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

Research on Acceleration Technology for FDTD Based on Vivado HLS

时域有限差分法(Finitedifferencetimedomainmethod，FDTD)是一种电磁学计算的基本方法，通过空间内电场和磁场的交替计算，得到整个研究空间的电磁分布情况。对于很多电磁学问题，不论从概念上还是可实现性上来讲，时域有限差分方法都是最简单的计算方法。时域有限差分法可以解决复杂的电磁计算问题，但同时要消耗大量的计算机资源，并且花费较长的计算时间。为了更快速高效地得到计算结果，可以利用硬件技术进行加速，这也是近年来FDTD方法研究领域比较受关注的部分。Xilinx公司新推出的高级综合工具VivadoHLS(HighLevelSynthesis)，直接通过C/C++语言开发硬...In the field of computational electromagnetics, finite difference time domain method (FDTD) has been widely used. Using FDTD, the electromagnetic distribution of the whole field is obtained by alternating calculation of the electric and magnetic field. For many electromagnetical computational problems, FDTD is the simplest method, in consideration of conception and achievability. Although FDTD can...学位：工程硕士院系专业：物理科学与技术学院_工程硕士(电子与通信工程)学号：3432014115280

Identification of Synchronous Machine Magnetization Characteristics From Calorimetric Core-Loss and No-Load Curve Measurements

The magnetic material characteristics of a wound-field synchronous machine are identified based on global calorimetric core-loss and no-load curve measurements. This is accomplished by solving a coupled experimental-numerical electromagnetic inverse problem, formulated to minimize the difference between a finite-element (FE) simulation-based Kriging surrogate model and the measurement results. The core-loss estimation in the FE model is based on combining a dynamic iron-loss model and a static vector Jiles-Atherton hysteresis model, the parameters for which are obtained by solving the inverse problem. The results show that reasonable hysteresis loops can be produced for a grid-supplied machine, while for an inverter-supplied machine the limitations in the FE and iron-loss models seemingly exaggerate the area of the loop. In addition, the effect of the measurement uncertainty on the inverse problem is quantitatively estimated.Peer reviewe

Virtual prototyping of pressure driven microfluidic systems with SystemC-AMS extensions

The design of "Lab on a Chip" microfluidic devices is, typically, preceded by a long and costly period of prototyping stages in which the system is gradually refined by an iterative process, involving the manufacturing of a physical prototype and the making of a lot of laboratory experiments. In this scenario, a virtual prototyping framework which allows the emulation of the behavior of the complete system is greatly welcome. This paper presents such a framework and details a virtual prototyping methodology able to soundly handle microfluidic behavior based on SystemC-AMS extensions. The use of these extensions will permit the communication of the developed microfluidic models with external digital or mixed signal devices. This allows the emulation of the whole Lab on a Chip system as it usually includes a digital control and a mixed-signal reading environment. Moreover, as SystemC-AMS is also being extended to cover other physical domains within the CATRENE CA701 project, interactions with these domains will be possible, for example, with electromechanical or optical parts, should they be part of the system. The presented extensions that can manage the modeling of a micro-fluidic system are detailed. Two approaches have been selected: to model the fluid analytically based on the Poiseuille flow theory and to model the fluid numerically following the SPH (Smoothed Particle Hydrodynamics) approach. Both modeling techniques are, by now, encapsulated under the TDF (Timed Data Flow) MoC (Model of Computation) of SystemC-AMS.This work has been supported by CATRENE CA701H-INCEPTION Projec

A High-Fidelity Computationally Efficient Transient Model of Interior Permanent-Magnet Machine With Stator Turn Fault

An accurate transient model of interior permanent-magnet (IPM) machine with stator turn fault with due account of magnetic saturation is essential to develop robust and sensitive interturn fault detection algorithms and to evaluate drive controller performance and stability under fault conditions. This paper proposes a general method of modeling stator turn fault using flux linkage map of IPM machine under fault extracted from finite-element (FE) analysis. Simulation results from the proposed fault model are compared against FE and experimental results. The results show that the proposed model matches well with experimental data

Influence of head models on neuromagnetic fields and inverse source localizations

