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DOE OSTI2022

Optimal Experimental Design for Inverse Problems in the Presence of Observation Correlations

Optimal experimental design (OED) is the general formalism of sensor placement and decisions on the data collection strategy for engineered or natural experiments. This problem is prevalent in many critical fields such as battery design, numerical weather prediction, geosciences, environmental and urban studies. State-of-the-art computational methods for experimental design do not accommodate correlation structure in observational errors produced by many expensive-to-operate devices such as X-ray machines, radars, and satellites. Discarding evident data correlations leads to biased results, higher expenses, and waste of valuable resources. We present a general formulation of the OED formalism for model-constrained large-scale Bayesian linear inverse problems, where measurement errors are generally correlated. The proposed approach utilizes the Hadamard product of matrices to formulate the weighted-likelihood, and is valid for both finite as well as infinite-dimensional Bayesian inverse problems. Extensive numerical experiments are carried out for empirical verification of the proposed approach using an advection-diffusion model, where the objective is to optimally place a small set of sensors, under a limited budget, to predict the concentration of a contaminant in a closed and bounded domain.

Correlated observations↗

DOE OSTI2022

Detection of Cyber Attacks in Grid-tied PV Systems Using Dynamic Watermarking

This paper presents of an active detection scheme for detecting cyber attacks on sensors controlling a grid-tied PV systems. Several cyber vulnerabilities in Grid tied PV Systems are discussed. The defense mechanism introduces a private (secret) watermarking signal into the control inputs of the grid-tied inverter system. This will enable the detection of any malicious manipulation of sensor measurements. Based on the measured data, two statistical tests are conducted to identify anomalies in the system using the presence of the watermarking signal. It shown that when a sensor data is compromised and/or replaced by a pre-recorded healthy signal, both test 1 and 2 exhibit high values indicating a possible malicious activity. The robustness of the proposed algorithm is tested and validated with several attack scenarios on a grid tied PV system. Select results from an experimental setup are discussed.

Ibrahim, Hasan↗

DOE OSTI2024

MODAQ (Modular Offshore Data Acquisition System) [SWR-24-58]

The National Renewable Energy Laboratory (NREL) has developed a data acquisition system to support laboratory (component) and in-water validation and testing of Marine Renewable Energy (MRE) technologies. The Modular Ocean Data AcQuisition System (MODAQ) is a robust system built on industrial grade hardware that can acquire high-quality and summary data from many sensor and instrument types. MODAQ includes modules to perform real-time QC screening, system health monitoring, and send alerts. MODAQ is also part of a larger data management, processing, visualization, and archiving system.

Raye, Robert↗

DOE OSTI2013

Marine Corps Base Camp Lejeune New River Estuary - Acoustic Doppler Current Profiler (ADCP) Results

