Digital Twins for Canal, Hydrokinetic Turbine and Array Power Modeling & Optimization
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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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MatLab(R) (MATrix LABoratory) is a numerical computation and simulation tool that is used by thousands Scientists and Engineers in many cou ntries. MatLab does purely numerical calculations, which can be used as a glorified calculator or interpreter programming language; its re al strength is in matrix manipulations. Computer algebra functionalities are achieved within the MatLab environment using "symbolic" toolbo x. This feature is similar to computer algebra programs, provided by Maple or Mathematica to calculate with mathematical equations using s ymbolic operations. MatLab in its interpreter programming language fo rm (command interface) is similar with well known programming languag es such as C/C++, support data structures and cell arrays to define c lasses in object oriented programming. As such, MatLab is equipped with most ofthe essential constructs of a higher programming language. M atLab is packaged with an editor and debugging functionality useful t o perform analysis of large MatLab programs and find errors. We belie ve there are many ways to approach real-world problems; prescribed methods to ensure foregoing solutions are incorporated in design and ana lysis of data processing and visualization can benefit engineers and scientist in gaining wider insight in actual implementation of their perspective experiments. This presentation will focus on data processing and visualizations aspects of engineering and scientific applicati ons. Specifically, it will discuss methods and techniques to perform intermediate-level data processing covering engineering and scientifi c problems. MatLab programming techniques including reading various data files formats to produce customized publication-quality graphics, importing engineering and/or scientific data, organizing data in tabu lar format, exporting data to be used by other software programs such as Microsoft Excel, data presentation and visualization will be discussed. The presentation will emphasize creating practIcal scripts (pro grams) that extend the basic features of MatLab TOPICS mclude (1) Ma trix and vector analysis and manipulations (2) Mathematical functions (3) Symbolic calculations & functions (4) Import/export data files (5) Program lOgic and flow control (6) Writing function and passing parameters (7) Test application programs
The Chicago Array of Things (AoT) project, funded by the US National Science Foundation, created an experimental, urban-scale measurement capability to support diverse scientific studies. Initially conceived as a traditional sensor network, collaborations with many science communities guided the project to design a system that is remotely programmable to implement Artificial Intelligence (AI) within the devices-at the “edge” of the network-as a means for measuring urban factors that heretofore had only been possible with human observers, such as human behavior including social interaction. The concept of “software-defined sensors” emerged from these design discussions, opening new possibilities, such as stronger privacy protections and autonomous, adaptive measurements triggered by events or conditions. We provide examples of current and planned social and behavioral science investigations uniquely enabled by software-defined sensors as part of the SAGE project, an expanded follow-on effort that includes AoT.
Future analysis of ATLAS data will involve new small-sized analysis formats to cope with the increased storage needs. The smallest of these, named DAOD_PHYSLITE, has calibrations already applied to allow fast downstream analysis and avoid the need for further analysis-specific intermediate formats. This allows for application of the "columnar analysis" paradigm where operations are applied on a per-array instead of a per-event basis. We will present methods to read the data into memory, using Uproot, and also discuss I/O aspects of columnar data and alternatives to the ROOT data format. Furthermore, we will show a representation of the event data model using the Awkward Array package and present proof of concept for a simple analysis application.
This research advances the understanding of spin decoherence mechanisms and addresses the grand challenges in quantum information science of generating and stabilizing quantum states that may be manipulated with single-atom precision. It is accomplished using unique capabilities developed at PNNL for ion soft landing and spatially resolved vibrational spectroscopy of well-defined supported molecular qubit (MQ) arrays. It addresses knowledge gaps impeding the development of MQ-based quantum devices by answering two scientific questions: 1) Can we minimize spin-lattice relaxation rates by tuning the substrate-MQ interface? 2) What is the optimum number of spin centers that enables sufficient coherent signal generation prior to decoherence through inter-MQ and MQ-support interactions? The objective is to develop predictive design principles for prolonging spin coherence lifetimes in arrays of optically-addressable MQs for quantum computing applications.
