Optimal sensor placement for maximizing information regarding optimization objectives
Explore the source record for details and available documents.
White, Rebekah Dale↗
SEARCH · Engineering Papers
Results for “sensor placement”
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.
Bayesian OED for Sensor Placement: Analysis and Optimization of Seismo-Acoustic Monitoring Networks with Bayesian Optimal Experimental Design
Explore the source record for details and available documents.
Callahan, Jacob Paul↗
Demonstration of Algorithm for Sensor Placement Optimization using Simulation Data
This deliverable reports FY26 progress in advancing a neural-network-based Green’s-function framework for reconstructing neutron-flux distributions from ex-core measurements and for translating reconstruction requirements into a practical detector-layout strategy. Building on the FY25 formulation, the present work had two main objectives: (1) refine and re-evaluate the reconstruction methodology on an updated Purdue University Reactor Number One (PUR-1) model, and (2) develop a systematic, Green’s-function-guided procedure for boundary detector placement.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
An analysis of multiple contaminant warning system design objectives for sensor placement optimization in water distribution networks.
Abstract not provided.
Watson, Jean-Paul↗
Design of Digital Twin Sensing Strategies Via Predictive Modeling and Interpretable Machine Learning
This work develops a methodology for sensor placement and dynamic sensor scheduling decisions for digital twins. The digital twin data assimilation is posed as a classification problem, and predictive models are used to train optimal classification trees that represent the map from observed data to estimated digital twin states. In addition to providing a rapid digital twin updating capability, the resulting classification trees yield an interpretable mathematical representation that can be queried to inform sensor placement and sensor scheduling decisions. The proposed approach is demonstrated for a structural digital twin of a 12 ft wingspan unmanned aerial vehicle. Offline, training data are generated by simulating scenarios using predictive reduced-order models of the vehicle in a range of structural states. Furthermore, these training data can be further augmented using experimental or other historical data. In operation, the trained classifier is applied to observational data from the physical vehicle, enabling rapid adaptation of the digital twin in response to changes in structural health. Within this context, we study the performance of the optimal tree classifiers and demonstrate how they enable explainable structural assessments from sparse sensor measurements and also inform optimal sensor placement.
47 OTHER INSTRUMENTATION↗
A general-purpose method for Pareto optimal placement of flow rate and concentration sensors in networked systems – With application to wastewater treatment plants
The advent of affordable computing, low-cost sensor hardware, and high-speed and reliable communications have spurred installation of ubiquitous sensors in complex engineered systems. However, ensuring reliable data quality remains a challenge. Exploitation of redundancy among sensor signals can help improving the precision of measured variables, detecting the presence of gross errors, and identifying faulty sensors. The cost of sensor ownership, maintenance efforts in particular, can still be cost-prohibitive however. Maximizing the ability to assess and control data quality while minimizing the cost of ownership thus requires a careful sensor placement. To solve this challenge, in this work we develop a generally applicable method to solve the multi-objective sensor placement problem in systems governed by linear and bilinear balance equations. Importantly, the method computes all Pareto-optimal sensor layouts with conventional computational resources and requires no information about the expected sensor quality.
