock-Period Delays in Telemetry Arraying

A set of special digital all-pass finite-impulse- response (FIR) filters produces phase shifts equivalent to delays that equal fractions of the sampling or clock period of a telemetry-data-processing system. These filters have been used to enhance the arraying of telemetry signals that have been received at multiple ground stations from spacecraft (see figure). Somewhat more specifically, these filters have been used to align, in the time domain, the telemetry-data sequences received by the various antennas, in order to maximize the signal-to-noise ratio of the composite telemetric signal obtained by summing the signals received by the antennas. The term arraying in this context denotes a method of enhanced reception of telemetry signals in which several antennas are used to track a single spacecraft. Each antenna receives a signal that comprises a sum of telemetry data plus noise, and these sum data are sent to an arraying combiner for processing. Correlation is the means used to align the set of data from one antenna with that from another antenna. After the data from all the antennas have been aligned in the time domain, they are all added together, sample by sample.

Fort, David↗

DOE OSTI2025

Expanding the physics reach of DUNE in the near and far detectors

The Deep Underground Neutrino Experiment (DUNE) is a next-generation long-baseline neutrino oscillation experiment. Its primary goal is the determination of the neutrino mass hierarchy and the CP-violating phase. The DUNE physics programme also includes the detection of astrophysical neutrinos and the search for beyond the Standard Model (BSM) phenomena. DUNE will consist of a near detector (ND) complex placed at Fermilab, and a modular Liquid Argon Time Projection Chamber (LArTPC) far detector (FD) to be built in the Sanford Underground Research Facility (SURF), approximately 1300 km away from the neutrino production point. This thesis describes three different projects within DUNE. First, a novel strategy to improve the triggering capabilities of the DUNE FD is proposed. It uses matched filters to enhance the production of online hits across all charge collection planes. Next, the possibility of detecting neutrinos coming from dark matter (DM) annihilations in the Sun with the FD is explored. The complementarity of DUNE to this kind of DM searches is shown. Finally, the simulation and reconstruction framework of ND-GAr, the gas argon ND proposed for Phase II of DUNE, is presented. A number of additions to this are described, particularly focused on the development of the particle identification (PID) capabilities of the detector. These are then used to perform the first event selection studies with an end-to-end simulation in ND-GAr, in particular the selection of pion exclusive samples in $\nu_{\mu}$ CC interactions. All three of these projects share the common goal of enhancing the physics programme of DUNE.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS1995

An Efficient Implementation of Forward-Backward LMS Adaptive Line Enhancer

An efficient implementation of the forward-backward least mean square (FBLMS) adaptive line enhancer is presented. Without changing the characteristics of the FBLMS adaptive line enhancer, the proposed implementation technique reduces multiplications by 25 percent and additions by 12.5 percent in two successive time samples in comparison to that of the direct implementation in both prediction and weight control. It can be applied to acquisition and tracking in both stationary and nonstationary environments.

adaptive line enhancers signal acquisition trackin↗

DOE OSTI2025

Tailoring additive manufacturing to optimize dynamic properties in 316L stainless steel

With the advent of additive manufacturing, manipulation of typical microstructural elements such as grain size, texture, and defect densities is now possible at a faster time scale. While the processing–structure–property relationship in additive manufactured metals has been well studied over the past decade, little work has been done in understanding how this process affects the dynamic behavior of materials. We postulate that additive manufacturing can be used to alter the material microstructure and used to enhance its dynamic strength. In this work, 316L stainless steel (SS) was manufactured via selected laser melting and its microstructure was altered through changing build parameters like laser power, speed, and hatch spacing systematically. These samples were then subjected to spall recovery experiments to measure the spall strength and quantify the amount of damage as a function of build parameters. By mapping the spall strength as a function of build parameters, this work demonstrated that indeed additive manufacturing can be used to tailor the spall strength of 316L SS. This work also determined the optimum build parameters (laser power=195W; scanning speed=1083mm/s; hatch spacing=0.09mm; layer thickness=0.02mm) to obtain the highest spall strength and the least amount of total damage in 316L SS. Microstructural characterization of the pre- and post-mortem samples revealed that increased grain average misorientation and textural index were the main driving force behind this higher spall strength. This work aims to enhance microstructural engineering techniques to design materials with greater resistance to dynamic shock loading.

