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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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At least 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Stability of Floodplain Subsurface Microbial Communities Through Seasonal Hydrological and Geochemical Cycles

Riparian floodplains represent an interaction zone between the terrestrial subsurface and rivers, where regional groundwater flows, infiltration, and evapotranspiration drive mixing of water and import/export of nutrients and contaminants. These dynamics create seasonally transient redox conditions that drive biogeochemical transformations, which strongly modify groundwater quality. Microbial responses to changing hydrological conditions are perhaps the critical step connecting hydrology to geochemical transformations and groundwater quality, yet are not well understood. We aimed to address this knowledge gap by monitoring seasonal transitions at the U.S. Department of Energy legacy uranium ore processing site in Riverton, WY, through spring-summer-fall hydrological transitions. Our goal was to characterize the microbial community throughout the soil profile, down to the saturated aquifer, and observe its response to wet-dry transitions across a full season and compare to changes in geochemistry and hydrology. Next-generation sequencing was employed to identify biogeochemically-relevant microbial taxa based on the 16S rRNA gene; we found a broad diversity of microbial clades including taxa involved in sulfur and metal cycling, as well as nitrification. These data were paired with measurements of soil moisture, major nutrients and cations, and trace elements. Overall microbial community composition was dependent on soil depth or type, with seasonal effects only observed in the topsoil or subsurface aquifer. This finding indicates that microbial communities in the transiently-reduced center of the soil profile at the Riverton, WY site are remarkably stable, despite moisture and redox inversions. In addition, these communities likely impact the communities in surrounding soil horizons through export of metabolites and solutes as the water table rises and falls throughout the season.

16S rRNA↗
DOE OSTI2024

Photovoltaic inverter-based quantification of snow conditions and power loss

Snow is a significant challenge for photovoltaic (PV) systems at northern latitudes, where the pace of deployment is rapid but snow-related power losses can exceed 30% of annual production. Accurate snow-related power loss estimation methods for utility-scale sites can support snow mitigation strategies, inform resource planning and validate predictive snow-loss models. This study builds on our previous work on inverter-based detection of snow, and its implications for utility-scale power production, by validating the accuracy of our snow-loss method across different PV sites and system designs and highlighting its value in bringing greater visibility to PV plant operations in winter. Our estimation method is both novel and scalable, requiring only standard monitoring data to correlate snow-related losses with meteorological data. As demonstrated here, our validation method involved three main steps: 1) estimation of performance losses for multiple systems by comparing measured inverter data to modeled data; 2) application of a detection framework to identify which performance losses are snow-related; and 3) comparison of snow-related losses among three utility-scale sites differing in tilt angle. Results show that utility-scale systems at higher tilt angles consistently shed snow more quickly/completely than their lower-tilt counterparts. Further, monthly and seasonal snow losses are inversely and non-linearly correlated with tilt angle when normalized for cumulative snowfall. These results are consistent with the findings of previous studies and support the broad applicability of this method to fixed-tilt utility-scale PV systems around the world that routinely experience snow-related performance losses.

Cooper, Emma C. (ORCID:0000000190554098)↗
DOE OSTI2024

Alternating Conditional Expectations: Introducing a Non‐Parametric Statistical Method to Interpret Long‐Term Greenhouse Gas Flux Measurements Over Semi‐Arid and Wetland Ecosystems

Abstract We explore the potential of using a non‐parametric statistical method called Alternating Conditional Expectations, ACE, to quantify functional relationships in biogeosciences. Here, ACE is used to quantify the non‐linear and multi‐faceted responses of greenhouse gas fluxes to a set of biophysical forcings, when the shapes of those response surfaces are unknown. We evaluated the statistical method over two contrasting ecosystems and two contrasting time steps. One case involved quantifying the biophysical controls of water vapor and carbon dioxide (CO 2 ) fluxes over a semi‐arid oak savanna using daily integrated fluxes. The other case evaluated the responses of CO 2 and methane (CH 4 ) flux measurements to a set of biophysical forcings at a restored tidal wetland using thirty‐minute averages. The statistical model, based on 4 independent variables, explained up over 90% of the variation in daily integrated flux densities of water vapor and net carbon dioxide exchange at the savanna site. This fit was defined by distinct non‐linear responses to such drivers as gross primary production, photosynthetically active radiation, air temperature, vapor pressure deficit and soil moisture. At the tidal wetland site, we evaluated net carbon dioxide and methane fluxes with short‐term measurements to capture the influence of rising and falling tides and seasonality in biological activity. The statistical model defined the shape of the forcing of fluxes due to the roles of carbon exudates, water table depth, oxygen level in the water column, temperature and vegetation status. The statistical fits of the greenhouse gas fluxes were less precise than the savanna case. The fetch varies on a run‐to‐run basis as it is comprised of a heterogeneous mosaic of open water and vegetation. Furthermore, it is difficult to monitor the environmental conditions of the archaea and bacteria in the sediments that produce methane and carbon dioxide.

