Rapid Determination of Galantamine in Human Plasma by Microchip Electrophoresis With a Highly Integrated Contactless Conductivity Detector.

A novel and rapid method was developed for the determination of galantamine in human plasma by microchip electrophoresis with a highly integrated contactless conductivity detector (CCD). The instrumental parameters affecting the response of the detector, such as excitation frequency and excitation voltage, were examined and optimized. The electrophoresis conditions that influenced the separation and detection of galantamine, including the composition of buffer solution, buffer pH, buffer concentration, additives, injection time, and separation voltage were systematically investigated. Under the optimal conditions, the peak height had a good linear relationship with the concentration of galantamine in human plasma from 10 to 160 µg/L, and the correlation coefficient was 0.9992, the limit of detection reached 1.1 µg/L. The recoveries were between 98.6% and 102.1%. This sensitive, rapid, and convenient method is a good alternative to existing methods for galantamine determination. Also, this highly integrated CCD holds great promise in clinical biochemical analysis.

Nondestructive Testing of 3D Printed Fiber-Reinforced Polymeric Composites: An Experimental Critical Comparison.

Polymer matrix composite (PMC) materials produced by additive manufacturing are a promising solution with several applications in industry. The presence of defects due to fabrication could undermine the performance of the component structure. PMC performance has been extensively studied using destructive tests, but reliable nondestructive testing (NDT) techniques are essential. In this study, PMC with unidirectional fibers were 3D printed with an adapted conventional fused filament fabrication printer. The matrix material was polylactic acid, and three different reinforcement fibers were used: Kevlar®, carbon, and glass fibers. The samples were 3D printed with artificial defects, to simulate delamination's 0.5 mm thick. Four NDT techniques were explored, benchmarking the inspection of PMC envisaging an automated noncontact imaging inspection for easier result interpretation. Active pulse thermography, air-coupled ultrasounds, continuous wave terahertz, and digital X-ray were the techniques chosen, and a critical comparison is presented, evaluating the performance of each technique in the detection of defects. NDT technique diversity, complementarity, and redundancy improve inspection reliability, as there is not a single inspection technique that can cover all material defects or characteristics.

Speckle Vibrometry for Contactless Instantaneous Heart Rate and Respiration Rate Monitoring on Mechanically Ventilated Patients.

Objective: Contactless monitoring of instantaneous heart rate and respiration rate has a significant clinical relevance. This work aims to use Speckle Vibrometry (i.e., based on the secondary laser speckle effect) to contactlessly measure these two vital signs in an intensive care unit. Methods: In this work, we propose an algorithm for the estimation of instantaneous heart rate and respiration rate from mechanically ventilated patients. The algorithm uses multiple regions, principal component analysis, and dominant angle analysis. A semi-automated peak detection method is implemented to precisely label the aortic valve opening peak within the cardiac waveform. Results: Compared with electrocardiography, the present work achieves limits of agreement of [-2.19, 1.73] beats per minute of instantaneous heart rate. The measurement spot is on the chest covered with two to three layers of duvet blankets. Compared with the airway flow signal measured by the mechanical ventilator, the present work achieves limits of agreement of [-0.68, 0.46] respirations per minute of instantaneous respiration rate. Conclusions: These results showcased Speckle Vibrometry's potential in vital sign monitoring in a clinical setting. Significance: This is the first human clinical study for Speckle Vibrometry.

One-Year Visual and Refractive Outcomes of SmartPulse® Technology in Transepithelial Photorefractive Keratectomy for Myopic and Astigmatic Patients.

Background/Objectives: This study aims to evaluate the efficacy, safety, and predictability of Transepithelial Photorefractive Keratectomy (TPRK) using the SmartPulse® technology excimer laser for the correction of myopia and myopic astigmatism, assessing visual and refractive outcomes over a one-year follow-up period. Methods: This retrospective cohort study analyzed data from patients who underwent TPRK at the Ophthalmology Department-"Victor Babes" University of Medicine and Pharmacy in Timisoara (Romania), between January 2019 and June 2023. The procedure was performed using the SmartPulse® Technology of the SmartSurfACE AMARIS 750S excimer laser (SCHWIND eye-tech-solutions, Kleinostheim, Germany). Preoperative assessments included visual acuity, refraction, and corneal measurements, with postoperative evaluations conducted for up to 12 months. Results: This study included 92 eyes from 46 patients (mean age 29.02 years, 63% male). At 12 months post-op, 100% achieved UDVA 20/25 or better, with an efficacy index of 1.01. Refractive accuracy was 96% within ±0.50 D of the target and astigmatism ≤ 0.50 D in 99% of eyes. The safety index was 1.01. Corneal haze occurred in 8.70% of eyes and was effectively managed with dexamethasone drops. Conclusions: TPRK with the SmartPulse® technology excimer laser demonstrated high efficacy and safety in correcting myopia and myopic astigmatism, achieving stable visual outcomes over one year. The procedure also showed excellent predictability with a low incidence of complications, supporting its use as a reliable refractive surgery option.

