Optimal Array Research Articles

The effects of solar insolation and cloud opacity on the optimum array size for a direct-coupled solar pumping system

The widespread adoption of solar photovoltaic pumping technology requires pertinent information on the sizing and design of these systems. This research investigated how the ratio of photovoltaic array peak power to motor power consumption, solar insolation and cloud opacity affects the water yield of a direct-coupled solar PV pumping system. The specific aim was to estimate the optimum photovoltaic power to motor power consumption (OPVM) ratio for a 3 W DC pump under Jamaican weather conditions. The PVM ratio is the ratio of peak array power to nominal motor power consumption. Four experimental direct coupled solar pumping systems were set up and the water yield for each measured daily for thirty days. Each system was identical in every respect except each having a different solar array peak power. The findings show that there is a strong correlation (Pearson coefficient of −0.903) between solar insolation and the optimum photovoltaic power to motor power consumption ratio. Additionally, there was found to be a strong correlation between average cloud opacity and optimum photovoltaic power to motor power consumption ratio (Pearson coefficient of 0.899). Both correlations were shown to be statistically significant, with both yielding p-values <0.001. The findings of the correlation analysis indicate that a relatively large PVM ratio would be economical when used on a site that regularly receives relatively low insolation and heavy cloud cover. Finally, the daily optimum photovoltaic power to motor power consumption ratio ranged from approximately 2.3 to 5.0. Generally, it was observed that the OPVM ratio shifted below 2.4 on clear sunny days and above 4.0 on very cloudy days. While these conclusions may not exactly extrapolate to scales of practical application, performance metrics of larger systems are expected to bear some similarity. Being guided by these results, additional studies at these larger scales are recommended.

Enhancing acoustic levitation capacity through array geometry optimization

The contactless micromanipulation capability and microgravity environment provided by the acoustic levitation technology have excellent application prospects in many fields, such as droplet dynamics, solidification state physics, life sciences, etc. However, the applications are severely constrained by the limitation of the transducer power. In this paper, we explore an array geometry optimization framework to improve the levitation capability. First, the acoustic pressure attenuation and directional characteristics of commercial transducers are analyzed to uncover the mechanism of the array geometry affecting the acoustic radiation force (ARF). Second, based on the Gor'kov potential theory, the array geometry form of N-elements is derived, and the arrangement in the spherical ring (ASR) method is proposed to obtain the element coordinates. Then, spherical radius and array height are adjusted to obtain the optimal array geometry. On this basis, the directional angle of each transducer is further optimized. Finally, simulation and physical verification are carried out. The simulation results indicate that the ARF increases and then decreases with the spherical radius and array height, which are the existing maximum values. Directional angle optimization (DAO) can improve the ARF, particularly when the array height exceeds the sphere diameter. These findings are consistent with the analytical results. Notably, we built a prototype physical system, and the physical experiments showed that the optimized array improved the axial ARF by 201.2% and the radial ARF by 115.9% over the conventional array. The works presented in this paper offer a valuable design approach for acoustic levitation arrays, thereby contributing to the advancement and application of ultrasonic array levitation technology.

From little things big things grow: enhancement of an acoustic telemetry network to monitor broad-scale movements of marine species along Australia’s east coast

BackgroundAcoustic telemetry has become a fundamental tool to monitor the movement of aquatic species. Advances in technology, in particular the development of batteries with lives of > 10 years, have increased our ability to track the long-term movement patterns of many species. However, logistics and financial constraints often dictate the locations and deployment duration of acoustic receivers. Consequently, there is often a compromise between optimal array design and affordability. Such constraints can hinder the ability to track marine animals over large spatial and temporal scales. Continental-scale receiver networks have increased the ability to study large-scale movements, but significant gaps in coverage often remain.MethodsSince 2007, the Integrated Marine Observing System’s Animal Tracking Facility (IMOS ATF) has maintained permanent receiver installations on the eastern Australian seaboard. In this study, we present the recent enhancement of the IMOS ATF acoustic tracking infrastructure in Queensland to collect data on large-scale movements of marine species in the northeast extent of the national array. Securing a relatively small initial investment for expanding receiver deployment and tagging activities in Queensland served as a catalyst, bringing together a diverse group of stakeholders (research institutes, universities, government departments, port corporations, industries, Indigenous ranger groups and tourism operators) to create an extensive collaborative network that could sustain the extended receiver coverage into the future. To fill gaps between existing installations and maximise the monitoring footprint, the new initiative has an atypical design, deploying many single receivers spread across 2,100 km of Queensland waters.ResultsThe approach revealed previously unknown broad-scale movements for some species and highlights that clusters of receivers are not always required to enhance data collection. However, array designs using predominantly single receiver deployments are more vulnerable to data gaps when receivers are lost or fail, and therefore “redundancy” is a critical consideration when designing this type of array.ConclusionInitial results suggest that our array enhancement, if sustained over many years, will uncover a range of previously unknown movements that will assist in addressing ecological, fisheries, and conservation questions for multiple species.

