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Over the last three decades, fiber optic sensors (FOS) have gained a lot of attention for their wide range of monitoring applications across many industries, including aerospace, defense, security, civil engineering, and energy. FOS technologies hold great promise to form the backbone for next‐generation intelligent sensing platforms that offer long‐distance, high‐accuracy, distributed measurement capabilities and multiparametric monitoring with resilience to harsh environmental conditions. The major limitations posed by FOS are 1) cross‐sensitivity, 2) enormous volume and large data generation, 3) low data processing speed, 4) degradation of signal‐to‐noise ratio over the fiber length, and 5) overall cost of sensor and interrogator systems. These challenges can be overcome by building advanced data analytics engines enabled by recent breakthroughs in machine learning (ML) and artificial intelligence (AI). This article presents a comprehensive review of recent studies that integrate ML and AI algorithms with FOS technologies. This review also highlights several FOS technology development directions that promise a significant impact on widespread use for several industrial applications, with an emphasis on energy systems monitoring. A perspective on future directions for further research development is also provided. Fiber optic sensor technologies hold great promise to form the backbone for next‐generation intelligent sensing platforms that offer long‐distance, high‐accuracy, distributed measurement capabilities, and multiparametric monitoring with resilience to harsh environmental conditions. The performance and capabilities of these technologies can be enhanced by integrating them with machine learning (ML)‐based data analytics engines.

Metal amorphous nanocomposite (MANC) alloys are an emerging class of soft magnetic materials showing promise for a range of inductive components targeted for higher power density and higher efficiency power conversion applications including inductors, transformers, and rotating electrical machinery. Magnetization reversal mechanisms within these alloys are typically determined by composition optimization as well as controlled annealing treatments to generate a nanocomposite structure composed of nanocrystals embedded in an amorphous precursor. Here we demonstrate the concept of spatially varying the permeability within a given component for optimization of performance by using the strain annealing process. The concept is realized experimentally through the smoothing of the flux profile from the inner to outer core radius achieved by a monotonic variation in tension during the strain annealing process. Great potential exists for an extension of this concept to a wide range of other power magnetic components and more complex spatially varying permeability profiles through advances in strain annealing techniques and controls.

A quantitative structure–property correlation study of thin films consisting of CoFe nanoparticles embedded in SiO 2 is presented, comparing film microstructure and chemistry with measured magnetic properties. SiO 2 was fully percolated for all films with > ~50% SiO 2 by volume, and decreasing CoFe-nanoparticle size and separation with increasing SiO 2 resulted in a transition to superparamagnetic behavior. Partial oxidation of transition-metal elements is observed by x-ray photoelectron spectroscopy, and evidence for interparticle magnetic interactions can be resolved in soft x-ray resonant small-angle scattering experiments, highlighting the need for additional detailed and quantitative studies in this class of soft magnetic materials.

A modified Bragg-Williams (B-W) model of α and α' FeCo is extended to estimate the effect of strong magnetic fields on the critical ordering temperature (T^sub ORDER^) taking into account long-range chemical and magnetic ordering. The model discussed here is generalized from our previous work in which only the larger average exchange per atom in the chemically ordered state was taken into account. A positive shift of critical temperatures for the higher order α [arrow right] α' order-disorder phase transformation has been predicted in the presence of a strong field. In this work, the experimentally observed dependence of the average magnetic moment of Fe atoms on the degree of chemical order has been accounted for explicitly. The estimated shift in the critical ordering temperature (ΔT^sub Order^) is larger when the dependence of the Fe moment on the degree of chemical order is taken into account, particularly in the case of Fe-rich compositions (e.g., ΔT^sub ORDER^ ~ 1 3 K vs ΔTqrder ~ 10 K for H ~ 50 T at equiatomic composition). For most compositions, however, the contribution to Ar0RDER associated with the larger average exchange per atom in the chemically ordered state accounts for the majority of the shift. The estimated effect remains quite small and is only expected to be experimentally observable in static fields larger than currently available in most laboratories (ΔT^sub Order^ is only predicted to be larger than ~2 to 3 K for H > ~10 T). [PUBLICATION ABSTRACT]

A modified Bragg-Williams (B-W) model of alpha and alpha' FeCo is extended to estimate the effect of strong magnetic fields on the critical ordering temperature (T ORDER) taking into account long-range chemical and magnetic ordering. The model discussed here is generalized from our previous work in which only the larger average exchange per atom in the chemically ordered state was taken into account. A positive shift of critical temperatures for the higher order alpha- > alpha' order-disorder phase transformation has been predicted in the presence of a strong field. In this work, the experimentally observed dependence of the average magnetic moment of Fe atoms on the degree of chemical order has been accounted for explicitly. The estimated shift in the critical ordering temperature (DeltaT ORDER) is larger when the dependence of the Fe moment on the degree of chemical order is taken into account, particularly in the case of Fe-rich compositions (e.g., DeltaT ORDER ~ 13 K vs DeltaT ORDER ~ 10 K for H ~ 50 T at equiatomic composition). For most compositions, however, the contribution to DeltaT ORDER associated with the larger average exchange per atom in the chemically ordered state accounts for the majority of the shift. The estimated effect remains quite small and is only expected to be experimentally observable in static fields larger than currently available in most laboratories (DeltaT ORDER is only predicted to be larger than ~2 to 3 K for H > ~10 T).

Advanced soft magnetic materials are needed to match high-power density and switching frequencies made possible by advances in wide band-gap semiconductors. Magnetics capable of operating at higher operating frequencies have the potential to greatly reduce the size of megawatt level power electronics. In this article, we examine the role of soft magnetic materials in high-frequency power applications and we discuss current material's limitations and highlight emerging trends in soft magnetic material design for high-frequency and power applications using the materials paradigm of synthesis -> structure -> property -> performance relationships.

Extended abstract of a paper presented at Microscopy and Microanalysis 2004 in Savannah, Georgia, USA, August 1–5, 2004.

Giant magnetoimpedance (GMI)-based devices offer potential as next-generation low-cost, flexible, ultrasensitive sensors. They can be used in applications that include current sensors, field sensors, stress sensors, and others. Challenging applications involve operation at high temperatures, and therefore studies of GMI temperature dependence and performance of soft magnetic materials are needed. We present a high-temperature GMI study on an amorphous soft magnetic microwire from room temperature to 560°C. The GMI ratio was observed to be nearly constant at ∼86% at low temperatures and to decrease rapidly at ∼290°C, finally reaching a near-zero value at 500°C. The rapid drop in GMI ratio at 290°C is associated with a reduction in the long-range ferromagnetic order as measured by the spontaneous magnetization ( M ) at the Curie temperature ( T c ). We also correlated the impedance with the magnetic properties of the material. From room temperature to 290°C, the impedance was found to be proportional to the square root of the magnetization to magnetic anisotropy ratio. Lastly, M ( T ) has been fit using a Handrich–Kobe model, which describes the system with a modified Brillouin function and an asymmetrical distribution of exchange interactions. We infer that the structural fluctuations of the amorphous phase result in a relatively small asymmetry in the fluctuation parameters.