Remote physiological signal recovery with efficient spatio-temporal modeling.

Contactless physiological signal measurement has great applications in various fields, such as affective computing and health monitoring. Physiological measurements based on remote photoplethysmography (rPPG) are realized by capturing the weak periodic color changes. The changes are caused by the variation in the light absorption of skin surface during systole and diastole stages of a functioning heart. This measurement mode has advantages of contactless measurement, simple operation, low cost, etc. In recent years, several deep learning-based rPPG measurement methods have been proposed. However, the features learned by deep learning models are vulnerable to motion and illumination artefacts, and are unable to fully exploit the intrinsic temporal characteristics of the rPPG. This paper presents an efficient spatiotemporal modeling-based rPPG recovery method for physiological signal measurements. First, two modules are utilized in the rPPG task: 1) 3D central difference convolution for temporal context modeling with enhanced representation and generalization capacity, and 2) Huber loss for robust intensity-level rPPG recovery. Second, a dual branch structure for both motion and appearance modeling and a soft attention mask are adapted to take full advantage of the central difference convolution. Third, a multi-task setting for joint cardiac and respiratory signals measurements is introduced to benefit from the internal relevance between two physiological signals. Last, extensive experiments performed on three public databases show that the proposed method outperforms prior state-of-the-art methods with the Pearson's correlation coefficient higher than 0.96 on all three datasets. The generalization ability of the proposed method is also evaluated by cross-database and video compression experiments. The effectiveness and necessity of each module are confirmed by ablation studies.

Corrigendum: SonoBox: development of a robotic ultrasound tomograph for the ultrasound diagnosis of paediatric forearm fractures.

[This corrects the article DOI: 10.3389/frobt.2024.1405169.].

A Deep Learning Method for Human Sleeping Pose Estimation with Millimeter Wave Radar.

Recognizing sleep posture is crucial for the monitoring of people with sleeping disorders. Existing contact-based systems might interfere with sleeping, while camera-based systems may raise privacy concerns. In contrast, radar-based sensors offer a promising solution with high penetration ability and the capability to detect vital bio-signals. This study propose a deep learning method for human sleep pose recognition from signals acquired from single-antenna Frequency-Modulated Continuous Wave (FMCW) radar device. To capture both frequency features and sequential features, we introduce ResTCN, an effective architecture combining Residual blocks and Temporal Convolution Network (TCN) to recognize different sleeping postures, from augmented statistical motion features of the radar time series. We rigorously evaluated our method with an experimentally acquired data set which contains sleeping radar sequences from 16 volunteers. We report a classification accuracy of 82.74% on average, which outperforms the state-of-the-art methods.

Contactless ultrasound droplet manipulation system for mixing chemical reagents.

Green chemistry has been a rising topic in environmental sustainability, with a focus on the waste and consumption reduction of chemical and biomedical industries. Traditional chemical handling processes require tools that contact chemical reagents to produce vast amounts of residues and disposals. This study presents a contactless chemical mixing system that integrates acoustic droplet ejection and levitation techniques. First, the acoustic droplet ejection system creates a droplet in mid-air from a designated liquid reservoir by focusing acoustic energy at the liquid-air junction. The droplet levitation system captures and transports the droplet along a predetermined path by shifting the focal points of the acoustic standing waves. This facilitates contactless mixing of chemicals in a defined ratio. Notably, this study employs piezoelectric discs in an acoustic droplet ejection system to eject droplets from liquids. The relationship between the duration of the driving bursts and height and size of ejected droplets was also investigated. The proposed acoustic standing wave levitation system captures droplets with weights between 2.8 and 5.2 mg. To assess the reliability of the proposed system, 25 droplets were sequentially generated and transported to the mixing well without failure. The root mean square error between the collected and expected liquid weights was only 0.098 mg. The proposed system offers a promising solution for reducing waste and promoting environmentally friendly practices in chemical and biomedical laboratories.

