ReMKiT1D -- A framework for building reactive multi-fluid models of the tokamak Scrape-Off Layer with coupled electron kinetics in 1D

In this manuscript we present the recently developed flexible framework for building both fluid and electron kinetic models of the tokamak Scrape-Off Layer in 1D - ReMKiT1D (Reactive Multi-fluid and Kinetic Transport in 1D). The framework can handle systems of non-linear ODEs, various 1D PDEs arising in fluid modelling, as well as PDEs arising from the treatment of the electron kinetic equation. As such, the framework allows for flexibility in fluid models of the Scrape-Off Layer while allowing the easy addition of kinetic electron effects. We focus on presenting both the high-level design decisions that allow for model flexibility, as well as the most important implementation aspects. A significant number of verification and performance tests are presented, as well as a step-by-step walkthrough of a simple example for setting up models using the Python interface

Adaptive physics-informed neural operator for coarse-grained non-equilibrium flows

This work proposes a new machine learning (ML)-based paradigm aiming to enhance the computational efficiency of non-equilibrium reacting flow simulations while ensuring compliance with the underlying physics. The framework combines dimensionality reduction and neural operators through a hierarchical and adaptive deep learning strategy to learn the solution of multi-scale coarse-grained governing equations for chemical kinetics. The proposed surrogate's architecture is structured as a tree, with leaf nodes representing separate neural operator blocks where physics is embedded in the form of multiple soft and hard constraints. The hierarchical attribute has two advantages: i) It allows the simplification of the training phase via transfer learning, starting from the slowest temporal scales; ii) It accelerates the prediction step by enabling adaptivity as the surrogate's evaluation is limited to the necessary leaf nodes based on the local degree of non-equilibrium of the gas. The model is applied to the study of chemical kinetics relevant for application to hypersonic flight, and it is tested here on pure oxygen gas mixtures. In 0-D scenarios, the proposed ML framework can adaptively predict the dynamics of almost thirty species with a maximum relative error of 4.5% for a wide range of initial conditions. Furthermore, when employed in 1-D shock simulations, the approach shows accuracy ranging from 1% to 4.5% and a speedup of one order of magnitude compared to conventional implicit schemes employed in an operator-splitting integration framework. Given the results presented in the paper, this work lays the foundation for constructing an efficient ML-based surrogate coupled with reactive Navier-Stokes solvers for accurately characterizing non-equilibrium phenomena in multi-dimensional computational fluid dynamics simulations

Simulating 3D Radiation Transport, a modern approach to discretisation and an exploration of probabilistic methods

Light, or electromagnetic radiation in general, is a profound and invaluable resource to investigate our physical world. For centuries, it was the only and it still is the main source of information to study the Universe beyond our planet. With high-resolution spectroscopic imaging, we can identify numerous atoms and molecules, and can trace their physical and chemical environments in unprecedented detail. Furthermore, radiation plays an essential role in several physical and chemical processes, ranging from radiative pressure, heating, and cooling, to chemical photo-ionisation and photo-dissociation reactions. As a result, almost all astrophysical simulations require a radiative transfer model. Unfortunately, accurate radiative transfer is very computationally expensive. Therefore, in this thesis, we aim to improve the performance of radiative transfer solvers, with a particular emphasis on line radiative transfer. First, we review the classical work on accelerated lambda iterations and acceleration of convergence, and we propose a simple but effective improvement to the ubiquitously used Ng-acceleration scheme. Next, we present the radiative transfer library, Magritte: a formal solver with a ray-tracer that can handle structured and unstructured meshes as well as smoothed-particle data. To mitigate the computational cost, it is optimised to efficiently utilise multi-node and multi-core parallelism as well as GPU offloading. Furthermore, we demonstrate a heuristic algorithm that can reduce typical input models for radiative transfer by an order of magnitude, without significant loss of accuracy. This strongly suggests the existence of more efficient representations for radiative transfer models. To investigate this, we present a probabilistic numerical method for radiative transfer that naturally allows for uncertainty quantification, providing us with a mathematical framework to study the trade-off between computational speed and accuracy. Although we cannot yet construct optimal representations for radiative transfer problems, we point out several ways in which this method can lead to more rigorous optimisation

Laser Induced Breakdown Spectroscopy For Detection Of Organic Residues Impact Of Ambient Atmosphere And Laser Parameters

