Variable Temperature Environments Research Articles

Thermal buckling analysis of composite doubly‐curved shells with viscoelastic core

AbstractComposite sandwich double‐curved shells (CSDCS) have been extensively employed in the engineering domain of variable temperature environments; consequently, it becomes very significant and essential to present the thermal buckling characteristics of CSDCS to supply some valuable numerical results. The no‐linear strain–displacement relationship due to temperature variation has been taken into consideration based on the Von‐Karman non‐linear strain theory, and Hamilton's principle is adapted to derive the buckling differential equations of the CSDCS. The Navier method is employed to solve the eigenvalue problem for obtaining the critical buckling temperature. Finally, through studying the variation rule of thermal buckling temperature (CBT), several interesting findings on thermal buckling of the CSDCS are shown, as follows: As the h3/h1 rises, the CBTs of CSDCS first decrease then increase. With the increase of radius value, the CBTs of spherical shells decline, the CBTs of models (1,1) and (2,2) of hyperbolic paraboloidal shells are essentially unchanged, but the ones of models (1,2) and (2,1) decrease. As the h2 increases, the CBT progressively grows after the initial reduction. With the increase in aspect ratio, the CBTs for modes (1,1) and (2,1) of CSDCS gradually increase, and the ones of modes (1,2) and (2,2) shows an increase after an initial decrease. The CBTs of CSDCS gradually increase as the shear module of damping layer increases.Highlights Thermal buckling of composite doubly‐curved sandwich shells was studied. A solution was proposed to derive the buckling differential equations. The change rules of critical buckling temperature were obtained.

Adaptive crack damage identification based on multi-scale sample entropy under variable temperature environment

High-speed trains inevitably experience the impact of complex temperature loads during operation, which can easily result in cracks. Structural health monitoring methods based on Lamb waves are affected by the impact of temperature on their propagation mechanism, leading to difficulties in constructing accurate damage diagnostic models. This paper proposes a crack damage identification method based on adaptive multi-scale sample entropy under variable temperature environment. A sliding window method is proposed to obtain multiple wave packets in a Lamb wave, which avoids the disadvantage of insufficient information of a single wave packet and improves the robustness and reliability of diagnosis. A variance-based multiscale transform method is proposed to process Lamb wave signals, which reduces the sensitivity of Lamb wave signals to temperature compared to the traditional mean-based multiscale transform method. Lamb waves in different sliding windows are transformed at multiple scales to extract damage Multi-scale sample entropy (MSE) at different scales. The Quantum genetic algorithm (QGA) is introduced to optimize sliding windows and MSE, achieving adaptive MSE extraction under variable temperature environments. Based on the advantage of adaptive multiscale entropy, a quantitative crack diagnosis model is established. To verify the effectiveness of the proposed method, crack detection experiments under variable temperatures were conducted. The results show that the proposed adaptive MSE has good resistance to temperature changes and can accurately identify crack length.

Li-Ion Cell in Operando Monitoring and Prognostication Using CMUT Devices

Capacitive micromachined ultrasonic transducer (CMUT) devices are ideal solutions for in operando battery monitoring applications especially for consumer electronics. Their minute size (2mm x 1mm x 0.5mm) allows direct placement on the battery without affecting the form factor of the device, the volume production techniques used for their manufacture allow for a very low-cost device, and they can be easily integrated with front-end integrated circuits either in a single device or in a discrete multichip form. Together with industry partners we have developed CMUT devices that we use to continuously monitor the behavior of a battery while it is cycling.For this work we used CMUTs (close-up shown in Fig. 1(a)) developed to operate with a low bias voltage (less than 30 V) which reduces the complexity of the driving hardware. The impulse response and its frequency content are shown in Fig. 1(b). In collapse mode, the peak frequency is 4.0 MHz and the one-way –3 dB BW is 76%, which allows flexibility in setting the drive conditions to interrogate the battery. Our testing also shows that the CMUTs show good device-to-device uniformity and they have a very stable performance in variable temperature environments (e.g. 0℃ - 60℃ ).The cell is interrogated in pulse-echo mode. The CMUT is excited by a wide-band unipolar pulse and the returned analog signal is filtered and amplified. The signal is then digitized and processed to extract features that allow us to quantify the state of the battery. Figure 1(c) depicts such signal features change under baseline cell cycling conditions (three cycles at a charge rate of C/4). These changes are the result of mechanical material property changes in the cell e.g., electrode stiffness and density [1] which directly affect the acoustic wave propagation inside the cell. Figure 1(d) depicts signal feature change due cell overcharge (red line). Ultrasound is especially sensitive to gas generation which occurs as the cell voltage extends beyond normal operating range [2]. Signal features provide early warning notice that cell is outside of normal operating conditions.Figure 1. a) Close-up image of CMUT device. b) CMUT impulse response and corresponding frequency content. c) Signal features (RMS and Time of Flight (ToF)) and thickness change during C/4 charge and discharge. d) Signal features during cell constant current overcharge. Cell overcharged after 4.4V (red line). Chang, W. et al. Measuring effective stiffness of Li-ion batteries via acoustic signal processing. Mater. Chem. A 8, 16624–16635 (2020).Appleberry, M. C. et al. Avoiding thermal runaway in lithium-ion batteries using ultrasound detection of early failure mechanisms. Journal of Power Sources 535, 231423 (2022). Figure 1

