Compact, semi-passive beam steering prism array for solar concentrators.

In order to maximize solar energy utilization in a limited space (e.g., rooftops), solar collectors should track the sun. As an alternative to rotational tracking systems, this paper presents a compact, semi-passive beam steering prism array which has been designed, analyzed, and tested for solar applications. The proposed prism array enables a linear concentrator system to remain stationary so that it can integrate with a variety of different solar concentrators, and which should be particularly useful for systems which require a low profile (namely rooftop-mounted systems). A case study of this prism array working within a specific rooftop solar collector demonstrates that it can boost the average daily optical efficiency of the collector by 32.7% and expand its effective working time from 6 h to 7.33 h. Overall, the proposed design provides an alternative way to "follow" the sun for a wide range of solar thermal and photovoltaic concentrator systems.

Feasibility of nanofluid-based optical filters.

In this article we report recent modeling and design work indicating that mixtures of nanoparticles in liquids can be used as an alternative to conventional optical filters. The major motivation for creating liquid optical filters is that they can be pumped in and out of a system to meet transient needs in an application. To demonstrate the versatility of this new class of filters, we present the design of nanofluids for use as long-pass, short-pass, and bandpass optical filters using a simple Monte Carlo optimization procedure. With relatively simple mixtures, we achieve filters with <15% mean-squared deviation in transmittance from conventional filters. We also discuss the current commercial feasibility of nanofluid-based optical filters by including an estimation of today's off-the-shelf cost of the materials. While the limited availability of quality commercial nanoparticles makes it hard to compete with conventional filters, new synthesis methods and economies of scale could enable nanofluid-based optical filters in the near future. As such, this study lays the groundwork for creating a new class of selective optical filters for a wide range of applications, namely communications, electronics, optical sensors, lighting, photography, medicine, and many more.

Feature-based statistical analysis of combustion simulation data.

We present a new framework for feature-based statistical analysis of large-scale scientific data and demonstrate its effectiveness by analyzing features from Direct Numerical Simulations (DNS) of turbulent combustion. Turbulent flows are ubiquitous and account for transport and mixing processes in combustion, astrophysics, fusion, and climate modeling among other disciplines. They are also characterized by coherent structure or organized motion, i.e. nonlocal entities whose geometrical features can directly impact molecular mixing and reactive processes. While traditional multi-point statistics provide correlative information, they lack nonlocal structural information, and hence, fail to provide mechanistic causality information between organized fluid motion and mixing and reactive processes. Hence, it is of great interest to capture and track flow features and their statistics together with their correlation with relevant scalar quantities, e.g. temperature or species concentrations. In our approach we encode the set of all possible flow features by pre-computing merge trees augmented with attributes, such as statistical moments of various scalar fields, e.g. temperature, as well as length-scales computed via spectral analysis. The computation is performed in an efficient streaming manner in a pre-processing step and results in a collection of meta-data that is orders of magnitude smaller than the original simulation data. This meta-data is sufficient to support a fully flexible and interactive analysis of the features, allowing for arbitrary thresholds, providing per-feature statistics, and creating various global diagnostics such as Cumulative Density Functions (CDFs), histograms, or time-series. We combine the analysis with a rendering of the features in a linked-view browser that enables scientists to interactively explore, visualize, and analyze the equivalent of one terabyte of simulation data. We highlight the utility of this new framework for combustion science; however, it is applicable to many other science domains.