Tuning of the functional beamforming resolution for wind tunnel measurements

Conventional Frequency Domain Beamforming (CB) is characterised by the frequency dependency of the mainlobe width and by the presence of sidelobes that limit its dynamic range. Functional Beamforming (FB) has been introduced with the aim to overcome these limitations, narrowing the mainlobe and reducing the sidelobe levels. This paper introduces a strategy to obtain a beamformer with a target mainlobe width that is constant over a desired frequency range. The idea is to properly adjust the Functional Beamforming order ν, frequency by frequency, to preserve the mainlobe width. A tuning procedure of the order ν is presented and applied to a typical wind tunnel setup. A detailed analysis of the dependency “order ν versus frequency” is discussed and a general formula describing this dependency is provided. Finally, the effectiveness of the proposed approach is shown both on simulated and experimental test case

A comparison between aeroacoustic source mapping techniques for the characterisation of wind turbine blade models with microphone arrays

Characterising the aeroacoustic noise sources generated by a rotating wind turbine blade provides useful information for tackling noise reduction of this mechanical system. In this context, microphone array measurements and acoustic source mapping techniques are powerful tools for the identification of aeroacoustic noise sources. This paper discusses a series of acoustic mapping strategies that can be exploited in this kind of applications. A single-blade rotor was tested in a semi-anechoic chamber using a circular microphone array. The Virtual Rotating Array (VRA) approach, which transforms the signals acquired by the physical static array into signals of virtual microphones synchronously rotating with the blade, hence ensuring noise-source stationarity, was used to enable the use of frequency domain acoustic mapping techniques. A comparison among three different acoustic mapping methods is presented: Conventional Beamforming, CLEAN-SC and Covariance Matrix Fitting based on Iterative Re-weighted Least Squares and Bayesian approach. The latter demonstrated to provide the best results for the application and made it possible a detailed characterization of the noise sources generated by the rotating blade at different operating conditions

In-Situ Component-Based TPA for Time-Variant Dynamic Systems: A State-Space Formulation

In this chapter, a methodology to calculate equivalent forces by taking into account the possible time-varying dynamic behavior of the components under analysis is presented. This methodology is based on the use of the state-space realization of the in-situ component-based TPA method. To take into account possible time-varying dynamic behavior of the systems under study, a local linear parameter varying (LPV) model identification approach is used. This approach enables the computation of state-space models representative of the components at each time instant by interpolating a given set of linear time-invariant (LTI) state-space models representative of the dynamics of the components under study for fixed operating conditions. By exploiting a numerical example, it is found that when dealing with structures presenting time-varying behavior, accurate equivalent forces can be computed in time domain by using the approaches presented in this chapter. Furthermore, it is clearly demonstrated that ignoring the time dependency of the dynamic behavior of mechanical systems can lead to an important deterioration of the results

Goal-Oriented Scheduling in Sensor Networks With Application Timing Awareness

— Taking inspiration from linguistics, the communications theoretical community has recently shown a significant recent interest in pragmatic, or goal-oriented, communication. In this paper, we tackle the problem of pragmatic communication with multiple clients with different, and potentially conflicting, objectives. We capture the goal-oriented aspect through the metric of Value of Information (VoI), which considers the estimation of the remote process as well as the timing constraints. However, the most common definition of VoI is simply the Mean Square Error (MSE) of the whole system state, regardless of the relevance for a specific client. Our work aims to overcome this limitation by including different summary statistics, i.e., value functions of the state, for separate clients, and a diversified query process on the client side, expressed through the fact that different applications may request different functions of the process state at different times. A query-aware Deep Reinforcement Learning (DRL) solution based on statically defined VoI can outperform naive approaches by 15-20%

Experimental acoustic modal analysis of an automotive cabin: Challenges and solutions

In this paper, a full Acoustic Modal Analysis (AMA) procedure to improve the CAE predictions of the car interior noise level is proposed. Some of the challenges that can be experienced during such an analysis are described and new solutions to face them are proposed. Particular AMA challenges range from the arrangement of the experimental setup to the post-processing analysis. Since a large number of microphones are needed, a smart localization procedure, which automatically determines the microphone three dimensional (3-D) positions and dramatically reduces the setup time, is presented herein. Furthermore, the need for a large number of sound sources spread across the cavity to assure a homogeneous sound field makes modal parameter estimation a nontrivial task. Traditional modal parameter estimators have indeed proven not to be effective in cases where many input excitation locations have to be used. Hence, a more suitable estimator, the Maximum Likelihood Modal Model-based (ML-MM) method, will be employed for such an analysis

Dielectric and optical evaluation of high-emissivity coatings for temperature measurements in microwave applications

