Entire Range Of Operation Research Articles

A novel Economic Nonlinear Model Predictive Controller for power maximisation on wind turbines

Reducing the Levelized Cost of Energy (LCoE) is one of the main objectives of the wind turbine industry. There are several ways to achieve this goal: reducing construction and installation costs, reducing Operating&Maintenance costs, or increasing the power output. In this work, an Economic Nonlinear Model Predictive Control strategy is developed to maximise the power production of wind turbines. A novel three-states, non-linear Reduced Order Model is developed to predict aerodynamic power, rotor thrust and generator temperature with suitable accuracy. The control action is obtained from a constrained optimisation problem that uses the developed model, where the objective is the maximisation of the integral of the aerodynamic power. A set of constraints (including a bound on the generator temperature and the rotor thrust) are imposed. First, the turbine model is validated against high-fidelity simulations, then the controller performance and robustness are assessed in the entire wind range of operation, obtaining a significant increase of average power. Apart from the assessment of the controller performance in OpenFAST, the controller robustness is verified, introducing errors in the estimation of incoming wind, up to the case of a complete lack of information. The controller (freely downloadable from a dedicated repository) is effective in all the operating regions without the need for logical switches. Moreover, thanks to the optimised numerical solver adopted, it can be applied to actual wind turbines (which require real-time algorithmic performance).

Fuzzy Control Strategy Applied to an Electromagnetic Frequency Regulator in Wind Generation Systems

This paper presents the implementation of a fuzzy control strategy for speed regulation of an electromagnetic frequency regulator (EFR) prototype, aiming to eliminate the dependence on knowledge of physical parameters in the most diverse operating conditions. Speed multiplication is one of the most important steps in wind power generation. Gearboxes are generally used for this purpose. However, they have a reduced lifespan and a high failure rate, and are still noise sources. The search for new ways to match the speed (and torque) between the turbine and the generator is an important research area to increase the energy, financial, and environmental efficiency of wind systems. The EFR device is an example of an alternative technology that this team of researchers has proposed. It considers the main advantages of an induction machine with the rotor in a squirrel cage positively. In the first studies, the EFR control strategy consisted of the conventional PID controllers, which have several limitations that are widely discussed in the literature. This strategy also limits the EFR’s performance, considering its entire operating range. The simulation program was developed using the Matlab/Simulink platform, while the experimental results were obtained in the laboratory emulating the EFR-based system. The EFR prototype has 2 poles, a nominal power of 2.2 kW, and a nominal frequency of 60 Hz. Experimental results were presented to validate the efficiency of the proposed control strategy.

A novel hybrid half‐bridge LLC resonant converter with double resonant tanks for high step‐down applications

AbstractConventional half‐bridge (HB) LLC converters have been a good candidate for the first stage in the two‐stage conversion structure because of its high conversion efficiency and high frequency operation ability. However, the voltage gain is only 0.5 exclusive of the transformer, resulting in the reduced efficiency for high step‐down applications. In order to solve this issue, in this paper, a novel hybrid HB LLC converter with double resonant tanks is firstly presented for high step‐down ratio applications. The distinctive features of the proposed converter are as follows: (1) the voltage gain of 0.25 is achieved by incorporating the switched capacitor circuit. Thus, the primary turns number for the transformer is halved compared with conventional HB LLC converters. Correspondingly, the smaller primary side winding losses and transformer volume can be implemented. (2) The power splitting and low input current ripple can be obtained due to the employed double resonant tanks. Thus, the uniform thermal distribution and low input filter capacitance are the second advantage for the proposed converter. (3) Soft switching over the entire operation range still can be maintained with larger magnetics inductance. The detailed operation principles, circuit analysis, and resonant tank design for the proposed converter are introduced and presented. A hardware prototype with 36–60 V input, 3 V/50 A‐output is built to demonstrate the declared features of the proposed hybrid converter.

Active Disturbance Rejection Control of a Magnetic Screw Motor for High Tracking Performance

A magnetic screw motor integrated by a magnetic screw and a permanent magnet synchronous motor offers high thrust density without friction and loss. However, its internal structure is complicated, resulting in difficulty to control its linear displacement to track the given signal in time and even getting out of control due to interference. In this article, to improve the tracking performance and anti-interference performance of the system, an active disturbance rejection controller (ADRC) is adopted to improve the current loop and makes up for nonlinear regional control in the closed-loop control system. To ensure the stability of the proposed scheme in the entire operation range, the design and the stability analysis are carried out in the continuous-time domain. The novel control algorithm enhances the flexibility of the magnetic screw motor by optimizing the nonlinear function in ADRC. In addition, to highlight the high-performance control of the ADRC for the magnetic screw motor, sliding mode control and PI control are added to compare with it. Finally, the reciprocating motion control is added to show the diversity of the functions of the magnetic screw motor, which verifies the effectiveness and feasibility of the control algorithm.