Experimental evaluation of ECG signal denoising methods based on HRV indices and their application in indoor thermal comfort study under different temperatures

The heart rate variability (HRV) features extracted from an electrocardiogram (ECG) signal have been widely used to help improve the performance of thermal comfort models, with the ECG signal denoising method being very important. In this study, 20 volunteers were recruited for experiments in a well-controlled chamber. Their ECG signals (a total of 6,000,000 sets of data) were collected under stable and variable temperature environments. With the use of the setup database, four denoising methods were compared: the fast Fourier transform (FFT), wavelet transform, Chebyshev filter, and Butterworth filter. When the performance of these methods was evaluated from ECG images, the Butterworth filter exhibited a more comprehensive and smoother representation of the original signal. Judging from denoising indices, the Butterworth filter exhibited the best performance, with its denoising indices surpassing those of fast Fourier transform and wavelet transform by differences ranging from 26% to 122%. Judging from HRV index and R-peak positioning, the data processed by Butterworth exhibited the highest reliability (93.5%), outperforming FFT (30.0%), the wavelet filter (72.5%), and the Chebyshev filter (78.5%). Furthermore, the 5th-order Butterworth filter exhibited the largest signal-to-noise ratio (SNR) with 1.1–34.4% larger and the smallest percent root-mean-square difference (PRD) with 15.2–66.1% smaller compared to other orders. Next, the characteristics of denoised HRV features under different temperatures were studied. During a temperature rise stage, the fitting R2 values of rMSSD, SDNN, and HR with temperature were 0.85, 0.64, and 0.78, respectively; meanwhile, during the subsequent temperature drop stage, the fitting R2 values were 0.78, 0.84, and 0.78, respectively. This suggests that changes in temperature can be effectively reflected by HRV indices. When gender was taken into consideration, the HR of females showed a 55.8% higher fitting level compared to males.

State-of-charge of lithium-ion battery based on equivalent circuit model – Relevance vector machine fusion model considering varying ambient temperatures

Ambient temperature significantly impacts battery characteristics and state-of-charge (SOC) accuracy, so it is crucial to estimate SOC under various temperatures. Most circuit models of lithium-ion batteries have poor temperature adaptability, especially at low temperatures. To solve this challenge, this paper proposes a fusion model of the Equivalent Circuit Model-Relevance Vector Machine to enhance SOC estimation under various temperatures. First, a second-order equivalent circuit model based on the forgetting factor recursive least squares is built to obtain the SOC value. Second, build a data-driven battery model based on the relevance vector machine (RVM) algorithm with a moving window method, and the SOC value is obtained based on the RVM model and the unscented Kalman filter algorithm. The weights of the models are merged using the Bayesian principle. The experiments verify that the fusion model accurately estimates SOC for different types of batteries under variable ambient temperature environments and exhibits strong robustness.

A Novel Catheter Shape-Sensing Method Based on Deep Learning with a Multi-Core Optical Fiber.

