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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Landmark-embedded Gaussian process with applications for functional data modeling

In practice, we often need to infer the value of a target variable from functional observation data. A challenge in this task is that the relationship between the functional data and the target variable is very complex: the target variable not only influences the shape but also the location of the functional data. In addition, due to the uncertainties in the environment, the relationship is probabilistic, that is, for a given fixed target variable value, we still see variations in the shape and location of the functional data. To address this challenge, we present a landmark-embedded Gaussian process model that describes the relationship between the functional data and the target variable. A unique feature of the model is that landmark information is embedded in the Gaussian process model so that both the shape and location information of the functional data are considered simultaneously in a unified manner. Gibbs-Metropolis-Hasting algorithm is used for model parameters estimation and target variable inference. The performance of the proposed framework is evaluated by extensive numerical studies and a case study of nano-sensor calibration.

42 ENGINEERING↗
DOE OSTI2026

Data for Impact of Vertical and Seasonal Variation in Leaf Traits on Simulating Soybean Canopy Photosynthesis via 1D and 3D Modeling

Accurate modeling of photosynthesis is crucial for predicting crop productivity and quantifying the carbon cycle in agroecosystems. Leaf traits are essential inputs for modeling canopy photosynthesis. Yet, many existing models still use fixed plant functional type (PTF)-based values to parameterize leaf traits under a big-leaf or two-big-leaf assumption, neglecting their vertical profiles and seasonal changes. This simplification may introduce significant uncertainties in estimating gross primary productivity (GPP). In this study, we simulated soybean GPP and tested the effects of vertical and seasonal variation in three key leaf photosynthetic traits: the maximum carboxylation rate at 25 °C (Vcmax25), leaf chlorophyll content (LCC), and leaf mass per area (LMA) in the 1D-SCOPE and 3D-Helios models. Weekly field measurements were conducted during the growing season of 2024 to support the simulation. We designed ten leaf trait parameterization schemes by incorporating different combinations of vertical profiles and seasonal changes, while assuming homogeneous canopy architecture in both models. Our results revealed that Vcmax25 vertical and seasonal variation had the strongest influence on simulated GPP in both 1D and 3D models, while LCC and LMA effects were minimal. Particularly, the scheme with an empirically parameterized Vcmax25 profile achieved comparable performance to the scheme with the measured Vcmax25 profile. Both 1D-SCOPE and 3D-Helios accurately modeled GPP (SCOPE: R2 = 0.87, Bias = 0.55 µmol m⁻² s⁻¹; Helios: R2 = 0.9, Bias = 0.22 µmol m⁻² s⁻¹) under the most complex scheme, and their responses to vertical and seasonal variation in leaf traits were consistent, demonstrating the robustness of our findings. Based on our findings, we propose a scalable framework for parameterizing leaf traits to improve GPP simulations. This study contributes to improving the representation of leaf trait dynamics in canopy-level photosynthesis models, potentially enhancing our ability to predict crop productivity and understand agroecosystem carbon dynamics.

Photosynthesis↗
DOE OSTI2015

DiaMonD: An Integrated Multifaceted Approach to Mathematics at the Interfaces of Data, Models, and Decisions

