Hybrid Analytical Model Research Articles

Capabilities toward adoption of outcome-based contracts

PurposeManufacturing enterprises have started to offer the “outcome” derived from machines with the help of outcome-based contracts (OBCs). Offering OBCs leads to benefits such as increased revenues, stronger customer relationships and sustainability. However, implementing OBCs requires critical capabilities. Existing literature has focused on identifying these necessary capabilities, but the prioritization and interrelationships among them remain unexplored. This study aims to address this gap.Design/methodology/approachOur study employs a hybrid analytical hierarchy process and interpretative structural modeling approach to prioritize and map interrelationships among OBC-related capabilities within small and medium-sized enterprises (SMEs).FindingsThe findings highlight the importance of digitalization capabilities such as data privacy and security, remote monitoring, and data analytics; and organizational and governance capabilities, including quantifying, controlling, and monitoring risks, teamwork, and leadership, are highlighted.Research limitations/implicationsWe quantitatively prioritize OBC capabilities and establish their level-wise structural interrelationships, which will facilitate a more effective and efficient implementation of OBCs. Due to the emergent nature of OBCs, our study could identify just one SME case company meeting our selection criteria.Originality/valueExisting OBC literature focusses on the design of OBCs in large companies. Similarly, earlier capability-related OBC literature is oriented toward identifying the OBC capabilities to perform specific functions. However, in the current study, we propose a systematic decision-making approach that comprehensively prioritizes and identifies the interrelationships among the capabilities necessary to provide OBCs, thus complementing the existing scientific literature on OBCs. In addition, we focus on SMEs, that have specific limitations and characteristics.

Hybrid Analytical Model of Permanent Magnet Linear Motor Considering Iron Saturation and End Effect

Hybrid Analytical Model of Permanent Magnet Linear Motor Considering Iron Saturation and End Effect

Transient Temperature at Tool-Chip Interface during Initial Period of Chip Formation in Orthogonal Cutting of Inconel 718.

Machining nickel-based super alloys such as Inconel 718 generates a high thermal load induced via friction and plastic deformation, causing these alloys to be among most difficult-to-cut materials. Localized heat generation occurring in machining induces high temperature gradients. Experimental techniques for determining cutting tool temperature are challenging due to the small dimensions of the heat source and the chips produced, making it difficult to observe the tool-chip interface. Therefore, theoretical analysis of cutting temperatures is crucial for understanding heat generation and temperature distribution during cutting operations. Periodic heating and cooling occurring during cutting and interruption, respectively, are modeled using a hybrid analytical and finite element (FE) transient thermal model. In addition to identifying a transition distance associated with initial period of chip formation (IPCF) from apparent coefficient of friction results using a sigmoid function, the transition temperature is also identified using the thermal model. The model is validated experimentally by measuring the tool-chip interface temperature using a two-color pyrometer at a specific cutting distance. Due to the cyclic behavior in interrupted cutting, where a steady-state condition may or may not be achieved, transient thermal modeling is required in this case. Input parameters required to identify the heat flux for the transient thermal model are obtained experimentally and the definitions of heat-flux-reducing factors along the cutting path are associated with interruptions and the repeating IPCF. The thermal model consists of two main parts: one is related to identifying the heat flux, and the other part involves the determination of the temperature field within the tool using a partial differential equation (PDE) solved numerically via a 2D finite element method.

Hybrid Analytical Model for Predicting the Electromagnetic Losses in Surface-Mounted Permanent-Magnet Motors

Hybrid Analytical Model for Predicting the Electromagnetic Losses in Surface-Mounted Permanent-Magnet Motors

Hybrid Analytical Model for AC Copper Loss Computation of Hairpin Winding

Hybrid Analytical Model for AC Copper Loss Computation of Hairpin Winding

A hybrid analytical model of open-circuit air gap magnetic field of Interior Permanent Magnet Synchronous Motor with non-uniform air gap structure considering saturation effect

A hybrid analytical model of open-circuit air gap magnetic field of Interior Permanent Magnet Synchronous Motor with non-uniform air gap structure considering saturation effect

Towards the cost-effective design of stormwater infiltration trenches: a hybrid model integrating cost–benefit analysis and an analytical stochastic approach

This study proposes a hybrid analytical model integrating two cost–benefit analysis approaches and an analytical stochastic approach for the proper design of infiltration trenches.