Electronic sensing combined with machine learning models for predicting soil nutrient content

Traditional methods for detecting soil nutrient content usually involve laborious and time-consuming experimental procedures, hindering the efficiency of soil analysis and making them less suitable for large-scale soil testing. Therefore, there exists a pressing need to develop innovative solutions. This study aimed to investigate the potential relationship between soil pyrolysis gas and soil nutrient content by establishing a model that combined electronic sensing and machine learning to predict soil nutrient content. The initial data sets were derived from utilizing pyrolysis gas response curves of soil samples acquired from 10 distinct gas sensors. Afterward, we innovatively proposed a sensor array optimization (SAO) method based on the dynamic feature importance (DFI) of the Random Forest-Pearson Correlation Coefficient (RF-PCC) to identify the optimal sensor combinations for four soil nutrients. Furthermore, Support Vector Machine (SVM), Random Forest (RF), Multilayer Perceptron (MLP), and Multilayer Perceptron-Random Forest (MLP-RF) models were employed to predict the soil nutrient content based on the electronic sense to address the challenges associated with developing a unified model. The MLP-RF models showed superior performance compared with the other three models, with the coefficient of determination (R2) for the four soil nutrients ranging from 0.54 to 0.94.Overall, our research not only provides approximately quantitative predictive outcomes but also contributes to a better understanding ofa new approach for detecting soil nutrient content.

Bioinspired Polymer Films with Surface Ordered Pyramid Arrays and 3D Hierarchical Pores for Enhanced Passive Radiative Cooling.

Passive radiative cooling (PRC) has been acknowledged to be an environmentally friendly cooling technique, and especially artificial photonic materials with manipulating light-matter interaction ability are more favorable for PRC. However, scalable production of radiative cooling materials with advanced biologically inspired structures, fascinating properties, and high throughput is still challenging. Herein, we reported a bioinspired design combining surface ordered pyramid arrays and internal three-dimensional hierarchical pores for highly efficient PRC based on mimicking natural photonic structures of the white beetle Cyphochilus' wings. The biological photonic film consisting of surface ordered pyramid arrays with a bottom side length of 4 μm together with amounts of internal nano- and micropores was fabricated by using scalable phase separation and a quick hot-pressing process. Optimization of pore structures and surface-enhanced photonic arrays enables the bioinspired film to possess an average solar reflectance of ∼98% and a high infrared emissivity of ∼96%. A temperature drop of ∼8.8 °C below the ambient temperature is recorded in the daytime. Besides the notable PRC capability, the bioinspired film exhibits excellent flexibility, strong mechanical strength, and hydrophobicity; therefore, it can be applied in many complex outdoor scenarios. This work provides a highly efficient and mold replication-like route to develop highly efficient passive cooling devices.

Online evolution of a phased array for ultrasonic imaging by a novel adaptive data acquisition method

Ultrasonic imaging, using ultrasonic phased arrays, has an enormous impact in science, medicine and society and is a widely used modality in many application fields. The maximum amount of information which can be captured by an array is provided by the data acquisition method capturing the complete data set of signals from all possible combinations of ultrasonic generation and detection elements of a dense array. However, capturing this complete data set requires long data acquisition time, large number of array elements and transmit channels and produces a large volume of data. All these reasons make such data acquisition unfeasible due to the existing phased array technology or non-applicable to cases requiring fast measurement time. This paper introduces the concept of an adaptive data acquisition process, the Selective Matrix Capture (SMC), which can adapt, dynamically, to specific imaging requirements for efficient ultrasonic imaging. SMC is realised experimentally using Laser Induced Phased Arrays (LIPAs), that use lasers to generate and detect ultrasound. The flexibility and reconfigurability of LIPAs enable the evolution of the array configuration, on-the-fly. The SMC methodology consists of two stages: a stage for detecting and localising regions of interest, by means of iteratively synthesising a sparse array, and a second stage for array optimisation to the region of interest. The delay-and-sum is used as the imaging algorithm and the experimental results are compared to images produced using the complete generation-detection data set. It is shown that SMC, without a priori knowledge of the test sample, is able to achieve comparable results, while preforming ∼\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim$$\\end{document}10 times faster data acquisition and achieving ∼\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim$$\\end{document} 10 times reduction in data size.

