Electrodeposition of arrays of Ru, Pt, and PtRu Alloy 1D metallic nanostructures

Arrays of Ru, Pt, and PtRu one dimensional 1D nanowires NWs and nanotubes NTs were prepared by electrodeposition through the porous structure of an anodic aluminum oxide AAO membrane. In each case, micrometer-long NW and NT were formed with an outer diameter of ca. 200 nm, close to the interior diameter of the porous AAO membrane. Arrays of NW and NT can be formed by varying the metallic salt concentration, the applied potential, and the conductivity of the electrolyte. The Ru and Pt deposition rates were measured in the various deposition conditions, using an electrochemical quartz crystal microbalance. The mechanisms responsible for the formation of Ru and Pt NW and NT are discussed based on the observed deposition rates and models found in the literature. Finally, it is shown that arrays of PtRu alloy NT and NW can be readily prepared and their compositions can be varied over the whole compositional range by changing the metallic salt concentration of the electrodeposition bath

Proportional-Integral Extremum Seeking for Optimizing Power of Vapor Compression Systems

Conventionally, online methods for minimizing power consumption of vapor compression systems rely on the use of physical models. These model-based approaches attempt to describe the influence of commanded inputs, disturbances and setpoints on the thermodynamic behavior of the system and the resultant consumed electrical power. These models are then used online to predict the combination of inputs for a measured set of thermodynamic conditions that both meets the heat load and minimizes power consumption. However, these models of vapor compression systems must contain nonlinear terms of sufficient complexity in order to accurately describe the region near the optimum operating point(s), but also must rely on simplifying assumptions in order to produce a mathematically tractable representation. For these reasons, model-based online optimization of vapor compression machines have not gained traction in application, and have created an opportunity for model-free techniques such as extremum seeking control, which is gradient descent optimization implemented as a feedback controller. While traditional perturbation-based extremum seeking controllers for vapor compression systems have proven effective at minimizing power without requiring a process model, the algorithm\u27s requirement for multiple distinct timescales has limited the applicability of this method to laboratory tests where boundary conditions can be carefully controlled, or simulation studies with unrealistic convergence times. Perturbation-based extremum seeking requires that the control input be manipulated with a time constant approximately two orders of magnitude slower than the slowest vapor compression system dynamics, otherwise instabilities in the closed loop system occur. As a result, convergence to the optimum for slow processes such as thermal systems is restrictive due to inefficient estimation of the gradient, and slow (integral-action dominated) adaptation in the extremum seeking control law. In order to address this timescale separation issue, we have previously developed an algorithm called ``time-varying extremum seeking that more efficiently estimates the gradient of the performance metric and applied this algorithm to the problem of setpoint optimization for compressor temperatures. That algorithm improved the convergence rate to one timescale slower than the vapor compression machine dynamics. In this paper, we optimize power consumption through the application of a newly-developed proportional--integral extremum seeking controller (PI-ESC) that converges at the same timescale as the process. This method uses the improved gradient estimation routines of time-varying extremum seeking but also modifies the control law to include terms proportional to the estimated gradient. This modification of the control law, in turn, requires a revision to the gradient estimator in order to avoid bias. PI-ESC is applied to the problem of compressor discharge temperature selection for a vapor compression system so that power consumption is minimized. Because of the improved convergence properties of PI-ESC, we show that optimum values of discharge temperature can be tracked in the presence of realistic disturbances such as variation in the outdoor air temperature---enabling application of extremum seeking control to vapor compression systems in environments where previous methods have failed. The method is demonstrated experimentally on a 2.8 kW split ductless room air conditioner and in simulation using a custom-developed Modelica model

Realtime Optimization of MPC Setpoints using Time-Varying Extremum Seeking Control for Vapor Compression Machines

Recently, model predictive control (MPC) has received increased attention in the HVAC community, largely due to its ability to systematically manage constraints while optimally regulating signals of interest to setpoints. For example, in a common formulation of an MPC control problem for variable compressor speed vapor compression machines, the setpoints often include the zone temperature and the evaporator superheat temperature. However, the energy consumption of vapor compression systems has been shown to be sensitive to these setpoints. Further, while superheat temperature is often preferred because it can be easily correlated to heat exchanger efficiency (and therefore cycle efficiency), direct measurement of superheat is not always available. Therefore, identifying alternate signals in the control of vapor compression machines that correlate to efficiency is desired. Conventionally, methods for maximizing the energy efficiency rely on the use of mathematical models of the physics of vapor compression systems. These model-based approaches attempt to describe the influence of commanded inputs on the thermodynamic behavior of the system and the consumed electrical energy, and they are used to predict the combination of inputs that both meet the heat load requirements and minimize energy consumption. However, these models of vapor compression systems rely on simplifying assumptions in order to produce a mathematically tractable representation. Further, they are difficult to derive and calibrate, and often do not describe variations over long time scales, such as those due to refrigerant losses or accumulation of debris on the heat exchangers. In this paper, we consider a model-free extremum seeking algorithm that adjusts setpoints provided to a model predictive controller. While perturbation-based extremum seeking methods have been known for some time, they suffer from slow convergence rates---a problem emphasized by the long time constants associated with thermal systems. Our method uses a new algorithm (time-varying extremum seeking), which has dramatically faster and more reliable convergence properties. In particular, we regulate the compressor discharge temperature using an MPC controller with setpoints selected from a model-free time-varying extremum seeking algorithm. We show that the relationship between compressor discharge temperature and power consumption is convex (a requirement for this class of realtime optimization), and use time-varying extremum seeking to drive these setpoints to values that minimize power. The results are compared to the traditional perturbation-based extremum seeking approach. Further, because the required cooling capacity (and therefore compressor speed) is a function of measured and unmeasured disturbances, the optimal compressor discharge temperature setpoint must vary according to these conditions. We show that the energy optimal discharge temperature is tracked with the time-varying extremum seeking algorithm in the presence of disturbances