BACKGROUND: The magnetoencephalograms (MEGs) are mainly due to the source currents. However, there is a significant contribution to MEGs from the volume currents. The structure of the anatomical surfaces, e.g., gray and white matter, could severely influence the flow of volume currents in a head model. This, in turn, will also influence the MEGs and the inverse source localizations. This was examined in detail with three different human head models. METHODS: Three finite element head models constructed from segmented MR images of an adult male subject were used for this study. These models were: (1) Model 1: full model with eleven tissues that included detailed structure of the scalp, hard and soft skull bone, CSF, gray and white matter and other prominent tissues, (2) the Model 2 was derived from the Model 1 in which the conductivity of gray matter was set equal to the white matter, i.e., a ten tissuetype model, (3) the Model 3 consisted of scalp, hard skull bone, CSF, gray and white matter, i.e., a five tissue-type model. The lead fields and MEGs due to dipolar sources in the motor cortex were computed for all three models. The dipolar sources were oriented normal to the cortical surface and had a dipole moment of 100 μA meter. The inverse source localizations were performed with an exhaustive search pattern in the motor cortex area. A set of 100 trial inverse runs was made covering the 3 cm cube motor cortex area in a random fashion. The Model 1 was used as a reference model. RESULTS: The reference model (Model 1), as expected, performed best in localizing the sources in the motor cortex area. The Model 3 performed the worst. The mean source localization errors (MLEs) of the Model 3 were larger than the Model 1 or 2. The contour plots of the magnetic fields on top of the head were also different for all three models. The magnetic fields due to source currents were larger in magnitude as compared to the magnetic fields of volume currents. DISCUSSION: These results indicate that the complexity of head models strongly influences the MEGs and the inverse source localizations. A more complex head model performs better in inverse source localizations as compared to a model with lesser tissue surfaces

A mixed-signal computer architecture and its application to power system problems

Radical changes are taking place in the landscape of modern power systems. This massive shift in the way the system is designed and operated has been termed the advent of the ``smart grid''. One of its implications is a strong market pull for faster power system analysis computing. This work is concerned in particular with transient simulation, which is one of the most demanding power system analyses. This refers to the imitation of the operation of the real-world system over time, for time scales that cover the majority of slow electromechanical transient phenomena. The general mathematical formulation of the simulation problem includes a set of non-linear differential algebraic equations (DAEs). In the algebraic part of this set, heavy linear algebra computations are included, which are related to the admittance matrix of the topology. These computations are a critical factor to the overall performance of a transient simulator. This work proposes the use of analog electronic computing as a means of exceeding the performance barriers of conventional digital computers for the linear algebra operations. Analog computing is integrated in the frame of a power system transient simulator yielding significant computational performance benefits to the latter. Two hybrid, analog and digital computers are presented. The first prototype has been implemented using reconfigurable hardware. In its core, analog computing is used for linear algebra operations, while pipelined digital resources on a field programmable gate array (FPGA) handle all remaining computations. The properties of the analog hardware are thoroughly examined, with special attention to accuracy and timing. The application of the platform to the transient analysis of power system dynamics showed a speedup of two orders of magnitude against conventional software solutions. The second prototype is proposed as a future conceptual architecture that would overcome the limitations of the already implemented hardware, while retaining its virtues. The design space of this future architecture has been thoroughly explored, with the help of a software emulator. For one possible suggested implementation, speedups of four orders of magnitude against software solvers have been observed for the linear algebra operations

The effect of meninges on the electric fields in TES and TMS: Numerical modeling with adaptive mesh refinement

Background When modeling transcranial electrical stimulation (TES) and transcranial magnetic stimulation (TMS) in the brain, the meninges – dura, arachnoid, and pia mater – are often neglected due to high computational costs. Objective We investigate the impact of the meningeal layers on the cortical electric field in TES and TMS while considering the headreco segmentation as the base model. Method We use T1/T2 MRI data from 16 subjects and apply the boundary element fast multipole method with adaptive mesh refinement, which enables us to accurately solve this problem and establish method convergence at reasonable computational cost. We compare electric fields in the presence and absence of various meninges for two brain areas ( and ) and for several distinct TES and TMS setups. Results Maximum electric fields in the cortex for focal TES consistently increase by approximately 30% on average when the meninges are present in the CSF volume. Their effect on the maximum field can be emulated by reducing the CSF conductivity from 1.65 S/m to approximately 0.85 S/m. In stark contrast to that, the TMS electric fields in the cortex are only weakly affected by the meningeal layers and slightly (∼6%) decrease on average when the meninges are included. Conclusion Our results quantify the influence of the meninges on the cortical TES and TMS electric fields. Both focal TES and TMS results are very consistent. The focal TES results are also in a good agreement with a prior relevant study. The solver and the mesh generator for the meningeal layers (compatible with SimNIBS) are available online

Uncertainty Quantification in Molecular Signals using Polynomial Chaos Expansion

Molecular signals are abundant in engineering and biological contexts, and undergo stochastic propagation in fluid dynamic channels. The received signal is sensitive to a variety of input and channel parameter variations. Currently we do not understand how uncertainty or noise in a variety of parameters affect the received signal concentration, and nor do we have an analytical framework to tackle this challenge. In this paper, we utilize Polynomial Chaos Expansion (PCE) to show to uncertainty in parameters propagates to uncertainty in the received signal. In demonstrating its applicability, we consider a Turbulent Diffusion Molecular Communication (TDMC) channel and highlight which parameters affect the received signals. This can pave the way for future information theoretic insights, as well as guide experimental design