The analytical and artificial intelligence methods to investigate the effects of aperture dimension ratio on electrical shielding effectiveness

This paper presents that the effect of single aperture size of metallic enclosure on electrical shielding effectiveness (ESE) at 0 – 1 GHz frequency range has been investigated by using both Robinson’s analytical formulation and artificial neural networks (ANN) methods that are multilayer perceptron (MLP) networks and a radial basis function neural network (RBFNN). All results including measurement have been compared each other in terms of aperture geometry of metallic enclosure. The geometry of single aperture varies from square to rectangular shape while the open area of aperture is fixed. It has been observed that network structure of MLP 3-40-1 in modeling with ANN modeled with fewer neurons in the sense of overlapping of faults and data and modeled accordingly. In contrast, the RBFNN 3-150-1 is the other detection that the network structure is modeled with more neurons and more. It can be seen from the same network-structured MLP and RBFNN that the MLP modeled better. In this paper, the impact of dimension of rectangular aperture on shielding performance by using RBFNN and MLP network model with ANN has been studied, as a novelty

Development of a SQUID magnetometry system for cryogenic neutron electric dipole moment experiment

A measurement of the neutron electric dipole moment (nEDM) could hold the key to understanding why the visible universe is the way it is: why matter should predominate over antimatter. As a charge-parity violating (CPV) quantity, an nEDM could provide an insight into new mechanisms that address this baryon asymmetry. The motivation for an improved sensitivity to an nEDM is to find it to be non-zero at a level consistent with certain beyond the Standard Model theories that predict new sources of CPV, or to establish a new limit that constrains them. CryoEDM is an experiment that sought to better the current limit of $|d_n| < 2.9 \times 10^{-26}\,e\,$cm by an order of magnitude. It is designed to measure the nEDM via the Ramsey Method of Separated Oscillatory Fields, in which it is critical that the magnetic field remains stable throughout. A way of accurately tracking the magnetic fields, moreover at a temperature $\sim 0.5\,$K, is crucial for CryoEDM, and for future cryogenic projects. This thesis presents work focussing on the development of a 12-SQUID magnetometry system for CryoEDM, that enables the magnetic field to be monitored to a precision of $0.1\,$pT. A major component of its infrastructure is the superconducting capillary shields, which screen the input lines of the SQUIDs from the pick up of spurious magnetic fields that will perturb a SQUID's measurement. These are shown to have a transverse shielding factor of $> 1 \times 10^{7}$, which is a few orders of magnitude greater than the calculated requirement. Efforts to characterise the shielding of the SQUID chips themselves are also discussed. The use of Cryoperm for shields reveals a tension between improved SQUID noise and worse neutron statistics. Investigations show that without it, SQUIDs have an elevated noise when cooled in a substantial magnetic field; with it, magnetostatic simulations suggest that it is detrimental to the polarisation of neutrons in transport. The findings suggest that with proper consideration, it is possible to reach a compromise between the two behaviours. Computational work to develop a simulation of SQUID data is detailed, which is based on the Laplace equation for the magnetic scalar potential. These data are ultimately used in the development of a linear regression technique to determine the volume-averaged magnetic field in the neutron cells. This proves highly effective in determining the fields within the $0.1\,$pT requirement under certain conditions

Data-driven modelling ofAC losses in high-temperature superconducting coils

Predicting the loss in superconductive power devices is of utmost importance when designing such devices. This is because the cooling system needs to be designed accordingly. The current methods for predicting AC Loss are either inaccurate or very time consuming. These conventional methods for predicting loss are of two types in which one is faster but inaccurate, while the other is very accurate but also very time consuming. While currently they are both employed in different stages of the design process, there is an interest in a faster, but still accurate, form of predicting AC Loss. Studies have time and time again shown that Artificial Neural Networks are capable of taking on complex tasks and handling them faster than regular computing. Because of this, in this work, an Artificial Neural Network based approach is proposed as to predict AC Loss in various configurations of HTS coils. This approach aims to replicate the accuracy of standard numerical models while being much faster than said models. This results in a final framework comprised of two distinct sequential Neural Networks that are capable of predicting the AC Loss for different configurations of HTS coils nearly instantaneously while still being very accurate and reliable in their predictions.A capacidade de previsão de perdas em dispositivos de potência supercondutores é um assunto de alta importância aquando do desenho dos mesmos. Isto deve-se ao facto de o sistema de arrefecimento necissitar de ser desenhado de acordo com as mesmas. Os métodos atuais de previsão de perdas AC são ou pouco fiávies, ou bastante demorados. Estes métodos atuais de previsão de perdas são de dois tipos em que um é mais rápido mas pouco preciso, enquanto o outro é bastante preciso mas, no entanto,muito demorado. Embora atualmente sejam ambos empregados em fases diferentes do processo de desenho, continua a existir interesse numa forma rápida e precisa de prever perdas AC. Estudos têm vindo a provar que as Redes Neuronais são capazes de enfrentar tarefas complexas e lidar com elas de forma mais rápida que a computação tradicional. Dado isto, neste trabalho propõe-se uma abordagem baseada em Redes Neuronais para prever perdas AC em várias configurações de bobinas HTS. Esta abordagem visa a replicar a fiabilidade de métodos numéricos sendo, no entanto, bastante mais rápida. Isto resulta numa framework final composta por duas Redes Neuronais distintas sequenciais que é capaz the prever perdas AC em diversas configurações de bobinas de forma quase instantânea sendo, no entanto, bastante correta e confiável nas suas previsões

