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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.

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At least 1,207 records · Page 67
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Robustness analysis of an air heating plant and control law by using polynomial chaos

This paper presents a robustness analysis of an air heating plant with a multivariable closed-loop control law by using the polynomial chaos methodology (MPC). The plant consists of a PVC tube with a fan in the air input (that forces the air through the tube) and a mass flux sensor in the output. A heating resistance warms the air as it flows inside the tube, and a thermo-couple sensor measures the air temperature. The plant has thus two inputs (the fan's rotation intensity and heat generated by the resistance, both measured in percent of the maximum value) and two outputs (air temperature and air mass flux, also in percent of the maximal value). The mathematical model is obtained by System Identification techniques. The mass flux sensor, which is nonlinear, is linearized and the delays in the transfer functions are properly approximated by non-minimum phase transfer functions. The resulting model is transformed to a state-space model, which is used for control design purposes. The multivariable robust control design techniques used is the LQG/LTR, and the controllers are validated in simulation software and in the real plant. Finally, the MPC is applied by considering some of the system's parameters as random variables (one at a time, and the system's stochastic differential equations are solved by expanding the solution (a stochastic process) in an orthogonal basis of polynomial functions of the basic random variables. This method transforms the stochastic equations in a set of deterministic differential equations, which can be solved by traditional numerical methods (That is the MPC). Statistical data for the system (like expected values and variances) are then calculated. The effects of randomness in the parameters are evaluated in the open-loop and closed-loop pole's positions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Global carbon budget 2014

Accurate assessment of anthropogenic carbon dioxide (CO 2 ) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and a methodology to quantify all major components of the global carbon budget, including their uncertainties, based on the combination of a range of data, algorithms, statistics, and model estimates and their interpretation by a broad scientific community. We discuss changes compared to previous estimates, consistency within and among components, alongside methodology and data limitations. CO 2 emissions from fossil fuel combustion and cement production ( E FF ) are based on energy statistics and cement production data, respectively, while emissions from land-use change ( E LUC ), mainly deforestation, are based on combined evidence from land-cover-change data, fire activity associated with deforestation, and models. The global atmospheric CO 2 concentration is measured directly and its rate of growth ( G ATM ) is computed from the annual changes in concentration. The mean ocean CO 2 sink ( S OCEAN ) is based on observations from the 1990s, while the annual anomalies and trends are estimated with ocean models. The variability in S OCEAN is evaluated with data products based on surveys of ocean CO 2 measurements. The global residual terrestrial CO 2 sink ( S LAND ) is estimated by the difference of the other terms of the global carbon budget and compared to results of independent dynamic global vegetation models forced by observed climate, CO 2 , and land-cover-change (some including nitrogen–carbon interactions). We compare the mean land and ocean fluxes and their variability to estimates from three atmospheric inverse methods for three broad latitude bands. All uncertainties are reported as ±1σ;, reflecting the current capacity to characterise the annual estimates of each component of the global carbon budget. For the last decade available (2004–2013), E FF was 8.9 ± 0.4 GtC yr⁻¹, E LUC 0.9 ± 0.5 GtC yr⁻¹, G ATM 4.3 ± 0.1 GtC yr⁻¹, S OCEAN 2.6 ± 0.5 GtC yr⁻¹, and S LAND 2.9 ± 0.8 GtC yr⁻¹. For year 2013 alone, E FF grew to 9.9 ± 0.5 GtC yr⁻¹, 2.3% above 2012, continuing the growth trend in these emissions, E LUC was 0.9 ± 0.5 GtC yr⁻¹, G ATM was 5.4 ± 0.2 GtC yr⁻¹, S OCEAN was 2.9 ± 0.5 GtC yr⁻¹, and S LAND was 2.5 ± 0.9 GtC yr⁻¹. G ATM was high in 2013, reflecting a steady increase in E FF and smaller and opposite changes between S OCEAN and S LAND compared to the past decade (2004–2013). The global atmospheric CO 2 concentration reached 395.31 ± 0.10 ppm averaged over 2013. We estimate that E FF will increase by 2.5% (1.3–3.5%) to 10.1 ± 0.6 GtC in 2014 (37.0 ± 2.2 GtCO 2 yr⁻¹), 65% above emissions in 1990, based on projections of world gross domestic product and recent changes in the carbon intensity of the global economy. From this projection of E FF and assumed constant E LUC for 2014, cumulative emissions of CO 2 will reach about 545 ± 55 GtC (2000 ± 200 GtCO 2 ) for 1870–2014, about 75% from E FF and 25% from E LUC . This paper documents changes in the methods and data sets used in this new carbon budget compared with previous publications of this living data set (Le Quéré et al., 2013, 2014). All observations presented here can be downloaded from the Carbon Dioxide Information Analysis Center ( doi:10.3334/CDIAC/GCP_2014 ).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A Synoptic VLBI Technique for Localizing Nonrepeating Fast Radio Bursts with CHIME/FRB