Environmental Sciences & Ecology↗
DOE OSTI2020

Surface reservoirs dominate dynamic gas-surface partitioning of many indoor air constituents

Human health is affected by indoor air quality. One distinctive aspect of the indoor environment is its very large surface area that acts as a poorly characterized sink and source of gas-phase chemicals. In this work, air-surface interactions of 19 common indoor air contaminants with diverse properties and sources were monitored in a house using fast-response, on-line mass spectrometric and spectroscopic methods. Enhanced-ventilation experiments demonstrate that most of the contaminants reside in the surface reservoirs and not, as expected, in the gas phase. They participate in rapid air-surface partitioning that is much faster than air exchange. Phase distribution calculations are consistent with the observations when assuming simultaneous equilibria between air and large weakly polar and polar absorptive surface reservoirs, with acid-base dissociation in the polar reservoir. Chemical exposure assessments must account for the finding that contaminants that are fully volatile under outdoor air conditions instead behave as semivolatile compounds indoors.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2020

Design, Development and Testing Plan for Energy Efficiency Algorithms Related to Building-Integrated Cooling, Heating, and Power Systems

Building-integrated cooling, heating and power (CHP) systems are more efficient than conventional systems at providing local power and thermal energy, and favorable fuel prices are bound to spur their increased adoption. It is estimated that the total CHP technical potential in the commercial building sector is approximately 76 GW, but only a small fraction of it is actually deployed. Advanced control and monitoring systems that realize the full potential of these CHP systems can transform this capacity into an asset for both the owner and the grid and also accelerate the adoption of these systems in the building sector. However, to realize the full benefit of the CHP systems, we must ensure persistence of energy efficient operations. It has been demonstrated by several studies that as much as 30% of building energy use is wasted. Much of the waste results from the inability to anticipate load variations (design day vs. non-design day operations; diurnal variation of loads), identify improper operation, and efficiently operate, control and maintain building systems. Moreover, many buildings, even those with adequate control infrastructure, lack knowledgeable staff to operate and maintain energy systems. Operators of buildings with CHP systems will constantly need to make tradeoffs (economic dispatch) between onsite power generation and purchasing power, between onsite heat production from distributed generation versus heating/cooling from conventional systems, and different passive and active load management measures. Much of the inefficiency in the current building operations can be eliminated by use of automated performance monitoring (PM), real-time commissioning verification (CxV) and automated fault detection and diagnostic (AFDD) tools. Automation can help system operators make intelligent decisions. Remote and continuous monitoring of system conditions and performance will enable better management and integration of CHP with existing building systems. Continuous PM, real-time CxV and AFDD could alleviate burdens for operations staff, enhance operations and maintenance (O&M), and improve reliability of building and CHP systems. To address the O&M challenges and to provide a means to maximize the rate-of-return of building-integrated CHP systems, the Building Technologies Office (BTO) within the U.S. Department of Energy’s (DOE’s) Office of Energy Efficiency and Renewable Energy (EERE) initiated a project to design, develop, and field test a VOLTTRON™-based supervisory controller and associated open-source algorithms. These algorithms will ensure real-time optimal operation of a building-integrated CHP system, support electric grid reliability, and lead to achieving the goal of a clean, efficient, reliable and affordable next-generation integrated energy systems. This report lists the components for which PM, real-time CxV and AFDD algorithms will be developed, how the algorithms will be tested, and metrics that will be used to validate the algorithms and easy of deployment. Deployment of these algorithms in the field will result in savings reduction in energy consumption of between 10% and 20%.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Long–short-term memory encoder–decoder with regularized hidden dynamics for fault detection in industrial processes