Early detection of deteriorating patients in general wards through continuous contactless vital signs monitoring.

Objective: To assess the efficacy of continuous contactless vital signs monitoring with an automated Early Warning System (EWS) in detecting clinical deterioration among patients in general wards. Methods: A prospective observational cohort study was conducted in the medical unit of a tertiary care hospital in India, involving 706 patients over 84,448 monitoring hours. The study used a contactless ballistocardiography system (Dozee system) to continuously monitor heart rate, respiratory rate, and blood pressure. The study assessed total, mean, and median alerts at 24, 48, 72, 96, 120 h, and length of stay (LOS) before patient deterioration or discharge. It analyzed alert sensitivity and specificity, average time from initial alert to deterioration, and healthcare practitioners (HCP) activity. Study was registered with the Clinical Trials Registry-India CTRI/2022/10/046404. Results: Out of 706 patients, 33 (5%) experienced clinical deterioration, while 673 (95%) did not. The deterioration group consistently had a higher number of alerts compared to those who were discharged normally, across all time-points. On average, the time between the initial alert and clinical deterioration was 16 h within the last 24 h preceding the event. The sensitivity of the Dozee-EWS varied between 67% and 94%. HCP spend 10% of their time on vital signs check and documentation. Conclusions: This study suggests that utilizing contactless continuous vital signs monitoring with Dozee-EWS in general ward holds promise for enhancing the early detection of clinical deterioration. Further research is essential to evaluate the effectiveness across a wider range of clinical settings.

SonoBox: development of a robotic ultrasound tomograph for the ultrasound diagnosis of paediatric forearm fractures.

Introduction: Paediatric forearm fractures are a prevalent reason for medical consultation, often requiring diagnostic X-rays that present a risk due to ionising radiation, especially concerning given the sensitivity of children's tissues. This paper explores the efficacy of ultrasound imaging, particularly through the development of the SonoBox system, as a safer, non-ionising alternative. With emerging evidence supporting ultrasound as a viable method for fracture assessment, innovations like SonoBox will become increasingly important. Materials and methods: In our project, we want to advance ultrasound-based, contact-free, and automated cross-sectional imaging for diagnosing paediatric forearm fractures. To this end, we are building a technical platform that navigates a commercially available ultrasound probe around the extremity within a water-filled tank, utilising intelligent robot control and image processing methods to generate a comprehensive ultrasound tomogram. Safety and hygiene considerations, gender and diversity relevance, and the potential reduction of radiation exposure and examination pain are pivotal aspects of this endeavour. Results: Preliminary experiments have demonstrated the feasibility of rapidly generating ultrasound tomographies in a water bath, overcoming challenges such as water turbulence during probe movement. The SonoBox prototype has shown promising results in transmitting position data for ultrasound imaging, indicating potential for autonomous, accurate, and potentially painless fracture diagnosis. The project outlines further goals, including the construction of prototypes, validation through patient studies, and development of a hygiene concept for clinical application. Conclusion: The SonoBox project represents a significant step forward in paediatric fracture diagnostics, offering a safer, more comfortable alternative to traditional X-ray imaging. By automating the imaging process and removing the need for direct contact, SonoBox has the potential to improve clinical efficiency, reduce patient discomfort, and broaden the scope of ultrasound applications. Further research and development will focus on validating its effectiveness in clinical settings and exploring its utility in other medical and veterinary applications.

Laser Fabrication of Humidity Sensors on Ethanol-Soaked Polyimide for Fully Contactless Respiratory Monitoring.

Humidity-sensor-based fully contactless respiratory monitoring can eliminate the discomfort and infection risks associated with any wearable device. However, challenges in the facile fabrication of highly sensitive humidity sensors continue to hinder their widespread application for fully contactless respiratory monitoring. In this study, we introduce a simple method to fabricate highly sensitive humidity sensors. Our method employs laser-induced graphene (LIG) on an ethanol-soaked polyimide (PI) film as the electrode of the humidity sensor. The ethanol-soaked PI between adjacent LIG electrodes functions as the sensing material, enabling ion-conductive humidity sensing. Compared to the LIG humidity sensors fabricated on untreated PI films, LIG humidity sensors fabricated on ethanol-soaked PI films exhibit superior performance with higher linearity (R2 = 0.9936), reduced hysteresis (ΔH = 5.1% RH), and increased sensitivity (0.65%/RH). Notably, the LIG humidity sensor fabricated on the ethanol-soaked PI film can detect a person's breathing from a distance of 30 cm, a capability not achieved by sensors fabricated on untreated PI films. Moreover, incorporating these LIG humidity sensors into an array further enhances both the detection distance and the sensitivity for respiratory monitoring. Experimental results demonstrate that the LIG humidity sensor array can be employed for fully contactless on-bed respiration monitoring and for continuous, fully contactless monitoring of the respiratory rate during treadmill exercise. These results highlight the great potential of our LIG humidity sensors for various practical applications in medicine and sports.

