Dimensional Constraints Research Articles

A Potential-Real-Time Thigh Orientation Prediction Method Based on Two Shanks-Mounted IMUs and Its Clinical Application

The detection and evaluation of gait kinematics is vital for patient diagnosis and rehabilitation. Aiming at limitations of commonly used optical capture and wearable sensing systems in clinical applications, this paper proposes a thigh attitude angle prediction method based on the hip error tolerance from the kinematic data of two shank-mounted IMUs (Inertial Measurement Unit). The novelties of the proposed method are summarized as follows: i) It develops a parallel approach to regress variation of hip error tolerance for different subjects. This parallel approach, by simultaneously deconstructing the shank kinematics data via different regression algorithm including support vector machine, boosting tree, and stepwise linearity regression, is able to well accommodate the characteristics of both health subjects and patients. ii) It develops some evaluation indices based on gait symmetry, consistency, and activity to fully evaluate the human lower limbs motion performance in gait by only two IMUs. The effectiveness of the proposed method is verified by the experimental results among 8 healthy subjects and 16 cerebral infarction patients. For the healthy subjects, the estimated error of thigh prediction compared with Xsens and Vicon are <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.3 \pm {0.3^ \circ }$</tex-math> </inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.5 \pm {0.7^ \circ }$</tex-math> </inline-formula> , respectively. For the patients, the estimated error compared with Xsens-measured angle is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.8 \pm {1.7^ \circ }$</tex-math> </inline-formula> . Its broad significance in actual intelligent healthcare and robotics-assisted rehabilitation is three-fold: First, it is a recursive real-time method as it only needs data from the previous gait cycle to predict the thigh angle. Second, its accuracy meets the actual clinical needs. Third, it is a low-cost method that only needs two IMUs and has high potentials of clinical applications. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Gait is of great significance to quantify the degree of movement disorders in clinical practice, and the existing equipment is rarely able to meet the needs of full dimension, low cost, and low place constraints in clinical practice. This paper presents an innovative gait kinematic prediction method, which only uses two IMUs attached to the shanks to predict the orientation of thigh. Compared with related works, the proposed methods have achieved high-precision, real-time and low-cost acquisition. This paper is inspired by the problems of large number, high motion interference, and high cost of wearable gait measurement devices in clinical practice. The proposed method have potential to be integrated into exoskeletons and medical walking AIDS, which could greatly improve the comfort and control precision of the wearable system.

Electromagnetic and Mechanical Topology Optimization for SynRM Rotors Considering High Dimensional Constraints

The knowledge-based parametric design process of the rotor topology of the synchronous reluctance motor (SynRM) requires a tedious parameter definition, selection, and searching process until an optimal design can be obtained. This research proposes a topology optimization approach based on solid isotropic material with penalization method for the SynRM rotor topology design. The aim is to pursue a more intelligent and automatic design process. Specifically, the proposed method aims to generate the SynRM rotor design of high output torque profile. Meanwhile, the mechanical constraints including structural compliance and stress etc. are also considered for promising the structural reliability of the rotor. Considering the high dimensional constraints caused by the multi-physics performances, the augmented Lagrangian method based optimization framework is developed to solve the minimization problem in a compact scheme. To prove the effectiveness of the developed method, simulations and analysis on the topology optimization of SynRMs are performed in this research. Prototyping and experiment are also conducted for verifying the performance of the optimized structure.

Efficient Autonomous Path Planning for Ultrasonic Non-Destructive Testing: A Graph Theory and K-Dimensional Tree Optimisation Approach

