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Concreting at elevated temperatures accelerates the hydration process, leading to quicker setting but potentially reducing the long-term strength of the concrete structure. Additionally, low air humidity fosters rapid moisture loss from the concrete, resulting in reduced workability, pronounced plastic shrinkage, and a higher likelihood of crazing.
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In cold weather, masonry construction requires specific precautions to ensure mortar does not freeze before curing, as this can significantly weaken its strength and watertightness. Mortar temperature should be maintained between 60°F and 80°F to support proper hydration and curing. Below 40°F, mortar water must be heated, but should not exceed 120°F as high temperatures can reduce mortar's compressive and bond strength.
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Practical rare event sampling for extreme mesoscale weather.

Robert J Webber1, David A Plotkin2, Morgan E O'Neill3

  • 1Courant Institute of Mathematical Sciences, New York University, New York, New York 10012, USA.

Chaos (Woodbury, N.Y.)
|June 4, 2019
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Summary
This summary is machine-generated.

A new rare event sampling algorithm, quantile diffusion Monte Carlo (quantile DMC), efficiently simulates extreme weather events. This method shows promise for improving tropical cyclone simulations and understanding extreme weather statistics.

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Area of Science:

  • Atmospheric Science
  • Computational Science
  • Climate Modeling

Background:

  • Extreme mesoscale weather events like tropical cyclones pose significant simulation challenges.
  • Accurate simulation is crucial for understanding and mitigating damage from severe weather.
  • Existing simulation strategies often lack the efficiency needed for extreme event analysis.

Purpose of the Study:

  • Introduce a novel rare event sampling algorithm, quantile diffusion Monte Carlo (quantile DMC).
  • Demonstrate the effectiveness of quantile DMC for simulating extreme tail behavior in various processes.
  • Assess the feasibility of quantile DMC for modeling extreme mesoscale weather phenomena.

Main Methods:

  • Developed and implemented the quantile diffusion Monte Carlo (quantile DMC) algorithm.
  • Applied quantile DMC to sample extreme realizations of historical tropical cyclones (Hurricane Earl and Hurricane Joaquin).
  • Compared the performance and efficiency of quantile DMC against other existing sampling methods.

Main Results:

  • Quantile DMC successfully sampled extremely intense realizations of tropical cyclones.
  • The algorithm demonstrated potential for providing low-variance extreme weather statistics.
  • Initial results indicate advantages over other sampling techniques, though further efficiency improvements are noted.

Conclusions:

  • Quantile DMC offers a promising approach for simulating extreme weather events.
  • The algorithm has the potential to enhance the accuracy and efficiency of mesoscale weather simulations.
  • Further research is needed to optimize quantile DMC for greater efficiency in practical applications.