Reliable Contactless Monitoring of Heart Rate, Breathing Rate, and Breathing Disturbance During Sleep in Aging: Digital Health Technology Evaluation Study.

BACKGROUND: Longitudinal monitoring of vital signs provides a method for identifying changes to general health in an individual, particularly in older adults. The nocturnal sleep period provides a convenient opportunity to assess vital signs. Contactless technologies that can be embedded into the bedroom environment are unintrusive and burdenless and have the potential to enable seamless monitoring of vital signs. To realize this potential, these technologies need to be evaluated against gold standard measures and in relevant populations. OBJECTIVE: We aimed to evaluate the accuracy of heart rate and breathing rate measurements of 3 contactless technologies (2 undermattress trackers, Withings Sleep Analyzer [WSA] and Emfit QS [Emfit]; and a bedside radar, Somnofy) in a sleep laboratory environment and assess their potential to capture vital signs in a real-world setting. METHODS: Data were collected from 35 community-dwelling older adults aged between 65 and 83 (mean 70.8, SD 4.9) years (men: n=21, 60%) during a 1-night clinical polysomnography (PSG) test in a sleep laboratory, preceded by 7 to 14 days of data collection at home. Several of the participants (20/35, 57%) had health conditions, including type 2 diabetes, hypertension, obesity, and arthritis, and 49% (17) had moderate to severe sleep apnea, while 29% (n=10) had periodic leg movement disorder. The undermattress trackers provided estimates of both heart rate and breathing rate, while the bedside radar provided only the breathing rate. The accuracy of the heart rate and breathing rate estimated by the devices was compared with PSG electrocardiogram-derived heart rate (beats per minute) and respiratory inductance plethysmography thorax-derived breathing rate (cycles per minute), respectively. We also evaluated breathing disturbance indexes of snoring and the apnea-hypopnea index, available from the WSA. RESULTS: All 3 contactless technologies provided acceptable accuracy in estimating heart rate (mean absolute error <2.12 beats per minute and mean absolute percentage error <5%) and breathing rate (mean absolute error ≤1.6 cycles per minute and mean absolute percentage error <12%) at 1-minute resolution. All 3 contactless technologies were able to capture changes in heart rate and breathing rate across the sleep period. The WSA snoring and breathing disturbance estimates were also accurate compared with PSG estimates (WSA snore: r2=0.76; P<.001; WSA apnea-hypopnea index: r2=0.59; P<.001). CONCLUSIONS: Contactless technologies offer an unintrusive alternative to conventional wearable technologies for reliable monitoring of heart rate, breathing rate, and sleep apnea in community-dwelling older adults at scale. They enable the assessment of night-to-night variation in these vital signs, which may allow the identification of acute changes in health, and longitudinal monitoring, which may provide insight into health trajectories. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.3390/clockssleep6010010.

Simultaneously performing Taylor dispersion analysis with fluorimetry, photometry, and contactless conductometry at the same detection window.

Taylor dispersion analysis (TDA) is a rapid and precise method for determining the hydrodynamic radius (RH) of various substances. We present a versatile TDA system with a flow-through sample injection device, two compact 3-in-1 detectors, and a high-voltage power supply. The 3D-printed detectors combine fluorimetry (FD), photometry (AD@255 nm), and contactless conductometry (C4D) in a single head, enabling simultaneous detection at one capillary window. Using bovine serum albumin (BSA) as a model analyte, we compare TDA with different detection methods. BSA labeled with fluorescein isothiocyanate (FITC) is analyzed in both pulse mode and capillary electrophoresis (CE) TDA. FD and AD detection yield similar RH values, except when FITC binds with small ions in the buffer. In phosphate buffer, C4D underestimates RH values by approximately 18 % due to BSA self-association. In Tris-based buffers, C4D values are 87%-96 % of AD values in pulse mode. With CE-TDA using Tris-CHES buffer, no statistical difference is found across all detections. The system is also applied to CE-TDA of various compounds, particularly charged saccharides. CE-TDA improves the accuracy of TDA results from C4D. We demonstrate the resolution of mixed C4D-TDA signals with assistance from FD and AD signals, successfully resolving gluconate peaks fully covered by another compound. The versatile system with 3-in-1 detection offers a powerful tool for TDA of mixtures and enhances sample throughput.