Optimal Microphone Array Placement Design Using the Bayesian Optimization Method.

In addition to the filter coefficients, the location of the microphone array is a crucial factor in improving the overall performance of a beamformer. The optimal microphone array placement can considerably enhance speech quality. However, the optimization problem with microphone configuration variables is non-convex and highly non-linear. Heuristic algorithms that are frequently employed take a long time and have a chance of missing the optimal microphone array placement design. We extend the Bayesian optimization method to solve the microphone array configuration design problem. The proposed Bayesian optimization method does not depend on gradient and Hessian approximations and makes use of all the information available from prior evaluations. Furthermore, Gaussian process regression and acquisition functions make up the Bayesian optimization method. The objective function is given a prior probabilistic model through Gaussian process regression, which exploits this model while integrating out uncertainty. The acquisition function is adopted to decide the next placement point based upon the incumbent optimum with the posterior distribution. Numerical experiments have demonstrated that the Bayesian optimization method could find a similar or better microphone array placement compared with the hybrid descent method and computational time is significantly reduced. Our proposed method is at least four times faster than the hybrid descent method to find the optimal microphone array configuration from the numerical results.

Application of Dandelion Optimization Algorithm in Pattern Synthesis of Linear Antenna Arrays

This paper introduces an application of the dandelion optimization (DO) algorithm in antenna arrays. This is the first time that the DO algorithm has been used for optimizing antenna arrays. For antenna array optimization, sidelobe level (SLL) and deep nulls are key technical indicators. A lower SLL can improve the signal-to-noise ratio and reduce the impact of clutter signals outside the main beam. Deep nulls need to be aligned with the direction of interference to eliminate the influence of interference sources. The combination of the two can effectively improve the anti-interference ability of the entire system. Therefore, antenna arrays with ultra-low sidelobes and ultra-deep nulls are currently hot in the field of antenna array design and are also some of the key technologies needed to achieve modern high-performance radar systems. As a new type of evolutionary algorithm inspired by nature, the DO algorithm is inspired by the wind propagation behavior of dandelions in nature. This algorithm iteratively updates the population from three stages of ascent, descent, and landing, ultimately finding the optimal position. It has good optimization ability in solving complex problems such as those involving nonlinearity, discreteness, and non-convexity, and the antenna array pattern synthesis optimization problem belongs to multivariate nonlinear problems. Therefore, the DO algorithm can be effectively applied in the field of antenna array optimization. In this work, we use the following method to obtain an optimized pattern of a linear array with the lowest sidelobe level (SLL), null placement in particular directions, and a lower notch in particular directions: by controlling the antenna array’s element spacing and leaving the phase unchanged to optimize the current amplitudes and by controlling the excitation current and phase fixation of the antenna array and changing the element spacing. In the first and second examples, different algorithms are used to reduce the SLL of the antenna. In the first example, the DO algorithm reduces the SLL to −33.37 dB, which is 2.67 dB, 2.67 dB, 3.77 dB, 2.74 dB, and 2.52 dB lower than five other algorithms. In the second example, the SLL optimized by the DO algorithm is −42.56 dB, which is 5.04 dB and 1.48 dB lower than two other algorithms. In both examples, the DO algorithm reduces the SLL lower than other algorithms when the main lobe of the antenna is not significantly widened. Examples 3, 4, and 5 use the DO algorithm to optimize the amplitude of the current, generating deep nulls and deep notches in specific directions. In Example 3, the DO algorithm obtains a depth of nulls equal to −187.6 dB, which is 66.7 dB and 44.3 dB lower than that of the flower pollination algorithm (FPA) and the chaotic colony predation algorithm (CCPA), respectively. In Example 4, the deep null obtained by the DO algorithm is as low as −98.69 dB, which is 6.67 dB lower than the deep null obtained by the grey wolf optimization (GWO) algorithm. In Example 5, the deep notch obtained by the DO algorithm is as low as −63.1 dB, which is 6.4 dB and 1.9 dB lower than the spider monkey optimization (SMO) algorithm and the grasshopper optimization algorithm (GOA), respectively. The data prove that the DO algorithm produces deeper nulls and notches than other algorithms. The last two examples involve reducing sidelobe levels and generating deep nulls by optimizing the spacing between elements. In Example 5, the SLL obtained using the DO algorithm is −22.8766 dB, which is 0.1998 dB lower than the lowest SLL of −22.6768 dB among other algorithms. In Example 6, the SLL obtained using the DO algorithm is −20.1012 dB, and the null depth is −125.1 dB, which is 1.592 dB lower than the SLL obtained by the cat swarm optimization (CSO) algorithm and 19.1 dB lower than the deep null obtained by the GWO algorithm, respectively. In summary, the results of six simulation experiments indicate that the DO algorithm has better optimization ability in linear array optimization than other evolutionary algorithms