36 MATERIALS SCIENCE↗

NASA NTRSDate not supplied

Progress on the Organic and Inorganic Modules of the Spacecraft Water Impurity Monitor, a Next Generation Complete Water Analysis System for Crewed Vehicles

The Spacecraft Water Impurity Monitor (SWIM) is a joint collaboration to develop an instrument platform that will perform in-flight measurements and deliver a more complete picture of water quality to decision makers. For exploration missions, returned water samples will not be an option, so spacecraft and habitats will need to be equipped with advanced water monitoring capabilities. Eventually, missions to the moon, Mars, and beyond should be equipped with analytical capabilities roughly analogous to those found in terrestrial labs. Based on what we know about current and future spacecraft environments, SWIM will seek to provide enhanced analytical capability that enables NASA to confidently send astronauts on distant missions without the possibility of returned water samples. The SWIM architecture can be broken down in an Organic Water Module (OWM) and an Inorganic Water Module (IWM), independent of each other but can be flown together if desired; an integrated system may share some commonality, e.g., single sample injection, sampling consumables, waste, etc. Each of these main modules can be broken down further into separation (if required) and detection modules. And, each separation module can be paired with one or more detection module depending on mission, spacecraft, customer needs, and size/mass/power constraints. This paper discusses the research and development progress toward the goal of a total water analysis system. For OWM, one of the analysis technologies that the SWIM team have been developing is a liquid-injection gas chromatograph mass spectrometer system; these systems are the workhorses of analytical chemistry laboratories world-wide. For IWM, the team is exploring a number of technologies ranging from traditional liquid chromatography technologies (e.g. ion chromatography, capillary electrophoresis) to flight-heritage technology such as ion-specific electrodes.

Stuart J Pensinger↗

NASA NTRSDate not supplied

Progress on the Organic and Inorganic Modules of the Spacecraft Water Impurity Monitor, a Next Generation Complete Water Analysis System for Crewed Vehicles

The Spacecraft Water Impurity Monitor (SWIM) is a joint collaboration to develop an instrument platform that will perform in-flight measurements and deliver a more complete picture of water quality to decision makers. For exploration missions, returned water samples will not be an option, so spacecraft and habitats will need to be equipped with advanced water monitoring capabilities. Eventually, missions to the moon, Mars, and beyond should be equipped with analytical capabilities roughly analogous to those found in terrestrial labs. Based on what we know about current and future spacecraft environments, SWIM will seek to provide enhanced analytical capability that enables NASA to confidently send astronauts on distant missions without the possibility of returned water samples. The SWIM architecture can be broken down in an Organic Water Module (OWM) and an Inorganic Water Module (IWM), independent of each other but can be flown together if desired; an integrated system may share some commonality, e.g., single sample injection, sampling consumables, waste, etc. Each of these main modules can be broken down further into separation (if required) and detection modules. And, each separation module can be paired with one or more detection module depending on mission, spacecraft, customer needs, and size/mass/power constraints. This paper discusses the research and development progress toward the goal of a total water analysis system. For OWM, one of the analysis technologies that the SWIM team have been developing is a liquid-injection gas chromatograph mass spectrometer system; these systems are the workhorses of analytical chemistry laboratories world-wide. For IWM, the team is exploring a number of technologies ranging from traditional liquid chromatography technologies (e.g. ion chromatography, capillary electrophoresis) to flight-heritage technology such as ion-specific electrodes.

Stuart J. Pensinger↗

NASA NTRS2023

Progress on the Organic and Inorganic Modules of the Spacecraft Water Impurity Monitor, a Next Generation Complete Water Analysis System for Crewed Vehicles

The Spacecraft Water Impurity Monitor (SWIM) is a joint collaboration to develop an instrument platform that will perform in-flight measurements and deliver a more complete picture of water quality to decision makers. For exploration missions, returned water samples will not be an option, so spacecraft and habitats will need to be equipped with advanced water monitoring capabilities. Eventually, missions to the moon, Mars, and beyond should be equipped with analytical capabilities roughly analogous to those found in terrestrial labs. Based on what we know about current and future spacecraft environments, SWIM will seek to provide enhanced analytical capability that enables NASA to confidently send astronauts on distant missions without the possibility of returned water samples. The SWIM architecture can be broken down in an Organic Water Module (OWM) and an Inorganic Water Module (IWM), independent of each other but can be flown together if desired; an integrated system may share some commonality, e.g., single sample injection, sampling consumables, waste, etc. Each of these main modules can be broken down further into separation (if required) and detection modules. And, each separation module can be paired with one or more detection module depending on mission, spacecraft, customer needs, and size/mass/power constraints. This paper discusses the research and development progress toward the goal of a total water analysis system. For OWM, one of the analysis technologies that the SWIM team have been developing is a liquid-injection gas chromatograph mass spectrometer system; these systems are the workhorses of analytical chemistry laboratories world-wide. For IWM, the team is exploring a number of technologies ranging from traditional liquid chromatography technologies (e.g. ion chromatography, capillary electrophoresis) to flight-heritage technology such as ion-specific electrodes.

