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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Nicotine Monitoring with a Wearable Sweat Band

The tobacco epidemic is a public health threat that has taken a heavy toll of lives around the globe each year. Smoking affects both the smokers and those who are exposed to secondhand smoke, and careful tracking of exposure can be key to mitigating the potential hazards. For smokers, the variation of chemical compositions between commercial cigarettes has led to ambiguity in estimating the health risks, both for active smokers and others involuntarily exposed to tobacco smoke and byproducts. In this regard, sweat possesses an attractive opportunity to monitor smoke exposure due to sweat's abundance in biomolecules and its great accessibility. In this paper, we present a wearable sweat band to monitor nicotine, a prominent ingredient in cigarettes, as a viable way to quantitatively assess a wearer's exposure to smoking. Both smokers and normal subjects are tested to demonstrate the use of this device for smoke-related health monitoring. Our results exhibit confirmable and elevated nicotine levels in sweat for subjects inhaling cigarette smoke. This continuous and personalized sweat sensing device is leverage to monitor smoke pollution for a potentially broad population.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Reward-Based Participant Management for Crowdsourcing Rainfall Monitoring: An Agent-Based Model Simulation

Crowdsourcing incorporates common citizens as rich sources of data and is promising for environmental monitoring. Here, we propose and test the idea of incorporating incentives to crowdsourcing management for rainfall monitoring. In particular, we model the allocation of incentives (quantitatively measurable and limited rewards) among crowdsourcing participants for a theoretical rainfall monitoring case. For this purpose, we develop an integrated model comprising a reward allocation component to represent the decision-making process of a central manager, an agent-based model to simulate the interactions between the manager and participants, and a rainfall simulation model to evaluate the effectiveness of various reward allocation policies. We simulate six reward allocation policies of varying levels of administrative cost, and consideration of participant and rainfall spatial heterogeneities. The findings indicate the performance of each policy to improve with the reward budget and their spatial uniformity. Among the six policies tested, we discover that the participant density weighted maximum participation policy yields the most accurate estimation of rainfall intensity due to its more explicit consideration of the spatial distribution of participants; however, this policy associates with a high administrative cost. This highlights the trade-off between performance and cost in designing effective reward allocation policies. This paper provides a physical and behavior simulation modeling tool to study the feasibility and complexity of reward-based participant management for crowdsourcing rainfall monitoring. The proposed crowdsourcing method is beneficial for a wide range of applications that require rainfall data with fine resolution, such as storm water management and water availability and biomass assessment for food and energy crops.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Thermographic Microstructure Monitoring in Electron Beam Additive Manufacturing

To reduce the uncertainty of build performance in metal additive manufacturing, robust process monitoring systems that can detect imperfections and improve repeatability are desired. One of the most promising methods for in-situ monitoring is thermographic imaging. However, there is a challenge in using this technology due to the difference in surface emittance between the metal powder and solidified part being observed that affects the accuracy of the temperature data collected. This developed a method for properly calibrating temperature profiles from thermographic data and then determining important characteristics of the build through additional processing. The thermographic data was analyzed to determine the transition of material from metal powder to a solid as-printed part. A corrected temperature profile was then assembled for each point using calibrations for these surface conditions. Using this data, we calculated the thermal gradient and solid-liquid interface velocity and correlated it to microstructural variation within the part experimentally. This work shows that by using a method of process monitoring, repeatability of a build could be monitored specifically in relation to microstructure control.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Thermal stability of curved ray tomography for corrosion monitoring

Guided wave tomography is being developed as an effective tool for continuous monitoring of corrosion and erosion depth in pipelines. A pair of transmit- and receive-ring arrays of ultrasonic transducers encircles the pipe and delimits the section to be monitored. In curved ray tomography (CRT), the depth profile is estimated from the time delay matrix, Δτ, whose ij-th entry is the phase traveltime difference between the current and baseline signals measured between transducers i and j of the transmit and receive-ring arrays, respectively. Under perfectly stable experimental conditions, the non-zero entries of Δτ are only due to the occurrence of damage and provide a reliable input to CRT. However, during field operation, Δτ can develop non-zero entries due to a number of environmental changes ranging from temperature variations to degradation of transducer-pipe coupling and transducer intrinsic performance. Here, we demonstrate that these sources of instability can be eliminated by exploiting the spatial diversity of array measurements in conjunction with EMAT transducer technology which is intrinsically stable owing to its non-contact nature. The study is based on a full-scale experiment performed on a schedule 40, 8’ diameter, 3 m length steel pipe, monitored with two EMAT ring arrays. It is shown that for an irregularly shaped defect the proposed method yields maximum depth estimations that are as accurate as single point ultrasonic thickness gaging measurements and over a wide temperature range up to 175°C. The results indicate that advanced inversion schemes in combination with EMAT transduction offer great potential for continuously monitoring the progression of corrosion or erosion damage in the oil and gas industry.

