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Modal structure inferred from static far-field noise directivity
Turbofan noise directivity calculated for two directivity models (equal modal amplitude and equal modal power or energy) was compared with experimental blade passing frequency data from two fans at 60 and 90 percent speeds. Experimental data indicated similar directivity patterns which were well represented by a single average data curve. Calculated points using the equal amplitude model showed over-prediction near the fan axis and near the 90-deg position. Calculated points using the equal power model showed a very good match with the average data lending support to theory of equipartition of modal power from a random source such as the interaction of the rotor with inlet flow distortion. The equal modal power model also gave good agreement with individual data points.
Phenomenological R-Matrix parameterization of direct, doorway, and compound nuclear reactions [Slides]
The presented R-matrix formalism could improve evaluations of resolved resonance nuclear cross section data by parameterizing direct reaction processes via an orthonormal channel mixing matrix and doorway state processes. Together, they can parameterize direct-semidirect capture. A formal similarity among the R-, K-, T- matrix formalisms was leveraged to parameterize direct and doorway processes. The advantage of this R-matrix parameterization is that it is easily extensible due to its application of projection operators directly upon the (H-E) on p. 6.
Wide area fault detection method using PMU data
The embodiments herein are directed to monitoring in real time, power system data so as to provide insights into global operation of a power grid. Such a scheme disclosed herein utilizes rules, which are created by analyzing PMU measurement data, in order to detect the fault location (bus and line) and fault type. Three common types of faults in a power grid, single-line-to-ground (SLG), line-to-line (LL), and three phase faults, can be detected using the methods herein.
An improved algorithm of image processing technique for film thickness measurement in a horizontal stratified gas-liquid two-phase flow
Due to the importance of the two-phase flow researches for the industrial safety analysis, many researchers developed various methods and techniques to study the two-phase flow phenomena on the industrial cases, such as in the chemical, petroleum and nuclear industries cases. One of the developing methods and techniques is image processing technique. This technique is widely used in the two-phase flow researches due to the non-intrusive capability to process a lot of visualization data which are contain many complexities. Moreover, this technique allows to capture direct-visual information data of the flow which are difficult to be captured by other methods and techniques. The main objective of this paper is to present an improved algorithm of image processing technique from the preceding algorithm for the stratified flow cases. The present algorithm can measure the film thickness (h{sub L}) of stratified flow as well as the geometrical properties of the interfacial waves with lower processing time and random-access memory (RAM) usage than the preceding algorithm. Also, the measurement results are aimed to develop a high quality database of stratified flow which is scanty. In the present work, the measurement results had a satisfactory agreement with the previous works.
The Artificial Scientist: in-Transit Machine Learning of Plasma Simulations
Large-scale simulations or scientific experiments produce petabytes of data per run. This poses massive challenges for I/O and storage when scientific analysis workflows are run manually offline. Unsupervised deep learning-based techniques to extract patterns and non-linear relations from these large amounts of data provide a way to build scientific understanding from raw data, reducing the need for manual pre-selection of analysis steps, but require exascale compute and memory to process the full dataset available. In this paper, we demonstrate a heterogeneous streaming workflow in which plasma simulation data is streamed directly to a Machine Learning (ML) application training a model on the simulation data in-transit, completely circumventing the capacity-constrained filesystem bottleneck. This workflow employs openPMD to provide a high level interface to describe scientific data and also uses ADIOS2, to transfer volumes of data that exceed the capabilities of the filesystem. We employ experience replay to avoid catastrophic forgetting in learning from this non-steady state process in a continual manner and adapt it to improve model convergence while learning in-transit. As a proof-of-concept, we approach the ill-posed inverse problem of predicting particle dynamics from radiation in a particle-incell (PIConGPU) simulation of the Kelvin-Helmholtz instability (KHI). We detail hardware-software co-design challenges as we scale PIConGPU to full Frontier, the Top-1 system as of June 2024 Top500 list.
