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Ferrofluid seals are known for their low friction torque and high tightness. However, they have some limitation due to the allowable rotational speed. The work presented here analyzes the performance of newly designed seals which are a combination of a ferrofluid and a centrifugal seal. The new seals can operate at high speeds. The purpose of this study is to theoretically predict the performance of combined seals.

Three seals were designed and selected for analysis. A version of the seals with a nonmagnetic insert is also considered, the purpose of which is to facilitate the installation and return of ferrofluid during low rotational speeds. The analyses were based on combining the results of numerical simulation of magnetic field distribution with mathematical models.

A combination of ferrofluid sealing and centrifugal sealing is possible. Analyses showed that the combined seal could hold a minimum pressure of 190 kPa in the velocity range of 0–100 m/s. The problem with this type of seal is the temperature.

New seal designs are presented. Key parameters that affect the seal operation are discussed. A methodology that can be used in the design of such seals is presented.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2023-0221/.

The pursuit of manufacturing new inks with low financial cost is an urgent economic demand. Thus, the purpose of this paper is to synthesize some new pigments derived from Lithol Rubine (LR) via a successful simple route and to investigate their physicochemical properties for usage in the inks industry.

Two novel pigments were generated during the reaction of LR with Mn(II) and Co(II) salts in ethanolic solutions. The obtained pigments were isolated as solid compounds and characterized through elemental analysis, UV–vis, Fourier transform infrared, 1H NMR spectra, oil absorption, specific gravity, melting point, molar conductivity and magnetic moment measurements. Their dyeing and durability characteristics were examined using American Standard Testing Methods. The synthesized pigments were then applied in inks formulation.

The printing inks containing the two new pigments (LR–Mn and LR–Co) were compared to (GF 59-606 and GF 59-616), respectively. The results of this study showed that the performance of newly prepared pigments was comparable to that of commercial pigments currently in use in the inks industry.

LR and its new derivative pigments can be used in other different applications such as paper coating, crayon, rubber and paint industries.

The authors designed an efficient synthesis for some novel pigments. The synthesis technique is featured by a short reaction time, high yields and ease of use. The pigments developed would be good and cost-effective substitute for the original commercially available and expensive pigments used in the inks industry.

The ability to characterise highly conducting objects, that may also be highly magnetic, by the complex symmetric rank–2 magnetic polarizability tensor (MPT) is important for metal detection applications including discriminating between threat and non-threat objects in security screening, identifying unexploded anti-personnel landmines and ordnance and identifying metals of high commercial value in scrap sorting. Many everyday non-threat items have both a large electrical conductivity and a magnetic behaviour, which, for sufficiently weak fields and the frequencies of interest, can be modelled by a high relative magnetic permeability. This paper aims to discuss the aforementioned idea.

The numerical simulation of the MPT for everyday non-threat highly conducting magnetic objects over a broad range of frequencies is challenging due to the resulting thin skin depths. The authors address this by employing higher order edge finite element discretisations based on unstructured meshes of tetrahedral elements with the addition of thin layers of prismatic elements. Furthermore, computer aided design (CAD) geometrical models of the non-threat and threat object are often not available and, instead, the authors extract the geometrical features of an object from an imaging procedure.

The authors obtain accurate numerical MPT characterisations that are in close agreement with experimental measurements for realistic physical objects. The assessment of uncertainty shows the impact of geometrical and material parameter uncertainties on the computational results.

The authors present novel computations and measurements of MPT characterisations of realistic objects made of magnetic materials. A novel assessment of uncertainty in the numerical predictions of MPT characterisations for uncertain geometry and material parameters is included.

The purpose of this paper is to develop surrogate models, using deep learning (DL), that can facilitate the application of EM analysis software. In the current status quo, electrical systems can be found in an ever-increasing range of products that are part of everyone’s daily live. With the advances in technology, industries such as the automotive, communications and medical devices have been disrupted with new electrical and electronic systems. The innovation and development of such systems with increasing complexity over time has been supported by the increased use of electromagnetic (EM) analysis software. Such software enables engineers to virtually design, analyze and optimize EM systems without the need for building physical prototypes, thus helping to shorten the development cycles and consequently cut costs.

The industry standard for simulating EM problems is using either the finite difference method or the finite element method (FEM). Optimization of the design process using such methods requires significant computational resources and time. With the emergence of artificial intelligence, along with specialized tools for automatic differentiation, the use of DL has become computationally much more efficient and cheaper. These advances in machine learning have ushered in a new era in EM simulations where engineers can compute results much faster while maintaining a certain level of accuracy.

This paper proposed two different models that can compute the magnetic field distribution in EM systems. The first model is based on a recurrent neural network, which is trained through a data-driven supervised learning method. The second model is an extension to the first with the incorporation of additional physics-based information to the authors’ model. Such a DL model, which is constrained by the laws of physics, is known as a physics-informed neural network. The solutions when compared with the ground truth, computed using FEM, show promising accuracy for the authors’ DL models while reducing the computation time and resources required, as compared to previous implementations in the literature.

The paper proposes a neural network architecture and is trained with two different learning methodologies, namely, supervised and physics-based. The working of the network along with the different learning methodologies is validated over several EM problems with varying levels of complexity. Furthermore, a comparative study is performed regarding performance accuracy and computational cost to establish the efficacy of different architectures and learning methodologies.