42 ENGINEERING↗
DOE OSTI2014

Comprehensive Nuclear-Test-Ban Treaty seismic monitoring: 2012 USNAS report and recent explosions, earthquakes, and other seismic sources

A comprehensive ban on nuclear explosive testing is briefly characterized as an arms control initiative related to the Non-Proliferation Treaty. The work of monitoring for nuclear explosions uses several technologies of which the most important is seismology-a physics discipline that draws upon extensive and ever-growing assets to monitor for earthquakes and other ground-motion phenomena as well as for explosions. This paper outlines the basic methods of seismic monitoring within that wider context, and lists web-based and other resources for learning details. It also summarizes the main conclusions, concerning capability to monitor for test-ban treaty compliance, contained in a major study published in March 2012 by the US National Academy of Sciences.

58 GEOSCIENCES↗
DOE OSTI2014

A non-invasive beam profile monitor for charged particle beams

Non-interceptive beam profile monitors are highly desirable in almost all particle accelerators. Such techniques are especially valuable in applications where real time monitoring of the beam properties is required while beam preservation and minimal influence on the vacuum are of the greatest importance. This applies to many kinds of accelerators such as high energy machines where the normal diagnostics cannot withstand the beam's power, medical machines where treatment time is valuable and cannot be allocated to diagnostics and also low energy, low intensity accelerators where the beam's properties are difficult to measure. This paper presents the design of a gas-jet based beam profile monitor which was developed and commissioned at the Cockcroft Institute and can operate in a very large background pressure range from 10{sup −7} down to below 10{sup −11} millibars. The functioning principle of the monitor is described and the first experimental results obtained using a 5 keV electron beam are discussed.

43 PARTICLE ACCELERATORS↗
DOE OSTI2015

Monitoring system for the quality assessment in additive manufacturing

Additive Manufacturing (AM) refers to a process by which a set of digital data -representing a certain complex 3dim design - is used to grow the respective 3dim real structure equal to the corresponding design. For the powder-based EOS manufacturing process a variety of plastic and metal materials can be used. Thereby, AM is in many aspects a very powerful tool as it can help to overcome particular limitations in conventional manufacturing. AM enables more freedom of design, complex, hollow and/or lightweight structures as well as product individualisation and functional integration. As such it is a promising approach with respect to the future design and manufacturing of complex 3dim structures. On the other hand, it certainly calls for new methods and standards in view of quality assessment. In particular, when utilizing AM for the design of complex parts used in aviation and aerospace technologies, appropriate monitoring systems are mandatory. In this respect, recently, sustainable progress has been accomplished by joining the common efforts and concerns of a manufacturer Additive Manufacturing systems and respective materials (EOS), along with those of an operator of such systems (MTU Aero Engines) and experienced application engineers (Carl Metrology), using decent know how in the field of optical and infrared methods regarding non-destructive-examination (NDE). The newly developed technology is best described by a high-resolution layer by layer inspection technique, which allows for a 3D tomography-analysis of the complex part at any time during the manufacturing process. Thereby, inspection costs are kept rather low by using smart image-processing methods as well as CMOS sensors instead of infrared detectors. Moreover, results from conventional physical metallurgy may easily be correlated with the predictive results of the monitoring system which not only allows for improvements of the AM monitoring system, but finally leads to an optimisation of the quality and insurance of material security of the complex structure being manufactured. Both, our poster and our oral presentation will explain the data flow between the above mentioned parties involved. A suitable monitoring system for Additive Manufacturing will be introduced, along with a presentation of the respective high resolution data acquisition, as well as the image processing and the data analysis allowing for a precise control of the 3dim growth-process.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Research Update: Electrical monitoring of cysts using organic electrochemical transistors

