The bounded coalescent model: conditioning a genealogy on a minimum root date

The coalescent model represents how individuals sampled from a population may have originated from a last common ancestor. The bounded coalescent model is obtained by conditioning the coalescent model such that the last common ancestor must have existed after a certain date. This conditioned model arises in a variety of applications, such as speciation, horizontal gene transfer or transmission analysis, and yet the bounded coalescent model has not been previously analysed in detail. Here we describe a new algorithm to simulate from this model directly, without resorting to rejection sampling. We show that this direct simulation algorithm is more computationally efficient than the rejection sampling approach. We also show how to calculate the probability of the last common ancestor occurring after a given date, which is required to compute the probability of realisations under the bounded coalescent model. Our results are applicable in both the isochronous (when all samples have the same date) and heterochronous (where samples can have different dates) settings. We explore the effect of setting a bound on the date of the last common ancestor, and show that it affects a number of properties of the resulting phylogenies. All our methods are implemented in a new R package called BoundedCoalescent which is freely available online.

Modelling and simulation of atmosphere-breathing electric propulsion intakes via direct simulation Monte Carlo

The Air-Breathing Ion Engine (ABIE) is an electric propulsion system capable of compensating for drag at low altitudes by ingesting the surrounding atmospheric particles to be utilized as propellant. The current state of the art of the ABIE performance is evaluated via Direct Simulation Monte Carlo (DSMC), due to the rarefied nature of the atmosphere in Very-Low Earth Orbit (VLEO). Nevertheless, the scarce availability of relevant simulation methodologies in the literature limits the repeatability of such numerical studies. Therefore, this paper proposes an independent methodology applicable to the modelling and simulation of Atmosphere-Breathing Electric Propulsion (ABEP) intakes that aims to validate the ABIE DSMC results retrieved from the literature. This is achieved by investigating the ABIE intake collection efficiency and compression ratio through the open-source solver dsmcFoam+ and by assessing the results against the available RARAC-3D DSMC data. First, the variation of grid transparency is discussed and compared between both solvers, yielding a mean percentage error of $$2.97\%$$ 2.97 % for the compression ratio and $$2.06\%$$ 2.06 % for the collection efficiency. Second, the absence of intermolecular collisions is verified for which errors of $$1.61\%$$ 1.61 % for collection efficiency and $$3.49\%$$ 3.49 % for compression ratio are observed. Then, the variation of flow incidence angle is simulated between $$0^{\circ }$$ 0 ∘ and $$15^{\circ }$$ 15 ∘ , yielding differences lower than $$1.80\%$$ 1.80 % . Consecutively, the intake aspect ratio is varied between 10 and 40, for which a maximum discrepancy of $$1.83\%$$ 1.83 % is measured and, finally, the drag coefficient of the intake is obtained in dsmcFoam+ to define the power density requirements.

A Critical Literature Review on Rock Petrophysical Properties Estimation from Images Based on Direct Simulation and Machine Learning Techniques

Estimation of petrophysical properties is essential for accurate reservoir predictions. In recent years, extensive work has been dedicated into training different machine-learning (ML) models to predict petrophysical properties of digital rock using dry rock images along with data from single-phase direct simulations, such as lattice Boltzmann method (LBM) and finite volume method (FVM). The objective of this paper is to present a comprehensive literature review on petrophysical properties estimation from dry rock images using different ML workflows and direct simulation methods. The review provides detailed comparison between different ML algorithms that have been used in the literature to estimate porosity, permeability, tortuosity, and effective diffusivity. In this paper, various ML workflows from the literature are screened and compared in terms of the training data set, the testing data set, the extracted features, the algorithms employed as well as their accuracy. A thorough description of the most commonly used algorithms is also provided to better understand the functionality of these algorithms to encode the relationship between the rock images and their respective petrophysical properties. The review of various ML workflows for estimating rock petrophysical properties from dry images shows that models trained using features extracted from the image (physics-informed models) outperformed models trained on the dry images directly. In addition, certain tree-based ML algorithms, such as random forest, gradient boosting, and extreme gradient boosting can produce accurate predictions that are comparable to deep learning algorithms such as deep neural networks (DNNs) and convolutional neural networks (CNNs). To the best of our knowledge, this is the first work dedicated to exploring and comparing between different ML frameworks that have recently been used to accurately and efficiently estimate rock petrophysical properties from images. This work will enable other researchers to have a broad understanding about the topic and help in developing new ML workflows or further modifying exiting ones in order to improve the characterization of rock properties. Also, this comparison represents a guide to understand the performance and applicability of different ML algorithms. Moreover, the review helps the researchers in this area to cope with digital innovations in porous media characterization in this fourth industrial age – oil and gas 4.0.