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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Spin-Up and Tuning of the Global Carbon Cycle Model Inside the GISS ModelE2 GCM

Planetary carbon cycle involves multiple phenomena, acting at variety of temporal and spacial scales. The typical times range from minutes for leaf stomata physiology to centuries for passive soil carbon pools and deep ocean layers. So, finding a satisfactory equilibrium state becomes a challenging and computationally expensive task. Here we present the spin-up processes for different configurations of the GISS Carbon Cycle model from the model forced with MODIS observed Leaf Area Index (LAI) and prescribed ocean to the prognostic LAI and to the model fully coupled to the dynamic ocean and ocean biology. We investigate the time it takes the model to reach the equilibrium and discuss the ways to speed up this process. NASA Goddard Institute for Space Studies General Circulation Model (GISS ModelE2) is currently equipped with all major algorithms necessary for the simulation of the Global Carbon Cycle. The terrestrial part is presented by Ent Terrestrial Biosphere Model (Ent TBM), which includes leaf biophysics, prognostic phenology and soil biogeochemistry module (based on Carnegie-Ames-Stanford model). The ocean part is based on the NASA Ocean Biogeochemistry Model (NOBM). The transport of atmospheric CO2 is performed by the atmospheric part of ModelE2, which employs quadratic upstream algorithm for this purpose.

Climate model↗
NASA NTRS2017

Self-Tuning Tunable Mass Dampers

A tunable mass damper incorporates a frame and a voice coil supported in the frame. A magnet concentric with the voice coil is movable relative to the housing via the voice coil. A plurality of flexures having a first end extending from the magnet and an arm releasably coupled to the frame are adjustable to an effective length for a desired frequency of reciprocation of the magnet.

Griffin, Steven F.↗
NASA NTRS2019

Tuning the Performance of a Computational Persistent Homology Package

In recent years, persistent homology has become an attractive method for data analysis. It captures topological features, such as connected components, holes, and voids from point cloud data and summarizes the way in which these features appear and disappear in a filtration sequence. In this project, we focus on improving the performanceof Eirene, a computational package for persistent homology. Eirene is a 5000-line open-source software library implemented in the dynamic programming language Julia. We use the Julia profiling tools to identify performance bottlenecks and develop novel methods to manage them, including the parallelization of some time-consuming functions on multicore/manycore hardware. Empirical results show that performance can be greatly improved.

Persistent Homology↗
NASA NTRS2021

Fine-tuning Polymers as Flexible Antenna Substrates for Aircraft

The pursuit to design more lightweight aircraft that can travel over greater distances never ceases. One area that has recieved much attention is the development of lighter and more aerodynamic antennas. Researchers at the NASA Glenn Research Center at the forefront of this effort. The research team, which includes Jessica Cashman, Baochau Nguyen, Haiquan Guo, Marcos Pantoja, Kevin Cavicchi, and Mary Ann Meador, is designing flexible polymer materials that can be used to construct lightweight, low-drag antennas that lie along the surface of an aircraft.

Haiquan Guo↗
NASA NTRS2021

Chapter 3: Suspensions, foams, emulsions, colloids, and granular materials--self-healing, tuning gravity, and life support for exploration

At the suggestion of NASA’s Physical Science Research Program in the Space Life and Physical Science Research and Application Division, Paul Chaikin, Noel Clark, and Sidney Nagel organized a focus session and workshop for the 2020 American Physical Society (APS) March meeting under the auspices of the Division of Soft Matter. Three overarching themes emerged from the workshop and are presented with additional details: • Machines made out of machines • Scalable self-sustaining ecosystems • Active materials and metamaterials This report lays out only some of the potential directions for soft matter dynamics over the next two decades. It also lays out the role that gravity plays in the organization of the basic building blocks of matter. Not only will research on soft matter have tremendous application towards understanding its behavior in our terrestrial environment, but also potentially in other NASA programs such as planetary science, exploration, robotics, etc. Attached is a White Paper for the Decadal Survey that consists of an extended Title along with the previous Introduction and Chapter 2.3 from NASA/CP-20205010493.

Soft matter↗
NASA NTRSDate not supplied

IMUFDIR Tuning In Response To Structural Deformation Of The Orion Capsule

During system level Thermal Vacuum (TVAC) testing it was discovered that the pressure differential of the crew cabin in a vacuum would deflect the capsule slightly, causing significant misalignment of Orion’s three Inertial Measurement Units (IMUs). This phenomenon has been likened to a balloon expanding. When this misalignment was modeled in simulation as a function of atmospheric pressure, it was determined that the rate of deflection on reentry was significant enough to cause a mis-compare of the angular rates measured by the IMUs. This mis-comparison of the rates caused Orion’s Fault Detection Isolation and Recovery (FDIR) algorithm to falsely fail one of the IMUs. This manuscript will discuss how the IMUFDIR algorithm was detuned to be insensitive to these ballooning effects, while still meeting FDIR requirements to capture real faults in an IMU. All testing to validate this detuning was performed in a Six Degree of Freedom (6 DOF) Monte Carlo Simulation Environment.

Nicholas Rahaim↗
NASA NTRSDate not supplied

Strategies for Automation of Model Tuning in Multifidelity Trajectory Uncertainty Propagation

Multi-model Monte Carlo methods are efficient strategies to perform forward uncertainty quantification studies in entry, descent, and landing (EDL) applications. These multi-model methods are based on the classical Monte Carlo estimator, but fuse predictions from several low-fidelity models to obtain estimators with greater precision given a prescribed computational budget. The effectiveness of these approaches relies on the magnitudes of correlations between the low-fidelity models and the high-fidelity model, as well as the relative computational costs of all models. Identifying and exploiting the best trade-off between correlation and cost, which ultimately depends on the selection of hyperparameters in the low-fidelity models, is a task often performed by hand or simply inspired by the deterministic understanding available for a specific application. This work extends a preliminary effort,

Marten Thompson↗