For many problems in computational science and engineering, the so-called forward problem-solution of the underlying mathematical model to yield output quantities of interest, given input parameters--is difficult enough for frontier complex models, which are often characterized by coupled multiphysics and possibly stochastic behavior over a wide range of length and time scales. However, we face the need to move beyond the forward problem, to address what is often the ultimate goal of CS&E: decision-making. This requires us to tackle a spectrum of mathematical problems that subsume, and are thus even more difficult than, the forward problem. First, given experimental data, we wish to estimate unknown parameters characterizing a model of a natural or engineered system by solving an inverse problem. Second, we seek the optimal con figuration of the system (or experiments) by solving an optimal design problem. Third, the optimal operation of the system must be determined by solving an optimal control problem. And fourth, we must quantify uncertainties as they propagate through all of the preceding problems, from data to model inference to prediction and finally to optimal design and control. Optimal mathematical methods and algorithms for such end-to-end, data-to-decisions modeling and simulation of complex problems--requiring integrated solution of forward, inverse, optimization, and UQ problems for large, multiphysics, multiscale models--entails challenges of the highest order, while also presenting great opportunities for applied mathematics research. Unfortunately, research on forward, inverse, optimization, and UQ problems has all-too-often progressed in isolation. This has led to mathematical methods that perform well when considering the forward simulation in isolation, but are prohibitive or suboptimal or unstable when combined with other methods within the framework of inversion, optimization, or UQ. The reverse is also true: general-purpose methods developed within the inversion, optimization, and UQ fields often become prohibitive when applied to complex CS&E problems since they do not exploit their structure. DiaMonD is a multi-institutional DOE MMICC effort that aims to address the challenges of end-to-end, data-to-decisions modeling and simulation for complex CS&E problems in a unifi ed, systematic, and integrated fashion. Institutions involved are Colorado State, Florida State, Los Alamos, MIT, Oak Ridge, Stanford, and UT-Austin. The goals of DiaMonD are (1) to develop advanced mathematical methods and analysis for multimodel, multiphysics, multiscale model problems driven by frontier DOE applications, including those in subsurface energy and environmental flows, materials for energy storage and conversion, and ice sheet dynamics; (2) to create theory and algorithms for integrated inversion, optimization, and uncertainty quantification for these complex problems; and (3) to disseminate the philosophy of an integrated end-to-end, data-to-decisions approach to modeling and simulation of complex problems to the broader applied math and computational science communities through workshops and other outreach.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

The global gridded crop model intercomparison: Data and modeling protocols for Phase 1 (v1.0)

We present protocols and input data for Phase 1 of the Global Gridded Crop Model Intercomparison, a project of the Agricultural Model Intercomparison and Improvement Project (AgMIP). The project consist of global simulations of yields, phenologies, and many land-surface fluxes using 12–15 modeling groups for many crops, climate forcing data sets, and scenarios over the historical period from 1948 to 2012. The primary outcomes of the project include (1) a detailed comparison of the major differences and similarities among global models commonly used for large-scale climate impact assessment, (2) an evaluation of model and ensemble hindcasting skill, (3) quantification of key uncertainties from climate input data, model choice, and other sources, and (4) a multi-model analysis of the agricultural impacts of large-scale climate extremes from the historical record.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1988

Helicopter main-rotor noise: Determination of source contributions using scaled model data

Acoustic data from a test of a 40 percent model MBB BO-105 helicopter main rotor are scaled to equivalent full-scale flyover cases. The test was conducted in the anechoic open test section of the German-Dutch Windtunnel (DNW). The measured data are in the form of acoustic pressure time histories and spectra from two out-of-flow microphones underneath and foward of the model. These are scaled to correspond to measurements made at locations 150 m below the flight path of a full-scale rotor. For the scaled data, a detailed analysis is given for the identification in the data of the noise contributions from different rotor noise sources. Key results include a component breakdown of the noise contributions, in terms of noise criteria calculations of a weighted sound pressure level (dBA) and perceived noise level (PNL), as functions of rotor advance ratio and descent angle. It is shown for the scaled rotor that, during descent, impulsive blade-vortex interaction (BVI) noise is the dominant contributor to the noise. In level flight and mild climb, broadband blade-turbulent wake interaction (BWI) noise is dominant due to the absence of BVI activity. At high climb angles, BWI is reduced and self-noise from blade boundary-layer turbulence becomes the most prominent.

Brooks, Thomas F.↗
NASA NTRS2003

Model-Data Assimilation of Internal Waves during ASIAEX-2001

In recent Asian Seas International Acoustics Experiment (ASIAEX), extensive moorings have been deployed around the continental shelf break area in the northeast of South China Sea in May 2001. Simultaneous RADARSAT SAR images have been collected during the field test to integrate with the in-situ measurements from moorings, ship-board sensors, and CTD casts. Besides it provides synoptic information, satellite imagery is very useful for tracking the internal waves, and locating surface fronts and mesoscale features. During ASIAEX in May 2001, many large internal waves were observed at the test area and were the major oceanic features for acoustic volume interaction. Based on the internal wave distribution maps compiled from satellite data, the wave crest can be as long as 200 km with amplitude of 100 m. Environmental parameters have been calculated based on extensive CTD casts data near the ASIAEX area. Nonlinear internal wave models have been applied to integrate and assimilate both SAR and mooring data. Using SAR data in deep water as an initial condition, numerical simulations produce the wave evolution on the continental shelf and compared reasonably well with the mooring measurements at the downstream station. The shoaling, turning, and dissipation of large internal waves on the shelf break, elevation solitons, and wave-wave interaction have been studied and are very important issues for acoustic propagation. The internal wave effects on acoustic modal coupling has been implicated and discussed.

