Measurement Science and Technology

Structural health monitoring (SHM) is the automation of the condition assessment process of an engineered system. When applied to geometrically large components or structures, such as those found in civil and aerospace infrastructure and systems, a critical challenge is in designing the sensing solution that could yield actionable information. This is a difficult task to conduct cost-effectively, because of the large surfaces under consideration and the localized nature of typical defects and damages. There have been significant research efforts in empowering conventional measurement technologies for applications to SHM in order to improve performance of the condition assessment process. Yet, the field implementation of these SHM solutions is still in its infancy, attributable to various economic and technical challenges. The objective of this Roadmap publication is to discuss modern measurement technologies that were developed for SHM purposes, along with their associated challenges and opportunities, and to provide a path to research and development efforts that could yield impactful field applications. The Roadmap is organized into four sections: distributed embedded sensing systems, distributed surface sensing systems, multifunctional materials, and remote sensing. Recognizing that many measurement technologies may overlap between sections, we define distributed sensing solutions as those that involve or imply the utilization of numbers of sensors geometrically organized within (embedded) or over (surface) the monitored component or system. Multi-functional materials are sensing solutions that combine multiple capabilities, for example those also serving structural functions. Remote sensing are solutions that are contactless, for example cell phones, drones, and satellites. It also includes the notion of remotely controlled robots.

Maximum permissible errors (MPEs) are an important measurement system specification and form the basis of periodic verification of a measurement system's performance. However, there is no standard methodology for determining MPEs, so when they are not provided, or not suitable for the measurement procedure performed, it is unclear how to generate an appropriate value with which to verify the system. Whilst a simple approach might be to take many measurements of a calibrated artefact and then use the maximum observed error as the MPE, this method requires a large number of repeat measurements for high confidence in the calculated MPE. Here, we present a statistical method of MPE determination, capable of providing MPEs with high confidence and minimum data collection. The method is presented with 1000 synthetic experiments and is shown to determine an overestimated MPE within 10% of an analytically true value in 99.2% of experiments, while underestimating the MPE with respect to the analytically true value in 0.8% of experiments (overestimating the value, on average, by 1.24%). The method is then applied to a real test case (probing form error for a commercial fringe projection system), where the efficiently determined MPE is overestimated by 0.3% with respect to an MPE determined using an arbitrarily chosen large number of measurements.

In this study, a self-sensing and self-heating natural fibre-reinforced cementitious composite for the shotcrete technique was developed using Kenaf fibres. For this purpose, a series of Kenaf fibre concentrations were subjected to initial chemical treatment, followed by integration into the cement-based composite containing hybrid carbon nanotubes (CNT) and graphene nanoplatelets (GNP). The investigation encompassed an examination of mechanical, microstructural, sensing, and joule heating performances of the environmentally friendly shotcrete mixture, with subsequent comparisons drawn against a counterpart blend featuring a conventionally synthesized polypropylene (PP) fibre. Following the experimental phase, a comprehensive 3D nonlinear finite difference (3D NLFD) model of an urban twin road tunnel, completed with all relevant components, was meticulously formulated using the FLAC3D (fast lagrangian analysis of continua in 3 dimensions) code. This model was subjected to rigorous validation procedures. The performances of this green shotcrete mixture as the lining of the inner shell of the tunnel were assessed comparatively using this 3D numerical model under static and dynamic loading. The twin tunnel was subjected to a harmonic seismic load as a dynamic load with a duration of 15 s. The laboratory findings showed a reduction in the composite sensing and heating potentials in both cases of Kenaf and PP fibre reinforcement. Incorporating a specific quantity of fibre yields a substantial enhancement in both the mechanical characteristics and microstructural attributes of the composite. An analysis of digital image correlation demonstrated that Kenaf fibres were highly effective in controlling cracks in cement-based composites. Furthermore, based on the static and dynamic 3DNLFD analysis, this green cement-based composite demonstrated its potential for shotcrete applications as the lining of the inner shell of the tunnel. This study opens an appropriate perspective on the extensive and competent contribution of natural fibres for multifunctional sustainable, reliable and affordable cement-based composite developments for today's world.

