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DOE OSTI2023

Commercial Building Sensors and Controls Systems: Barriers and Drivers: Preprint

Building sensors and controls systems, including building automation systems, comprise the sensor-based devices installed in buildings as well as the control and automation of those devices. Optimized sensors and controls systems could lead to 29% annual energy savings in commercial buildings and are integral to the growth of grid-interactive efficient buildings. Only 13% of small commercial buildings, however, have installed sensors and controls systems, largely because of cost barriers. To accelerate adoption, this work seeks to increase the transparency of system costs and identify specific barriers and drivers. To gather industry insights, the team reached out to building owners, vendors, and contractors and conducted 21 interviews with the goal of collecting cost data and market characteristics regarding building sensors and controls. We collected the cost data in the form of invoices and used it to develop a percentage-based cost category breakdown. The interview data were analyzed using grounded theory to identify overarching concepts such as barriers, drivers, and future directions. From this analysis, we found the primary barriers to be complex and confusing systems, lack of user skills, and financial concerns, and the primary drivers to be operational benefits, insight into operations, and remote access to data. The future directions analysis highlighted the potential technological solutions to address gaps and barriers, as well as predicted drivers to increase adoption. This greater understanding of the costs, barriers, and drivers associated with commercial building sensors and controls systems lays the groundwork for increasing system adoption, reducing energy consumption, and transforming the market.

building automation system↗

DOE OSTI2019

Operational Planning Strategy for a Sequim Bay Underwater UXO Test Site (SBU2)

The Department of Defense (DoD) has identified more than 400 underwater sites that are potentially contaminated with munitions. Most areas are in shallow water (0-35 m) where the munitions pose a threat to human health and the environment. The Strategic Environmental Research and Development Program (SERDP) and Environmental Security Technology Certification Program (ESTCP) held a workshop in 2018 to establish the requirements, framework, protocols, responsibilities, and time lines for development of a series of underwater UXO Standardized Demonstration sites (“test beds”) for the purpose of testing the efficacy of sensors, equipment, and technologies to detect UXO. The workshop recommended a phased approach to building test beds to capitalize on: 1) incorporation of lessons learned from existing DoD funded projects, 2) leveraging existing underwater test bed environments funded by other programs, and 3) supporting recursive learning from early phases of the test bed development (i.e. efficiently optimizing through a scaling-up approach). This strategy document provides the framework to establish a working operational plan for the Sequim Bay Underwater UXO (SBU2) Test Bed (Phase II). Details of the plan are dependent on earlier phases of the effort as shown in Figure 1. Results from Phase I are being evaluated, and when complete, will inform the next iteration of this plan. As such, it should be considered a living document that will be informed by the lessons learned and updated as new information becomes known and requirements are refined. This approach will allow SBU2 to evolve to meet the needs of the remediation community.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2015

Low drift type N thermocouples in out-of-pile advanced gas reactor mock-up test: metallurgical analysis

Thermocouples are the most commonly used sensors for temperature measurement in nuclear reactors. They are crucial for the control of current nuclear reactors and for the development of GEN IV reactors. In nuclear applications thermocouples are strongly affected by intense neutron fluxes. As a result of the interaction with neutrons, the thermoelements of the thermocouples undergo transmutation, which produces a time dependent change in composition and, as a consequence, a time dependent drift of the thermocouple signal. Thermocouple drift can be very significant for in-pile temperature measurements and may render the temperature sensors unreliable after exposure to nuclear radiation for relatively short times compared to the life required for temperature sensors in nuclear applications. Previous experiences with type K thermocouples in nuclear reactors have shown that they are affected by neutron irradiation only to a limited extent. Similarly type N thermocouples are expected to be only slightly affected by neutron fluxes. Currently the use of Nickel based thermocouples is limited to temperatures lower than 1000 deg. C due to drift related to phenomena other than nuclear irradiation. As part of a collaboration between Idaho National Laboratory (INL) and the University of Cambridge a variety of Type N thermocouples have been exposed at INL in an Advanced Gas Reactor mock-up test at 1150 deg. C for 2000 h, 1200 deg. C for 2000 h, 125 deg. C for 200 h and 1300 deg. C for 200 h, and later analysed metallurgically at the University of Cambridge. The use of electron microscopy allows to identify the metallurgical changes occurring in the thermocouples during high temperature exposure and correlate the time dependent thermocouple drift with the microscopic changes experienced by the thermoelements of different thermocouple designs. In this paper conventional Inconel 600 sheathed type N thermocouples and a type N using a customized sheath developed at the University of Cambridge have been investigated. The rationale for the superior performance of the type N using a customized sheath developed at the University of Cambridge is explained in comparison with the behavior of conventional type N Inconel 600 sheathed thermocouples. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2020