Laser Induced Breakdown Spectroscopy (LIBS) is showing great potential as an atomic analytical technique. With its ability to rapidly analyze all forms of matter, with little-to-no sample preparation, LIBS has many advantages over conventional atomic emission spectroscopy techniques. With the maturation of the technologies that make LIBS possible, there has been a growing movement to implement LIBS in portable analyzers for field applications. In particular, LIBS has long been considered the front-runner in the drive for stand-off detection of trace deposits of explosives. Thus there is a need for a better understanding of the relevant processes that are responsible for the LIBS signature and their relationships to the different system parameters that are helping to improve LIBS as a sensing technology. This study explores the use of LIBS as a method to detect random trace amounts of specific organic materials deposited on organic or non-metallic surfaces. This requirement forces the limitation of single-shot signal analysis. This study is both experimental and theoretical, with a sizeable component addressing data analysis using principal components analysis to reduce the dimensionality of the data, and quadratic discriminant analysis to classify the data. In addition, the alternative approach of ‘target factor analysis’ was employed to improve detection of organic residues on organic substrates. Finally, a new method of characterizing the laser-induced plasma of organics, which should lead to improved data collection and analysis, is introduced. The comparison between modeled and experimental measurements of plasma temperatures and electronic density is discussed in order to improve the present models of low-temperature laser induced plasmas

Vacuum ultraviolet laser induced breakdown spectroscopy (VUV-LIBS) for pharmaceutical analysis

Laser induced breakdown spectroscopy (LIBS) allows quick analysis to determine the elemental composition of the target material. Samples need little\no preparation, removing the risk of contamination or loss of analyte. It is minimally ablative so negligible amounts of the sample is destroyed, while allowing quantitative and qualitative results. Vacuum ultraviolet (VUV)-LIBS, due to the abundance of transitions at shorter wavelengths, offers improvements over LIBS in the visible region, such as achieving lower limits of detection for trace elements and extends LIBS to elements\samples not suitable to visible LIBS. These qualities also make VUV-LIBS attractive for pharmaceutical analysis. Due to success in the pharmaceutical sector molecules representing the active pharmaceutical ingredients (APIs) have become increasingly complex. These organic compounds reveal spectra densely populated with carbon and oxygen lines in the visible and infrared regions, making it increasingly difficult to identify an inorganic analyte. The VUV region poses a solution as there is much better spacing between spectral lines. VUV-LIBS experiments were carried out on pharmaceutical samples. This work is a proof of principle that VUV-LIBS in conjunction with machine learning can tell pharmaceuticals apart via classification. This work will attempt to test this principle in two ways. Firstly, by classifying pharmaceuticals that are very different from one another i.e., having different APIs. This first test will gauge the efficacy of separating into different classes analytes that are essentially carbohydrates with distinctly different APIs apart from one another using their VUV emission spectra. Secondly, by classifying two different brands of the same pharmaceutical, i.e., paracetamol. The second test will investigate of the ability of machine learning to abstract and identify the differences in the spectra of two pharmaceuticals with the same API and separate them. This second test presents the application of VUV-LIBS combined with machine learning as a solution for at-line analysis of similar analytes e.g., quality control. The machine learning techniques explored in this thesis were convolutional neural networks (CNNs), support vector machines, self-organizing maps and competitive learning. The motivation for the application of principal component analysis (PCA) and machine learning is for the classification of analytes, allowing us to distinguish pharmaceuticals from one another based on their spectra. PCA and the machine learning techniques are compared against one another in this thesis. Several innovations were made; this work is the first in LIBS to implement the use of a short-time Fourier transform (STFT) method to generate input images for a CNN for VUV-LIBS spectra. This is also believed to be the first work in LIBS to carry out the development and application of an ellipsoidal classifier based on PCA. The results of this work show that by lowering the pulse energy it is possible to gather more useful spectra over the surface of a sample. Although this yields spectra with poorer signal-to-noise, the samples can still be classified using the machine learning analytics. The results in this thesis indicate that, of all the machine learning techniques evaluated, CNNs have the best classification accuracy combined with the fastest run time. Prudent data augmentation can significantly reduce experimental workloads, without reducing classification rates

Machine learning and uncertainty quantification framework for predictive ab initio Hypersonics

