Sentence examples for time series pressure from inspiring English sources

Travel times calculated from time series pressure and EC data collected in the Mitchell River in northern Australia are used to demonstrate application of this combined approach.

The large-scale tornado simulation facility of Texas Tech University, known as VorTECH, was used to acquire the time-series velocities over a cross-section through the apparatus center as well as the time-series pressure on the floor.

Time-series pressure measurements for all sensors were grouped into nine anatomical masks [ 5, 13, 14].

(1) Statistical pressure derivative utilises the 2nd differencing of pressure and time series since pressure change and subsurface flow rate are nonstationary series and then integrates the residual of its 1st differences using simple statistical functions such as sum of square error SSE, standard deviation, moving average MA and covariance of these series to formulate the model.

Time mean pressure and time series of pressure fluctuations were measured at different axial positions in the dipleg with particle mass fluxes ranging from 50.0 to 385.0 kg m−2 s−1.

The method utilises the 2nd differencing of pressure and time series since pressure change and subsurface flow rate are nonstationary series and then integrate the residual of its 1st differences using simple statistical functions such as sum of square error SSE, standard deviation, moving average MA and covariance of data to formulate the model.

The time mean pressure and the time series of pressure fluctuations in the cyclone dipleg are discussed based on the measured pressure data.

The recently developed technique of wavelet transform based on localized wavelet functions is applicable to the analysis of nonlinear or nonstationary pressure fluctuation signals: the time series of pressure fluctuation signals have been analyzed by means of discrete wavelet transform coefficients and multiresolution decomposition.

Comparisons of the prediction results by the POD-BPNN approach and those from the wind tunnel test demonstrate that the BPNN combined with POD method can successfully and efficiently predict the time series of pressure data on all surfaces of a high-rise building on the basis of wind tunnel pressure measurements from a certain number of pressure taps.

Analysestransitiof velocitiestandarding from the chaotic measures andeviationndand deviation show good agreements.

We consider identification of absolute permeability (hydraulic conductivity) based on time series of pressure data in sparsely distributed wells for two-phase porous-media flow.