Critical military training and testing on lands along the nation’s coastal and estuarine shorelines are increasingly placed at risk because of development pressures in surrounding areas, impairments due to other anthropogenic disturbances, and increasing requirements for compliance with environmental regulations. The U.S. Department of Defense (DoD) intends to enhance and sustain its training and testing assets and to optimize its stewardship of natural resources through the development and application of an ecosystem-based management approach on DoD installations. To accomplish this goal, particularly for installations in estuarine/coastal environments, the Strategic Environmental Research and Development Program (SERDP) launched the Defense Coastal/Estuarine Research Program (DCERP) as a minimum 10-year effort at Marine Corps Base Camp Lejeune (MCBCL) in North Carolina. The results of the first six years of the program (DCERP1) are presented here.The overarching objectives of DCERP are to: (1) understand the effects of military training activities, infrastructure development, and other coastal military installations; (2) develop models, tools, and indicators to evaluate ecosystem health; and (3) recommend adaptive management strategies to sustain ecosystem natural resources within the context of an active military installation.This data package contains a zipfile of results from Acoustic Doppler Current Profilers (ADCPs) used to collect current profiles throughout the New River Estuary in 2009: Teledyne RD Instrument’s acoustic Doppler current profilers (ADCPs) were deployed throughout the NRE to understand current flows and tidal extent. (Current velocity and water level data collected by the ADCPs were also used to validate a hydrodynamic model of the NRE.) Two ADCPS were deployed by Marker 12 and the Highway 172 bridge from late-February to early-July 2009. These ADCPs were attached to steel frames that rested on the sediment surface with the ADCP looking upward to make full water column profiles of water velocity minus a 1-m blanking between the instrument and sediment surface. Also attached to the steel frames were conductivity-temperature-depth (CTD) sensors, which provided concomitant salinity, temperature, and water depth data with the ADCP current measurements. Two ADCPS were also deployed in close proximity to the AVPs in Stones and Morgan Bays from mid-July to mid-December 2009. These ADCPs were deployed flush to the estuary floor to maximize the full vertical sampling capability at these shallow stations and the AVPs collected coincidental temperature, salinity, and depth measurements. For all deployments, the ADCPs were programmed in fast ping-rate mode 12 using 20 pings per ensemble and collected profiles in 10-cm bins every 6 minutes yielding a standard deviation in velocity measurements of 1.27 cm s-1. Current velocity and water level data were analyzed to understand the tidal influence on circulation within the NRE. Harmonic analyses were performed to extract the dominant tidal constituents from the velocity and depth records using T-tide run in MATLAB (Pawlowicz et al., 2002). These measurements of tidal amplitude and phase were then used to validate the high-resolution Advanced Circulation (ADCIRC) Model of the NRE region.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Optimization of the number and locations of the calibration stations needed to monitor soil moisture using distributed temperature sensing systems: A proof-of-concept study

The single-probe heat-pulse (SPHP) technique combined with the Fiber-optic Distributed Temperature Sensing (DTS) technology can offer novel high-resolution measurements of soil moisture (θ) over spatial scales ranging from several centimeters to several kilometers. However, the key limitation of this method is in obtaining the calibration relationship between θ and soil thermal conductivity (λ) across a specific field. In a previous study, a new methodology using a Gaussian processes model was presented to account for the spatial variability in the λ-θ relationship. The model aggregated θ measurements from soil moisture sensors scattered over the SPHP transect with the corresponding DTS λ measurements at their locations. In this study, a novel methodology is tested to optimize the number and locations of soil moisture sensors required to account for the spatial variability of the λ - θ relationship to achieve higher accuracy from the SPHP technique. The proposed methodology utilizes hierarchical clustering to analyze the information contained in the spatial structure of the SPHP measurements as the soil dries from a nearly-saturated condition. The proposed methodology was tested using data from a field in Oklahoma. Monte-Carlo simulation was performed to validate the performance of the proposed methodology. The predictions obtained from the proposed methodology resulted in θ measurements accuracy comparable to those obtained from the 10% best Monte-Carlo iterations of randomly assigned soil moisture locations. Further, this study demonstrates that the proposed methodology is more efficient than the traditional practice of randomly spreading calibration soil moisture sensors along the SPHP transect.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

A Flexible Framework for Building Occupancy Detection Using Spatiotemporal Pattern Networks: Preprint

This paper presents a reliable, scalable, and transferable framework to predict occupancy in a building utilizing diverse, multi-modal information. We propose a new methodology for learning-driven occupancy detection built on the concepts of probabilistic graphical modeling and observable Markov chain modeling. To capture the relationship between multi-sensor data and occupancy, we propose this Occ-STPN framework that is flexible to support both multivariate and univariate formulations. While the multivariate Occ-STPN performs feature-level fusion of multiple predictors and occupancy time-series data, the univariate Occ-STPN involves decision fusion of occupancy predictions using individual predictors based on a mutual information weighted fusion scheme. We also propose a new metric to evaluate the performance of occupancy prediction algorithms. Two popular datasets are used to validate our approach and demonstrate that our framework is scalable in terms of the number of information sources (e.g., sensors) as well as it is possible to transfer trained models from one building to another without significant reduction in performance. Reliability of the algorithm is also tested by injecting noise into the datasets.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2019