A hybrid analytical model for thermodynamic calculations of supercritical carbon dioxide centrifugal compressors

A hybrid analytical model for thermodynamic calculations of supercritical carbon dioxide centrifugal compressors

Evaluating the performance of machine learning CFD-based and hybrid analytical models for transient flow prediction in temperature-compensated digital flow units

This investigation utilized binary-coded, parallel-connected on-off valves that can achieve high flow rates with fewer valves while addressing flow peak challenges. By considering temperature and refining modeling techniques, the study rectifies certain limitations observed in previous research, such as neglecting temperature, imprecise valve orifice flow coefficients, and absent flow pattern visualization, thereby enhancing flow prediction accuracy.The results for the ML_CFD-based model suggest that although extrapolation challenges exist in rarely data-driven systems, the proposed approach exhibits errors under 5 % across diverse metrics, attributable to the effectiveness of well-constrained overparameterized models and the segmented structure of digital flow control units. On the other hand, while the simplified Hybrid Analytical model shows minor deviations and offers easier implementation, it encounters constraints when processing data beyond its pre-tuned coefficient of discharge values.

Hybrid analytical surrogate-based process optimization via Bayesian symbolic regression

Modular chemical process simulators are widespread in chemical industries to design and optimize production processes with sufficient accuracy. However, convergence issues and entrapment in local optima during process optimization are still challenges to overcome. To circumvent them, surrogate models of first principles simulations have attracted attention as they are easier to handle, with hybrid surrogates combining data-driven surrogate models with mechanistic equations becoming particularly appealing. In this context, this work explores the use of Bayesian symbolic regression to construct and globally optimize hybrid analytical surrogate models of process flowsheets, where some units are approximated with tailored analytical expressions rather than with neural networks or Gaussian processes, which might be harder to globally optimize. Comparing with other prevalent black-box surrogate modeling & optimization approaches, such as kriging and Bayesian optimization, we find that our approach can find better solutions than those identified with pure black-box methodologies, yet model building is much more computationally demanding.

Risk Assessment in Sustainable Production: Utilizing a Hybrid Evaluation Model to Identify the Waste Factors in Steel Plate Manufacturing

In the realm of industrial development, steel has consistently played a pivotal role due to its extensive applications. This research aims to refine the process of steel plate manufacturing, focusing on reducing waste as a critical step towards embracing sustainable development and aligning with the Sustainable Development Goals (SDGs). Our approach integrates a hybrid analytical model grounded in Failure Mode and Effects Analysis (FMEA) to thoroughly investigate the waste-producing elements in steel plate production. The methodology of this study is structured in a three-pronged approach, as follows: Initially, it involves meticulous on-site inspections across various factories to pinpoint potential sources of waste. Subsequently, we employ the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method to intricately analyze the interconnectedness and impact of various risk factors. The final phase utilizes the Performance Calculation technique within the Integrated Multiple Multi-Attribute Decision-Making (PCIM-MADM) framework for aggregating and evaluating risk scores. This multifaceted approach aids in establishing the priorities for corrective actions aimed at waste reduction. Our findings present innovative solutions for identifying and mitigating critical waste factors, contributing to a more efficient and sustainable steel manufacturing process. These strategies promise scalability and adaptability for broader industrial applications and provide critical insights into resource optimization. This research directly supports the objectives of SDG 9, which focuses on building resilient infrastructure and promoting sustainable industrialization. Furthermore, it resonates with SDG 12, advocating for sustainable consumption and production patterns. By enhancing the efficiency and cost effectiveness of steel plate production, this study significantly contributes to minimizing waste and elevating the sustainability of industrial practices.

