scholarly journals Pointing Errors of Large Telescopes Due to Wind

1984 ◽  
Vol 79 ◽  
pp. 177-181
Author(s):  
Bobby L. Ulich
Keyword(s):  
Mechanical Properties ◽  
Wind Speed ◽  
Scaling Laws ◽  
Tracking Error ◽  
Structural Materials ◽  
Pointing Errors ◽  
Direct Measurements ◽  
Large Telescopes ◽  
Mirror Telescope

AbstractScaling laws are presented which show the dependence of the tracking error of large telescopes on structural materials, mechanical properties and dimensions of the mount, and wind speed. Based on direct measurements on the Multiple Mirror Telescope, predictions are made for future very large telescopes. It is shown that good tracking can be achieved most of the time even without a traditional dome to block the wind, and this may result in better images by eliminating “dome seeing”.

Micro/meso-scale computational study of dislocation-stacking-fault tetrahedron interactions in copper

2009 ◽  
Vol 24 (12) ◽  
pp. 3628-3635 ◽  
Author(s):  
Jaime Marian ◽  
Enrique Martínez ◽  
Hyon-Jee Lee ◽  
Brian D. Wirth
Keyword(s):  
Mechanical Properties ◽  
Nuclear Power ◽  
Scaling Laws ◽  
Computational Study ◽  
Numerical Algorithms ◽  
Structural Materials ◽  
Material Behavior ◽  
Dislocation Dynamics ◽  
Dynamics Simulations ◽  
New Generation

In a carbon-free economy, nuclear power will surely play a fundamental role as a clean and cost-competitive energy source. However, new-generation nuclear concepts involve temperature and irradiation conditions for which no experimental facility exists, making it exceedingly difficult to predict structural materials performance and lifetime. Although the gap with real materials is still large, advances in computing power over the last decade have enabled the development of accurate and efficient numerical algorithms for materials simulations capable of probing the challenging conditions expected in future nuclear environments. One of the most important issues in metallic structural materials is the degradation of their mechanical properties under irradiation. Mechanical properties are intimately related to the glide resistance of dislocations, which can be increased severalfold due to irradiation-produced defects. Here, we present a combined multiscale study of dislocation-irradiation obstacle interactions in a model system (Cu) using atomistic and dislocation dynamics simulations. Scaling laws generalizing material behavior are extracted from our results, which are then compared with experimental measurements of irradiation hardening in Cu, showing good agreement.

Mechanical properties and scaling laws of nanoporous gold

2013 ◽  
Vol 113 (2) ◽  
pp. 023505 ◽  
Author(s):  
Xiao-Yu Sun ◽  
Guang-Kui Xu ◽  
Xiaoyan Li ◽  
Xi-Qiao Feng ◽  
Huajian Gao
Keyword(s):  
Mechanical Properties ◽  
Scaling Laws ◽  
Nanoporous Gold

Intermetallics of Aluminum

2019 ◽  
Author(s):  
Georg Frommeyer ◽  
Sven Knippscheer
Keyword(s):  
Crystal Structure ◽  
Mechanical Properties ◽  
Plastic Deformation ◽  
High Temperature ◽  
Physical Properties ◽  
Intermetallic Compounds ◽  
Oxidation Behavior ◽  
Physical And Mechanical Properties ◽  
Structural Materials

Aluminum-rich intermetallic compounds of the Al3X-type with transmission metals (X = Ti. Zr, Nb, V) of Groups IVb and Vb are of interest in the development of novel high-temperature and lightweight structural materials. This article describes the important physical and mechanical properties of trialuminides with DO22 structure and their L12 variations. Topical coverage includes: crystal structure and selected physical properties, plastic deformation, oxidation behavior, and applications.

scholarly journals Seedability of Winter Orographic Clouds

1986 ◽  
Vol 43 ◽  
pp. 127-138 ◽  
Author(s):  
Geoffrey E. Hill
Keyword(s):  
Wind Speed ◽  
Liquid Water ◽  
Supercooled Liquid ◽  
Direct Measurements ◽  
Orographic Clouds ◽  
The Subject ◽  
Relationship Of ◽  
Increasing Precipitation ◽  
Cloud Top Temperature ◽  
Limiting Value

Abstract This article is a review of work on the subject of seedability of winter orographic clouds for increasing precipitation. Various aspects of seedability are examined in the review, including definitions, distribution of supercooled liquid water, related meteorological factors, relationship of supercooled liquid water to storm stage, factors governing seedability, and the use of seeding criteria. Of particular interest is the conclusion that seedability is greatest when supercooled liquid water concentrations are large and at the same time precipitation rates are small. Such a combination of conditions is favored if the cloud-top temperature is warmer than a limiting value and as the cross-barrier wind speed at mountaintop levels increases. It is also suggested that cloud seeding is best initiated in accordance with direct measurements of supercooled liquid water, precipitation, and cross-barrier wind speed. However, in forecasting these conditions or in continuation of seeding previously initiated, the cloud-top temperature and cross-barrier wind speed are the most useful quantities.

Mechanical properties and scaling laws of polycrystalline CuZr shape memory alloy

2021 ◽  
Vol 130 (15) ◽  
pp. 155106
Author(s):  
Yuhang Zhang ◽  
Jiejie Li ◽  
Yiqun Hu ◽  
Suhang Ding ◽  
Fuying Du ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Shape Memory Alloy ◽  
Shape Memory ◽  
Scaling Laws

scholarly journals Extraction of mechanical properties of materials through deep learning from instrumented indentation

2020 ◽  
Vol 117 (13) ◽  
pp. 7052-7062 ◽  
Author(s):  
Lu Lu ◽  
Ming Dao ◽  
Punit Kumar ◽  
Upadrasta Ramamurty ◽  
George Em Karniadakis ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Deep Learning ◽  
Scaling Laws ◽  
Three Dimensional ◽  
Instrumented Indentation ◽  
Multilayered Structures ◽  
Mechanical Properties Of Materials ◽  
Properties Of Materials ◽  
Indentation Problem ◽  
And Function

Instrumented indentation has been developed and widely utilized as one of the most versatile and practical means of extracting mechanical properties of materials. This method is particularly desirable for those applications where it is difficult to experimentally determine the mechanical properties using stress–strain data obtained from coupon specimens. Such applications include material processing and manufacturing of small and large engineering components and structures involving the following: three-dimensional (3D) printing, thin-film and multilayered structures, and integrated manufacturing of materials for coupled mechanical and functional properties. Here, we utilize the latest developments in neural networks, including a multifidelity approach whereby deep-learning algorithms are trained to extract elastoplastic properties of metals and alloys from instrumented indentation results using multiple datasets for desired levels of improved accuracy. We have established algorithms for solving inverse problems by recourse to single, dual, and multiple indentation and demonstrate that these algorithms significantly outperform traditional brute force computations and function-fitting methods. Moreover, we present several multifidelity approaches specifically for solving the inverse indentation problem which 1) significantly reduce the number of high-fidelity datasets required to achieve a given level of accuracy, 2) utilize known physical and scaling laws to improve training efficiency and accuracy, and 3) integrate simulation and experimental data for training disparate datasets to learn and minimize systematic errors. The predictive capabilities and advantages of these multifidelity methods have been assessed by direct comparisons with experimental results for indentation for different commercial alloys, including two wrought aluminum alloys and several 3D printed titanium alloys.

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