Le développement de l'aisance à l'oral en français langue seconde au cours d'un programme d'immersion à l'étranger de courte durée de 5 semaines

L'objectif principal de la présente étude était de déterminer si la durée d'un programme d'immersion à l'étranger (PIE) de cinq semaines était suffisante pour observer une amélioration significative au niveau du développement de l'aisance à l'oral (AAO) en français langue seconde (L2) chez des anglophones adultes (n^lOO). À l'aide d'une épreuve de narration effectuée au début et la fin du PIE, les résultats principaux, calculés à partir de diverses valeurs temporelles de la parole, ont démontré que 1) la durée du PIE a eu un effet positif sur le développement de l'AAO en français L2 chez tous les apprenants, 2) le PIE était particulièrement bénéfique pour le développement des apprenants (n=33) qui avaient un degré d'AAO faible en L2 et 3) pour certaines mesures chez les apprenants les plus aisés (n=33), il était possible d'afficher un degré d'AAO similaire aux locuteurs natifs (n=23)

Le développement de l'aisance à l'oral en français langue seconde au cours d'un programme d'immersion à l'étranger de courte durée de 5 semaines

L’objectif principal de la présente étude était de déterminer si la durée d’un programme d’immersion à l’étranger (PIE) de cinq semaines était suffisante pour observer une amélioration significative au niveau du développement de l’aisance à l’oral (AAO) en français langue seconde (L2) chez des anglophones adultes (n=100). À l’aide d’une épreuve de narration effectuée au début et la fin du PIE, les résultats principaux, calculés à partir de diverses valeurs temporelles de la parole, ont démontré que 1) la durée du PIE a eu un effet positif sur le développement de l’AAO en français L2 chez tous les apprenants, 2) le PIE était particulièrement bénéfique pour le développement des apprenants (n=33) qui avaient un degré d’AAO faible en L2 et 3) pour certaines mesures chez les apprenants les plus aisés (n=33), il était possible d’afficher un degré d’AAO similaire aux locuteurs natifs (n=23)

Comparing Realtime Energy-Optimizing Controllers for Heat Pumps

As the vapor compression machine has become more sophisticated (for example, through the adoption of variable speed compressor technology, electronic expansion valves and variable speed fans), the opportunities to improve efficiency are increasingly realized through the control algorithms that operate machine actuators. However, designing control algorithms that minimize energy consumption is not straightforward: the heat load disturbances to be rejected are not measured, the governing dynamics are nonlinear and interactive, and the machine exhibits strong coupling between the multivariate inputs and outputs. Further, many heat pumps must also operate in cooling mode, forcing compromises in sensor locations and actuator selection. This paper compares two controllers for realtime (online) energy optimization of heat pumps. The first energy-optimizing controller is model-based. A custom multi-physical model of the dynamics of a heat pump is developed in the Modelica modeling language and used to obtain the relationship between control inputs and power consumption as a function of the operating conditions. The gradient of this relationship is computed symbolically and used to derive a gradient descent control law that is shown to drive actuator inputs such that the system power consumption is minimized. To address the concern of modeling error on optimization performance, the controller based on a model of a heat pump will be tested on a physical system in an experimental setting for the submitted paper. We expect the convergence rate to be exponential, and will quantify the sensitivity between modeling errors and the non-optimality of the stabilized system. The second approach is model-free and based on the authors\u27 time-varying (TV) and proportional-integral (PI) extremum seeking control (ESC) algorithms. Briefly, extremum seeking controllers use an estimate of the gradient between a plant\u27s manipulated inputs and an objective signal (i.e., power consumption) to steer the system toward an optimum operating point, under the assumption that this relationship is convex. Whereas traditional ESC methods exhibit slow and non-robust convergence, our TV-ESC and PI-ESC methods have demonstrated higher performance due to the estimation routine that tracks the gradient as a time-varying parameter. We expect this algorithm to converge faster than transitional perturbation-based ESC methods (as we have previously demonstrated), but perhaps slower than the model-based approach. However, we expect this controller to converge to a neighborhood around the true optimum since modeling errors are not applicable in this model-free algorithm. The final paper will compare convergence properties of these two methods through experiments obtained on a commercial four-zone heat pump installed in calorimetric-style testing chambers, and the resultant coefficients-of-performance (COPs) will be measured

Topographical coloured plasmonic coins

The use of metal nanostructures for colourization has attracted a great deal of interest with the recent developments in plasmonics. However, the current top-down colourization methods based on plasmonic concepts are tedious and time consuming, and thus unviable for large-scale industrial applications. Here we show a bottom-up approach where, upon picosecond laser exposure, a full colour palette independent of viewing angle can be created on noble metals. We show that colours are related to a single laser processing parameter, the total accumulated fluence, which makes this process suitable for high throughput industrial applications. Statistical image analyses of the laser irradiated surfaces reveal various distributions of nanoparticle sizes which control colour. Quantitative comparisons between experiments and large-scale finite-difference time-domain computations, demonstrate that colours are produced by selective absorption phenomena in heterogeneous nanoclusters. Plasmonic cluster resonances are thus found to play the key role in colour formation.Comment: 9 pages, 5 figure