A Flexible Framework for Building Occupancy Detection Using Spatiotemporal Pattern Networks

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Validating a Tethered Balloon System and Optical Technologies for Marine Wildlife Detection and Tracking

The interactions between marine wildlife and marine energy devices are not well understood, leading to regulatory delays for device deployments and testing. Technologies that enable marine wildlife observations can help to fill data gaps and reduce uncertainties about animal–device interactions. A validation test conducted in Galveston Bay near La Porte, Texas, in December 2022 used a technology package consisting of a tethered balloon system and three independent sensor systems, including three-band visible, eight-band multispectral, and single-band thermal to detect three marine-mammal-shaped surrogates. The field campaign aimed to provide an initial step to evaluating the use of the TBS and the effectiveness of the sensor suite for marine wildlife observations and detection. From 2 December to 7 December 2022, 6 flights were conducted under varying altitudes and environmental conditions resulting in the collection of 5454 images. A subset of the images was classified and analyzed with two collection criteria including Beaufort wind force scale and TBS altitude to assess a range of observations of a surrogate from near-shore to offshore based on pixel count. The results of this validation test demonstrate the potential for using TBSs and imaging sensors for marine wildlife observations and offer valuable information for further development and application of this technology for marine energy and other blue economy sectors.

47 OTHER INSTRUMENTATION↗

DOE OSTI2019

Validation of an inverse model of zone air heat balance

Our work introduces the validation method and results of an inverse model of zone air heat balance. The inverse model, implemented in EnergyPlus and published in a previous article [1], calculates highly uncertain model parameters such as internal thermal mass and infiltration airflow by inversely solving the zone air heat balance equation using the easy-to-measure zone air temperature data. The paper provides technical details of validation from the experiments using LBNL's Facility for Low Energy eXperiment in Buildings (FLEXLAB) that measures zone air temperature under the controlled experiment of two levels of internal mass and four levels of infiltration airflow. The simulation results of the zone infiltration airflow and internal thermal mass from the inverse model agree well with the measured data from the FLEXLAB experiments. The validated inverse model in EnergyPlus can be utilized to enhance the energy modeling of existing buildings that enables energy performance assessments for energy efficiency improvements.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2015

Comparison of Calibration of Sensors Used for the Quantification of Nuclear Energy Rate Deposition

This present work deals with a collaborative program called GAMMA-MAJOR 'Development and qualification of a deterministic scheme for the evaluation of GAMMA heating in MTR reactors with exploitation as example MARIA reactor and Jules Horowitz Reactor' between the National Centre for Nuclear Research of Poland, the French Atomic Energy and Alternative Energies Commission and Aix Marseille University. One of main objectives of this program is to optimize the nuclear heating quantification thanks to calculation validated from experimental measurements of radiation energy deposition carried out in irradiation reactors. The quantification of the nuclear heating is a key data especially for the thermal, mechanical design and sizing of irradiation experimental devices in specific irradiated conditions and locations. The determination of this data is usually performed by differential calorimeters and gamma thermometers such as used in the experimental multi-sensors device called CARMEN 'Calorimetric en Reacteur et Mesures des Emissions Nucleaires'. In the framework of the GAMMA-MAJOR program a new calorimeter was designed for the nuclear energy deposition quantification. It corresponds to a single-cell calorimeter and it is called KAROLINA. This calorimeter was recently tested during an irradiation campaign inside MARIA reactor in Poland. This new single-cell calorimeter differs from previous CALMOS or CARMEN type differential calorimeters according to three main points: its geometry, its preliminary out-of-pile calibration, and its in-pile measurement method. The differential calorimeter, which is made of two identical cells containing heaters, has a calibration method based on the use of steady thermal states reached by simulating the nuclear energy deposition into the calorimeter sample by Joule effect; whereas the single-cell calorimeter, which has no heater, is calibrated by using the transient thermal response of the sensor (heating and cooling steps). The paper will concern these two kinds of calorimetric sensors. It will focus in particular on studies on their out-of-pile calibrations. Firstly, the characteristics of the sensor designs will be detailed (such as geometry, dimension, material sample, assembly, instrumentation). Then the out-of-pile calibration methods will be described. Furthermore numerical results obtained thanks to 2D axisymmetrical thermal simulations (Finite Element Method, CAST3M) and experimental results will be presented for each sensor. A comparison of the two different thermal sensor behaviours will be realized. To conclude a discussion of the advantages and the drawbacks of each sensor will be performed especially regarding measurement methods. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2026