In this paper, we propose a novel shape-sensing method based on deep learning with a multi-core optical fiber for the accurate shape-sensing of catheters and guidewires. Firstly, we designed a catheter with embedded multi-core fiber containing three sensing outer cores and one temperature compensation middle core. Then, we analyzed the relationship between the central wavelength shift, the curvature of the multi-core Fiber Bragg Grating (FBG), and temperature compensation methods to establish a Particle Swarm Optimization (PSO) BP neural network-based catheter shape sensing method. Finally, experiments were conducted in both constant and variable temperature environments to validate the method. The average and maximum distance errors of the PSO-BP neural network were 0.57 and 1.33 mm, respectively, under constant temperature conditions, and 0.36 and 0.96 mm, respectively, under variable temperature conditions. This well-sensed catheter shape demonstrates the effectiveness of the shape-sensing method proposed in this paper and its potential applications in real surgical catheters and guidewire.

A compact, friction self-matching, non-inertial piezo motor with scanning capability

Maintaining friction matching is the core issue for non-inertial piezo motors (PMs); this challenge severely limits their application in complex conditions such as variable temperature environments. To address this issue, a compact, optimal friction self-matching PM with non-inertial driving is reported in this paper. The motor is implemented with a narrow 5.5 mm-outer diameter piezoelectric scanner tube (PST) whose outer electrode is equally divided into two independently controllable PSTs. The PST, divided into two parts, clamps a sapphire rod between dual sapphire ball structures at both ends and an elastically supported sapphire ball at the centre. The device features a balanced normal force distribution scheme that allows friction forces acting on the sapphire rod at both ends and on the intermediate section to be approximately equal along the axial direction of the PST, achieving automatic optimal matching of friction, then it can operate like an inchworm motor. The feasibility of this scheme is verified by testing with a low threshold voltage down to 35 V at room temperature and 160 V at liquid nitrogen temperature. The motor dimensions are 5.5 mm × 5.5 mm ×35 mm (length × width × height). At room temperature, step size ranges from 0.1 μm to 1 μm. It has a maximum stroke about 5 mm and a maximum load of 40 g. This PM’s extreme compactness, low machine tolerance requirements, and smooth sequence make it ideally suited for building superior quality, atomically resolved scanning probe microscopy devices compatible with narrow spaces and extreme conditions.

A review of the effects of different parameters on salt-based solar thermal energy storage systems

Phase change materials (PCMs) used for thermal energy storage (TES) have shown to be particularly promising, especially in light of the growing need for a wide variety of energy-related applications. This article discusses the usage of packed beds made from phase change materials in solar energy storage devices for variable temperature environments. The primary heating/cooling technologies and associated thermal energy storage difficulties have also been examined from the perspective of engineering applications. The packed bed’s history from a thermodynamic perspective, the temperature dispersion inside the packed bed, and several design factors that influence its performance are all covered in the brief description of the packed bed. Energy and exergy efficacy are the foundations of its analysis. The article delves into numerous thermal energy storage methods, with phase change materials being the primary topic and a beneficial means of storing thermal energy. In addition, phase change materials also keep latent thermal energy during phase transitions at almost constant temperatures.

Temperature-insensitive conductive composites for noninterference strain sensing

Very few thin-film strain gauges (TFSGs) that exhibit temperature self-compensation have been developed. Their temperature insensitivity range is typically < 100 °C, and they cannot be used for static strain sensing in variable-temperature environments. In this study, environmental interference owing to temperature fluctuations was successfully eliminated by controlling electron scattering and thermally assisted hopping transport in conductive composites. A new temperature-insensitive material was developed. The proposed strategy enables noninterference dynamic strain monitoring over an ultrawide temperature range of 30–300 °C, which is approximately 200 °C higher than that of noninterference TFSGs that have been reported. In particular, we propose the smallest feature unit of electron transport in conducting composites, that is, the pathway of an electron through a conductive particle and adjacent junction. This unit retains all the features of the entire conductive network, such as the temperature-resistivity effect, and is the smallest unit that can fully reflect the electron transport behavior. We manipulated the microstructure such that the static strain affects only the arrangement and number of temperature-insensitive feature units without destroying its internal structure, enabling temperature independence under static strain. The first noninterference TFSG was designed with a gauge factor of > 2300 for static strain sensing. The thermal output strain error at 30–200 °C was < 0.0016%.

