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NASA NTRS2018

Assessing Version 4 of the SMAP L4_SM Data Product

Version 4 of the SMAP Level 4 Surface and Root Zone Soil Moisture (L4_SM) product benefits from an improved land surface modeling system. Surface soil moisture is typically drier by several volumetric percent in Version 4 compared to Version 3, whereas root zone soil moisture is wetter in Version 4 in some regions and drier in others. Results from core validation site comparisons show that Version 4 of the L4_SM data product meets the accuracy requirement, which is formulated in terms of the RMSE after removal of the long-term mean difference (ubRMSE). The overall ubRMSE of the 3-hourly L4_SM data at the 9 km scale is 0.039 m3 m-3 for surface soil moisture and 0.029 m3 m-3 for root zone soil moisture, below the 0.04 m3 m-3 requirement. L4_SM surface and root zone soil moisture estimates are more skillful than model-only simulation estimates that are not informed by SMAP brightness temperature observations, with statistically significant improvements at the 5% level for surface soil moisture R and anomaly R values. Results from comparisons of the L4_SM product to in situ measurements from more than 400 sparse network sites corroborate the core validation site results.

Reichle, R.↗

DOE OSTI2025

Dual‐Transformer Deep Learning Framework for Seasonal Forecasting of Great Lakes Water Levels

Abstract The Great Lakes of North America form one of the largest freshwater systems on Earth, and their lake‐wide average water levels (lake levels) can fluctuate by more than 0.5 m on a seasonal scale. These fluctuations pose substantial challenges for coastal resilience, flood risk management, and navigation planning. Accurate seasonal forecasting of lake levels using traditional mechanistic models is challenging due to the complex physical mechanisms and coupled hydroclimatic processes involved. Recently, deep learning has gained prominence in geoscience applications for its ability to recognize intricate patterns within multiphysical data sets. Here, we introduce a novel Dual‐Transformer deep learning framework, tested on the Great Lakes. This architecture integrates two modified Transformer models: the Prophet, which predicts underlying trends, and the Critic, which refines the Prophet's predictions. The final lake level prediction is derived by weighting the outputs of both models through a multi‐layer perceptron, jointly trained with the Prophet and Critic to enhance overall accuracy. Our results demonstrate that the innovative learning framework achieves the highest prediction accuracy compared to established deep learning models when using identical input features. It attains a root mean square error of 4–7 cm in predicting lake levels up to 6 months in advance across the lakes. Additionally, the Dual‐Transformer model runs six orders of magnitude faster than conventional mechanistic models, producing results in less than one second on a typical personal computer. These findings suggest that our deep learning framework has strong potential to advance lake level prediction and carries important implications for water management and disaster mitigation, thereby enhancing the quality of life in coastal regions.

Chen, Yi [Great Lakes Research Center Michigan Tec↗

DOE OSTI2022

Development and validation of a mathematical model of heart rate response to fluid perturbation

Physiological closed-loop controlled (PCLC) medical devices monitor and automatically adjust the patient’s condition by using physiological variables as feedback, ideally with minimal human intervention to achieve the target levels set by a clinician. PCLC devices present a challenge when it comes to evaluating their performance, where conducting large clinical trials can be expensive. Virtual physiological patients simulated by validated mathematical models can be utilized to obtain preclinical evidence of safety and assess the performance of the PCLC medical device during normal and worst-case conditions that are unlikely to happen in a limited clinical trial. A physiological variable that plays a major role during fluid resuscitation is heart rate (HR). For in silico assessment of PCLC medical devices regarding fluid perturbation, there is currently no mathematical model of HR validated in terms of its predictive capability performance. This paper develops and validates a mathematical model of HR response using data collected from sheep subjects undergoing hemorrhage and fluid infusion. The model proved to be accurate in estimating the HR response to fluid perturbation, where averaged between 21 calibration datasets, the fitting performance showed a normalized root mean square error (NRMSE) of 7.41 ± 2.8%. The model was also evaluated in terms of model predictive capability performance via a leave-one-out procedure (21 subjects) and an independent validation dataset (6 subjects). Two different virtual cohort generation tools were used in each validation analysis. The generated envelope of virtual subjects robustly met the defined acceptance criteria, in which 95% of the testing datasets presented simulated HR patterns that were within a deviation of 50% from the observed data. In addition, out of 16000 and 18522 simulated subjects for the leave one-out and independent datasets, the model was able to generate at least one virtual subject that was close to the real subject within an error margin of 9.56 ± 3.15% and 11.1 ± 1.22% NRMSE, respectively. In conclusion, the model can generate valid virtual HR physiological responses to fluid perturbation and be incorporated into future non-clinical simulated testing setups for assessing PCLC devices intended for fluid resuscitation.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2010