Hypersonics represents one of the most challenging applications for predictive science. Due to the multi-scale and multi-physics characteristics, high-Mach phenomena are generally complex from both the computational and the experimental perspectives. Nevertheless, the related simulations typically require high accuracy, as their outcomes inform design and decision-making processes in safety-critical applications. Ab initio approaches aim to improve the predictive accuracy by making the calculations free from empiricism. In order to achieve this goal, these methodologies move the computational resolution down to the interatomic level by relying on first-principles quantum physics. As side effects, the increase in model complexity also results in: i) more physics that could be potentially misrepresented and ii) dramatic inflation of the computational cost. This thesis leverages machine learning (ML), uncertainty quantification (UQ), data science, and reduced order models (ROMs) for tackling these downsides and improving the predictive capabilities of ab initio Hypersonics. The first part of the manuscript focuses on formulating and testing a systematic approach to the reliability assessment of ML-based models based on their non-deterministic extensions. In particular, it introduces a novel methodology for the quantification of uncertainties associated with potential energy surfaces (PESs) computed from first-principles quantum mechanical calculations. The methodology relies on Bayesian inference and ML techniques to construct a stochastic PES and to express the inadequacies associated with the ab initio data points and their fit. The resulting stochastic surface is efficiently forward propagated via quasi-classical trajectory (QCT) and master equation calculations by combining high fidelity calculations and reduced order modeling. In this way, the PES contribution to the uncertainty on predefined quantities of interest (QoIs) is explicitly determined. This study is done at both microscopic (e.g., rovibrational-specific rate coefficients) and macroscopic (e.g., thermal and chemical relaxation properties) levels. A correlation analysis is finally applied to identify the PES regions that require further refinement, based on their effects on the QoI reliability. The methodology is applied to the study of singlet (11A') and quintet (25A') PESs describing the interaction between O2 molecules and O atoms in their ground electronic state. The investigation of the singlet surface reveals a negligible uncertainty on the kinetic properties and relaxation times, which are found to be in excellent agreement with the ones previously published in the literature. On the other hand, the methodology demonstrated significant uncertainty on the quintet surface due to inaccuracies in the description of the exchange barrier and the repulsive wall. When forward propagated, this uncertainty is responsible for the variability of one order of magnitude in the vibrational relaxation time and of factor four in the exchange reaction rate coefficient, both at 2,500 K. The second part of this thesis presents a data-informed and physics-driven coarse-graining strategy aimed to reduce the computational cost of ab initio simulations. At first, an in-depth discussion of the physics governing the non-equilibrium dissociation of O2 molecules colliding with O atoms is proposed. A rovibrationally-resolved database for all of the elementary collisional processes is constructed by including all nine adiabatic electronic states of O3 in the QCT calculations. A detailed analysis of the ab initio data set reveals that, for a rovibrational level, the probability of dissociating is mostly dictated by its deficit in internal energy compared to the centrifugal barrier. Due to the assumption of rotational equilibrium, the conventional vibrational-specific calculations fail to characterize such a dependence, and the new ROM strategy is proposed based on this observation. By relying on a hybrid technique made of rovibrationally-resolved excitation coupled to coarse-grained dissociation, the novel approach is compared to the vibrational-specific model and the direct solution of the rovibrational state-to-state master equation. Simulations are performed in a zero-dimensional isothermal and isochoric chemical reactor for a wide range of temperatures (1,500 - 20,000 K). The study shows that the main contribution to the model inadequacy of vibrational-specific approaches originates from the incapability of characterizing dissociation, rather than the energy transfers. Even when constructed with only twenty groups and only 20% of the original computational cost, the new reduced order model outperforms the vibrational-specific one in predicting all of the QoIs related to dissociation kinetics. At the highest temperature, the accuracy in the mole fraction is improved by 2,000%

The cool hydrogen-deficient carbon stars and their atmospheres

Photoelectric photometry of a large sample of R CrB and hydrogen-deficient carbon stars was obtained over a period of five months in order to search for variability and determine the period if variable. All the stars in the sample were found to be variable. Only for the stars S Aps, U Aqr and V CrA were sufficient observations obtained to enable periods to be identified. The determined periods were 39.7, 41.8 and 69.0 days respectively. These periods are in agreement with the theoretical period-temperature relationship. Photoelectric photometry of the hot hydrogen-deficient star DY Cen was obtained over a period of four weeks. DY Cen was confirmed to be variable and the dominate period of 3.8 days determined. This period was consistent with the period-temperature relationship. Model atmospheres were calculated for hydrogen-deficient compositions with temperatures between 5000-8000K and surface gravities between 0.0 and 4.4. The models included the effects of molecular formation, convection and line-blanketing. It was shown that the temperature structure was strongly dependent on the composition, in particular the ratios of C/He and H/He. R CrB was re-analysed using these new models. The derived atmospheric parameters were T[subscript(eff)] = 7400 ± 500K, log g = 0.55 ± 0.25, ξ[subscript(t)] = 8 ± 2kms⁻¹ and C/He=0.005. High resolution spectra were obtained of RY Sgr in order to do a similar analysis. The derived parameters were T[subscript(eff)] = 7000 ± 500K, log g = 0.65 ± 0.25, ξ[subscript(t)] = 10 ± 2kms⁻¹ and C/He=0.005. Both stars were found to have solar metallicities with no over-abundances of s-process elements. The abundances of C, N and O were all enhanced relative to the solar values. Medium resolution spectra were obtained at the Isaac Newton telescope of suspected R CrB stars in order to correctly classify them. The stars were classified on the basis of the strength of the hydrogen lines and the G band. BG Cep, LO Cep, CC Cep, DZ And, RZ Vul, VZ Vul, V638 Her and V1405 Cyg were all classified as not being R CrB stars. UV Cas, SU Tau and SV Sge were classified as R CrB stars