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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2000

Pilot Biofeedback Training in the Cognitive Awareness Training Study (CATS)

One of the ongoing problems that pilots face today is a diminished state of awareness such as boredom, sleepiness, or fatigue during cruise conditions that could result in various pilot errors. This study utilized a cognitive training exercise to sharpen the pilot's awareness during simulated flight thereby providing them with a means to overcome these diminished states of awareness. This study utilizes psychophysiological methods in an attempt to assess a pilot's state of awareness more directly. In turn, the pilots will be able to train themselves to recognize these states of awareness and be more mentally sharp during mundane tasks such as those experienced in cruise conditions. The use of these measurement tools may be beneficial for researchers working within the NASA Aviation Safety Program. This paper will provide the reader with some background information concerning the motivation for the study, a brief description of the experimental setup and design matrix, the dependent and independent variables that were employed, and some preliminary findings based on some of the subjective and objective data that was collected. These preliminary findings are of part of an ongoing study being conducted at the NASA Langley Research Center in Hampton, Virginia.

Uenking, M.↗
DOE OSTI2021

Constrained Block Nonlinear Neural Dynamical Models

Neural network modules conditioned by known priors can be effectively trained and combined to represent systems with nonlinear dynamics. This work explores a novel formulation for data-efficient learning of deep control-oriented nonlinear dynamical models by embedding local model structure and constraints. The proposed method consists of neural network blocks that represent input, state, and output dynamics with constraints placed on the network weights and system variables. For handling partially observable dynamical systems, we utilize a state observer neural network to estimate the states of the system's latent dynamics. We evaluate the performance of the proposed architecture and training methods on system identification tasks for three nonlinear systems: a continuous stirred tank reactor, a two tank interacting system, and an aerodynamics body. Models optimized with a few thousand system state observations accurately represent system dynamics in open loop simulation over thousands of time steps from a single set of initial conditions. Experimental results demonstrate an order of magnitude reduction in open-loop simulation mean squared error for our constrained, block-structured neural models when compared to traditional unstructured and unconstrained neural network models.

Skomski, Elliott↗
DOE OSTI2025

Leveraging unlabeled SEM datasets with self-supervised learning for enhanced particle segmentation

Scanning Electron Microscopes (SEMs) are widely used in experimental science laboratories, often requiring cumbersome and repetitive user analysis. Automating SEM image analysis processes is highly desirable to address this challenge. In particle sample analysis, Machine Learning (ML) has emerged as the most effective approach for particle segmentation. However, the time-intensive process of manually annotating thousands of SEM images limits the applicability of supervised learning approaches. Self-Supervised Learning (SSL) offers a promising alternative by enabling knowledge extraction from raw, unlabeled data. This study presents a framework for evaluating SSL techniques in SEM image analysis, focusing on novel methods leveraging the ConvNeXtV2 architecture for particle detection. A dataset comprising 25,000 SEM images is curated to benchmark these proposed SSL methods. The results demonstrate that ConvNeXtV2 models, with varying parameter counts, consistently outperform other techniques in particle detection across different length scales, achieving up to a 34% reduction in relative error compared to established SSL methods. Furthermore, an ablation study explores the relationship between dataset size and SSL performance, providing actionable insights for practitioners regarding model selection and resource efficiency. This research advances the integration of SSL into autonomous analysis pipelines and supports its application in accelerating materials science discovery.