NASA SBIR abstracts of 1991 phase 1 projects

The objectives of 301 projects placed under contract by the Small Business Innovation Research (SBIR) program of the National Aeronautics and Space Administration (NASA) are described. These projects were selected competitively from among proposals submitted to NASA in response to the 1991 SBIR Program Solicitation. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 301, in order of its appearance in the body of the report. Appendixes to provide additional information about the SBIR program and permit cross-reference of the 1991 Phase 1 projects by company name, location by state, principal investigator, NASA Field Center responsible for management of each project, and NASA contract number are included

Magnetic and Newtonian noises in Advanced Virgo: evaluation and mitigation strategies

In the present study, I table the first detailed estimation of the magnetic noise contribution to the Advanced Virgo sensitivity to gravitational waves. I tackle the topic by performing experimental assessments and numerical finite element simulations, all accompanied by careful data analysis. Results suggest that the magnetic noise impact for Advanced Virgo is not dramatic, but it will eventually be a considerable issue once the detector will approach its final design. In anticipation of that, I propose a mitigation strategy based on passive magnetic field shielding. In the second part, I deal with seismic newtonian noise, focusing on two crucial aspects involving the noise cancellation pipeline. These are the choice of the subtraction filter and the optimization of the seismic sensor array placement. The former issue required the definition of a machine learning algorithm based on deep neural networks, and its fine tuning. Results give some indication of good performances compared to the standard Wiener filter approach. The problem of the sensors deployment is instead addressed with the finite element analysis of the actual Virgo infrastructure and underground soil layers surrounding the test masses

A Bayesian regularization-backpropagation neural network model for peeling computations

Bayesian regularization-backpropagation neural network (BR-BPNN) model is employed to predict some aspects of the gecko spatula peeling viz. the variation of the maximum normal and tangential pull-off forces and the resultant force angle at detachment with the peeling angle. K-fold cross validation is used to improve the effectiveness of the model. The input data is taken from finite element (FE) peeling results. The neural network is trained with 75% of the FE dataset. The remaining 25% are utilized to predict the peeling behavior. The training performance is evaluated for every change in the number of hidden layer neurons to determine the optimal network structure. The relative error is calculated to draw a clear comparison between predicted and FE results. It is shown that the BR-BPNN model in conjunction with k-fold technique has significant potential to estimate the peeling behavior.Comment: 18 pages, 9 figure

Transmembrane potential induced on the internal organelle by a time-varying magnetic field: a model study

<p>Abstract</p> <p>Background</p> <p>When a cell is exposed to a time-varying magnetic field, this leads to an induced voltage on the cytoplasmic membrane, as well as on the membranes of the internal organelles, such as mitochondria. These potential changes in the organelles could have a significant impact on their functionality. However, a quantitative analysis on the magnetically-induced membrane potential on the internal organelles has not been performed.</p> <p>Methods</p> <p>Using a two-shell model, we provided the first analytical solution for the transmembrane potential in the organelle membrane induced by a time-varying magnetic field. We then analyzed factors that impact on the polarization of the organelle, including the frequency of the magnetic field, the presence of the outer cytoplasmic membrane, and electrical and geometrical parameters of the cytoplasmic membrane and the organelle membrane.</p> <p>Results</p> <p>The amount of polarization in the organelle was less than its counterpart in the cytoplasmic membrane. This was largely due to the presence of the cell membrane, which "shielded" the internal organelle from excessive polarization by the field. Organelle polarization was largely dependent on the frequency of the magnetic field, and its polarization was not significant under the low frequency band used for transcranial magnetic stimulation (TMS). Both the properties of the cytoplasmic and the organelle membranes affect the polarization of the internal organelle in a frequency-dependent manner.</p> <p>Conclusions</p> <p>The work provided a theoretical framework and insights into factors affecting mitochondrial function under time-varying magnetic stimulation, and provided evidence that TMS does not affect normal mitochondrial functionality by altering its membrane potential.</p