Liu, Antony↗
DOE OSTI2022

YEASTRACT+: a portal for the exploitation of global transcription regulation and metabolic model data in yeast biotechnology and pathogenesis

Abstract YEASTRACT+ (http://yeastract-plus.org/) is a tool for the analysis, prediction and modelling of transcription regulatory data at the gene and genomic levels in yeasts. It incorporates three integrated databases: YEASTRACT (http://yeastract-plus.org/yeastract/), PathoYeastract (http://yeastract-plus.org/pathoyeastract/) and NCYeastract (http://yeastract-plus.org/ncyeastract/), focused on Saccharomyces cerevisiae, pathogenic yeasts of the Candida genus, and non-conventional yeasts of biotechnological relevance. In this release, YEASTRACT+ offers upgraded information on transcription regulation for the ten previously incorporated yeast species, while extending the database to another pathogenic yeast, Candida auris. Since the last release of YEASTRACT+ (January 2020), a fourth database has been integrated. CommunityYeastract (http://yeastract-plus.org/community/) offers a platform for the creation, use, and future update of YEASTRACT-like databases for any yeast of the users’ choice. CommunityYeastract currently provides information for two Saccharomyces boulardii strains, Rhodotorula toruloides NP11 oleaginous yeast, and Schizosaccharomyces pombe 972h-. In addition, YEASTRACT+ portal currently gathers 304 547 documented regulatory associations between transcription factors (TF) and target genes and 480 DNA binding sites, considering 2771 TFs from 11 yeast species. A new set of tools, currently implemented for S. cerevisiae and C. albicans, is further offered, combining regulatory information with genome-scale metabolic models to provide predictions on the most promising transcription factors to be exploited in cell factory optimisation or to be used as novel drug targets. The expansion of these new tools to the remaining YEASTRACT+ species is ongoing.

Teixeira, Miguel Cacho (ORCID:0000000256766174)↗
DOE OSTI2013

Data-Model Assimilation at the FACE and AmeriFlux Sites Toward Predictive Understanding of Carbon Sequestration at Ecosystem and Regional Scales

The project was conducted during the period from 9/1/2007 to 8/31/2011 with three major tasks: (1) development of data assimilation (DA) techniques for terrestrial carbon research; (2) applications of DA techniques to analysis of carbon cycle at Duke and other FACE sites; and (3) inverse analysis at AmeriFlux sites. During this period, we have developed a variety of techniques, including (1) ensemble Kalman filter to estimate model parameters or state variables (Gao et al. 2011), (2) Conditional inversion to estimate parameters of a carbon cycle model (Wu et al. 2009), and (3) various methods to quantify uncertainty of estimated parameters and predicted C sinks (e.g., Weng et al. 2011), and (4) information theory to evaluate information content of different model structures and data sets (Weng and Luo 2011). We applied the DA techniques to and did modeling at the Duke FACE and other global change experimental sites. We addressed the following issues: (1) interactive effects of CO 2 , warming and precipitation on ecosystem processes (e.g., Luo et al. 2008, Weng and Luo 2008, Zhou et al. 2008), (2) effects of warming on estimated parameters related to photosynthesis and residence times (Zhou et al. 2010); and (3) uncertainty in estimated parameters and predicted C sequestration (Gao et al. 2011, Weng and Luo 2011). In addition, we have done data assimilation to estimate carbon residence and carbon sequestration in US continent (Zhou and Luo 2008) and temperature sensitivity at the global scale (Zhou et al. 2009).

58 GEOSCIENCES↗
DOE OSTI2017

A structured data model frame for human factors application - 175

Building a Human Factors analysis and Verification and Validation case for new control rooms and even for control room modifications is a highly labor intensive project. Any optimization that can be made in the process translates into savings in the overall control room project schedule and budget. Several support tools from word, to excel, mind maps and databases have been traditionally used by human factors practitioners to conduct the analysis and document its results. While the use of these tools has had satisfactory results, they don't provide in our experience strong enough means for configuration control management, to avoid duplicities, streamline the analysis processes and allow collaborative team efforts. This paper documents how the established practices (methods, processes and tools) were questioned, which were the sources for comparison and how the results were translated into requirements for the new tool constituting the data frame model for Human Factors Engineering applications. The exercise has allowed us to evaluate the efficiency of our current practices in comparison to previous ones while improving our compliance with the quality assurance standards. (authors)