Time-gated (TG) Raman spectroscopy (RS) has been shown to be an effective technical solution for the major problem whereby sample-induced fluorescence masks the Raman signal during spectral detection. Technical methods of fluorescence rejection have come a long way since the early implementations of large and expensive laboratory equipment, such as the optical Kerr gate. Today, more affordable small sized options are available. These improvements are largely due to advances in the production of spectroscopic and electronic components, leading to the reduction of device complexity and costs. An integral part of TG Raman spectroscopy is the temporally precise synchronization (picosecond range) between the pulsed laser excitation source and the sensitive and fast detector. The detector is able to collect the Raman signal during the short laser pulses, while fluorescence emission, which has a longer delay, is rejected during the detector dead-time. TG Raman is also resistant against ambient light as well as thermal emissions, due to its short measurement duty cycle.

In recent years, the focus in the study of ultra-sensitive and fast detectors has been on gated and intensified charge coupled devices (ICCDs), or on CMOS single-photon avalanche diode (SPAD) arrays, which are also suitable for performing TG RS. SPAD arrays have the advantage of being even more sensitive, with better temporal resolution compared to gated CCDs, and without the requirement for excessive detector cooling. This review aims to provide an overview of TG Raman from early to recent developments, its applications and extensions.

Digital twinning is a rapidly growing area of research. Digital twins combine models and data to provide up-to-date information about the state of a system. They support reliable decision-making in fields such as structural monitoring and advanced manufacturing. The use of metrology data to update models in this way offers benefits in many areas, including metrology itself. The recent activities in digitalisation of metrology offer a great opportunity to make metrology data 'twin-friendly' and to incorporate digital twins into metrological processes. This paper discusses key features of digital twins that will inform their use in metrology and measurement, highlights the links between digital twins and virtual metrology, outlines what use metrology can make of digital twins and how metrology and measured data can support the use of digital twins, and suggests potential future developments that will maximise the benefits achieved.

Time-correlated single photon counting (TCSPC) is a widely used, robust and mature technique to measure the photon arrival time in applications such as fluorescence spectroscopy and microscopy, LIDAR and optical tomography. In the past few years there have been significant developments with wide-field TCSPC detectors, which can record the position as well as the arrival time of the photon simultaneously. In this review, we summarise different approaches used in wide-field TCSPC detection, and discuss their merits for different applications, with emphasis on fluorescence lifetime imaging.

Experimentally-measured pressure fields play an important role in understanding many fluid dynamics problems. Unfortunately, pressure fields are difficult to measure directly with non-invasive, spatially resolved diagnostics, and calculations of pressure from velocity have proven sensitive to error in the data. Omnidirectional line integration methods are usually more accurate and robust to these effects as compared to implicit Poisson equations, but have seen slower uptake due to the higher computational and memory costs, particularly in 3D domains. This paper demonstrates how omnidirectional line integration approaches can be converted to a matrix inversion problem. This novel formulation uses an iterative approach so that the boundary conditions are updated each step, preserving the convergence behavior of omnidirectional schemes while also keeping the computational efficiency of Poisson solvers. This method is implemented in Matlab and also as a GPU-accelerated code in CUDA-C++. The behavior of the new method is demonstrated on 2D and 3D synthetic and experimental data. Three-dimensional grid sizes of up to 125 million grid points are tractable with this method, opening exciting opportunities to perform volumetric pressure field estimation from 3D PIV measurements.

Due to the necessary transition to renewable energy, the transport of electricity over long distances will become increasingly important, since the sites of sustainable electricity generation, such as wind or solar power parks, and the place of consumption can be very far apart. Currently, electricity is mainly transported via overhead AC lines. However, studies have shown that for long distances, transport via DC offers decisive advantages. To make optimal use of the existing route infrastructure, simultaneous AC and DC, or hybrid transmission, should be employed. The resulting electric field strengths must not exceed legally prescribed thresholds to avoid potentially harmful effects on humans and the environment. However, accurate quantification of the resulting electric fields is a major challenge in this context, as they can be easily distorted (e.g. by the measurement equipment itself). Nonetheless knowledge of the undisturbed field strengths from DC up to several multiples of the fundamental frequency of the power-grid (up to 1 kHz) is required to ensure compliance with the thresholds. Both AC and DC electric fields can result in the generation of corona ions in the vicinity of the line. In the case of pure AC fields, the corona ions generated typically recombine in the immediate vicinity of the line and, therefore, have no influence on the field measurement further away. Unfortunately, this assumption does not hold for DC fields and hybrid fields, where corona ions can be transported far away from the line (e.g. by wind), and potentially interact with the measurement equipment yielding incorrect measurement results. This review will provide a comprehensive overview of the current state-of-the-art technologies and methods which have been developed to address the problems of measuring the electric field near hybrid power lines.