Organotypic three-dimensional (3D) cell culture models have the potential to act as surrogate tissues in vitro, both for basic research and for drug discovery/toxicology. 3D cultures maintain not only 3D architecture but also cell-cell and cell extracellular matrix interactions, particularly when grown in cysts or spheroids. Characterization of cell cultures grown in 3D formats, however, provides a significant challenge for cell biologists due to the incompatibility of these structures with commonly found optical or electronic monitoring systems. Electronic impedance spectroscopy is a cell culture monitoring technique with great potential; however, it has not been possible to integrate 3D cultures with commercially available systems to date. Cyst-like 3D cultures are particularly challenging due to their small size and difficulty in manipulation. Herein, we demonstrate isolation of cyst-like 3D cultures by capillarity and subsequent integration with the organic electrochemical transistor for monitoring the integrity of these structures. We show not only that this versatile device can be adapted to the cyst format for measuring resistance and, therefore, the quality of the cysts, but also can be used for quantitative monitoring of the effect of toxic compounds on cells in a 3D format. The ability to quantitatively predict effects of drugs on 3D cultures in vitro has large future potential for the fields of drug discovery and toxicology.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Upgrading of data acquisition software for centralized radiation monitoring system in Malaysian Nuclear Agency

With the growth of technology, many devices and equipments can be connected to the network and internet to enable online data acquisition for real-time data monitoring and control from monitoring devices located at remote sites. Centralized radiation monitoring system (CRMS) is a system that enables area radiation level at various locations in Malaysian Nuclear Agency (Nuklear Malaysia) to be monitored centrally by using a web browser. The Local Area Network (LAN) in Nuclear Malaysia is utilized in CRMS as a communication media for data acquisition of the area radiation levels from radiation detectors. The development of the system involves device configuration, wiring, network and hardware installation, software and web development. This paper describes the software upgrading on the system server that is responsible to acquire and record the area radiation readings from the detectors. The recorded readings are called in a web programming to be displayed on a website. Besides the main feature which is acquiring the area radiation levels in Nuclear Malaysia centrally, the upgrading involves new features such as uniform time interval for data recording and exporting, warning system and dose triggering.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Photonic methods and technologies for monitoring the ocean and atmosphere

In this paper, we review photonic methods and technologies that are promising for monitoring the ocean and atmosphere and have been implemented mainly in recent years at the Institute of Automation and Control Processes, Far Eastern Branch, Russian Academy of Sciences. We present results of lidar studies that have made it possible to understand key features of ocean – atmosphere interaction processes under continent – ocean transition conditions, which determine specific features of the atmospheric aerosol distribution, small gaseous components of the atmosphere and its optical characteristics. We consider methods and tools for combined optical and laser fluorescence monitoring of the ocean surface. Particular attention is paid to results of research on remote methods and tools for real-time laser-induced and laser fluorescence environmental monitoring of the ocean, including specialised fibre-optic probes and mobile underwater robotic systems. We present results of the development and investigation of highly sensitive, noise-proof fibre-optic hydro- and seismic/acoustic sensors for remote monitoring of the ocean and robotic systems for underwater laser protection of marine vessels, hydraulic structures and oceanographic instruments against the negative impact of biofouling. (invited paper)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

A Review of Online Monitoring within Used Nuclear Fuel Recycling Processes

The processing of used nuclear fuels and related materials is often complex and variable. The ability to quickly optimize conditions to the material being processed can aid in increasing efficiency and safety, but requires very quick determination of the conditions present in the feedstock, the process, and the product. Furthermore, accurate quantification of materials such as enriched uranium and plutonium aids in maintaining material accountancy and avoiding nuclear proliferation risks. Traditional analytical methods require process samples to be collected and analyzed in a laboratory, which often takes days to weeks. Online monitoring is suitable for collecting this information nearly instantaneously, enabling much faster optimization of the process or detection of material diversion. Online monitoring is also beneficial as it is typically based on robust and nondestructive analytical methods, so no material is removed as samples. This review examines online monitoring relevant to used nuclear fuel processing for the determination of both chemical and physical parameters. The chemical parameters include quantities such as concentration, isotopic composition, and speciation. These values are often well suited to spectroscopic or spectrometric measurements as they are fast, nondestructive, and easily implemented in an online manner. Physical quantities are often more varied and include temperature, pressure, tank fill levels, and others. Due to the specificity of these quantities, specialized instrumentation is often used. However, this instrumentation is often amendable to online monitoring.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2014