Deformable Object Manipulation in Caregiving Scenarios: A Review

This paper reviews the robotic manipulation of deformable objects in caregiving scenarios. Deformable objects like clothing, food, and medical supplies are ubiquitous in care tasks, yet pose modeling, control, and sensing challenges. This paper categorises caregiving deformable objects and analyses their distinct properties influencing manipulation. Key sections examine progress in simulation, perception, planning, control, and system designs for deformable object manipulation, along with end-to-end deep learning’s potential. Hybrid analytical data-driven modeling shows promise. While laboratory successes have been achieved, real-world caregiving applications lag behind. Enhancing safety, speed, generalisation, and human compatibility is crucial for adoption. The review synthesises critical technologies, capabilities, and limitations, while also pointing to open challenges in deformable object manipulation for robotic caregiving. It provides a comprehensive reference for researchers tackling this socially valuable domain. In conclusion, multi-disciplinary innovations combining analytical and data-driven methods are needed to advance real-world robot performance and safety in deformable object manipulation for patient care.

A 2D hybrid analytical electromagnetic model of the dual‐stator axial‐field flux‐switching permanent magnet motor

AbstractThe two‐dimensional (2D) analytical model for the calculation of the components of the flux density distribution in the air gap in a dual‐stator axial‐field flux‐switching permanent magnet (PM) motor (DSAFFSPM) is presented. The novelties of this study are deriving a 2D hybrid analytical model and replacing the rotor teeth with some surface currents for the calculation of air‐gap magnetic flux density for the first time in the DSAFFSPM motor. The 2D analytical model for DSAFFSPMs is more challenging due to the doubly salient structure and inner PM of the stator. 1D analytical interior PMs are first transferred to the bore of the stator body using the magnetic equivalent circuit model (MEC). Next, the effects of the rotor teeth are taken into consideration by injecting virtual surface currents for 2D analytical model. Applying boundary conditions and solving the Laplace/Poisson equations, the vertical and tangential flux components of the flux density distribution in the air‐gap DSAFFSPM motor are computed. The verification of the proposed method and the obtained results are validated by the 3D finite element method.

Analytical Model for Electromagnetic Performance Prediction of IPM Motors Considering Different Rotor Topologies

Aiming at the problem of long computation time of finite element analysis (FEA) and poor calculation universality of existing analytical models, this paper proposes a general hybrid analytical model to predict the no-load and load performance of interior permanent magnet motors with any slot-pole combination, where the form of magnet can be V-shape, U-shape or flat-shape. The magnets are equivalent correspondingly according to the rotor structure in the modeling process and the motor can be divided into eight types of subdomains, the no-load and load magnetic field of the motors can be obtained by solving the equations established by boundary conditions, where the nonlinear of core is calculated by local MEC. The proposed model can be used to calculate the air-gap magnetic field, no-load back electromotive force, cogging torque and electromagnetic torque of IPM motors with different rotor topologies. The effectiveness of the proposed model is verified by both FEA and experiments. Compared with FEA, this model can consume less time with the acceptable accuracy, which brings convenience to the design and optimization of related types of motors.

Hybrid analytical model for air‐gap magnetic field prediction of surface‐mounted permanent magnet motors with a quasi‐regular polygon rotor

AbstractIn this article, a hybrid analytical model for a quasi‐regular polygon rotor (QPR) is proposed. It mainly uses the subdomain method and conformal mapping to solve a non‐circular boundary in the QPR. Firstly, the subdomain method combined with an equivalent surface current method is used to calculate the air‐gap magnetic field considering a slotted stator with a circular rotor. Secondly, by segmenting the non‐circular boundary in the QPR, the complex relative air‐gap permeance of the QPR can be calculated using the conformal mapping. Thirdly, this complex relative air‐gap permeance modifies the air‐gap magnetic field calculated in the first step to obtain the actual magnetic field distributions. Consequently, no‐load and loaded air‐gap flux densities, back‐electromotive force and torque can be obtained. A 12‐pole/3‐phase permanent magnet motor is modelled using the proposed hybrid analytical model, which is validated by finite‐element analysis and experiment. This proposed hybrid analytical model presents a new way of processing the QPR. Its calculation speed is nearly 50 times faster than the finite‐element analysis, which is of great help to the initial design and optimisation of machines with QPRs.

