An Analysis of Equity and Equality Constraints on Power System Planning
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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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Presentation on NREL's new capabilities in electron microscopy and AI-guided interface that are relevant to the Si PV community.
Machine intelligence has the potential to revolutionize materials science, enabling autonomous synthesis, self-driving characterization, and accelerated modeling. However, despite the promise, successful implementation of these methods in day-to-day research remains a challenge. This talk will delve into the reasons behind this, exploring how truly intelligent experiments are hindered by opaque experiment control, a lack of domain-specific models, and human-centric design. Through a focus on the characterization of next-generation microelectronics and energy storage materials, I will share insights from both successful and failed attempts to implement machine intelligence. We will then explore the next steps necessary to unlock the full potential of machine intelligence in materials science, creating a future where intelligent systems work seamlessly alongside researchers to drive innovation and discovery.
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A new computer code was developed to analyze the chemically reactive flow and spray combustion processes occurring inside a stratified-charge rotary engine. Mathematical and numerical details of the new code were recently described by the present authors. The results are presented of limited, initial computational trials as a first step in a long-term assessment/validation process. The engine configuration studied was chosen to approximate existing rotary engine flow visualization and hot firing test rigs. Typical results include: (1) pressure and temperature histories, (2) torque generated by the nonuniform pressure distribution within the chamber, (3) energy release rates, and (4) various flow-related phenomena. These are discussed and compared with other predictions reported in the literature. The adequacy or need for improvement in the spray/combustion models and the need for incorporating an appropriate turbulence model are also discussed.
Recent advances in the field of data-driven dynamics allow for the discovery of ODE systems using state measurements. One approach, known as Sparse Identification of Nonlinear Dynamics (SINDy), assumes the dynamics are sparse within a predetermined basis in the states and finds the expansion coefficients through linear regression with sparsity constraints. This approach requires an accurate estimation of the state time derivatives, which is not necessarily possible in the high-noise regime without additional constraints. We present an approach called Derivative-based SINDy (DSINDy) that combines two novel methods to improve ODE recovery at high-noise levels. First, we denoise the state variables by applying a projection operator that leverages the assumed basis for the system dynamics. Second, we use a second order cone program (SOCP) to find the derivative and governing equations simultaneously. We derive theoretical results for the projection-based denoising step, which allow us to estimate the values of hyperparameters used in the SOCP formulation. This underlying theory helps limit the number of required user-specified parameters. Finally, we present results demonstrating that our approach leads to improved system recovery for the Van der Pol oscillator, the Duffing oscillator, the Rössler attractor, and the Lorenz 96 model.
We propose a scalable framework for the learning of high-dimensional parametric maps via adaptively constructed residual network (ResNet) maps between reduced bases of the inputs and outputs. When just few training data are available, it is beneficial to have a compact parametrization in order to ameliorate the ill-posedness of the neural network training problem. By linearly restricting high-dimensional maps to informed reduced bases of the inputs, one can compress high-dimensional maps in a constructive way that can be used to detect appropriate basis ranks, equipped with rigorous error estimates. A scalable neural network learning framework is thus to learn the nonlinear compressed reduced basis mapping. Unlike the reduced basis construction, however, neural network constructions are not guaranteed to reduce errors by adding representation power, making it difficult to achieve good practical performance. Inspired by recent approximation theory that connects ResNets to sequential minimizing flows, we present an adaptive ResNet construction algorithm. This algorithm allows for depth-wise enrichment of the neural network approximation, in a manner that can achieve good practical performance by first training a shallow network and then adapting. We prove universal approximation of the associated neural network class for $L^2_v$ functions on compact sets. Our overall framework allows for constructive means to detect appropriate breadth and depth, and related compact parametrizations of neural networks, significantly reducing the need for architectural hyperparameter tuning. Numerical experiments for parametric PDE problems and a 3D CFD wing design optimization parametric map demonstrate that the proposed methodology can achieve remarkably high accuracy for limited training data, and outperformed other neural network strategies we compared against.
Tensor analysis is one of the more abstruse, even if one of the more useful, higher math subjects enjoined by students of physics and engineering. It is abstruse because of the intellectual gap that exists between where most physics and engineering mathematics leave off and where tensor analysis traditionally begins. It is useful because of its great generality, computational power, and compact, easy to use, notation. This paper bridges the intellectual gap. It is divided into three parts: algebra, calculus, and relativity. Algebra: In tensor analysis, coordinate independent quantities are sought for applications in physics and engineering. Coordinate independence means that the quantities have such coordinate transformations as to leave them invariant relative to a particular observer s coordinate system. Calculus: Non-zero base vector derivatives contribute terms to dynamical equations that correspond to pseudoaccelerations in accelerated coordinate systems and to curvature or gravity in relativity. These derivatives have a specific general form in tensor analysis. Relativity: Spacetime has an intrinsic geometry. Light is the tool for investigating that geometry. Since the observed geometry of spacetime cannot be made to match the classical geometry of Euclid, Einstein applied another more general geometry differential geometry. The merger of differential geometry and cosmology was accomplished in the theory of relativity. In relativity, gravity is equivalent to curvature.