OPTICAL MONITORING OF TWO BRIGHTEST NEARBY QUASARS, PHL 1811 AND 3C 273

Variability is one of the most observable characteristics of active galactic nuclei, and it is important when considering the emission mechanism. In this paper, we report optical photometry monitoring of two nearby brightest quasars, PHL 1811 and 3C 273, using the ST-6 camera attached to the Newtonian focus and the Ap6E CCD camera attached to the primary focus of the 70 cm meniscus telescope at the Abastumani Observatory, Georgia. PHL 1811 was monitored during the period from 2002 September to 2012 December, while 3C 273 was monitored during the period from 1998 February to 2008 May. During our monitoring period, the two sources did not show any significant intra-day variability. The largest detected variations are ΔR = 0.112 ± 0.010 mag. for PHL 1811, ΔB = 0.595 ± 0.099 mag, ΔV = 0.369 ± 0.028 mag, ΔR = 0.495 ± 0.076 mag, and ΔI = 0.355 ± 0.009 mag for 3C 273. When the periodicity analysis methods are adopted for the observations of the sources, a period of p = 5.80 ± 1.12 yr is obtained for PHL 1811 in the R light curve in the present work, and periods of p = 21.10 ± 0.14, 10.00 ± 0.14, 7.30 ± 0.09, 13.20 ± 0.09, 2.10 ± 0.06, and 0.68 ± 0.05 yr are obtained for 3C 273 based on the data in the present work combined with historical works.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

The FRAM robotic telescope for atmospheric monitoring at the Pierre Auger Observatory

FRAM (F/Photometric Robotic Atmospheric Monitor) is a robotic telescope operated at the Pierre Auger Observatory in Argentina for the purposes of atmospheric monitoring using stellar photometry. As a passive system which does not produce any light that could interfere with the observations of the fluorescence telescopes of the observatory, it complements the active monitoring systems that use lasers. We discuss the applications of stellar photometry for atmospheric monitoring at optical observatories in general and the particular modes of operation employed by the Auger FRAM. We describe in detail the technical aspects of FRAM, the hardware and software requirements for a successful operation of a robotic telescope for such a purpose and their implementation within the FRAM system.

Optics↗
DOE OSTI2025

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↗
DOE OSTI2024

Automated Framework for Groundwater Monitoring Using DWT with LSTM and Transformers

Environmental monitoring is critical for safeguarding public health and ecological well-being. Traditional data structuring and workflow monitoring methods consume significant time and effort, hindering timely insights and effective decision-making. Our study addresses this challenge by presenting an AI framework that automates data cleaning, structuring, and modeling processes, specifically targeting applications in groundwater monitoring. By leveraging automation for data processing and model training, our framework establishes a novel and efficient paradigm for environmental monitoring, with its potential application to the vast network of over a hundred Department of Energy Environmental Management (DoE-EM) cleanup sites across the country. It analyzes data streams from a network of groundwater Internet-of-Things (IoT) sensors deployed at the Savannah River Site (SRS) for prediction modeling. This allows human experts to focus on analysis and decision-making, ultimately leading to better environmental outcomes.The framework employs multivariate time-series forecasting methods to study and model the behavior of varying chemical analytes. The continuous learning process is enabled by utilizing deep learning techniques. It allows the framework to become more nuanced in its analysis over time, adapting to the specific characteristics of the environmental site and the evolving nature of contaminant behavior. Deep learning models known for sequence modeling, LSTM, and Transformers are employed for time series forecasting. Data processing and structuring are essential components significantly impacting the final model's performance. This hypothesis was proven by presenting a comparative analysis of model performance with processed and unprocessed data. The feature engineering approach utilized was the Discrete Wavelet Transform, which works well with time series data.

