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At least 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Variations in boundary layer stability across Antarctica: a comparison between coastal and interior sites

The range of boundary layer stability profiles, from the surface to 500 m a.g.l. (above ground level), present in radiosonde observations from two continental-interior (South Pole Station and Dome Concordia Station) and three coastal (McMurdo Station, Georg von Neumayer Station III, and Syowa Station) Antarctic sites, is examined using the self-organizing maps (SOMs) neural network algorithm. A wide range of potential temperature profiles is revealed, from shallow boundary layers with strong near-surface stability to deeper boundary layers with weaker or near-neutral stability, as well as profiles with weaker near-surface stability and enhanced stability aloft, above the boundary layer. Boundary layer regimes were defined based on the range of profiles revealed by the SOM analysis; 20 boundary layer regimes were identified to account for differences in stability near the surface as well as above the boundary layer. Strong, very strong, or extremely strong stability, with vertical potential temperature gradients of 5 to in excess of 30 K per 100 m, occurred more than 80 % of the time at South Pole and Dome Concordia in the winter. Weaker stability was found in the winter at the coastal sites, with moderate and strong stability (vertical potential temperature gradients of 1.75 to 15 K per 100 m) occurring 70 % to 85 % of the time. Even in the summer, moderate and strong stability is found across all five sites, either immediately near the surface or aloft, just above the boundary layer. While the mean boundary layer height at the continental-interior sites was found to be approximately 50 m, the mean boundary layer height at the coastal sites was deeper, around 110 m. Further, a commonly described two-stability-regime system in the Arctic associated with clear or cloudy conditions was applied to the 20 boundary layer regimes identified in this study to understand if the two-regime behavior is also observed in the Antarctic. It was found that moderate and strong stability occur more often with clear- than cloudy-sky conditions, but weaker stability regimes occur almost equally for clear and cloudy conditions.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2016

Applied Time Domain Stability Margin Assessment for Nonlinear Time-Varying Systems

The baseline stability margins for NASA's Space Launch System (SLS) launch vehicle were generated via the classical approach of linearizing the system equations of motion and determining the gain and phase margins from the resulting frequency domain model. To improve the fidelity of the classical methods, the linear frequency domain approach can be extended by replacing static, memoryless nonlinearities with describing functions. This technique, however, does not address the time varying nature of the dynamics of a launch vehicle in flight. An alternative technique for the evaluation of the stability of the nonlinear launch vehicle dynamics along its trajectory is to incrementally adjust the gain and/or time delay in the time domain simulation until the system exhibits unstable behavior. This technique has the added benefit of providing a direct comparison between the time domain and frequency domain tools in support of simulation validation. This technique was implemented by using the Stability Aerospace Vehicle Analysis Tool (SAVANT) computer simulation to evaluate the stability of the SLS system with the Adaptive Augmenting Control (AAC) active and inactive along its ascent trajectory. The gains for which the vehicle maintains apparent time-domain stability defines the gain margins, and the time delay similarly defines the phase margin. This method of extracting the control stability margins from the time-domain simulation is relatively straightforward and the resultant margins can be compared to the linearized system results. The sections herein describe the techniques employed to extract the time-domain margins, compare the results between these nonlinear and the linear methods, and provide explanations for observed discrepancies. The SLS ascent trajectory was simulated with SAVANT and the classical linear stability margins were evaluated at one second intervals. The linear analysis was performed with the AAC algorithm disabled to attain baseline stability margins. At each time point, the system was linearized about the current operating point using Simulink's built-in solver. Each linearized system in time was evaluated for its rigid-body gain margin (high frequency gain margin), rigid-body phase margin, and aero gain margin (low frequency gain margin) for each control axis. Using the stability margins derived from the baseline linearization approach, the time domain derived stability margins were determined by executing time domain simulations in which axis-specific incremental gain and phase adjustments were made to the nominal system about the expected neutral stability point at specific flight times. The baseline stability margin time histories were used to shift the system gain to various values around the zero margin point such that a precise amount of expected gain margin was maintained throughout flight. When assessing the gain margins, the gain was applied starting at the time point under consideration, thereafter following the variation in the margin found in the linear analysis. When assessing the rigid-body phase margin, a constant time delay was applied to the system starting at the time point under consideration. If the baseline stability margins were correctly determined via the linear analysis, the time domain simulation results should contain unstable behavior at certain gain and phase values. Examples will be shown from repeated simulations with variable added gain and phase lag. Faithfulness of margins calculated from the linear analysis to the nonlinear system will be demonstrated.