42 ENGINEERING↗
Use of Micro-Gravity Sensors for External Fluid Level Monitoring in Waste and Nuclear Related Applications - 20315
There are a number of applications in nuclear energy and hazardous waste disposal that require monitoring of fluids under extreme environments, including high levels of temperature, pressure, toxicity and radioactivity. Many of these applications will benefit from a monitoring technique that is external and non-invasive. Currently the sensors used are invasive, must reside inside the pressurized vessels and must penetrate the vessel walls, which can create a weakness in the vessel. Additionally, instruments that are used inside such containers must be exceptionally hardened to the environment. Information Systems Laboratories (ISL) has developed an external mass (gravimetric) measuring technique for monitoring nuclear coolant in Small Modular Reactors (SMRs), which will also work for measuring fluid levels in waste tanks, that avoids the problems inherent in invasive sensors. It utilizes a COTS gravitational sensor of unprecedented accuracy, leveraged via proper sensor placement geometry, to detect fluid changes of small amplitude from an outside position, obviating the need to penetrate the vessel. The technique is called Gravisense{sup TM}. ISL has proven via simulation and experiment that this concept can be usefully applied to monitoring fluid levels in both nuclear reactors and large waste tanks. Numerical simulation algorithms were developed to calculate the gravity effect of small changes in water level, which were verified by experiments at the NIST Physical Simulator facility at the Oregon State University. The measured ultralow noise levels of the superconducting gravimeter type which utilizes a Niobium sphere suspended in a magnetic field to attain its phenomenal accuracy, demonstrated that fluid levels in SMRs can be measured at least to within 3 cm. Furthermore, the method can distinguish between a contained leak (from reactor to containment vessel) from an external leak (from reactor to outside of containment). Additionally, simulations of waste canisters that hold spent fuel rods show that the fluid level measuring accuracy can potentially do better than 1 cm accuracy by measuring from below the vessel, and judicious placement of sensors on top of large waste tanks can potentially achieve a very impressive 2 mm measurement accuracy. These encouraging results prove that the Gravisense{sup TM} technique for fluid determination can be very useful in nuclear energy generation, testing, and research, as well as in waste monitoring situations that are difficult to monitor via traditional sensing technology. We believe that the next step should be to test the technique on canisters of the type that are currently storing waste in various DOE locations. (authors)
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Assessing the design of integrated methane sensing networks
Abstract While methane is the second largest contributor to global warming after carbon dioxide, it has a larger warming effect over a much shorter lifetime. Despite accelerated technological efforts to radically reduce global carbon dioxide emissions, rapid reductions in methane emissions are needed to limit near-term warming. Being primarily emitted as a byproduct from agricultural activities and energy extraction, methane is currently monitored via bottom–up (i.e. activity level) or top–down (via airborne or satellite retrievals) approaches. However, significant methane leaks remain undetected and emission rates are challenging to characterize with current monitoring frameworks. In this paper, we study the design of a layered monitoring approach that combines bottom–up and top–down approaches as an integrated sensing network. By recognizing that varying meteorological conditions and emission rates impact the efficacy of bottom–up monitoring, we develop a probabilistic approach to optimal sensor placement in its bottom–up network. Subsequently, we derive an inverse Bayesian framework to quantify the improvement that a design-optimized integrated framework has on emission-rate quantifications and their uncertainties. We find that under realistic meteorological conditions, the overall error in estimating the true emission rates is approximately 1.3 times higher, with their uncertainties being approximately 2.4 times higher, when using a randomized network over an optimized network, highlighting the importance of optimizing the design of integrated methane sensing networks. Further, we find that optimized networks can improve scenario coverage fractions by more than a factor of 2 over experimentally-studied networks, and identify a budget threshold beyond which the rate of optimized-network coverage improvement exhibits diminishing returns, suggesting that strategic sensor placement is also crucial for maximizing network efficiency.
54 ENVIRONMENTAL SCIENCES↗
A machine learning approach to water quality forecasts and sensor network expansion: Case study in the Wabash River Basin, United States
Abstract Midwestern cities require forecasts of surface nitrate loads to bring additional treatment processes online or activate alternative water supplies. Concurrently, networks of nitrate monitoring stations are being deployed in river basins, co‐locating water quality observations with established stream gauges. However, tools to evaluate the future value of expanded networks to improve water quality forecasts remains challenging. Here, we construct a synthetic data set of stream discharge and nitrate for the Wabash River Basin—one of the United States’ most nutrient polluted basins—using the established Agro‐IBIS and THMB models. Synthetic data enables rapid, unbiased and low‐cost assessment of potential sensor placements to support management objectives, such as near‐term forecasting. Using the synthetic data, we established baseline 1‐day forecasts for surface water nitrate at 12 cities in the basin using support vector machine regression (SVMR; RMSE 0.48–3.3 ppm). Next, we used the SVMRs to evaluate the improvement in forecast performance associated with deployment of additional nitrate sensors. We identified the optimal sensor placement to improve forecasts at each city, and the relative value of sensors at each candidate location. Finally, we assessed the co‐benefit realized by other cities when a sensor is deployed to optimize a forecast at one city, finding significant positive externalities in all cases. Ultimately, our study explores the potential for machine learning to make near‐term predictions and critically evaluate the improvement realized by expanding a monitoring network. While we use nitrate pollution in the Wabash River Basin as a case study, this approach could be readily applied to any problem where the future value of sensors and network design are being evaluated.