Particle image velocimetry (PIV) has become the chief experimental technique for velocity field measurements in fluid flows. The technique yields quantitative visualizations of the instantaneous flow patterns, which are typically used to support the development of phenomenological models for complex flows or for validation of numerical simulations. However, due to the complex relationship between measurement errors and experimental parameters, the quantification of the PIV uncertainty is far from being a trivial task and has often relied upon subjective considerations. Recognizing the importance of methodologies for the objective and reliable uncertainty quantification (UQ) of experimental data, several PIV-UQ approaches have been proposed in recent years that aim at the determination of objective uncertainty bounds in PIV measurements.

This topical review on PIV uncertainty quantification aims to provide the reader with an overview of error sources in PIV measurements and to inform them of the most up-to-date approaches for PIV uncertainty quantification and propagation. The paper first introduces the general definitions and classifications of measurement errors and uncertainties, following the guidelines of the International Organization for Standards (ISO) and of renowned books on the topic. Details on the main PIV error sources are given, considering the entire measurement chain from timing and synchronization of the data acquisition system, to illumination, mechanical properties of the tracer particles, imaging of those, analysis of the particle motion, data validation and reduction. The focus is on planar PIV experiments for the measurement of two- or three-component velocity fields.

Approaches for the quantification of the uncertainty of PIV data are discussed. Those are divided into a-priori UQ approaches, which provide a general figure for the uncertainty of PIV measurements, and a-posteriori UQ approaches, which are data-based and aim at quantifying the uncertainty of specific sets of data. The findings of a-priori PIV-UQ based on theoretical modelling of the measurement chain as well as on numerical or experimental assessments are discussed. The most up-to-date approaches for a-posteriori PIV-UQ are introduced, highlighting their capabilities and limitations.

As many PIV experiments aim at determining flow properties derived from the velocity fields (e.g. vorticity, time-average velocity, Reynolds stresses, pressure), the topic of PIV uncertainty propagation is tackled considering the recent investigations based on Taylor series and Monte Carlo methods. Finally, the uncertainty quantification of 3D velocity measurements by volumetric approaches (tomographic PIV and Lagrangian particle tracking) is discussed.

Digital image correlation (DIC) is a widely used technique in experimental mechanics for full field measurement of displacements and strains. The subset matching based DIC requires surfaces containing a random pattern. Even though there are several techniques to create random speckle patterns, their applicability is still limited. For instance, traditional methods such as airbrush painting are not suitable in the following challenging scenarios: (i) when time available to produce the speckle pattern is limited and (ii) when dynamic loading conditions trigger peeling of the pattern. The development and application of some novel techniques to address these situations is presented in this paper. The developed techniques make use of commercially available materials such as temporary tattoo paper, adhesives and stamp kits. The presented techniques are shown to be quick, repeatable, consistent and stable even under impact loads and large deformations. Additionally, they offer the possibility to optimise and customise the speckle pattern. The speckling techniques presented in the paper are also versatile and can be quickly applied in a variety of materials.