Extended Kalman Filter-Based Vehicle Tracking Using Uniform Planar Array for Vehicle Platoon Systems.

We develop an extended Kalman filter-based vehicle tracking algorithm, specifically designed for uniform planar array layouts and vehicle platoon scenarios. We first propose an antenna placement strategy to design the optimal antenna array configuration for precise vehicle tracking in vehicle-to-infrastructure networks. Furthermore, a vehicle tracking algorithm is proposed to improve the position estimation performance by specifically considering the characteristics of the state evolution model for vehicles in the platoon. The proposed algorithm enables the sharing of corrected error transition vectors among platoon vehicles, for the purpose of enhancing the tracking performance for vehicles in unfavorable positions. Lastly, we propose an array partitioning algorithm that effectively divides the entire antenna array into sub-arrays for vehicles in the platoon, aiming to maximize the average tracking performance. Numerical studies verify that the proposed tracking and array partitioning algorithms improve the position estimation performance.

Micro Photosynthetic Power Cell Array for Energy Harvesting: Bio-Inspired Modeling, Testing and Verification

A micro-photosynthetic power cell (µPSC) generates electricity through the exploitation of living photosynthetic organisms through the principles of photosynthesis and respiration. Modeling such systems will enhance insights into the µPSC that can be employed to design real-time applications from µPSC. In this study, the bio-inspired electrical equivalent modeling of the array of µPSC is elucidated. The model is validated for array configurations of the micro-photosynthetic power cells. The developed arrayed model foresees the steady-state response at various electrical loadings. The polarization characteristics of the current-voltage (I-V) and current-power (I-P) characteristics of the array of µPSC in series and parallel, and their combinations in series and parallel connected µPSCs were validated with the experimental results. From this analysis, it is predicted that the arraying of the µPSC in the combination of series and parallel is the optimal array strategy to obtain the desired voltage and current from the µPSC such that it can be used to power real-time low and ultra-low power devices.

Enhancing 5G Sub-3.5 GHz Networks through Optimization of Microstrip Antenna Arrays with Sequential Rotation-Based MIMO using Machine Learning Techniques

The research concerns the design and optimization of microstrip antenna arrays with sequential rotation-based MIMO configurations for 5G sub-3.5 GHz networks. In total, the use of five optimization approaches was accessed, namely, Bayesian optimization, sparse learning, genetic algorithms, particle swarm optimization, and convolutional neural networks, to evaluate their efficiency in improving antenna properties. In general, Bayesian optimization outperformed the other methodologies most consistently, with the value of the average antenna gain ranging from 0.84 to 0.88, SNR between 14.8 and 15.3, and radiation efficiency ranging between 0.90 and 0.93. The use of sparse learning also ensured a high level of performance, with the average gain ranging between 0.80 and 0.85, and the values of SNR between 15.2 and 15.7. Thus, this approach showed the highest resistance to noise. Genetic algorithms, particle swarm optimization , and CNN are also quite close to each other in the efficiency of properties, with the average antenna gain value ranging between 0.85 and 0.89 and the value of SNR between 14.7 and 15.1. In overall, the results of the research show that there is a variety of available approaches to the optimization of antenna design, and each of them has a number of advantages that can be of use for the improvement of the properties and makes it possible to derive a solution that outweighs others in terms of several properties and the efficiency of its application.

