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All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Building complex event processing capability for intelligent environmental monitoring

Rapid evolution of Internet-of-Things is driving the increased deployment of smart sensors in environmental applications, contributing to many big data characteristics of environmental monitoring. Most of the current environmental monitoring systems are not designed to handle real-time datastreams, and the best practices for datastream processing and predictive analytics are yet to be established. This work presents a complex event processing (CEP) engine for detecting anomalies in real time, and demonstrates it using a series of real monitoring data from the geological carbon sequestration domain. We show that the service-based CEP engine is instrumental for enabling environmental intelligent monitoring systems to ingest heterogeneous datastreams with scalable performance. Our CEP framework requires minimal coding from the user and can be easily extended to other similar environmental monitoring applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

Atmospheric and soil-gas monitoring for surface leakage at the San Juan Basin CO{sub 2} pilot test site at Pump Canyon New Mexico, using perfluorocarbon tracers, CO{sub 2} soil-gas flux and soil-gas hydrocarbons

Near-surface monitoring and subsurface characterization activities were undertaken in collaboration with the Southwest Regional Carbon Sequestration Partnership on their San Juan Basin coal-bed methane pilot test site near Navajo City, New Mexico. Nearly 18,407 short tons (1.670 × 107 kg) of CO{sub 2} were injected into 3 seams of the Fruitland coal between July 2008 and April 2009. Between September 18 and October 30, 2008, two additions of approximately 20 L each of perfluorocarbon (PFC) tracers were mixed with the CO{sub 2} at the injection wellhead. PFC tracers in soil-gas and in the atmosphere were monitored over a period of 2 years using a rectangular array of permanent installations. Additional monitors were placed near existing well bores and at other locations of potential leakage identified during the pre-injection site survey. Monitoring was conducted using sorbent containing tubes to collect any released PFC tracer from soil-gas or the atmosphere. Near-surface monitoring activities also included CO{sub 2} surface flux and carbon isotopes, soil-gas hydrocarbon levels, and electrical conductivity in the soil. The value of the PFC tracers was demonstrated when a significant leakage event was detected near an offset production well. Subsurface characterization activities, including 3D seismic interpretation and attribute analysis, were conducted to evaluate reservoir integrity and the potential that leakage of injected CO{sub 2} might occur. Leakage from the injection reservoir was not detected. PFC tracers made breakthroughs at 2 of 3 offset wells which were not otherwise directly observable in produced gases containing 20–30% CO{sub 2}. These results have aided reservoir geophysical and simulation investigations to track the underground movement of CO{sub 2}. 3D seismic analysis provided a possible interpretation for the order of appearance of tracers at production wells.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Modeling of time-lapse seismic monitoring using CO 2 leakage simulations for a model CO 2 storage site with realistic geology: Application in assessment of early leak-detection capabilities

Time-lapse surface seismic surveys have been widely used at carbon sequestration sites for site characterization, monitoring subsurface CO 2 plume migration, and detecting potential CO 2 leakage from a storage reservoir. Monitoring in the first permeable unit directly above the primary seal is important for early detection of CO 2 leakage. Forward modeling of time-lapse seismic data can be used to assess the utility of the seismic method for CO 2 leakage detection. We develop a workflow for forward modeling of time-lapse seismic data, including constructing seismic velocity models using flow simulation outputs, modeling of pre-stack and post-stack synthetic seismic data following seismic data processing sequence and analysis of processed synthetic time-lapse seismic data. We apply the forward modeling and analysis workflow to assessing the detectability and the earliest detection time of seismic monitoring using the hypothetical CO 2 leakage scenarios for a model geologic storage site with realistic geology. We then derive the detection thresholds using the simulated normalized root-mean-square (NRMS) differences for the no-leakage case at a range of signal-to-noise ratios, representing the background noise levels in seismic data. We then compare NRMS differences triggered by the CO 2 leakage to the detection thresholds at each time step to quantify the detectability and the earliest detection time of seismic monitoring. We analyze the effects of the acquisition parameters and elastic parameters on the produced synthetic seismic data and earliest detection time. Our modeling results indicate that high signal-to-noise ratio is needed to detect the CO 2 leakage at the model site. Minimizing the background noise in seismic data is crucial for improving the detectability of the seismic method. Increasing the shot density or increasing the dominant frequency of the source wavelet is likely to increase the possibility of the leakage detection and reduce the time needed for the detection. The elastic parameters used in the rock physics modeling have significant effects on the resultant seismic velocity models and synthetic seismic data, highlighting their critical roles in understanding and interpreting time-lapse seismic reflection data associated with CO 2 monitoring, verification and accounting activities.