Within the domain of robotic non-destructive testing (NDT) of complex structures, the existing methods typically utilise an offline robot-path-planning strategy. Commonly, for robotic inspection, this will involve full coverage of the component. An NDT probe oriented normal to the component surface is deployed in a raster scan pattern. Here, digital models are used, with the user decomposing complex structures into manageable scan path segments, while carefully avoiding obstacles and other geometric features. This is a manual process that requires a highly skilled robotic operator, often taking several hours or days to refine. This introduces several challenges to NDT, including the need for an accurate model of the component (which, for NDT inspection, is often not available), the requirement of skilled personnel, and careful consideration of both the NDT inspection method and the geometric structure of the component. This paper addresses the specific challenge of scanning complex surfaces by using an automated approach. An algorithm is presented, which is able to learn an efficient scan path by taking into account the dimensional constraints of the footprint of an ultrasonic phased-array probe (a common inspection method for NDT) and the surface geometry. The proposed solution harnesses a digital model of the component, which is decomposed into a series of connected nodes representing the NDT inspection points within the NDT process—this step utilises graph theory. The connections to other nodes are determined using nearest neighbour with KD-Tree optimisation to improve the efficiency of node traversal. This enables a trade-off between simplicity and efficiency. Next, movement restrictions are introduced to allow the robot to navigate the surface of a component in a three-dimensional space, defining obstacles as prohibited areas, explicitly. Our solution entails a two-stage planning process, as follows: a modified three-dimensional flood fill is combined with Dijkstra’s shortest path algorithm. The process is repeated iteratively until the entire surface is covered. The efficiency of this proposed approach is evaluated through simulations. The technique presented in this paper provides an improved and automated method for NDT robotic inspection, reducing the requirement of skilled robotic path-planning personnel while ensuring full component coverage.

Empirical formulas for predicting the energy attenuation behavior of primary stress waves in 1D composite chain of viscoelastic spheres

One-dimensional composite chain of viscoelastic spheres are potential candidates for novel impact buffers. Understanding the energy attenuation behavior of primary stress waves in the composite chain of viscoelastic spheres is the premise for high-performance impact buffers design. Although the current research included viscoelasticity into the motion equations of spheres, most of them were limited to homogeneous chains. Moreover, the models of discrete system could not give the explicit expressions of the key factors governing the energy attenuation of primary stress waves. The absence of explicit formulas for the key factors hindered efficient optimization design of impact buffers constructed by one-dimensional composite chain of viscoelastic spheres. In this study, empirical formulas for predicting the input amplitude, the attenuation coefficient of the contact force and the transmission coefficient at the interface were constructed. Multivariate nonlinear regression method and dimensional constraint were applied to ensure that all the empirical formulas only employ the impact velocity and the parameters of the used spheres as independent variables. Intermediate factor was introduced for individual key factor to evaluate the accuracy of the empirical formula. Though the groups of empirical formulas have degraded performance in the low range of the intermediate factors, their accuracy in quantitatively predicting the energy attenuation behavior of the stress waves in one-dimensional composite chain of viscoelastic spheres deserves satisfactory.

Topological relation expression and verification of symmetrical parallel mechanism based on the evolution of chemical molecule

AbstractThe research of parallel mechanism (PM) configuration involves many problems. From topology to configuration, dimensional constraint, etc., how to establish the relationship between topology and configuration with effective methods is a long-term challenge for the configuration design. In this paper, the chemical molecular spatial structure (CMSS) is linked with the configuration of symmetrical parallel mechanism (SPM). Starting from the methane molecule (CH4), a spatial structural topological relation is obtained. Based on graph theory and the spatial structural topological relation, a new expression method with topological graph and its kinematic pair adjacency matrix for spatial SPM is proposed. Then, the expression and analysis for the characteristics of spatial SPM are obtained. Finally, by taking the 3-RPS PM and the 3-RRC PM as examples, the effectiveness and corresponding consistency of the proposed expression method are successfully verified. The proposed new expression method paves the way for the subsequent digital and automated design and analysis of the SPM configuration.

Hierarchical 3D Percolation Network of Ag-Au Core-Shell Nanowire-Hydrogel Composite for Efficient Biohybride Electrodes.

Metal nanomaterials are highly valued for their enhanced surface area and electrochemical properties, which are crucial for energy devices and bioelectronics. However, their practical applications are often limited by challenges, such as scalability and dimensional constraints. In this study, we developed a synthesis method for highly porous Ag-Au core-shell nanowire foam (AACNF) using a one-pot process based on a simultaneous nanowelding synthesis method. The unique characteristics of AACNF as metal-based electrodes show the lowest density among metal-based electrodes while demonstrating high electrical conductivity (99.33-753.04 S/m) and mechanical stability. The AACNF's excellent mass transport properties enable multiscale hierarchical incorporation with functional materials including polymeric precursors and living cells. The enhanced mechanical stability at the nanowelded junctions allows AACNF-hydrogel composites to exhibit large stretching (∼700%) and 10,000 times higher electrical conductivity than hydrogel-nanowire composites without the junction. Large particles in the 1-10 μm scale, including fibroblast cells and exoelectrogenic microbes, are also successfully incorporated with AACNF. AACNF-based microbial fuel cells show high power density (∼330.1 W/m3) within the optimal density range. AACNF's distinctive ability to form a hierarchical structure with substances in various scales showcases its potential for advanced energy devices and biohybrid electrodes in the future.