Railway axles are safety-critical components of the railroad rolling stock and the consequences of possible in-service failures can have a huge impact. Axle fatigue cracks are relatively common defects during train operation, but how to intelligently identify axle fatigue cracks in running trains is still a great challenge. In order to identify axle fatigue cracks more intelligently, the problem that needs to be solved is how to overcome the manual extraction of features by manual experience as well as shallow networks. Therefore, in this paper, an acoustic emission signal identification method based on deep belief networks (DBNs) for axle fatigue cracks is proposed. In this method, a DBN model is constructed. The axle fatigue crack acoustic emission signal data were obtained by our designed acquisition experimental setup, and these data were used to verify the accuracy of the constructed DBN network model identification. The experimental results show that the method of identification of axle fatigue cracks based on DBN, compared with the traditional fault diagnosis method, eliminates the operations of data feature extraction, feature screening, feature fusion, etc and makes complete use of all the information contained in the fault data. The method can not only identify fatigue crack signals but also has a high identification rate of fatigue cracks at different stages. In the axle fatigue crack acoustic emission identification field, it can be seen that the proposed method in this paper will be a promising approach.

Resonance demodulation is one of the most commonly used methods in rolling bearing fault diagnosis, yet determining the optimal demodulation band has been a significant challenge. The vibration signal from a faulty bearing may include not only periodic fault impulses but also discrete harmonic interferences, random impulses, Gaussian white noise, among others. To enhance fault information and attenuate the impact of interference signals, this paper proposes an improved envelope spectrum via Hoyer index-gram (IESHoyergram). By utilizing the Hoyer index of the spectrum-related enhanced envelope spectrum as the frequency band filtering criterion, the proposed method extracts periodic impulses while suppressing interference from random impulses and other sources. Moreover, owing to the multilevel segmentation based on the different trend components in the spectral correlation spectrogram, IESHoyergram avoids the shortcomings of traditional segmentation methods. The proposed method is validated through both simulated and experimentally acquired data, demonstrating its capability not only to enhance the characteristics of a single fault but also to separate multiple component faults.

One of the key objectives in tunnel illness detection is identifying tunnel lining leakage, and deep learning-based image semantic segmentation approaches can automatically locate tunnel lining leakage. However, in order to meet the real-time processing needs of professional mobile inspection equipment, existing leakage image segmentation approaches have difficulties in identifying real-time, dealing with voids, and dealing with edge discontinuities in the leaking zone. To address the aforementioned issues, this study introduces the PP-LiteSeg-Attn model, which takes the real-time semantic segmentation model PP-LiteSeg-B as baseline model, and combines the multi-layer CBAM attention mechanism and the CoT attention mechanism. Using the publically available dataset Water-Leakage, we trained and validated the PP-LiteSeg-Attn model, and attained IoU and F1 values of 88.18% and 93.72%, respectively, outperforming similar models in both measures. Extensive experiments show that the segmentation speed of the PP-LiteSeg-Attn model reaches 112.28 FPS, which meets real-time requirements, and that the model can effectively solve problems such as the appearance of voids in the seepage area, discontinuity, and fuzzy segmentation of seepage edges. The PP-LiteSeg-Attn model is better applicable to complicated tunnel settings, offering technical references for real-time diagnosis of tunnel illnesses.

The inner space of tubular and hole-like workpieces, such as oil pipes and cylinders, is limited in volume and has no effective means of measuring the inner surface under geometric constraints.

This paper proposes a non-coaxial optical path line structured light method for inner surface inspection, which can significantly reduce the device size and working distance. The method constructs two non-coaxial optical paths for light source and imaging, and uses two fixed reflectors to achieve the deflection of the optical axes of the light source and imaging paths.

After theoretical derivation and verification, a sample device was designed and fabricated for experiments. The device measures approximately 22 mm by 24 mm in cross-section, the maximum insertion distance is about 200 mm, and the minimum inner diameter of the accessible space is 50 mm.

The results show that the optical path design does not destroy the original homography relationship, and can still ensure the measurement capability comparable to the existing methods when reducing the device size to 10% of the original. The method has obvious advantages when applied to the inspection of tubular and hole-like workpieces in fields such as petrochemical industry, military equipment, etc.

The control system of unmanned vehicles must demonstrate strong capability to promptly diagnose and address system faults. Such a capability can improve transportation efficiency, ensure the smooth execution of production tasks, and to a certain extent, mitigate the risk of human casualties. To ensure the upkeep of unmanned vehicles and address the diagnostic requirements of control systems, this study integrates traditional wheeled vehicle control systems with digital twin (DT) technology to establish a framework for control system fault diagnosis and maintenance, with the primary objective of fulfilling the fault diagnosis task. By this framework, a method for detecting faults in unmanned vehicle control systems based on DT technology has been developed. This method involves the design of a data-driven model using multiple sensors and the application of a DT-improved particle filter fault diagnosis algorithm, utilizing a multi-domain model approach. A case study of the proposed method and simulation results are presented to illustrate its feasibility.