58 GEOSCIENCES↗
DOE OSTI2019

Risk-based monitoring designs for detecting CO 2 leakage through abandoned wellbores: An application of NRAP’s WLAT and DREAM tools

We report that one suggested approach to managing the risk of geologic CO 2 storage projects is to perform risk-based subsurface monitoring targeting early leak detection. The challenge in risk-based monitoring is that leakage impacts are expected to be small in comparison with the footprint of the stored CO 2 plume and could be at depths ranging from the depth of storage (often ~1 km bgs) up to surficial aquifers. Addressing this challenge requires a multidisciplinary effort capable of characterizing potential subsurface leakage in terms of likelihood, location, magnitude, impacts to overlying aquifers, and uncertainty as well as innovative monitoring technology design and implementation. The National Risk Assessment Partnership (NRAP) was established to develop related risk assessment tools to provide guidance on such assessments. Here we demonstrate the application workflow of two of the NRAP computational tools, WLAT (Wellbore Leakage Analysis Tool) and DREAM (Designs for Risk Evaluation and Management), to a hypothetical CO 2 storage site based on a study area in the Midwestern United States. This workflow shows how CO 2 leakage may be estimated, evaluated, and monitored in terms of risk. Results provide guidance on monitoring configurations and technologies which minimize the time to leak detection, maximize the probability detection, and provide cost considerations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Optimal design of microseismic monitoring network: Synthetic study for the Kimberlina CO2 storage demonstration site

Microseismic monitoring can play a crucial role to ensure safe long-term geological carbon storage. For reliable long-term monitoring for CO2-injection-induced microseismic events, a surface seismic array is desirable in addition to borehole geophone sensors. Optimal design of surface seismic network is of great interest to achieve cost-effective monitoring. We develop a methodology to determine the optimal number of surface seismic stations with a geometrically satisfactory distribution for given monitoring regions. We design an optimal microseismic monitoring network based on widely-accepted guiding principles, and the relationship between the location accuracy of microseismic events and the total number of seismic stations. We determine the optimal number of seismic stations based on the trade-off curve of the event location accuracy vs. the total number of seismic stations. We apply our optimal design method to the Kimberlina carbon storage site in California. We use a synthetic Kimberlina model to show that approximately 20 surface seismic stations with a geometrically satisfactory distribution are preferred for the best trade-off between the cost and the event location accuracy.

58 GEOSCIENCES↗
DOE OSTI2022

Seismic monitoring of CO 2 geosequestration using multi-well 4D DAS VSP: Stage 3 of the CO2CRC Otway project

An important part of any CO 2 geosequestration project is to ensure CO 2 containment and conformance in the subsurface. This is generally done by implementing a comprehensive, risk-based Measurement, Monitoring and Verification plan, a key element of which is active time-lapse seismic monitoring. However, high cost and environmental impact of the standard surface seismic monitoring dictate the need for a cost-effective and environmentally friendly alternative. An opportunity to develop such method emerges with advances in distributed acoustic sensing (DAS) technology, which turns an optical fibre into a seismic sensor with dense spatial sampling. DAS can be permanently deployed in multiple wells across the geosequestration site providing a robust and non-intrusive network of seismic receivers. This approach was developed and tested in the CO2CRC Otway project, where injection of 15 kt of CO 2 at 1.5 km depth was monitored with a 4D vertical seismic profiling (VSP) using five borehole DAS arrays and mobile vibroseis sources. The 4D DAS VSP in each of the five wells provides broadly consistent images of the CO 2 plume with some differences due to different illumination of the target horizon, lateral variation of velocities, and seismic anisotropy. When the newly injected CO 2 reaches a CO 2 plume created as a result of an earlier injection into the same formation ~600 m updip, 4D DAS VSP shows a change in reflectivity in that area and beyond. Furthermore, this shows a potential of 4D DAS VSP for monitoring gas injection into gas-saturated reservoirs.