With Impressions Chosen from Another Time: Core Technologies and Debitage Production at the Lower Palaeolithic Site of Notarchirico (670–695 ka; layers F to I2)

The earliest evidence of bifaces in western Europe is dated to the initial phase of the Middle Pleistocene (la Noira, Notarchirico, Moulin Quignon, 700–670 ka), with the findings of Barranc de la Boella (1.0–0.9 Ma) considered to be an earlier local evolution. No transition assemblages are recorded during this time frame, and the “abrupt” appearance of bifaces during this time frame is associated with significant cognitive shifts in human technological behaviours (Acheulean techno-complex). The new investigations conducted at the site of Notarchirico unearthed 30 ka of repeated human occupation (695–670 ka, layers F-I2) during MIS 17, with evidence of bifacial tools in layer G (680 ka) and F along with other heavy-duty implements (LCTs, pebble tools, etc.). Massive production of debitage products realised on local raw materials collected in situ through simple and efficient core technologies characterises a large part of the lithic assemblage with a high ratio of diversified light-duty tools, including modified chert nodules. Despite core and flake assemblages being a recurrent trait of Lower Pleistocene contexts, the increase in retouched implements recorded at the onset of the Middle Pleistocene has been considered a significant technological shift. The technological analysis of the debitage products presented in this work highlights recurrent and systematic technological behaviours of the hominins of Notarchirico—who proved to efficiently overcome the raw materials dimensional constraints—even in the layers without bifaces. This may shed light on the meaning of cultural and behavioural innovation that the Acheulean techno-complex is thought to bring over Europe. It is plausible that given the substantial homogeneity of the lithic strategies within the sequence of Notarchirico, which only the “introduction” of the bifaces in the upper layers seems to interrupt, a supposed behavioural or cultural change in the site might have already occurred in the lowermost portion of the sequence. In this work, we evaluate the degree of change—if any—from a technological perspective by analysing the debitage reduction sequences.

Seed State-Detection Sensor for a Cotton Precision Dibble

In Xinjiang, precision hole-sowing technology is used for cotton cultivation. A disc-type seed disperser has problems with missing seeds and multi-seeding; therefore, an interdigital (multiple pairs of coplanar electrodes crossed) capacitance sensor is designed to determine the seed pick-up status by gathering electrical capacity information. Firstly, a theoretical derivation is performed for calculating the capacitance of the sensor, and it is concluded that the interdigital spacing, interdigital width, and interdigital logarithm all affect the output capacitance. Then, by analyzing the working process of the dibble, the assemblage position of the sensor and the dimensional constraints were determined. In order to explore the impact of various structural parameters on the sensor’s performance (signal strength and capacitance variation), a Maxwell simulation platform was established, and orthogonal tests were created to optimize the structural parameters. In addition, the STM32 microcontroller is utilized as the core, and it is linked with the PCAP01-AD chip to form a tiny capacitance-detecting circuit. Finally, the capacitance threshold division test determined the capacitance threshold at different seed states. The test results demonstrate that the interdigital capacitive sensor can accurately determine the precision dibble’s seeding status, with detection accuracies of 96.9% for normal seeding, 99.67% for miss-seeding, and 93.77% for multiple seeds. These results can be used as a research reference for capacitive seeding status-detection technology.