Optical fiber shape sensing of flexible medical instruments with temperature compensation

Needle shape monitoring techniques ensure the safety of modern interventional surgery. Optical fiber sensors such as fiber Bragg grating (FBG) are being increasingly employed for needle shape sensing. However, existing studies have overlooked the simultaneous strain and temperature sensitivities of the sensors in the fiber grating sensing array, directly affecting the shape reconstruction accuracy under variable temperature environments. Thus, this study proposed a method to optimize shape reconstruction-based fiber optic sensing by introducing a strain and temperature sensitivity matrix. First, the relationship between the wavelength shift of the fiber grating implanted in the needle and its strain and temperature was analyzed based on the fiber grating sensing theory. Thereafter, the strain sensitivity matrix was introduced to improve the accuracy of the shape reconstruction algorithm. Moreover, temperature compensation eliminated temperature influence on the sensor. Next, the local unit geometric parameter relationship and coordinate transformation equation of the needle were derived, and the shape sensing model of the FBG array-based needle was established. Finally, the effectiveness of the method was verified by conducting variable temperature shape reconstruction experiments. The proposed method had increased accuracy over the conventional method in shape reconstruction under variable temperature environments, thus indicating its superiority and application feasibility.

Improved spectral demodulation algorithm with EEMDAD for sapphire fiber Fabry-Perot high-temperature sensing.

In this paper, an improved spectral demodulation algorithm with the ensemble empirical mode decomposition average denoising is proposed to suppress order jumps in the sapphire fiber Fabry-Perot high-temperature sensing system. It is proven that the signal-to-noise ratio of the sensor is closely related to the severity of the demodulation jumps. The proposed algorithm can reduce the fluctuations of key parameters by reducing the noise in the spectrum, thus overcoming this obstacle. The simulations and experiment demonstrate that the algorithm can effectively eliminate the order jumps in both stable and variable temperature environments. The proposed algorithm solves the order jumping problem that has long plagued the demodulation of this system, improves demodulation accuracy, ensures the reliable operation of the high-temperature sensor, and exhibits excellent demodulation performance.

3D nano-micro structured NiCoP/CC as high-performance cathode for hybrid supercapacitor

Transition-metal phosphides are desirable candidates for supercapacitors. Herein, bimetallic phosphides NiCoP/CC is obtained with 3D nano-micro structure. This unique as well as the altered electronic structure effectively improved the energy density of NiCoP/CC//AC hybrid supercapacitors (HSCs). As compared to Ni2P/CC, NiCoP/CC shows a metallic conductivity and has more electrons close to the Fermi level, increasing the intrinsic reactivity effectively. Moreover, the 3D nano-micro structure with a 20 nm staggered nanosheet provides a multidirectional pathway for charge transfer. Thus, NiCoP/CC cathode holds the capacity of 3.42 C cm−2 at 4 mA cm−2, which is much higher than that of Ni2P/CC. As a result, the HSC device delivers a greater energy density of 31.8 Wh kg−1 than that of most reported devices. More importantly, the device works well over a wide temperature range. It can still maintain 100% of its initial capacity after 450 cycles under variable temperatures from 5 ℃ to 50 ℃. In summary, this work starts with structural engineering and then analyzes the performance of HSC under variable temperature environments, offering a universal approach for the rational development of high-performance electrode materials in the field of supercapacitors.

Lithium Battery State-of-Charge Estimation Based on a Bayesian Optimization Bidirectional Long Short-Term Memory Neural Network

State of charge (SOC) is the most important parameter in battery management systems (BMSs), but since the SOC is not a directly measurable state quantity, it is particularly important to use advanced strategies for accurate SOC estimation. In this paper, we first propose a bidirectional long short-term memory (BiLSTM) neural network, which enhances the comprehensiveness of information by acquiring both forward and reverse battery information compared to the general one-way recurrent neural network (RNN). Then, the parameters of this network are optimized by introducing a Bayesian optimization algorithm to match the data characteristics of lithium batteries with the network topology. Finally, two sets of lithium battery public data sets are used to carry out experiments under different constant temperature and variable temperature environments. The experimental results show that the proposed model can effectively fit the actual measurement curve. Compared with traditional long short-term memory network (LSTM) and BiLSTM models, the prediction accuracy of the Bayes-BiLSTM model is the best, with a root mean square error (RMSE) within 1%, achieving a better ability for capturing long-term dependencies. Overall, the model exhibits high accuracy, adaptability, and generalization for the SOC estimation of batteries with different chemical compositions.