Multi-Gas Sensors for Enhanced Reliability of SOFC Operation

GE Research, in partnership with SUNY Polytechnic Institute (SUNY Poly), designed, built, and tested gas sensors for in situ monitoring of H 2 and CO anode tail gases produced with on-site steam reforming in solid oxide fuel cell (SOFC) systems. The knowledge of the H 2 /CO ratio of these anode tail gases should allow accurate determination and control of the efficiency of the reforming process in the SOFC system and should deliver a lower operating cost for SOFC customers. The project objectives were to achieve multi-gas monitoring capability with a single multivariable sensor, and to sustain this performance in the presence of gaseous interferences and a potential poison for the sensor. The duration of the project was 24 months with the project structure that included three technical tasks such as (1) development of design rules of photonic nanostructures for H 2 and CO gas detection, (2) laboratory validation of photonic nanostructures for selective H 2 and CO gas detection, and (3) validation of photonic nanostructures for initial stability and poison-resistance against H 2 S. To build multivariable sensors for selective H 2 and CO gas detection in the presence of interferences, we expanded our earlier knowledge of multi-gas sensors into new fabrication and functionalization methodologies as well as into new methodologies for the spectral data analysis of multi-gas responses. We have advanced our design rules of the three-dimensional (3D) photonic nanostructures that allowed detection of H 2 and CO at high temperatures as individual gases and as their mixtures and rejection of interferences such as CO 2 , H 2 O, CH 4 , and other hydrocarbons for SOFC applications. Our advanced design rules should be attractive for building the new generation of cost-effective industrial sensors. Stability and poison resistance of our 3D photonic nanostructures was tested in the laboratory conditions. While initially we utilized conventional machine learning data analysis tools, we have found that they were unable to correct for the sensor drift. Thus, we have implemented new methods of machine learning for the analysis of our spectral data. These learnings pave the way to move the future studies into advanced testing of effects of interferences, aging and field tests. In future, our work will continue to advance our sensing designs to operate in conditions with known and unknown interferences by implementing nanostructures with enhanced spectral diversity of responses to gaseous species of interest and interferences. Our systematic reduction of technical risks in this completed project and in future studies will ensure transition of this sensing technology to commercialization.

03 NATURAL GAS↗

DOE OSTI2018

Sensitive and selective colorimetric detection of Pb2+ by silver nanoparticles synthesized from Aconitum violaceum plant leaf extract