Including the power regulation strategy in aerodynamic optimization of wind turbines for increased design freedom

AbstractIn wind turbine optimization, the standard power regulation strategy follows a constrained trajectory based on the maximum power coefficient. It can be updated automatically during the optimization process by solving a nested maximization problem at each iteration. We argue that this model does not take advantage of the load alleviation potential of the regulation strategy and additionally requires significant computational effort. An alternative approach is proposed, where the rotational speed and pitch angle control points for the entire operation range are set as design variables, changing the problem formulation from nested to one‐level. The nested and one‐level formulations are theoretically and numerically compared on different aerodynamic blade design optimization problems for AEP maximization. The aerodynamics are calculated with a steady‐state blade element momentum method. The one‐level approach increases the design freedom of the problem and allows introducing a secondary objective in the design of the regulation strategy. Numerical results indicate that a standard regulation strategy can still emerge from a one‐level optimization. Second, we illustrate that novel optimal regulation strategies can emerge from the one‐level optimization approach. This is demonstrated by adding a thrust penalty term and a constraint on the maximum thrust. A region of minimal thrust tracking and a peak‐shaving strategy appear automatically in the optimal design.

Combining the strengths of radiologists and AI for breast cancer screening: a retrospective analysis

We propose a decision-referral approach for integrating artificial intelligence (AI) into the breast-cancer screening pathway, whereby the algorithm makes predictions on the basis of its quantification of uncertainty. Algorithmic assessments with high certainty are done automatically, whereas assessments with lower certainty are referred to the radiologist. This two-part AI system can triage normal mammography exams and provide post-hoc cancer detection to maintain a high degree of sensitivity. This study aimed to evaluate the performance of this AI system on sensitivity and specificity when used either as a standalone system or within a decision-referral approach, compared with the original radiologist decision. We used a retrospective dataset consisting of 1 193 197 full-field, digital mammography studies carried out between Jan 1, 2007, and Dec 31, 2020, from eight screening sites participating in the German national breast-cancer screening programme. We derived an internal-test dataset from six screening sites (1670 screen-detected cancers and 19 997 normal mammography exams), and an external-test dataset of breast cancer screening exams (2793 screen-detected cancers and 80 058 normal exams) from two additional screening sites to evaluate the performance of an AI algorithm on sensitivity and specificity when used either as a standalone system or within a decision-referral approach, compared with the original individual radiologist decision at the point-of-screen reading ahead of the consensus conference. Different configurations of the AI algorithm were evaluated. To account for the enrichment of the datasets caused by oversampling cancer cases, weights were applied to reflect the actual distribution of study types in the screening programme. Triaging performance was evaluated as the rate of exams correctly identified as normal. Sensitivity across clinically relevant subgroups, screening sites, and device manufacturers was compared between standalone AI, the radiologist, and decision referral. We present receiver operating characteristic (ROC) curves and area under the ROC (AUROC) to evaluate AI-system performance over its entire operating range. Comparison with radiologists and subgroup analysis was based on sensitivity and specificity at clinically relevant configurations. The exemplary configuration of the AI system in standalone mode achieved a sensitivity of 84·2% (95% CI 82·4-85·8) and a specificity of 89·5% (89·0-89·9) on internal-test data, and a sensitivity of 84·6% (83·3-85·9) and a specificity of 91·3% (91·1-91·5) on external-test data, but was less accurate than the average unaided radiologist. By contrast, the simulated decision-referral approach significantly improved upon radiologist sensitivity by 2·6 percentage points and specificity by 1·0 percentage points, corresponding to a triaging performance at 63·0% on the external dataset; the AUROC was 0·982 (95% CI 0·978-0·986) on the subset of studies assessed by AI, surpassing radiologist performance. The decision-referral approach also yielded significant increases in sensitivity for a number of clinically relevant subgroups, including subgroups of small lesion sizes and invasive carcinomas. Sensitivity of the decision-referral approach was consistent across the eight included screening sites and three device manufacturers. The decision-referral approach leverages the strengths of both the radiologist and AI, demonstrating improvements in sensitivity and specificity surpassing that of the individual radiologist and of the standalone AI system. This approach has the potential to improve the screening accuracy of radiologists, is adaptive to the requirements of screening, and could allow for the reduction of workload ahead of the consensus conference, without discarding the generalised knowledge of radiologists. Vara.