We demonstrate the blind interferometric detection and localization of two fast radio bursts (FRBs) with subarcminute precision on the 400 m baseline between the Canadian Hydrogen Intensity Mapping Experiment (CHIME) and the CHIME Pathfinder. In the same spirit as Very Long Baseline Interferometry (VLBI), the telescopes were synchronized to separate clocks, and the channelized voltage (herein referred to as baseband) data were saved to a disk with correlation performed offline. The simultaneous wide field of view and high sensitivity required for blind FRB searches implies a high data rate—6.5 terabits per second (Tb/s) for CHIME and 0.8 Tb s{sup −1} for the Pathfinder. Since such high data rates cannot be continuously saved, we buffer data from both telescopes locally in memory for ≈40 s, and write to the disk upon receipt of a low-latency trigger from the CHIME Fast Radio Burst Instrument (CHIME/FRB). The ≈200 deg{sup 2} field of view of the two telescopes allows us to use in-field calibrators to synchronize the two telescopes without needing either separate calibrator observations or an atomic timing standard. In addition to our FRB observations, we analyze bright single pulses from the pulsars B0329+54 and B0355+54 to characterize systematic localization errors. Our results demonstrate the successful implementation of key software, triggering, and calibration challenges for CHIME/FRB Outriggers: cylindrical VLBI outrigger telescopes which, along with the CHIME telescope, will localize thousands of single FRB events with sufficient precision to unambiguously associate a host galaxy with each burst.

47 OTHER INSTRUMENTATION↗
DOE OSTI2017

Development and Implementation of an Automatic Continuous Online Monitoring and Control Platform for Polymerization Reactions to Sharply Boost Energy and Resource Efficiency in Polymer Manufacturing

The project goal was to create an energy saving paradigm shift in how polymers are manufactured in the 21st century. It used Automatic Continuous Online Monitoring of Polymerization reactions (ACOMP) integrated for the first time with automatic active control to create the innovative ‘ACOMP/Control Interface’, or ‘ACOMP/CI’. ACOMP/CI will begin the transformation from old, inefficient processes into highly evolved, energy and resource efficient ones. The ACOMP platform is broadly applicable to many types of reactions and processes throughout the vast polymer industry. The industry provides materials for sectors such as automotive, aerospace, oil recovery, agriculture, paints, resins, adhesives, pharmaceuticals and therapeutic proteins, optics, electronics, lightweight building materials, and many more. The U.S. chemical industry is one of the last major sectors in which the U.S. has top global stature. It consumes 24% of all U.S. manufacturing energy, produces over $800B of product annually, supports 25% of the U.S. GDP and employs over 6 million people. It is also a major source of GHG emissions. Polymers make up approximately 30% of this sector. It is estimated that annually 60 TBtu of energy could be saved and 3 million tons less of GHG emissions produced by optimizing production in the polyolefin manufacturing sector alone. The project scope included first time design and prototyping of an ACOMP/CI, creation of active reaction controllers, and demonstration of control capabilities on ideal, low concentration polymerization reactions. All these elements of the scope were met, including advances and findings not originally anticipated. Extensions to more complex reactions, beyond the reactor capabilities of the current project ACOMP/CI, such as polyolefins and other high pressure/high temperature reactions, are being proposed in Fall 2017 to CESMII, a DoE based NNMI. The initial proposal was for a three year funded project, but this was reduced to a two year project and budget due to funding constraints. Hence, some of the original plans, such as adaptation of the ACOMP/CI to more relevant industrial processes, such as emulsion and dispersion technologies, could not be carried out. A third year of funding was requested at the end of the project, but DoE did not have resources to grant this. The sub-contractor Fluence Analytics (previously Advanced Polymer Monitoring Technologies, Inc) designed, prototyped, and commissioned a working ACOMP/CI by June 2015. The reaction characteristics to be automatically controlled were i) conversion kinetics, ii) molecular weight, iii) copolymer composition, and iv) simultaneous molecular weight and composition. A two pronged control strategy was used. The Tulane/Fluence group took a basic principles approach that did not rely on kinetic models. The LSU group took a more complex, non-linear model-oriented approach involving complete kinetic descriptions of the reaction system Each of these approaches proved successful in their own way. By April 2016 fully automatic control of conversion and weight average molecular weight, Mw, trajectories was achieved using the Tulane/Fluence (TF) basic principles controller. Similar results were obtained by the LSU non-linear model controller by August 2016. The demonstration system was aqueous free radical polymerization of acrylamide, Am. The control variables were temperature and semi-batch feed to the reactor of Am monomer and initiator. A demonstration of active manual conversion control in an industrial process using high solids in inverse emulsion polymerization of Am was achieved. During Summer 2016 the TF controller was used in conjunction with a chain transfer agent, another control variable, to automatically produce multi-modal molecular weight distributions, MWD, in a single reactor. Industrially, multi-modal MWD are produced by mixing products made in separate reactors, requiring significant extra time, energy, and reactor resources. Recognizing the industrial potential a patent on automatic production of multi-modal polymers was filed, and DoE acknowledged. In Fall 2016 the TF team developed a basic principles controller for copolymer composition and demonstrated it on aqueous free radical copolymerization of comonomers Am and styrene sulfonate, SS. TF then fused the Mw and composition controllers to achieve simultaneous control of both Mw and copolymer composition trajectories. Numerous simultaneous trajectories were demonstrated, including a trimodal composition distribution with constant Mw. Meanwhile, the LSU group developed a Kalman filter to improve the results of their automatic Mw and conversion controller and successful tests were made. During the project the TF team developed a means of computing full MWD during polymer synthesis without need for any chromatographic separation, based on model distributions. This means the polymer product is ‘born characterized’ and this can eliminate post-manufacture analytical laboratory quality control. TF filed a joint patent application on this new approach to chromatography-free determination of MWD with acknowledgment to DoE. The Tulane group obtained a 60MHz NMR during the project and recently completed the first work on separating three comonomers, Am, SS, and Na-acrylate, with a first demonstration of terpolymer composition control with the TF basic principles controller. Widespread dissemination of ACOMP/CI in the polymer manufacturing sector will bolster DoE goals of energy efficiency and reduced GHG emissions: The ability to monitor and actively control polymerization reactions will lead to more efficient use of energy and non-renewable resources, plant and labor time, increase the safety of manufacturing personnel, and will enhance product quality and lead to feasibility of manufacturing of polymers currently too complex for industrial scale production, while leading to less GHG emissions per kilo of product, and allowing for increased U.S. competitiveness in this enormous manufacturing sector. When ACOMP/CI is expanded to the polyolefin industry it is estimated that 60 TeraBTU/year of energy can be saved. Much of this saving is anticipated to come from optimized control of grade changeovers in steady state reactors and maintenance of steady states. Conclusions: ACOMP’s ability to provide continuous realtime data streams of measured polymer and reaction characteristics made it possible, for the first time, to directly and automatically control free radical polymerization reactions. An industrial client of Fluence Analytics has requisitioned the first ACOMP/CI which uses the TF basic principles controller. This sets the stage for FA to add control features to the ACOMP systems it has begun to install on the industrial scale beginning in 2014. Recommendations: This successful project was mainly limited to ideal polymerizations not of an industrial sort. The most energy intensive portion of polymer manufacturing is polyolefins. Adoption of ACOMP/CI to this enormous industrial sector faces the enormous challenges of high temperature, high pressure continuous sampling and high temperature sensor operation to obtain the continuous data needed for direct reaction control. The project team has a strategy for achieving this ambitious goal and will present it in Fall 2017 as a proposal to CESMII/DoE. It is recommended that this upcoming proposal be funded in order to make full use of the achievements of this just ended DoE project as the next step towards making polyolefin ACOMP/CI an energy saving reality. It is projected that ACOMP/CI can have its first polyolefin testbed demonstrations within two years of beginning the proposed project.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

