Gaussian process regression and conditional Karhunen-Loève models for data assimilation in inverse problems
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97 MATHEMATICS AND COMPUTING↗
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Results for “data conditioning”
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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Why Firn Quakes
Snow dampens sounds, but anecdotal reports concisely describe audible propagating collapse events—firnquakes—in Antarctic and Arctic snowfields. We propose combining granular and continuum mechanics to form a testable theory for conditioning, triggering, and propagation of firnquakes consistent with scarce data. A central condition for collapse events is unconsolidated firn at depth. As firn grains compact, stresses are transmitted along force chains which carry the overburden and transition into a continuous medium by pressure sintering. This granular legacy creates solid-like supports of denser layers that keep the material below unconsolidated. Dynamic amplification triggers local brittle failure of the supports, which induces a cascade of collapse propagation. Using bulk density from ice cores as proxy for stiffness, we find the flexural wave speed by collapsing supports matches the recorded firnquake velocities on the order of 100 m/s. Our theory is to be tested in firn sheets and other compacting granular materials.
Voigtländer, A. [Lawrence Berkeley National Labora↗
Requirements for Cataloging Hanford Geophysical Datasets
Environmental management activities at the Hanford Site produce extensive data about site conditions, contaminants, cleanup, and more. Managing and archiving that data requires a high degree of collaboration among site contractors and a high level of awareness by project managers and staff. Part of that effort is developing a Hanford Environmental Information and Data Index (HEIDI) to organize the data and maximize its value by making it findable and available for reuse. The objective is to catalog the disparate data sets collected to address the evolving needs of planning, executing, and documenting cleanup over several decades up to the present day, including links to active data sources when available. A properly implemented data catalog makes finding environmental datasets related to an area or theme a routine, reliable process, without requiring the searcher to have special knowledge that a data set exists and where it may be stored. In this project, a working group, including the U.S. Department of Energy, the Hanford Site contractors, and Pacific Northwest National Laboratory staff, identified needs and requirements for handling complex site data. Geophysical data was chosen as a test case because it can be large and complex and often involves multiple processing steps to extract the information incorporated into deliverables. The ability to document those steps was one of the requirements identified for the catalog. In addition to developing requirements, other activities included selecting a metadata schema and initial testing with the objective of determining whether the workflow and capabilities of selected data catalog software platforms were sufficient to implement and impose the identified requirements. This initial testing involved running the default catalog instance using the software platform of interest and altering the configuration to achieve each requirement, if possible. Where configuration alone was insufficient, the possibility of modifying the software by changing the code was examined, but not implemented. A follow-on task is planned to reprogram the code as necessary to implement requirements in a prototype catalog.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Smoke density studies with the Arapahoe and NBS chambers
Smoke density studies on twelve cellulosic and five synthetic materials using the Arapahoe and NBS smoke chambers show an empirical relationship between smoke weight as per cent of initial weight in the Arapahoe test and maximum specific optical density under piloted conditions in the NBS test. The NBS optical density data under piloted conditions exhibited sensitivity to test artifacts such as type of pilot burner.
Hilado, C. J.↗
Launch commit criteria performance trending analysis, phase 1, revision A. SRM and QA mission services
An assessment of quantitative methods and measures for measuring launch commit criteria (LCC) performance measurement trends is made. A statistical performance trending analysis pilot study was processed and compared to STS-26 mission data. This study used four selected shuttle measurement types (solid rocket booster, external tank, space shuttle main engine, and range safety switch safe and arm device) from the five missions prior to mission 51-L. After obtaining raw data coordinates, each set of measurements was processed to obtain statistical confidence bounds and mean data profiles for each of the selected measurement types. STS-26 measurements were compared to the statistical data base profiles to verify the statistical capability of assessing occurrences of data trend anomalies and abnormal time-varying operational conditions associated with data amplitude and phase shifts.