Rettenberger, Luca↗
DOE OSTI2022

Soil-related developments of the Biome-BGCMuSo v6.2 terrestrial ecosystem model

Terrestrial biogeochemical models are essential tools to quantify climate–carbon cycle feedback and plant–soil relations from local to global scale. In this study, a theoretical basis is provided for the latest version of the Biome-BGCMuSo biogeochemical model (version 6.2). Biome-BGCMuSo is a branch of the original Biome-BGC model with a large number of developments and structural changes. Earlier model versions performed poorly in terms of soil water content (SWC) dynamics in different environments. Moreover, lack of detailed nitrogen cycle representation was a major limitation of the model. Since problems associated with these internal drivers might influence the final results and parameter estimation, additional structural improvements were necessary. In this paper the improved soil hydrology as well as the soil carbon and nitrogen cycle calculation methods are described in detail. Capabilities of the Biome-BGCMuSo v6.2 model are demonstrated via case studies focusing on soil hydrology, soil nitrogen cycle, and soil organic carbon content estimation. Soil-hydrology-related results are compared to observation data from an experimental lysimeter station. The results indicate improved performance for Biome-BGCMuSo v6.2 compared to v4.0 (explained variance increased from 0.121 to 0.8 for SWC and from 0.084 to 0.46 for soil evaporation; bias changed from -0.047 to -0.007 m 3 m -3 for SWC and from -0.68 to -0.2 mm d -1 for soil evaporation). Simulations related to nitrogen balance and soil CO 2 efflux were evaluated based on observations made in a long-term field experiment under crop rotation. The results indicated that the model is able to provide realistic nitrate content estimation for the topsoil. Soil nitrous oxide (N 2 O) efflux and soil respiration simulations were also realistic, with overall correspondence with the observations (for the N 2 O efflux simulation bias was between -0.13 and -0.1 mgNm -2 d -1 , and normalized root mean squared error (NRMSE) was 32.4 %–37.6 %; for CO 2 efflux simulations bias was 0.04–0.17 gCm -2 d -1 , while NRMSE was 34.1 %–40.1 %). Sensitivity analysis and optimization of the decomposition scheme are presented to support practical application of the model. The improved version of Biome-BGCMuSo has the ability to provide more realistic soil hydrology representation as well as nitrification and denitrification process estimation, which represents a major milestone.

58 GEOSCIENCES↗
DOE OSTI2025

A segmented approach to modeling building height: Delineating high-rise and low-rise buildings for enhanced height estimation

Understanding building height is imperative to the overall study of energy efficiency, population distribution, urban morphologies, emergency response, among others. Currently, existing approaches for modeling building height at scale are hindered by two pervasive issues. First, there is no consistent approach to quantify what a high-rise building is at a macro scale, leaving researchers unable to accurately compare results across geographies and domains. Second, high-rise buildings represent a small fraction of the built environment, implying data imbalance challenges that negatively affect current approaches. This is a problem of practical relevance since information on high-rise buildings is important for studies on urban heat islands, population dynamics, and pollution dispersion. Here, we introduce a novel approach to map building height which first identifies two distinct distributions within the built environment, with one being composed of low-rise buildings and one composed of high-rise buildings. We then develop an ensemble scheme where discrete specialist models are trained for each subset of low-rise buildings and high-rise buildings to infer building height from morphology features. For experiments mapping heights of 4.85 million buildings in Japan, we show an increase of 34 % in accuracy within 3m error when compared to the current state-of-the-art when modeling high-rise buildings, which based on KNN experimentation we define as any building > 12m . Our findings show that such an ensemble framework outperforms the current state-of-the-art approaches, which is especially relevant in relation to inferring height for high-rise buildings, a prominent issue of existing approaches for mapping the built environment.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Ripening of Rh Nanoparticle Catalysts in Reverse Water–Gas Shift via a Data-Driven Model Combining Physics, Theory, and Experiment