Aeroelastic Stability Analysis of a Quad-Rotor Wind Turbine

This paper presents the aeroelastic stability analysis of a quad-rotor wind turbine and identifies modes with low damping that are otherwise not present on a single-rotor wind turbine. The non-linear dynamics of a wind turbine model in SIMPACK, without considering wind shear and tower shadow, is linearized around a steady-state; followed by MBC transformation resulting in LTI model. Standard eigenvalue analysis of LTI model is carried out to capture the natural frequency and damping of a quad-rotor wind turbine as a function of wind speed. Results show that several modes with low damping (besides the low damping modes observed on a single-rotor wind turbines) are identified where the damping ratio is less than 1% over the entire operation range.

Autogenerated Multilocal PLS Models without Pre-classification for Quality Monitoring of Nonlinear Processes with Unevenly Distributed Data

To achieve the desired product qualities for an operating process, the operational status must be monitored. Operating conditions are changed because of the variability in the quality of raw materials and equipment characteristics. The operating conditions are also changed to produce various product grades and to meet time-to-market demands. This makes the process data collected within a wide region to be distributed unevenly, so conventional multivariate statistical process control techniques that use a single global model are not suited to monitoring. Data is partitioned to construct each local model, but partitioning data without considering variable correlations can cause the information to be lost. This study uses a multilocal partial least-squares (ML-PLS) model to monitor a wide operation process. Without pre-selecting the data for each local model, ML-PLS automatically reinforces the data that pertains to each local model and weakens the data that does not pertain to other local models. The entire range of operation is clustered, and multiple PLS models are constructed using this clustered data. Statistical indices for quality-based process monitoring in the latent and observed variable spaces are derived using the learned ML-PLS models. The effectiveness and accuracy of the proposed method are demonstrated using a numerical example and a practical application for a chemical process.

Theory, Design, and Characterization of Nanoelectromechanical Relays for Stiction-Based Non-Volatile Memory

Diverse areas such as the Internet of Things (IoT), aerospace and industrial electronics increasingly require non-volatile memory to work under high-temperature, radiation-hard conditions, with zero standby power. Nanoelectromechanical (NEM) relays uniquely have the potential to work at 300 °C and absorb high levels of radiation, with zero leakage current across the entire operational range. While NEM relays that utilise stiction for non-volatile operation have been demonstrated, it is not clear how to design a relay to reliably achieve given programming and reprogramming voltages, an essential requirement in producing a memory. Here, we develop an analytical, first-principle physics-based model of rotational NEM relays to provide detailed understanding of how the programming and reprogramming voltages vary based on the device dimensions and surface adhesion force. We then carry out an experimental parametric study of relays with a critical dimension of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\approx ~\mathrm {80~~\, \text {n} \text {m} }$ </tex-math></inline-formula> to characterise the surface adhesion force, and derive guidelines for how a NEM relay should be dimensioned for a given contact surface force, feature size constraints and operating requirements. We carry out a scaling study to show that voltages of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\approx ~\mathrm {1~~\, \text {V}}$ </tex-math></inline-formula> and a footprint under <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\approx ~\mathrm {2~~\, \mu \text {m} ^{2}}$ </tex-math></inline-formula> can be achieved with a critical dimension of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\approx ~\mathrm {10~~\, \text {n} \text {m} }$ </tex-math></inline-formula> , with this device architecture. [2021-0138]

Multiphysics based Modal and Harmonic Analysis of Axial Turbines

The presented work discusses the vibration behavior of an axial turbine, analyzed using the finite element method (FEM) using commercial software ANSYS®. The turbine was modeled as a 2-D plate type of structure discretized with 4-noded shell 63 elements to save computational time. Constraints were applied keeping in view the actual operating conditions. The turbine was modeled with more than one FE meshes. Mesh sensitivity analysis was carried out to ensure the quality and independence of the results. Modal analysis was conducted to calculate a few initial natural frequencies. Results were studied against the operating frequency of the turbine. After carrying out the modal analysis, harmonic analysis was performed to see the response of the turbine under dynamic loading. The nature and cause of the dynamic loading are also discussed in relation to dynamic behavior. It was observed that the turbine is safe in its entire range of operation as far as the phenomenon of resonance is concerned. Also, it was observed that the maximum harmonic response of the turbine on the application of dynamic loading is far lesser than its failure limit within the specified operating range.

