ALBERT

Description: Commercial and residential building is one of the four major final energy consumption and end-use sectors. In this sector, cooling loads represent an important part of the energy consumption, and therefore, they must be minimized, improving the energy efficiency of buildings. Ventilated façades are one of the most widely used passive elements that are integrated into buildings, precisely with the aim of reducing these loads. This reduction is due to the airflow induced in the air cavity by the buoyancy forces, when the solar radiation heats the outer layer of the façade. In the open joint ventilated facades (OJVF), ventilation is attained through the open joints between the panels composing the outer layer. Despite the steadily growing research in the characterization of this type of system, few studies combine the numerical modelling of OJVF with experimental results for the assessment of the airflow in the ventilated cavities. This paper experimentally validates a numerical simulation model of an OJVF. Firstly, the façade performance has been experimentally assessed in a laboratory model determining the temperatures in the panels and air gap and measuring the flow field at the gap using particle image velocimetry (PIV) techniques. Secondly, a numerical model has been developed using advanced Computational Fluid Dynamics (CFD) simulation tools. Finally, an experimental validation of the numerical model has been done. Experimental and numerical results are compared in different planes inside the ventilated cavity. The discrete ordinates (DO) radiation model and the k-ε renormalisation group (RNG) turbulence model better adjust the simulated results to the experimental ones.

Description: Many hybrid data assimilation systems currently used for NWP employ some form of dual-analysis system approach. Typically a hybrid variational analysis is responsible for creating initial conditions for high-resolution forecasts, and an ensemble analysis system is responsible for creating sample perturbations used to form the flow-dependent part of the background error covariance required in the hybrid analysis component. In many of these, the two analysis components employ different methodologies, e.g., variational and ensemble Kalman filter. In such cases, it is not uncommon to have observations treated rather differently between the two analyses components; recentering of the ensemble analysis around the hybrid analysis is used to compensated for such differences. Furthermore, in many cases, the hybrid variational high-resolution system implements some type of four-dimensional approach, whereas the underlying ensemble system relies on a three-dimensional approach, which again introduces discrepancies in the overall system. Connected to these is the expectation that one can reliably estimate observation impact on forecasts issued from hybrid analyses by using an ensemble approach based on the underlying ensemble strategy of dual-analysis systems. Just the realization that the ensemble analysis makes substantially different use of observations as compared to their hybrid counterpart should serve as enough evidence of the implausibility of such expectation. This presentation assembles numerous anecdotal evidence to illustrate the fact that hybrid dual-analysis systems must, at the very minimum, strive for consistent use of the observations in both analysis sub-components. Simpler than that, this work suggests that hybrid systems can reliably be constructed without the need to employ a dual-analysis approach. In practice, the idea of relying on a single analysis system is appealing from a cost-maintenance perspective. More generally, single-analysis systems avoid contradictions such as having to choose one sub-component to generate performance diagnostics to another, possibly not fully consistent, component.