Interconnected MXene/Graphene Network Constructed by Soft Template for Multi-Performance Improvement of Polymer Composites

HighlightsInterconnected MXene/graphene networks are constructed by soft template through a facile dispersion dip-coating approach for multi-performance improvement of polymer composites.The superior through-plane thermal conductivity, electromagnetic interference shielding, and mechanical property performance of polymer composites are achieved.The polymer composites obtained improve phase change and thermomechanical properties, which provided the possibility for the application in variable temperature environments.

A 4-Channel High-Precision Real-Time Pressure Test System for Irregularly Variable High Temperature Environments

In this study, a 4-channel high-precision pressure test system was designed. It implements high-precision measurement of pressure parameters at multiple test points in situ and synchronously in irregularly variable high-temperature environments. The test system consists of a pressure&#x2013;temperature double-parameter composite sensor along with signal acquisition, temperature compensation algorithm processing, and real-time online display modules. The temperature-sensitive chip of the sensor was integrated on the surface of the differential pressure-sensitive chip for in-situ temperature compensation. The collected pressure-temperature parameters were calculated and displayed using the temperature compensation algorithm processing and real-time online display modules. Finally, a high-precision pressure test was conducted. The proposed test system realizes the pressure test in high-temperature environments with irregular variations in the range of 24&#x2013;400 &#x00B0;C, with a test error of less than 0.95&#x0025; and a sensitivity of 6.378 mV/kPa. The study results can be used for real-time monitoring of high-precision pressure of key components operating in irregularly variable high-temperature environments, which provides a significant guarantee for the normal operation of equipment.

Thermal conductivity evolution of early-age concrete under variable curing temperature: Effect mechanism and prediction model

The thermal conductivity and microevolution mechansim of early-age concrete (EAC) under variable curing temperature environments significantly influence the temperature cracks in massive concrete. An experiment was carried out to compare thermal conductivity evolution differences of EAC under variable curing temperature (VCT) and standard curing temperature (SCT) conditions. The concrete specimens of moisture and dry states were used to consider the effect of moisture content on thermal conductivity. Results showed that the moisture concrete thermal conductivity under VCT condition decreases with curing ages and is higher than SCT condition. For the dry samples, the thermal conductivity of VCT condition increases with curing ages and is lower than the SCT condition. The difference in thermal conductivity between VCT and SCT increases from zero to seven days and decreases after seven days. The scanning electron microscope (SEM) tests showed that the VCT leads to slower growth of pore structure and larger porosity of EAC due to the slower hydration rate under VCT condition. The thermal conductivity difference between VCT and SCT is mainly caused by the differences in water content, chemical components in solid phase, and porosity (solid-phase volume fraction). Meanwhile, the relation among thermal conductivity, porosity and most probable pore size shows that curing temperature has a great effect on hydration degree and controls the thermal conductivity evolution. Besides, a prediction model of EAC thermal conductivity under VCT condition was built based on hydration kinetics.

Sensitivity calibration of a three-axis accelerometer under different temperature conditions using the hybrid GA–PSO–BPNN algorithm

