Computer Vision-Based Classification of Flow Regime and Vapor Quality in Vertical Two-Phase Flow

This paper presents a method to classify flow regime and vapor quality in vertical two-phase (vapor-liquid) flow, using a video of the flow as the input; this represents the first high-performing and entirely camera image-based method for the classification of a vertical flow regime (which is effective across a wide range of regimes) and the first image-based tool for estimating vapor quality. The approach makes use of computer vision techniques and deep learning to train a convolutional neural network (CNN), which is used for individual frame classification and image feature extraction, and a deep long short-term memory (LSTM) network, used to capture temporal information present in a sequence of image feature sets and to make a final vapor quality or flow regime classification. This novel architecture for two-phase flow studies achieves accurate flow regime and vapor quality classifications in a practical application to two-phase CO2 flow in vertical tubes, based on offline data and an online prototype implementation, developed as a proof of concept for the use of these models within a feedback control loop. The use of automatically selected image features, produced by a CNN architecture in three distinct tasks comprising flow-image classification, flow-regime classification, and vapor quality prediction, confirms that these features are robust and useful, and offer a viable alternative to manually extracting image features for image-based flow studies. The successful application of the LSTM network reveals the significance of temporal information for image-based studies of two-phase flow

Representation of simulation errors in single step methods using state dependent noise

The local error of single step methods is modelled as a function of the state derivative multiplied by bias and zero-mean white noise terms. The deterministic Taylor series expansion of the local error depends on the state derivative meaning that the local error magnitude is zero in steady state and grows with the rate of change of the state vector. The stochastic model of the local error may include a constant, “catch-all” noise term. A continuous time extension of the local error model is developed and this allows the original continuous time state differential equation to be represented by a combination of the simulation method and a stochastic term. This continuous time stochastic differential equation model can be used to study the propagation of the simulation error in Monte Carlo experiments, for step size control, or for propagating the mean and variance. This simulation error model can be embedded into continuous-discrete state estimation algorithms. Two illustrative examples are included to highlight the application of the approach

Quasi-steady state aerodynamics of the cheetah tail

During high-speed pursuit of prey, the cheetah (Acinonyx jubatus) has been observed to swing its tail while manoeuvring (e.g. turning or braking) but the effect of these complex motions is not well understood. This study demonstrates the potential of the cheetah's long, furry tail to impart torques and forces on the body as a result of aerodynamic effects, in addition to the well-known inertial effects. The first-order aerodynamic forces on the tail are quantified through wind tunnel testing and it is observed that the fur nearly doubles the effective frontal area of the tail without much mass penalty. Simple dynamic models provide insight into manoeuvrability via simulation of pitch, roll and yaw tail motion primitives. The inertial and quasi-steady state aerodynamic effects of tail actuation are quantified and compared by calculating the angular impulse imparted onto the cheetah's body and its shown aerodynamic effects contribute to the tail's angular impulse, especially at the highest forward velocities

The dinoflagellateDinophysis norvegica: biological and ecological observations in the Baltic Sea

Observations of the dinoflagellate Dinophysis norvegica in the Baltic Sea during the summers of 1991–1993 indicate that maximal abundances (c 40–150 × 103 cells l-1) were found at the thermocline, typically at 12°C. Maximum densities were usually between 12 and 15 m where 2·9% and 1·5% of surface photon irradiances, respectively, were measured. No diel vertical migration was observed, and cell densities in the mixed layer were always low. Photosynthesis versus irradiance measurements with an oxygen electrode indicated that these populations had a P max of 2·47 [coefficient of variation (CV) 7·3%] and 3·4 (CV 4·7%) mg O2 mg Chl a -1 h-1, and compensation values of photon irradiance were 16·5 and 83 μmol m-2 s-1 in 1992 and 1993, respectively. Both oxygen electrode and 14C light/dark bottle measurements indicated that D. norvegica had very little net photosynthesis at the depths where it was most abundant; it would have had about 2·5-fold greater capacity at photon irradiances present closer to the surface. Calculated carbon doubling times via photosynthesis averaged 4–11 months. There was no observable diel rhythym of DNA synthesis, suggesting that either D. norvegica was not dividing synchronously (asynchronous division is common in heterotrophs) or not dividing at all. Electron microscopy did not reveal the presence of food vacuoles, but feeding and digestion could have been extracellular. The data suggest that this species is a mixotroph which received its primary nutrition via heterotrophic means during our observation periods in the summers of 1991–1993

Optimal LED-based illumination control via distributed convex optimization

Achieving illumination and energy consumption targets is essential in indoor lighting design. The provision of localized illumination to occupants, and the utilization of natural light and energy-efficient light-emitting diode (LED) luminaires can help meet both objectives. Localized illumination control schemes require suitable coordination mechanisms to obtain luminaire dimming levels that achieve the desired illuminance levels and reduce energy costs. This paper presents several distributed optimal LED-based illumination control schemes that provide localized illuminance to occupants. The lighting system consists of multiple LED-based luminaires, each of which has a controller that can process information locally and communicate with nearby controllers. The illuminance requirements and energy costs for the lighting system are expressed as a linear programming problem. This optimization problem is solved, in a distributed manner, across the network of controllers using local communication amongst the controllers. State-of-the-art distributed optimization methods, based on accelerated first-order methods, are applied to parallelize the computational tasks among multiple controllers. Important practical aspects such as the rate of convergence, computational complexity, and communication requirements are investigated via simulations