Role of Subsurface Physics in the Assimilation of Surface Soil Moisture Observations

Root zone soil moisture controls the land-atmosphere exchange of water and energy and exhibits memory that may be useful for climate prediction at monthly scales. Assimilation of satellite-based surface soil moisture observations into a land surface model is an effective way to estimate large-scale root zone soil moisture. The propagation of surface information into deeper soil layers depends on the model-specific representation of subsurface physics that is used in the assimilation system. In a suite of experiments we assimilate synthetic surface soil moisture observations into four different models (Catchment, Mosaic, Noah and CLM) using the Ensemble Kalman Filter. We demonstrate that identical twin experiments significantly overestimate the information that can be obtained from the assimilation of surface soil moisture observations. The second key result indicates that the potential of surface soil moisture assimilation to improve root zone information is higher when the surface to root zone coupling is stronger. Our experiments also suggest that (faced with unknown true subsurface physics) overestimating surface to root zone coupling in the assimilation system provides more robust skill improvements in the root zone compared with underestimating the coupling. When CLM is excluded from the analysis, the skill improvements from using models with different vertical coupling strengths are comparable for different subsurface truths. Finally, the skill improvements through assimilation were found to be sensitive to the regional climate and soil types.

Reichle, R. H.↗

DOE OSTI2017

Nutrient Cycle Impacts on Forest Ecosystem Carbon Cycling: Improved Prediction of Climate Feedbacks from Coupled C–Nutrient Dynamics from Ecosystem to Regional Scales

One of the largest sources of uncertainty in the projected global mean temperature response to anthropogenic carbon (C) emissions is the role of the land surface in contributing to or mediating the rise in atmospheric CO2 [IPCC, 2007]. Development of ecosystem processes in global land surface models (LSMs) is far from complete, with major components such as the nitrogen (N) cycle only recently being implemented in some LSMs [Thornton et al., 2007; Sokolov et al., 2008; Xu-Ri and Prentice, 2008; Fisher et al., 2010]. Inclusion of the N cycle can reduce terrestrial C uptake by up to ~50 Pg C·y-1 globally [Figure 1: Fisher et al., 2010]. However, even these early representations of the N cycle in LSMs are missing critical plant–soil interactions that have been shown empirically to exert a major influence over ecosystem C fluxes. Here we propose to use a newly developed plant N model by PI Fisher as a theoretical and analytical framework for integrating novel field-based methods and long-term flux observations to quantify the impacts of belowground processes on fluxes of C, water and energy, from ecosystem to regional scales. Although the model (Fixation & Uptake of Nitrogen: FUN) is one of the more advanced global plant N models available, the model is dependent on theoretical C flux curves optimizing plant C allocation to root exudates, symbiotic bacteria and fungi, and retranslocation enzymes that have not been confirmed or validated with measurements—primarily because the measurement techniques had not been available, until recently. Parallel to the model developments by Fisher et al. were developments in in situ direct measurement techniques by Phillips et al. [2008; 2011], who were able to measure C fluxes from roots to soil. Not only do these techniques have the capacity to confirm and/or modify the modeling components in the Fisher et al. plant N model, but they also provide insight into how belowground C supply from roots couples the C–N cycles at the micro-site and ecosystem scales. Collectively, the integration of measurements and modeling provides empirical grounding for theoretical process behavior. Three critical components will enable us to successfully carry out this research: I) new methodological approaches developed by co-I Phillips for in situ measurement of root exudates, and belowground C fluxes that allow for testing and refinement of FUN [Phillips et al., 2008; Phillips et al., 2011]; II) an innovative research design that involves data integration across an ecological gradient, including a DOE-supported AmeriFlux tower operated by co-I Dragoni, which is essential to determining the ecosystem flux impacts [Dragoni et al., 2011]; and, III) new results from co-I Evans showing unique spectral signatures of trees in association with ectomycorrhizae (ECM) and arbuscular mycorrhizae (AM) fungi from Landsat data using spectral mixture analysis (SMA), which is key to scaling up belowground processes to regional scales [Andersson et al., 2009]. Each component provides feedback to and is intimately linked with all other components; no part operates in isolation. Our team and approach combines expertise in modeling, remote sensing, ecosystem ecology, plant physiology, soil biogeochemistry, and micrometeorology to address these inherently interdisciplinary problems and questions. This proposal directly responds to the focused Science Area of the FOA: “The role of belowground processes and mechanisms across scales (e.g., soil carbon transformation, root dynamics, mycorrhizal interactions, and plant mediated (e.g., root exudates) biogeochemical transformations) associated with a changing climate.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Surrogate model evaluation and building energy benchmarking for commercial buildings