Stuart J Pensinger↗

NASA NTRS2023

Progress on the Organic and Inorganic Modules of the Spacecraft Water Impurity Monitor, a Next Generation Complete Water Analysis System for Crewed Vehicles

The Spacecraft Water Impurity Monitor (SWIM) is a joint collaboration to develop an instrument platform that will perform in-flight measurements and deliver a more complete picture of water quality to decision makers. For exploration missions, returned water samples will not be an option, so spacecraft and habitats will need to be equipped with advanced water monitoring capabilities. Eventually, missions to the moon, Mars, and beyond should be equipped with analytical capabilities roughly analogous to those found in terrestrial labs. Based on what we know about current and future spacecraft environments, SWIM will seek to provide enhanced analytical capability that enables NASA to confidently send astronauts on distant missions without the possibility of returned water samples. The SWIM architecture can be broken down in an Organic Water Module (OWM) and an Inorganic Water Module (IWM), independent of each other but can be flown together if desired; an integrated system may share some commonality, e.g., single sample injection, sampling consumables, waste, etc. Each of these main modules can be broken down further into separation (if required) and detection modules. And, each separation module can be paired with one or more detection module depending on mission, spacecraft, customer needs, and size/mass/power constraints. This paper discusses the research and development progress toward the goal of a total water analysis system. For OWM, one of the analysis technologies that the SWIM team have been developing is a liquid-injection gas chromatograph mass spectrometer system; these systems are the workhorses of analytical chemistry laboratories world-wide. For IWM, the team is exploring a number of technologies ranging from traditional liquid chromatography technologies (e.g. ion chromatography, capillary electrophoresis) to flight-heritage technology such as ion-specific electrodes.

Stuart Pensinger↗

NASA NTRS2010

Geolab in NASA's First Generation Pressurized Excursion Module: Operational Concepts

We are building a prototype laboratory for preliminary examination of geological samples to be integrated into a first generation Habitat Demonstration Unit-1/Pressurized Excursion Module (HDU1-PEM) in 2010. The laboratory GeoLab will be equipped with a glovebox for handling samples, and a suite of instruments for collecting preliminary data to help characterize those samples. The GeoLab and the HDU1-PEM will be tested for the first time as part of the 2010 Desert Research and Technology Studies (DRATS), NASAs annual field exercise designed to test analog mission technologies. The HDU1-PEM and GeoLab will participate in joint operations in northern Arizona with two Lunar Electric Rovers (LER) and the DRATS science team. Historically, science participation in DRATS exercises has supported the technology demonstrations with geological traverse activities that are consistent with preliminary concepts for lunar surface science Extravehicular Activities (EVAs). Next years HDU1-PEM demonstration is a starting point to guide the development of requirements for the Lunar Surface Systems Program and test initial operational concepts for an early lunar excursion habitat that would follow geological traverses along with the LER. For the GeoLab, these objectives are specifically applied to enable future geological surface science activities. The goal of our GeoLab is to enhance geological science returns with the infrastructure that supports preliminary examination, early analytical characterization of key samples, insight into special considerations for curation, and data for prioritization of lunar samples for return to Earth.

Evans, C. A.↗

DOE OSTI2024

Leveraging neural control variates for enhanced precision in lattice field theory

Results obtained with stochastic methods have an inherent uncertainty due to the finite number of samples that can be achieved in practice. In lattice QCD this problem is particularly salient in some observables like, for instance, observables involving one or more baryons and it is the main problem preventing the calculation of nuclear forces from first principles. The method of control variables has been used extensively in statistics and it amounts to computing the expectation value of the difference between the observable of interest and another observable whose average is known to be zero but is correlated with the observable of interest. Recently, control variates methods emerged as a promising solution in the context of lattice field theories. In our current study, instead of relying on an educated guess to determine the control variate, we utilize a neural network to parametrize this function. Using 1 + 1 dimensional scalar field theory as a testbed, we demonstrate that this neural network approach yields substantial improvements. Notably, our findings indicate that the neural network ansatz is particularly effective in the strong coupling regime. Published by the American Physical Society 2024