The ability of recurrent neural networks (RNN) to model nonlinear dynamics of high dimensional process data has enabled data-driven RNN-based fault detection algorithms. Previous studies have focused on detecting faults by identifying the discrepancies in data distribution between the faulty and normal data, as reflected in prediction errors generated by RNN models. However, in industrial processes, variations in data distribution can also result from changes in normal control setpoints and compensatory control adjustments in response to disturbances, making it hard to differentiate between normal and faulty conditions. This paper proposes a fault detection method utilizing a long short-term memory (LSTM) encoder–decoder structure with regularized hidden dynamics and reversible instance normalization (RevIN) to compactly represent high-dimensional measurements for effective monitoring. During training, the hidden states of the model are regularized to form a low-dimensional latent space representation of the original multivariate time series data. As a result, the prediction errors of the latent states can be used to monitor the abnormal dynamic variations, while the reconstruction errors of the measured variables are used to monitor the abnormal static variations. Furthermore, the proposed indices can reflect operating conditions, even when the distribution of test data changes, which helps distinguish faults from normal adjustments and disturbances that controllers can settle. Here, data from numerical simulation and the Tennessee Eastman process are used to illustrate the effectiveness of the proposed fault detection method.

42 ENGINEERING↗
DOE OSTI2026

Overview of IMPACT Data Acquisition System and Data Reduction Process

This report documents the development of the data acquisition system (DAS) and data reduction methodologies for the Irradiated Material Property Accelerated Characterization Test (IMPACT) experiment at the Advanced Test Reactor (ATR). The IMPACT experiment is designed to enable in-pile measurement of thermal conductivity in metallic nuclear fuels, specifically U-10Zr, using an instrumented thermal conductivity probe. The DAS supports both passive temperature monitoring and active thermal interrogation of the probe through controlled AC and DC excitation. Significant modifications to laboratory-scale systems were required to accommodate the higher resistance paths associated with the in-pile application. Custom electronics and relay-controlled measurement sequencing were developed to enable the measurement and sufficient power delivery to the sensing region. A reduced-order, axisymmetric thermal model based on the thermal quadrupoles method is presented to support data interpretation. This model enables efficient evaluation of transient heat transfer behavior and facilitates solution of the inverse problem required to extract thermal properties from measured signals. Multiple boundary condition formulations are discussed to address varying experimental time scales and geometries. Additionally, machine learning techniques are introduced to support data reduction and improve confidence in inverse solutions. Convolutional neural networks are applied to identify the presence of gas gaps and other evolving geometric features that significantly impact thermal response during irradiation. These efforts contribute to the broader integration of digital twin frameworks and real-time modeling capabilities within the Advanced Fuels Campaign.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2022

Multistage mixed precision iterative refinement

Abstract Low precision arithmetic, in particular half precision (16‐bit) floating point arithmetic, is now available in commercial hardware. Using lower precision can offer significant savings in computation and communication costs with proportional savings in energy. Motivated by this, there has been a renewed interest in mixed precision iterative refinement schemes for solving linear systems , and new variants of GMRES‐based iterative refinement have been developed. Each particular variant with a given combination of precisions leads to different condition number‐based constraints for convergence of the backward and forward errors, and each has different performance costs. The constraints for convergence given in the literature are, as an artifact of the analyses, often overly strict in practice, and thus could lead a user to select a more expensive variant when a less expensive one would have sufficed. In this work, we develop a multistage mixed precision iterative refinement solver which aims to combine existing mixed precision approaches to balance performance and accuracy and improve usability. For a user‐specified initial combination of precisions, the algorithm begins with the least expensive approach and convergence is monitored via inexpensive computations with quantities produced during the iteration. If slow convergence or divergence is detected using particular stopping criteria, the algorithm switches to use a more expensive, but more reliable variant. A novel aspect of our approach is that, unlike existing implementations, our algorithm first attempts to use “stronger” GMRES‐based solvers for the solution update before resorting to increasing the precision(s). In some scenarios, this can avoid the need to refactorize the matrix in higher precision. We perform extensive numerical experiments on a variety of random dense problems and problems from real applications which confirm the benefits of the multistage approach.

Oktay, Eda↗
DOE OSTI2020

A Mixed Length Scale Model for Migrating Fluvial Bedforms

With the expansion of hydropower, in-stream converters, flood-protection infrastructures, and growing concerns on deltas fragile ecosystems, there is a pressing need to evaluate and monitor bedform sediment mass flux. It is critical to estimate real-time bedform size and migration velocity and provide a theoretical framework to convert easily accessible time histories of bed elevations into spatially evolving patterns. In this study, we collected spatiotemporally resolved bathymetries from laboratory flumes and the Colorado River in statistically steady, homogeneous, subcritical flow conditions. Wave number and frequency spectra of bed elevations show compelling evidence of scale-dependent velocity for the hierarchy of migrating bedforms observed in the laboratory and field. New scaling laws were applied to describe the full range of migration velocities as function of two dimensionless groups based on the bed shear velocity, sediment diameter, and water depth. Further simplification resulted in a mixed length scale model estimating scale-dependent migration velocities, without requiring bedform classification or identification.