Comparison of CNN-Based Image Classification Approaches for Implementation of Low-Cost Multispectral Arcing Detection

Camera-based sensing has benefited in recent years from developments in machine learning data processing methods, as well as improved data collection options such as Unmanned Aerial Vehicles (UAV) mounted sensors. However, cost considerations, both for the initial purchase of sensors as well as updates, maintenance, or potential replacement if damaged, can limit adoption of more expensive sensing options for some applications. To evaluate more affordable options with less expensive, more available, and more easily replaceable hardware, we examine the use of machine learning-based image classification with custom datasets, utilizing deep learning based-image classification and the use of ensemble models for sensor fusion. Utilizing the same models for each camera to reduce technical overhead, we showed that for a very representative training dataset, camera-based detection can be successful for detection of electrical arcing. We also use multiple validation datasets, based on conditions expected to be of varying difficulty, to evaluate custom data. These results show that ensemble models of different data sources can mitigate risks from gaps in training data, though the system will be less redundant for those cases unless other precautions are taken. We found that with good quality custom datasets, data fusion models can be utilized without specialization in design to the specific cameras utilized, allowing for less specialized, more accessible equipment to be utilized as multispectral camera components. This approach can provide an alternative to expensive sensing equipment for applications in which lower-cost or more easily replaceable sensing equipment is desirable.

convolutional neural networks↗

DOE OSTI2024

Space Nuclear Power Autonomous Control Algorithm and Control Element Test Bed

Nuclear thermal rockets are currently NASA’s preferred option for use in a manned mission to Mars in the 2040s. The communication delay between an Earth ground station and a spacecraft heading toward Mars can be up to 20 min. Therefore, controlling the nuclear rocket engine would require either a full-time reactor operator on the mission or an autonomous control system for the reactor. The latter idea of making space nuclear reactors fully autonomous has drawn more interest from stakeholders, but such an autonomous control system must be rigorously tested and validated before it is certified for human use. The cost of a full ground test for a space nuclear reactor is tremendous, so a nonnuclear mock reactor test bed was created to test and validate control elements and control algorithms for space nuclear reactors. The test bed consists of control element hardware that inputs physical measurement data into a reactor emulator to produce the reactor’s performance under steady-state, transient, and fault conditions. The control element hardware consists of six full-sized control drums equipped with servo drives and motors and is instrumented with optical encoders, resolvers, and torque sensors for drum movement characterization. In addition to the drums, a two-phase flow loop was designed and built to mimic the valves and turbomachinery associated with the propellant flow through a nuclear thermal rocket engine; components such as pressure sensors, flow meters, thermocouples, and tachometers are instrumented throughout the loop to characterize the fluid flow, valve, and turbomachinery behavior of the system. The data from the physical hardware (e.g., drum position, propellant flow rates) are input to a nuclear reactor simulator to determine the actual nuclear reactor parameters, and the data are sent back to a control algorithm to complete the control loop. The ability to conduct numerous tests of the control systems and autonomous algorithms can help validate the instrumentation and control aspects for a space nuclear reactor for every possible fault situation.