58 GEOSCIENCES↗
DOE OSTI2015

Organic liquid scintillation detectors for on-the-fly neutron/gamma alarming and radionuclide identification in a pedestrian radiation portal monitor

Here, we present new experimental results from a radiation portal monitor based on the use of organic liquid scintillators. The system was tested as part of a 3 He-free radiation portal monitor testing campaign at the European Commission’s Joint Research Centre in Ispra, Italy, in February 2014. The radiation portal monitor was subjected to a wide range of test conditions described in ANSI N42.35, including a variety of gamma-ray sources and a 20,000 n/s 252 Cf source. A false alarm test tested whether radiation portal monitors ever alarmed in the presence of only natural background. The University of Michigan Detection for Nuclear Nonproliferation Group’s system triggered zero false alarms in 2739 trials. It consistently alarmed on a variety of gamma-ray sources travelling at 1.2 m/s at a 70 cm source to detector distance. The neutron source was detected at speeds up to 3 m/s and in configurations with up to 8 cm of high density polyethylene shielding. The success of on-the-fly radionuclide identification varied with the gamma-ray source measured as well as with which of two radionuclide identification methods was used. Both methods used a least squares comparison between the measured pulse height distributions to library spectra to pick the best match. The methods varied in how the pulse height distributions were modified prior to the least squares comparison. Correct identification rates were as high as 100% for highly enriched uranium, but as low as 50% for 241 Am. Lastly, both radionuclide identification algorithms produced mixed results, but the concept of using liquid scintillation detectors for gamma-ray and neutron alarming in radiation portal monitor was validated.

3He replacement technology↗
DOE OSTI2026

A call to standardize metrics for monitoring baleen whales near marine construction activities

Effective monitoring is necessary to protect marine mammal species during the construction of offshore infrastructure. The tools for detecting or monitoring marine mammals span traditional (e.g., visual observers, optical cameras), to newer (e.g., passive acoustic monitoring, infrared cameras, tags), and emerging (e.g., satellite imagery, environmental DNA, dimethyl sulfide concentration) technologies. Some are better suited for use during offshore development; however, peer-reviewed literature does not typically evaluate and report on the performance of these various technologies. We define a minimum set of metrics related to efficacy (i.e., confusion matrix, precision and recall, probability of missed mitigation), detection range (i.e., maximum and reliable detection range, spatial resolution), and data delivery (i.e., detection latency, system reliability, temporal resolution) that we recommend are needed to assess the utility of monitoring technologies for this purpose. Following a literature review of relevant studies, we highlight which publications reported these metrics and used multiple technologies to compare relative performance. We also emphasize the benefits of multi-modal approaches and recommend performance assessments through modeling or large-scale collaborative field testing. These metrics will standardize data collection, reporting, and analysis; promote consistent and comparable results; and foster collaboration among developers, regulatory agencies, and scientists. This may lead to the co-development of technology that achieves multiple goals, has greater application, and can answer research questions while collecting data to fulfill permitting requirements. These metrics may also inform decisions on what systems regulatory agencies might consider using and reduce monitoring costs, which is critical to support the marine sector's rapid growth alongside marine mammal conservation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Heat pulse testing at monitoring wells to estimate subsurface fluid velocities in geological CO 2 storage

