Sentence examples for environment predictions from inspiring English sources

Aerothermodynamic environment predictions play an important role in the heatshield design of Mars entry vehicle.

In order to be more consistent with debris environment predictions and to be useful for spacecraft design, such BLCs need to be drawn in units that are consistent with environment flux models used in that design process.

The aero-thermal environment predictions over several critical regions of the hypothesized lifting body vehicle, including the stagnation region of the nose-cap, cylindrically swept leading edges, fuselage-upper, and fuselage-lower surfaces, are discussed.

Key thermal environmental data predictions and monitored values are compared, and if significant differences are identified, thermal environment predictions are modified.

In summary, our method achieves high accuracy in predicting intense geomagnetic storms and could be applied in space environment prediction.

This paper briefly reviews theoretical and engineering significance of aircraft vibration response environment prediction firstly.

GEORAD suite is applicable to radiation environment prediction at LEO, medium earth orbit (MEO) and geosynchronous earth orbit (GEO) at quiet solar periods.

Aircraft vibration response environment prediction, which is adopted in aircraft initial vehicle development, has not been got enough attention and wide application yet.

Then the paper summarizes the main aircraft vibration response environment prediction methods and indicates their advantages, disadvantages and applicability scopes, including extrapolation of similar structure, theory analysis and analytical solution of differential dynamical equation, statistical parameter modeling, simulation calculation modeling and machine learning.

The key enhancement is to propose an environment prediction model using meteorology to simulate harvesting capability during a scenario.

We have also learned through extensive analysis that prediction performance of the proposed method can be improved by learning microphone dependent clustering parameters, that is, learning microphone specific clustering parameters and use them for environment prediction for recordings made.