Building energy consumption benchmarking involves challenges associated with various energy patterns for different building types; heating, ventilating, and air-conditioning (HVAC) system types; and climates. Given significant variation in energy use patterns, accurate prediction of long-term energy use using surrogate models remains challenging. Multiple linear regression (MLR) is commonly used for building energy benchmarking because of its simple structure; however, it lacks accuracy compared to other black-box models. Although many studies have compared surrogate models and offer guidance on model selection based on metrics, they do not provide detailed analysis on improving the surrogate model accuracy. In this paper, we implement a surrogate model using polynomial ridge regression (i.e., MLR with interaction terms combined with ridge regularization) for small office and retail strip mall buildings across six HVAC system types and all climate zones, for electricity and natural gas in baseline and proposed scenarios. A simulation workflow is developed using OpenStudio TM /EnergyPlus TM to generate simulation data using measures over a wide range of efficiency inputs. Enhancements based on statistical insights are used for improving the model accuracy using filters, input transformations, and change points. Surrogate models achieved average coefficient of variation of the root mean squared error (CVRMSE) values of 2.17, 1.06, 2.05, and 3.26 for proposed electricity, proposed natural gas, baseline electricity, and baseline natural gas, respectively, with enhancements reducing CVRMSE by an average of 14.9% across all combinations. We provide model interpretation via Shapley additive explanations to determine which input variables most influence energy consumption and provide supportive arguments for enhancements.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS1996

A Comparison of Pressure Measurements Between a Full-Scale and a 1/6-Scale F/A-18 Twin Tail During Buffet

In 1993, tail buffet tests were performed on a full-scale, production model F/A-18 in the 80 x 120-foot Wind Tunnel at NASA Ames Research Center. Steady and unsteady pressures were recorded on both sides of the starboard vertical tail for an angle-of-attack range of 20 to 40 degrees and at a sideslip range of -1 6 to 16 degrees at freestream velocities up to 100 knots (Mach 0.15, Reynolds number 1.23 x 10(exp 7). The aircraft was equipped with removable leading edge extension (LEX) fences that are used in flight to reduce tail buffet loads. In 1995, tail buffet tests were performed on a 1/6-scale F-18 A/B model in the Transonic Dynamics Tunnel (TDT) at NASA Langley Research Center. Steady and unsteady pressures were recorded on both sides of both vertical tails for an angle-of-attack range of 7 to 37 degrees at freestream velocities up to 65 knots (Mach 0.10). Comparisons of steady and unsteady pressures and root bending moments are presented for these wind-tunnel models for selected test cases. Representative pressure and root bending moment power spectra are also discussed, as are selected pressure cross-spectral densities.