54 ENVIRONMENTAL SCIENCES↗
Analysis and optimization of seismic monitoring networks with Bayesian optimal experimental design
SUMMARY Monitoring networks increasingly aim to assimilate data from a large number of diverse sensors covering many sensing modalities. Bayesian optimal experimental design (OED) seeks to identify data, sensor configurations or experiments which can optimally reduce uncertainty and hence increase the performance of a monitoring network. Information theory guides OED by formulating the choice of experiment or sensor placement as an optimization problem that maximizes the expected information gain (EIG) about quantities of interest given prior knowledge and models of expected observation data. Therefore, within the context of seismo-acoustic monitoring, we can use Bayesian OED to configure sensor networks by choosing sensor locations, types and fidelity in order to improve our ability to identify and locate seismic sources. In this work, we develop the framework necessary to use Bayesian OED to optimize a sensor network’s ability to locate seismic events from arrival time data of detected seismic phases at the regional-scale. This framework requires five elements: (i) A likelihood function that describes the distribution of detection and traveltime data from the sensor network, (ii) A prior distribution that describes a priori belief about seismic events, (iii) A Bayesian solver that uses a prior and likelihood to identify the posterior distribution of seismic events given the data, (iv) An algorithm to compute EIG about seismic events over a data set of hypothetical prior events, (v) An optimizer that finds a sensor network which maximizes EIG. Once we have developed this framework, we explore many relevant questions to monitoring such as: how to trade off sensor fidelity and earth model uncertainty; how sensor types, number and locations influence uncertainty; and how prior models and constraints influence sensor placement.
58 GEOSCIENCES↗
The NREL Sensor Laboratory Detection of Hydrogen Emissions
The development of a functional hydrogen detection system is a multifaceted process that integrates hardware, deployments strategies, and analytics which can be supported by the NREL Sensor Laboratory: 1. Support of the design, validation and optimization of sensing prototypes; 2. Guide optimized sensing element development, including control electronics; 3. Laboratory testing to validate/optimize metrological performance (measurement range, detection limit, etc.); 4. Provide test sites for field deployments representative of real-world scenarios with controlled hydrogen releases; 5. Develop sensor placement and operation guidance; 6. Provide guidance on electronics to accommodate facility integration; 7. Electrical safety designs to allow for operation within restricted zones; 8. Integration into facility monitoring and control systems; 9. Guide incorporation of cyber security elements to protect facilities from malicious attacks; 10. Modeling and application of advanced analytics to detect and quantify emissions; 11. Higher Order dispersion models to guide sensor placement for reliable detection; 12. Advanced analytics for improved metrological performances, and to inform inverse modeling; 13. Market support and commercialization (national and international markets); 14. Commercial deployments in H2@SCALE markets (e.g., HUBs and other large-scale hydrogen markets); and 15. Leverage off international collaborations/partnerships (e.g., NREL is on the advisory board for the European initiative "pre-Normative Research on Hydrogen Releases Assessment"-NHyRA).
08 HYDROGEN↗
Stochastic Learning Approach for Binary Optimization: Application to Bayesian Optimal Design of Experiments
Here, we present a novel stochastic approach to binary optimization suited for optimal experimental design (OED) for Bayesian inverse problems governed by mathematical models such as partial differential equations. The OED utility function, namely, the regularized optimality criterion, is cast into a stochastic objective function in the form of an expectation over a multivariate Bernoulli distribution. The probabilistic objective is then solved by using a stochastic optimization routine to find an optimal observational policy. This formulation (a) is generally applicable to binary optimization problems with soft constraints and is ideal for OED and sensor placement problems; (b) does not require differentiability of the original objective function (e.g., a utility function in OED applications) with respect to the design variable, and thus it enables direct employment of sparsity-enforcing penalty functions such as $\ell_0$, without needing to utilize a continuation procedure or apply a rounding technique; (c) exhibits much lower computational cost than traditional gradient-based relaxation approaches; and (d) can be applied to both linear and nonlinear OED problems with proper choice of the utility function. The proposed approach is analyzed from an optimization perspective with detailed convergence analysis of the optimization approach and is also analyzed from a machine learning perspective with correspondence to policy gradient reinforcement learning. The approach is demonstrated numerically by using an idealized two-dimensional Bayesian linear inverse problem and validated by extensive numerical experiments carried out for sensor placement in a parameter identification setup.
