The GRACE Follow-On satellite mission measures distance variations between its two satellites in order to derive monthly gravity field maps, indicating mass variability on Earth on a scale of a few 100 km originating from hydrology, seismology, climatology and other sources. This mission hosts two ranging instruments, a conventional microwave system based on K(a)-band ranging (KBR) and a novel laser ranging instrument (LRI), both relying on interferometric phase readout. In this paper, we show how the phase measurements can be converted into range data using a time-dependent carrier frequency (or wavelength) that takes into account potential intraday variability in the microwave or laser frequency. Moreover, we analyze the KBR-LRI residuals and discuss which error and noise contributors limit the residuals at high and low Fourier frequencies. It turns out that the agreement between KBR and LRI biased range observations can be slightly improved by considering intraday carrier frequency variations in the processing. Although the effect is probably small enough to have little relevance for gravity field determination at the current precision level, this analysis is of relevance for detailed instrument characterization and potentially for future more precise missions.
Abstract. The sizing of industrial structures with a given material requires to understand completely its fatigue behaviour. Yet, the full estimation of the fatigue lifetime of a material requires time-consuming and expensive fatigue testing campaigns, and a huge number of samples. An alternative experimental procedure is based on the monitoring of the self-heating properties of the studied material. The aim is to correlate the fatigue limit with a change of the thermal behaviour of the material during the self-heating tests. The main advantage of these tests is that they need only a limited number of mechanical cycles, and require few samples to test. Ultimately, self-heating tests lead to an accelerated estimation of the fatigue behaviour. The purpose of the present paper is to validate this approach for a woven carbon/thermoplastic composite material.
<p align="justify"><span lang="en-GB">The new Laser Ranging Interferometer (LRI) on GRACE Follow-On is measuring, just like the microwave instrument (MWI), the distance variations between the two satellites, but with a significantly higher precision. The Albert Einstein Institute (AEI) in Hannover was involved in the development of the LRI and is currently concerned with instrument operation and data analysis. &#160;In order to verify and validate the correctness of the Science Data System (SDS) derived LRI1B data product, currently available as release 04, we started to implement an own processing chain to convert data from raw level0 or level 1A to level1B, where the latter is usually employed in gravity field recovery. Besides the validation, we are interested in testing alternative processing strategies, which could improve the data quality and that might get adopted by official processing centers at some point.<br /></span><span lang="en-GB">We will provide an overview on our processing strategy, which includes five major steps 1) Deglitching of the piston phase in order to remove phase jumps that occur when the attitude control thrusters are activated. 2) Conversion of time-tags from LRI time to GPS time and forming the phase difference of master and transponder measurements. &#160;This removes the common 10 MHz ramp in the measurements. 3) Conversion of the phase to a physical length, the non-instantaneous biased range 4) Filtering and down-sampling of the data to the LRI1B rate of 0.5 Hz. &#160;5) Finally, the light time correction (LTC) is calculated and allows to transform the non-instantaneous biased range and its derivatives to the instantaneous or corrected biased range. We will highlight the main differences in our processing to the RL04 processing, as far as known to us.<br /></span><span lang="en-GB">In the end, we compare the RL04 and our data set at the 1B level, which shows a slightly lower noise and uses less empirical parameters.</span></p>
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