Wilson, Brandon↗

DOE OSTI2021

Development of NDE/NDT Tools for High-Volume & High-Speed Inspection of CFRP Structures in Automotive Manufacturing

Main advantages of the air-coupled ultrasound testing (ACUT) and electromagnetic testing (EMT) techniques for NDE of CFRP composites were non-contact sensing, scalability for high-speed inspection, cost-effectiveness, and non-hazardous operation. Despite these advantages, no systems that would satisfy the project requirements were commercially available. Hence, one of the major efforts of the Michigan State University (MSU) team at the initial stage of the project was to close this technological gap by developing, optimizing, and validating array sensors that would provide sufficient sensitivity, spatial coverage, and resolution for robust defect detection. Optimization of the ACUT and EMT sensor designs was performed using experimentally validated finite element models. Initial experiments using array probes were conducted on relatively flat CFRP samples. In parallel, the MSU team designed and assembled a portable platform with two robotic arms. The robots were equipped with newly designed sensors that enabled high-speed NDE of curved CFRP parts. Presently, the developed robotic platform can be used as a demo/template NDE system, which is easily adaptable to manufacturing environments and in-line NDE. The ACUT NDE system developed by the MSU team used a high-power 4-channel pulser receiver for parallel data acquisition. The array probes were designed by stacking commercially available ACUT transducers, which operated in the frequency range between 100 kHz and 500 kHz. MSU optimized the excitation procedure and developed wave focusing cones so as to reduce the crosstalk between the transducers and to provide higher pulse repletion frequency (PRF). The through-transmission (TT) and single-side access (SSA) inspection modes were successfully implemented. In the TT-ACUT, structural defects in CFRP were detected by passing ultrasonic waves through the test part. Hence, the ACUT transmitters and receivers needed to be placed on the opposite sides of the test part. In the SSA-ACUT, guided waves (GW) were excited in the test part using the transmitters and were sensed by the receivers from the same side. Multi-channel TT-ACUT and SSA-ACUT provided high-speed NDE, and were successfully validated on CFRP test samples with interlaminar delaminations and other embedded defects The EM techniques developed by the MSU team included: 1) eddy current testing (ECT), 2) capacitive imaging (CI) and hybrid dual-mode imaging. In ECT, structural damage was detected in CFRP using coils sensor arrays. In ECT, the excitation magnetic field is generated by passing an alternating current through a coil, which is placed above the test sample. The excitation field penetrates the conductive sample and induces the eddy currents in its transect. In turn, the eddy currents generate the reaction field, which affects the total field sensed by a coil. Hence, the presence of structural flaws will alter the eddy current flow and the picked-up signal. ECT is mostly sensitive to local changes of the electric conductivity of the test sample, and CFRPs are mostly conductive in the direction of carbon fibers. Hence, ECT was well suited for the detection of fiber damage/fiber irregularities. The MSU team developed printed circuit boards (PCB) with coil sensor arrays optimized for NDE of CFRP. Unlike most commercial probes designed for ECT of metallic structures, the MSU array probes were designed for operation in [1-10] MHz frequency range, which was optimal for low-conductive CFRP. Multiple sensing topologies (coil groups excitation/sensing arrangements) were implemented and successfully validated. Capacitive Imaging (CI) technique developed by MSU was complementary to ECT. In contrast to ECT, which was sensitive to local changes of the electrical conductivity, the CI was sensitive to local changes of the dielectric constant. Therefore, CI could provide information about matrix damage/matrix irregularities in CFRP. The MSU CI sensor arrays were made of multiple circular or rectangular open-plate capacitors printed on PCB. Sensors of this type are not commercially available. In addition to ECT and CI, the MSU team developed a hybrid (dual-mode) inductive/capacitive measurement technique that synergistically combined the benefits of inductive and capacitive sensing for rapid NDE of fiber reinforced polymer (FRP) composite structures. Fiber damage and fiber irregularities in FRPs were detected by configuring hybrid sensors as coil sensors. Similarly, matrix damage, matrix irregularities and interlaminar delaminations were detected by configuring hybrid sensors as capacitive sensors. ECT and CI were performed sequentially by means of electronic switching. Hence, eliminating the need for mounting two separate sensor arrays on the probe. Portable robotic platform was developed by MSU for multi-technique high-speed NDE of CFRP test parts. The platform had two 6-axis robots, which enabled inspection of curved parts in approximately a 6×6×6 ft 3 active scan area. On the software side, the MSU team integrated scripts for NDE hardware control with scripts for robot motion control. MSU also implemented automated path planning for the robots, reconstruction of part’s surfaces via stereovision, 3D rendering of inspection data, and image processing algorithms for enhanced defect detection. Automotive composite parts manufactured by Plasan Composites from Phase I were used to validate the ACUT and EMT techniques on representative testbeds. Among those parts were three X-braces for a Dodge Viper, one composite calibration plaque with known defects at known locations, and four other test sections, including sections from a front splitter, a corner section from a composite hood, and a high-pressure RTM panel made using non crimp fabric. Other test samples included CFRP and GFRP calibration plates with fiber/matrix defects fabricated at MSU/CVRC.