Monitoring the injected CO 2 during geological CO 2 storage (GCS) is essential to assure containment and identify CO 2 leakage. Here in this work, a new approach is introduced to estimate the evolution of the downhole fluid velocity at a monitoring well and identify CO 2 arrival time using in-well heat pulse/tracer test. The proposed technique involves using a downhole heater to generate a series of heat pulses and measuring their corresponding temperature response. The surface temperature of the downhole heater is controlled by the supplied electrical power and the heat loss by convection to the surroundings. Convective heat transfer is well described using Newton's law of cooling in which the temperature difference between the heater and the surrounding fluids drives the heat transfer, for which the convection heat transfer coefficient (h) controls the magnitude of heat loss. Among various factors that control h, it depends on the type of the flowing fluid and its velocity. Through analyzing the measured temperature at different heat pulses, the changes in h - due to mobilization of the in-situ brine or CO 2 arrival - can be estimated. Consequently, the velocity of the flowing fluid across the heater can be obtained. Since heat transfer by convection is sensitive to the type of the surrounding fluid, intrusion of CO 2 can be detected from the relatively higher surface temperature obtained at CO 2 arrival. Churchill and Bernstein (1977)'s correlation is adopted to estimate the change of fluid velocity in terms of the change in h. To demonstrate the validity of the proposed technique, the results are applied and validated against those of COMSOL Multiphysics simulation tool for single-phase brine (before CO 2 arrival) and single-phase CO 2 (after CO 2 arrival). The observed temperature heating is sensitive to the flowing fluid velocity and fluid type. The temperature signal observed at CO 2 arrival is large and easily detectable using temperature monitoring tool which provides reliable indication for tracking CO 2 arrival at monitoring wells compared with passive temperature monitoring. The results obtained using the proposed technique agree very well with the numerical results obtained from the simulation tool with a maximum estimation error of 7 percent.

02 PETROLEUM↗
DOE OSTI2024

A comparative study on deep learning models for condition monitoring of advanced reactor piping systems

Advanced nuclear reactors offer innovative applications due to their portability, reliability, resiliency, and high capacity factors. To operate them on a wider scale, reducing maintenance life-cycle costs while ensuring their integrity is essential. Autonomous operations in advanced nuclear reactors using augmented Digital Twin (DT) technology can serve as a cost-effective solution by increasing awareness about the system’s health. A key component of nuclear DT frameworks is the condition monitoring of safety systems, such as piping-equipment systems, which involves acquiring and monitoring the plant’s sensor data. Here, this research proposes a condition monitoring methodology utilizing deep learning algorithms, such as multilayer perceptions (MLP) and convolutional neural networks (CNNs), to detect degradation and its severity in nuclear piping-equipment systems. Sensor signals are processed to obtain the power spectral density and the Short-Time Fourier transform, and feature extraction methodologies are proposed to develop degradation-sensitive data repositories. The performance of MLP, one-dimensional (1D) CNN, and 2D CNN within the proposed condition monitoring framework is compared using a finite element model of a 3D piping system subjected to seismic loads as the application case study. Various approaches, such as dropout, k-Fold validation, regularization, and early stopping of training the network, are investigated to avoid overfitting the models to the input sensor data. The predictive capability and computational capacity of the deep learning algorithms are also compared to detect degradation in the Z-pipe system of the Experimental Breeder Reactor II (EBRII). The Z-pipe system is subjected to harmonic excitations that represent normal operating loads, such as pump-induced vibrations. The findings of the study indicate that the proposed artificial intelligence (AI)-driven condition monitoring framework demonstrates superior prediction accuracies with a 2D CNN, whereas the MLP exhibits higher computational efficiency.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2014

Systems engineering approach towards performance monitoring of emergency diesel generator

Systems engineering is an interdisciplinary approach and means to enable the realization of successful systems. In this study, systems engineering approach towards the performance monitoring of Emergency Diesel Generator (EDG) is presented. Performance monitoring is part and parcel of predictive maintenance where the systems and components conditions can be detected before they result into failures. In an effort to identify the proposal for addressing performance monitoring, the EDG boundary has been defined. Based on the Probabilistic Safety Analysis (PSA) results and industry operating experiences, the most critical component is identified. This paper proposed a systems engineering concept development framework towards EDG performance monitoring. The expected output of this study is that the EDG reliability can be improved by the performance monitoring alternatives through the systems engineering concept development effort.