A hybrid analytical and numerical model for cross-over and performance decay in a unit cell vanadium redox flow battery

Developing an accurate and efficient model for cross-over is critical for improving the long-term performance of redox flow batteries (RFBs). This work presents a hybrid analytical and numerical model that combines a two-dimensional analytical solution to the active species, a one-dimensional analytical model for cross-over mechanisms, and a zero-dimensional numerical model for outlet concentrations of reactants. By comparing with experiment over 41 cycles (ca. 144 h), the model reported a mean voltage difference of 0.0089 V, mean time difference of 14 s per cycle (of 3.5 h), maximum relative difference for capacity and energy of 1.34% and 1.63%, respectively. The predicted mean concentrations for V2+, V3+, and VO2+ in membrane are 65% ∼ 77% of measured values from the literature. Upon validation, the model reproduced behaviors in electrolyte imbalance similar to those observed in experiments and numerical models, and revealed the control of cross-over, self-discharge, stoichiometry of side reactions, and Coulombic efficiency on electrolyte imbalance. The model also demonstrates excellent computational efficiency for simulating 41 cycles (around 37,000 points) within 3 ∼ 4 s. The demonstrated efficiency and accuracy for predicting cross-over and its impacts on voltage, capacity&energy decay, membrane concentrations, and electrolyte imbalance makes it a reliable tool for optimizing RFBs’ long-term performance.

A Sparse Protocol Parsing Method for IIoT Based on BPSO-vote-HMM Hybrid Model

With the development of the Industrial Internet of Things, industrial control systems have become more open and intelligent. However, large numbers of unknown protocols exist in IIoT, threatening the security of IIoT devices and systems. Protocol reverse engineering extracts the grammar and semantics of the protocol by monitoring and analyzing the traffic trace or the execution process of instructions, without the need for protocol description. As the executable programs are mainly integrated into the IIoT devices and the communication traffic is relatively sparse, the traditional protocol analyzing method is not suitable for the IIoT environment. This paper proposes an improved sparse protocol parsing method of IIoT protocol based on the BPSO-vote-HMM hybrid model. The binary particle swarm optimization algorithm is introduced to expand the captured IIoT protocol message sequence, solving the problems of sparse samples in IIoT and the low efficiency of the GA-based data expansion model. Besides, we improve on the parameter training part to improve the efficiency and get better model parameters by dividing the training set into several sub-sets, conducting the parameter update parallel, and inputting the results into a voter to generate the final parameter of HMM, which is used in protocol field prediction. Finally, by combining the BPSO-based data expansion model and the protocol field parsing model based on vote-HMM, a hybrid analytical model is constructed to improve the analytical accuracy in a gradual evolutionary manner. Through a series of comparative experiments, the improved protocol field parsing model has better performance on IIoT protocol.

Hybrid Analytical Modeling of Force Dense Segmented Magnetic Linear Actuator with Non-Dimensional Parametric Modeling of the Magnetic Flux Effects