Degradation via sintering is an ongoing challenge that impedes the broad commercial success of supported metallic nanoparticle catalysts. To mitigate degradation via informed catalyst design and process operations, here we aim to disambiguate the underlying mechanisms of sintering by combining theory and experiment in a quantitative framework. While mechanistic sintering models exist, they only model a single sintering pathway, even though multiple sintering mechanisms can occur simultaneously or dominate at different stages of the process. Data-driven machine learning models have emerged as a means to represent complex processes through data regression. However, machine learning models have very large data needs and lack mechanistic insights due to their black-box encoding. To develop an interpretive model of catalyst degradation via sintering, we constructed a hybrid model combining mechanistic “physics-based” models and data-driven methods to obtain both reliable predictions and mechanistic insights regarding experimentally observed sintering phenomena. Focusing on nanoparticle sintering in the Rh–TiO 2 catalyst for the reverse water–gas shift (RWGS) reaction, the hybrid model couples a mechanistic term for Ostwald ripening with energy values calculated via density functional theory (DFT) with a parametric, data-driven discrepancy function term for unmodeled mechanisms. The hybrid model is trained using Bayesian inference with data collected from small-angle X-ray scattering (SAXS) in situ experiments wherein average nanoparticle diameter versus time was measured at three relevant operating temperatures. The calibrated hybrid model results show that an Ostwald ripening-only model parameterized with fixed DFT energies does not fully capture the time and temperature dependence of the SAXS-observed sintering kinetics, and that an additional functional contribution, or DFT energy calibration, is required to reconcile simulation and experiment. Analysis of the hybrid-model error confirms that the hybrid model outperforms both the purely mechanistic and purely data-driven alternatives in terms of expected predictive accuracy for time-evolving average particle sizes. Furthermore, the results support the hypothesis that the Ostwald ripening mechanism is less important for explaining the sintering phenomena as operating temperature increases under an assumed fixed DFT parameterization. This could be explained in one of two ways: either latent, unmodeled sintering mechanisms dominate at higher temperatures, or the DFT uncertainty increases with temperature. The proposed modeling approach directly links theory to experiments and simulations via a statistical hybrid modeling framework and can be extended to other catalytic systems to improve predictive models and mechanistic understanding.

Bayesian hybrid modeling↗
NASA NTRS2000

Application of Three-Component PIV to a Hovering Rotor Wake

The key to accurate predictions of rotorcraft aerodynamics, acoustics, and dynamics lies in the accurate representation of the rotor wake. The vortical wake computed by rotorcraft CFD analyses typically suffer from numerical dissipation before the first blade passage. With some a priori knowledge of the wake trajectory, grid points can be concentrated along the trajectory to minimize the dissipation. Comprehensive rotorcraft analyses based on lifting-line theory rely on classical vortex models and/or semi-empirical information about the tip vortex structure. Until the location, size, and strength of the trailed tip vortex can be measured over a range of wake ages, the analyses will continue to be adjusted on a trial and error basis in order to correctly predict blade airloads, acoustics, dynamics, and performance. Using the laser light sheet technique, tip vortex location can be acquired in a straightforward manner. Measuring wake velocities and vortex core size, however, has been difficult and tedious using point-measurement techniques such as laser velocimetry. Recently, the Particle Image Velocimetry (PIV) technique has proven to be an efficient method for acquiring velocity measurements over relatively large areas and volumes of a rotor wake. The work reported to date, however, has been restricted to 2-component velocity measurements of the rotor wake. Three-component velocity measurements of a hovering rotor wake were acquired at NASA Ames Research Center in May 1999. This experiment represents a major step toward understanding the detailed structure of a rotor wake. This paper will focus primarily on the experimental technique used in acquiring this data. The accuracy and limitations of the current technique will also be discussed. Representative velocity field measurements will be included.

Yamauchi, Gloria K.↗
NASA NTRS2016

Hybrid Reynolds-Averaged/Large Eddy Simulation of the Flow in a Model SCRamjet Cavity Flameholder