42 ENGINEERING↗
DOE OSTI2014

The status and prospective of environmental radiation monitoring stations in Saudi Arabia

The use of nuclear technology requires an environmental monitoring program to ensure the safety of the environment, and to protect people from the hazards of radioactive materials, and nuclear accidents. Nuclear accidents are unique, for they incur effects that surpass international frontiers, and can even have a long lasting impact on Earth. Such was the case of the Chernobyl accident in the Ukraine on April 6, 1986. For that purpose, international and national efforts come together to observe for any nuclear or radioactive accident. Many states, including Saudi Arabia which oversees the operation of the National Radiation, Environmental and Early Monitoring Stations, The Radiation Monitoring Stations(RMS’s) are currently scattered across 35 cities in the country,. These locations are evaluated based on various technological criteria such as border cities, cities of high population density, wind direction, etc. For new nuclear power plants hovering around, it is strongly recommended to increase the number of radiation monitoring stations to warn against any threat that may arise from a nuclear leak or accident and to improve the performance of the existing RMS’s. SARA (Spectroscopic Monitoring Station for air) should be implemented due to the high sensitivity to artificial radiation, automatic isotope identification, free of maintenance, and fully independent due to solar power supply (incl. battery backup) and wireless communication (GPRS)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Effects-Based Monitoring of Geomagnetically-Induced Current Using a Convolutional Neural Network

Geomagnetically-induced current (GIC) due to space weather can flow in the power grid causing undesirable effects such as transformer overheating, misoperation of protection devices, and potential blackouts. It is therefore important to monitor GIC in the power grid to improve online situational awareness and decision-making of system operators during a geomagnetic disturbance. To avoid the costly installation of GIC monitors at transformers’ neutrals, it is desirable to find correlations between GIC and already-monitored parameters. Hence, this work proposed the use of a convolutional neural network (CNN) to compute GIC amplitudes from learned patterns in the time-series data of transformer even harmonic currents. Using an electromagnetic transient program, GIC injection simulations were performed for a modeled Dominion Energy Virginia (DEV) substation with two 504 MVA, 500/230 kV transformers. Data collected from these offline simulations were used to train the CNN to provide online GIC monitoring. Testing the CNN performance involved using real GIC measurements from published literature and from a physical GIC monitor in the DEV area. Finally, the results showed that the proposed method was able to provide GIC readings with a root mean squared error of 1.56 A/phase (equivalent to an average accuracy of 94%) for these real GIC waveforms.

42 ENGINEERING↗
DOE OSTI2019

Condition Monitoring of Wind Turbine Planetary Gearboxes Under Different Operating Conditions

Digitally enhanced services for wind power could reduce Operations and Maintenance (O&M) costs as well as the Levelised Cost Of Energy (LCOE). Therefore, there is a continuous need for advanced monitoring techniques which can exploit the opportunities of Internet of Things (IoT) and Big Data Analytics, revolutionizing the future of the energy sector. The heart of wind turbines is a rather complex epicyclic gearbox. Among others, extremely critical gearbox components which are often responsible for machinery stops are the rolling element bearings. The vibration signatures of bearings are rather weak compared to other components, such as gears, and as a result an extended number of signal processing techniques and tools have been proposed during the last decades, focusing towards accurate, early, and on time bearing fault detection with limited false alarms and missed detections. Envelope Analysis is one of the most important methodologies, where an envelope of the vibration signal is estimated usually after filtering around a frequency band excited by impacts due to the bearing faults. Different tools, such as Kurtogram, have been proposed in order to accurately select the optimum filter parameters (center frequency and bandwidth). Cyclic Spectral Correlation and Cyclic Spectral Coherence, based on Cyclostationary Analysis, have been proved as very powerful tools for condition monitoring. The monitoring techniques seem to have reached a mature level in case a machinery operates under steady speed and load. On the other hand, in case the operating conditions change, it is still unclear whether the change of the monitoring indicators is due to the change of the health of the machinery or due to the change of the operating parameters. Recently, the authors have proposed a new tool called IESFOgram, which is based on Cyclic Spectral Coherence and can automatically select the filtering band. Furthermore, the Cyclic Spectral Coherence is integrated along the selected frequency band leading to an Improved Envelope Spectrum. In this paper, the performance of the tool is evaluated and further extended on cases operating under different speeds and different loads. The effectiveness of the methodology is tested and validated on the National Renewable Energy Laboratory (NREL) wind turbine gearbox vibration condition monitoring benchmarking data set which includes various faults with different levels of diagnostic complexity as well as various speed and load operating conditions.