A parametric study of squealer tip geometries applied in a hydraulic axial turbine used in a rocket engine turbopump

Axial turbines are machines widely used in different engineering applications. Due to their constructive characteristics, they must have a space between the rotor blades and the turbine casing, called tip clearance. Unfortunately, this gap allows a part of the fluid to leak from the pressure side to the suction side of the rotor blades. This leakage is undesirable and represents an energy loss. A way to avoid part of this loss is through the use of desensitization techniques. Although the use of these techniques is widely known, no studies in the open literature have evaluated these techniques in hydraulic turbines. This work presents a numerical analysis of squealer desensitization techniques applied in a hydraulic axial turbine. The turbomachine under study is the first stage of the hydraulic axial turbine used in the Low Pressure Oxidizer Turbopump (LPOTP) of the Space Shuttle Main Engine (SSME). Numerical simulations were performed using CFX v.19.2 software, and computational meshes were generated in ICEM v.19.2 software. Initially, the computational model was validated, using the experimental results published by the National Aeronautics and Space Administration (NASA). A parametric analysis was performed considering the variation in squealer cavity depth and rim thickness. The study found that the squealer cavity depth has a greater influence on the stage performance than its rim thickness. The tendency is that the greater the cavity depth, the greater the stage efficiency. One of the squealer geometries analyzed allowed an average increased efficiency of 1.43%, over the entire turbine operational range. The results obtained also show that the application of the proposed geometries would enable the reduction in cavitation close to the trailing edge of the rotor blades. This result is extremely valuable, as it can impact the life cycle of the turbine.

Description of the method of fuzzy situational control based on a composite hybrid model of a complex technical system

Objective. The aim of the study is to generalize the accumulated experience of fuzzy situational control based on a compositional hybrid model of a complex technical system in the form of an algorithm and, on this basis, to form recommendations on the methodology for the formation and identification of situations, determining parameters and solutions for managing a complex technical system under conditions incomplete data to improve the accuracy of control decisions. Method. The use of a compositional hybrid model solves the problem of describing and modeling the system in conditions of incomplete data and the impossibility of obtaining information about the entire range of the system's operation. Fuzzy situational control makes it possible to develop control decisions in accordance with the chosen control strategy and take into account the specifics of the system thanks to the compositional model. Result. An algorithm for fuzzy situational control of complex technical systems based on compositional hybrid models is proposed. The stages, features, advantages and disadvantages of fuzzy situational control for this type of systems are considered. The procedure for determining and unambiguously identifying emerging fuzzy situations for the system is given, and a method for analyzing and developing typical control strategies is also considered. The compositional hybrid model of a complex technical system considered in the article describes the operation of the experimental compressor unit ETsK-55. Conclusion. The main advantages of the developed fuzzy situational method for managing complex technical systems include: integration of the control system with existing elements of the system; better use of available resources; adaptability and reliability of a control method based on fuzzy situational networks and a composite hybrid model of the system. Management strategies have been defined to meet the customer's requirements for product quality, as well as the safety of personnel and equipment, trouble-free production and saving resources.

Digital twins for prognostics of electro-hydraulic actuators: novel simplified fluid dynamic models for aerospace valves

In the design and development phases of electro-hydraulic actuators (EHAs) used for aircraft flight controls, it is often necessary to carry out accurate and high-fidelity fluid dynamics simulations to evaluate the system behaviour within its entire operating range and, if necessary, investigate its most critical issues. These high-fidelity simulations (nowadays achievable with different techniques and commercial software) generally become pretty expensive from a computational perspective. Therefore, especially in the preliminary design phases or implementing system health monitoring algorithms (in real-time), the need to adopt simplified models emerges definitely (albeit capable of guaranteeing the appropriate level of detail and accuracy). These simplified models are also essential for developing effective and reliable model-based prognostic strategies capable of performing early health assessments of EHA valves. This work proposes a new lumped-parameters simplified numerical model, which, despite having a very compact formulation and reduced computational costs, simulates the internal fluid dynamics of the valve, overcoming some critical issues typical of other models available in the literature. It evaluates valve performance as a function of spool position and environmental conditions (e.g. supply pressure), better-assessing flow rate feedback, internal leakages, and other operating conditions (e.g. spool fine adjustment, pressure supply variable, overpressure, or water hammer). The performance of this numerical model is evaluated comparing with other simplified models published in the literature. Moreover, it is validated with a high-fidelity digital twin that simulates the behaviour of the valve, taking into account the geometry of the spool, the properties of the hydraulic fluid, and the local internal fluid-dynamics (laminar or turbulent regime, cavitation, etc.).