Radio Frequency Antenna Designs and Methodologies for Human Brain Computer Interface and Ultrahigh Field Magnetic Resonance Imaging

Brain Computer Interface (BCI) and Magnetic Resonance Imaging (MRI) are two powerful medical diagnostic techniques used for human brain studies. However, wired power connection is a huge impediment for the clinical application of BCI, and most current BCIs have only been designed for immobile users in a carefully controlled environment. For the ultrahigh field (≥7T) MRI, limitations such as inhomogeneous distribution of the transmit field (B1+) and potential high power deposition inside the human tissues have not yet been fully combated by existing methods and are central in making ultrahigh field MRI practical for clinical use. In this dissertation, radio frequency (RF) methods are applied and RF antennas/coils are designed and optimized in order to overcome these barriers. These methods include: 1) designing implanted miniature antennas to transmit power wirelessly for implanted BCIs; 2) optimizing a new 20-channel transmit array design for 7 Tesla MRI neuroimaging applications; and 3) developing and implementing a dual-optimization method to design the RF shielding for fast MRI imaging methods. First, three miniaturized implanted antennas are designed and results obtained using finite difference time domain (FDTD) simulations demonstrate that a maximum RF power of up to 1.8 miliwatts can be received at 2 GHz when the antennas are implanted at the dura, without violating the government safety regulations. Second, Eigenmode arrangement of the 20-channel transmit coil allows control of RF excitation not only at the XY plane but also along the Z direction. The presented results show the optimized eigenmode could generate 3D uniform transmit B1+ excitations. The optimization results have been verified by in-vivo experiments, and they are applied with different protocol sequences on a Siemens 7 Tesla MRI human whole body scanner equipped with 8 parallel transmit channels. Third, echo planar imaging (EPI), B1+ maps and S matrix measurements are used to verify that the proposed RF shielding can suppress the eddy currents while maintaining the RF characteristics of the transmit coil. The contributions presented here will provide a long-term and safer power transmission path compared to the wire-connected implanted BCIs and will bring ultrahigh field MRI technology closer to clinical applications

Magnetic material characterization and magnet axis displacement measurement for particle accelerators

Bending and focusing magnets, both normal- or super-conducting, are crucial elements for the performance of any particle accelerator. Their design requirements are always more tighten regarding components’ misalignment and magnetic properties. This dissertation proposes new solutions for characterizing magnetic materials and monitoring solenoids’ magnetic axis misalignments. A superconducting permeameter is designed to characterize the new-generation superconducting magnet yokes at their operational temperature and saturation level. As proof of principle, the magnetic characterization of ARMCO® Pure Iron was performed at the cryogenic temperature of 4.2 K and a saturation level of nearly 3 T. A case study based on the new HL-LHC superconducting magnets quantifies the impact of the magnetic properties of the yoke on the performances of the superconducting magnets. A flux-metric based method is proposed to identify the relative magnetic permeability of weakly magnetic materials. As proof of principle, the magnetic properties of the ITER TF coils quench detection stainless steel are analyzed. This method is not suitable to test materials with a relative permeability lower than 1.1. Hence, a measurement system based on a new magneto-metric method is conceived and validated employing a standard reference sample. The methods proposed in this thesis are currently employed at CERN’s magnetic laboratory to face an increasing number of requests concerning not only the magnetic characterization of materials for magnets but also for shielding systems and compatibility of various components with high magnetic fields. In this thesis, the results of the evaluation of ARMCO® Pure Iron as the yoke of the new LHC superconducting magnets and CRYOPHY as the magnetic shield for the cryomodule prototypes of HL-LHC Crab Cavities are reported. Finally, a new Hall-sensor method is conceived and implemented for monitoring the coils alignment in multi-coil magnets, directly during their operation in particle accelerators. The proposed method is suitable even for those cases when almost the whole magnet aperture is not accessible. Requiring only a few measurements of the magnetic field at fixed positions inside the magnet aperture, the method overcomes the main drawback of the other Hall sensor-based methods which is having to deal with sturdy mechanics of the moving stages. The method is validated numerically on a challenging case study related to the Solenoid B of the project ELI-NP