The health condition of rolling bearings has a direct impact on the safe operation of rotating machinery. And their working environment is harsh and the working condition is complex, which brings challenges to fault diagnosis. With the development of computer technology, deep learning has been applied in the field of fault diagnosis and has rapidly developed. Among them, convolutional neural network (CNN) has received great attention from researchers due to its powerful data mining ability and feature adaptive learning ability. Based on recent research hotspots, the development history and trend of CNN is summarized and analyzed. Firstly, the basic structure of CNN is introduced and the important progress of classical CNN models for rolling bearing fault diagnosis in recent years is studied. The problems with the classic CNN algorithm have been pointed out. Secondly, to solve the above problems, combined with recent research achievements, various methods and principles for optimizing CNN are introduced and compared from the perspectives of deep feature extraction, hyperparameter optimization, network structure optimization. Although significant progress has been made in the research of fault diagnosis of rolling bearings based on CNN, there is still room for improvement and development in addressing issues such as low accuracy of imbalanced data, weak model generalization, and poor network interpretability. Therefore, the future development trend of CNN networks is discussed finally. And transfer learning models are introduced to improve the generalization ability of CNN and interpretable CNN is used to increase the interpretability of CNN networks.

This work addresses the historical development of techniques and methodologies oriented to the measurement of the internal diameter of transparent tubes since the original contributions of Anderson and Barr published in 1923 in the first issue of Measurement Science and Technology. The progresses on this field are summarized and highlighted the emergence and significance of the measurement approaches supported by the optical fiber.

Mechanical fault diagnosis is crucial for ensuring the normal operation of mechanical equipment. With the rapid development of deep learning technology, the methods based on big data-driven provide a new perspective for the fault diagnosis of machinery. However, mechanical equipment operates in the normal condition most of the time, resulting in the collected data being imbalanced, which affects the performance of mechanical fault diagnosis. As a new approach for generating data, generative adversarial network (GAN) can effectively address the issues of limited data and imbalanced data in practical engineering applications. This paper provides a comprehensive review of GAN for mechanical fault diagnosis. Firstly, the development of GAN-based mechanical fault diagnosis, the basic theory of GAN and various GAN variants (GANs) are briefly introduced. Subsequently, GANs are summarized and categorized from the perspective of labels and models, and the corresponding applications are outlined. Lastly, the limitations of current research, future challenges, future trends and selecting the GAN in the practical application are discussed.

Predictive maintenance (PdM) is currently the most cost-effective maintenance method for industrial equipment, offering improved safety and availability of mechanical assets. A crucial component of PdM is the remaining useful life (RUL) prediction for machines, which has garnered increasing attention. With the rapid advancements in industrial internet of things and artificial intelligence technologies, RUL prediction methods, particularly those based on pattern recognition (PR) technology, have made significant progress. However, a comprehensive review that systematically analyzes and summarizes these state-of-the-art PR-based prognostic methods is currently lacking. To address this gap, this paper presents a comprehensive review of PR-based RUL prediction methods. Firstly, it summarizes commonly used evaluation indicators based on accuracy metrics, prediction confidence metrics, and prediction stability metrics. Secondly, it provides a comprehensive analysis of typical machine learning methods and deep learning networks employed in RUL prediction. Furthermore, it delves into cutting-edge techniques, including advanced network models and frontier learning theories in RUL prediction. Finally, the paper concludes by discussing the current main challenges and prospects in the field. The intended audience of this article includes practitioners and researchers involved in machinery PdM, aiming to provide them with essential foundational knowledge and a technical overview of the subject matter.

