Ares I–X Flight Test Vehicle similitude to the Ares I Crew Launch Vehicle

The Ares I–X Flight Test Vehicle is the first in a series of flight test vehicles that will take the Ares I Crew Launch Vehicle design from development to operational capability. Ares I–X is scheduled for a 2009 flight date, early enough in the Ares I design and development process so that data obtained from the flight can impact the design of Ares I before its Critical Design Review. Decisions on Ares I–X scope, flight test objectives, and FTV fidelity were made prior to the Ares I systems requirements being baselined. This was necessary in order to achieve a development flight test to impact the Ares I design. Differences between the Ares I–X and the Ares I configurations are artifacts of formulating this experimental project at an early stage and the natural maturation of the Ares I design process. This paper describes the similarities and differences between the Ares I–X Flight Test Vehicle and the Ares I Crew Launch Vehicle. Areas of comparison include the outer mold line geometry, aerosciences, trajectory, structural modes, flight control architecture, separation sequence, and relevant element differences. Most of the outer mold line differences present between Ares I and Ares I–X are minor and will not have a significant effect on overall vehicle performance. The most significant impacts are related to the geometric differences in Orion Crew Exploration Vehicle at the forward end of the stack. These physical differences will cause differences in the flow physics in these areas. Even with these differences, the Ares I–X flight test is poised to meet all five primary objectives and six secondary objectives. Knowledge of what the Ares I–X flight test will provide in similitude to Ares I—as well as what the test will not provide—is important in the continued execution of the Ares I–X mission leading to its flight and the continued design and development of Ares I.     

Order of magnitude analysis of convective effects in dendritic solidification

The different types of convective phenomena which may occur during the dendritic solidification of metallic alloys are discussed from an order of magnitude analysis. Bulk thermal convection and/or interdendritic solutal convection have to be considered according to the values of the experimental data. Scaling laws for the solute boundary layer resulting from bulk thermal convection have already been derived. It is shown here that the interdendritic flow depends on a solutal Grashof number Gr based on the horizontal density gradient and a characteristic length L_s which is of the order of the liquid channels width. For Gr < 1, which is generally verified in practical cases, the interdendritic flow velocity U_r is proportional to the Grashof number. This a priori law compares favorably with the results of horizontal solidification experiments where the mean interdendritic flow velocity has been estimated from the resulting measured macrosegregation. In these experiments, as well as for most horizontal dendritic solidifications of metallic alloys at 1 g, the ratio $\text{U}{}_{\mathrm{r}}\text{R}$ (R is the growth rate) is of order one. In order to cancel the interdendritic flow effects, this ratio has to be lowered by one order of magnitude. According to our analysis, this can be obtained by performing the experiments either at a slightly reduced g level (～10^?1 g), or at 1 g in a vertical stable configuration with a sufficiently low residual horizontal thermal gradient.     

风车状态下转子性能及通道内流动分析

为探究低展弦比压气机转子在风车状态下由压气机模式向涡轮模式转化过程中性能、内部流场结构以及气动损失的演化过程,提出了一种基于叶片和流体间能量传递的简化数值计算方法,以获得某转速下的风车状态临界流量点。在数值模拟的基础上,重点对比了同一转速线上压气机工况点(小流量工况)、风车临界点和涡轮工况点下叶尖泄漏损失的演化机制,同时探究了叶片通道内流动分离的演化过程。 结果显示,随着转速的增加,转子风车状态临界流量呈现近似线性的变化趋势。而同转速下随流量增大,叶尖泄漏流从吸力面流向压力面,并与压力面上的低能量流体进行掺混,造成了流动堵塞。同时,从压气机模式转向涡轮模式的过程中,叶尖区域的流动分离从吸力面分离转变为压力面分离,随后分离强度和尺寸逐渐增大,造成的气动损失显著增加;而在轮毂区域,流动分离始终保持吸力面分离,其分离尺度沿径向有所发展。     