Highlights: • A new colorimetric sensor for the detection of Pb{sup 2+} using Aconitum violaceum leaf extract synthesized AgNPs was developed. • The color and spectral variations of the assay was easily detected by naked eye or spectrophotometer. • The bio-functionalized AgNPs were capable of detecting Pb{sup 2+} in the range of 0.5 × 10{sup −7}–2.5 × 10{sup −5} M with good linearity. • The proposed method showed high specificity and good recoveries for Pb{sup 2+} in river water. - Abstract: In this work, we propose a simple and cost effective colorimetric method for the detection of Pb{sup 2+} using silver nanoparticles (AgNPs) prepared from the leaf extract of Aconitum violaceum. Due to the presence of significant amount of polyphenols in the extract, the AgNPs were successfully synthesized and were then characterized by UV–vis spectroscopy, Fourier transform infrared spectroscopy (FT-IR), X-Ray diffraction (XRD) and transmission electron microscopy (TEM). The as prepared AgNPs were employed as colorimetric probes for the detection of Pb{sup 2+}. In the presence of Pb{sup 2+}, the biosynthesized AgNPs showed a color transformation from yellow to red, which was readily determined by UV–vis spectroscopy. The novel sensor exhibited good linearity for the detection of Pb{sup 2+} in the concentration range of 0.5 - 25 μM (R{sup 2 }= 0.996) with a detection limit (LOD) of 0.1 μM. Also, this sensing system presented a highly selective response for Pb{sup 2+} determination.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Development of the Senseiver for efficient field reconstruction from sparse observations

Abstract The reconstruction of complex time-evolving fields from sensor observations is a grand challenge. Frequently, sensors have extremely sparse coverage and low-resource computing capacity for measuring highly nonlinear phenomena. While numerical simulations can model some of these phenomena using partial differential equations, the reconstruction problem is ill-posed. Data-driven-strategies provide crucial disambiguation, but these suffer in cases with small amounts of data, and struggle to handle large domains. Here we present the Senseiver, an attention-based framework that excels in reconstructing complex spatial fields from few observations with low overhead. The Senseiver reconstructs n -dimensional fields by encoding arbitrarily sized sparse sets of inputs into a latent space using cross-attention, producing uniform-sized outputs regardless of the number of observations. This allows efficient inference by decoding only a sparse set of output observations, while a dense set of observations is needed to train. This framework enables training of data with complex boundary conditions and extremely large fine-scale simulations. We build on the Perceiver IO by enabling training models with fewer parameters, which facilitates field deployment, and a training framework that allows a flexible number of sensors as input, which is critical for real-world applications. We show that the Senseiver advances the state-of-the-art of field reconstruction in many applications.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Hydrogen Detection Strategies to Support H2@SCALE - The NREL Sensor Laboratory

Hydrogen represents a major pathway to decarbonize and stabilize the national and international energy industry and select manufacturing markets. To facilitate the development of hydrogen markets, the US Department of Energy initiated H2@Scale to bring together stakeholders to advance affordable hydrogen production, transport, storage, and utilization to increase revenue opportunities in multiple energy sectors. One major impediment to hydrogen implementation is cost. To expedite the use of hydrogen in energy and other markets, the United States announced in 2021 the Hydrogen Shot, which seeks to reduce the cost of clean hydrogen by 80% to $1 per 1 kilogram in 1 decade ("1 1 1"). As the cost of hydrogen drops, new applications will emerge that will require unique configurations of existing equipment and infrastructure, and eventually lead to advances in the generation and utilization of hydrogen. As the hydrogen economy expands, sensors and detection methods will need to adapt to changing infrastructure demands to address the primary targets of health & safety, emissions monitoring, and process control. The NREL Sensor Laboratory is playing a pivotal role in advancing the use of hydrogen sensors and detection methodologies in each of these categories to support DOE's mission for safe and efficient utilization in emerging markets. Health & safety monitors are required to ensure that operators and facilities can react to unintended hydrogen releases, either as GH2, LH2, or as a constituent of blends (e.g., natural gas or ammonia). Current detection methodologies focus on safety applications to detect near its lower flammable limit (4 vol %), and typically include point sensors in applications such as fixed or mobile detectors (e.g., personal gas monitors). Methodologies amenable for area detection include acoustic, emerging optical imaging methods, and flame detectors. Comparable detection strategies can be utilized for emissions monitoring and quantization, however few methods can simultaneously cover both low (emissions) and high (health & safety) levels. Deployment of emission level detectors will be required to 1) reduce product loss through small but potentially significant leaks from an environmental or cost perspective, 2) reduce downtime of high demand systems by early identification of eminent system failures (leaks through pump or compressor seals indicative of impending failure), and 3) address potential emission monitoring requirements that may be set by regulating bodies. The first two points should be adopted by industry to reduce the cost-of-goods-sold. The third main category for hydrogen detection relates to process control and may be advantageous for many existing applications. Two main applications are emerging. For example, the purity requirements for hydrogen that is dispensed from refueling systems for hydrogen fuel cell electric vehicles (FCEV) is rigorously regulated by the Standard SAE J2719, which prescribes maximum allowable levels of multiple impurities in the hydrogen fuel and must be verified by a regulatory body. Hydrogen contaminant detectors (HCD) integrated to the fueling station can assure this compliance. HCDs must be able operate in 100% H2 backgrounds and be able to distinguish between multiple contaminants at low ppm to low ppb levels. Secondly, as a strategy to decarbonize the natural gas grid, there are proposals to blend hydrogen with natural gas. This blending will affect transport applications (pipeline infrastructure), stationary combustion systems (turbines), and consumer and commercial appliances. In the short-term, hydrogen levels up to 20% are proposed. Variations in the hydrogen level can have dramatic impact on the combustion process and on the potential response of safety sensors. These mixtures may be regulated so that the concentration at a delivery point must be monitored with high precision. However, routine maintenance may introduce background gases such as ambient air (with water) or maintenance gases (introduced with welding processes or adhesive outgassing.) Therefore, the detection methodology must be robust enough to recover or respond to various contaminants. Several reviews can be found in literature addressing sensing and detection technologies, including their limitations and applications. However, for most applications, limitations can be alleviated by combining various detection techniques either through system integration or implementation of machine learning methods (artificial intelligence). In this presentation, we will discuss several applications, highlight their current approach for hydrogen detection, and suggest detection strategies to supplement their limitations.