Astronomy & Astrophysics↗

NASA NTRS1992

Enhanced magnetic field production during oblique hypervelocity impacts

The natural remanent magnetization of the lunar surface as displayed in returned lunar samples and the data returned by the Apollo subsatellite magnetometer has an unexpectedly high magnitude and exhibits spatial variation at all scales. The origin of the lunar remanent fields may be due to crustal remanence of a core dynamo field occurring early in lunar history prior to extensive modification by impact or remanence of transient fields, particularly associated with impacts, occurring on a local scale throughout lunar history. The presence of an early core dynamo field would have strong consequences for the formation and early evolution of the Moon, yet to deconvolve the role that an internally generated core dynamo field may have had, it is necessary to understand how the magnetic state of the lunar surface has developed through time. Impact-induced magnetism may be an important component of the present magnetic state of the lunar surface. New theoretical considerations suggest that transient magnetic fields within plasma produced by hypervelocity meteorite impacts may have greater significance at larger scales than previously thought.

Crawford, D. A.↗

DOE OSTI2025

Machine Learning for Well Log Analysis in Uranium Mining

This project explores the use of Artificial Intelligence (AI) and Machine Learning (ML) techniques to automate well log analysis for uranium mining. Geophysical log data—spontaneous potential, resistivity, and gamma ray—were used to classify lithology, correlate well logs and identify roll front zonation patterns, which are critical for locating uranium ore bodies. Supervised ML algorithms such as eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Random Forest were trained to classify lithology with high accuracy. Gradient Boosting Machines (GBM), XGBoost, Random Forest, and Neural Networks were also used for role front zone identification. Moreover, a Fast Dynamic Time Warping (FastDTW) algorithm was employed for well log correlation. Additionally, sample lag was addressed using dynamic programming. Results demonstrate the potential of AI and ML to streamline well log analysis and enhance uranium exploration workflows.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRS2001

Thermal Processing Effects on the Adhesive Strength of PS304 High Temperature Solid Lubricant Coatings

In this paper the effects of post deposition heat treatments on the cohesive and adhesive strength properties of PS304, a plasma sprayed nickel-chrome based, high temperature solid lubricant coating deposited on stainless steel, are studied. Plasma spray deposited coating samples were exposed in air at temperatures from 432 to 650 C for up to 500 hr to promote residual stress relief, enhance particle to particle bonding and increase coating to substrate bond strength. Coating pull-off strength was measured using a commercial adhesion tester that utilizes 13 mm diameter aluminum pull studs attached to the coating surface with epoxy. Pull off force was automatically recorded and converted to coating pull off strength. As deposited coating samples were also tested as a baseline. The as-deposited (untreated) samples either delaminated at the coating-substrate interface or failed internally (cohesive failure) at about 17 MPa. Samples heat treated at temperatures above 540 C for 100 hr or at 600 C or above for more than 24 hr exhibited strengths above 31 MPa, nearly a two fold increase. Coating failure occurred inside the body of the coating (cohesive failure) for nearly all of the heat-treated samples and only occasionally at the coating substrate interface (adhesive failure). Metallographic analyses of heat-treated coatings indicate that the Nickel-Chromium binder in the PS304 appears to have segregated into two phases, a high nickel matrix phase and a high chromium precipitated phase. Analysis of the precipitates indicates the presence of silicon, a constituent of a flow enhancing additive in the commercial NiCr powder. The exact nature and structure of the precipitate phase is not known. This microstructural change is believed to be partially responsible for the coating strength increase. Diffusion bonding between particles may also be playing a role. Increasing the heat treatment temperature, exposure time or both accelerate the heat treatment process. Preliminary measurements indicate that the heat treatment also results in a one time, permanent coating thickness increase of about 3%. Based upon these results, the incorporation of a heat treatment prior to final finishing has been incorporated in the application process of this coating technology.

DellaCorte, Christopher↗

NASA NTRS1995

Prototype development and test results of a continuous ambient air monitoring system for hydrazine at the 10 ppb level