The Vera C. Rubin Observatory Data Preview 1

We present Rubin Data Preview 1 (DP1), the first data from the National Science Foundation–Department of Energy Vera C. Rubin Observatory, comprising raw and calibrated single-epoch images, coadds, difference images, detection catalogs, and ancillary data products. DP1 is based on 1792 optical–near-infrared exposures acquired over 48 distinct nights by the Rubin Commissioning Camera (LSSTComCam) on the Simonyi Survey Telescope at the Summit Facility on Cerro Pachón, Chile in late 2024. DP1 covers ∼15 deg 2 distributed across seven roughly equal-sized noncontiguous fields, each independently observed in six broad photometric bands, ugrizy. The median FWHM of the point-spread function across all bands is approximately 1"14, with the sharpest images reaching about 0." 58. The 5σ point-source depths for coadded images in the deepest field, the Extended Chandra Deep Field South, are u = 24.55, g = 26.18, r = 25.96, i = 25.71, z = 25.07, and y = 23.1. Other fields are no more than 2.2 mag shallower in any band, where they have nonzero coverage. DP1 contains approximately 2.3 million distinct astrophysical objects, of which 1.6 million are extended in at least one band in coadds, and 431 solar system objects, of which 93 are new discoveries. DP1 is approximately 3.5 TB in size and is available to Vera C. Rubin Observatory data rights holders via the Rubin Science Platform, a cloud-based environment for the analysis of petascale astronomical data. While small compared to future LSST releases, its high quality and diversity of data support a broad range of early science investigations ahead of full operations in 2026.

Ground-based astronomy↗
DOE OSTI2013

Assessment of Interfraction Patient Setup for Head-and-Neck Cancer Intensity Modulated Radiation Therapy Using Multiple Computed Tomography-Based Image Guidance