36 MATERIALS SCIENCE↗

DOE OSTI2024

A machine learning approach for particle accelerator errant beam prediction using spatial phase deviation

Particle accelerators are extremely complex systems that are expected to operate on high availability. Predicting impending failures only by utilizing data collected from diagnostic equipment already on board can help operators to avoid installing expensive sensors, unscheduled downtime and associated costs. For this purpose we explore the predictive power of Machine Learning algorithms to detect faulty beams prior to the failure. In this study, we propose a Machine Learning approach to model mapping from a pair of sensors located across the accelerator. While the model is trained to represent normal operation, we evaluate the predictive performance on known faulty beam pulses. We also investigate the model performance on unseen data through k-fold cross-validation. Then we recap the analysis with a neural architecture search and hyperparameter optimization study to fine tune our initial model. In conclusion, this paper will also introduce a sustainable framework that can standardize Machine Learning workflow applied to particle accelerators.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2016

Time-resolved measurement of subchannel void distribution for boiling two-phase flow in 5 x 5 rod bundle at high-pressure and high-temperature

A subchannel void sensor (SCVS) was improved to acquire time-resolve boiling two-phase flow in a 5-by-5 heated rod bundle at high-pressure and high-temperature. The SCVS consists of 6-by-6 wire electrodes and 5-by-5 rod electrodes. The SCVS can acquire a time series data of local void fraction at 32 points of central subchannel region in addition to 100 points of rod surface regions between rods. The devised sensors are installed in the heated rod bundle at eight height levels to acquire two-phase flow dynamics and its development. The axial and radial power profile of the heated rod bundle are uniform, and eight pairs of sheath thermocouples are embedded on the heated rod to monitor its surface temperature distribution. The paper addresses the axial and cross-sectional distributions of void fraction at various pressure and rod bundle power conditions, which simulates an accidental condition in reactor core and in a spent fuel pool of a boiling water reactor (BWR). The experimental data are suitable to validate the computational multi-fluid dynamics (CMFD) and subchannel codes. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2016

High Accuracy Signal Validation Framework for Sensor Calibration Assessment in NPPs