In this work, several commercial high-emissivity coatings have been characterized in terms of emissivity, chemical composition and dielectric properties as a function of temperature, under microwave irradiation. Accurate knowledge of their response under exposure to microwaves provides new and crucial information about their practical usability for non-contact temperature measurements in microwave environments. Due to their high metallic content, some of the studied coatings exhibited unexpected microwave-triggered reactions that hindered their use up to the maximum temperature specified by the manufacturers. Emissivity and chemical analyses before and after the heating cycles confirmed the degradation of some of the samples predicted by dielectric measurements. This work illustrates how a careful characterization of optical and dielectric properties under representative operating conditions (temperature range, microwave exposure) is vital in order to select the appropriate reference coating to obtain reliable temperature measurements in microwave applications

Learning to speak on behalf of a group: medium access control for sending a shared message

The rapid development of Internet of Things (IoT) technologies has not only enabled new applications, but also presented new challenges for reliable communication with limited resources. In this work, we define a novel problem that can arise in these scenarios, in which a set of sensors need to communicate a joint observation. This observation is shared by a random subset of the nodes, which need to propagate it to the rest of the network, but coordination is complex: as signaling constraints require the use of random access schemes over shared channels, sensors need to implicitly coordinate, so that at least one transmission gets through without collisions. Unlike the majority of existing medium access schemes, the goal is to make sure that the shared message gets through, regardless of the sender. We analyze this coordination problem theoretically and provide low-complexity solutions. While a clustering-based approach is near-optimal if the sensors have prior knowledge, we provide a distributed multi-armed bandit (MAB) solution for the more general case and validate it by simulation

Measurement of the structural behaviour of a 3D airless wheel prototype by means of optical non-contact techniques

Additive Manufacturing (AM) is becoming a widely employed technique also in mass production. In this field, compliances with geometry and mechanical performance standards represent a crucial constrain. Since 3D printed products exhibit a mechanical behaviour that is difficult to predict and investigate due to the complex shape and the inaccuracy in reproducing nominal sizes, optical non-contact techniques are an appropriate candidate to solve these issues. In this paper, 2D digital image correlation and thermoelastic stress analysis are combined to map the stress and the strain performance of an airless wheel prototype. The innovative airless wheel samples are 3D-printed by fused deposition modelling and stereolithography in poly-lactic acid and photopolymer resin, respectively. The static mechanical behaviour for different wheel-ground contact configurations is analysed using the aforementioned non-contact techniques. Moreover, the wheel-ground contact pressure is mapped, and a parametric finite element model is developed. The results presented in the paper demonstrate that several factors have great influence on 3D printed airless wheels: a) the type of material used for manufacturing the specimen, b) the correct transfer of the force line (i.e., the loading system), c) the geometric complexity of the lattice structure of the airless wheel. The work confirms the effectiveness of the proposed non-contact measurement procedures for characterizing complex shaped prototypes manufactured using AM

EpiDiP/NanoDiP: a versatile unsupervised machine learning edge computing platform for epigenomic tumour diagnostics.

DNA methylation analysis based on supervised machine learning algorithms with static reference data, allowing diagnostic tumour typing with unprecedented precision, has quickly become a new standard of care. Whereas genome-wide diagnostic methylation profiling is mostly performed on microarrays, an increasing number of institutions additionally employ nanopore sequencing as a faster alternative. In addition, methylation-specific parallel sequencing can generate methylation and genomic copy number data. Given these diverse approaches to methylation profiling, to date, there is no single tool that allows (1) classification and interpretation of microarray, nanopore and parallel sequencing data, (2) direct control of nanopore sequencers, and (3) the integration of microarray-based methylation reference data. Furthermore, no software capable of entirely running in routine diagnostic laboratory environments lacking high-performance computing and network infrastructure exists. To overcome these shortcomings, we present EpiDiP/NanoDiP as an open-source DNA methylation and copy number profiling suite, which has been benchmarked against an established supervised machine learning approach using in-house routine diagnostics data obtained between 2019 and 2021. Running locally on portable, cost- and energy-saving system-on-chip as well as gpGPU-augmented edge computing devices, NanoDiP works in offline mode, ensuring data privacy. It does not require the rigid training data annotation of supervised approaches. Furthermore, NanoDiP is the core of our public, free-of-charge EpiDiP web service which enables comparative methylation data analysis against an extensive reference data collection. We envision this versatile platform as a useful resource not only for neuropathologists and surgical pathologists but also for the tumour epigenetics research community. In daily diagnostic routine, analysis of native, unfixed biopsies by NanoDiP delivers molecular tumour classification in an intraoperative time frame