RL-ANN-Based Minimum-Current-Stress Scheme for the Dual-Active-Bridge Converter With Triple-Phase-Shift Control

Aiming to reduce the current stress and improve the power efficiency of the dual-active-bridge (DAB) converter, this article proposes a reinforcement learning (RL) &#x002B; artificial neural network (ANN)-based minimum-current-stress scheme. In the first stage, Q-learning as a typical algorithm of the RL method is adopted for offline training. The aim of the first stage is to solve the optimized control strategy based on the triple-phase-shift (TPS) control. More specifically, the zero-voltage-switching (ZVS) constraints and each effective operation mode are taken into consideration during the training process of the Q-learning algorithm. Therefore, the minimum-current-stress scheme while maintaining the soft switching can be obtained after the first stage. In the second stage, the training results of the Q-learning algorithm are used to train an ANN, in order to reduce the computational time and memory allocation. After that, the trained agent of the ANN, which likes an implicit function, can provide optimal phase-shift-angle online in real time under the entire continuous operation range. Finally, the detailed simulation and experimental results are given to demonstrate the effectiveness of the proposed optimized scheme.

Assembly and Characterization of an External Driver for the Generation of Sub-Kilohertz Oscillatory Flow in Microchannels.

Microfluidic technology has become a standard tool in chemical and biological laboratories for both analysis and synthesis. The injection of liquid samples, such as chemical reagents and cell cultures, is predominantly accomplished through steady flows that are typically driven by syringe pumps, gravity, or capillary forces. The use of complementary oscillatory flows is seldom considered in applications despite its numerous advantages as recently demonstrated in the literature. The significant technical barrier to the implementation of oscillatory flows in microchannels is likely responsible for the lack of its widespread adoption. Advanced commercial syringe pumps that can produce oscillatory flow, are often more expensive and only work for frequencies less than 1 Hz. Here, the assembly and operation of a low-cost, plug-and-play type speaker-based apparatus that generates oscillatory flow in microchannels is demonstrated. High-fidelity harmonic oscillatory flows with frequencies ranging from 10-1000 Hz can be achieved along with independent amplitude control. Amplitudes ranging from 10-600 µm can be achieved throughout the entire range of operation, including amplitudes > 1 mm at the resonant frequency, in a typical microchannel. Although the oscillation frequency is determined by the speaker, we illustrate that the oscillation amplitude is sensitive to fluid properties and channel geometry. Specifically, the oscillation amplitude decreases with increasing channel circuit length and liquid viscosity, and in contrast, the amplitude increases with increasing speaker tube thickness and length. Additionally, the apparatus requires no prior features to be designed on the microchannel and is easily detachable. It can be used simultaneously with a steady flow created by a syringe pump to generate pulsatile flows.

Asymmetric loading multi-roller planetary traction drive: Modeling and performance analysis

The development of high-speed motors has stimulated the demand for high-speed reducers. Aiming at the lack of high-speed reducer and the NVH problem of high-speed gear transmission, an asymmetric loading multi-roller planetary traction drive (AL-MPTD) scheme is proposed to fill the gap of traction drive with large speed ratio, zero spin and self-adaptively loading. By comprehensively considering the effects of axis tilt, elastic deformation, rough surface contact and elastohydrodynamic lubrication (EHL), this paper establishes a more refined theoretical analysis model and provides the analysis method under the condition of the non-smooth surface and non-ideal contact. This paper also studies the transmission performance of AL-MPTD from the aspects of traction ability, transmission efficiency and so on. The results obtained by theoretical calculations show that AL-MPTD can be considered to achieve zero-spin transmission in the entire operation range. In addition, the loading mechanism achieves a good self-adaptively loading effect, effectively reducing the sliding of the AL-MPTD and improving its efficiency, with the peak of the global sliding coefficient being only 0.402% and the maximum transmission efficiency of 98.5%.