Purpose: Various image guidance systems are commonly used in conjunction with intensity modulated radiation therapy (IMRT) in head-and-neck cancer irradiation. The purpose of this study was to assess interfraction patient setup variations for 3 computed tomography (CT)-based on-board image guided radiation therapy (IGRT) modalities. Methods and Materials: A total of 3302 CT scans for 117 patients, including 53 patients receiving megavoltage cone-beam CT (MVCBCT), 29 receiving kilovoltage cone-beam CT (KVCBCT), and 35 receiving megavoltage fan-beam CT (MVFBCT), were retrospectively analyzed. The daily variations in the mediolateral (ML), craniocaudal (CC), and anteroposterior (AP) dimensions were measured. The clinical target volume-to-planned target volume (CTV-to-PTV) margins were calculated using 2.5Σ + 0.7 σ, where Σ and σ were systematic and random positioning errors, respectively. Various patient characteristics for the MVCBCT group, including weight, weight loss, tumor location, and initial body mass index, were analyzed to determine their possible correlation with daily patient setup. Results: The average interfraction displacements (± standard deviation) in the ML, CC, and AP directions were 0.5 ± 1.5, −0.3 ± 2.0, and 0.3 ± 1.7 mm (KVCBCT); 0.2 ± 1.9, −0.2 ± 2.4, and 0.0 ± 1.7 mm (MVFBCT); and 0.0 ± 1.8, 0.5 ± 1.7, and 0.8 ± 3.0 mm (MVCBCT). The day-to-day random errors for KVCBCT, MVFBCT, and MVCBCT were 1.4-1.6, 1.7, and 2.0-2.1 mm. The interobserver variations were 0.8, 1.1, and 0.7 mm (MVCBCT); 0.5, 0.4, and 0.8 mm (MVFBCT); and 0.5, 0.4, and 0.6 mm (KVCBCT) in the ML, CC, and AP directions, respectively. The maximal calculated uniform CTV-to-PTV margins were 5.6, 6.9, and 8.9 mm for KVCBCT, MVFBCT, and MVCBCT, respectively. For the evaluated patient characteristics, the calculated margins for different patient parameters appeared to differ; analysis of variance (ANOVA) and/or t test analysis found no statistically significant setup difference in any direction. Conclusions: Daily random setup errors and CTV-to-PTV margins for treatment of head-and-neck cancer were affected by imaging quality. Our data indicated that larger margins were associated with MVFBCT and MVCBCT, compared with smaller margins for KVCBCT. IGRT modalities with better image quality are encouraged in clinical practice.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

Global Carbon Budget 2016

Accurate assessment of anthropogenic carbon dioxide (CO 2 ) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere – the “global carbon budget” – is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and methodology to quantify all major components of the global carbon budget, including their uncertainties, based on the combination of a range of data, algorithms, statistics, and model estimates and their interpretation by a broad scientific community. We discuss changes compared to previous estimates and consistency within and among components, alongside methodology and data limitations. CO 2 emissions from fossil fuels and industry ( E FF ) are based on energy statistics and cement production data, respectively, while emissions from land-use change ( E LUC ), mainly deforestation, are based on combined evidence from land-cover change data, fire activity associated with deforestation, and models. The global atmospheric CO 2 concentration is measured directly and its rate of growth ( G ATM ) is computed from the annual changes in concentration. The mean ocean CO 2 sink ( S OCEAN ) is based on observations from the 1990s, while the annual anomalies and trends are estimated with ocean models. The variability in S OCEAN is evaluated with data products based on surveys of ocean CO 2 measurements. The global residual terrestrial CO 2 sink ( S LAND ) is estimated by the difference of the other terms of the global carbon budget and compared to results of independent dynamic global vegetation models. We compare the mean land and ocean fluxes and their variability to estimates from three atmospheric inverse methods for three broad latitude bands. All uncertainties are reported as ±1 σ , reflecting the current capacity to characterise the annual estimates of each component of the global carbon budget. For the last decade available (2006–2015), E FF was 9.3 ± 0.5 GtC yr -1 , E LUC 1.0 ± 0.5 GtC yr -1 , G ATM 4.5 ± 0.1 GtC yr -1 , S OCEAN 2.6 ± 0.5 GtC yr -1 , and S LAND 3.1 ± 0.9 GtC yr -1 . For year 2015 alone, the growth in E FF was approximately zero and emissions remained at 9.9 ± 0.5 GtC yr -1 , showing a slowdown in growth of these emissions compared to the average growth of 1.8 % yr -1 that took place during 2006–2015. Also, for 2015, E LUC was 1.3 ± 0.5 GtC yr -1 , G ATM was 6.3 ± 0.2 GtC yr -1 , S OCEAN was 3.0 ± 0.5 GtC yr -1 , and S LAND was 1.9 ± 0.9 GtC yr -1 . G ATM was higher in 2015 compared to the past decade (2006–2015), reflecting a smaller S LAND for that year. The global atmospheric CO 2 concentration reached 399.4 ± 0.1 ppm averaged over 2015. For 2016, preliminary data indicate the continuation of low growth in E FF with +0.2 % (range of -1.0 to +1.8 %) based on national emissions projections for China and USA, and projections of gross domestic product corrected for recent changes in the carbon intensity of the economy for the rest of the world. In spite of the low growth of E FF in 2016, the growth rate in atmospheric CO 2 concentration is expected to be relatively high because of the persistence of the smaller residual terrestrial sink ( S LAND ) in response to El Niño conditions of 2015–2016. From this projection of E FF and assumed constant E LUC for 2016, cumulative emissions of CO 2 will reach 565 ± 55 GtC (2075 ± 205 GtCO 2 ) for 1870–2016, about 75 % from E FF and 25 % from E LUC . This living data update documents changes in the methods and data sets used in this new carbon budget compared with previous publications of this data set (Le Quéré et al., 2015b, a, 2014, 2013). All observations presented here can be downloaded from the Carbon Dioxide Information Analysis Center ( doi:10.3334/CDIAC/GCP_2016 ).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Mic-hackathon 2024: hackathon on machine learning for electron and scanning probe microscopy