17 WIND ENERGY↗
DOE OSTI2012

Monitoring Thermal Fatigue Damage In Nuclear Power Plant Materials Using Acoustic Emission

Proactive aging management of nuclear power plant passive components requires technologies to enable monitoring and accurate quantification of material condition at early stages of degradation (i.e., pre-macrocrack). Acoustic emission (AE) is well-suited to continuous monitoring of component degradation and is proposed as a method to monitor degradation during accelerated thermal fatigue tests. A key consideration is the ability to separate degradation responses from external sources such as water spray induced during thermal fatigue testing. Water spray provides a significant background of acoustic signals, which can overwhelm AE signals caused by degradation. Analysis of AE signal frequency and energy is proposed in this work as a means for separating degradation signals from background sources. Encouraging results were obtained by applying both frequency and energy filters to preliminary data. The analysis of signals filtered using frequency and energy provides signatures exhibiting several characteristics that are consistent with degradation accumulation in materials. Future work is planned to enable verification of the efficacy of AE for thermal fatigue crack initiation detection. While the emphasis has been placed on the use of AE for crack initiation detection during accelerated aging tests, this work also has implications with respect to the use of AE as a primary tool for early degradation monitoring in nuclear power plant materials. The development of NDE tools for characterization of aging in materials can also benefit from the use of a technology such as AE which can continuously monitor and detect crack initiation during accelerated aging tests.

acoustic emission↗
DOE OSTI2012

Automated size-specific CT dose monitoring program: Assessing variability in CT dose

Purpose: The potential health risks associated with low levels of ionizing radiation have created a movement in the radiology community to optimize computed tomography (CT) imaging protocols to use the lowest radiation dose possible without compromising the diagnostic usefulness of the images. Despite efforts to use appropriate and consistent radiation doses, studies suggest that a great deal of variability in radiation dose exists both within and between institutions for CT imaging. In this context, the authors have developed an automated size-specific radiation dose monitoring program for CT and used this program to assess variability in size-adjusted effective dose from CT imaging. Methods: The authors radiation dose monitoring program operates on an independent health insurance portability and accountability act compliant dosimetry server. Digital imaging and communication in medicine routing software is used to isolate dose report screen captures and scout images for all incoming CT studies. Effective dose conversion factors (k-factors) are determined based on the protocol and optical character recognition is used to extract the CT dose index and dose-length product. The patient's thickness is obtained by applying an adaptive thresholding algorithm to the scout images and is used to calculate the size-adjusted effective dose (ED{sub adj}). The radiation dose monitoring program was used to collect data on 6351 CT studies from three scanner models (GE Lightspeed Pro 16, GE Lightspeed VCT, and GE Definition CT750 HD) and two institutions over a one-month period and to analyze the variability in ED{sub adj} between scanner models and across institutions. Results: No significant difference was found between computer measurements of patient thickness and observer measurements (p= 0.17), and the average difference between the two methods was less than 4%. Applying the size correction resulted in ED{sub adj} that differed by up to 44% from effective dose estimates that were not adjusted by patient size. Additionally, considerable differences were noted in ED{sub adj} distributions between scanners, with scanners employing iterative reconstruction exhibiting significantly lower ED{sub adj} (range: 9%-64%). Finally, a significant difference (up to 59%) in ED{sub adj} distributions was observed between institutions, indicating the potential for dose reduction. Conclusions: The authors developed a robust automated size-specific radiation dose monitoring program for CT. Using this program, significant differences in ED{sub adj} were observed between scanner models and across institutions. This new dose monitoring program offers a unique tool for improving quality assurance and standardization both within and across institutions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