42 ENGINEERING↗
NASA NTRS2005

Human Exploration of the Moon and Mars: Space Radiation Data, Modeling and Instrumentation Needs

On January 14, 2004 President Bush announced the Vision for Space Exploration, a program for long-term human and robotic exploration of the solar system which will include a return of humans to the moon not later than 2020, followed by human missions to Mars. Since this announcement, NASA has been developing plans and mission architectures for these human missions as well as robotic precursor missions. Among the critical needs for research and development in support of this Vision are investigations on the ionizing radiation environment and development of instrumentation to guide NASA in managing the radiation exposure of the crew during the manned missions. For mission planning, models are needed for a reference worst-case solar energetic particle event and a reference worst-case galactic cosmic ray environment. During Lunar missions it will be necessary to carefully manage the radiation exposure of the crew in real time because of the variability of the radiation environment due to solar activity. In particular, prompt warnings will be needed when large solar energetic particle events occur. Accurate predictions will also be needed of the particle flux and flux history at the moon to support critical mission management decisions. A new generation of dosimeters and radiation monitors will also be needed to accompany the crew. These instruments must return data in real time so that they can be used in the critical decisions that must be made if a large solar energetic particle event occurs. This is especially true if it occurs during a lunar excursion. A substantial radiation exposure on extended lunar missions and Mars missions comes from galactic cosmic rays. This exposure must be mitigated by radiation shielding and other measures. During Mars missions the galactic cosmic ray exposure occurs primarily during the cruse phase between the Earth and Mars. This is especially true for opposition class missions. These missions would typically last -430 days with only 30-90 days on Mars. Solar energetic particle events are less of a concern on Mars because of its greater distance from the Sun (approximately 1.5 AU) and the partial protection afforded by its atmosphere (approximately 20 grams per square centimeter). The talk will describe the current plans for future human missions to Earth orbit, the Moon and Mars. The needs for data and models of the radiation environment and radiation detectors to support these missions will be discussed.

Adams, James H.↗
DOE OSTI2021

Modeling Data Movement Performance on Heterogeneous Architectures

The cost of data movement on parallel systems varies greatly with machine architecture, job partition, and nearby jobs. Performance models that accurately capture the cost of data movement provide a tool for analysis, allowing for communication bottlenecks to be pinpointed. Modern heterogeneous architectures yield increased variance in data movement as there are a number of viable paths for inter-GPU communication. In this paper, we present performance models for the various paths of inter-node communication on modern heterogeneous architectures, including the trade-off between GPUDirect communication and copying to CPUs. Furthermore, we present a novel optimization for inter-node communication based on these models, utilizing all available CPU cores per node. Finally, we show associated performance improvements for MPI collective operations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

Application of Spatial Data Modeling Systems, Geographical Information Systems (GIS), and Transportation Routing Optimization Methods for Evaluating Integrated Deployment of Interim Spent Fuel Storage Installations and Advanced Nuclear Plants

The objective of this siting study work is to support DOE in evaluating integrated advanced nuclear plant and ISFSI deployment options in the future. This study looks at several nuclear power plant growth scenarios that consider the locations of existing and planned commercial nuclear power plants integrated with the establishment of consolidated interim spent fuel storage installations (ISFSIs). This research project is aimed at providing methodologies, information, and insights that inform the process for determining and optimizing candidate areas for new advanced nuclear power generation plants and consolidated ISFSIs to meet projected US electric power demands for the future.

Geographical Information Systems (GIS)↗
NASA NTRS2010

Statistical Analysis of Model Data for Operational Space Launch Weather Support at Kennedy Space Center and Cape Canaveral Air Force Station

