Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Condition Monitoring”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Stable isotopes contain substantial additive information about terrestrial carbon and water cycling

Abstract Stable isotope ratios of H ( δ 2 H ), O ( δ 18 O), and C ( δ 13 C) are linked to key biogeochemical processes of the water and carbon cycles; however, the degree to which isotope-associated processes are reflected in macroscale ecosystem flux observations remains unquantified. Here through formal information assessment, new measurements of δ 13 C of net ecosystem exchange ( NEE ) as well as δ 2 H and δ 18 O of latent heat ( LH ) fluxes across the United States National Ecological Observation Network (NEON) are used to determine conditions under which isotope measurements are informative of environmental exchanges. We find all three isotopic datasets individually contain comparable amounts of information about NEE and LH fluxes as wind speed observations. Such information from isotope measurements, however, is largely unique. Generally, δ 13 C provides more information about LH as aridity increases or mean annual precipitation decreases. δ 2 H provides more information about LH as temperatures or mean annual precipitation decreases, and also provides more information about NEE as temperatures decrease. Overall, we show that the stable isotope datasets collected by NEON contribute non-trivial amounts of new information about bulk environmental fluxes useful for interpreting biogeochemical and ecohydrological processes at landscape scales. However, the utility of this new information varies with environmental conditions at continental scales. This study provides an approach for quantifying the value adding non-traditional sensing approaches to environmental monitoring sites and the patterns identified here are expected to aid in modeling and data interpretation efforts focused on constraining carbon and water cycles’ mechanisms.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Neural Network-Based Electric Vehicle Range Prediction for Smart Charging Optimization

Range prediction is a standard feature in most modern road vehicles, allowing drivers to make informed decisions about when to refuel. Most vehicles make range predictions through data- or model-driven means, monitoring the average fuel consumption rate or using a tuned vehicle model to predict fuel consumption. The uncertainty of future driving conditions makes the range prediction problem challenging, particularly for less pervasive battery electric vehicles (BEV). Most contemporary machine learning-based methods attempt to forecast the battery SOC discharge profile to predict vehicle range. In this work, we propose a novel approach using two recurrent neural networks (RNNs) to predict the remaining range of BEVs and the minimum charge required to safely complete a trip. Each RNN has two outputs that can be used for statistical analysis to account for uncertainties; the first loss function leads to mean and variance estimation (MVE), while the second results in bounded interval estimation (BIE). These outputs of the proposed RNNs are then used to predict the probability of a vehicle completing a given trip without charging, or if charging is needed, the remaining range and minimum charging required to finish the trip with high probability. Training data was generated using a low-order physics model to estimate vehicle energy consumption from historical drive cycle data collected from medium-duty last-mile delivery vehicles. Here, the proposed method demonstrated high accuracy in the presence of day-to-day route variability, with the root-mean-square error (RMSE) below 6% for both RNN models.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

DISCOVR: Development of Integrated Screening, Cultivar Optimization, and Verification Research

The DISCOVR consortium has developed and is applying an integrated platform and workflow for standardized, deep characterization of highly productive and resilient microalgae strains. New strains are screened using three consecutive tiers in a conceptual down-selection funnel to arrive at the most promising strains. At tier I, strains are screened on gradient incubators to determine their temperature and salinity tolerance range, and to evaluate their resistance to infectious agents (e.g., chytrids). At tier II, the strains’ winter and summer season biomass productivities are quantified in Laboratory Environmental Algae Pond Simulator (LEAPS) photobioreactors, and biomass composition is determined under N-replete and N-deplete conditions. At tier III, the best winter and summer season strains are evaluated in indoor crash ponds to determine their resilience to induced infections, and strains are tested in outdoor raceway ponds to confirm high biomass productivity and culture stability. The top strains are then evaluated in seasonal SOT outdoor pond culture trials at AzCATI, the dedicated Department of Energy SOT (State of Technology) testbed. Additional research is carried out on evaluating growth promoting molecules, reducing the impact of oxygen inhibition, optimizing pond operational conditions, measuring compositional shift dynamics, developing integrated pest management strategies, analysing culture health using 96 well-plate tools, implementing spectroradiometric monitoring and using machine learning to predict culture crashes.