Microscopy is one of the primary sources of information on materials structure and functionality at the nanometer and atomic scales. The data generated through microscopy is often contained in well-structured datasets, enriched with extensive metadata and sample histories, although not always with the same level of detail or storage format. The broad incorporation of data management plans by major funding agencies ensures the preservation and accessibility of this data. However, deriving insights from these rich datasets remains challenging due to the lack of established code ecosystems, standardized benchmarks, and integration strategies. Correspondingly, the efficiency of data usage is very low, and time expenditures at the analysis stage are enormous. In addition to post-acquisition data analysis, the emergence of application programming interfaces by major microscope manufacturers now creates opportunities for real-time ML-based data analytics to enable automated decision making, and particularly ML-agent controlled real-time microscope operation. Despite these opportunities, there is a significant gap in integrating the ML community with the broader microscopy community, limiting the value that these methods bring to physics and materials discovery and materials optimization. Hackathons address these challenges by fostering collaboration between ML experts and microscopy professionals, encouraging the development of innovative solutions that leverage ML for microscopy and preparing the workforce of the future both for microscopy-intensive domains areas, instrument manufacturers, and ML scientists interested in real world applications for fundamental research, materials optimization, and manufacturing. The hackathon generated benchmark datasets and digital twins of microscopes that further contribute to the development of the field and establish data analysis ecosystems. All the codes can be found at GitHub(https://github.com/KalininGroup/Mic-hackathon-2024-codes-publication/tree/1.0.0.1) and Zenodo (https://zenodo.org/records/15579940).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Magnetic Navigation for GPS-Denied Airborne Applications

Most current flight systems are dependent on GPS for navigation. Recently, however, navigation in GPS-denied environments has become an area of intensive research. Additional navigation sensor data can be obtained from visual observations (stars or terrain), inertial measurement units, radar, measurements of the local magnetic field, or perhaps even gravity. Absolute and relative positioning via magnetic field measurements have been shown to be viable in many applications including ground navigation, low altitude aircraft flight, and spaceflight. There is greater variability in the magnetic field over shorter distances when flying at low altitude and in ground applications, leading to more accurate positioning. However, ground-based magnetic navigation is often heavily influenced by man-made structures, especially in urban environments. This is not the case for airborne magnetic navigation since the influence of buildings, roads, etc. is negligible for typical aircraft altitudes. For absolute magnetic navigation, the positioning accuracy decreases as altitude increases for a given vehicle velocity, but the observed time variability in the field can be reclaimed by traveling faster through the field. Thus, navigation accuracy becomes a balance of speed and altitude since the higher altitude can be counterbalanced by higher velocity. To understand these effects quantitatively, we explored various techniques to aid a simulated inertial measurement unit with magnetic information. Using a technique known as two-dimensional magnetic map matching, we simulated the performance of airborne magnetic navigation at fixed speed while varying the altitude, flight direction, magnetometer data collection time, reference magnetic map bias error, and type of trajectory (over land or over ocean).

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

INTEGRATION OF DATA ANALYTICS WITH SYSTEM HEALTH PROGRAMS

Industry equipment reliability and asset management programs are essential elements that help ensure the safe and economical operation of nuclear power plants. The effectiveness of these programs is addressed in several industry developed and regulatory programs. However, these programs have proven to be labor intensive and expensive. There is an opportunity to significantly enhance the collection, analysis, and use of this information to provide more cost-effective plant operation. Additionally, there is an acute industry need to leverage advanced technology to reduce costs and improve operational effectiveness. The goal of this paper is to provide effective and efficient analytical methods and tools to support risk-informed decisions for the equipment reliability and asset management programs at nuclear power plants. This is accomplished by creating a direct bridge between component health/lifecycle data and decision making (e.g., maintenance scheduling and project prioritization). Here we are supporting typical system engineer decisions regarding maintenance activity scheduling and component ageing management. This is performed in a risk-informed context where herein the term “risk” is broadly constructed to include both plant reliability and economics. This framework combines data analytics tools to analyze equipment reliability data with risk-informed methods designed to support system engineer decisions (e.g., maintenance and replacement schedules, optimal maintenance posture) in a customizable workflow. A challenge is that the structure of this workflow strongly depends on the decision that needs to be made, the type of data available, and the constraints that need to be considered. Current methods are designed to provide specific answers to specific problems; however, these methods might prove to be inadequate even when problem settings slightly change (e.g., different types of requirements, additional dependencies between system reliability and economics). We tackled this challenge by designing framework in a flexible and modular fashion such that the user can assemble and customize his/her own workflow that integrates SSC economic lifecycle models (e.g., maintenance and replacement costs), system reliability models, and optimization methods.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2018

The Open-source Data Inventory for Anthropogenic CO 2 , version 2016 (ODIAC2016): a global monthly fossil fuel CO 2 gridded emissions data product for tracer transport simulations and surface flux inversions