Discrete Wavelet Transform (DWT)↗
DOE OSTI2021

Connect the Dots: In Situ 4-D Seismic Monitoring of CO 2 Storage With Spatio-Temporal CNNs

4-D seismic imaging has been widely used in CO 2 sequestration projects to monitor the fluid flow in the volumetric subsurface region that is not sampled by wells. Ideally, real-time monitoring and near-future forecasting would provide site operators with great insights to understand the dynamics of the subsurface reservoir and assess any potential risks. However, due to obstacles such as high deployment cost, availability of acquisition equipment, exclusion zones around surface structures, only very sparse seismic imaging data can be obtained during monitoring. That leads to an unavoidable and growing knowledge gap over time. The operator needs to understand the fluid flow throughout the project lifetime and the seismic data are only available at a limited number of times. This is insufficient for understanding reservoir behavior. To overcome those challenges, we have developed spatio-temporal neural-network-based models that can produce high-fidelity interpolated or extrapolated images effectively and efficiently. Specifically, our models are built on an autoencoder, and incorporate the long short-term memory (LSTM) structure with a new loss function regularized by optical flow. We validate the performance of our models using real 4-D post-stack seismic imaging data acquired at the Sleipner CO 2 sequestration field. We employ two different strategies in evaluating our models. Numerically, we compare our models with different baseline approaches using classic pixel-based metrics. We also conduct a blind survey and collect a total of 20 responses from domain experts to evaluate the quality of data generated by our models. Finally, via both numerical and expert evaluation, we conclude that our models can produce high-quality 2-D/3-D seismic imaging data at a reasonable cost, offering the possibility of real-time monitoring or even near-future forecasting of the CO 2 storage reservoir.

4-D seismic imaging↗
DOE OSTI2021

Smart Meters Enabling Voltage Monitoring and Control Functionalities: The Last-Mile Voltage Stability Issue

It is a demanding yet challenging task to design the next generation of smart meters. This work investigates the new voltage monitoring and control function for the next-generation smart meters, and identifies its added values for power system voltage stability issues. In terms of voltage monitoring, the risk benefit analysis for adding voltage magnitudes to smart meter measurements is presented, and the risk mitigation strategies along with the co-simulation validation via GridLAB-D and NS-3 are proposed, all providing insight into the new function evaluation. In terms of voltage control, a new voltage stability control scheme is developed, which uses voltage measurements from smart meters and utilizes the observability and controllability of distributed energy resources and controllable loads. Different from traditional voltage control schemes focusing on voltage regulation and power quality monitoring from the utility perspective, the proposed control scheme can achieve the maximum real power transfer margin at the end user side, enabling the voltage stability issues being solved at the grid edge, i.e. the last-mile segment. It is the first work that considers voltage monitoring and control collectively for smart meter investment. It can help power engineering community supplement smart meter applications and shape the next-generation smart meters.

42 ENGINEERING↗
DOE OSTI2025

Deep Neural Network Assisted Distributed Strain and Temperature Fiber Sensor System for Natural Gas Pipeline Monitoring

Natural gas pipeline integrity monitoring is crucial to detect potential leaks, find structural issues, and prevent environmental damage. This article presents a system of natural gas pipeline monitoring that uses a specialized double Brillouin peak sensing fiber along with the Brillouin optical time domain analysis (BOTDAs) technique. The calibrated sensing fiber coefficients for strain and temperature are 41.8 kHz/ με and 0.9 MHz/°C for peak 1; and 47.2 kHz/ με , and 1.11 MHz/°C for peak 2, respectively. Initially, lab tests were performed by installing a short section of double Brillouin peak fiber (DBPF) on a 1-in steel pipe under pressure up to 1000 per square inch (psi) at elevated temperatures. Simultaneous distributed measurements of temperature and pressure-induced hoop strain were successfully measured. Considering the long processing speed to extract Brillouin frequency shift (BFS), we employ a novel probabilistic deep neural network (PDNN) framework for rapid BFS prediction. Additionally, using the Finite Element Method, the effects of the pipeline pressure on hoop strain were modeled and compared to the experimental hoop strain under the same set of pipeline conditions. Finally, an actual 4-in outer diameter steel natural gas pipeline was used for pilot-scale tests, where hoop strain was measured at various pressure levels. Leaks were simulated to demonstrate accurate pipeline integrity monitoring. At an internal pipe pressure of 1000 psi, hoop strain of approximately 300 με was observed, and the sensitivity was calculated as 0.28 με /psi. The results of this pilot-scale study demonstrated that the system is capable of performing distributed monitoring sufficient to detect pipeline pressure and the presence of leaks to ensure the safe operation of gas pipelines in the field.

03 NATURAL GAS↗