Moses, Robert W.↗

NASA NTRS1997

A Comparison of Pressure Measurements Between a Full-Scale and a 1/16-Scale F/A-18 Twin Tail During Buffet

In 1993, tail buffet tests were performed on a full-scale, production model F/A-18 in the 80-by-120 Foot Wind Tunnel at NASA Ames Research Center. Steady and unsteady pressures were recorded on both sides of the starboard vertical tail for an angle of attack range of 20 to 40 degrees and at a sideslip range of -16 to 16 degrees at freestream velocities up to 100 knots (Mach 0.15, Reynolds number 1.23 x 10(exp 7)). The aircraft was equipped with removable leading edge extension (LEX) fences that are used in flight to reduce tail buffet loads. In 1995, tail buffet tests were performed on a 1/6-scale F-18 A/B model in the Transonic Dynamics Tunnel (TDT) at NASA Langley Research Center. Steady and unsteady pressures were recorded on both sides of both vertical tails for an angle-of-attack range of 7 to 37 degrees at freestream velocities up to 65 knots (Mach 0.10). Comparisons of steady and unsteady pressures and root bending moments are presented for these wind-tunnel models for selected test cases. Representative pressure and root bending moment power spectra are also discussed, as are selected pressure cross-spectral densities.

Moses, Robert W.↗

NASA NTRS1991

Post-glacial relaxation of a viscously stratified compressible mantle

The postglacial relaxation phenomenon of a viscously compressible nonself-gravitating spherical-shell model is investigated. Analytical solutions to an exponentially depth-dependent viscosity are developed with various types of compressible density models such as those with an exponential dependence of the radius and an algebraic-root dependence of the radius. The solutions of a viscously compressible multilayered model with constant thermodynamic properties and viscosity are developed by the propagator matrix method. The results show that inferences of deep mantle viscosity from postglacial rebound would be hampered by mantle compressibility for long-wavelength harmonics because of the smaller excitation of compressible eigenfunctions in the lower mantle. The relaxation times and velocity fields are more sensitive to higher viscosity contrast for the exponentially varying viscosity than for models with discrete jumps in the viscosity structure.

Wu, J.↗

NASA NTRS2000

Controlling Microbial Byproducts using Model-Based Substrate Monitoring and Control Strategies

We have developed a computer-controlled bioreactor system to study various aspects of microbially-mediated nitrogen cycling. The system has been used to investigate methods for controlling microbial denitrification (the dissimilatory reduction of nitrate to N2O and N2) in hydroponic plant growth chambers. Such chambers are key elements of advanced life support systems being designed for use on long duration space missions, but nitrogen use efficiency in them is reduced by denitrification. Control software architecture was designed which permits the heterogeneous control of system hardware using traditional feedback control, and quantitative and qualitative models of various system features. Model-based feed forward control entails prediction of future systems in states and automated regulation of system parameters to achieve desired and avoid undesirable system states. A bacterial growth rate model based on the classic Monod model of saturation kinetics was used to evaluate the response of several individual denitrifying species to varying environmental conditions. The system and models are now being applied to mixed microbial communities harvested from the root zone of a hydroponic growth chamber. The use of a modified Monod organism interaction model was evaluated as a means of achieving more accurate description of the dynamic behavior of the communities. A minimum variance parameter estimation routine was also' used to calibrate the constant parameters in the model by iterative evaluation of substrate (nitrate) uptake and growth kinetics. This representation of processes and interactions aids in the formulation of control laws. The feed forward control strategy being developed will increase system autonomy, reduce crew intervention and limit the accumulation of undesirable waste products (NOx).

