Unified form language: A domain-specific language for weak formulations of partial differential equations

We present the Unified Form Language (UFL), which is a domain-specific language for representing weak formulations of partial differential equations with a view to numerical approximation. Features of UFL include support for variational forms and functionals, automatic differentiation of forms and expressions, arbitrary function space hierarchies formultifield problems, general differential operators and flexible tensor algebra. With these features, UFL has been used to effortlessly express finite element methods for complex systems of partial differential equations in near-mathematical notation, resulting in compact, intuitive and readable programs. We present in this work the language and its construction. An implementation of UFL is freely available as an open-source software library. The library generates abstract syntax tree representations of variational problems, which are used by other software libraries to generate concrete low-level implementations. Some application examples are presented and libraries that support UFL are highlighted. © 2014 ACM.

Development of a time and space resolved sampling probe diagnostic for engine exhaust hydrocarbons

In order to understand how unburned hydrocarbons emerge from SI engines and, in particular, how non-fuel hydrocarbons are formed and oxidized, a new gas sampling technique has been developed. A sampling unit, based on a combination of techniques used in the Fast Flame Ionization Detector (FFID) and wall-mounted sampling valves, was designed and built to capture a sample of exhaust gas during a specific period of the exhaust process and from a specific location within the exhaust port. The sampling unit consists of a transfer tube with one end in the exhaust port and the other connected to a three-way valve that leads, on one side, to a FFID and, on the other, to a vacuum chamber with a high-speed solenoid valve. Exhaust gas, drawn by the pressure drop into the vacuum chamber, impinges on the face of the solenoid valve and flows radially outward. Once per cycle during a specified crank angle interval, the solenoid valve opens and traps exhaust gas in a storage unit, from which gas chromatography (GC) measurements are made. The port end of the transfer tube can be moved to different locations longitudinally or radially, thus allowing spatial resolution and capturing any concentration differences between port walls and the center of the flow stream. Further, the solenoid valve's opening and closing times can be adjusted to allow sampling over a window as small as 0.6 ms during any portion of the cycle, allowing resolution of a crank angle interval as small as 15°CA. Cycle averaged total HC concentration measured by the FFID and that measured by the sampling unit are in good agreement, while the sampling unit goes one step further than the FFID by providing species concentrations. Comparison with previous measurements using wall-mounted sampling valves suggests that this sampling unit is fully capable of providing species concentration information as a function of air/fuel ratio, load, and engine speed at specific crank angles. © Copyright 1996 Society of Automotive Engineers, Inc.

Scalable, low-energy hybrid photonic space switch

A scalable monolithically integrated photonic space switch is proposed which uses a combination of Mach-Zehnder modulators and semiconductor optical amplifiers (SOAs) for improved crosstalk performance and reduced switch loss. This architecture enables the design of high-capacity, high-speed, large-port count, low-energy switches. Extremely low crosstalk of better than -50 dB can be achieved using a 2 × 2 dilated hybrid switch module. A 'building block' approach is applied to make large port count optical switches possible. Detailed physical layer multiwavelength simulations are used to investigate the viability of a 64 × 64 port switch. Optical signal degradation is estimated as a function of switch size and waveguide induced crosstalk. A comparison between hybrid and SOA switching fabrics highlights the power-efficient, high-performance nature of the hybrid switch design, which consumes less than one-third of the energy of an equivalent SOA-based switch. The significantly reduced impairments resulting from this switch design enable scaling of the port count, compared to conventional SOA-based switches. © 1983-2012 IEEE.

Bayesian inference and learning in Gaussian process state-space models with Particle MCMC

State-space models are successfully used in many areas of science, engineering and economics to model time series and dynamical systems. We present a fully Bayesian approach to inference and learning (i.e. state estimation and system identification) in nonlinear nonparametric state-space models. We place a Gaussian process prior over the state transition dynamics, resulting in a flexible model able to capture complex dynamical phenomena. To enable efficient inference, we marginalize over the transition dynamics function and, instead, infer directly the joint smoothing distribution using specially tailored Particle Markov Chain Monte Carlo samplers. Once a sample from the smoothing distribution is computed, the state transition predictive distribution can be formulated analytically. Our approach preserves the full nonparametric expressivity of the model and can make use of sparse Gaussian processes to greatly reduce computational complexity.