58 GEOSCIENCES↗
DOE OSTI2025

Rapid monitoring of fermentations: a feasibility study on biological 2,3-butanediol production

2,3-butanediol (2,3-BDO) is an economically important platform chemical that can be produced by the fermentation of sugars using an engineered strain of Zymomonas mobilis . These fermentations require continuous monitoring and modification of fermentation conditions to maximize 2,3-BDO yields and minimize the production of the undesired coproducts glycerol and acetoin. Because of the time required for sampling and off-line chromatographic measurement of fermentation samples, the ability of fermentation scientists to modify fermentation conditions in a timely manner is limited. The goal of this study was to test if near-infrared spectroscopy (NIRS) along with multivariate statistics could reduce the time needed for this analysis and enable real-time monitoring and control of the fermentation. In this work we developed partial least squares (PLS) calibration models to predict the concentrations of glucose, xylose, 2,3-BDO, acetoin, and glycerol in fermentations via NIRS using two different spectrometers and two different spectroscopy modalities. We first evaluated the feasibility of rapid NIRS monitoring through experiments where we measured the signals from each analyte of interest and built NIRS-based PLS models using spectra from synthetic samples containing uncorrelated concentrations of these analytes. All analytes showed unique spectral signatures, and this initial modeling showed that all analytes could be detected simultaneously. We then began work with samples from laboratory fermentation experiments and tested the feasibility of regression model development across two spectral collection modalities (at-line and on-line) and two instruments: a laboratory-grade instrument and a low-cost instrument with a more limited spectral range. All modalities showed promise in the ability to monitor Z. mobilis fermentations of glucose and xylose to 2,3-BDO. The low-cost instrument displayed a lower signal-to-noise ratio than the laboratory-grade instrument, which led to comparatively lower performance overall, but still provided sufficient accuracy to monitor fermentation trends. While the ease of use of on-line monitoring systems was favored as compared to at-line systems due to the lack of sampling required and potential for automated process control, we observed some decrease in performance due to the additional complexity of the sample matrix. We have demonstrated that NIRS combined with multivariate analysis can be used for at-line and on-line monitoring of the concentrations of glucose, xylose, 2,3-BDO, acetoin, and glycerol during Z. mobilis fermentations. The decrease in signal-to-noise ratio when using a low-cost spectrometer led to greater prediction error than the laboratory-grade spectrometer for at-line monitoring. The on-line monitoring modality showed great promise for real time process control via NIRS.

09 BIOMASS FUELS↗
DOE OSTI2021

Welding of Haynes 282 to Steels to Enable Modular Rotors for Advanced Ultra Super-Critical Steam Turbines

Steam turbines for an Advanced Ultra Super Critical (AUSC) fossil fired power plant will operate at temperatures well above those of current commercial steam cycles with inlet temperatures more than 760 C , which is beyond the capabilities of alloy steels presently used for steam turbine applications and requires advanced materials. Large components such as steam turbine rotors may be made of nickel based super alloys such as Haynes 282 (H282). However, monolithic forgings of superalloys in the sizes required for large steam turbines rotors can be prohibitively expensive besides many technical challenges. To minimize cost and alleviate the related technical challenges, superalloy use needs to be limited to locations on the steam turbine rotor where strength and temperature requirements cannot be met by conventional steels. This is possible if nickel-based superalloys can be successfully welded to steels and the related technical challenges - machining parts made of dissimilar welded materials, non-destructive examination of such welds for flaw detection; and, material properties of such hybrid components – are sufficiently addressed. In this technology development project, we successfully welded H282 to plates up to ~ 75 mm (~ 3 inches) to a 3.5NiCrMoV steel of similar thickness. Advanced ultrasonic inspection technique called Phased Array Ultrasonic Testing (PAUT) was employed to examine the dissimilar H282-Steel welds, into which flat bottomed side drilled holes (SDH) of various diameters were introduced, to determine the minimum detectable feature sizes; it was shown that with PAUT SDH of dia. down to 0.5 mm could be detected in the base alloys and SDH with dia. down to 2.4 mm could be detected in the weld metal under multiple orientations successfully. An autonomous machining process monitoring system was developed and demonstrated whereby the forces acting on the cutting tool could be actively monitored as the cutting tool transitioned from H282 to Steel across the weld using which the machining parameters can be potentially altered without interruption to extend tool life. This project successfully achieved its objectives of - i. Developing a welding methodology and viable welding geometries, to successfully join H282 to steel 3.5CrMoNiV steel to enable manufacture of modular steam turbine rotors for AUSC applications (conditions of at least 760 °Celsius and 3,100 psia (pounds per square inch absolute pressure) and evaluate the material properties of the welded specimen. ii. Employ the advanced ultrasonic inspection technique to the dissimilar weld metal joint and determine the minimum detectable feature sizes. iii. Develop effective machining techniques to machine such hybrid structures with online tool force monitoring and effect machine state metrics for optimal results.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2022