Computational Fluid Dynamics Simulations to Support Efficiency Improvements in Aluminum Smelting Process
Smelting is broadly described as the extraction of a metal from its ore. In the United States, aluminum is commonly produced by smelting alumina in bauxite using the Hall-Héroult process. Optimization of equipment and processes in conventional smelting is crucial to enhancing process efficiency and productivity, is necessary for improving the techno-economic feasibility, which directly manifests as the growth of the American economy. To achieve optima, insightful data on the multiphysics phenomena that are inherent to the process must be obtained through physical investigation or high-fidelity numerical simulations. The resolution of relevant scales in time and space for smelting operations requires intensive, high-performance computing (HPC) simulations. Hostile operating conditions limit physical data acquisition to specific techniques; therefore, these data do not describe the multiscale interaction of simultaneous effects. Fortunately, in recent decades, significant advancements in computing hardware and computational methods have made the numerical resolution of such a complex process possible. In this study, a high-fidelity simulation of aluminum smelting was performed using an open-source tool, OpenFOAM, which analyzed many parameters characteristic to underlying phenomena. A multiphysics model based on the Eulerian-Eulerian multifluid approach was adopted. This model can resolve critical issues in the electrolytic smelting of aluminum, such as bubbling of carbon dioxide from the anode(s), magnetohydrodynamics from electromagnetic effects, ionic dissolution of the alumina in the electrolyte, and the evolution of thermal profiles. This study provides valuable connectivity for characteristic data that can direct the future designs of efficient smelters. A basic framework to model and simulate the smelting process using OpenFOAM is presented for user modification in keeping with process development. Of relevance to the flow field, a detailed investigation of vortices produced by bubble motion and electromagnetics is discussed, along with their impact on the evolution of thermal profiles. The predictions show small-scale vortices in the clearance between the anode and cathode caused by magnetic forces. Predictions also indicate relatively large-scale vortices in the inter-anode space resulting from carbon dioxide rising through the electrolytic flow field. The formation of vortices at the edges of anodes was shown to direct alumina charged by the feeder to the bottom of the anodes, thus preventing the entrapment of gas bubbles in the periphery of the bottom of the anode. Symmetry was observed in the location of cold spots in the electrolytic mixture in the vicinity of the feeder. Cold spots were also observed in the clearance between the anode and cathode due to the flow’s transmission of unconverted alumina to this region.
Soda Pop: A Time-Series Clustering, Alarming and Disease Forecasting Application
To introduce Soda Pop, an R/Shiny application designed to be a disease agnostic time-series clustering, alarming, and forecasting tool to assist in disease surveillance “triage, analysis and reporting” workflows within the Biosurveillance Ecosystem (BSVE). In this poster, we highlight the new capabilities that are brought to the BSVE by Soda Pop with an emphasis on the impact of methodological decisions. The Biosurveillance Ecosystem (BSVE) is a biological and chemical threat surveillance system sponsored by the Defense Threat Reduction Agency (DTRA). BSVE is intended to be user-friendly, multi-agency, cooperative, modular and threat agnostic platform for biosurveillance. In BSVE, a web-based workbench presents the analyst with applications (apps) developed by various DTRA funded researchers, which are deployed on-demand in the cloud(e.g., Amazon Web Services). These apps aim to address emerging needs and refine capabilities to enable early warning of chemical and biological threats for multiple users across local, state, and federal agencies. Soda Pop is an app developed by Pacific Northwest National Laboratory (PNNL) to meet the current needs of the BSVE for early warning and detection of disease outbreaks. Aimed for use bya diverse set of analysts, the application is agnostic to data source and spatial scale enabling it to be generalizable across many diseases and locations. To achieve this, we placed a particular emphasis on clustering and alerting of disease signals within Soda Pop without strong prior assumptions on the nature of observed diseased counts. Although designed to be agnostic to the data source, Soda Pop was initially developed and tested on data summarizing Influenza-Like Illness in military hospitals from collaboration with the Armed Forces Health Surveillance Branch. Currently, the data incorporated also includes the CDC’s National Notifiable Diseases Surveillance System(NNDSS) tables and the WHO’s Influenza A/B Influenza Data(Flunet). These data sources are now present in BSVE’s Postgres data storage for direct access. Soda Pop is designed to automate time-series tasks of data summarization, exploration, clustering, alarming and forecasting. Built as an R/Shiny application, Soda Pop is founded on the powerful statistical tool R.. Where applicable, Soda Pop facilitates nonparametric seasonal decomposition of time-series; hierarchical agglomerative clustering across reporting areas and between diseases within reporting areas; and a variety of alarming techniques including Exponential Weighted Moving Average alarms and Early Aberration Detection. Soda Pop embeds these techniques within a user-interface designed to enhance an analyst’s understanding of emerging trends in their data and enables the inclusion of its graphical elements into their dossier for further tracking and reporting. The ultimate goal of this software is to facilitate the discovery of unknown disease signals along with increasing the speed of detection of unusual patterns within these signals. Soda Pop organizes common statistical disease surveillance tasks in a manner integrated with BSVE data source inputs and outputs. The app analyzes time-series disease data and supports a robust set of clustering and alarming routines that avoid strong assumptions on the nature of observed disease counts. This attribute allows for flexibility in the data source, spatial scale, and disease types making it useful to a wide range of analysts.
Bistatic radar sea state monitoring field test
Recent advances in understanding the physical phenomena controlling the interaction of electromagnetic energy with the ocean surface have revealed the possiblity of remote measurement of the two-dimensional surface wave height spectrum of the ocean using bistatic radar techniques. The basic feasibility of such a technique operating at frequencies in the HF region (3 to 30 MHz) was examined during previous studies and hardware for an experimental verification experiment was specified. The activities have resulted in a determination of the required hardware and system parameters for both satellite and aircraft systems, the development, assembly, and testing of hardware for an experimental aircraft system, the development and initial testing of data processing procedures, and the conduct of an initial flight test experiment. Activities were devoted to completing the assembly and testing of the experimental hardware, completing the experiment planning, conducting a field test experiment, and the processing and analysis of the experimental data. Even though directional spectrum maps of the test area cannot be generated from the measured data, the hardware concept employed appears viable, and solutions to the problems encountered have been identified.