The Open-source Data Inventory for Anthropogenic CO 2 (ODIAC) is a global high-spatial-resolution gridded emissions data product that distributes carbon dioxide (CO 2 ) emissions from fossil fuel combustion. The emissions spatial distributions are estimated at a 1 × 1 km spatial resolution over land using power plant profiles (emissions intensity and geographical location) and satellite-observed nighttime lights. This work describes the year 2016 version of the ODIAC emissions data product (ODIAC2016) and presents analyses that help guide data users, especially for atmospheric CO 2 tracer transport simulations and flux inversion analysis. Since the original publication in 2011, we have made modifications to our emissions modeling framework in order to deliver a comprehensive global gridded emissions data product. Major changes from the 2011 publication are (1) the use of emissions estimates made by the Carbon Dioxide Information Analysis Center (CDIAC) at the Oak Ridge National Laboratory (ORNL) by fuel type (solid, liquid, gas, cement manufacturing, gas flaring, and international aviation and marine bunkers); (2) the use of multiple spatial emissions proxies by fuel type such as (a) nighttime light data specific to gas flaring and (b) ship/aircraft fleet tracks; and (3) the inclusion of emissions temporal variations. Using global fuel consumption data, we extrapolated the CDIAC emissions estimates for the recent years and produced the ODIAC2016 emissions data product that covers 2000–2015. Our emissions data can be viewed as an extended version of CDIAC gridded emissions data product, which should allow data users to impose global fossil fuel emissions in a more comprehensive manner than the original CDIAC product. Our new emissions modeling framework allows us to produce future versions of the ODIAC emissions data product with a timely update. Such capability has become more significant given the CDIAC/ORNL's shutdown. The ODIAC data product could play an important role in supporting carbon cycle science, especially modeling studies with space-based CO 2 data collected in near real time by ongoing carbon observing missions such as the Japanese Greenhouse gases Observing SATellite (GOSAT), NASA's Orbiting Carbon Observatory-2 (OCO-2), and upcoming future missions. The ODIAC emissions data product including the latest version of the ODIAC emissions data (ODIAC2017, 2000–2016) is distributed from http://db.cger.nies.go.jp/dataset/ODIAC/ with a DOI (https://doi.org/10.17595/20170411.001).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

SU-F-R-48: Early Prediction of Pathological Response of Locally Advanced Rectal Cancer Using Perfusion CT:A Prospective Clinical Study

Purpose: To prospectively evaluate the tumor vascularity assessed by perfusion CT for prediction of chemo-radiation treatment (CRT) response in locally advanced rectal cancer (LARC). Methods: Eighteen consecutive patients (61.9±8.8 years, from March–June 2015) diagnosed with LARC who underwent 6–8 weeks CRT followed by surgery were included. The pre-treatment perfusion CT was acquired after a 5s delay of contrast agent injection for 45s with 1s interval. A total of 7-cm craniocaudal range covered the tumor region with 3-mm slice thickness. The effective radiation dose is around 15mSv, which is about 1.5 the conventional abdomen/pelvis CT dose. The parametric map of blood flow (BF), blood volume (BV), mean transit time (MTT), permeability (PMB), and maximum intensity map (MIP) were obtained from commercial software (Syngo-CT 2011A, Siemens). An experienced radiation oncologist outlined the tumor based on the pre-operative MR and pathologic residual region, but was blinded with regards to pathological tumor stage. The perfusion parameters were compared to histopathological response quantified by tumor regression grade as good-responder (GR, TRG 0-1) vs. non-good responder (non-GR). Furthermore, the predictive value for pathological complete response (pCR) was also investigated. Results: Both BV (p=0.02) and MTT (P=0.02) was significantly higher and permeambility was lower (p=0.004) in the good responders. The BF was higher in GR group but not statistically significant. Regarding the discrimination of pCR vs non-pCR, the BF was higher in the pCR group (p=0.08) but none of those parameters showed statistically significant differences. Conclusion: BV and MTT can discriminate patients with a favorable response from those that fail to respond well, potentially selecting high-risk patients with resistant tumors that may benefit from an aggressive preoperative treatment approach. However, future studies with more patient data are needed to verify the prognostic value of perfusion CT especially for pCR prediction. This work is supported by the National High-tech R&D program for Young Scientists by the Ministry of Science and Technology of China (Grant No. 2015AA020917), Natural Science Foundation of China (NSFC Grant No. 81201091).