MO-G-BRE-01: A Real-Time Virtual Delivery System for Photon Radiotherapy Delivery Monitoring

Purpose: Treatment delivery monitoring is important for radiotherapy, which enables catching dosimetric error at the earliest possible opportunity. This project develops a virtual delivery system to monitor the dose delivery process of photon radiotherapy in real-time using GPU-based Monte Carlo (MC) method. Methods: The simulation process consists of 3 parallel CPU threads. A thread T1 is responsible for communication with a linac, which acquires a set of linac status parameters, e.g. gantry angles, MLC configurations, and beam MUs every 20 ms. Since linac vendors currently do not offer interface to acquire data in real time, we mimic this process by fetching information from a linac dynalog file at the set frequency. Instantaneous beam fluence map (FM) is calculated. A FM buffer is also created in T1 and the instantaneous FM is accumulated to it. This process continues, until a ready signal is received from thread T2 on which an inhouse developed MC dose engine executes on GPU. At that moment, the accumulated FM is transferred to T2 for dose calculations, and the FM buffer in T1 is cleared. Once the calculation finishes, the resulting 3D dose distribution is directed to thread T3, which displays it in three orthogonal planes overlaid on the CT image for treatment monitoring. This process continues to monitor the 3D dose distribution in real-time. Results: An IMRT and a VMAT cases used in our patient-specific QA are studied. Maximum dose differences between our system and treatment planning system are 0.98% and 1.58% for the two cases, respectively. The average time per MC calculation is 0.1sec with <2% relative uncertainty. The update frequency of ∼10Hz is considered as real time. Conclusion: By embedding a GPU-based MC code in a novel data/work flow, it is possible to achieve real-time MC dose calculations to monitor delivery process.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

TH-C-17A-09: Direct Visualization and Monitoring of Medical Radiation Beams in Air

Purpose: Radiation therapy errors are rare but potentially catastrophic. Recent fatal incidents could have been avoided by utilizing real-time methods of monitoring delivery of radiation during treatment. However, few existing methods are practical enough to be used routinely. The study presents the first experimental demonstration of a novel non-perturbing method of monitoring radiation therapy through the phenomena of air scintillation. Methods: Monitoring of radiation delivery was devised by leveraging the phenomena of nitrogen excitation in air by ionizing radiation. The excitation induced weak luminescence in the 300–400 nm range, a process called air scintillation. An electron-multiplication charge-coupled device camera (f/0.95 lens; 440 nm shortpass) was set-up in a clinical treatment vault and was used to capture air scintillation images of kilovoltage and megavoltage beams. Monte Carlo simulations were performed to determine the correlation of radiation dose to air scintillation. Results: Megavoltage beams from a Varian Clinac 21EX and kilovoltage beams from an orthovoltage unit (50 kVp, 30 mA) were visualized with a relatively short exposure time (10 s). Cherenkov luminescence produced in a plastic transparent phantom did not interfere with detection of air scintillation. The image intensity displayed an inverse intensity falloff (r{sup 2} = 0.89) along the central axis and was proportional to dose rate (r{sup 2} = 0.9998). As beam energy increased, the divergence of the imaged beam decreased. Last, air scintillation was visualized during a simulated total skin irradiation electron treatment. Conclusion: Air scintillation can be clinically detected to monitor a radiation beam in an inexpensive and non-perturbing manner. This new method is advantageous in monitoring for gross delivery and uniquely capable of wide area in a single acquisition, such as the case for online verification of total body / skin / lymphoid irradiation treatments.

60 APPLIED LIFE SCIENCES↗