The market for service robots is expanding as labor costs continue to rise. Faced with intricate working environments, fault detection and diagnosis are crucial to ensure the proper functioning of service robots. The objective of this review is to systematically investigate the realm of service robots' fault diagnosis through the application of Structural Topic Modeling. A total of 289 papers were included, culminating in ten topics, including advanced algorithm application, data learning-based evaluation, automated equipment maintenance, actuator diagnosis for manipulator, non-parametric method, distributed diagnosis in multi-agent systems, signal-based anomaly analysis, integrating complex control framework, event knowledge assistance, mobile robot particle filtering method. These topics spanned service robot hardware and software failures, diverse service robot systems, and a range of advanced algorithms for fault detection in service robots. Asia-Pacific, Europe, and the Americas, recognized as three pivotal regions propelling the advancement of service robots, were employed as covariates in this review to investigate regional disparities. The review found that current research tends to favor the use of artificial intelligence (AI) algorithms to address service robots' complex system faults and vast volumes of data. The topics of algorithms, data learning, automated maintenance, and signal analysis are advancing with the support of AI, gaining increasing popularity as a burgeoning trend. Additionally, variations in research focus across different regions were found. The Asia-Pacific region tends to prioritize algorithm-related studies, while Europe and the Americas show a greater emphasis on robot safety issues. The integration of diverse technologies holds the potential to bring forth new opportunities for future service robot fault diagnosis.Simultaneously, regional standards about data, communication, and other aspects can streamline the development of methods for service robots' fault diagnosis.

Inspection of surface and nanostructure imperfections play an important role in high-throughput manufacturing across various industries. This paper introduces a novel, parallelised version of the metrology and inspection technique: Coherent Fourier scatterometry (CFS). The proposed strategy employs parallelisation with multiple probes, facilitated by a diffraction grating generating multiple optical beams and detection using an array of split detectors. The article details the optical setup, design considerations, and presents results, including independent detection verification, calibration curves for different beams, and a data stitching process for composite scans. The study concludes with discussions on the system's limitations and potential avenues for future development, emphasizing the significance of enhancing scanning speed for the widespread adoption of CFS as a commercial metrology tool.

Global navigation satellite system (GNSS) positioning performance in the urban dense environment experiences significant deterioration due to frequent non-line-of-sight (NLOS) and multipath errors. An accurate weighting scheme is critical for positioning, especially in urban environment. Traditional methods for determining the weights of observations typically rely on the carrier-to-noise density ratio (C/N0) and the elevations from satellites to receivers. Nevertheless, the performance of these methods is degraded in the dense urban settings, as C/N0 and elevation measurements fail to fully capture the intricacies of NLOS and multipath errors. In this paper, a novel GNSS observations weighting scheme based on Hopular GNSS signal classifier, which can accurately identify the LOS/NLOS signals using medium-sized training dataset, is proposed to improve the urban kinematic navigation solution in real-time kinematic positioning mode. Four GNSS features: C/N0, time-differenced code-minus-carrier, loss of lock indicator and satellite's elevation, are employed in the training of the Hopular based signal classifier. The performance of the new method is validated using two urban kinematic datasets collected by a U-blox F9P receiver with a low-cost antenna, in downtown Calgary. For the first testing dataset, the results show that the Hopular based weighting scheme outperforms the three most commonly used GNSS observations weighting schemes: C/N0, elevation, and a combined C/N0-elevation approach. Approximately 10.089 m of horizontal root-mean-squared (RMS) positioning error and 12.592 m of vertical RMS error are achieved using the proposed method; with improvements of 78.83%, 46.82% and 43.27% on horizontal positioning accuracy and 54.00%, 47.51% and 49.69% on vertical positioning accuracy, compared to using C/N0, elevation and C/N0-elevation combined weighting schemes, respectively. For the second testing dataset, a similar performance is achieved with nearly 11.631 m of horizontal RMS error and 10.158 m of vertical RMS error; improvements of 64.58%, 32.90% and 22.40% on horizontal positioning accuracy and 71.99%, 65.24% and 55.88% on vertical positioning accuracy are achieved, compared to using C/N0, elevation and C/N0-elevation combined weighting schemes, respectively.