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Global Carbon Budget 2015

Accurate assessment of anthropogenic carbon dioxide (CO 2 ) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and a methodology to quantify all major components of the global carbon budget, including their uncertainties, based on the combination of a range of data, algorithms, statistics, and model estimates and their interpretation by a broad scientific community. We also discuss changes compared to previous estimates as well as consistency within and among components, alongside methodology and data limitations. CO 2 emissions from fossil fuels and industry ( E FF ) are based on energy statistics and cement production data, while emissions from land-use change ( E LUC ), mainly deforestation, are based on combined evidence from land-cover-change data, fire activity associated with deforestation, and models. The global atmospheric CO 2 concentration is measured directly and its rate of growth ( G ATM ) is computed from the annual changes in concentration. Moreover, the mean ocean CO 2 sink ( S OCEAN ) is based on observations from the 1990s, while the annual anomalies and trends are estimated with ocean models. The variability in S OCEAN is evaluated with data products based on surveys of ocean CO 2 measurements. The global residual terrestrial CO 2 sink ( S LAND ) is estimated by the difference of the other terms of the global carbon budget and compared to results of independent dynamic global vegetation models forced by observed climate, CO 2 , and land-cover change (some including nitrogen–carbon interactions). We compare the mean land and ocean fluxes and their variability to estimates from three atmospheric inverse methods for three broad latitude bands. All uncertainties are reported as ±1σ, reflecting the current capacity to characterise the annual estimates of each component of the global carbon budget. For the last decade available (2005–2014), E FF was 9.0 ± 0.5 GtC yr -1 , E LUC was 0.9 ± 0.5 GtC yr -1 , G ATM was 4.4 ± 0.1 GtC yr -1 , S OCEAN was 2.6 ± 0.5 GtC yr -1 , and S LAND was 3.0 ± 0.8 GtC yr -1 . For the year 2014 alone, E FF grew to 9.8 ± 0.5 GtC yr -1 , 0.6 % above 2013, continuing the growth trend in these emissions, albeit at a slower rate compared to the average growth of 2.2 % yr -1 that took place during 2005–2014. Also, for 2014, E LUC was 1.1 ± 0.5 GtC yr -1 , G ATM was 3.9 ± 0.2 GtC yr -1 , S OCEAN was 2.9 ± 0.5 GtC yr -1 , and S LAND was 4.1 ± 0.9 GtC yr -1 . G ATM was lower in 2014 compared to the past decade (2005–2014), reflecting a larger S LAND for that year. The global atmospheric CO 2 concentration reached 397.15 ± 0.10 ppm averaged over 2014. For 2015, preliminary data indicate that the growth in E FF will be near or slightly below zero, with a projection of -0.6 [range of -1.6 to +0.5] %, based on national emissions projections for China and the USA, and projections of gross domestic product corrected for recent changes in the carbon intensity of the global economy for the rest of the world. From this projection of E FF and assumed constant E LUC for 2015, cumulative emissions of CO 2 will reach about 555 ± 55 GtC (2035 ± 205 GtCO 2 ) for 1870–2015, about 75 % from E FF and 25 % from E LUC . Finally, this living data update documents changes in the methods and data sets used in this new carbon budget compared with previous publications of this data set (Le Quéré et al., 2015, 2014, 2013). All observations presented here can be downloaded from the Carbon Dioxide Information Analysis Center ( doi:10.3334/CDIAC/GCP_2015 ).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

DETECTING FIRE WITH MACHINE LEARNING-ENABLED VISUAL MONITORING FOR NUCLEAR POWER PLANT ENVIRONMENTS

Nuclear power plants are experiencing significant cost challenges to remain competitive with other energy-generation utilities. Unlike other industries, the cost of operation and maintenance activities is mostly attributed to workforce costs. To mitigate this, nuclear power plant stakeholders are increasingly interested in the development and deployment of machine learning methods to potentially automate or augment manually intensive tasks to reduce costs, especially for monitoring activities. One monitoring function that is visually demanding and that can occur frequently to meet the requirements of a fire protection program is visually monitoring an area for fire occurrence. Currently, fire watch activities consist of a worker physically stationed at a given location with the sole responsibility of observing a given area to ensure a fire is detected and mitigated promptly. This effort focused on the development and evaluation of a suitable deep convolutional neural network to classify individual video frames at a sub-second frequency for the occurrence of “fire” and “no fire” in varying industrial environments similar to nuclear power plants. It is believed that a trained neural network model could be integrated with existing facility video surveillance camera feeds to generate alerts when fire inferences occur in individual frames captured at sub-second temporal resolutions. Extensive effort was dedicated to identifying and curating suitable imagery training data representing varying environments and scene settings with and without flame features to maximize generalization in nuclear power plant environments. The data collection effort resulted in the aggregation of a large, labeled image library exceeding 12,000 images to support model training for diverse industrial environments. A deep neural network model incorporating parallel multi-scale capabilities was developed and trained to support accurate image-based detection of flame incidents of varying sizes and spectral feature properties within heterogeneous scenes. Analysis results show that the trained model can achieve high inference accuracy despite heterogeneous scene environments and components. Testing accuracy exceeded 95.0 percent with very low false positive and false negative inferences.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

AI-Based Analytics and Energy Modeling Framework for Characterizing Urban Energy Systems

Developing location-specific district energy models is essential for understanding energy patterns and supporting efficient management and planning decisions. However, accurately characterizing these models remains challenging due to gaps in building characteristics and labor-intensive traditional modeling workflows. To address these challenges, we develop an AI-based framework that integrates top-down and bottom-up building energy data to automate urban energy model characterization. The framework trains multimodal deep learning models using heterogeneous ResStockTM datasets to infer missing building characteristics from varying levels of known information and generate simulation-ready inputs for district-scale energy modeling. It also employs a conditioning-based injection approach to generate ”what-if” scenarios, enabling users to explore retrofit, efficiency, and technology-upgrade pathways. Integrated within URBANoptTM, a bottom-up district energy modeling platform for simulating co-located buildings, the framework infers detailed building-level inputs required for bottom-up simulations. Both localized and generalized AI models are developed to learn relationships across categorical, numerical, and time-series data, enabling reconstruction of missing attributes and generation of targeted upgrade scenarios. We demonstrate this methodology on a residential neighborhood in Baltimore, MD, assessing internal consistency against ResStock reference data and URBANopt simulation, and comparing selected attributes against real-world building characteristics. Results show strong overall predictive accuracy in data completion and scenario generation, with localized and generalized models offering complementary trade-offs between precision and scalability. Overall, our automated framework streamlines energy modeling and provides a reliable framework for urban building energy characterization.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