Huesemann, Michael H↗
DOE OSTI2022

Use Attainability Analysis Aquatic Life Uses for Perennial Reach of Sandia Canyon [Slides]

The following information will be presented during the February 23, 2022 Accord Pueblos Technical Meeting. The purpose of the study is to determine if natural thermal conditions are preventing the attainment of Coldwater Aquatic Life Use in the perennial reach of the upper Sandia Canyon Assessment Unit (Sandia AU). The New Mexico Water Quality Standards allow for a change in the designated use if a Use Attainability Analysis (UAA) demonstrates that the use is not attainable due to one or more of six factors listed in 40 CFR 131.10(g), including naturally occurring pollutant concentrations. The Sandia AU is located in a perennial reach of upper Sandia Canyon between Sigma Canyon and NPDES Outfall 001. The classified Segment 20.6.4.126 NMAC comprises perennial waters within Los Alamos National Laboratory boundaries and includes the Sandia AU. Persistent surface flows originate from NPDES permitted effluent releases. These releases have occurred since the early 1950’s and continue today. The UAA examines several lines of evidence. NMED’s Air-Water Temperature Correlation (AWTC) model is used for identifying appropriate stream classifications and attainable aquatic life use subcategories. The model correlates between July average air temperatures (ATEMP) and maximum weekly average stream temperatures. Air temperatures are obtained from PRISM and LANL Meteorological Towers and used to derive ATEMP. Thermographs were placed in the Sandia AU to obtain measured stream temperatures during summer months when stream temperatures are the highest. Measured data is used with AWTC modeled data to determine if the Sandia AU is meeting its natural air temperature driven thermal condition.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Application of Partial Least Squares Approaches to Pyroprocessing ER Data

Multivariate approaches show promise for application to process monitoring for safeguards of pyroprocessing. Past MPACT work explored the application of Principal Component Analysis (PCA) to detect off-normal conditions in pyroprocessing electrorefiner (ER) data from in the Hot Fuel Examination Facility (HFEF) at Idaho National Laboratory (INL) known as the Scalable Pyrochemical Recycling testbed (SPyRe) ER. PCA, however, does not consider the output variables. In FY24, multivariate analysis was extended from PCA to Partial Least Squares (PLS) analysis. PLS maximizes the variance between both the input signals and output variables. In the case of this work, PLS was applied in two different manners: Predictive PLS and Discriminant PLS. Predictive PLS maximizes the covariance between the process variables of the ER and the measured U concentration from in-situ voltammetry. Discriminant PLS maximizes the covariance between the process variables and a set of training process “states” such as known off-normal conditions. By projecting into the latent variable space in PLS, the process variables can be regressed onto the outputs and predictions can be made for new data sets. In this work, by applying predictive PLS, a penalized non-linear PLS approach was able to make predictions of concentration based on test and training data and detect when operations were off-normal. However, the predictive PLS does not classify the signals to which off-normal operations are attributable. Discriminant PLS can be used to classify off-normal operations but is inadequate to properly classify specific off-normal classes like power supply faults when the Discriminant PLS model is only specifically trained to detect that off-normal class. When all faults are trained against the observation data, all three operational classes are accurately classified and distinguished. Thus, future application of latent variable techniques should not select any given method, but should use a mixture of PCA, Predictive PLS, and Discriminant PLS.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Lawrence Berkley National Laboratory Building 59

The building management system in Building 59 is monitoring and archiving building-level electricity usage, HVAC and lighting system states (e.g., setpoint, temperature, flow rate, pressure), indoor environmental conditions (air temperature, relative humidity, CO2), on-site weather (air temperature, relative humidity), and especially occupant counts as well as other metrics such as Wi-Fi signal. This dataset could support multiple use cases, such as model predictive control and occupant related demand management.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Lawrence Berkley National Laboratory Building 59 (Raw)

The building management system in Building 59 is monitoring and archiving building-level electricity usage, HVAC and lighting system states (e.g., setpoint, temperature, flow rate, pressure), indoor environmental conditions (air temperature, relative humidity, CO2), on-site weather (air temperature, relative humidity), and especially occupant counts as well as other metrics such as Wi-Fi signal. This dataset could support multiple use cases, such as model predictive control and occupant related demand management. Raw data.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Lawrence Berkeley National Lab Building 59

The building management system in Building 59 is monitoring and archiving building-level electricity usage, HVAC and lighting system states (e.g., setpoint, temperature, flow rate, pressure), indoor environmental conditions (air temperature, relative humidity, CO2), on-site weather (air temperature, relative humidity), and especially occupant counts as well as other metrics such as Wi-Fi signal. This dataset could support multiple use cases, such as model predictive control and occupant related demand management.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Physics-Based Method for Generating Fully Synthetic IV Curve Training Datasets for Machine Learning Classification of PV Failures