Performance Assessment for the Environmental Restoration Disposal Facility (Annual Status Report FY 2021)

DOE O 435.1 and DOE M 435.1-1 require that a determination of continued adequacy of the performance assessment (PA) (CP-60089), composite analysis (CA), and disposal authorization statement (DAS) be made on an annual basis, and that the determination must consider the results of data collection and analysis from research, field studies, and monitoring as well as the need to update any Radioactive Waste Management Basis (RWMB) documents. Beginning in 1996, the Environmental Disposal Facility (ERDF) started accepting low-level radioactive, hazardous, and mixed wastes that were generated during cleanup activities at the Hanford Site. ERDF is composed of a series of cells or disposal areas and can accommodate future design expansions as needed. Currently, there are eight cells and two supercells in ERDF. Each supercell is the equivalent of two cells. During this reporting period (fiscal year 2021, extending from October 1, 2020, through September 30, 2021), approximately 9.14E+04 metric tons (1.01E+05 U.S. tons) of waste was disposed at ERDF. From ERDF inception through September 30, 2021, approximately 16.9 million metric tons (18.9 U.S. tons) of waste has been disposed of at ERDF, which equates to consumption of approximately 88.7% of the currently constructed disposal volume. According to the design of ERDF, the facility has the ability to be expanded as needed. As a condition of the DAS, disposal operation within ERDF must be in accordance with the waste acceptance criteria (ERDF-00011) that provide specific radionuclide disposal limits, waste form restrictions, and descriptions of acceptable waste packages in compliance with the requirements of DOE M 435-1.1. The ERDF waste acceptance criteria stipulate that waste destined for disposal at ERDF be controlled based on source, physical form, and contaminant concentration and activity levels. There have been no changes to the physical configuration of ERDF or to the waste forms (source, physical form, etc.). No new unreviewed disposal question screenings or evaluations have been generated in this reporting period. Therefore, there are no noted impacts to the PA, CA, DAS, or RWMB resulting from the evaluations and screenings. Sum-of-fractions analysis shows that the disposed inventory meets both the concentration and inventory threshold requirements. A sum-of-fractions value is computed for ERDF sensitive radionuclides contributing to the all pathways and the air pathway inventory limits. Computed values were 7.63E-03 and 1.49E-03, respectively. The disposed waste inventory remained well under the PA imposed limits, as shown in Table 4 and Table 5 in the main text of this report. Required monitoring was satisfactorily completed during the fiscal year reporting period. Compliance with performance objectives were met as each of the reported values were well below the established limit. Overall, there are no substantive changes to primary PA assumptions or changes to the PA analysis conclusion; therefore, compliance with DOE O 435.1 and the DAS is maintained.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Development of a Nuclear Fuel Dissolution Monitor Based on Raman Spectroscopy

The processing of spent nuclear fuel and other nuclear materials is a critical component of nuclear material management with implications for global security. The first step of fuel processing is dissolution, with several charges of fuel sequentially added to a batch of solvent. The incomplete dissolution of a charge precludes the addition of the next charge. As the dissolution takes place in a heated, highly corrosive and radiological vessel, direct monitoring of the process is not possible. We discuss the use of Raman spectroscopy to indirectly monitor dissolution through an analysis of the gaseous emissions from the dissolver. Challenges associated with the implementation of Raman spectroscopy include the composition and physical characteristics of the offgas stream and the impact of operating conditions. Nonetheless, we observed that NO2 concentrations serve as a reliable indicator of process activity and correlate to the amount of fuel material that remains undissolved. These results demonstrate the promise of the method for monitoring nuclear material dissolution.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Performance Assessment for the Environmental Restoration Disposal Facility (Annual Status Report FY 2022)