This paper analyses the efficiency of various frequency cepstral coefficients (FCC) in a non-speech application, specifically in classifying acoustic impulse events-gunshots. There are various methods for such event identification available. The majority of these methods are based on time or frequency domain algorithms. However, both of these domains have their limitations and disadvantages. In this article, an FCC, combining the advantages of both frequency and time domains, is presented and analyzed. These originally speech features showed potential not only in speech-related applications but also in other acoustic applications. The comparison of the classification efficiency based on features obtained using four different FCC, namely mel-FCC (MFCC), inverse mel-frequency cepstral coefficients (IMFCC), linear-frequency cepstral coefficients (LFCC), and gammatone-frequency cepstral coefficients (GTCC) is presented. An optimal frame length for an FCC calculation is also explored. Various gunshots from short guns and rifle guns of different calibers and multiple acoustic impulse events, similar to the gunshots, to represent false alarms are used. More than 600 acoustic events records have been acquired and used for training and validation of two designed classifiers, support vector machine, and neural network. Accuracy, recall and Matthew's correlation coefficient measure the classification success rate. The results reveal the superiority of GFCC to other analyzed methods.

Gravity measurements have uses in a wide range of fields including geological mapping and mine-shaft inspection. The specific application in question sets limits on the survey and the amount of information that can be obtained. For example, in a conventional gravity survey at the Earth's surface a gravimeter is translated on a two-dimensional planar grid taking measurements of vertical component of gravity. If, however, the survey points cannot be chosen so freely, for example if the gravimeter is constrained to operate in a tunnel where only a one-dimensional line of data could be taken, less information will be obtained. To address this situation, we investigate an alternative approach, in the form of an instrument which rotates around a central point measuring the gravitational potential on the boundary of a sphere around the centre of the instrument. The ability to record additional components of gravity by rotating the gravimeter will give more information than obtained with a single measurement traditionally taken at each point on a survey, consequently reducing ambiguities in interpretation. We term a device which measures the potential, or its radial derivatives, around the surface of a sphere a gravitational eye. In this article we explore ideas of resolution and propose a thought experiment for comparing the performance of diverse types of gravitational eye. We also discuss radial analytic continuation towards sources of gravity and the resulting resolution enhancement, before finally discussing the possibility of using cold-atom gravimetry and gradiometry to construct a gravitational eye. If realised, the gravitational eye will offer revolutionary capability enabling the maximum information to be obtained about features in all directions around it.

High-resolution reconstruction of flow-field data from low-resolution and noisy
measurements is of interest due to the prevalence of such problems in experimental
fluid mechanics, where the measurement data are in general sparse, incomplete and
noisy. Deep-learning approaches have been shown suitable for such super-resolution
tasks. However, a high number of high-resolution examples is needed, which may not
be available for many cases. Moreover, the obtained predictions may lack in complying
with the physical principles, e.g. mass and momentum conservation. Physics-informed
deep learning provides frameworks for integrating data and physical laws for learning.
In this study, we apply physics-informed neural networks (PINNs) for super-resolution
of flow-field data both in time and space from a limited set of noisy measurements
without having any high-resolution reference data. Our objective is to obtain a
continuous solution of the problem, providing a physically-consistent prediction at
any point in the solution domain. We demonstrate the applicability of PINNs for the
super-resolution of flow-field data in time and space through three canonical cases:
Burgers' equation, two-dimensional vortex shedding behind a circular cylinder and the
minimal turbulent channel flow. The robustness of the models is also investigated
by adding synthetic Gaussian noise. Our results show the adequate capabilities of
PINNs in the context of data augmentation for experiments in fluid mechanics.

To improve the temporal resolution in an optical delay system that uses a conventional mechanical delay stage, we integrate an in-line liquid crystal (LC) wave retarder. Previous implementations of LC optical delay methods are limited due to the small temporal window provided. Using a conventional mechanical delay stage system in series with the LC wave retarder, the temporal window is lengthened. Additionally, the limitation on temporal resolution resulting from the minimum optical path alteration (resolution of 400 nm) of the conventionally used mechanical delay stage is reduced via the in-line wave retarder (resolution of 50 nm). Interferometric autocorrelation measurements are conducted at multiple laser emission frequencies (349, 357, 375, 393, and 405 THz) using the in-line LC and conventional mechanical delay stage systems. The in-line LC system is compared to the conventional mechanical delay stage system to determine the improvements in temporal resolution relating to maximum resolvable frequency. This work demonstrates that the integration of the in-line LC system can extend the maximum resolvable frequency from 375 to 3000 THz. The in-line LC system is also applied for measurement of terahertz pulses.