Kiefer, J. M.↗
DOE OSTI2012

Stabilizing chock assembly for caster wheel and associated method of use

A stabilizing chock assembly for a caster wheel or the like, including: a first stabilizing member or piece and a second stabilizing member or piece; and a clamping mechanism coupling the first stabilizing member to the second stabilizing member, the clamping mechanism operable for selectively biasing the first stabilizing member and the second stabilizing member together about a wheel; wherein the first stabilizing member and the second stabilizing member collectively define a conformal recess that is configured to selectively receive and retain a lower portion of the wheel. Optionally, the first stabilizing member and the second stabilizing member each define an arcuate ramp structure that forms a portion of the conformal recess. Optionally, the chock assembly also includes a friction surface disposed on a bottom surface of each of the first stabilizing member and the second stabilizing member.

42 ENGINEERING↗
DOE OSTI2023

Stabilizers for a vehicle cooling module

A stabilizing arrangement of a cooling system module of a vehicle includes stabilizer pins attached to the vehicle cooling system module. Stabilizer slots are incorporated into a component of the chassis of the vehicle, such as a frame extender. The stabilizer slots may be formed as an upwards opening U-shape. Bushings are attached to the stabilizer pins. The stabilizer pins and bushings extend laterally into and engage with the stabilizer slots. Upon installation, the stabilizer pins and bushings are aligned with the stabilizer slots as the cooling system module is lowered onto the ISO mounts. The stabilizer pins and bushings slide vertically downwards into the stabilizer slots as the cooling system module is lowered into place. The simpler stabilizer pins, bushings, and stabilizer slots thereby take the place of expensive, time consuming, complex, labor intensive, and space consuming stay rod arrangements.

Wu, Shouhao↗
DOE OSTI2022

Community stability is related to animal diversity change

Abstract Understanding the drivers of community stability in times of increasing anthropogenic pressure is an urgent issue. Biodiversity is known to promote community stability, but studies of the biodiversity–stability relationship rarely consider the full complexity of biodiversity change. Furthermore, finding generalities that hold across taxonomic groups and spatial and temporal scales remains challenging because most investigations have narrow taxonomic, spatial, and temporal scopes. We used organismal data collected through the National Ecological Observatory Network (NEON) at sites across the contiguous United States to evaluate linkages between community stability and biodiversity change for four taxonomic groups: small mammals, ground beetles, fish, and freshwater macroinvertebrates. We defined community stability as constancy of aggregate species' abundance. We quantified change in biodiversity as (1) dissimilarity in community taxonomic and functional composition and species replacement and richness change components of that dissimilarity and (2) change in species' abundance distributions as captured by change in species rank, richness, and evenness. We found that community stability increased with species replacement and with contribution of species replacement to overall dissimilarity for all taxonomic groups, but declined with increasing change in species richness and evenness. This is consistent with the notion that temporal fluctuations in species abundance can help stabilize community properties. We also found that community stability was highest when change in community functional composition was either lower or higher than expected given reshuffling of each community's taxonomic composition. This suggests that long‐term community stability can result from fluctuations of functionally similar species in assemblages with high taxonomic reshuffling. On the contrary, the functional uniqueness of fluctuating species compensates for lower taxonomic reshuffling to drive stabilization of community properties. Our study provides an initial assessment of the relationship between community stability and biodiversity change and illustrates the utility of fine temporal resolution data collected across ecosystems and biomes to understand the general mechanisms underlying biodiversity–stability relationships.

54 ENVIRONMENTAL SCIENCES↗