A finite difference scheme for inviscid flows with non-eequilibrium chemistry and internal energy

A finite difference scheme is presented for the inviscid terms of the equations of compressible fluid dynamics with general non-equilibrium chemistry and internal energy.

Sea-breeze modification of the growth of a marine internal boundary layer

A numerical mesoscale model is used to make a high-resolution simulation of the marine boundary layer in the Persian Gulf, during conditions of offshore flow from Saudi Arabia. A marine internal boundary layer (MIBL) and a sea-breeze circulation (SBC) are found to co-exist. The sea breeze develops in the mid-afternoon, at which time its front is displaced several tens of kilometres offshore. Between the coast and the sea-breeze system, the MIBL that occurs is consistent with a picture described in the existing literature. However, the MIBL is perturbed by the SBC, the boundary layer deepening significantly seaward of the sea-breeze front. Our analysis suggests that this strong, localized deepening is not a direct consequence of frontal uplift, but rather that the immediate cause is the retardation of the prevailing, low-level offshore wind by the SBC. The simulated boundary-layer development can be accounted for by using a simple 1D Lagrangian model of growth driven by the surface heat flux. This model is obtained as a straightforward modification of an established MIBL analytic growth model.

Inverting recurrent neural networks for internal model control of nonlinear systems

In this paper, we show how a set of recently derived theoretical results for recurrent neural networks can be applied to the production of an internal model control system for a nonlinear plant. The results include determination of the relative order of a recurrent neural network and invertibility of such a network. A closed loop controller is produced without the need to retrain the neural network plant model. Stability of the closed-loop controller is also demonstrated.

Internal model control of nonlinear systems through the inversion of recurrent neural networks

Recurrent neural networks can be used for both the identification and control of nonlinear systems. This paper takes a previously derived set of theoretical results about recurrent neural networks and applies them to the task of providing internal model control for a nonlinear plant. Using the theoretical results, we show how an inverse controller can be produced from a neural network model of the plant, without the need to train an additional network to perform the inverse control.

Multiple hydrogen bonds induce formation of nanoparticles with internal microemulsion structure by an amphiphilic copolymer

A particulate microemulsion is generated in a simple two-component system comprising an amphiphilic copolymer (Pluronic P123) in mixtures with tannic acid. This is correlated to complexation between the poly(ethylene oxide) in the Pluronic copolymer and the multiple hydrogen bonding units in tannic acid which leads to the breakup of the ordered structure formed in gels of Pluronic copolymers, and the formation of dispersed nanospheres containing a bicontinuous internal structure. These novel nanoparticles termed ‘‘emulsomes’’ are self-stabilized by a coating layer of Pluronic copolymer. The microemulsion exhibits a pearlescent appearance due to selective light scattering from the emulsion droplets. This simple formulation based on a commercial copolymer and a biofunctional and biodegradable additive is expected to find applications in the fast moving consumer goods sector.