Steady-state and scale-resolving simulations have been performed for flow in and around a model scramjet combustor flameholder. Experimental data available for this configuration include velocity statistics obtained from particle image velocimetry. Several turbulence models were used for the steady-state Reynolds-averaged simulations which included both linear and non-linear eddy viscosity models. The scale-resolving simulations used a hybrid Reynolds-averaged/large eddy simulation strategy that is designed to be a large eddy simulation everywhere except in the inner portion (log layer and below) of the boundary layer. Hence, this formulation can be regarded as a wall-modeled large eddy simulation. This e ort was undertaken to not only assess the performance of the hybrid Reynolds-averaged / large eddy simulation modeling approach in a flowfield of interest to the scramjet research community, but to also begin to understand how this capability can best be used to augment standard Reynolds-averaged simulations. The numerical errors were quantified for the steady-state simulations, and at least qualitatively assessed for the scale-resolving simulations prior to making any claims of predictive accuracy relative to the measurements. The steady-state Reynolds-averaged results displayed a high degree of variability when comparing the flameholder fuel distributions obtained from each turbulence model. This prompted the consideration of applying the higher-fidelity scale-resolving simulations as a surrogate "truth" model to calibrate the Reynolds-averaged closures in a non-reacting setting prior to their use for the combusting simulations. In general, the Reynolds-averaged velocity profile predictions at the lowest fueling level matched the particle imaging measurements almost as well as was observed for the non-reacting condition. However, the velocity field predictions proved to be more sensitive to the flameholder fueling rate than was indicated in the measurements.

Baurle, R. A.↗
DOE OSTI2023

Towards Pareto optimal high entropy hydrides via data-driven materials discovery

The ability to rapidly screen material performance in the vast space of high entropy alloys is of critical importance to efficiently identify optimal hydride candidates for various use cases. Given the prohibitive complexity of first principles simulations and large-scale sampling required to rigorously predict hydrogen equilibrium in these systems, we turn to compositional machine learning models as the most feasible approach to screen on the order of tens of thousands of candidate equimolar high entropy alloys (HEAs). Critically, we show that machine learning models can predict hydride thermodynamics and capacities with reasonable accuracy (e.g. a mean absolute error in desorption enthalpy prediction of ~5 kJ mol H 2 –1 ) and that explainability analyses capture the competing trade-offs that arise from feature interdependence. We can therefore elucidate the multi-dimensional Pareto optimal set of materials, i.e., where two or more competing objective properties can't be simultaneously improved by another material. This provides rapid and efficient down-selection of the highest priority candidates for more time-consuming density functional theory investigations and experimental validation. Various targets were selected from the predicted Pareto front (with saturation capacities approaching two hydrogen per metal and desorption enthalpy less than 60 kJ mol H 2 –1 ) and were experimentally synthesized, characterized, and tested amongst an international collaboration group to validate the proposed novel hydrides. Finally, additional top-predicted candidates are suggested to the community for future synthesis efforts, and we conclude with an outlook on improving the current approach for the next generation of computational HEA hydride discovery efforts.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Effects of spin-orbit coupling and very large supercells on the description of acceptors in CdTe

Predicting accurate shallow donor and acceptor levels in semiconductors has been quite challenging using periodic boundary conditions as implemented in current density functional theory codes. The reason is that the wave functions associated with the shallow centers are quite extended and are not fully contained in the supercell (typically with a few hundred atoms) so impurities in the periodically repeated image cells interact with each other. Errors of ~0.1 eV are expected, and these are of the same order of magnitude as the ionization energies themselves. In the case of acceptors in CdTe, this problem is exacerbated by the strong spin-orbit coupling that split the Te-related states at the top of the valence band, making the calculations at least 8 times more expensive. CdTe is an important solar-cell material with record high efficiency of 22%. One of the main limiting factors to increasing the efficiency towards the theoretical limit of ~30% is the often reported very low hole concentration. Pushing to the limit of computational capability by using very large supercells with spin-orbit coupling we report the results of hybrid functional calculations of group-V acceptors in CdTe. We show that extrapolation to the dilute limit leads to an interpretation of the experimental data that is qualitatively different from previous DFT and hybrid functional calculation reports. We find that the group-V impurities indeed behave as shallow acceptors and that the corresponding compensating AX-centers are unstable and do not limit p-type doping. We address the differences between our results and previous theoretical predictions and show that our calculated ionization energies predict hole concentrations that are in excellent agreement with recent temperature-dependent Hall measurements on high-quality single-crystal samples.