ENERGY STORAGE,HYDROGEN↗

DOE OSTI2020

Cognition at the Point of Sensing

Over the last 15 years, compressive sensing techniques have been developed which have the potential to greatly reduce the amount of data collected by systems while preserving the amount of information obtained. A cost of this efficiency is that a computationally-intensive optimization routine must be used to put the sensed data into a form that a person can interpret. At the same time, machine learning techniques have experienced tremendous growth as well. Machines have demonstrated the ability learn how to effectively perform tasks such as detection and classification at speeds much faster than humanly possible. Our goal in this project was to study the feasibility of using compressive sensing systems "at the edge." That is, how can compressive sensing sensors be deployed such that information is created at the remote sensor rather than sending raw data to a central processing location? Studies were performed to analyze whether machine learning could be done on the compressively sensed data in its raw form. If a machine is performing the task, is it possible to do so without putting the data into a human interpretable form? We show that this is possible for some systems, in particular a compressive sensing snapshot imaging spectrometer. Machine learning tasks were demonstrated to be more effective and more robust to noise when the machine learning algorithm worked on data in its raw form. This system is shown to outperform a traditional spectrometer. Techniques for reducing the complexity of the reconstruction routine were also analyzed. Techniques for such as data regularization, deep neural networks, and matrix completion were studied and shown to have benefits over traditional reconstruction techniques. In this project we showed that compressive sensing sensors are indeed feasible at the edge. As always, sensors and algorithms must be carefully tuned to work in the constrained environment. In this project we developed tools and techniques to enable those analyses.

47 OTHER INSTRUMENTATION↗

DOE OSTI2014

Development of an X-ray pixel detector with multi-port charge-coupled device for X-ray free-electron laser experiments

This paper presents development of an X-ray pixel detector with a multi-port charge-coupled device (MPCCD) for X-ray Free-Electron laser experiments. The fabrication process of the CCD was selected based on the X-ray radiation hardness against the estimated annual dose of 1.6 × 10{sup 14} photon/mm{sup 2}. The sensor device was optimized by maximizing the full well capacity as high as 5 Me- within 50 μm square pixels while keeping the single photon detection capability for X-ray photons higher than 6 keV and a readout speed of 60 frames/s. The system development also included a detector system for the MPCCD sensor. This paper summarizes the performance, calibration methods, and operation status.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