Two mathematical representations of noise due to atmospheric turbulence are presented. These representations are derived and used in computer simulations of the Bartlett Estimate implementation of beamforming. Beamforming is an array processing technique employing an array of acoustic sensors used to determine the bearing of an acoustic source. Atmospheric wind conditions introduce noise into the beamformer output. Consequently, the accuracy of the process is degraded and the bearing of the acoustic source is falsely indicated or impossible to determine. The two representations of noise presented here are intended to quantify the effects of mean wind passing over the array of sensors and to correct for these effects. The first noise model is an idealized case. The effect of the mean wind is incorporated as a change in the propagation velocity of the acoustic wave. This yields an effective phase shift applied to each term of the spatial correlation matrix in the Bartlett Estimate. The resultant error caused by this model can be corrected in closed form in the beamforming algorithm. The second noise model acts to change the true direction of propagation at the beginning of the beamforming process. A closed form correction for this model is not available. Efforts to derive effective means to reduce the contributions of the noise have not been successful. In either case, the maximum error introduced by the wind is a beam shift of approximately three degrees. That is, the bearing of the acoustic source is indicated at a point a few degrees from the true bearing location. These effects are not quite as pronounced as those seen in experimental results. Sidelobes are false indications of acoustic sources in the beamformer output away from the true bearing angle. The sidelobes that are observed in experimental results are not caused by these noise models. The effects of mean wind passing over the sensor array as modeled here do not alter the beamformer output as significantly as expected.
Modeling using mathematical optimization dynamics is a design tool used in magnetic suspension system development. MATLAB (software) is used to calculate minimum cost and other desired constraints. The parameters to be measured are programmed into mathematical equations. MATLAB will calculate answers for each set of inputs; inputs cover the boundary limits of the design. A Magnetic Suspension System using Electromagnets Mounted in a Plannar Array is a design system that makes use of optimization modeling.
MatLab(TradeMark)(MATrix LABoratory) is a numerical computation and simulation tool that is used by thousands Scientists and Engineers in many countries. MatLab does purely numerical calculations, which can be used as a glorified calculator or interpreter programming language; its real strength is in matrix manipulations. Computer algebra functionalities are achieved within the MatLab environment using "symbolic" toolbox. This feature is similar to computer algebra programs, provided by Maple or Mathematica to calculate with mathematical equations using symbolic operations. MatLab in its interpreter programming language form (command interface) is similar with well known programming languages such as C/C++, support data structures and cell arrays to define classes in object oriented programming. As such, MatLab is equipped with most of the essential constructs of a higher programming language. MatLab is packaged with an editor and debugging functionality useful to perform analysis of large MatLab programs and find errors. We believe there are many ways to approach real-world problems; prescribed methods to ensure foregoing solutions are incorporated in design and analysis of data processing and visualization can benefit engineers and scientist in gaining wider insight in actual implementation of their perspective experiments. This presentation will focus on data processing and visualizations aspects of engineering and scientific applications. Specifically, it will discuss methods and techniques to perform intermediate-level data processing covering engineering and scientific problems. MatLab programming techniques including reading various data files formats to produce customized publication-quality graphics, importing engineering and/or scientific data, organizing data in tabular format, exporting data to be used by other software programs such as Microsoft Excel, data presentation and visualization will be discussed.
Stochasticity in magnetic nanodevices is an essential characteristic for harnessing these devices to computing based on population coding or the building blocks of probabilistic computing, p-bits. A magnetic tunneling junction (MTJ) consisting of a patterned magnetic element is considered a promising computing unit in the concept of artificial neurons and p-bits. A comprehensive understanding of the stochasticity in the switching of patterned magnetic elements is crucial for realizing MTJ-based probabilistic computing technology. In the present work, the stochastic behavior in the switching process of a perpendicularly magnetized Co/Pt disk within an array was directly observed utilizing full-field soft X-ray microscopy. Within 50 repeated hysteretic cycles, the stochastic magnetization switching of individual Co/Pt disks within disk arrays is identified. We found that the stochasticity in the magnetization switching of disks considerably depends on the disk size. The stochasticity initially decreases as the disk radius gets bigger from 125 to 375 nm (region I), then increases with further enlarging the disk size to 625 nm (region II). The variance of thermal fluctuation relevant to the disk size and the multilevel switching within a disk are severely involved in the observed size-dependent stochasticity. Furthermore, this work provides the way for controlling the stochasticity in the switching of nanopatterned elements, which is a key aspect of MTJ-based probabilistic computing.