A physics-informed neural network based simulation tool for reacting flow with multicomponent reactants

The reacting flow involves nonlinear partial differential equations which are mostly solved numerically for autocatalytic reaction between multi reactants. One-dimensional convection-diffusion-reaction equations are widely used for modeling reacting flows. This paper developed a method to solve the convection-diffusion-reaction equations using a neural network model, called the physics-informed neural network. It was applied to solve the nonlinear reacting flow for forward as well as inverse problems. To enhance accuracy and to eliminate non-physical oscillations, a derivative constraint of the spatial dimension was employed. In order to avoid the slow training speed caused by the derivative constraint, a new neural network connection structure is proposed. For validation, three different forward reacting flow problems involving steep fronts were analyzed, including the convection reaction, and steady and unsteady convection-diffusion-reaction systems. For the inverse problem, except for the kinetic parameters, the unknown convection velocity and diffusion coefficients were correctly predicted, demonstrating the capability of the proposed method for identifying reacting flow parameters. Moreover, under the guidance of the improved algorithm, the sensitivity of the reacting flow to certain kinetic parameter was investigated.

Unlocking the Potential of Field Effect Transistor (FET) Biosensors: A Perspective on Methodological Advances in Computational and Molecular Biology

AbstractField‐effect transistor (FET)‐based sensors are increasingly gaining relevance in diagnostic, healthcare, and environmental monitoring applications. A FET operates by transducing chemical interactions between a surface‐immobilized bioreceptor and the target analyte into a detectable electrical signal. FET biosensors can detect and monitor molecules (i.e., biomarkers, small molecules, viruses, bacterias) present in liquid samples, making a “liquid gate” configuration of FETs the most suitable approach. However, this FET architecture presents dimensional constraints that affect bioreceptors’ stability and immobilization in the liquid phase. To overcome these limitations, herein, a combination of computational and molecular biology techniques for improving bioreceptors' applicability in biosensing is proposed. This results in the optimized and problem‐tailored protein receptors for specific FET biosensors applications, thus enhancing their overall performance. The interplay between the computational and experimental approaches will represent a ground‐breaking solution for the development of next‐generation biosensors.

A quantitative Khintchine–Groshev theorem for [formula omitted]-arithmetic diophantine approximation

In Schmidt (1960), Schmidt studied a quantitative type of Khintchine–Groshev theorem for general (higher) dimensions. Recently, a new proof of the theorem was found, which made it possible to relax the dimensional constraint and more generally, to add on the congruence condition (Alam et al., 2021).In this paper, we generalize this new approach to S-arithmetic spaces and obtain a quantitative version of an S-arithmetic Khintchine–Groshev theorem. During the process, we consider a new, but still natural S-arithmetic analog of Diophantine approximation, which is different from the one formerly established (see Kleinbock and Tomanov, 2007). Hence for the sake of completeness, we also deal with the convergent case of the Khintchine–Groshev theorem, based on this new generalization.

Allowance Extraction Considering of Inner and Outer Contour and Experimental Research on Belt Grinding of Hollow Blade

Aero-engine fan blades often use a cavity structure to improve the thrust-to-weight ratio of the aircraft. However, the use of the cavity structure brings a series of difficulties to the manufacturing and processing of the blades. Due to the limitation of blade manufacturing technology, it is difficult for the internal cavity structure to achieve the designed contour shape, so the blade has uneven wall thickness and poor consistency, which affects the fatigue performance and airflow dynamic performance of the blade. In order to reduce the influence of uneven wall thickness, this paper proposes a grinding allowance extraction method considering the double dimension constraints (DDC) of the inner and outer contours of the hollow blade. Constrain the two dimensions of the inner and outer contours of the hollow blade. On the premise of satisfying the outer contour constraints, the machining model of the blade is modified according to the distribution of the inwall contour to obtain a more reasonable distribution of the grinding allowance. On the premise of satisfying the contour constraints, according to the distribution of the inwall contour, the machining model of the blade is modified to obtain a more reasonable distribution of the grinding allowance. Through the grinding experiment of the hollow blade, the surface roughness is below Ra0.4 μm, and the contour accuracy is between − 0.05~0.14 mm, which meets the processing requirements. Compared with the allowance extraction method that only considers the contour, the problem of poor wall thickness consistency can be effectively improved. It can be used to extract the allowance of aero-engine blades with hollow features, which lays a foundation for the study of hollow blade grinding methods with high service performance.