Real-time monitoring of trace noble gases using laser-induced breakdown spectroscopy—An investigation of the impact of bulk gas on plasma properties and sensitivity

The impact of Ar and He bulk gases on laser-induced breakdown spectroscopy (LIBS) real-time monitoring of trace Xe and Kr was assessed. LIBS is being developed as a monitoring tool for measuring noble gas transport in molten salt systems, in which traditional sensors may face challenges associated with radiation, corrosive materials, and/or mixed phases. The plasma temperature and electron densities of LIBS plasmas were measured in both static and various flowing Ar and He streams (0–5 L min −1 ). The use of an Ar bulk gas resulted in higher plasma temperature, greater electron densities by an order of magnitude, and extended plasma lifetime compared with when He bulk gas was used. Gas flow rate was found to have little impact on plasma temperature; however, its effect on electron density was significant, indicating the need to consider flow rate–specific models. Matrix effects on emission peaks were reported for both bulk gases. Due to these matrix effects, multivariate models were developed for Xe and Kr ranging from 0 to 700 ppm in both bulk gases. Although the predictive behavior was similar (root mean square error of prediction ranging from 11.1 to 20.6 ppm), the limits of detection were superior in He (Xe: 22.9 ppm, Kr: 30.4 ppm). Furthermore, these models were employed in demonstrative real-time tests (>1 h), which showed strong predictive precision (relative standard deviation <5 %) regardless of the bulk gas. Ultimately, this study provides a guide for the considerations required when developing gaseous LIBS models for real-time monitoring.

Gas flow effects↗

DOE OSTI2019

Influence of Resonances on the Noise Performance of SQUID Susceptometers

Scanning Superconducting Quantum Interference Device (SQUID) Susceptometry simultaneously images the local magnetic fields and susceptibilities above a sample with sub-micron spatial resolution. Further development of this technique requires a thorough understanding of the current, voltage, and flux ( I V Φ ) characteristics of scanning SQUID susceptometers. These sensors often have striking anomalies in their current–voltage characteristics, which we believe to be due to electromagnetic resonances. The effect of these resonances on the performance of these SQUIDs is unknown. To explore the origin and impact of the resonances, we develop a model that qualitatively reproduces the experimentally-determined I V Φ characteristics of our scanning SQUID susceptometers. We use this model to calculate the noise characteristics of SQUIDs of different designs. We find that the calculated ultimate flux noise is better in susceptometers with damping resistors that diminish the resonances than in susceptometers without damping resistors. Such calculations will enable the optimization of the signal-to-noise characteristics of scanning SQUID susceptometers.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2021

Power Plant Testing of Ultrasonic Measurements of Temperature Distributions and Heat Fluxes to Heat Exchange Surfaces

Extreme environmental conditions are common in energy conversion applications, in which the most hardened insertion sensors often do not perform reliably for long. On the other hand, ultrasonic measurements can be acquired noninvasively, with sensitive components kept away from the damaging environment. We have previously developed the ultrasonic method for measuring the spatial distribution of temperatures in solid materials applicable when large thermal gradients are present. In the developed approach, we use the echogenically segmented ultrasound propagation path, structured to contain engineered or naturally occurring echogenic features, to produce a train of echoes in response to an external pulse of ultrasonic excitation. This paper outlines the testing results of this approach applied to the temperature distribution measurements to a waterwall of a 500 MW power-plant utility boiler. The validation results obtained over the prolonged power plant test show that the estimated temperature profile is correctly captured, and the measurement accuracy can be comparable with traditional insertion sensors, such as thermocouples. Overall, the testing has confirmed that the developed approach has matured to become an attractive alternative to conventional sensing in solving challenging problems of long-term temperature measurements in extreme environments. Heat fluxes and thermal stresses in the structure can then be characterized noninvasively using the measured temperature distribution as the basis.