14 SOLAR ENERGY↗
DOE OSTI2026

Describing Point Defect Topology in 2D Energy Materials Through Computer Vision

Point defects such as vacancies and impurity atoms strongly impact the performance of 2D materials. Traditional efforts often rely on manual detection, a process that is time-intensive, prone to human error, and challenging to scale. Here we leverage machine learning (ML) methods to identify and quantify vacancies within 2D transition metal carbides (Ti3C2, MXenes), aiming to expedite detection while improving accuracy. MXenes exhibit valuable defect-defined electrochemical properties, but we currently lack statistical understanding of defect topology needed to fully harness these materials. Here we employ a convolutional neural network for semantic segmentation of experimental MXene images, opening an opportunity to conduct a rigorous statistical study on defect hierarchy while investigating local relaxation in the lattice. We show how the integration of ML can yield fundamental insight into point defects, providing a powerful tool that will play an increasingly crucial role in the future of materials science. ML is often not just a matter of straightforward application, and pretrained models proved ineffective in this case. Instead, we trained our own neural network (NN) and applied data augmentation techniques and fine-tuning to the training dataset. Since labeled microscopy data is often scarce, we developed training data from a previously published wide-frame MXene image, using customized Gaussian fitting to locate atomic positions. Our trained model was then applied to a large dataset of experimental images, enabling a statistical study of defect configurations across three samples prepared with different HF etchant concentrations (5%, 9.1%, and 12.5%), as shown in Fig. 1. This also allowed us to investigate local strain around vacancies, though we find that we are limited by the precision of measurements using high-angle annular dark field (HAADF) images, as shown in Fig. 2. This study demonstrates how ML enables large-scale, quantitative analysis of atomic defects - an otherwise infeasible task with traditional methods. While our NN was specialized for Ti3C2 MXenes, the pipeline we developed provides a foundation for future ML models tailored to other materials. Ultimately, we envision embedding the NN onto the microscope to give real-time feedback to the user. To make this a reality, continued work is necessary to fully understand the NN's capabilities and limitations. This study gets one step closer to our goals of automated experimentation moving away from traditional methods of manual labeling. As ML capabilities advance, we hope to continue adapting and applying these techniques in microscopy.

2D materials↗
NASA NTRS1982

Wind-tunnel wall interference corrections for three-dimensional flows

A procedure for the evaluation of wall interference corrections for three-dimensional aircraft configurations is presented. The Mach number and angle-of-attack corrections are obtained by numerically solving the Laplace equation in a parallelepiped with boundary conditions supplied mainly from experimental pressure measurements. A portion of these measurements and other wind-tunnel data required by the procedure may be replaced by theoretical estimates if not available from experiments. The accuracy of the correction results will then depend on the accuracy of these estimates. The correction procedure is applied to an isolated wing and to a wing-tail configuration in a solid-wall wind tunnel. It is found that neglecting twist and camber corrections for the wing effectively increases the tail angle-of-attack correction. Two different Mach number corrections can be calculated for the wing and tail. However, since only one Mach number correction is allowed for both the wing and the tail, and since the wing surface area is larger than the tail surface area, the final correction tends to be closer to the required wing correction. This is a source of error for the tail results.

