Sentence examples for variable buoyancy from inspiring English sources

In September 1960, he successfully descended to a depth of 45m, in the process setting the first of 17 records in the variable buoyancy category.

A two-mass variable buoyancy engine has been designed and implemented.

The effect of the variable buoyancy is examined in terms of the gliding angle, velocity and angle of attack.

Over the era in which he competed, Maiorca's depth records in what is known as the "variable buoyancy" category were pushed from 45m in 1960 to an astonishing 101m in 1988.

The objectives of the present study are to understand the glider dynamics adopting a CFD approach, fabricate the glider and its variable buoyancy engine and test its trajectory in water and compare it with numerically obtained trajectory in the vertical plane.

Variable buoyancy of propagules can change the vertical dispersal of a larva in the water column (e.g. [91]).

The evolution of the vertical structure of wave motions can be calculated from given initial conditions with a one-dimensional finite-difference version of the model with or without forcing/dissipation under a variable thermal buoyancy condition in height.

Thermal development is slower and the effects of buoyancy and variable properties are both weaker.

Thus, one useful approach would be to utilize crustal density and magma buoyancy as common intensive variables to quantitatively compare thermobarometric constraints from erupted magmas to regional models of crustal density structure from geophysical observations.

Generalized linear mixed-effect models with the animal as the random variable and five explanatory variables (body mass, buoyancy, dive depth, dive phase, and drag) showed that swim speed was best predicted by two variables, drag and dive phase (AIC = −139).

We used a generalized liner mixed-effect model (GLMM) to determine interactions among swim speed (dependent variable) and five explanatory variables (body mass, buoyancy, dive depth, drag, and dive phase: descent or ascent phase) with individual identification as a random effect (family  =  Gaussian; link  =  identity).