This summer I am working in the Educational Programs Office. My task is to convert the Aeronautics Interactive Workstation from a Macintosh (Mac) platform to a Personal Computer (PC) platform. The Aeronautics Interactive Workstation is a workstation in the Aerospace Educational Laboratory (AEL), which is one of the three components of the Science, Engineering, Mathematics, and Aerospace Academy (SEMAA). The AEL is a state-of-the-art, electronically enhanced, computerized classroom that puts cutting-edge technology at the fingertips of participating students. It provides a unique learning experience regarding aerospace technology that features activities equipped with aerospace hardware and software that model real-world challenges. The Aeronautics Interactive Workstation, in particular, offers a variety of activities pertaining to the history of aeronautics. When the Aeronautics Interactive Workstation was first implemented into the AEL it was designed with Macromedia Director 4 for a Mac. Today it is being converted to Macromedia DirectorMX2004 for a PC. Macromedia Director is the proven multimedia tool for building rich content and applications for CDs, DVDs, kiosks, and the Internet. It handles the widest variety of media and offers powerful features for building rich content that delivers red results, integrating interactive audio, video, bitmaps, vectors, text, fonts, and more. Macromedia Director currently offers two programmingkripting languages: Lingo, which is Director's own programmingkripting language and JavaScript. In the workstation, Lingo is used in the programming/scripting since it was the only language in use when the workstation was created. Since the workstation was created with an older version of Macromedia Director it hosted significantly different programming/scripting protocols. In order to successfully accomplish my task, the final product required correction of Xtra and programming/scripting errors. I also had to convert the Mac platform file extensions into compatible file extensions for a PC.
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With the advent of high speed computing Monte Carlo ray tracing techniques has become the preferred method for evaluating spacecraft orbital heats. Monte Carlo has its greatest advantage where there are many interacting surfaces. However Monte Carlo programs are specialized programs that suffer from some inaccuracy, long calculation times and high purchase cost. A general orbital heating integral is presented here that is accurate, fast and runs on MathCad, a generally available engineering mathematics program. The integral is easy to read, understand and alter. The integral can be applied to unshaded primitive surfaces at any orientation. The method is limited to direct heating calculations. This integral formulation can be used for quick orbit evaluations and spot checking Monte Carlo results.
For over fifty years of crewed spaceflight missions, NASA astronauts have taken remarkable photographs of the Earth that offer various perspectives for viewing dynamic Earth processes. The International Space Station (ISS) today provides a unique platform to view and capture imagery of Earth at a variety of viewing angles, seasons, and times of day. Astronaut photos comprise a variable true-color (RGB - Red Green Blue) dataset taken with multiple handheld digital (and historically, film) cameras and lens types that differ from nadir-looking satellite-based remote sensing instruments with fixed temporal and spatial resolutions. The Crew Earth Observations (CEO) Facility within the Earth Science and Remote Sensing Unit at NASA’s Johnson Space Center supports the acquisition, analysis, and curation of astronaut photography of Earth’s surface and atmosphere. CEO’s website, the Gateway to Astronaut Photography of Earth (eol.jsc.nasa.gov), provides free public access to view, search, and download over three million images taken by astronauts from Mercury, Gemini, and Apollo missions to current ISS missions. Handheld images taken by astronauts from the ISS are used for scientific research and public engagement in STEM (Science, Technology, Engineering, Mathematics) fields, particularly earth science. While segments of the public are actively interested in STEM fields, there may be groups who are underexposed to and disinterested in earth science or are unaware of astronaut photography from the ISS. A broader public audience can be introduced to earth science, STEM fields, and ISS research through artistic or abstract photos of the Earth. Wide angle pictures taken of Earth have identifiable landforms and bodies of water, however zoomed in, very high resolution photos without obvious geographic reference can attract new viewing audiences on a purely aesthetic basis. The CEO Facility actively curates a collection of exceptional astronaut photos of Earth as art used to reach a wider population through increased digital products and outreach events. This continuously-updated collection feeds development of freely accessible and high-quality downloadable materials including scientific articles, videos, computer wallpapers, and social media content. These materials are presented with science-based information that augments the artistic qualities of the imagery, and facilitate connection between art-engaged or non-STEM audiences to the ISS research platform and the need for future innovative STEM research.
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Abstract The accurate prediction of aerodynamic drag on satellites orbiting in the upper atmosphere is critical to the operational success of modern space technologies, such as satellite‐based communication or navigation systems, which have become increasingly popular in the last few years due to the deployment of constellations of satellites in low‐Earth orbit. As a result, physics‐based models of the ionosphere and thermosphere have emerged as a necessary tool for the prediction of atmospheric outputs under highly variable space weather conditions. This paper proposes a high‐fidelity approach for physics‐based space weather modeling based on the solution of the Navier–Stokes equations using a high‐order discontinuous Galerkin method, combined with a matrix‐free strategy suitable for high‐performance computing on GPU architectures. The approach consists of a thermospheric model that describes a chemically frozen neutral atmosphere in nonhydrostatic equilibrium driven by the external excitation of the Sun. A novel set of variables is considered to treat the low densities present in the upper atmosphere and to accommodate the wide range of scales present in the problem. At the same time, and unlike most existing approaches, radial and angular directions are treated in a nonsegregated approach. The study presents a set of numerical examples that demonstrate the accuracy of the approximation and validate the current approach against observational data along a satellite orbit, including estimates of established empirical and physics‐based models of the ionosphere‐thermosphere system. Finally, a one‐dimensional radial derivation of the physics‐based model is presented and utilized for conducting a parametric study of the main thermal quantities under various solar conditions.