Classification machine learning models require high-quality labeled datasets for training. Among the most useful datasets for photovoltaic array fault detection and diagnosis are module or string current-voltage (IV) curves. Unfortunately, such datasets are rarely collected due to the cost of high fidelity monitoring, and the data that is available is generally not ideal, often consisting of unbalanced classes, noisy data due to environmental conditions, and few samples. In this paper, we propose an alternate approach that utilizes physics-based simulations of string-level IV curves as a fully synthetic training corpus that is independent of the test dataset. In our example, the training corpus consists of baseline (no fault), partial soiling, and cell crack system modes. The training corpus is used to train a 1D convolutional neural network (CNN) for failure classification. The approach is validated by comparing the model’s ability to classify failures detected on a real, measured IV curve testing corpus obtained from laboratory and field experiments. Results obtained using a fully synthetic training dataset achieve identical accuracy to those obtained with use of a measured training dataset. When evaluating the measured data’s test split, a 100% accuracy was found both when using simulations or measured data as the training corpus. When evaluating all of the measured data, a 96% accuracy was found when using a fully synthetic training dataset. The use of physics-based modeling results as a training corpus for failure detection and classification has many advantages for implementation as each PV system is configured differently, and it would be nearly impossible to train using labeled measured data.

Hopwood, Michael W. (ORCID:0000000161901767)↗
DOE OSTI2024

Experimental Investigation of Low-Frequency Distributed Acoustic Sensor Responses to Two Parallel Propagating Fractures

Low-frequency distributed acoustic sensing (LF-DAS) is a diagnostic tool for hydraulic fracture propagation with far-field monitoring using fiber optic sensors. LF-DAS senses strain rate variation caused by stress field change due to fracture propagation. Fiber optic sensors are installed in the monitoring wells in the vicinity of a fractured well. From the strain responses, fracture propagation can be evaluated. To understand subsurface conditions with multiple propagating fractures, a laboratory-scale hydraulic fracture experiment was performed simulating the LF-DAS response to fracture propagation with embedded distributed optical fiber strain sensors under these conditions. The experiment was performed using a transparent cube of epoxy with two parallel radial initial flaws centered in the cube. Fluid was injected into the sample to generate fractures along the initial flaws. The experiment used distributed high-definition fiber optic strain sensors with tight spatial resolutions. The sensors were embedded at two different locations on opposite sides of the initial flaws, serving as observation/monitoring locations. We also employed finite element modeling to numerically solve the linear elastic equations of equilibrium continuity and stress–strain relationships. The measured strains from the experiment were compared to simulation results from the finite element model. The experimentally derived strain and strain-rate waterfall plots from this study show the responses to both fractures propagating, while the fracture at the lower position took most of the fluid during the experiment. Interestingly, a fracture first began propagating from the upper flaw of the two flaws, but once the lower fracture was initiated, it grew much faster than the upper fracture. Both fibers were intercepted by the lower fracture, further verifying the strain signature as a fracture is approaching and intersecting an offset fiber.

Chemistry↗
DOE OSTI2023

Quantification of oil and gas methane emissions in the Delaware and Marcellus basins using a network of continuous tower-based measurements

Abstract. According to the United States Environmental Protection Agency (US EPA), emissions from oil and gas infrastructure contribute 30 % of all anthropogenic methane (CH4) emissions in the US. Studies in the last decade have shown emissions from this sector to be substantially larger than bottom-up assessments, including the EPA inventory, highlighting both the increased importance of methane emissions from the oil and gas sector in terms of their overall climatological impact and the need for independent monitoring of these emissions. In this study we present continuous monitoring of regional methane emissions from two oil and gas basins using tower-based observing networks. Continuous methane measurements were taken at four tower sites in the northeastern Marcellus basin from May 2015 through December 2016 and five tower sites in the Delaware basin in the western Permian from March 2020 through April 2022. These measurements, an atmospheric transport model, and prior emission fields are combined using an atmospheric inversion to estimate monthly methane emissions in the two regions. This study finds the mean overall emission rate from the Delaware basin during the measurement period to be 146–210 Mg CH4 h−1 (energy-normalized loss rate of 1.1 %–1.5 %, gas-normalized rate of 2.5 %–3.5 %). Strong temporal variability in the emissions was present, with the lowest emission rates occurring during the onset of the COVID-19 pandemic. Additionally, a synthetic model–data experiment performed using the Delaware tower network shows that the presence of intermittent sources is not a significant source of uncertainty in monthly quantification of the mean emission rate. In the Marcellus, this study finds the overall mean emission rate to be 19–28 Mg CH4 h−1 (gas-normalized loss rate of 0.30 %–0.45 %), with relative consistency in the emission rate over time. These totals align with aircraft top-down estimates from the same time periods. In both basins, the tower network was able to constrain monthly flux estimates within ±20 % uncertainty in the Delaware and ±24 % uncertainty in the Marcellus. The results from this study demonstrate the ability to monitor emissions continuously and detect changes in the emissions field, even in a basin with relatively low emissions and complex background conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Irradiation Testing of Sensors - ASI Program Review 2022

