Modeling a Ship’s Ferromagnetic Signatures

Holmes, John J.

doi:10.1007/978-3-031-01698-1

Cited by 21 publications

(21 citation statements)

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“…Finite element method (FEM) software packages are continually being enhanced to eliminate the modeling difficulties associated with magnetic signature evaluation. FEM can solve Maxwell's differential equations using numerical techniques along with discretizing geometries [12], [14], [16].…”

Section: Magnetic Signature Numerical Modelingmentioning

confidence: 99%

“…The FEM approximates solutions to Maxwell's equations by discretizing the space inside and outside the vessel, including the hull. The scalar magnetic potential can be evaluated at each node in space and interpolated solutions are generated at locations between the nodes [12], [15]. Some of the numerical vessel modeling challenges and how to alleviate them will be demonstrated in the following parts [12], [14].…”

Section: A Theoretical Basismentioning

confidence: 99%

“…Propeller shafts and rudders are the major vessel parts that are mainly made of steel. While modeling and measurement of marine vessel induced magnetic signatures have been demonstrated and studied extensively by Isa et al [1], [6], JC Aird [7], I. Gloza et al [8], [9], Holmes [10]- [12], P. Jankowski et al [13], X. Brunotte et al [14], and G. Rosu et al [15], they mainly focused on the overall vessel geometry, hull thickness, materials, and even inside ship equipment, while the effect of propeller shafts and rudders has not been studied. Unshielded by the hull, the propeller shaft and rudder intensify the geomagnetic field in their corresponding regions, resulting in a deviation of the induced magnetization from the pre-existing conditions prior to their existence; the latter effect is under-evaluated and poorly described in the literature.…”

Section: Introductionmentioning

confidence: 99%

“…The measured and evaluated signatures will be used to investigate the effect of these particular underwater geometry vessel details on the induced magnetic signature of the full-scale vessel, and to demonstrate the extent to which the effect has an impact on the vessel's magnetic detectability for magnetic threats. For decades, scaled vessel models have been extensively employed for the reproduction and extrapolation of marine vessel magnetic signatures [12]. Signature measurements of a properly scaled vessel model were used to verify the numerical model.…”

Section: Introductionmentioning

confidence: 99%

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Analyzing the Effect of Flux Intensification Due to Propeller Shaft and Rudder Upon the 3D Induced Magnetic Signature of Marine Vessels

2022

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This study investigates and analyzes the effect of flux intensification due to the propeller shaft and rudder on the 3D mapped induced magnetic signature of marine vessels. The study was designed to extrapolate the 3D induced magnetic signature and to analyze the difference between signature components in two different cases: a vessel without propeller shaft or rudder and a vessel with the propeller shaft and rudder attached. Numerical modeling was conducted using full-scale vessel dimensions and finiteelement analysis (FEA) modeling software. Measurements on a 1:100 properly scaled and designed physical scale model (PSM) were conducted in a magnetically clean area. To enrich the analysis, both signature components from the numerical evaluation and experimental measurements were 3D mapped. Both the scaling technique and scaling effect on magnetic signature were investigated. The signature measurements of the scaled vessel model were performed to verify the numerical model. Both the numerical and experimental results are both discussed and shown to be consistent. The extrapolated signature components revealed that the studied effect became prominent as the saliency of the 3D mapped signature components increased. Consequently, this effect increases the magnetic susceptibility of the vessel to detection or destruction. Finally, this study suggests solutions to reduce this effect.

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Section: Magnetic Signature Numerical Modelingmentioning

confidence: 99%

Section: A Theoretical Basismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Analyzing the Effect of Flux Intensification Due to Propeller Shaft and Rudder Upon the 3D Induced Magnetic Signature of Marine Vessels

2022

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“…The source of the ferromagnetic target's magnetic field is usually very complex, so it is difficult to model it accurately [18][19][20]. However, when the distance from the target to the magnetometer is long enough, a magnetic target can usually be regarded as a magnetic dipole [21].…”

Section: Magnetic Target Modelmentioning

confidence: 99%

A deep neural network based method for magnetic anomaly detection

Wang

Han

Zhao

et al. 2021

IET Science Measure & Tech

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Magnetic anomaly detection (MAD) is a technique to find ferromagnets hiding in strong and complicated magnetic background. In many practical cases, the targets are very far from the detection sensor, which leads to low signal-to-noise ratio (SNR) and high detection difficulty. Most of the current methods determine the existence of target by some approaches based on signal analysis, such as the orthogonal basis function (OBF) and the minimum entropy (ME). However, although these methods consume low resources, the detection performances are not satisfactory enough. In recent years, due to the increase of computer capability, complex methods become applicable in MAD. In this study, a deep neural network (DNN) is adopted to detect the magnetic anomalies. The DNN has shown its better ability to represent natural data in many applications. A feature automatically learned by a DNN from data in the raw form is more effective for detecting target signals and suppressing irrelevant variations. Herein, a convolutional network with residual structure to implement the feature extraction is designed and an MAD method based on it is proposed. Through the semi-real tests, the proposed method exhibits a strong capability to extract features and shows excellent performances on detection.

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Modeling of active shimming of metallic needles for interventional MRI

Sengupta

2020

Magnetic Resonance in Med

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Purpose Artifacts caused by large magnetic susceptibility differences between metallic needles and tissue are a persistent problem in many interventional MRI applications. The signal void caused by the needle can hide procedure targets and prevent accurate image‐based monitoring. In this paper, a solution to this problem is presented in the form of an active shim insert inspired from degaussing coils used in naval vessels, that is designed to correct the field disturbance (ΔB0) caused by the needle. Methods The ΔB0 induced by a 10 gauge hollow single‐beveled titanium needle at 3T is modeled in different orientations. A set of 63 orthogonal coil pairs with unique tip paths are evaluated for shimming performance, and an optimal coil pair is chosen. Shimming performance and current demands are evaluated over a range of needle orientations. Results Robust correction of the titanium needle induced ΔB0 is predicted using a flat no‐loop coil combined with an orthogonal 1½ turn loop coil angled at the bevel angle for most orientations, with currents well below 1 amp per coil. Reductions in ΔB0 standard deviations with shimming ranged from ~49% to ~10% depending on needle orientation, with performance worsening as the needle is aligned more along B0. Conclusion Simulations predict that it is possible to minimize metallic probe induced ΔB0 and signal losses using externally supplied direct current shim coil inserts in arbitrary orientations for potential benefits in many interventional MRI applications.

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Modeling a Ship’s Ferromagnetic Signatures

Cited by 21 publications

References 0 publications

Analyzing the Effect of Flux Intensification Due to Propeller Shaft and Rudder Upon the 3D Induced Magnetic Signature of Marine Vessels

Analyzing the Effect of Flux Intensification Due to Propeller Shaft and Rudder Upon the 3D Induced Magnetic Signature of Marine Vessels

A deep neural network based method for magnetic anomaly detection

Modeling of active shimming of metallic needles for interventional MRI

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