SU-G-JeP3-12: Use of Cone Beam CT and Deformable Image Registration for Assessing Geometrical and Dosimetric Variations During Lung Radiotherapy

Purpose: To assess the value of cone beam CT (CBCT) combined with deformable image registration in estimating the accuracy of the delivered treatment and the suitability of the applied target margins. Methods: Two patients with lung tumor were selected. Using their CT images intensity modulated radiation therapy (IMRT) treatment plans were developed to deliver 66Gy to the 95% of the PTV in 2Gy fractions. Using the Velocity AI software, the planning CT of each patient was registered with the fractional CBCT images that were obtained through the course of the treatment. After a CT to CBCT deformable image registration (DIR), the same fractional deformation matrix was used for the deformation of the planned dose distributions, as well as of all the contoured volumes, to each CBCT dataset. The dosimetric differences between the planning target volume (PTV) and various organs at risk (OARs) were recorded and compared. Results: CBCT data such as CTV volume change and PTV coverage was analyzed. There was a moderate relationship between volume changes and contouring method (automatic contouring using the DIR transformation vs. manual contouring on each CBCT) for patient #1 (r = 0.49), and a strong relationship for patient #2 (r = 0.83). The average PTV volume coverage from all the CBCT datasets was 91.2% for patient #1 and 95.6% for patient #2. Conclusion: Daily setup variations, tumor volume motion and lung deformation due to breathing yield differences in the actual delivered dose distributions versus the planned ones. The results presented indicate that these differences are apparent even with the use of daily IGRT. In certain fractions, the margins used seem to be insufficient to ensure acceptable lung tumor coverage. The observed differences notably depend on the tumor volume size and location. A larger cohort of patient is under investigation to verify those findings.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-E-T-437: Four-Dimensional Treatment Planning for Lung VMAT-SBRT

Purpose: To assess optimal treatment planning approach of Volumetric Modulated Arc Therapy for lung Stereotactic Body Radiation Therapy (VMAT-SBRT). Methods: Subjects were 10 patients with lung cancer who had undergone 4DCT. The internal target volume (ITV) volume ranged from 2.6 to 16.5cm{sup 3} and the tumor motion ranged from 0 to 2cm. From 4DCT, which was binned into 10 respiratory phases, 4 image data sets were created; maximum intensity projection (MIP), average intensity projection (AIP), AIP with the ITV replaced by 0HU (RITV-AIP) and RITV-AIP with the planning target volume (PTV) minus the internal target volume was set to −200 HU (HR-AIP). VMAT-SBRT plans were generated on each image set for a patient. 48Gy was prescribed to 95% of PTV. The plans were recalculated on all phase images of 4DCT and the dose distributions were accumulated using a deformable image registration software MIM Maestro™ as the 4D calculated dose to the gross tumor volume (GTV). The planned dose to the ITV and 4D calculated dose to the GTV were compared. Results: In AIP plan, 10 patients average of all dose parameters (D1%, D-mean, and D99%) discrepancy were 1Gy or smaller. MIP and RITV-AIP plans resulted in having common tendency and larger discrepancy than AIP plan. The 4D dose was lower than the planned dose, and 10 patients average of all dose parameters discrepancy were in range 1.3 to 2.6Gy. HR-AIP plan had the largest discrepancy in our trials. 4D calculated D1%, D-mean, and D99% were resulted in 3.0, 4.1, and 6.1Gy lower than the expected in plan, respectively. Conclusion: For all patients, the dose parameters expected in AIP plan approximated to 4D calculated. Using AIP image set seems optimal treatment planning approach of VMAT-SBRT for a mobile tumor. Funding Support: This work was supported by the Japan Society for the Promotion of Science Core-to-Core program (No. 23003)

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Informing Plant Asset Reliability and Availability Through AI-Driven Analysis of Operator Logs

The availability and reliability of nuclear power plant (NPP) structures, systems, and components (SSCs) are critical parameters for NPP safety. Tracking these parameters is necessary but costly and labor-intensive, requiring the collection and evaluation of SSC event data such as shutdowns, startups, and failures. To show how these events are needed for the parameters an example is given: one measure of reliability is based on the number of equipment failure events and the number of run hours (i.e., the time from a startup event to a shutdown event). Here, this work investigates using artificial intelligence (AI) to mine NPP operator log entry texts for SSC event data. Four AI approaches were explored for identifying these events, including natural language processing (NLP) methods, generative AI, generative AI combined with NLP, and topic modeling. A key challenge addressed with all four approaches is the brevity of operator log entries. Among these four a neural network–based NLP method was shown to be the most promising for this application, achieving F1 scores of 86.0% for shutdowns, 92.2% for startups, and 80.4% for failures on a subject-matter-expert-curated dataset from NPP operator logs, compared to a baseline of 66.6% for a random classifier. This shows that NLP methods can perform better than generative AI. Additionally, the NLP methods combined with generative AI were shown to perform better than generative AI alone. Generative AI was most successful at providing the background information for the NLP methods to use. This work demonstrates the potential to use AI to automate parameter collection from NPP operator log entries and other records.

97 - MATHEMATICS AND COMPUTING↗