Probabilistic screening and behavior of solar cells under Gaussian parametric uncertainty using polynomial chaos representation model

The paper presents a hierarchical polynomial chaos expansion-based probabilistic approach to analyze the single diode solar cell model under Gaussian parametric uncertainty. It is important to analyze single diode solar cell model response under random events or factors due to uncertainty propagation. The optimal values of five electrical parameters associated with the single diode model are estimated using six deterministic optimization techniques through the root-mean-square minimization approach. Values corresponding to the best objective function response are further utilized to describe the probabilistic design space of each random electrical parameter under uncertainty. Adequate samples of each parameter corresponding Gaussian uncertain distribution are generated using Latin hypercube sampling. Furthermore, a multistage probabilistic approach is adopted to evaluate the model response using low-cost polynomial chaos series expansion and perform global sensitivity analysis under specified Gaussian distribution. Coefficients of polynomial basis functions are calculated using least square and least angle regression techniques. Unlike the highly non-linear and complex single diode representation of solar cells, the polynomial chaos expansion model provides a low computational burden and reduced complexity. To ensure reproducibility, probabilistic output response computed using proposed polynomial chaos expansion model is compared with the true model response. Finally, a multidimensional sensitivity analysis is performed through Sobol decomposition of polynomial chaos series representation to quantify the contribution of each parameter to the variance of the probabilistic response. The validation and assessment result shows that the output probabilistic response of the solar cell under Gaussian parametric uncertainty correlates to a Rayleigh probability distribution function. Output response is characterized by a mean value of 0.0060 and 0.0760 for RTC France and Solarex MSX83 solar cells, respectively. The standard deviation of $$ \pm $$ ± 0.0034 and $$ \pm $$ ± 0.0052 was observed in the probabilistic response for RTC France and Solarex MSX83 solar cells, respectively.

Reward reduces habitual errors by enhancing the preparation of goal-directed actions

People’s goals often conflict with their habits, leading people to perform worse than desired. Research shows people are better at overcoming their habits and achieving their goals when they are motivated by the prospect of reward. However, it is not known whether expected reward leads to improved performance via the inhibition of habits, the facilitation of goals, or a mixture of both. We addressed this using forced-response conflict tasks and a probabilistic response preparation model that dissociates the preparation of habitual and goal-directed actions. Across two experiments, we find evidence that reward selectively accelerates the preparation of goal-directed actions.

Probabilistic Response of a Bladed Disk With Uncertain Geometry

Geometric uncertainties in the blade manufacturing process have important consequences in terms of dynamical properties of bladed disks. In this paper, we address the problem of modeling a full bladed disk composed by blades having uncertain geometry. The geometric imperfection of the blades is represented and analyzed according to a procedure previously presented by the authors, based on the principal component analysis (PCA) and the mesh morphing. The dynamical model of the full disk is constructed following the component mode synthesis (CMS) approach. The blade geometry is represented using a probabilistic model constructed from an experimental dataset. The effect of the geometric uncertainties is assessed using a linear uncertainty propagation approach, leading to a procedure that is fast enough to be embedded into a Monte Carlo simulation (MCS) loop.

Probabilistic Response of a Bladed Disk With Uncertain Geometry

Geometric uncertainties in the blade manufacturing process have important consequences in terms of dynamical properties of bladed disks. In this paper we address the problem of modeling a full bladed disk composed by blades having uncertain geometry. The geometric imperfection of the blades is represented and analyzed according to a procedure previously presented by the authors, based on the principal component analysis and the mesh morphing. The dynamical model of the full disk is constructed following the component mode synthesis approach. The blade geometry is represented using a probabilistic model constructed from an experimental dataset. The effect of the geometric uncertainties is assessed using a linear propagation approach, leading to a procedure that is fast enough to be embedded into a Monte Carlo simulation loop.