Source record↗
Combustion of Gaseous Fuels with High Temperature Air in Normal- and Micro-gravity Conditions
The objective of this study is determine the effect of air preheat temperature on flame characteristics in normal and microgravity conditions. We have obtained qualitative (global flame features) and some quantitative information on the features of flames using high temperature combustion air under normal gravity conditions with propane and methane as the fuels. This data will be compared with the data under microgravity conditions. The specific focus under normal gravity conditions has been on determining the global flame features as well as the spatial distribution of OH, CH, and C2 from flames using high temperature combustion air at different equivalence ratio.
Wang, Y.↗
Acoustic measurements of a full-scale coaxial hingeless rotor helicopter
Acoustic data were obtained during a full-scale test of the XH-59A Advancing Blade Concept Technology Demonstrator in the 40- by 80-Foot Wind Tunnel. The XH-59A is a research helicopter with two coaxial rotors and hingeless blades. Performance, vibration, and noise at various forward speeds, rotor lift coefficients and rotor shaft angles of attack were investigated. The noise data were acquired over an isolated rotor lift coefficient range of 0.024 to 0.162, an advance ratio range of 0.23 to 0.45 corresponding to tunnel wind speeds of 89 to 160 knots, and angles of attack from 0 deg to 10 deg. Acoustic data are presented for seven microphone locations for all run conditions where the model noise is above the background noise. Model test configuration and performance information are also listed. Acoustic waveforms, dBA, and 1/3-octave spectra as functions of operating condition for selected data points and microphones are presented. In general, the noise level is shown to increase with rotor lift coefficient except under certain operating conditions where significant impulsive blade/vortex interactions increase noise levels.
Peterson, R. L.↗
Development of a Low Data Event Timer for Monitoring an Advancing Crack in Fracture
Monitoring the crack position and velocity in a fracture specimen can be difficult and laborious. In addition, the data storage requirements can be considerable depending upon the testing conditions. A low data event timer was developed to alleviate these problems. The test apparatus was applied to cantilever beams bonded with a structural epoxy and tested under different conditions such as stable to unstable transitions and different temperature extremes. The results indicate that the approach eliminates problems associated with other types of crack measurement and greatly simplifies the measuring process.
Macon, D. J.↗
Heavy-Duty Low-Temperature and Diesel Combustion & Heavy-Duty Combustion Modeling (FY 2018 Annual Progress Report)
Regulatory drivers and market demands for lower pollutant emissions, lower carbon dioxide emissions, and lower fuel consumption motivate the development of clean and fuel-efficient engine operating strategies. Most current production engines use a combination of both in-cylinder and exhaust emission control strategies to achieve these goals. The emissions and efficiency performance of in-cylinder strategies depend strongly on flow and mixing processes associated with fuel injection. Both heavy- and light-duty engine/vehicle manufacturers use multiple-injection strategies to reduce noise, emissions, and fuel consumption. For both conventional and low-temperature diesel combustion, the state of knowledge and modeling tools for multiple injections are far less advanced than for single-injection strategies. Engine efficiency is limited to some degree by tradeoffs that must be accepted to meet particulate matter (including soot) emissions limits. Recent work on this project has filled some knowledge gaps on soot oxidation with multiple injections, and the current work for Fiscal Year (FY) 2018 addresses knowledge gaps on soot formation for multiple injections. While total in-cylinder soot is readily measured, discerning formation from oxidation is difficult. The FY 2018 experiments are designed to create in-cylinder conditions at the threshold of soot formation, where processes that affect soot formation can be more readily discerned. Soot formation pathways under such conditions are fraught with uncertainties, and soot models significantly overpredict polyaromatic hydrocarbon (PAH) and soot, so experimental data at these conditions will provide much needed data for improvements to PAH and soot models.