Smernoff, David T.↗

NASA NTRS2005

Business Intelligence Modeling in Launch Operations

This technology project is to advance an integrated Planning and Management Simulation Model for evaluation of risks, costs, and reliability of launch systems from Earth to Orbit for Space Exploration. The approach builds on research done in the NASA ARC/KSC developed Virtual Test Bed (VTB) to integrate architectural, operations process, and mission simulations for the purpose of evaluating enterprise level strategies to reduce cost, improve systems operability, and reduce mission risks. The objectives are to understand the interdependency of architecture and process on recurring launch cost of operations, provide management a tool for assessing systems safety and dependability versus cost, and leverage lessons learned and empirical models from Shuttle and International Space Station to validate models applied to Exploration. The systems-of-systems concept is built to balance the conflicting objectives of safety, reliability, and process strategy in order to achieve long term sustainability. A planning and analysis test bed is needed for evaluation of enterprise level options and strategies for transit and launch systems as well as surface and orbital systems. This environment can also support agency simulation .based acquisition process objectives. The technology development approach is based on the collaborative effort set forth in the VTB's integrating operations. process models, systems and environment models, and cost models as a comprehensive disciplined enterprise analysis environment. Significant emphasis is being placed on adapting root cause from existing Shuttle operations to exploration. Technical challenges include cost model validation, integration of parametric models with discrete event process and systems simulations. and large-scale simulation integration. The enterprise architecture is required for coherent integration of systems models. It will also require a plan for evolution over the life of the program. The proposed technology will produce long-term benefits in support of the NASA objectives for simulation based acquisition, will improve the ability to assess architectural options verses safety/risk for future exploration systems, and will facilitate incorporation of operability as a systems design consideration, reducing overall life cycle cost for future systems. The future of business intelligence of space exploration will focus on the intelligent system-of-systems real-time enterprise. In present business intelligence, a number of technologies that are most relevant to space exploration are experiencing the greatest change. Emerging patterns of set of processes rather than organizational units leading to end-to-end automation is becoming a major objective of enterprise information technology. The cost element is a leading factor of future exploration systems.

Bardina, Jorge E.↗

DOE OSTI2023

Toward accurate prediction of partial-penetration laser weld performance informed by three-dimensional characterization – Part II: μCT based finite element simulations

The mechanical behavior of partial-penetration laser welds exhibits significant variability in engineering quantities such as strength and apparent ductility. Understanding the root cause of this variability is important when using such welds in engineering designs. In Part II of this work, we develop finite element simulations with geometry derived from micro-computed tomography (μCT) scans of partial-penetration 304L stainless steel laser welds that were analyzed in Part I. We use these models to study the effects of the welds’ small-scale geometry, including porosity and weld depth variability, on the structural performance metrics of weld ductility and strength under quasi-static tensile loading. We show that this small-scale geometry is the primary cause of the observed variability for these mechanical response quantities. Additionally, we explore the sensitivity of model results to the conversion of the μCT data to discretized model geometry using different segmentation algorithms, and to the effect of small-scale geometry simplifications for pore shape and weld root texture. The modeling approach outlined and results of this work may be applicable to other material systems with small-scale geometric features and defects, such as additively manufactured materials.

36 MATERIALS SCIENCE↗

NASA NTRS2020

Fisher's Peak Ecological Forecasting - Mapping Biomass to Inform Conservation Planning of a Future State Park in Southern Colorado

Fisher’s Peak is a 77.5 km2 property southeast of Trinidad, Colorado that is planned to become Colorado’s newest state park. The area has experienced limited anthropogenic disturbance and is home to an abundance of unique habitats and species. A rapid, approximately 900 m change in elevation over the extent of the area nurtures a variety of plants and animals, including the endangered New Mexico meadow jumping mouse. In 2019, the State of Colorado obtained Fisher’s Peak with plans to make it Colorado’s second largest state park. A diverse group of collaborators, including the Colorado State Forest Service and The Nature Conservancy, worked closely to design the state park to maximize recreation opportunity while conserving the property’s rich habitats and biodiversity. The Fisher’s Peak Ecological Forecasting Team utilized Light Detection and Ranging (LiDAR) surveys, in situ forest inventory data, and Earth observations from Landsat 8 Operational Land Imager (OLI), Sentinel-1 C-band Synthetic Aperture Radar (C-SAR), Sentinel-2 Multispectral Instrument (MSI), Advanced Land Observing Satellite 2 (ALOS-2) Phased Array type L-band Synthetic Aperture Radar (PALSAR-2) and the Shuttle Radar Topography Mission (SRTM) to quantify and map biomass over the extent of the study area. The results from modeling biomass had an out-of-bag root mean square error of 55 Mg/ha and an R2 of 12. The resulting map indicates areas where carbon storage on the property is high, informing decision-making processes for future park development. While more in situ training data may improve modeling capacity for biomass in the Fisher’s Peak area, this work represents a feasible attempt to better understand biomass distribution using earth observation

