## What is/are Temporally Evolving?

Temporally Evolving - The goal of this paper is to demonstrate the urgent need for, and suggest an approach for developing, an improved suite of temporally evolving, spatially distributed snowproducts to help understand howdynamics in snowscape properties impact wildlife, with a specific focus onAlaska and northwesternCanada.^{[1]}

## direct numerical simulation

Direct numerical simulations of temporally evolving supersonic turbulent channel flows of thermally perfect gas are conducted at Mach number 3.^{[1]}A direct numerical simulation (DNS) initialized with an implicit large eddy simulation (ILES) is performed for temporally evolving planar jets and turbulent boundary layers.

^{[2]}In the present work, nonpremixed temporally evolving planar spray jet flames are simulated using both direct numerical simulation (DNS) and the composition transported probability density function (TPDF) method.

^{[3]}The study is based on direct-numerical simulation (DNS) and large-eddy simulation (LES), comparing different subgrid-scale (SGS) models for incompressible, uniform-density, temporally evolving forced shear-layer flows.

^{[4]}The structure of vorticity field in compressible mixing layers is studied using direct numerical simulations of temporally evolving mixing layers.

^{[5]}

## dynamic light scattering

Methods Phase-decorrelation OCT (PhD-OCT), based in the theory of dynamic light scattering, is a method to spatially resolve endogenous random motion by calculating the decorrelation rate, Γ, of the temporally evolving complex-valued OCT signal.^{[1]}Methods Phase-decorrelation OCT (PhD-OCT), based in the theory of dynamic light scattering, is a method to spatially resolve endogenous random motion by calculating the decorrelation rate, Γ, of the temporally evolving complex-valued OCT signal.

^{[2]}Methods Phase-decorrelation OCT (PhD-OCT), based in the theory of dynamic light scattering, is a method to spatially resolve endogenous random motion by calculating the decorrelation rate, Γ, of the temporally evolving complex-valued OCT signal.

^{[3]}Methods Phase-decorrelation OCT (PhD-OCT), based in the theory of dynamic light scattering, is a method to spatially resolve endogenous random motion by calculating the decorrelation rate, Γ, of the temporally evolving complex-valued OCT signal.

^{[4]}Methods Phase-decorrelation OCT (PhD-OCT), based in the theory of dynamic light scattering, is a method to spatially resolve endogenous random motion by calculating the decorrelation rate, Γ, of the temporally evolving complex-valued OCT signal.

^{[5]}

## temporally evolving complex

Methods Phase-decorrelation OCT (PhD-OCT), based in the theory of dynamic light scattering, is a method to spatially resolve endogenous random motion by calculating the decorrelation rate, Γ, of the temporally evolving complex-valued OCT signal.^{[1]}Methods Phase-decorrelation OCT (PhD-OCT), based in the theory of dynamic light scattering, is a method to spatially resolve endogenous random motion by calculating the decorrelation rate, Γ, of the temporally evolving complex-valued OCT signal.

^{[2]}Methods Phase-decorrelation OCT (PhD-OCT), based in the theory of dynamic light scattering, is a method to spatially resolve endogenous random motion by calculating the decorrelation rate, Γ, of the temporally evolving complex-valued OCT signal.

^{[3]}Methods Phase-decorrelation OCT (PhD-OCT), based in the theory of dynamic light scattering, is a method to spatially resolve endogenous random motion by calculating the decorrelation rate, Γ, of the temporally evolving complex-valued OCT signal.

^{[4]}Methods Phase-decorrelation OCT (PhD-OCT), based in the theory of dynamic light scattering, is a method to spatially resolve endogenous random motion by calculating the decorrelation rate, Γ, of the temporally evolving complex-valued OCT signal.

^{[5]}

## temporally evolving turbulent

The resulting time-integration techniques are employed in a 3D simulation of a temporally evolving turbulent planar flame with dimethyl ether/air chemistry, and improvements with respect to the computational efficiency and strong scalability are examined.^{[1]}The scalar dissipation rate of the progress variable from temporally evolving turbulent lean premixed H2-air flames in the thin reaction zone regime is analyzed using the chemical explosive mode analysis to understand its dependence on combustion modes and transient flame features.

^{[2]}In the present work we assess the capabilities of neural networks to predict temporally evolving turbulent flows.

^{[3]}

## temporally evolving pattern

These complex post fracture immune responses were sexually dichotomous and interacted in temporally evolving patterns that generated post traumatic nociceptive sensitization in both sexes lasting for up to 5 months.^{[1]}In this study we use a vertically integrated thermomechanical ice dynamics model to simulate the temporally evolving patterns of surficial moraine, stratification, foliation, and folding of glacier ice, and the density and orientation of traces of former crevasses.

^{[2]}

## temporally evolving planar

A direct numerical simulation (DNS) initialized with an implicit large eddy simulation (ILES) is performed for temporally evolving planar jets and turbulent boundary layers.^{[1]}In the present work, nonpremixed temporally evolving planar spray jet flames are simulated using both direct numerical simulation (DNS) and the composition transported probability density function (TPDF) method.

^{[2]}

## temporally evolving shear

Energy spectra obtained from the present homogeneous shear turbulence agree well with the spectra from temporally evolving shear layers.^{[1]}Numerical simulations of weakly compressible, temporally evolving shear layers are used to verify theoretical results and confirm that if the log slope of the one-dimensional density spectrum in the inertial subrange is -mρ, the optical phase distortion spectral slope is given by -(mρ + 1).

^{[2]}

## temporally evolving force

Although it is possible to computationally infer cell-cell forces from a mechanical model of collective cell behavior, doing so for temporally evolving forces in a manner robust to digitization difficulties is challenging.^{[1]}While it is possible to computationally infer cell-cell forces from a mechanical model of collective cell behavior, doing so for temporally evolving forces in a manner that is robust to digitization difficulties is challenging.

^{[2]}

## temporally evolving environment

Whereas mature simultaneous localization and mapping (SLAM) approaches assume a static environment, this work extends pose graph SLAM to spatiotemporally evolving environments.^{[1]}Modern systems operate in spaiotemporally evolving environments, and similar spatiotemporal scenarios are likely to be tied with similar decision solutions.

^{[2]}