Joint Array Design and Pattern Synthesis Optimization for Flexible Conformal Arrays

Joint Array Design and Pattern Synthesis Optimization for Flexible Conformal Arrays

Predicting turbulent wake flow of marine hydrokinetic turbine arrays in large-scale waterways via physics-enhanced convolutional neural networks

We present a physics-enhanced convolutional neural network (PECNN) algorithm for reconstructing the mean flow and turbulence statistics in the wake of marine hydrokinetic (MHK) turbine arrays installed in large-scale meandering rivers. The algorithm embeds the mass and momentum conservation equations into the loss function of the PECNN algorithm to improve the physical realism of the reconstructed flow fields. The PECNN is trained using large eddy simulation (LES) results of the wake flow of a single row of turbines in a virtual meandering river. Subsequently, the trained PECNN is applied to predict the wake flow of MHK turbines with arrangements and positionings different than those considered during the training process. The PECNN predictions are validated using the results of separately performed LES. The results show that the PECNN algorithm can accurately predict the wake flow of MHK turbine farms at a small fraction of the cost of LES. The PECNN can improve the accuracy by around 1% and reduce the physical constraint indices by around 50% compared to the CNN without physical constraints. This work underscores the potential of PECNN to develop reduced-order models for control co-design and optimization of MHK turbine arrays in natural riverine environments.

Integration of electronic nose, electronic tongue, and colorimeter in combination with chemometrics for monitoring the fermentation process of Tremella fuciformis

This study proposes an efficient method for monitoring the submerged fermentation process of Tremella fuciformis (T. fuciformis) by integrating electronic nose (e-nose), electronic tongue (e-tongue), and colorimeter sensors using a data fusion strategy. Chemometrics is employed to establish qualitative identification and quantitative prediction models. The Pearson correlation analysis was applied to extract features from the e-nose and tongue sensor arrays. The optimal sensor arrays for monitoring the deep fermentation process of T. fuciformis were obtained, and four different data fusion methods were developed by incorporating the colorimeter data features. To achieve qualitative identification, the physicochemical data and principal component analysis (PCA) results are utilized to determine three stages of the fermentation process. The fusion signal based on full features proves to be the optimal data fusion method, exhibiting the highest accuracy across different models. Notably, random forest (RF) is shown to be the most accurate pattern recognition method in this paper. For quantitative prediction, partial least squares regression (PLSR) and support vector regression (SVR) are employed to predict the sugar content and dry cell weight during fermentation. The best respective predictive R2 values for reducing sugar, tremella polysaccharide and dry cell weight are found to be 0.965, 0.988, and 0.970. Furthermore, due to its ability to capture nonlinear data relationships, SVR had superior performance in prediction modeling than PLSR. The results demonstrated that the combination of electronic sensor fusion signals and chemometrics provides a promising method for effectively monitoring T. fuciformis fermentation.

Parametric design optimization and thermodynamic analysis of plate fin heat exchanger for helium liquefaction system

The plate-fin heat exchanger is one of the most important components of helium liquefaction system as it recovers cold energy from helium gas in the cold box. Because of larger heat transfer surface area and compact geometrical configuration, plate-fin heat exchangers are widely used. The design of heat exchanger using helium as a cryogenic fluid needs high thermodynamic performance with minimum pressure drop which particularly requires the optimization of dimensional array and its geometrical configurations. In this regard, a parametric investigation was performed to compute the effective geometrical parameters and its effect on thermodynamic (effectiveness and log-mean temperature difference) performance has been discussed in detail. The present study aims to design optimization and thermodynamic performance evaluation of plate-fin type heat exchangers used in helium liquefaction systems. The developed helium liquefaction model has been simulated for the most critical liquefier with and without liquid nitrogen precooling, mixed mode, and refrigeration mode. Each component of the liquefaction model has been technically and thermodynamically analyzed and, in particular, four plate-fin heat exchangers have been considered and designed accurately using Aspen EDR®. Initially, the sensitivity analysis was carried out to characterize the most significant geometrical parameters (fin thickness, height, number and arrangement of layers, frequency, and fin type) and their effect on thermodynamic performance and pressure drop. After that, the artificial intelligence model was used to determine the optimal range of such geometrical variables in which the heat exchanger has maximum effectiveness and log mean temperature difference. Finally, the operating conditions obtained from cycle modeling and optimized geometrical datasets have been applied in commercial simulation software Aspen EDR® for plate-fin heat exchangers design. Finally, a comparative thermodynamic analysis has been carried out to determine the performance of all the designed heat exchangers operating at four different modes (boundary conditions).