A Hydrazine Vapor Area Monitor (HVAM) system is currently being field tested as a detector for the presence of hydrazine in ambient air. The MDA/Polymetron Hydrazine Analyzer has been incorporated within the HVAM system as the core detector. This analyzer is a three-electrode liquid analyzer typically used in boiler feed water applications. The HVAM system incorporates a dual-phase sample collection/transport method which simultaneously pulls ambient air samples containing hydrazine and a very dilute sulfuric acid solution (0.0001 M) down a length of 1/4 inch outside diameter (OD) tubing from a remote site to the analyzer. The hydrazine-laden dilute acid stream is separated from the air and the pH is adjusted by addition of a dilute caustic solution to a pH greater than 10.2 prior to analysis. Both the dilute acid and caustic used by the HVAM are continuously generated during system operation on an "as needed" basis by mixing a metered amount of concentrated acid/base with dilution water. All of the waste water generated by the analyzer is purified for reuse by Barnstead ion-exchange cartridges so that the entire system minimizes the generation of waste materials. The pumping of all liquid streams and mixing of the caustic solution and dilution water with the incoming sample are done by a single pump motor fitted with the appropriate mix of peristaltic pump heads. The signal to noise (S/N) ratio of the analyzer has been enhanced by adding a stirrer in the MDA liquid cell to provide mixing normally generated by the high liquid flow rate designed by the manufacturer. An onboard microprocessor continuously monitors liquid levels, sample vacuum, and liquid leak sensors, as well as handles communications and other system functions (such as shut down should system malfunctions or errors occur). The overall system response of the HVAM can be automatically checked at regular intervals by measuring the analyzer response to a metered amount of calibration standard injected into the dilute acid stream. The HVAM system provides two measurement ranges (threshold limit value (TLV): 10 to 1000 parts per billion (ppb)/LEAK: 100 ppb to 10 parts per million (ppm)). The LEAK range is created by dilution of the sulfuric acid/hydrazine liquid sample with pure water. This dual range capability permits the analyzer to quantify ambient air samples whose hydrazine concentrations range from 10 ppb to as high as 10 ppm. The laboratory and field prototypes have demonstrated total system response times on the order of 10 to 12 minutes for samples ranging from 10 to 900 ppb in the lLV mode and is greater than 2 minutes for samples ranging from 100 to 1300 ppb in the LEAK mode. Service intervals of over 3 months have been demonstrated for continuous 24 hour/day, 7 day/week usage. The HVAM is made up of a purged cabinet that contains power supplies, RS422 signal transmission capabilities, a UPS, an on-site warning system, and a Line Replaceable Unit (LRU). The LRU includes all of the liquid flow system, the analyzer, the control/data system microprocessor and assorted flow and liquid-level sensors. The LRU is mounted on a track slide system so it can be serviced inplace or totally removed and quickly exchanged with another calibrated unit, thus minimizing analyzer downtime. Once an LRU is removed from an analyzer enclosure, it can be brought to a laboratory facility for complete calibration and periodic maintenance.

Meneghelli, Barry↗

DOE OSTI2025

AutoTandemML: Active Learning Enhanced Tandem Neural Networks for Inverse Design Problems

Inverse design in science and engineering involves determining optimal design parameters that achieve desired performance outcomes, a process often hindered by the complexity and high dimensionality of design spaces, leading to significant computational costs. To tackle this challenge, we propose a novel hybrid approach that combines active learning with Tandem Neural Networks to enhance the efficiency and effectiveness of solving inverse design problems. Active learning allows to selectively sample the most informative data points, reducing the required dataset size without compromising accuracy. We investigate this approach using three benchmark problems: airfoil inverse design, photonic surface inverse design, and scalar boundary condition reconstruction in diffusion partial differential equations. We demonstrate that integrating active learning with Tandem Neural Networks outperforms standard approaches across the benchmark suite, achieving better accuracy with fewer training samples.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Recyclability of reversible polymer networks over a Dozen reprocessing cycles

Reversible polymer networks capable of reversible reactions along their backbone provide a promising strategy for addressing waste management challenges associated with conventional thermoset products. However, reversible polymer networks cannot be recycled infinitely. Unavoidable side reactions eventually cause significant degradation of mechanical properties or loss of recyclability. This study aims to answer a simple yet critical question on reversible polymer network systems: how many reprocessing cycles can be achieved before profound mechanical degradation or a loss of recyclability? The recyclability of Diels–Alder (DA) network samples was evaluated by repeating consistent reprocessing cycles until they were no longer reprocessable. To enhance their recyclability, the chemical structure of the maleimide precursor was tailored to mitigating side reactions by maleimide homopolymerization. Remarkably, by employing a maleimide precursor with alkyl substitutions on its phenyl group, the DA network system attained 12 reprocessing cycles using injection molding at 160 °C without significant degradation of mechanical properties. However, the 13th reprocessing cycle did not succeed. Rheological analysis revealed the accumulation of non-reversible bonds within the network structure during repeated reprocessing, despite fairly consistent mechanical properties under operating conditions. This study demonstrates that the rational design of the maleimide precursor is an effective means to enhance the reprocessability of DA networks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