Topics covered in the presentation include: Primary causes of current performance on KNL/Haswell processers; compiler discussion; array-syntax; compiler-option/code modifications; and, performance results.
This presentation presents a technique that allows passing of Fortran nested-type hierarchies interoperably to C++. The technique is motivated by the Eulerian Application Project’s need to port code from Fortran to C++ to utilize the Kokkos performance portability library. The resulting method allows hierarchies of types to become interoperable while at the same time transforming Fortran array members of the original Fortran type into Kokkos::Views in the resulting C++ type.
The combined effects of imaging gathering, sampling and reconstruction are analyzed in terms of image fidelity. The analysis is based upon a standard end-to-end linear system model which is sufficiently general so that the results apply to most line-scan and sensor-array imaging systems. Shift-variant sampling effects are accounted for with an expected value analysis based upon the use of a fixed deterministic input scene which is randomly shifted (mathematically) relative to the sampling grid. This random sample-scene phase approach has been used successfully by the author and associates in several previous related papers.
An ultra-small micro spectrometer with less than 1mm diameter was constructed using Fresnel diffraction. The fabricated spectrometer has a diameter of 750 nmicrometers and a focal length of 2.4 mm at 533nm wavelength. The micro spectrometer was built with a simple negative zone plate that has an opaque center with an ecliptic shadow to remove the zero-order direct beam to the aperture slit. Unlike conventional approaches, the detailed optical calculation indicates that the ideal spectral resolution and resolving power do not depend on the miniaturized size but only on the total number of rings. We calculated 2D and 3D photon distribution around the aperture slit and confirmed that improved micro-spectrometers below 1mm size can be built with Fresnel diffraction. The comparison between mathematical simulation and measured data demonstrates the theoretical resolution, measured performance, misalignment effect, and improvement for the sub-1mm Fresnel micro-spectrometer. We suggest the utilization of an array of micro spectrometers for tunable multi-spectral imaging in the ultra violet range.
The VIS-AD data model integrates metadata about the precision of values, including missing data indicators and the way that arrays sample continuous functions, with the data objects of a scientific programming language. The data objects of this data model form a lattice, ordered by the precision with which they approximate mathematical objects. We define a similar lattice of displays and study visualization processes as functions from data lattices to display lattices. Such functions can be applied to visualize data objects of all data types and are thus polymorphic.
Space Weather UnderGround (SWUG) is an educational outreach program that provides hands-on experiences in Science, Technology, Engineering, and Mathematics (STEM) to high school and undergraduate students. It has three ultimate goals: 1. Educating the future STEM workforce. 2. Building a cost-effective, research-capable ground 3. magnetometer array across the United States. Collecting geomagnetic field data at high spatial resolution for heliophysics research. SWUG students build, test, and deploy the Simple Aurora Monitor (SAM)-III kit, purchased by Reeve Engineers. The SAM-III kit is a fluxgate magnetometer that measures geomagnetic field changes at a resolution of 1 nT/sec. Dr. Charles Smith initiated the SWUG program at the University of New Hampshire, and it has since expanded to Alaska and Maryland. This presentation introduces the Maryland SWUG activities. The MD-SWUG program was started in the Fall of 2022 with the goal of deploying magnetometers to the solar eclipse sites in 2023 and 2024. Two student-built magnetometers were deployed to the Southwest Research Institute and the Los Alamos National Laboratory for the 2023 annular solar eclipse, with plans to deploy at least four magnetometers to the 2024 total solar eclipse sites, including the University of Texas at Dallas. During a solar eclipse, reduced solar irradiance weakens ionospheric currents and reduces geomagnetic fields by up to 30 nT at the eclipse sites. A recent study suggested that solar eclipses impact geomagnetic fields not only along the eclipse path but also at their magnetic conjugate locations. The SWUG program will help us understand this electrodynamic coupling between the conjugate locations during a solar eclipse by providing better data coverage along the solar eclipse path.
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Stability and control, telecommunication system, solar array design, propellant feed system, and infrared model of Mars for Voyager project