Dynamics of scalar dissipation in isotropic turbulence: a numerical and modelling study

The physical mechanisms underlying the dynamics of the dissipation of passi ve scalar fluctuations with a uniform mean gradient in stationary isotropic turbulence are studied using data from direct numerical simulations (DNS), at grid resolutions up to 512(3). The ensemble-averaged Taylor-scale Reyno lds number is up to about 240 and the Schmidt number is from 1/8 to 1. Spec ial attention is given to statistics conditioned upon the energy dissipatio n rate because of their important role in the Lagrangian spectral relaxatio n (LSR) model of turbulent mixing. In general, the dominant physical proces ses are those of nonlinear amplification by strain rate fluctuations, and d estruction by molecular diffusivity. Scalar dissipation tends to form elong ated structures in space, with only a limited overlap with zones of intense energy dissipation. Scaler gradient fluctuations are preferentially aligne d with the direction of most compressive strain rate, especially in regions of high energy dissipation. Both the nature of this alignment and the time scale of the resulting scalar gradient amplification appear to be nearly un iversal in regard to Reynolds and Schmidt numbers. Most of the terms appear ing in the budget equation for conditional scalar dissipation show neutral behaviour at low energy dissipation but increased magnitudes at high energy dissipation. Although homogeneity requires that transport terms have a zer o unconditional average, conditional molecular transport is found to be sig nificant, especially at lower Reynolds or Schmidt numbers within the simula tion data range. The physical insights obtained from DNS are used for a pri ori testing and development of the LSR model. In particular, based on the D NS data, improved functional forms are introduced for several model coeffic ients which were previously taken as constants. Similar improvements includ ing new closure schemes for specific terms are also achieved for the modell ing of conditional scalar variance.