The 12-km resolution North American Mesoscale (NAM) model (MesoNAM) is used by the 45th Weather Squadron (45 WS) Launch Weather Officers at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) to support space launch weather operations. The 45 WS tasked the Applied Meteorology Unit to conduct an objective statistics-based analysis of MesoNAM output compared to wind tower mesonet observations and then develop a an operational tool to display the results. The National Centers for Environmental Prediction began running the current version of the MesoNAM in mid-August 2006. The period of record for the dataset was 1 September 2006 - 31 January 2010. The AMU evaluated MesoNAM hourly forecasts from 0 to 84 hours based on model initialization times of 00, 06, 12 and 18 UTC. The MesoNAM forecast winds, temperature and dew point were compared to the observed values of these parameters from the sensors in the KSC/CCAFS wind tower network. The data sets were stratified by model initialization time, month and onshore/offshore flow for each wind tower. Statistics computed included bias (mean difference), standard deviation of the bias, root mean square error (RMSE) and a hypothesis test for bias = O. Twelve wind towers located in close proximity to key launch complexes were used for the statistical analysis with the sensors on the towers positioned at varying heights to include 6 ft, 30 ft, 54 ft, 60 ft, 90 ft, 162 ft, 204 ft and 230 ft depending on the launch vehicle and associated weather launch commit criteria being evaluated. These twelve wind towers support activities for the Space Shuttle (launch and landing), Delta IV, Atlas V and Falcon 9 launch vehicles. For all twelve towers, the results indicate a diurnal signal in the bias of temperature (T) and weaker but discernable diurnal signal in the bias of dewpoint temperature (T(sub d)) in the MesoNAM forecasts. Also, the standard deviation of the bias and RMSE of T, T(sub d), wind speed and wind direction indicated the model error increased with the forecast period all four parameters. The hypothesis testing uses statistics to determine the probability that a given hypothesis is true. The goal of using the hypothesis test was to determine if the model bias of any of the parameters assessed throughout the model forecast period was statistically zero. For th is dataset, if this test produced a value >= -1 .96 or <= 1.96 for a data point, then the bias at that point was effectively zero and the model forecast for that point was considered to have no error. A graphical user interface (GUI) was developed so the 45 WS would have an operational tool at their disposal that would be easy to navigate among the multiple stratifications of information to include tower locations, month, model initialization times, sensor heights and onshore/offshore flow. The AMU developed the GUI using HyperText Markup Language (HTML) so the tool could be used in most popular web browsers with computers running different operating systems such as Microsoft Windows and Linux.

Bauman, William H., III↗
DOE OSTI2019

DiaMonD: An Integrated Multifaceted Approach to Mathematics at the Interfaces of Data, Models, and Decisions

For many problems in computational science and engineering, the so-called forward problem—solution of the underlying mathematical model to yield output quantities of interest, given input parameters—is difficult enough for frontier complex models, which are often characterized by coupled multiphysics and possibly stochastic behavior over a wide range of length and time scales. However, we face the need to move beyond the forward problem, to address what is often the ultimate goal of CS&E: decision making. This requires us to tackle a spectrum of mathematical problems that subsume, and are thus even more difficult than, the forward problem. First, given experimental data, we wish to estimate unknown parameters characterizing a model of a natural or engineered system by solving an inverse problem. Second, we seek the optimal configuration of the system (or experiments) by solving an optimal design problem. Third, the optimal operation of the system must be determined by solving an optimal control problem. And fourth, we must quantify uncertainties as they propagate through all of the preceding problems, from data to model inference to prediction and finally to optimal design and control. Optimal mathematical methods and algorithms for such end-to-end, data-to-decisions modeling and simulation of complex problems—requiring integrated solution of forward, inverse, optimization, and UQ problems for large, multiphysics, multiscale models—entails challenges of the highest order, while also presenting great opportunities for applied mathematics research. Unfortunately, research on forward, inverse, optimization, and UQ problems has all-too-often progressed in isolation. This has led to mathematical methods that perform well when considering the forward simulation in isolation, but are prohibitive or suboptimal or unstable when combined with other methods within the framework of inversion, optimization, or UQ. The reverse is also true: general-purpose methods developed within the inversion, optimization, and UQ fields often become prohibitive when applied to complex CS&E problems since they do not exploit their structure.

58 GEOSCIENCES↗
DOE OSTI2025

Block Island Noise Modeling Data

Noise propagation near Block Island was simulated to assess environmental impacts of impact pile driving during wind turbine construction. Computational models complement field-recorded acoustic data, providing insights into sound attenuation, spectral variability, and propagation dynamics. The dataset includes: 1. Propagation Models: Simulated underwater sound fields documenting sound pressure and directional variability across frequency bands and distances. 2. Spectral Analysis (LTSA): Long-term averages and processed outputs calculating acoustic intensity over time. 3. Visualization Files: Graphs, 2D/3D simulation results, and reference calculations used in sound modeling.