This is a powerpoint presentation for the annual Advanced Sensors and Instrumentation (ASI) program webinar. The following is a summary of the presentation. Advanced instrumentation enables testing of nuclear fuels and materials in support of the US advanced nuclear technology industry. A number of promising sensor technologies are in the pipeline. The early part of sensor development can be done outside of the reactor environment, but full technical readiness requires experience gained from in-core performance testing. Customers usually have only one shot to conduct their irradiation experiments. Because of the high costs test sponsors are frequently reluctant to incorporate unproven technologies in reactor experiments. Therefore, it is vital to demonstrate newly-developed sensors in operational conditions, prior to incorporating them into long-term high-value experiments. Successful completion of these activities will create new monitoring capabilities in high-power test reactors (such as ATR) as well as specialty reactors such as TREAT.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Simple Wins: Driving Effective Implementation of Energy Efficiency Projects

Small- and medium-sized manufacturers (SMMs) in the United States, making up over 98% of manufacturing firms, account for 48.3% of the sector’s energy consumption. Despite access to energy assessments and technical resources, such as those from the US Department of Energy’s Better Plants program and Industrial Training and Assessment Centers, implementation rates for identified energy-saving measures remain lower than expectations, averaging 47%. Some barriers were identified by other researchers as limited workforce capacity, inadequate training, and time constraints hinder progress. This article studied the development of customized checklists together with the energy assessment report as a practical solution to improve implementation rates for energy efficiency projects among SMMs. The framework for the checklists was grounded in five principles: short, precise, actionable, relevant, and codeveloped (SPARC). A case study of a 500,000-squarefoot SMM demonstrates how tailored checklists targeting significant energy users—such as heating, ventilating and air conditioning (HVAC), lighting, and compressed air systems for this facility—can simplify the process for staff to do facility walkthroughs to monitor best practices and track the progress of energy efficiency project implementation.

Guo, Wei [Oak Ridge National Laboratory (ORNL), Oa↗
DOE OSTI2022

Real-time laser ultrasonic monitoring of laser-induced thermal processes

Abstract Intra- and inter-layer integrity of components fabricated with advanced manufacturing techniques, such as laser powder bed fusion, is dependent upon rapid heating, melting, and solidification processes. There is a need for new techniques to provide in situ feedback of these processes. Here a laser-based ultrasonic technique to probe thermal effects induced by a high-power continuous wave laser in titanium samples is described. Numerical simulations were performed to show that, for a spatially uniform heating beam, laser-induced surface acoustic waves are strongly influenced by surface heating conditions, are dispersive in the case of rapid heating, and that an abrupt velocity reduction happens upon the onset of surface melting. Furthermore, laser-based ultrasound experimental results which monitor the transient change of surface wave travel time associated with high power laser surface heating are provided. A pulsed laser is used to generate high frequency surface acoustic waves that propagate through the laser-heated region and are detected using a photorefractive crystal-based interferometer. Qualitative agreement is observed between theory and experiment with both showing a rapid reduction in the surface wave velocity at the onset of illumination and further decrease in surface wave velocity associated with melting. It is demonstrated that changes in the surface wave velocity can be used to track local heating and detect the onset of surface melting in real time.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Measuring the Aerosol Collection Efficiency and Detector Face Deposition of the Bladewerx KatanaGBM™ (Glove Box Monitor) Continuous Air Monitor