97 MATHEMATICS AND COMPUTING↗
Integrated Harsh Environment Gas / Temperature Wireless Microwave Acoustic Sensor System for Fossil Energy Applications
There is a significant need for sensors capable of detecting gases, such as H 2 , O 2 , NO x , SO x within harsh environments encountered in power plants, industrial manufacturing, oil and gas exploration, and aerospace applications. This project successfully demonstrated the use of wireless microwave acoustic sensor technology for the detection of gases (H 2 or O 2 ) from ambient temperatures up to 650°C. The work focused on langasite (LGS) based surface acoustic wave resonator (SAWR) sensors as the harsh-environment sensor platform and explored multiple combinations of high-temperature thin films and device structures which were used to increase the sensor platform stability and detection capability at temperatures in the operational range of 150°C to 700°C. Specific material configurations that were investigated include: yttria-stabilized-zirconia (YSZ) decorated with Pt nanoparticles, atomic layer deposited (ALD) Al 2 O 3 , palladium, and Pt/Al 2 O 3 co-deposited electrode alloys. Through the deposition of YSZ at temperatures as high as 850°C and the use of graded alloy concentrations of Pt in the fabrication of the Pt/Al 2 O 3 electrode structures, film stress problems were mitigated, and sensor operation and stability achieved. To test and evaluate SAWR sensor performance for the detection of H 2 and O 2 under the influence of temperature variations, a comprehensive gas sensor control system and test apparatus was created to operate within a laboratory box furnace-controlled environment. In addition to the advancement in thin film materials through the deposition and fabrication techniques mentioned above, the work characterized the performance of sensors containing these films in the presence of oxidizing and reducing gases between 25°C and 700°C. In particular, the work revealed that the exposure of the SAWR sensor surfaces to oxidizing environments significantly improve the sensor response to H 2 , whereas the exposure of the sensor to reducing environments at high temperatures (≈ 500°C) renders the sensor irresponsive to H 2 , requiring sensor surface treatment at high temperatures (above 500°C) to recover the responsiveness to H 2 . The SAWR sensors have been also tested for wireless operation and array operation using multiple orientations to resolve the detection of gases under temperature variations. The work developed at the University of Maine was aided by a collaboration with the NETL Research and Innovation Center, Pittsburgh, PA, where thin film materials and device structures fabricated at UMaine were tested and characterized using NETL gas reactors and surface analysis techniques. SAWR sensors fabricated at UMaine were exposed multiple times to temperatures up to 700°C and H 2 concentrations up to 100% in the NETL facilities to measure the sensor performance. Environetix Technologies Corporation, a UMaine harsh-environment sensor spin-off company, also provided support and assistance in sensor system testing and implementation. The sensor small size and configuration allows flexible sensor placement and embedding of multiple sensor arrays into a variety of components within power systems and other aerospace or industrial settings that need to be interrogated wirelessly. The SAW platform is an attractive option for high-temperature harsh-environment gas sensing applications due to its inherent features, namely small size, capability of battery-free and wireless operation, and cost effective scale production using well-established production techniques from the semiconductor industry. The research findings achieved in this work, particularly advances regarding the fabrication and performance of the SAWR gas sensor platform, can be adapted and transferred to industrial power plants and other harsh environments.
01 COAL, LIGNITE, AND PEAT↗
Impacts of New Sensor Types for Selected Advanced Controls
Sensors are critical components for controls in buildings. They collect desired information to input into controls for the completion of subsequent control actions. When sensors work in unhealthy or faulty conditions, the benefits of the control benefits will be compromised regardless of the control’s quality. For buildings, multiple components directly influence the sensor placement and deployment, such as sensor errors, sensor locations, sensor types, and sensor costs.