A new advanced two-dimensional hybrid analytical model of a segmented magnet linear actuator (MLA) comprised of surface permanent magnets (PM) is developed in this paper. This model is used to predict and evaluate the performance of the segmented MLA with proper correction on magnetic Flux Effects, validated by computational modeling. An MLA design with non-uniform PM segmentation was applied in this research to improve its performance compared with conventional radially magnetized MLA and uniform segmented Halbach Array based MLA. For MLA thrust force prediction, the previous published analytical model does not consider losses due to two observed magnetic Flux Effects: (1) the magnetic edge effect—the diminishing nature of the magnetic flux at the edge of the MLA, and (2) the observed magnetic interaction effect—the inconsistent peaks of individual magnetic flux lines, lower than the overall peak flux. In the proposed hybrid model for the segmented MLA, the shaft magnetic field distribution is based on a scalar potential theory subdomain method and the ring magnetic field is based on equivalent surface distributed currents. Collectively, these models are combined with three-dimensional finite element analysis (FEA), to estimate the magnetic thrust force. A data driven pole correction factor is introduced, based on the FEA computational database of three-dimensional MLA, to capture the losses associated with the magnetic flux, which is not considered in the analytical subdomain method. Finally, a normalized pole correction is proposed to generalize the model to different magnetic grades, different dimensional constraints, and varying magnet ratios of the segmented magnets. The developed model provides the design basis for manufacturing optimized force dense segmented MLAs for rotary to linear actuation, based on the force required for the application without the need for running FEA analysis after each design iteration, reducing costs and time required for the optimal design.

A Hybrid Analytical Model for the Electromagnetic Analysis of Surface-Mounted Permanent-Magnet Machines Considering Stator Saturation

This article presents the process of building a hybrid analytical model (HAM) for surface-mounted permanent-magnet machines. The HAM couples a reluctance network (RN) model in the stator region with a magnetic scalar potential analytical model in the air gap and magnets regions. This hybrid model can deal with the slotting effect with straight teeth, and takes magnetic saturation into account in the stator iron material using the RN model. It is calculated under open-circuit and loaded conditions. The magnetic flux density, flux linkage, back electromotive force (EMF), and torque of the machines are also calculated. This hybrid model is compared with the subdomain method. It is also compared with the finite element method (FEM) both in terms of the size of the matrix that needs to be calculated and in terms of the torque error. We analyzed this method for two surface-mounted permanent-magnet machines, one with a symmetry factor of four and another with a symmetry factor of three. In both cases, HAM reduced the size of the matrix that needed to be solved compared to FEM. In the machine with a symmetry factor of three, when the matrix size of both FEM and HAM was around 1700 × 1700, the torque error of FEM was 2.62% compared to the high-mesh-density FEM simulation, while the torque error of HAM was only 0.17% compared to the same simulation. HAM also had significant advantages over the subdomain method, as it reduced the torque error from 16.8% to 0.08% in the case of high magnetic saturation. The HAM can, hence, play a significant role in the design and optimization of surface-mounted permanent-magnet machines, especially in cases where magnetic saturation is present.

Investigation on minimum uncut chip thickness and size effect in micro milling of glow discharge polymer (GDP)

Glow discharge polymer (GDP) is used as the ablator material in inertial confinement fusion (ICF) applications. Its machining surface quality and geometric accuracy directly affect its high energy performance. Minimum uncut chip thickness (MUCT) and size effect are the keys to obtain higher surface quality during micro cutting. In this work, a hybrid analytical model of MUCT using cutting forces and effective rake angle based on minimum energy method (Model A), and micro milling experiment are applied to determine the MUCT of GDP. Furthermore, a specific cutting energy-based model (Model B), considering the mechanical properties, strain evolution, and tool geometry, is established to verify model A. Additionally, the material removal mechanism and size effect are also studied. It is found that the MUCT value of GDP determined by model A is between 0.268 and 0.532 μm, and the normalized MUCT value is between 0.148 and 0.295. These results are verified by the specific cutting energy obtained from model B. It is also proved that model B could preliminarily evaluate the MUCT value of GDP without conducting milling experiment. Moreover, below the MUCT threshold, the severe size effect and ploughing effect cause nonlinearly increase of specific cutting energy, and the random variation of cutting force and surface roughness. When the ratio of feed per tooth to cutting edge radius is larger than 0.61, the size effect could be ignored since the cutting mode is shearing.