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Recently, the cooling system of hydraulic excavator is often designed using the thermal and fluid analysis to improve the cooling performance. The reliability of the analysis results is important, since it directly influences on the efficiency of development. In the present study, the uncertain parameters were estimated using the data assimilation method to increase the reliability of the thermal and fluid analysis in an engine room of a hydraulic excavator. The ensemble Kalman filter (EnKF) was adapted as a data assimilation method, and the thermal and fluid analysis was conducted with the three-dimensional steady simulation based on the Reynolds-average Navier-Stokes equations. The estimated parameters were set to the total heat quantities released by heat exchangers and the flow rates of the coolants. The total heat quantity is a parameter used for the heat release calculation of a heat exchanger, and the flow rate of a coolant is specified at the inlet boundary. As measurement data, temperatures of coolants which were measured at the upstream and downstream of the heat exchangers were used. Initial parameters were generated by setting parameter values in a random manner. The simulation using estimated parameters successfully predicted temperatures at the heat exchangers, where the maximum error was 3K. In addition, the reductions of the standard deviations of the uncertain parameters were confirmed. That means the reliability of the simulation was increased.

It is important to construct 3D virtual models of man-made fields in which people work and live. Recent mid-range and long-range laser scanners can be used to acquire 3D shapes of cities, buildings, factories, heavy goods, transportation infrastructure, and so on. However, they tend to produce outliers and very noisy points near silhouettes and sharp edges of objects. This problem makes it difficult to reconstruct bounded faces. In addition, since enormous volumes of point-clouds are captured from a broad range of scenes, efficient processing methods are required. In this paper, we propose a robust edge detection method and an efficient GPU-based smoothing method for reconstructing primitive surfaces. We first calculate straight edge lines and silhouette lines from raw scanned data, and then eliminate noises and outliers by our GPU-based smoothing method for calculating surface equations. Then primitive surfaces are extracted using sharp edges, silhouette lines and surface equations. Our method is useful to robustly extract surface primitives from practical noisy pointclouds.