Aircraft Interrogation Scheduling with ASTRO-DABS

In the ASTRO-DABS concept for surveillance and data link, aircraft are interrogated by one of three geostationary transmitter satellites, each covering 1/3 of the contiguous United States. Interrogation scheduling involves a roll call such that aircraft responses to receiving satellites do not overlap (garble). A simple approach is developed which utilizes range ordering of aircraft with respect to transmitter satellites, but is independent of receiver satellite locations and aircraft distribution. Bounds on roll-call duration are established, showing that interrogation of 80 000 aircraft requires between 4.0 and 6.4 seconds with the ASTRO-DABS transmission format. If aircraft distribution is regionally concentrated (i.e., clustered), the roll-call duration nears the lower bound, since fewer gaps between interrogations are needed to preclude garbling.     

Upgrading CCIR's foF2 maps using available ionosondes and genetic algorithms

We have developed a new approach towards a new database of the ionospheric parameter $f_{o}F2$. This parameter, being the frequency of the maximum of the ionospheric electronic density profile and its main modeller, is of great interest not only in atmospheric studies but also in the realm of radio propagation. The current databases, generated by CCIR (Committee Consultative for Ionospheric Radiowave propagation) and URSI (International Union of Radio Science), and used by the IRI (International Reference Ionosphere) model, are based on Fourier expansions and have been built in the 60s from the available ionosondes at that time. The main goal of this work is to upgrade the databases by using new available ionosonde data. To this end we used the IRI diurnal/spherical expansions to represent the $f_{o}F2$ variability, and computed its coefficients by means of a genetic algorithm (GA). In order to test the performance of the proposed methodology, we applied it to the South American region with data obtained by RAPEAS (Red Argentina para el Estudio de la Atmósfera Superior, i.e. Argentine Network for the Study of the Upper Atmosphere) during the years 1958–2009. The new GA coefficients provide a global better fit of the IRI model to the observed $f_{o}F2$ than the CCIR coefficients. Since the same formulae and the same number of coefficients were used, the overall integrity of IRI’s typical ionospheric feature representation was preserved. The best improvements with respect to CCIR are obtained at low solar activities, at large (in absolute value) modip latitudes, and at night-time. The new method is flexible in the sense that can be applied either globally or regionally. It is also very easy to recompute the coefficients when new data is available. The computation of a third set of coefficients corresponding to days of medium solar activity in order to avoid the interpolation between low and high activities is suggested. The same procedure as for $f_{o}F2$ can be perfomed to obtain the ionospheric parameter M(3000)F2.     

Application of SVM on satellite images to detect hotspots in Jharia coal field region of India

The present paper deals with the application of Support Vector Machine (SVM) and image analysis techniques on NOAA/AVHRR satellite image to detect hotspots on the Jharia coal field region of India. One of the major advantages of using these satellite data is that the data are free with very good temporal resolution; while, one drawback is that these have low spatial resolution (i.e., approximately 1.1 km at nadir). Therefore, it is important to do research by applying some efficient optimization techniques along with the image analysis techniques to rectify these drawbacks and use satellite images for efficient hotspot detection and monitoring. For this purpose, SVM and multi-threshold techniques are explored for hotspot detection. The multi-threshold algorithm is developed to remove the cloud coverage from the land coverage. This algorithm also highlights the hotspots or fire spots in the suspected regions. SVM has the advantage over multi-thresholding technique that it can learn patterns from the examples and therefore is used to optimize the performance by removing the false points which are highlighted in the threshold technique. Both approaches can be used separately or in combination depending on the size of the image. The RBF (Radial Basis Function) kernel is used in training of three sets of inputs: brightness temperature of channel 3, Normalized Difference Vegetation Index (NDVI) and Global Environment Monitoring Index (GEMI), respectively. This makes a classified image in the output that highlights the hotspot and non-hotspot pixels. The performance of the SVM is also compared with the performance obtained from the neural networks and SVM appears to detect hotspots more accurately (greater than 91% classification accuracy) with lesser false alarm rate. The results obtained are found to be in good agreement with the ground based observations of the hotspots. This type of work will be quite helpful in the near future to develop a hotspots monitoring system using these operational satellites data.