17 WIND ENERGY↗
DOE OSTI2017

Objective tropical cyclone extratropical transition detection in high-resolution reanalysis and climate model data

This paper describes an objective technique for detecting the extratropical transition (ET) of tropical cyclones (TCs) in high-resolution gridded climate data. The algorithm is based on previous observational studies using phase spaces to define the symmetry and vertical thermal structure of cyclones. Storm tracking is automated, allowing for direct analysis of climate data. Tracker performance in the North Atlantic is assessed using 23 years of data from the variable-resolution Community Atmosphere Model (CAM) at two different resolutions (DX 55 km and 28 km), the Climate Forecast System Reanalysis (CFSR, DX 38 km), and the ERA-Interim Reanalysis (ERA-I, DX 80 km). The mean spatiotemporal climatologies and seasonal cycles of objectively detected ET in the observationally constrained CFSR and ERA-I are well matched to previous observational studies, demonstrating the capability of the scheme to adequately find events. High resolution CAM reproduces TC and ET statistics that are in general agreement with reanalyses. One notable model bias, however, is significantly longer time between ET onset and ET completion in CAM, particularly for TCs that lose symmetry prior to developing a cold-core structure and becoming extratropical cyclones, demonstrating the capability of this method to expose model biases in simulated cyclones beyond the tropical phase.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Accurate Holdup Calculations with Predictive Modeling & Data Integration

In facilities that process special nuclear material (SNM) it is important to account accurately for the fissile material that enters and leaves the plant. Although there are many stages and processes through which materials must be traced and measured, the focus of this project is material that is “held-up” in equipment, pipes, and ducts during normal operation and that can accumulate over time into significant quantities. Accurately estimating the holdup is essential for proper SNM accounting (vis-à-vis nuclear non-proliferation), criticality and radiation safety, waste management, and efficient plant operation. Usually it is not possible to directly measure the holdup quantity and location, so these must be inferred from measured radiation fields, primarily gamma and less frequently neutrons. Current methods to quantify holdup, i.e. Generalized Geometry Holdup (GGH), primarily rely on simple source configurations and crude radiation transport models aided by ad hoc correction factors. This project seeks an alternate method of performing measurement-based holdup calculations using a predictive model that employs state-of-the-art radiation transport codes capable of accurately simulating such situations. Inverse and data assimilation methods use the forward transport model to search for a source configuration that best matches the measured data and simultaneously provide an estimate of the level of confidence in the correctness of such configuration. In this work the holdup problem is re-interpreted as an inverse problem that is under-determined, hence may permit multiple solutions. A probabilistic approach is applied to solving the resulting inverse problem. This approach rates possible solutions according to their plausibility given the measurements and initial information. This is accomplished through the use of Bayes’ Theorem that resolves the issue of multiple solutions by giving an estimate of the probability of observing each possible solution. To use Bayes’ Theorem, one must have a model y(x) that maps the state variables x (the solution in this case) to the measurements y. In this case, the unknown state variables are the configuration and composition of the heldup SNM. The measurements are the detector readings. Thus, the natural model is neutral-particle radiation transport where a wealth of computational tools exists for performing these simulations accurately and efficiently. The combination of predictive model and Bayesian inference forms the Data Integration with Modeled Predictions (DIMP) method that serves as foundation for this project. The cost functional describing the model-to-data misfit is computed via a norm created by the inverse of the covariance matrix of the model parameters and responses. Since the model y(x) for the holdup problem is nonlinear, a nonlinear optimization on Q is conducted via Newton-type iterative methods to find the optimal values of the model parameters x. This project comprised a collaboration between NC State University (NCSU), the University of South Carolina (USC), and Oak Ridge National Laboratory (ORNL). The project was originally proposed in seven main tasks with an eighth contingency task to be performed if time and funding permitted; in fact time did not permit commencement of the contingency task and it was not performed. The remaining tasks involved holdup analysis with gamma detection strategies and separately with neutrons based on coincidence counting. Early in the project, and upon consultation with experts in coincidence counting it became evident that this approach is not viable for holdup applications and this task was replaced with an alternative, but valuable investigation that was carried out by the USC partner. Nevertheless, the experimental 4 measurements at ORNL of both gamma and neutron sources for the purpose of constructing Detector Response Functions (DRFs) with the associated uncertainties were indeed completed.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