Walton, Kenneth↗

DOE OSTI2024

Superconducting quasiparticle-amplifying transmon: A qubit-based sensor for meV-scale phonons and single terahertz photons

With great interest from the quantum computing community, an immense amount of R&D effort has been invested into improving superconducting qubits. The technologies developed for the design and fabrication of these qubits can be directly applied to applications for ultralow-threshold particle detectors, e.g., low-mass dark matter and far-infrared photon sensing. We propose a novel energy-resolving sensor based on the transmon qubit architecture combined with a signal-enhancing superconducting quasiparticle amplification stage. We refer to these sensors as SQUATs: superconducting quasiparticle-amplifying transmons. We detail the operating principle and design of this new sensor and predict that, with minimal R&D effort, solid-state-based detectors patterned with these sensors can achieve sensitivity to single terahertz photons, and sensitivity to 1 meV phonons in the detector absorber substrate on the microsecond timescale. Published by the American Physical Society 2024

47 OTHER INSTRUMENTATION↗

DOE OSTI2016

Assessment of Sensor Technologies for Advanced Reactors

47 OTHER INSTRUMENTATION↗

DOE OSTI2016

Assessment of Sensor Technologies for Advanced Reactors

Sensors and measurement technologies provide information on processes, support operations and provide indications of component health. They are therefore crucial to plant operations and to commercialization of advanced reactors (AdvRx). This report, developed by a three-laboratory team consisting of Argonne National Laboratory (ANL), Oak Ridge National Laboratory (ORNL) and Pacific Northwest National Laboratory (PNNL), provides an assessment of sensor technologies and a determination of measurement needs for AdvRx. It provides the technical basis for identifying and prioritizing research targets within the instrumentation and control (I&C) Technology Area under the Department of Energy’s (DOE’s) Advanced Reactor Technology (ART) program and contributes to the design and implementation of AdvRx concepts. The report is organized under two broad categories: High Temperature Reactors and Fast Reactors. For the purposes of this report, the scope of “High temperature reactors” includes Gen IV reactors whose coolant exit temperatures exceed ~650 °C and are moderated (as opposed to fast reactors). We included in this category gas-cooled reactors that have been built and operated throughout the world to date-including those that use carbon dioxide (CO 2 ) or Helium (He) as coolant-and the molten salt reactor. However, for the sake of conciseness this report uses the high temperature gas reactor (HTGR) as an example in many cases. With regard to fast reactors, there are several reactor years of experience with several technology variants (including sodium fast reactors and other fast reactors such as the LBFR). In particular, several experimental, prototype and demonstration units have been designed and operated in several countries, including France, India, Japan, USSR/Russia, UK, and the United States. To bound the scope, this report reviewed relevant operating experience from US-operated Sodium Fast Reactor (SFR) and relevant test experience from the Fast Flux Test Facility (FFTF).

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2016

Modeling and Analysis Methods for an On-line Enrichment Monitor

The International Atomic Energy Agency (IAEA) has developed an On-Line Enrichment Monitor (OLEM) as one possible component in a new generation of safeguards measures for uranium enrichment plants. The OLEM measures 235U emissions from the UF6 gas flowing through a unit header pipe using NaI(Tl) spectrometers, and corrects for gas density changes using pressure and temperature sensors in order to determine the enrichment of the gas as a function of time. In parallel with the OLEM instrument development, a Virtual OLEM (VOLEM) software tool has been developed that is capable of producing synthetic gamma-ray, pressure, and temperature data representative of a wide range of enrichment plant operating conditions. VOLEM complements instrument development activities and allows the study of OLEM for scenarios that will be difficult or impossible to evaluate empirically. Uses of VOLEM include: investigation of hardware design options; inter-comparison of candidate gamma-ray spectral analysis and enrichment estimation algorithms; uncertainty budget analysis and performance prediction for typical and atypical operational scenarios; and testing of the OLEM data acquisition, analysis and reporting software. This paper describes the technical foundations of VOLEM and illustrates how it can be used. An overview of the nominal instrument design and deployment scenario for OLEM is provided, with emphasis on the key online-assay measurement challenge: accurately determining the portion of the total 235U signal that comes from a background that includes solid uranium deposits on the piping walls. Monte Carlo modeling tools, data analysis algorithms and uncertainty quantification methods are described. VOLEM is then used to quantitatively explore the uncertainty budgets and predicted instrument performance for a plausible range of typical plant operating parameters, and one set of candidate analysis algorithms. Additionally, a series of VOLEM case studies illustrates how an online enrichment monitor might respond in scenarios expected to be atypical, for example weak pressure transients or significant changes in background levels.