02 PETROLEUM↗
Deep learning applications in visual data for benign and malignant hematologic conditions: a systematic review and visual glossary
Deep learning (DL) is a subdomain of artificial intelligence algorithms capable of automatically evaluating subtle graphical features to make highly accurate predictions, which was recently popularized in multiple imaging-related tasks. Because of its capabilities to analyze medical imaging such as radiology scans and digitized pathology specimens, DL has significant clinical potential as a diagnostic or prognostic tool. Coupled with rapidly increasing quantities of digital medical data, numerous novel research questions and clinical applications of DL within medicine have already been explored. Similarly, DL research and applications within hematology are rapidly emerging, although these are still largely in their infancy. Given the exponential rise of DL research for hematologic conditions, it is essential for the practising hematologist to be familiar with the broad concepts and pitfalls related to these new computational techniques. This narrative review provides a visual glossary for key deep learning principles, as well as a systematic review of published investigations within malignant and non-malignant hematologic conditions, organized by the different phases of clinical care. In order to assist the unfamiliar reader, this review highlights key portions of current literature and summarizes important considerations for the critical understanding of deep learning development and implementations in clinical practice.
60 APPLIED LIFE SCIENCES↗
Comparison of Methods for Determining Boundary Layer Edge Conditions for Transition Correlations
Data previously obtained for the X-33 in the NASA Langley Research Center 20-Inch Mach 6 Air Tunnel have been reanalyzed to compare methods for determining boundary layer edge conditions for use in transition correlations. The experimental results were previously obtained utilizing the phosphor thermography technique to monitor the status of the boundary layer downstream of discrete roughness elements via global heat transfer images of the X-33 windward surface. A boundary layer transition correlation was previously developed for this data set using boundary layer edge conditions calculated using an inviscid/integral boundary layer approach. An algorithm was written in the present study to extract boundary layer edge quantities from higher fidelity viscous computational fluid dynamic solutions to develop transition correlations that account for viscous effects on vehicles of arbitrary complexity. The boundary layer transition correlation developed for the X-33 from the viscous solutions are compared to the previous boundary layer transition correlations. It is shown that the boundary layer edge conditions calculated using an inviscid/integral boundary layer approach are significantly different than those extracted from viscous computational fluid dynamic solutions. The present results demonstrate the differences obtained in correlating transition data using different computational methods.
Liechty, Derek S.↗
Spectral reflectance characteristics and automated data reduction techniques which identify wetland and water quality conditions in the Chesapeake Bay
Progress on research designed to test the usability of multispectral, high altitude, remotely sensed data to analyze ecological and hydrological conditions in estuarine environments is presented. Emphasis was placed on data acquired by NASA aircraft over the Patuxent River Chesapeake Bay Test Site, No. 168. Missions were conducted over the Chesapeake Bay at a high altitude flight of 18,460 m and a low altitude flight of 3070. The principle objectives of the missions were: (1) to determine feasibility of identifying source and extent of water pollution problems in Baltimore Harbor, Chesapeake Bay and major tributaries utilizing high altitude, ERTS analogous remote sensing data; (2) to determine the feasibility of mapping species composition and general ecological condition of Chesapeake Bay wetlands, utilizing high altitude, ERTS analogous data; (3) to correlate ground spectral reflectance characteristics of wetland plant species with tonal characteristics on multispectral photography; (4) to determine usefulness of high altitude thermal imagery in delinating isotherms and current patterns in the Chesapeake Bay; and (5) to investigate automated data interpretive techniques which may be usable on high altitude, ERTS analogous data.