A method is presented for fitting the projected centres of spheres in cone beam x-ray imaging. By using a suitable coordinate system, the method allows direct and exact calculation of the sphere centre without fitting the projection shape with an ellipse and correcting from the ellipse centre to the sphere centre. Advantages in numerical implementation result from the number of unknown variables being reduced compared to ellipse fits. Additionally, the orientation of the detector relative to the x-ray source can be obtained from fitting the shapes of projections of multiple spheres without knowledge of the positions or dimensions of the spheres. The accuracy of the method is compared to other techniques using simulated x-ray projections.

Scanning probe microscopy (SPM) is a broad family of diagnostic methods. Common restraint of SPM is only surficial interaction with specimen, especially troublesome in case of complex volumetric systems, e.g. microbial or microelectronic. Scanning thermal microscopy (SThM) overcomes that constraint, since thermal information is collected from broader space. We present transformer bridge-based setup for resistive nanoprobe-based microscopy. With low-frequency (approx. 1 kHz) detection signal bridge resolution becomes independent on parasitic capacitances present in the measurement setup. We present characterization of the setup and metrological description – with resolution of the system 2 mK with sensitivity as low as 5 mV/K. Transformer bridge setup brings galvanic separation, enabling measurements in various environments, pursued for purposes of molecular biology. We present results SThM measurement results of high-thermal contrast sample of carbon fibers in an epoxy resin. Finally, we analyze influence of thermal imaging on topography imaging in terms of information channel capacity (ICC). We state that transformer bridge-based SThM system is a fully functional design along with low driving frequencies and resistive thermal nanoprobes by Kelvin Nanotechnology.

Fully developed laser speckle patterns are, due to their high contrast
and statistical nature, well suited to measure strain and displacement via an
appropriately designed measurement system. Laser speckle patterns are formed
when a sufficiently coherent light source, such as a HeNe-laser, illuminates an
optically rough surface. Therefore, methods based on laser speckle patterns can
be applied to any surface scatterer with a minimum mean surface roughness of
about a quarter of the laser's wavelength. This includes also materials such as thin
natural and technical fibres as well as foils, for which the presented measurement
system, including the digital signal processing, was designed.
In order to achieve the best possible resolution of a speckle-based measurement
system, combined with a sufficiently small measurement uncertainty, all available
design parameters must be optimised. One of these parameters is the speckle size,
which is dependant on the properties of the imaging optics.
In this paper a subjective laser speckle-based measurement system based on
a 4f−optical setup is presented. This setup allows the speckle size to
be controlled in both axial and lateral dimensions separately, which is achieved
with the help of an aperture in the Fourier plane of the optics. It is shown that
the optimal speckle size for the presented measurement system, not only depends
on the physical setup, but also on the signal processing applied. The signal
processing routine estimates displacements of the speckle pattern, leading to an
estimate for the strain. Additionally, it is demonstrated that the optimal speckle
size can be lower than the commonly reported optimum between two and five pixel
pitches, necessary to circumvent aliasing in the image data. While this is shown
for a measurement setup using 4f−optics, the results are of general importance
to speckle-based strain or displacement measurement systems and should be
taken into account.

A laminar separation bubble is studied on an SD7003 foil in a water towing tank at a Reynolds number of $6\cdot10^4$ and an angle of attack of 6^∘ by means of the temperature sensitive paint single-shot lifetime method in order to resolve the footprints and dynamics of vortical structures at low inflow turbulence levels. A heat flux is created by applying a carbon based heating layer on the suction side of the foil. The influence of the surface heating on the transition behaviour is analyzed using 2D2C-PIV and found to be negligible. The results demonstrate the capability of the single-shot lifetime method to quantify salient time-averaged flow characteristics, as well as to resolve and characterize the footprints of the dominant coherent structures.