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Summary Reaction kinetics between calcite and acid systems have been studied using the rotating disk apparatus (RDA). However, simplifying assumptions have been made to develop the current equations used to interpret RDA experiments to enable solving them analytically in contrast to using numerical methods. Previous work has revealed inadequacies of some of these assumptions, which necessitates the use of a computational fluid dynamics (CFD) model to investigate their impact on the RDA results. The objectives of the current work are to develop a calibrated CFD and proxy model to simulate the reaction in the RDA and use this model to estimate the diffusion coefficient and the reaction rate coefficient of the reaction in the RDA. The present work developed the first calibrated CFD model to determine the diffusion coefficient and the reaction rate coefficient in the RDA with minimum assumptions in the hydrochloric acid (HCl) carbonate reaction. More specifically, the model relaxes the constant fluid properties, infinite acting reactor boundaries, and constant reaction surface area assumptions. The proxy model obtained results in reduced computational time with minimal compromise on accuracy. Finally, the proposed model showed an improvement of 63% in predicting the reaction kinetics between calcite and HCl compared to traditional methods.

We combine in situ environmental transmission electron microscopy (E-TEM) with automated image processing and statistical machine learning to uniquely formulate interpretable mathematical models an...

Accurate short-term forecasts are indispensable for the integration of wind energy in power grids. On a wind farm, local wind conditions exhibit sizeable variations at a fine temporal resolution. Existing statistical models may capture the in-sample variations in wind behavior, but are often shortsighted to those occurring in the near future, that is, in the forecast horizon. The calibrated regime-switching method proposed in this paper introduces an action of regime dependent calibration on the predictand (here the wind speed variable), which helps correct the bias resulting from out-of-sample variations in wind behavior. This is achieved by modeling the calibration as a function of two elements: the wind regime at the time of the forecast (and the calibration is therefore regime dependent), and the runlength, which is the time elapsed since the last observed regime change. In addition to regime-switching dynamics, the proposed model also accounts for other features of wind fields: spatio-temporal dependencies, transport effect of wind and nonstationarity. Using one year of turbine-specific wind data, we show that the calibrated regime-switching method can offer a wide margin of improvement over existing forecasting methods in terms of both wind speed and power.

Power curve, the functional relationship that governs the process of converting a set of weather variables experienced by a wind turbine into electric power, is widely used in the wind industry to estimate power output for planning and operational purposes. Existing methods for power curve estimation have three main limitations: (i) they mostly rely on wind speed as the sole input, thus ignoring the secondary, yet possibly significant effects of other environmental factors, (ii) they largely overlook the complex marine environment in which offshore turbines operate, potentially compromising their value in offshore wind energy applications, and (ii) they solely focus on the first-order properties of wind power, with little (or null) information about the variation around the mean behavior, which is important for ensuring reliable grid integration, asset health monitoring, and energy storage, among others. In light of that, this study investigates the impact of several wind-and wave-related factors on offshore wind power variability, with the ultimate goal of accurately predicting its first two moments. Our approach couples OpenFAST—a multi-physics wind turbine simulator—with Gaussian Process (GP) regression to reveal the underlying relationships governing offshore weather-to-power conversion. We first find that a multi-input power curve which captures the combined impact of wind speed, direction, and air density, can provide double-digit improvements, in terms of prediction accuracy, relative to univariate methods which rely on wind speed as the sole explanatory variable (e.g. the standard method of bins). Wave-related variables are found not important for predicting the average power output, but interestingly, appear to be extremely relevant in describing the fluctuation of the offshore power around its mean. Tested on real-world data collected at the New York/New Jersey bight, our proposed multi-input models demonstrate a high explanatory power in predicting the first two moments of offshore wind generation, testifying their potential value to the offshore wind industry.

Statistical quality control in semiconductor manufacturing hinges on effective diagnostics of wafer bin maps, wherein a key challenge is to detect how defective chips tend to spatially cluster on a wafer--a problem known as spatial pattern recognition. Recently, there has been a growing interest in mixed-type spatial pattern recognition--when multiple defect patterns, of different shapes, co-exist on the same wafer. Mixed-type spatial pattern recognition entails two central tasks: (1) spatial filtering, to distinguish systematic patterns from random noises; and (2) spatial clustering, to group filtered patterns into distinct defect types. Observing that spatial filtering is instrumental to high-quality mixed-type pattern recognition, we propose to use a graph-theoretic method, called adjacency-clustering, which leverages spatial dependence among adjacent defective chips to effectively filter the raw wafer maps. Tested on real-world data and compared against a state-of the-art approach, our proposed method achieves at least 46% gain in terms of internal cluster validation quality (i.e., validation without external class labels), and about ~5% gain in terms of Normalized Mutual Information--an external cluster validation metric based on external class labels. Interestingly, the margin of improvement appears to be a function of the pattern complexity, with larger gains achieved for more complex-shaped patterns.