Anderson, R. R.↗
Vortex Wake Geometry of a Model Tilt Rotor in Forward Flight
A full-span 0.25-scale V-22 tiltrotor was tested in the NASA Ames 40-by 80-Foot Wind Tunnel in November 2000. The main objective of the test was to acquire a comprehensive database to validate tiltrotor analyses. Figure 1 shows the model installed in the Ames 40- by 80-Foot Wind Tunnel. Rotor and vehicle performance measurements were taken in addition to wing pressures, acoustics, and flow visualization. A dual acoustic traverse system was installed to measure blade-vortex interaction (BVI) noise levels and directivity. Test conditions included hover and forward flight in helicopter mode. Angle-of-attack and thrust sweeps for three tunnel speeds were acquired before model problems caused the premature conclusion of the test. The test will resume in the Ames 80- by 120-Foot Wind Tunnel in late 2001. This paper will focus on the wake geometry measurements that were acquired during the test. The wake geometry measurements were a small subset of a larger matrix of planned measurements designed to study the development and structure of the dual vortex system generated during BVI conditions. The present paper will provide wake geometry data for four test conditions. In addition, the data will be compared with previously acquired wake measurements from an isolated tiltrotor
Wadcock, Alan J.↗
A comparison of unsupervised classification procedures on LANDSAT MSS data for an area of complex surface conditions in Basilicata, Southern Italy
Two unsupervised classification procedures were applied to ratioed and unratioed LANDSAT multispectral scanner data of an area of spatially complex vegetation and terrain. An objective accuracy assessment was undertaken on each classification and comparison was made of the classification accuracies. The two unsupervised procedures use the same clustering algorithm. By on procedure the entire area is clustered and by the other a representative sample of the area is clustered and the resulting statistics are extrapolated to the remaining area using a maximum likelihood classifier. Explanation is given of the major steps in the classification procedures including image preprocessing; classification; interpretation of cluster classes; and accuracy assessment. Of the four classifications undertaken, the monocluster block approach on the unratioed data gave the highest accuracy of 80% for five coarse cover classes. This accuracy was increased to 84% by applying a 3 x 3 contextual filter to the classified image. A detailed description and partial explanation is provided for the major misclassification. The classification of the unratioed data produced higher percentage accuracies than for the ratioed data and the monocluster block approach gave higher accuracies than clustering the entire area. The moncluster block approach was additionally the most economical in terms of computing time.
Justice, C.↗
Identification and interpretation of patterns in rocket engine data: Artificial intelligence and neural network approaches
This paper describes an expert system which is designed to perform automatic data analysis, identify anomalous events, and determine the characteristic features of these events. We have employed both artificial intelligence and neural net approaches in the design of this expert system. The artificial intelligence approach is useful because it provides (1) the use of human experts' knowledge of sensor behavior and faulty engine conditions in interpreting data; (2) the use of engine design knowledge and physical sensor locations in establishing relationships among the events of multiple sensors; (3) the use of stored analysis of past data of faulty engine conditions; and (4) the use of knowledge-based reasoning in distinguishing sensor failure from actual faults. The neural network approach appears promising because neural nets (1) can be trained on extremely noisy data and produce classifications which are more robust under noisy conditions than other classification techniques; (2) avoid the necessity of noise removal by digital filtering and therefore avoid the need to make assumptions about frequency bands or other signal characteristics of anomalous behavior; (3) can, in effect, generate their own feature detectors based on the characteristics of the sensor data used in training; and (4) are inherently parallel and therefore are potentially implementable in special-purpose parallel hardware.