Artificial intelligence‐based correlation: Process side heat transfer coefficient for helical coils in stirred tank reactors

AbstractStirred tank reactor systems often employ jackets/limpets, single/multiple internal coils for heat transfer. Internal coils are used for heat removal in the case of exothermic reaction systems. The desired heat transfer area can be accurately estimated based on the overall heat transfer coefficient (HTC) considering the process side, utility side, and wall conduction resistance. For estimation of process side HTC, a large number of empirical/semi‐empirical correlations are available in the published literature. These correlations account for impeller Reynolds number, fluid Prandtl number, viscosity ratio, dimensionless geometric factors, and so forth. However, all the factors over the entire range of industrial operation do not get covered in a single study, hence the predictions may involve significant errors for industrial cases. The advanced artificial intelligence‐based techniques can provide unified correlation based on the experimental data set extracted from various investigations and the predictions can be accurate and reliable. In the present work, three techniques random forests (RF), artificial neural networks with Bayesian optimization (ANN‐BO), and support vector regression with grid search optimization (SVR‐GS) have been used with a data set of 1297 points with R2 values of 0.995, 0.972, 0.990 on the training data set and 0.962, 0.945, 0.905 respectively on the testing data set. Furthermore, the statistical measures show that RF provides a better fit as compared with other AI models, so RF is used for parametric sensitivity analysis. The permutation feature importance shows that the agitation speed, impeller dimensions, and positioning has a significant impact on the HTC.

An Optimal Reduced-Control-Set Model Predictive Flux Control For 3L-NPC Fed Induction Motor Drive

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">–</i> This paper proposes an efficient and optimal reduced control set model predictive flux control (RCS-MPFC) for a three-level neutral-point-clamped voltage source inverter (3L-NPC VSI) fed induction motor. The proposed algorithm reduces the computational time in the prediction stage without causing any suboptimality. The optimal voltage vector selected by the proposed method produces the same cost function value as that of the conventional FCS-MPFC which requires enumerating all 27 voltage vectors. Therefore, the proposed algorithm achieves the same performance as the conventional method in the entire range of operation of IM drives while the computational effort is significantly reduced. Experimental results verify the effectiveness of the proposed algorithm and its superior performance compared to the existing RCS-MPFC scheme.

Polarization-insensitive dual-wideband fractal meta-absorber for terahertz applications.

Terahertz (THz) metamaterial absorbers have realized a prodigious reputation due to the limitation of natural absorbing materials in this range. Getting wideband absorption characteristics is challenging and arduous, especially in the THz band. Self-similar repeated fractal elements offer a promising solution to attain broadband absorption response due to their inherent multiple resonance characteristics. Therefore, by captivating the advantage of fractal geometry, we proposed a dual and wideband meta-absorber operating in the THz regime. The metamaterial absorber design comprises the assembly of self-similar square-shaped blocks arranged in a specific pattern to construct the fractal geometry. The proposed THz absorber demonstrates 90% absorption under normal incident waves for two operating bands from 9.5-10.55 THz and 12.3-13.35 THz. The suggested metamaterial absorber also shows good and stable absorption responses under different oblique incidence angles for transverse electric (TE) and transverse magnetic (TM) wave polarization. Moreover, this absorber manifests over 85% absorptivity in its entire operating range (9-14 THz) under the incidence angle of 60° and 70° for TM mode. Furthermore, it gives a polarization-insensitive behavior under the effect of different polarization angles. This kind of wideband absorber catches fascinating applications in THz detection, imaging, cloaking, and optoelectronic devices.

Comparison of Gray-Box and Black-Box Two-Level Three-Phase Inverter Models for Electrical Drives

The accuracy of the set voltage compared to its reference value has a high impact on the control performance of three-phase electrical drives, especially if the voltage is an input variable of integrated observers (e.g., flux observer). Voltage deviations are generally a disturbance and, therefore, they lead to a less-than-ideal control performance of the drive. In the literature, most approaches only model the basic inverter effects but do not sufficiently address its strongly nonlinear behavior at very high or low duty cycles. To enable accurate voltage estimation in the entire operation range of a given drive, a black-box inverter model utilizing machine learning is presented in this article. By means of artificial neural networks, a direct mapping of available signals in the control framework and the actual inverter output voltage is realized. For comparison, a gray-box inverter model whose parameters are identified by using half-automatized recorded datasets and a particle swarm optimization is derived. Comprehensive experimental investigations prove the effectiveness of both approaches. The gray-box model can estimate the phase voltages per switching period precisely with a root-mean-square error of less than 1.1 V at a 560 V DC-link voltage level, whereas the machine-learning-based black-box approach even achieves an error of less than 0.65 V.