DOE O 435.1 and DOE M 435.1-1 require that a determination of continued adequacy of the performance assessment (PA) (CP-60089), composite analysis (CA), and disposal authorization statement (DAS) be made on an annual basis, and that the determination must consider the results of data collection and analysis from research, field studies, and monitoring as well as the need to update any Radioactive Waste Management Basis (RWMB) documents. Beginning in 1996, the Environmental Restoration Disposal Facility (ERDF) started accepting low-level radioactive, hazardous, and mixed wastes that were generated during cleanup activities at the Hanford Site. ERDF is composed of a series of cells or disposal areas and can accommodate future design expansions as needed. Currently, there are eight cells and two supercells in ERDF. Each supercell is the equivalent of two cells. During this reporting period (fiscal year 2022, extending from October 1, 2021, through September 30, 2022), approximately 8.52E+04 metric tons (9.39E+04 U.S. tons) of waste was disposed at ERDF. From ERDF inception through September 30, 2022, approximately 17.0 million metric tons (18.7 million U.S. tons) of waste has been disposed at ERDF, which equates to consumption of approximately 89.1% of the currently constructed disposal volume. According to the design of ERDF, the facility has the ability to be expanded as needed. As a condition of the DAS, disposal operations within ERDF must be in accordance with the waste acceptance criteria (ERDF-00011) that provide specific radionuclide disposal limits, waste form restrictions, and descriptions of acceptable waste packages in compliance with the requirements of DOE M 435-1.1. The ERDF waste acceptance criteria stipulate that waste destined for disposal at ERDF be controlled based on source, physical form, and contaminant concentration and activity levels. There have been no changes to the physical configuration of ERDF or to the waste forms (source, physical form, etc.). No new unreviewed disposal question screenings or evaluations have been generated in this reporting period. Therefore, there are no noted impacts to the PA, CA, DAS, or RWMB resulting from the evaluations and screenings. Sum-of-fractions analysis shows that the disposed inventory meets both the concentration and inventory threshold requirements. A sum-of-fractions value is computed for ERDF sensitive radionuclides contributing to the groundwater pathways and the air pathway inventory limits. Computed values were 5.50E-04 and 3.30E-03, respectively. The disposed waste inventory remained well under the PA imposed limits, as shown in Table 4 and Table 5 in the main text of this report. Required monitoring was satisfactorily completed during the fiscal year reporting period. Compliance with performance objectives were met as each of the reported values were well below the established limit. Overall, there are no substantive changes to primary PA assumptions or changes to the PA analysis conclusion; therefore, compliance with DOE O 435.1 and the DAS is maintained.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Machine learning-based real-time monitoring system for smart connected worker to improve energy efficiency

Recent advances in machine learning and computer vision brought to light technologies and algorithms that serve as new opportunities for creating intelligent and efficient manufacturing systems. In this study, the real-time monitoring system of manufacturing workflow for the Smart Connected Worker (SCW) is developed for the small and medium-sized manufacturers (SMMs), which integrates state-of-the-art machine learning techniques with the workplace scenarios of advanced manufacturing systems. Specifically, object detection and text recognition models are investigated and adopted to ameliorate the labor-intensive machine state monitoring process, while artificial neural networks are introduced to enable real-time energy disaggregation for further optimization. The developed system achieved efficient supervision and accurate information analysis in real-time for prolonged working conditions, which could effectively reduce the cost related to human labor, as well as provide an affordable solution for SMMs. The competent experiment results also demonstrated the feasibility and effectiveness of integrating machine learning technologies into the realm of advanced manufacturing systems.