Effective and efficient health management of sensing channels and instrumentation supports reliable and accurate process monitoring capacities in any industry. Current practice in the nuclear industry relies on periodic calibration assessment and correction activities. This time based maintenance technique consists of three steps: i) removal of sensor to a test environment, ii) testing and recalibration, and iii) reinstallation of sensor into its working environment. This procedure is highly labor intensive, adds to the outage cost and timeline, and contributes to worker radiation exposure and ALARA requirements. Furthermore, the human element contributes to error factors in all stages of the procedure. The effectiveness of this calibration procedure after sensors are returned to working environmental variables also raises concerns, and the practicability of this periodic approach is uncertain for future reactor designs with longer operating cycles between refueling outages. Studies indicate that periodic calibration assessment find only 10% of sensing instrumentations with calibration issues during a single outage (18- 24 months), meaning at least 90% of sensors are undergoing unnecessary maintenance actions. This provides an opportunity to maintain the current levels of safety and functional integrity with fewer, more targeted maintenance actions and lower economic burden. Performance-based maintenance planning shows much promise over current periodic maintenance approaches. Specifically, online monitoring (OLM) utilizes the continually collected process data to non-intrusively monitor the calibration status of process instrumentation and detect the onset of de-calibration, thereby minimizing the number of recalibration procedures required. Ultimately, this can lead to termination of time based recalibration at NPPs. OLM utilizes the process measurements made for process monitoring and control to evaluate the calibration status of the sensors and instrumentation making the measurements. OLM can detect and differentiate sensor faults and process anomalies, thus providing a valuable tool to suit the industry's and regulatory body's requirements. Though supported by more than a decade of research, OLM for sensor calibration assessment is yet to be implemented in the US fleet due to regulatory requirements and constraints. As shown in Figure 1, OLM utilizes process data to train statistical models that predict normal, error-free process behavior. Measured process values are compared with error-corrected predictions to ascertain the presence of faults. The reliability of OLM is dependent on the quality of its predictions, measured in part by the uncertainty in these predictions. This research develops a framework to accurately quantify the prediction uncertainty based on Bayesian inference techniques to provide the required confidence levels for OLM systems. The expected outcomes include signal validation by high-accuracy, high-confidence uncertainty quantification for variable sensor types and operating ranges. Furthermore, the framework will facilitate fault detection capacities and so-called virtual sensors, a capability to replace faulty sensor measurements with error-corrected values when a sensor fault is detected. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

LogPath: Log data based energy consumption analysis enabling electric vehicle path optimization

Vehicle navigation and path optimization require a more meticulous approach when it deals with EVs (electric vehicles) and SDVs (software-defined vehicles), due to lengthy charging times and the lack of charging infrastructure. Long-distance freight EV trucking needs path guidance with accurate energy consumption estimates to prevent charging-related failures. We developed a novel energy consumption estimation approach that only uses battery log data to extract major vehicle parameters to increase EV navigation accuracy without additional sensors. This is enabled by extracting multiple drive modes from the log data for analysis. The system provides 1) routes, 2) charge locations, 3) charging times, and 4) optimal vehicle speeds that guarantee the shortest travel time. Here we successfully validated the system using log data collected from an EV and Tesla's Supercharging map in the US and compared it with the commercially available navigation system, Tesla's trip planner, whose capabilities solely include charging time and routing.

EV (Electric vehicles) navigation↗

DOE OSTI2019

Regional Algal Feedstock Testebed (RAFT) Final Report

The RAFT project completed detailed characterization of 12 twelve potential production strains and extensively tested 3 the three best strains in long term seasonal cultivation trials. A total of x 272 cultivation experiments were completed at the three outdoor testbed locations as part of long term cultivation trials conducted over a three year period. The cultivation experiments tested various harvest strategies, crop protection strategies, and seasonal effects along with strain rotation strategies. A sensor, that continuously monitors cell density, was developed and a patent application was submitted. Models were develop and validated for predicting strain performance, determining biomass productivity, evaluating cultivation system design and operations, and evaluating large scale production scenarios. A detailed data base was established for the long term cultivation trials with discrete data from the x 272 testbed cultivation experiments. This data base will be maintained at the University of Arizona https://raft.arizona.edu/cultivation-data/ for public access and continued academic evaluations.

09 BIOMASS FUELS↗