The unprecedented scale and sophistication of wind turbine technologies call for wind forecasts of high spatial resolution, i.e. turbine-tailored forecasts, to inform several operational decisions at the turbine level. Towards that, this paper is concerned with leveraging the hub-height measurements collected from a fleet of turbines on a farm to make turbine-specific short-term wind speed and power predictions. We find that the wind propagation across a dense grid of turbines induces strong spatial and temporal dependencies in the within-farm wind field, but also gives rise to high-frequency high-magnitude fluctuations which may compromise the predictive accuracy of several data-driven forecasting methods. To capture both aspects, we propose to model the total variability in the within-farm wind speed field as a combination of two independent stochastic process terms. The first term reconstructs and extrapolates the wind speed field by learning the complex spatio-temporal dependence structure using hub-height turbine-level data. The second term accounts for high-frequency high-magnitude fluctuations that are not informed by near-term spatio-temporal dependencies. The two terms are coupled to make probabilistic wind speed forecasts at each turbine, which are then translated into turbine-specific power predictions via wind power curves. Evaluation on more than 3,000,000 data points from a wind farm dataset provides a strong empirical evidence in favor of the proposed method’s forecasting accuracy. On average, our proposed method achieves 9% accuracy improvement relative to persistence forecasts, and 7–9% relative to a set of widely recognized forecasting methods such as autoregressive-based models and Gaussian Processes.

A growing research trend in additive manufacturing (AM) calls for layerwise anomaly detection as a step toward enabling real-time process control, in contrast to ex situ or postprocess testing and characterization. We propose a method for layerwise anomaly detection during laser powder-bed fusion (L-PBF) metal AM. The method uses high-speed thermal imaging to capture melt pool temperature and is composed of the following four-step anomaly detection procedure: (1) using the captured thermal images, a process signature of a just-fabricated layer is generated. Next, a signature difference is obtained by subtracting the process signature of that particular layer from a prespecified reference signature, (2) a screening step selects potential regions of interests (ROIs) within the layer that are likely to contain process anomalies, hence reducing the computational burden associated with analyzing the full layer data, (3) the spatial dependence of these ROIs is modeled using a Gaussian process model, and then pixels with statistically significant deviations are flagged, and (4) using the quantity and the spatial pattern of the flagged pixels as predictors, a classifier is trained and implemented to determine whether the process is in- or out-of-control. We validate the proposed method using a case study on a commercial L-PBF system custom-instrumented with a dual-wavelength imaging pyrometer for capturing the thermal images during fabrication.

Reaction kinetics between calcite and acid systems has been studied using the rotating disk apparatus (RDA). However, simplifying assumptions have been made to develop the current equations used to interpret RDA experiments to enable solving them analytically in contrast to using numerical methods. Experimental results revealed inadequacy of some of these assumptions, which necessitates the use of a computational fluid dynamics (CFD) model to investigate their impact on the RDA results. The objectives of the current work are threefold: (1) develop a CFD model to simulate the reaction in the RDA, (2) Identify the error associated with the assumptions in the original equations, and (3) develop a proxy model from the results that can accurately represent the reaction in the RDA. In developing the CFD model, the averaged-continuum approach was used to simulate the chemical reaction on the disk surface. Both Newtonian and non-Newtonian fluids were studied to investigate the adequacy of the equations’ assumptions. To validate the model, simulations were compared with experimental results. Experiments were run at 0.25, 0.5, 1, and 1.25M HCl with marble using the RDA at 250°F. Rotation speeds of 200, 400, 600, and 1,000 rpm were tested at each acid concentration. The diffusion coefficient was then calculated. Parameters of the CFD model were then adjusted to match the rock dissolved throughout the RDA experiments. The rock dissolved in the disk from the CFD model matched the results from the RDA experiments. The transition from mass-transfer to the kinetics-limited reaction behavior was captured by the CFD model. The velocity and viscosity profiles for both Newtonian and non-Newtonian fluids showed the effect of the container's boundaries on the flow. Results indicate that this effect is pronounced in the case of Newtonian fluids at high rotational speeds. Moreover, the impact of varying viscosities in the case of non-Newtonian fluids resulted in errors in estimating the reaction kinetics. Finally, a proxy model was obtained to reduce the computational time involved in accurately simulating the experiments. The present work developed the first CFD model to accurately evaluate reaction kinetics and diffusion coefficient in the RDA with minimum assumptions. More specifically, the model relaxes the infinite acting, constant fluid properties, and constant reaction surface area assumptions. Finally, the proxy model obtained results in reduced computational time with minimal compromise on accuracy.

The massive amounts of spatio-temporal data collected in today's wind farms have created a necessity for accurate spatio-temporal models. Despite the growing recognition for non-separable spatio-temporal models, a significant reliance on separable, symmetric models is still the norm in today's renewable industry. We discover that the broad use of separable models is due to the handling of wind data in a setting that does not reveal their fine-scale spatio-temporal structure. The contribution of this research is two-fold. First, we devise a special pair of spatio-temporal "lens" that allows us to see the fine-scale spatio-temporal variations and interactions, and subsequently, we conclude that local wind fields exhibit strong signs of non-separability and asymmetry. Using one year of turbine-specific wind measurements, we show that asymmetry can in fact be detected in more than 93% of the time. Second, making use of the spatio-temporal lens, we propose an enhanced procedure for short-term wind speed forecast. Substantial improvements in forecast accuracy in both wind speed and wind power were observed. When combined with certain intelligent methods such as support vector machine, additional improvements are possible.