User-oriented end-to-end transport protocols for the real-time distribution of telemetry data from NASA spacecraft
A set of standard telemetry protocols for downlink data flow facilitating the end-to-end transport of instrument data from the spacecraft to the user in real time is proposed. The direct switching of data by autonomous message 'packets' that are assembled by the source instrument on the spacecraft is discussed. The data system consists thus of a format on a message rather than word basis, and such packet telemetry would include standardized protocol headers. Standards are being developed within the NASA End-to-End Data System (NEEDS) program for the source packet and transport frame protocols. The source packet protocol contains identification of both the sequence number of the packet as it is generated by the source and the total length of the packet, while the transport frame protocol includes a sequence count defining the serial number of the frame as it is generated by the spacecraft data system, and a field specifying any 'options' selected in the format of the frame itself.
Interpretation of gamma-ray burst source count statistics
Ever since the discovery of gamma-ray bursts, the so-called log N-log S relation has been used for determination of their distances and distribution. This task has not been straightforward because of varying thresholds for the detection of bursts. Most of the current analyses of these data are couched in terms of ambiguous distributions, such as the distribution of Cp/Clim, the ratio of peak to threshold photon count rates, or the distribution of V/Vmax = (Cp/Clim) exp -3/2. It is shown that these distributions are not always a true reflection of the log N-log S relation. Some kind of deconvolution is required for obtaining the true log N-log S. Therefore, care is required in the interpretation of results of such analyses. A new method of analysis of these data is described, whereby the bivariate distribution of Cp and Clim is obtained directly from the data.
TRACER CLAMPS2 Doppler Lidar VAD Data
These files contain 24 hour periods of data collected from the CLAMPS2 Halo Streamline XR+ Doppler lidar. The Doppler lidar conducts regular conical scans at a set elevation angle. These data are then passed through a typical VAD algorithm to retrieve horizontal wind speed and direction profiles. These data were collected during the TRACER project.
Asynoptic meteorological data assimilation by means of a time coordinate transformation
A possible method for assimilating large quantities of asynoptic data, such as satellite observations, into numerical weather forecasts, is investigated. The method is based on a transformation of the time coordinate to coincide with satellite paths, and a well-posed problem is solved in order to obtain forecasts beyond the time of data acquisition. Simulated barotropic forecasts based on the proposed technique are compared with forecasts derived from the direct insertion of data. Results indicate that only in ideal situations, when asynoptic data are obtained both continuosly and accurately, will the method function well as compared with direct insertion techniques.
TRACER SPARC Doppler Lidar VAD Data
These files contain 24 hour periods of data collected from the SPARC Halo Streamline XR Doppler lidar. The Doppler lidar conducts regular conical scans at a set elevation angle. These data are then passed through a typical VAD algorithm to retrieve horizontal wind speed and direction profiles. These data were collected during the TRACER project.
Observation of early shell-dopant mix in OMEGA direct-drive implosions and comparisons with radiation-hydrodynamic simulations
Temporally, spatially, and spectrally resolved x-ray image data from direct-drive implosions on OMEGA were interpreted with the aid of radiation-hydrodynamic simulations. Neither clean calculations nor those using a turbulent mix model can explain fully the observed migration of shell-dopant material (titanium) into the core. Shell-dopant migration was observed via time-dependent, spatially integrated spectra, and spatially and spectrally resolved x-ray images of capsule implosions and resultant dopant emissions. The titanium emission was centrally peaked in narrowband x-ray images. In post-processed clean simulations, the peak titanium emission forms in a ring in self-emission images as the capsule implodes. Post-processed simulations with mix reproduce trends in time-dependent, spatially integrated spectra, as well having centrally peaked Ti emission in synthetic multiple monochromatic imager. However, mix simulations still do not transport Ti to the core as is observed in the experiment. This suggests that phenomena in addition to the turbulent mix must be responsible for the transport of Ti. Simple diffusion estimates are unable to explain the early Ti mix into the core. Mechanisms suggested for further study are capsule surface roughness, illumination non-uniformity, and shock entrainment.
AWAKEN CLAMPS1 Doppler Lidar VAD Data
These files contain 24 hour periods of data collected from the CLAMPS1 Halo Streamline XR+ Doppler lidar. The Doppler lidar conducts regular conical scans at a set elevation angle. These data are then passed through a typical VAD algorithm to retrieve horizontal wind speed and direction profiles. These data were collected during the AWAKEN project.
AWAKEN CLAMPS2 Doppler Lidar VAD Data
These files contain 24 hour periods of data collected from the CLAMPS2 Halo Streamline XR+ Doppler lidar. The Doppler lidar conducts regular conical scans at a set elevation angle. These data are then passed through a typical VAD algorithm to retrieve horizontal wind speed and direction profiles. These data were collected during the AWAKEN project.