What Radio Waves Tell Us about Sleep!

The ability to assess sleep at home, capture sleep stages, and detect the occurrence of apnea (without on-body sensors) simply by analyzing the radio waves bouncing off people's bodies while they sleep is quite powerful. Such a capability would allow for longitudinal data collection in patients' homes, informing our understanding of sleep and its interaction with various diseases and their therapeutic responses, both in clinical trials and routine care. In this article, we develop an advanced machine learning algorithm for passively monitoring sleep and nocturnal breathing from radio waves reflected off people while asleep. Validation results in comparison with the gold standard (i.e., polysomnography) (n=880) demonstrate that the model captures the sleep hypnogram (with an accuracy of 80.5% for 30-second epochs categorized into Wake, Light Sleep, Deep Sleep, or REM), detects sleep apnea (AUROC = 0.89), and measures the patient's Apnea-Hypopnea Index (ICC=0.90; 95% CI = [0.88, 0.91]). Notably, the model exhibits equitable performance across race, sex, and age. Moreover, the model uncovers informative interactions between sleep stages and a range of diseases including neurological, psychiatric, cardiovascular, and immunological disorders. These findings not only hold promise for clinical practice and interventional trials but also underscore the significance of sleep as a fundamental component in understanding and managing various diseases.

Contactless integrated photonic probes: fundamentals, characteristics, and applications.

On-chip optical power monitors are indispensable for functional implementation and stabilization of large-scale and complex photonic integrated circuits (PICs). Traditional on-chip optical monitoring is implemented by tapping a small portion of optical power from the waveguide, which leads to significant loss. Due to its advantages like non-invasive nature, miniaturization, and complementary metal-oxide-semiconductor (CMOS) process compatibility, a transparent monitor named the contactless integrated photonic probe (CLIPP), has been attracting great attention in recent years. The CLIPP indirectly monitors the optical power in the waveguide by detecting the conductance variation of the local optical waveguide caused by the surface state absorption (SSA) effect. In this review, we first introduce the fundamentals of the CLIPP including the concept, the equivalent electric model and the impedance read-out method, and then summarize some characteristics of the CLIPP. Finally, the functional applications of the CLIPP on the identification and feedback control of optical signal are discussed, followed by a brief outlook on the prospects of the CLIPP.

Leveraging 3D convolutional neural network and 3D visible-near-infrared multimodal imaging for enhanced contactless oximetry.

Significance: Monitoring oxygen saturation ( SpO 2 ) is important in healthcare, especially for diagnosing and managing pulmonary diseases. Non-contact approaches broaden the potential applications of SpO 2 measurement by better hygiene, comfort, and capability for long-term monitoring. However, existing studies often encounter challenges such as lower signal-to-noise ratios and stringent environmental conditions. Aim: We aim to develop and validate a contactless SpO 2 measurement approach using 3D convolutional neural networks (3D CNN) and 3D visible-near-infrared (VIS-NIR) multimodal imaging, to offer a convenient, accurate, and robust alternative for SpO 2 monitoring. Approach: We propose an approach that utilizes a 3D VIS-NIR multimodal camera system to capture facial videos, in which SpO 2 is estimated through 3D CNN by simultaneously extracting spatial and temporal features. Our approach includes registration of multimodal images, tracking of the 3D region of interest, spatial and temporal preprocessing, and 3D CNN-based feature extraction and SpO 2 regression. Results: In a breath-holding experiment involving 23 healthy participants, we obtained multimodal video data with reference SpO 2 values ranging from 80% to 99% measured by pulse oximeter on the fingertip. The approach achieved a mean absolute error (MAE) of 2.31% and a Pearson correlation coefficient of 0.64 in the experiment, demonstrating good agreement with traditional pulse oximetry. The discrepancy of estimated SpO 2 values was within 3% of the reference SpO 2 for ∼ 80 % of all 1-s time points. Besides, in clinical trials involving patients with sleep apnea syndrome, our approach demonstrated robust performance, with an MAE of less than 2% in SpO 2 estimations compared to gold-standard polysomnography. Conclusions: The proposed approach offers a promising alternative for non-contact oxygen saturation measurement with good sensitivity to desaturation, showing potential for applications in clinical settings.