Smith, Leon E.↗

DOE OSTI2025

Data and scripts from: “Denoising autoencoder for reconstructing sensor observation data and predicting evapotranspiration: noisy and missing values repair and uncertainty quantification”

This data package includes data and scripts from the manuscript “Denoising autoencoder for reconstructing sensor observation data and predicting evapotranspiration: noisy and missing values repair and uncertainty quantification”.The study addressed common challenges faced in environmental sensing and modeling, including uncertain input data, missing sensor observations, and high-dimensional datasets with interrelated but redundant variables. Point-scaled meteorological and soil sensor observations were perturbed with noises and missing values, and denoising autoencoder (DAE) neural networks were developed to reconstruct the perturbed data and further predict evapotranspiration. This study concluded that (1) the reconstruction quality of each variable depends on its cross-correlation and alignment to the underlying data structure, (2) uncertainties from the models were overall stronger than those from the data corruption, and (3) there was a tradeoff between reducing bias and reducing variance when evaluating the uncertainty of the machine learning models.This package includes:(1) Four ipython scripts (.ipynb): “DAE_train.ipynb” trains and evaluates DAE neural networks, “DAE_predict.ipynb” makes predictions from the trained DAE models, “ET_train.ipynb” trains and evaluates ET prediction neural networks, and “ET_predict.ipynb” makes predictions from trained ET models.(2) One python file (.py): “methods.py” includes all user-defined functions and python codes used in the ipython scripts.(3) A “sub_models” folder that includes five trained DAE neural networks (in pytorch format, .pt), which could be used to ingest input data before being fed to the downstream ET models in ‘ET_train.ipynb” or ‘ET_predict.ipynb’.(4) Two data files (.csv). Daily meteorological, vegetation, and soil data is in “df_data.csv”, where “df_meta.csv” contains the location and time information of “df_data.csv”. Each row (index) in “df_meta.csv” corresponds to each row in “df_data.csv”. These data files are formatted to follow the data structure requirements and be directly used in the ipython scripts, and they have been shuffled chronologically to train machine learning models. The meteorological and soil data was collected using point sensors between 2019-2023 at(4.a) Three shrub-dominated field sites in East River, Colorado (named “ph1”, “ph2” and “sg5” in “df_meta.csv”, where “ph1” and “ph2” were located at PumpHouse Hillslopes, and “sg5” was at Snodgrass Mountain meadow) and(4.b) One outdoor, mesoscale, and herbaceous-dominated experiment in Berkeley, California (named “tb” in “df_meta.csv”, short for Smartsoils Testbed at Lawrence Berkeley National Lab).- See "df_data_dd.csv" and "df_meta_dd.csv" for variable descriptions and the Methods section for additional data processing steps. See "flmd.csv" and "README.txt" for brief file descriptions.- All ipython scripts and python files are written in and require PYTHON language software.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

A SENSOR CHARACTERIZATION METADATA ARCHITECTURE

Presentation conducted at the 11th Annual International AMS Tech Exchange in Spiez, Switzerland addressing the next generation algorithms require metadata and contextual data not currently recorded and/or digitized. Need to develop more robust and interchangability to perform more complex tasks for a wider range of international users.

47 OTHER INSTRUMENTATION↗