Investigations of defects in TiO2/HgTe/MoO3 heterostructures and their influence on transport properties

The need for simple-to-manufacture, solution-processable, tunable infrared active optoelectronic materials led to the development of infrared colloidal quantum dots, whose band gaps may be easily regulated by dimensional constraints resulting from the quantum confinement. Due to electronic deep-level trap states that limit effective mobility and function as effective recombination centers, nanocrystal (NC)-based optoelectronic devices still perform less well than what is predicted theoretically. The performance of NC-based optoelectronic devices can be considerably improved by identifying and passivating these defect levels. In this study, we use low-temperature I–V, C–V, C–F and the microcontroller-based deep level transient spectroscopy (DLTS) system to investigate the defect levels in the FTO/TiO2/HgTe/MoO3/Au device. The I–V measurements at low temperatures demonstrated the existence of exponential trap states, with an activation energy of 0.24 eV and a trap density of . Low-temperature C–V and C–F measurements established the existence of deep trap states. With DLTS, we have located two deep trap levels (traps 1 and 2), with energies of 0.25 and 0.46 eV, capture cross-sections and and a concentration of , respectively. The surface states at the HgTe NCs and the oxygen vacancies at the TiO2 are the leading causes of the trap levels, which are primarily present at the interface of the TiO2/HgTe heterojunction. The transport mechanism in these heterojunction devices was happening mainly through these interface trap states. Passivating these defect levels is vital to increase the device effectiveness.

Shallow and deep subsurface sediment remobilization and intrusion in the Middle Jurassic to Lower Cretaceous Agardhfjellet Formation (Svalbard)

Abstract Sedimentary injectites are increasingly documented in many hydrocarbon plays at various scales, either interpreted as potential risks (e.g., topseal bypass, a drilling hazard) or benefits (e.g., reservoir interconnection, increased hydrocarbon volumes) for production operations. As such, they have potential critical implications for the assessment of suitability for CO2 injection and sequestration. Detailed characterization of such units, especially in terms of diagenesis and (paleo) fluid flow, is directly achievable at outcrop scale, overcoming dimensional and time constraints otherwise unresolvable at seismic scale. Two sedimentary injection complexes have been recognized in the succession of the Middle Jurassic–Lower Cretaceous Agardhfjellet Formation exposed at Deltaneset, central Spitsbergen, Norway, at different stratigraphic levels. The upper complex comprises two main clastic dikes characterized by different orientation and consolidation, tapering out vertically (upward and downward) within a stratigraphic thickness and lateral extent of more than 50 m and 200 m, respectively. The lower complex is coarser grained, made up by a network of interconnected dikes and sills, shooting off from isolated lenticular and morphologically articulated bodies, interpreted as sedimentary intrusions linked to seafloor extrusion (sand volcano). Petrographic and micromorphological analyses were used to identify the underlying lithologies of the Late Triassic to Middle Jurassic Wilhelmøya Subgroup as the possible source of this remobilized material for both the upper and lower complexes. This subsurface remobilization and consequent intrusion were first achieved in the lower complex during the Late Jurassic at shallow burial conditions, and then at higher confinement pressure for the upper complex, probably during the Late Cretaceous. These results highlight how field data can be used to constrain longlived spatiotemporal relationships of sedimentary intrusions, allowing a finely tuned upscaling of seismic data and interpretations.

Integrated approach to stainless steel 316L parts repair for pitting corrosion using laser metal deposition

With the development of additive manufacturing (AM) and in particular laser metal deposition (LMD), new possibilities for the repair of damaged metal components are developing. In this context, the use of AM offers an efficient way to restore functionality to a defective part, especially for high value parts. In this study, a numerical chain is proposed to automate the repair process of stainless steel naval parts affected by pitting corrosion. An ellipsoidal repair volume is generated at each iteration by intersecting the nominal surface and the ellipsoid surface, which initial dimensions are those of the pitting corrosion defect. This optimization considers dimensional and technical constraints, which results in minimizing the repair volume in order to reduce manufacturing time and costs. Finally, repair tests are performed on ellipsoidal pre-machining, in order to observe the influence of the surface conditions and the pattern deposition on the final quality of the repair (porosity, microstructure and microhardness). The repaired parts highlight a heterogeneous microstructure and microhardness. The surface condition of the pre-machining has no influence on the repair. However, the deposition pattern influences the presence of porosities and the microhardness value in the deposit.