Ali, Moonis↗
Calibration of the Diffusivity Predictions of Centipede Using Approximate Bayesian Computation and Applications in Nyx (Engineering Scale) and Xolotl-MARMOT (Meso-Scale) Simulations
Fission gas evolution and release in UO 2 nuclear fuel are important fuel performance metrics and occur in several distinct stages: 1) nucleation, growth and resolution of intra-granular bubbles, 2) diffusion to grain boundaries and 3) nucleation and growth of bubbles at grain boundaries, which eventually form a connected network (percolation) enabling release of gas from grain boundaries through connections to triple junctions, grain edges or free surfaces. The NE-SciDAC project is developing several computational tools to model this problem, which are connected in a hierarchical multi-scale framework. The information transfer in the multi-scale framework is a critical step that, in addition to best-estimates, should include uncertainty quantification. Despite taking a first-principles multi-scale approach, there is a need to perform parameter calibration to ensure consistency with available experimental data. In the present study, uncertainty quantification (UQ) and parameter calibration is demonstrated for one of the lower length scale codes in the multi-scale framework (Centipede) and then the results, including instances of the propagated uncertainties, are used in other codes within the framework, specifically Nyx and Xolotl-MARMOT. We calibrated the model parameters in Centipede, a computer code used to predict diffusivities of uranium (U) and xenon (Xe) in the context of the simulation of fission gas in uranium oxide (UO 2 ) nuclear fuel. The Centipede code depends on 183 parameters, all of which are subject to uncertainty. The three data sets used in our calibration effort are taken from the literature. This data is available as a set of measurements, including measurement errors. Our goal is to calibrate a statistical model that predicts both the value of the measurement and the uncertainty associated with the measurement. We perform a Bayesian calibration of the model parameters using a dedicated approximate Bayesian computation (ABC) likelihood function. To avoid excessive computational costs, we replace the expensive Centipede simulation code by a higher-order surrogate model, constructed using only the 9 most important parameters. These important parameters are identified by a preliminary global sensitivity analysis (GSA) study. Among the important parameters are T0 (the temperature at which UO 2 is perfectly stoichiometric) and Hf_pO2 (the temperature dependence of the oxygen (O) partial pressure) that should be considered as operating conditions to be estimated along with the other parameters. We consider two different cases: one where we define one set of these operating conditions for all data sets, and one where we define distinct operating condition parameters for each data set. The Xe diffusivities predicted by the latter case show distinct features that could not be observed in the former. Next, we use the diffusivity predictions by Centipede as input to Nyx, a reduced order fuel performance code focused on gas behavior alone, in order to estimate quantities associated with inter-granular bubble formation at conditions specified by the experiments. Finally, the diffusivities obtained from the calibrated Centipede runs were used in coupled Xolotl-MARMOT simulations of intra- and inter-granular gas evolution. The results are compared to simulations using the baseline diffusivities from Turnbull et al.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Integration of Condition-Based, Diagnostic, Prognostic, And Anomaly Detection Data into Reliability Models to Support a Predictive Maintenance Context
Reliability data employed in plant reliability models are an approximated integral representation of the past industrywide operational experience, and they neglect the present asset health status (available, for example, from online monitoring data and diagnostic assessments) and forecasted health projection (when available from prognostic models). Ideally, in a predictive maintenance context, system reliability models should support decision making by propagating actual health information from the asset to the system level in order to provide a quantitative snapshot of system health and identify the most critical assets. Asset health should be informed solely by that specific asset’s current and historical performance data and should not be an approximated integral representation of the past industrywide operational experience (as currently performed by system reliability models through Bayesian updating processes). This paper proposes a reliability modeling approach that relies on asset diagnostic and prognostic assessments, along with monitoring data to measure asset health. We show how state-of-the art condition-based, diagnostic, prognostic, and anomaly detection models can be linked to system reliability models not in probability terms, but in terms of margin where margin is defined as the “distance” between the present status and an undesired event (e.g., failure or unacceptable performance). Then, we show how the propagation of margin data from the asset to the system level is performed through classical reliability models such as fault trees or reliability block diagrams. The described method is in fact able to propagate heterogenous health data from the asset to the system level in order to analytically assess system health.
97 MATHEMATICS AND COMPUTING↗
Specifications and a Prototype Software to Demonstrate a Data Catalog for Hanford Datasets
Environmental management activities at the Hanford Site produce extensive data about site conditions, contaminants, and cleanup activities. Managing, archiving, and accessing that data requires a high degree of collaboration among site contractors and a high level of awareness by project managers and staff. The Hanford Site has a range of databases (e.g., Hanford Environmental Information System [HEIS]) and their associated user interfaces (e.g., Environmental Dashboard Application [EDA], Virtual Library [VL]), as well as other document management systems (e.g., Integrated Document Management System [IDMS]). However, Hanford lacks a single unified resource to find data (which itself comes in multiple formats) amongst the multiple disparate systems, not to mention ad hoc data not contained in an official repository/database.
54 ENVIRONMENTAL SCIENCES↗