Lauren Lad↗

NASA NTRS2020

Fisher's Peak Ecological Forecasting: Mapping Biomass to Inform Conservation Planning of a Future State Park in Southern Colorado

Fisher’s Peak is a 77.5 km2 property southeast of Trinidad, Colorado that is planned to become Colorado’s newest state park. The area has experienced limited anthropogenic disturbance and is home to an abundance of unique habitats and species. A rapid, approximately 900 m change in elevation over the extent of the area nurtures a variety of plants and animals, including the endangered New Mexico meadow jumping mouse. In 2019, the State of Colorado obtained Fisher’s Peak with plans to make it Colorado’s second largest state park. A diverse group of collaborators, including the Colorado State Forest Service and The Nature Conservancy, worked closely to design the state park to maximize recreation opportunity while conserving the property’s rich habitats and biodiversity. The Fisher’s Peak Ecological Forecasting Team utilized Light Detection and Ranging (LiDAR) surveys, in situ forest inventory data, and Earth observations from Landsat8 Operational Land Imager (OLI), Sentinel-1 C-band Synthetic Aperture Radar (C-SAR), Sentinel-2 Multispectral Instrument (MSI), Advanced Land Observing Satellite 2 (ALOS-2) Phased Array type L-band Synthetic Aperture Radar (PALSAR-2) and the Shuttle Radar Topography Mission (SRTM) to quantify and map biomass over the extent of the study area. The results from modeling biomass had an out-of-bag root mean square error of 55 Mg/ha and an R2 of 12. The resulting map indicates areas where carbon storage on the property is high, informing decision-making processes for future park development. While more in situ training data may improve modeling capacity for biomass in the Fisher’s Peak area, this work represents a feasible attempt to better understand biomass distribution using earth observation.

DEVELOP Tech Paper↗

NASA NTRS1987

Development of control strategies for safe microburst penetration: A progress report

A single-engine, propeller-driven, general-aviation model was incorporated into the nonlinear simulation and into the linear analysis of root loci and frequency response. Full-scale wind tunnel data provided its aerodynamic model, and the thrust model included the airspeed dependent effects of power and propeller efficiency. Also, the parameters of the Jet Transport model were changed to correspond more closely to the Boeing 727. In order to study their effects on steady-state repsonse to vertical wind inputs, altitude and total specific energy (air-relative and inertial) feedback capabilities were added to the nonlinear and linear models. Multiloop system design goals were defined. Attempts were made to develop controllers which achieved these goals.

Psiaki, Mark L.↗

DOE OSTI2022

Analytics-at-scale of Sensor Data for Digital Monitoring in Nuclear Plants (3 rd Annual Report)

Nuclear power plants collect and store large volumes of heterogeneous data from various components and systems. With recent advances in machine learning (ML) techniques, these data can be leveraged to develop diagnostic and short-term forecasting models to better predict future equipment condition. Maintenance operations can then be planned in advance whenever degraded performance is predicted, thus resulting in fewer unplanned outages and the optimization of maintenance activities. This enables lower maintenance costs and improves the overall economics of nuclear power. This report primarily focuses on developing a short-term forecasting process that leverages a feature selection process to distill large volumes of heterogeneous data and predict specific equipment parameters. A variety of feature selection methods, including Shapley Additive Explanations (SHAP) and variance inflation factor (VIF), were used to select the optimal features as inputs for three ML methods: long short-term memory (LSTM) networks, support vector regression (SVR), and random forest (RF). Each combination of model and input features was used to predict a pump bearing temperature both 1 and 24 hours in advance, based on actual plant system data. The optimal inputs for the LSTM and SVR were selected using the SHAP values, while the optimal input for the RF consisted solely of the response variable itself. Each model produced similar 1-hour-ahead predictions, with root mean square errors (RMSEs) of roughly 0.006. For the 24-hour-ahead predictions, differences could be seen between LSTM, SVR, and RF, as reflected by model performances of 0.036 ± 0.014, 0.0026 ± 0, and 0.063 ± 0.004 RMSE, respectively. As big data and continuous online monitoring become more widely available, the proposed feature selection process can be used for many applications beyond the prediction of process parameters within nuclear infrastructure. This report summarizes the Fiscal Year 2021 research progress encompassing the (1) data cleaning and feature selection necessary for ML applications; (2) development of short-term forecasting models to predict future plant process parameters for both single and multiple time steps ahead; and (3) validation of the feature selection methods and short-term forecasting models given new data from different systems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