47 OTHER INSTRUMENTATION↗
Soil and groundwater environmental sensor data, Wax Lake Delta, Louisiana, March 2023 - March 2024
This study evaluates how environmental parameters that integrate biogeochemical processes vary with water table fluctuations in the freshwater Wax Lake Delta (WLD) in Louisiana, U.S.A. This data package contains seven *.csv files and one Excel file that compiles all the data from the individual .csv files. This dataset reports high frequency (15-min) observations of water level, soil redox potential, specific conductance, and pH made for one year along elevation transects located on the older, proximal (OT) and younger, distal (YT) ends of a deltaic island. Water depth relative to the ground surface (cm; HOBO U20L-04; error ± 0.4 cm), water pH and temperature (HOBO MX2501), and specific conductance and temperature (HOBO U24-001) sensors were installed in March 2023. Water depth was corrected for barometric pressure recorded by a separate logger secured to a platform above the highest water level. Soil redox probes (SWAP ORP-40-4-B) were also installed in March 2023. Each probe had four Pt sensors (2 mm width) placed at 10 cm, 20 cm, 30 cm, and 40 cm below the ground surface. Redox data were referenced to an external Ag0/AgCl (3M KCl) reference probe placed in saturated ground and recorded on CR1000X dataloggers (Campbell Scientific) powered by solar panels. A second reference probe was positioned near the primary reference probe for backup and data correction. The tops of the soil redox probes and soil moisture probes were flush with the soil surface so that sensors are reported at their indicated depths below ground surface. Here, we report data collected between 15 March 2023 to 15 March 2024 for all sensors, with some differences due to exact dates of sensor placement or data gaps associated with sensor malfunction. For example, water depth at OT4 was not recorded between March to November 2023. Data flags indicate whether a value is valid (1) or was excluded from data analysis in the associated manuscript (-1).
EARTH SCIENCE > LAND SURFACE > SOILS↗
Applying Infrared Thermography as a Method for Online Monitoring of Turbine Blade Coolant Flow
As gas turbine engine manufacturers strive to implement condition-based operation and maintenance, there is a need for blade monitoring strategies capable of early fault detection and root-cause determination. Given the importance of blade cooling flows to turbine blade health and longevity, there is a distinct lack of methodologies for coolant flowrate monitoring. The present study addresses this identified opportunity by applying an infrared thermography system on an engine-representative research turbine to generate data-driven models for prediction of blade coolant flowrate. Thermal images were used as inputs to a linear regression and regularization algorithm to relate blade surface temperature distribution with blade coolant flowrate. Additionally, this study investigates how coolant flowrate prediction accuracy is influenced by the number and breadth of diagnostic measurements. Here, the results of this study indicate that a source of high-fidelity training data can be used to predict blade coolant flowrate within about six percent error. Furthermore, identification of prioritized sensor placement supports application of this technique across multiple sensor technologies capable of measuring blade surface temperature in operating gas turbine engines, including spatially resolved and point-based measurement techniques.
42 ENGINEERING↗
Hydrogen Dispersion Modeling for Development of Smart Distributed Monitoring
Studying hydrogen dispersion is crucial for ensuring the safe and effective deployment of hydrogen as an energy carrier. This study presents a comprehensive CFD modeling framework for simulating hydrogen dispersion at a real-world hydrogen production, storage, and utilization facility. Utilizing the Hydrogen Research Facility under the Advanced Research on Integrated Energy Systems (ARIES) at the National Renewable Energy Laboratory's (NREL) Flatirons campus, controlled hydrogen releases at 27 kg-H2/hr were simulated. The model incorporated site-specific atmospheric conditions, including hourly wind speeds and temperatures recorded between 8 AM and 8 PM from October to December 2023. To reduce computational demands, a statistical reduction technique was applied to condense the dataset to 100 representative scenarios, validated by statistical tests for wind speeds and power law coefficients. Simulations were conducted using the Reynolds-Averaged Navier-Stokes equations. Results demonstrated that wind speed substantially influences hydrogen dispersion, with low wind conditions forming concentrated clouds and higher wind speeds stretching the plume. Additionally, clustering analysis informed optimal sensor placement at various elevations with up to 10 sensor locations on each elevation. This framework offers a robust approach for understanding hydrogen behavior in ambient conditions and informing detection strategies.
08 HYDROGEN↗