No abstract

Wildland fires are complex multi-physics problems that span wide spatial scale ranges. Capturing this complexity in computationally affordable numerical simulations for process studies and "outer-loop" techniques (e.g., optimization and uncertainty quantification) is a fundamental challenge in reacting flow research. Further complications arise for propagating fires where a priori knowledge of the fire spread rate and direction is typically not available. In such cases, static mesh refinement at all possible fire locations is a computationally inefficient approach to bridging the wide range of spatial scales relevant to wildland fire behavior. In the present study, we address this challenge by incorporating adaptive mesh refinement (AMR) in fireFoam, an OpenFOAM solver for simulations of complex fire phenomena. The AMR functionality in the extended solver, called wildFireFoam, allows us to dynamically track regions of interest and to avoid inefficient over-resolution of areas far from a propagating flame. We demonstrate the AMR capability for fire spread on vertical panels and for large-scale fire propagation on a variable-slope surface that is representative of real topography. We show that the AMR solver reproduces results obtained using much larger statically refined meshes, at a substantially reduced computational cost.

The distribution of mass density along the field lines affects the ratios of toroidal (azimuthally oscillating) Alfvén frequencies, and given the ratios of these frequencies, we can get information about that distribution. Here we assume the commonly used power law form for the field line distribution, m = m,eq (LR E ∕R) , where m,eq is the value of the mass density m at the magnetic equator, L is the L shell, R E is the Earth's radius, R is the geocentric distance to a point on the field line, and is the power law coefficient. Positive values of indicate that m increases away from the magnetic equator, zero value indicates that m is constant along the magnetic field line, and negative indicates that there is a local peak in m at the magnetic equator. Using 12 years of observations of toroidal Alfvén frequencies by the Geostationary Operational Environmental Satellites, we study the typical dependence of inferred values of on the magnetic local time (MLT), the phase of the solar cycle as specified by the F 10.7 extreme ultraviolet solar flux, and geomagnetic activity as specified by the auroral electrojet (AE) index. Over the mostly dayside range of the observations, we find that decreases with respect to increasing MLT and F 10.7 , but increases with respect to increasing AE. We develop a formula that depends on all three parameters, F 10.7 , that models the binned values of within a standard deviation of 0.3. While we do not yet have a complete theoretical understanding of why should depend on these parameters in such a way, we do make some observations and speculations about the causes. At least part of the dependence is related to that of m,eq ; higher , corresponding to steeper variation with respect to magnetic latitude, occurs when m,eq is lower.

Conventional compression-ignition (CI) engines have long offered high thermal efficiencies and torque across a wide range of loads, but often require extensive exhaust gas treatment that decreases efficiency to meet ever-increasing emissions regulations. One strategy to decrease emissions is to split the fuel injection into a series of smaller injections. In this paper, we explore a new way of discovering optimal control strategies for the next generation of CI engines using deep reinforcement learning (DRL). We outline a DRL procedure to maximize the weighted reward of engine work while minimizing end-of-cycle NO x emissions. Through the procedure outlined in this paper, we show that the DRL agent is able to reduce NO x emissions threefold while only decreasing network by 2%. We demonstrate the use of transfer learning (TL) across hierarchies of physical models to accelerate the learning process, making this approach feasible for a range of control problems within this space. This paper presents a framework and demonstration for using DRL to design control systems in technology areas such as multi-pulse engine control where a hierarchy of models combined with multi-objective rewards are used for optimal operation.