42 ENGINEERING↗
DOE OSTI2021

Impact of soiling on Si and CdTe PV modules: Case study in different Brazil climate zones

Soiling, particulate accumulation on photovoltaic (PV) module surfaces, reduces the available solar resource and the resulting generated device power. This case-study summarizes initial results of 5-year research on the contrasting soiling conditions in the tropical, subtropical, and semi-arid climates in Brazil. A major objective is to present a case study of the effects of soiling on PV module performance in different Brazil climate zones that represent the primary areas for the current and expanding Brazil solar installations. For this, the paper presents methodologies to quantify the soiling ratio (SRatio) and soiling rate (SRate) for two representative commercial technologies, polycrystalline or multicrystalline silicon (mc-Si) and thin-film cadmium telluride (CdTe) modules, through soiling monitoring stations deployed in the selected climate regions. An aim is to add to the growing soiling-research knowledge base through addressing these key factors and their relationships to critical electrical, solar resource, thermal, and local meteorological and environmental parameters. This paper presents, evaluates, and compares soiling rates and losses in Belo Horizonte, Minas Gerais (Equatorial Tropical: 19.92° S, 43.99° W), Porto Alegre, Rio Grande do Sul (Humid-Subtropical: 30.05° S, 51.17° W), and Brotas de Macaúbas, Bahia (Semi-Arid: 12.00° S, 42.63° W). The results show that soiling is moderate in all 3-regions, for example with 0.1%/day < SRate < 0.2%/day for Belo Horizonte. Precipitation dominates the cleaning of the modules in the summertime in this climate zone, while it is the major factor year-round in Rio Grande do Sul. Wind is the major issue mitigating the soiling accumulation for the Bahia installation. The methodology incorporates several key refinements, including the normalization and adjustment for the meteorological parameters (temperature, irradiance, wind, precipitation). The evaluations include the region-specific differing effects of non-uniform soiling, natural cleaning, and ambient temperatures.

14 SOLAR ENERGY↗
DOE OSTI2025

Temperature Field Reconstruction of Surfaces Heated Through Radiative Heat Transfer Using Convolutional Neural Networks

Microreactors could play a crucial role in decarbonizing our energy portfolio. However, their development and implementation come with specific challenges, particularly regarding cost. Due to their compact size and the harsh operational environment, collecting real-time data on reactor operation can be challenging. Many probe designs are unable to withstand extreme conditions (e.g., temperature, radiation) in the reactor. In this context, using convolutional neural networks (CNNs) can pave the way for developing a nonintrusive approach that relies solely on ex-core sensors. A well-trained physics-informed CNN can reconstruct the distribution of a given physical quantity over a domain using only a few sensors, allowing us to reconstruct the desired field distribution even in a limited space or complex geometries where a large array of sensors is impractical. In this work, we present the initial steps toward developing a real-time tool for monitoring the thermal behavior of nuclear reactor pressure vessels. Based on an experimental setup, a computational model using the Multiphysics Object-Oriented Simulation Environment (moose) framework was built, where the Ray Tracing and Heat Conduction modules were used to evaluate the temperature distribution over a convex metal surface heated through radiative heat transfer. This metal surface represents a section of a heated nuclear reactor vessel wall. The model also accounts for solid mechanics physics through the moose Solid Mechanics module. In situ experimental data, acquired from a Texas A&M facility, were used to validate the computational model. Part of the data generated by the moose model was used to train the convolutional neural network to reconstruct the vessel wall's outer surface temperature. The CNN generalization was then compared against the experimental and computational data.

Aldeia Machado, Luiz Carlos↗
DOE OSTI2021

Raman Spectroscopy to Detect and Measure NOW Pheromones. Annual Report [Jan 2021]

Insect sex pheromones are chemical compounds that insects release to attract their partners over distances of hundreds of meters or even kilometers, in complete darkness and without any audible signals. Use of synthetic forms of key compounds have in some cases become an essential component of monitoring and/or managing key pests of agricultural crops, including navel orangeworm (Amyelois transitella) (NOW) in California tree nuts. These pheromone-based strategies can include monitoring, mating disruption, mass trapping, attract-and-kill and push-pull. There are currently multiple commercially available mating disruption products available for NOW and recent studies have demonstrated that they can be effectively used to reduce crop damage by this pest. Just how mating disruption works is not fully established and likely varies across products and target species. For instance, the extent to which synthetic pheromones compete with natural pheromone is not well understood, or in the case of monitoring, how efficaciously the insect follows the diffusing plumes, especially across large blocks and at plot borders. Furthermore, there may be specific conditions under which poor or impeded diffusion of synthetic pheromone diminishes the disruption effect, which could result in some males effectively locating females for reproduction. At the same time, many commercially available synthetic lures are also available, and growers have been effectively using them to track population development – although the attractive range of these lures remains unclear. That is, while pheromone lures can attract many moths, the relationship between trap capture and local populations, much less crop damage, remains unclear.

47 OTHER INSTRUMENTATION↗