To assist Bladewerx LLC (the Requestor) in testing their new CAM (continuous air monitor) sampler model Bladewerx™ KatanaGBM™ (Glove Box Monitor), the Laboratory (LANL, i.e. Los Alamos National Laboratory) measured the aerosol particle collection efficiency and detector face deposition for several experimental test conditions. Bladewerx LLC provided a prototype KatanaGBM with a set of requested tests. According to these parameters, LANL designed and performed a series of experiments to (A.) Measure the aerosol particle collection efficiency and detector face deposition of the KatanaGBM at three air flow rates of 5, 42, and 70 ALPM (ambient liters per minute), (B.) Measure the collection efficiency and detector face deposition using two sizes of oil droplet particles: 3±1 and 10±1 µm (micron) AED (aerodynamic equivalent diameter), and (C.) Test the KatanaGBM for aerosol collection efficiency and detector face deposition with the wind tunnel’s air flow at three different angles 0°, 45° and 90° (compared to the KatanaGBM’s filter face).

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2026

Plant-Nitrifier Interactions in Topsoil and Subsoil

Plants can influence soil microbes through resource acquisition and interference competition, with consequences for ecosystem function such as nitrification. However, how plants alter soil conditions to influence nitrifiers and nitrification rates remains poorly understood, especially in the subsoil. Here, coupling the 15N isotopic pool dilution technique, high throughput sequencing and in situ soil O2 monitoring, we investigated how a deep-rooted perennial grass, miscanthus, versus an adjacent shallow-rooted turfgrass reference shapes nitrifier assembly and function along 1 m soil profiles. In topsoil, the suppression of ammonia (NH3) oxidizing archaea (AOA) and gross nitrification rates in miscanthus relative to the reference likely resulted from nitrifiers being outcompeted by plant roots and heterotrophic bacteria for ammonium (NH4+). The stronger tripartite competition under miscanthus may have been caused in part by the lower soil organic matter (SOM) content, which supported lower gross nitrogen (N) mineralization, the major soil process that produces NH4+. In contrast, below 10 cm soil depth, significantly greater gross nitrification rates were observed in miscanthus compared to the reference. This was likely driven by the significantly lower oxygen (O2) in miscanthus than reference subsoil, which selected against aerobic heterotrophic bacteria but in favor of AOA. Overall, we found that plants can regulate AOA community structure and function through different mechanisms in topsoil and subsoil, with suppression of nitrification in topsoil and enhancement of nitrification in subsoil.

Field Data↗
DOE OSTI2021

Chemometric optimisation of pressurised liquid extraction for the determination of alliin and S‐allyl‐cysteine in giant garlic ( Allium ampeloprasum L.) by liquid chromatography tandem mass spectrometry

Abstract Introduction Giant garlic is a functional food that contains different kinds of bioactive molecules with beneficial effects on chronic noncommunicable diseases like diabetes and cardiovascular conditions. Considering biosynthesis pathways, abundance, and biological activity, alliin and S‐allyl‐cysteine were used as chemical markers of organosulphur compounds present in giant garlic. Objective To establish a chemometric optimisation of pressurised liquid extraction for the determination of alliin and S‐allyl‐cysteine in giant garlic by liquid chromatography tandem mass spectrometry (LC–MS/MS). Methodology Samples were blanched (ca. 90°C for 10 min) to inactivate alliinase and γ‐glutamyl transpeptidase enzymes and then freeze‐dried. Chemometric optimisation was performed via response surface methodology based on central composite design (CCD). Organosulphur compound yields were determined applying a validated LC–MS/MS method in multiple reaction monitoring (MRM) mode using the following transitions: for alliin m/z 178 → 74 and for S‐allyl‐cysteine m/z 162 → 41. Results According to CCD results, under constant conditions of pressure (1500 psi) and time (20 min), the optimal conditions for pressurised liquid extraction of alliin and S‐allyl‐cysteine were 70.75 and 68.97% v/v of ethanol in extraction solvent and 76.45 and 98.88°C as extraction temperature, respectively. Multiple response optimisation for the simultaneous extraction of both organosulphur compounds was established via desirability function. Under these conditions, 2.70 ± 0.27 mg g −1 dry weight (DW) of alliin and 2.79 ± 0.22 mg g −1 DW of S‐allyl‐cysteine were extracted. Conclusions These results clearly demonstrated that pressurised liquid extraction is an efficient green technique to extract bioactive organosulphur compounds from giant garlic. Extraction yields were significantly ( p < 0.05) higher than those obtained with conventional ultra‐turrax extraction.

Peterssen‐Fonseca, Darlene↗