PurposeThree-axis accelerometers play a vital role in monitoring the vibrations in aircraft machinery, especially in variable flight temperature environments. The sensitivity of a three-axis accelerometer under different temperature conditions needs to be calibrated before the flight test. Hence, the authors investigated the efficiency and sensitivity calibration of three-axis accelerometers under different conditions. This paper aims to propose the novel calibration algorithm for the three-axis accelerometers or the similar accelerometers.Design/methodology/approachThe authors propose a hybrid genetic algorithm–particle swarm optimisation–back-propagation neural network (GA–PSO–BPNN) algorithm. This method has high global search ability, fast convergence speed and strong non-linear fitting capability; it follows the rules of natural selection and survival of the fittest. The authors describe the experimental setup for the calibration of the three-axis accelerometer using a three-comprehensive electrodynamic vibration test box, which provides different temperatures. Furthermore, to evaluate the performance of the hybrid GA–PSO–BPNN algorithm for sensitivity calibration, the authors performed a detailed comparative experimental analysis of the BPNN, GA–BPNN, PSO–BPNN and GA–PSO–BPNN algorithms under different temperatures (−55, 0 , 25 and 70 °C).FindingsIt has been showed that the prediction error of three-axis accelerometer under the hybrid GA–PSO–BPNN algorithm is the least (approximately ±0.1), which proved that the proposed GA–PSO–BPNN algorithm performed well on the sensitivity calibration of the three-axis accelerometer under different temperatures conditions.Originality/valueThe designed GA–PSO–BPNN algorithm with high global search ability, fast convergence speed and strong non-linear fitting capability has been proposed to decrease the sensitivity calibration error of three-axis accelerometer, and the hybrid algorithm could reach the global optimal solution rapidly and accurately.

Effects of open-circuit voltage tests and models on state-of-charge estimation for batteries in highly variable temperature environments: Study case nano-satellites

Nano-satellites are a rapidly developing field of the space industry. Their structure and environmental conditions impose requirements for their functioning and reliability. One of key systems to keep nano-satellites in operation is the energy storage, often in a form of batteries, which supplies power, when there is not enough generation from solar panels. Thus, it is desirable to monitor the battery states, such as the state-of-charge (SOC). This topic was investigated in connection to other applications as electric vehicles or stationary storage; however, their working conditions are different from nano-satellites’. We propose a SOC estimation method, which takes into account these conditions, especially wide and rapidly changing temperature. The SOC is estimated through an unscented Kalman filter (UKF), which uses open-circuit voltage as pseudo-measurement, obtained from an online parameter identification method. Furthermore, to achieve a high accuracy SOC estimation, various open-circuit voltage models and characterization test procedures were evaluated. Finally, the SOC root mean square error of 0.53% was reached for a rapid temperature varying nano-satellite mission profile with thermal gradient of 22.4°C/hour by using an extended Kalman filter for online parameter identification and UKF for SOC estimation, based on the analytical OCV model characterized from quasi open-circuit voltage test.

Improving the performance of ultrasonic motors in low-pressure, variable-temperature environments

Ultrasonic motor (USM) performance is dependent on the tribological characteristics of the interface between a stator and rotor, which are sensitive to ambient environments and driving parameters. Thus, the effects of vacuum and temperature on the mechanical output performance and energy conversion efficiency of a traveling wave rotary ultrasonic motor 60 (TRUM-60) with a novel micro-dimpled PI-based friction material and optimized input parameters were systematically investigated for the first time. The degree of vacuum had a negligible effect on the performance of the TRUM-60, whereas temperature influenced its mechanical output torque, rotational speed, and output power. The results can aid in the design of USM devices for aerospace applications.

Assessment of Wear Properties under Dry Sliding Conditions on AISI 301LN Austenitic Stainless Steel by Plasma Nitriding Process

In industry, mechanical components must operate under conditions conditions such as variable load, speed, temperature and various chemical environments. Materials are selected depending on their application. They are selected for commercial availability, cost and properties such as strength, hardness etc. Many engineering errors are due to fatigue, corrosion and poor wear resistance occurring on its surface. This causes cracks in the surface that shortens the service life of the material. Also, the surfaces of materials are exposed to strong thermal, chemical and shock loads. Chosen for this research work, AISI 301LN materials have low surface hardness and poor wear properties, which can limit their applications were components mate each other. AISI 301LN stainless steel was treated with plasma nitriding at a low temperature of about 650°C. It was observed that a mixture of ferrites and gradually nitrogenous matter accumulates in the following layers namely iron nitride. Further nitriding was carried out to 20 hrs, 40 hrs, 60 hrs and the specimen were named as PL1, PL2 and PL3 respectively. It was observed that Cr-N layer were formed on the outer surface. Wear tests were carried out on a tribological wear test machine to study the wear behavior. A comparison is made between treated and untreated specimens. The microstructures are investigated with scanning electron microscope.