Turbulent dynamics at two sites (C and D) in a hypoxic zone on the Texas- Louisiana continental shelf were studied by investigating turbulence quantities i.e. turbulence kinetic energy (TKE), dissipation rate of TKE (E), Reynolds stress (τ ), dissipation rate of temperature variance (χ), eddy diffusivity of temperature (ν't), and eddy diffusivity of density (ν'p). Numerical models were also applied to test their capability of simulating these turbulence quantities. At site D, TKE, E, and τ were calculated from velocity measurements in the bot- tom boundary layer (BBL), using the Kolmogorov’s -5/3 law in the inertial subrange of energy spectra of vertical velocity fluctuations in each burst measurement. Four second-moment turbulence closure models were applied for turbulence simulations, and modeled turbulence quantities were found to be consistent with those observed. It was found from inter-model comparisons that models with the stability functions of Schumann and Gerz predicted higher values of turbulence quantities than those of Cheng in the mid layer, which might be due to that the former stability functions are not sensitive to buoyancy. At site C, χ, E, v’t, and ν’p were calculated from profile measurements throughout the water column, and showed high turbulence level in the surface boundary layer and BBL, as well as in the mid layer where shear stress was induced by advected non-local water above a hypoxic layer. The relatively high dissolved oxygen in the non-local water resulted in upward and downward turbulent oxygen fluxes, and the bottom hypoxia will deform due to turbulence in 7.11 days. Two of the four models in the study at site D were implemented, and results showed that turbulence energy resulting from the non-local water was not well reproduced. We attribute this to the lack of high-resolution velocity measurements for simulations. Model results agreed with observations only for χ and E simulated from the model with the stability function of Cheng in the BBL. Discrepancies between model and observational results lead to the following conclusions: 1) the stability functions of Schumann and Gerz are too simple to represent the turbulent dynamics in stratified mid layers; 2) detailed velocity profiles measurements are required for models to accurately predict turbulence quantities. Missing such observations would result in underestimation,

In this work, various topics regarding (urban) boundary simulations for a city situated in the alps will be discussed. First of all, we will present novel parameterization adopted to take into account the effect of mitigation strategies, such as rooftop Photovoltaic Panels and Green Roofs, on the urban environment, and their effect on average temperature and energy consumption by buildings. Secondly, a new turbulence closure, that adopts a diagnostic equation for dissipation rate, and then independent on mixing length scales, will be introduced. The new turbulence closure, implemented into the WRF model, has been coupled with multi-layer urban parameterization schemes and compared with high-resolution CFD and LES simulations.

<p dir="ltr">New system-level codes are being developed for advanced reactors for safety analysis and licensing purposes. Thermal-hydraulics of advanced reactors is a challenging problem due to complex flow scenarios assisted by free jets and stratified flows that lead to turbulent mixing. For these reasons, the 0D or 1D models used for reactor plena in traditional safety analysis codes like RELAP cannot capture the physics accurately and introduce a large degree of modeling uncertainty. System-level calculation codes based on the advection-diffusion equation neglect turbulent fluctuations. These fluctuations are extremely important as they introduce higher-order moments, which are responsible for vortex stretching and the passage of energy to smaller scales. Alternatively, extremely detailed simulations with velocity coupling from the Navier-Stokes equations are able to capture turbulence effects accurately using DNS. These solutions are accurate because they resolve the flow into the smallest possible length and time scales (Kolmogorov scale) important to the flow, which makes DNS computationally expensive for simple geometries and impossible at the system level.</p><p dir="ltr">The flow field can be described through a reduced-order model using the principles of statistical mechanics. Statistical mechanics-based methods provide a method for extracting statistics from data and modeling that data using easily represented differential equations. The Kramers-Moyal (KM) expansion method can be used as a subgrid-scale (SGS) closure for solving the momentum equation. The stochastic Burgers equation is solved using DNS, and the DNS solutions are used to calculate the KM coefficients, which are then implemented as an SGS closure model. The KM method outperforms traditional methods in capturing the multi-scale behavior of Burgers turbulence. The functional dependencies of the KM coefficients are also uniform for several boundary conditions, meaning the closure model can be extended to multiple flow scenarios. </p><p dir="ltr">For the case of the Navier-Stokes equations, each particle trajectory tends to follow some scaling law. Kolmogorov hypothesized that the flow velocity field follows a -5/3 scaling in the inertial region where Markovian characteristics can be invoked to model the interaction between eddies of adjacent sizes. This law holds true in the inertial region where the flow is Markovian. For scalar turbulence, the scaling laws are affected by thermal diffusion. If a fluid has a Prandtl number close to one, the thermal behavior is dominated by momentum, so the spectra for velocity and temperature are similar. For small Prandtl number fluids, such as liquid metals, the thermal diffusion dominates the lower scales and the slope of the spectrum shifts from the -5/3 slope to a -3 slope, also called the Batchelor region. System-level thermal hydraulics codes need to be able to capture these behaviors for a range of Prandtl number fluids. The KM-based model can also be used as a surrogate for velocity or temperature fluctuations in scalar turbulence. Using DNS solutions for turbulent channel flow, the KM model is used to provide a surrogate for temperature and velocity signals at different wall locations in the channel for Pr = 0.004, Pr = 0.025, and Pr = 0.71. The KM surrogate matches well for all wall locations, but is not able to capture the viscous dissipation in the velocity signal, or the thermal dissipation in the low Prandtl number cases. The dissipation can be captured by implementing a Gaussian filter.</p><p dir="ltr">Statistical mechanics-based methods are not limited to modeling turbulence in a reactor. Renewable power generation, such as wind, can be modeled using the Ornstein-Uhlenbeck (OU) method, which allows the long-term trends and short-term fluctuations of wind power to be decoupled. This allows for large fluctuations in wind power to be scaled down to a level that a reactor can accommodate safely. </p><p dir="ltr">Since statistical mechanics methods are based in physics, the calculated coefficients provide some information about the inputted signal. In a high-temperature gas-cooled reactor, strong heating can cause flow that is expected to be turbulent to show laminar characteristics. This laminarization results in reduced heat removal. The KM coefficients can be used to classify the laminarization from probed velocity signals more effectively than traditional statistical analyses.</p>