Development of Short-Term Forecasting Models Using Plant Asset Data and Feature Selection

Nuclear power plants collect and store large volumes of heterogeneous data from various components and systems. With recent advances in machine learning (ML) techniques, these data can be leveraged to develop diagnostic and short-term forecasting models to better predict future equipment condition. Maintenance operations can then be planned in advance whenever degraded performance is predicted, thus resulting in fewer unplanned outages and the optimization of maintenance activities. This enables lower maintenance costs and improves the overall economics of nuclear power. This paper focuses on developing a short-term forecasting process that leverages a feature selection process to distill large volumes of heterogeneous data and predict specific equipment parameters. A variety of feature selection methods, including Shapley Additive Explanations (SHAP) and variance inflation factor (VIF), were used to select the optimal features as inputs for three ML methods: long short-term memory (LSTM) networks, support vector regression (SVR), and random forest (RF). Each combination of model and input features was used to predict a pump bearing temperature both 1 and 24 hours in advance, based on actual plant system data. The optimal inputs for the LSTM and SVR were selected using the SHAP values, while the optimal input for the RF consisted solely of the response variable itself. Each model produced similar 1-hour-ahead predictions, with root mean square errors (RMSEs) of roughly 0.006. For the 24-hour-ahead predictions, differences could be seen between LSTM, SVR, and RF, as reflected by model performances of 0.036 +- 0.014, 0.0026 +- 0, and 0.063 +- 0.004 RMSE, respectively. As big data and continuous online monitoring become more widely available, the proposed feature selection process can be used for many applications beyond the prediction of process parameters within nuclear infrastructure.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS1987

Characterization of root agravitropism induced by genetic, chemical, and developmental constraints

The patterns and rates of organelle redistribution in columella (i.e., putative statocyte) cells of agravitropic agt mutants of Zea mays are not significantly different from those of columella cells in graviresponsive roots. Graviresponsive roots of Z. mays are characterized by a strongly polar movement of 45Ca2+ across the root tip from the upper to the lower side. Horizontally-oriented roots of agt mutants exhibit only a minimal polar transport of 45Ca2+. Exogenously-induced asymmetries of Ca result in curvature of agt roots toward the Ca source. A similar curvature can be induced by a Ca asymmetry in normally nongraviresponsive (i.e., lateral) roots of Phaseolus vulgaris. Similarly, root curvature can be induced by placing the roots perpendicular to an electric field. This electrotropism increased with 1) currents between 8-35 mA, and 2) time between 1-9 hr when the current is constant. Electrotropism is reduced significantly by treating roots with triiodobenzoic acid (TIBA), an inhibitor of auxin transport. These results suggest that 1) if graviperception occurs via the sedimentation of amyloplasts in columella cells, then nongraviresponsive roots apparently sense gravity as do graviresponsive roots, 2) exogenously-induced asymmetries of a gravitropic effector (i.e., Ca) can induce curvature of normally nongraviresponsive roots, 3) the gravity-induced downward movement of exogenously-applied 45Ca2+ across tips of graviresponsive roots does not occur in nongraviresponsive roots, 4) placing roots in an electrical field (i.e., one favoring the movement of ions such as Ca2+) induces root curvature, and 5) electrically-induced curvature is apparently dependent on auxin transport. These results are discussed relative to a model to account for the lack of graviresponsiveness by these roots.

NASA Discipline Number 40-10↗