Determination of amino acids and peptides without their pre-column derivatization by capillary electrophoresis with ultraviolet and contactless conductivity detection. An overview.

This review provides an overview of recent works focusing on the determination of amino acids (AAs) and peptides using capillary electrophoresis with contactless conductivity detection and ultraviolet (UV) detection, which is the most widespread detection in capillary electromigration techniques, without pre-capillary derivatization. Available options for the UV detection of these analytes, such as indirect detection, complexation with transition metal ions, and in-capillary derivatization are described. Developments in the field of direct detection of UV-absorbing AAs and peptides as well as progress in chiral separation are described. A separate section is dedicated to using on-line sample preconcentration methods combined with capillary electrophoresis-UV.

Predicting Sleep Quality through Biofeedback: A Machine Learning Approach Using Heart Rate Variability and Skin Temperature.

Sleep quality (SQ) is a crucial aspect of overall health. Poor sleep quality may cause cognitive impairment, mood disturbances, and an increased risk of chronic diseases. Therefore, assessing sleep quality helps identify individuals at risk and develop effective interventions. SQ has been demonstrated to affect heart rate variability (HRV) and skin temperature even during wakefulness. In this perspective, using wearables and contactless technologies to continuously monitor HR and skin temperature is highly suited for assessing objective SQ. However, studies modeling the relationship linking HRV and skin temperature metrics evaluated during wakefulness to predict SQ are lacking. This study aims to develop machine learning models based on HRV and skin temperature that estimate SQ as assessed by the Pittsburgh Sleep Quality Index (PSQI). HRV was measured with a wearable sensor, and facial skin temperature was measured by infrared thermal imaging. Classification models based on unimodal and multimodal HRV and skin temperature were developed. A Support Vector Machine applied to multimodal HRV and skin temperature delivered the best classification accuracy, 83.4%. This study can pave the way for the employment of wearable and contactless technologies to monitor SQ for ergonomic applications. The proposed method significantly advances the field by achieving a higher classification accuracy than existing state-of-the-art methods. Our multimodal approach leverages the synergistic effects of HRV and skin temperature metrics, thus providing a more comprehensive assessment of SQ. Quantitative performance indicators, such as the 83.4% classification accuracy, underscore the robustness and potential of our method in accurately predicting sleep quality using non-intrusive measurements taken during wakefulness.

Contribution to energy conservation and quality improvement of frozen pork via contact/contactless immersion freezing in NaCl solution.

High energy consumption and quality deterioration are major challenges in the meat freezing process. In this study, the energy consumption and qualities of frozen pork were investigated using three freezing methods: nonpackaged pork air freezing (NAF), contactless immersion freezing (PIF) and contact immersion freezing (NIF) with NaCl solution as a refrigerant. The results indicated that NIF could improve the energy conservation and freezing efficiency in >4 freezing treatment-times by increasing the unfrozen water content, decreasing the frozen heat load, shortening the freezing time and reducing evaporation loss. NIF could also increase the a* value of the pork and improve the water-holding capacity by facilitating the conversion of free water to immobilized-water. The two immersion freezing methods could reduce freezing-thawing loss and protein loss by alleviating muscle tissue freezing damage. These results provide a suitable application of immersion freezing with energy conservation, high efficiency and good quality of frozen-pork.

Optoelectronic Strain-Measurement System Demonstrated on Scaled-Down Flywheels.

Monitoring the strain in the rotating flywheel in a kinetic energy storage system is important for safe operation and for the investigation of long-term effects in composite materials like carbon-fiber-reinforced plastics. An optoelectronic strain-measurement system for contactless deformation and position monitoring of a flywheel was investigated. The system consists of multiple optical sensors measuring the local relative in-plane displacement of the flywheel rotor. A special reflective pattern, which is necessary to interact with the sensors, was applied to the surface of the rotor. Combining the measurements from multiple sensors makes it possible to distinguish between the deformation and in-plane displacement of the flywheel. The sensor system was evaluated using a low-speed steel rotor for single-sensor performance investigation as well as a scaled-down high-speed rotor made from PVC plastic. The PVC rotor exhibits more deformation due to centrifugal stresses than a steel or aluminum rotor of the same dimensions, which allows experimental measurements at a smaller flywheel scale as well as a lower rotation speed. Deformation measurements were compared to expected deformation from calculations. The influence of sensor distance was investigated. Deformation and position measurements as well as derived imbalance measurements were demonstrated.