The ALTIUS-instrument is a three-channel spectral imager, measuring in the ultraviolet, visible and near infrared wavelength domains, that is bound to fly aboard a PROBA-satellite. The goal of the ALTIUS-instrument is to make hyper spectral images of the limb of the earth. In each of ALTIUS' three channels an AOTF (Acousto-Optical Tunable Filter) will be used. For each channel an RF-generator will be developed and subjected to a suite of environmental tests such as thermal-vacuum, vibration, radiation, shock and Electromagnetic Compatibility (EMC). For the AOTF RF-generator different solutions are possible. Currently the design of an analog Hilbert transform and a high frequency phase-locked loop (PLL) are proposed as possible solutions. The choice of components, the PCB-design and the manufacturing of both designs has to be done in accordance to space qualified standards. This limits the choice of design and lifts the design challenge to a higher level. Because of the limited choice of space qualified components, general commercial approaches are not possible. For this reason specific designs have to be investigated and research has to be done to select the appropriate solution.@en

The permeability of a pore structure is typically described by stochastic representations of its geometrical attributes (e.g. pore-size distribution, porosity, coordination number). Database-driven numerical solvers for large model domains can only accurately predict large-scale flow behavior when they incorporate upscaled descriptions of that structure. The upscaling is particularly challenging for rocks with multimodal porosity structures such as carbonates, where several different type of structures (e.g. micro-porosity, cavities, fractures) are interacting. It is the connectivity both within and between these fundamentally different structures that ultimately controls the porosity–permeability relationship at the larger length scales. Recent advances in machine learning techniques combined with both numerical modelling and informed structural analysis have allowed us to probe the relationship between structure and permeability much more deeply. We have used this integrated approach to tackle the challenge of upscaling multimodal and multiscale porous media. We present a novel method for upscaling multimodal porosity–permeability relationships using machine learning based multivariate structural regression. A micro-CT image of Estaillades limestone was divided into small 603 and 1203 sub-volumes and permeability was computed using the Darcy–Brinkman–Stokes (DBS) model. The microporosity–porosity–permeability relationship from Menke et al. (Earth Arxiv, https://doi.org/10.31223/osf.io/ubg6p, 2019) was used to assign permeability values to the cells containing microporosity. Structural attributes (porosity, phase connectivity, volume fraction, etc.) of each sub-volume were extracted using image analysis tools and then regressed against the solved DBS permeability using an Extra-Trees regression model to derive an upscaled porosity–permeability relationship. Ten test cases of 3603 voxels were then modeled using Darcy-scale flow with this machine learning predicted upscaled porosity–permeability relationship and benchmarked against full DBS simulations, a numerically upscaled Darcy flow model, and a Kozeny–Carman model. All numerical simulations were performed using GeoChemFoam, our in-house open source pore-scale simulator based on OpenFOAM. We found good agreement between the full DBS simulations and both the numerical and machine learning upscaled models, with the machine learning model being 80 times less computationally expensive. The Kozeny–Carman model was a poor predictor of upscaled permeability in all cases.@en