Multi-material topology optimization for additive manufacturing considering dimensional constraints

Recent advances in additive manufacturing have enabled the fabrication of structures in which members made of multiple materials are placed in appropriate positions. However, dimensional constraints must be considered in such a process because of the size limitations of the 3D printer. This paper proposes a topology optimization method that considers the dimensional constraints of each material component. An extended level-set method is used to represent multiple material phases in the structural design. Constraint functions are formulated to control the size of each component. The weighted covariance matrix is used for the bounding box constraint. The sensitivity is based on the topological derivative and adjoint variable method. The proposed method was applied to solving the minimum compliance problem for two- and three-dimensional numerical examples to demonstrate its effectiveness and its potential contribution to enriching designs in additive manufacturing.

Optimization of Electrode-Spacer Arrangement Using Simplex Algorithm

The present article presents the optimization of electric stress distribution over the live electrode of an axisymmetric electrode-spacer arrangement that is used in a Gas insulated substation (GIS) typically for 12–420 kV operations. The technique is based on a classical approach employing Simplex Algorithm which is used to solve constrained linear programming problems (LPPs). For this purpose, the objective function is obtained by applying multiple linear regression analysis by which a mathematical relationship between the maximum resultant electric stress <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$({E}_{\text {max}}^{R})$ </tex-math></inline-formula> over the surface of the live electrode and the critical dimensions affecting this stress. For this purpose, a set of 71 data is used for building the regression model, which is prepared by varying the critical dimensions within the constraints of the overall dimension of the system and calculating the value of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${E}_{\text {max}}^{R}$ </tex-math></inline-formula> over the surface of the live electrode by employing the indirect boundary element method (BEM).

Conditions and constraints of implementing a mathematics lesson study-based PD program for Japanese pre-service teachers

In this study, we aim to explore the conditions and constraints of implementing a lesson study-based professional development (LSPD) program for Japanese pre-service primary teachers. Based on the anthropological theory of the didactic–a well-established theoretical framework in mathematics education research–it offers three dimensions (institutional, educational, and personal) to frame and analyze the various conditions to implement the LSPD program and the constraints that impede them. We used a case study of an LSPD program for Japanese pre-service primary teachers, which offered pre-service teachers an opportunity to engage in mathematics lesson planning and participate in a lesson study conference. The results illustrated the conditions of the institutional context in which the LSPD program appeared, and the constraints in the difficulty of making the lesson study and PD coexist. We also found that the personal constraints on pre-service teachers’ lesson preparation work were affected by the conditions and constraints in other dimensions (institutional or educational). Thus, this study contributes to a deeper understanding of the constraints that may shape or hinder teachers’ practice and knowledge in the LSPD program at diverse but interrelated levels.

Optimization of Electric Stress in a Vacuum Interrupter Using Charnes’ Big M Algorithm

This article presents the optimization of electric stress distribution over the live electrode of an axisymmetric vacuum interrupter which is employed for 11 kV switching operations. The optimization is carried out by a classical approach using Charnes’ Big M algorithm used for solving constrained linear programming problems (LPPs). For this purpose, the objective function is obtained by applying multiple linear regression analysis by which a mathematical relationship between the maximum resultant electric field intensity ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${E}^{\text {max}}$ </tex-math></inline-formula> ) over the surface of the live electrode and the critical dimensions affecting this stress. For this purpose, a set of 173 data is used which is prepared by varying the critical dimensions within the constraints of the overall dimension of the system and calculating the value of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${E}^{\text {max}}$ </tex-math></inline-formula> over the surface of the live electrode by employing the boundary element method (BEM).

Switched Reluctance Motor Design for a Light Sport Aircraft Application

This paper presents the design of a switched reluctance motor (SRM) for a direct-drive propulsion application for a light sport aircraft. The SRM is designed to replace a 70 kW permanent magnet synchronous motor used in aerospace application with similar dimensional constraints. As a means of achieving high torque density and efficiency, a multi-objective design framework is used to optimize the geometry parameters of the motor. In order to further reduce the weight, rotor cutouts are implemented. The conduction angles for the asymmetric bridge converter are selected by employing a multi-objective genetic algorithm to map the torque speed characteristics of the motor. The core losses are evaluated with the modified Bertotti method to calculate the motor efficiency and determine the steady-state and transient thermal performance at the base speed. The designed coil winding is wound on a spindle winder, and the coil fitting, fill factor, and the coil retention are validated experimentally.