Rizk, M. H.↗
NASA NTRS2005

Reconfigurable Antennas for High Data Rate Multi-beam Communication Systems

High-speed (2-100 Mb/sec) wireless data communication - whether land- or satellite-based - faces a major challenge: high error rates caused by interference and unpredictable environments. A planar antenna system that can be reconfigured to respond to changing conditions has the potential to dramatically improve data throughput and system reliability. Moreover, new planar antenna designs that reduce array size, weight, and cost can have a significant impact on terrestrial and satellite communication system performance. This research developed new individually-reconfigurable planar antenna array elements that can be adjusted to provide multiple beams while providing increased scan angles and higher aperture efficiency than traditional diffraction-limited arrays. These new elements are microstrip spiral antennas with specialized tuning mechanisms that provide adjustable radiation patterns. We anticipate that these new elements can be used in both large and small arrays for inter-satellite communication as well as tracking of multiple mobile surface-based units. Our work has developed both theoretical descriptions as well as experimental prototypes of the antennas in both single element and array embodiments. The technical summary of the results of this work is divided into six sections: A. Cavity model for analysis and design of pattern reconfigurable antennas; B. Performance of antenna in array configurations for broadside and endfire operation; C. Performance of antenna in array configurations for beam scanning operation; D. Simulation of antennas in infinite phased arrays; E. Demonstration of antenna with commercially-available RF MEMS switches; F. Design of antenna MEMS switch combinations for direct simultaneous fabrication.

Bernhard, Jennifer T.↗
NASA NTRS2013

Modeling Non-homologous End Joining

Non-homologous end joining (NHEJ) is the dominant DNA double strand break (DSB) repair pathway and involves several NHEJ proteins such as Ku, DNA-PKcs, XRCC4, Ligase IV and so on. Once DSBs are generated, Ku is first recruited to the DNA end, followed by other NHEJ proteins for DNA end processing and ligation. Because of the direct ligation of break ends without the need for a homologous template, NHEJ turns out to be an error-prone but efficient repair pathway. Some mechanisms have been proposed of how the efficiency of NHEJ repair is affected. The type of DNA damage is an important factor of NHEJ repair. For instance, the length of DNA fragment may determine the recruitment efficiency of NHEJ protein such as Ku [1], or the complexity of the DNA breaks [2] is accounted for the choice of NHEJ proteins and subpathway of NHEJ repair. On the other hand, the chromatin structure also plays a role of the accessibility of NHEJ protein to the DNA damage site. In this talk, some mathematical models of NHEJ, that consist of series of biochemical reactions complying with the laws of chemical reaction (e.g. mass action, etc.), will be introduced. By mathematical and numerical analysis and parameter estimation, the models are able to capture the qualitative biological features and show good agreement with experimental data. As conclusions, from the viewpoint of modeling, how the NHEJ proteins are recruited will be first discussed for connection between the classical sequential model [4] and recently proposed two-phase model [5]. Then how the NHEJ repair pathway is affected, by the length of DNA fragment [6], the complexity of DNA damage [7] and the chromatin structure [8], will be addressed

Li, Yongfeng↗
NASA NTRS1986

An experimental investigation of the aerodynamics of a NACA 64A010 airfoil-flap combination with and without flap oscillations. Part 1: Steady-state characteristics

A NACA 64A010 airfoil with a sealed-gap 1/4-chord flap was tested between splitter plates in the NASA Ames 11- by 11-Foot Transonic Wind Tunnel at Mach numbers from 0.50 to 0.85, and Reynolds numbers based on chord from 3 to 13 million. Although the main purpose of the test was to obtain unsteady pressure data with the flap oscillating, no unsteady data are presented in this paper. The steady-state data are presented and compared with other test data to provide a basis for evaluating the results. Pressure data at two span stations are used to deduce early boundary-layer transitions at the midspan at higher Mach numbers, angles of attack, and flap angles. The effects of flap angle on pressures, normal force, pitching moment, and hinge moment are also presented in the report. Mach number errors caused by the splitter-plate configuration and the angle of attack are evaluated using pressure measurements near the floor and ceiling of the wind tunnel.