Valorization of sugarcane bagasse for high-yield production of laccase through Aspergillus terreus for effective azo dye decolourization

Synthetic dyes such as azo dyes are significant pollutants in the wastewater released from various textile industries. The low biodegradability and production from synthetic sources with high shelf life make azo dyes a challenging material for degradation. This study used chemically mutated Aspergillus terrus in the laccase production under solid-state fermentation using sugarcane bagasse. Initially, the wild-type strain produced a laccase activity of 4.12 U/mL. Later, the alkaline pretreatment of sugarcane bagasse showed a significant increase in laccase activity by 38.9%. Further, random mutagenesis treatment with 100 mM EMS generated a hyper laccase-producing strain with a 2.3-fold increment in laccase activity compared to the wild-type strain. The enzyme displayed optimal activity at pH 6.5 and 35 °C. The metal ions such as Fe3+ (29.4 U/mL), Fe2+ (20.8 U/mL) and Cu2+ (18.05 U/mL) showed positive effects on laccase activity. The crude laccase was used to bioremediate Congo red, a prominent azo dye used in textile and pharmaceutical industries. The preliminary studies with a crude enzyme displayed 68.86% dye decolourization after 24 h of incubation. Additionally, with Taguchi orthogonal array optimization experiments, the maximal dye decolorization of 78.24% was achieved by maintaining crude enzyme concentration (20 U), dye concentration (25 mg/L) and pH 4.5.

Vertically spliced tabletop light field cave display with extended depth content and separately optimized compound lens array.

Tabletop three-dimensional light field display is a kind of compelling display technology that can simultaneously provide stereoscopic vision for multiple viewers surrounding the lateral side of the device. However, if the flat panel light field display device is simply placed horizontally and displayed directly above, the visual frustum will be tilted and the 3D content outside the display panel will be invisible, the large oblique viewing angle will also lead to serious aberrations. In this paper, we demonstrate what we believe to be a new vertical spliced light field cave display system with an extended depth content. A separate optimization of different compound lens array attenuates the aberration from different oblique viewing angles, and a local heating fitting method is implemented to ensure the accuracy of fabrication process. The image coding method and the correction of the multiple viewpoints realize the correct construction of spliced voxels. In the experiment, a high-definition and precisely spliced 3D city terrain scene is demonstrated on the prototype with a correct oblique perspective in 100-degree horizontal viewing range. We envision that our research will provide more inspiration for future immersive large-scale glass-free virtual reality display technologies.

Topology Optimization Design Method for Acoustic Imaging Array of Power Equipment.

Acoustic imaging technology has the advantages of non-contact and intuitive positioning. It is suitable for the rapid positioning of defects such as the mechanical loosening, discharge, and DC bias of power equipment. However, the existing research lacks the optimization design of microphone array topology. The acoustic frequency domain characteristics of typical power equipment are elaborately sorted out. After that, the cut-off frequencies of acoustic imaging instruments are determined, to meet the needs of the full bandwidth test requirements. Through a simulation calculation, the circular array is demonstrated to be the optimal shape. And the design parameters affect the imaging performance of the array to varying degrees, indicating that it is difficult to obtain the optimal array topology by an exhaustive method. Aimed at the complex working conditions of power equipment, a topology optimization design method of an acoustic imaging array for power equipment is proposed, and the global optimal solution of microphone array topology is obtained. Compared with the original array, the imaging performance of the improved LF and HF array is promoted by 54% and 49%, respectively. Combined with the simulation analysis and laboratory test, it is verified that the improved array can not only accurately locate the single sound source but also accurately identify the main sound source from the interference of the contiguous sound source.

Design, scalable construction, and test of optimal linear Halbach arrays for mobile applications

Advancing permanent magnet arrays for linear electric machines is a compelling endeavor with the potential to further improve efficiencies in the fields of power generation or passenger and cargo transportation. Permanent magnets offer an unparalleled advantage by providing a consistent and efficient source of magnetic flux density without the need for external power input. This remarkable property allows for the development of smaller, lighter, and more energy-efficient electric machines, making them ideal for applications in several industries. Here, we present the design of an optimal linear Halbach array with a sinusoidal distribution of the magnetic flux density component Bz perpendicular to the surface of the array. A large scale and easy scalable six-segmented Halbach array has been built without the use of a steel backplate enabling a lightweight and compact design. Numerical results of the magnetic flux density distributions are calculated by relying on finite element methods and are compared with experimental measurements of the magnetic flux density, showing good agreement. The demonstration revealed that, with an optimal magnet configuration, the absence of a backplate does not result in any diminishment of magnetic flux density strength.

Modeling and Optimization of CMUTs Arrays for Improved Transmission and Reception Performance in Immersion

Modeling and Optimization of CMUTs Arrays for Improved Transmission and Reception Performance in Immersion