Buell, Donald A.↗
DOE OSTI2023

Enabling AI in synthetic biology through Construction File specification

The Construction File (CF) specification establishes a standardized interface for molecular biology operations, laying a foundation for automation and enhanced efficiency in experiment design. It is implemented across three distinct software projects: PyDNA_CF_Simulator, a Python project featuring a ChatGPT plugin for interactive parsing and simulating experiments; ConstructionFileSimulator, a field-tested Java project that showcases 'Experiment' objects expressed as flat files; and C6-Tools, a JavaScript project integrated with Google Sheets via Apps Script, providing a user-friendly interface for authoring and simulation of CF. The CF specification not only standardizes and modularizes molecular biology operations but also promotes collaboration, automation, and reuse, significantly reducing potential errors. The potential integration of CF with artificial intelligence, particularly GPT-4, suggests innovative automation strategies for synthetic biology. While challenges such as token limits, data storage, and biosecurity remain, proposed solutions promise a way forward in harnessing AI for experiment design. This shift from human-driven design to AI-assisted workflows, steered by high-level objectives, charts a potential future path in synthetic biology, envisioning an environment where complexities are managed more effectively.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Design Optimization and Measurement Uncertainty of an Electromagnetic Level Sensor for Liquid Metal Reactors

Here, this article describes the design and operation of a prototype mutual inductance level sensor (MILS) and the development and validation of a finite-element analysis (FEA) model describing its behavior. The MILS was designed for use in liquid sodium up to temperatures of 650 °C in the Mechanisms Engineering Test Loop (METL) at Argonne National Laboratory (ANL). Preliminary testing was performed in a room temperature test stand with aluminum acting as an analog for the sodium to better understand sensor performance and provide accurate code validation data. This experimental data, along with material properties found in literature, were used to validate an FEA model in ANSYS Maxwell. The validated ANSYS Maxwell model was used to examine the performance of the MILS in various environments and under various operating conditions. Simulations suggest the following: The MILS will perform adequately in liquid sodium and liquid lead at temperatures up to 650 °C . The temperature dependence of electrical conductivity imposes a temperature dependence on the sensor that requires proper compensation. The MILS signal sensitivity is maximized when mutual inductance between sensor coils is maximized, and sensor geometry should be selected to account for this factor. The operating frequency of the MILS can be optimized and is dependent on process fluid material, operating temperature, and materials/geometry of sensor system. Finally, the use of a stainless-steel isolating thimble does not adversely affect the sensor signal. The primary sources of error for this MILS system are the accuracy of the calibration standard against which the sensor is calibrated, and the temperature dependence of the sensor. This work contains all the necessary details to recreate the FEA model and results. This model can be used to optimize the performance of a MILS in any operating environment to read any electrically conductive working fluid.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Data-driven acoustic measurement of moisture content in flowing biomass

Measuring the moisture content in flowing biomass is critical to processes such as liquid biofuel conversion, such as biogasoline, biodiesel, bio jet kerosene, etc. However, biomass tends to flow in aggregates, which results in significant inhomogeneities in the amount of biomass flowing in front of a sensor at a given time, and there can be significant overlap in the material properties of dry vs wet biomass, leading to poor signal-to-noise ratio. We present a technique for identifying biomass moisture content using a series of acoustic pitch-catch measurements to quantify the sound speed and acoustic amplitude through the biomass, in conjunction with classical machine learning techniques, including Naive Bayes, Random Forest, and K-Nearest Neighbors classification. We amplify the differences between the acoustic measurements in different moisture levels by collecting a series of pulse-echo measurements, which we sort in order of ascending sound speed. We test the accuracy of the technique on experimentally-prepared batches of corn stover biomass with specified moisture levels and measure the average error in the estimated moisture level as a function of the number of pitch-catch measurements used. We observe average estimation errors as low as 6.7% by increasing the number of measurements and optimizing the hyperparameters. This work presents a novel method determining moisture content in flowing biomass with inhomogeneous flow. Additionally, this technique has application in optimizing biomass conversion processes, as well as other fields including, paper production, natural fiber processing, and mineral extraction.

09 BIOMASS FUELS↗