Physical Parameters Of System Research Articles

Data-driven fault detection framework for offshore wind-hydrogen systems

Offshore wind-hydrogen systems operate in harsh marine environments for extended periods, posing risks of low accessibility and high failure rates. This paper proposes a data-driven fault detection framework for offshore wind-hydrogen systems, aiming to promptly detect potential faults based on the system's physical parameters. The research establishes a comprehensive model based on the operating principles of offshore wind-hydrogen systems. The study uses fault injection techniques to simulate common partial faults in offshore wind-hydrogen systems and collects relevant physical parameters (such as wind turbine power generation, energy storage battery charging/discharging current, and electrolyzer hydrogen production rate). A fault detection model was constructed using a combination of Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) to enable precise detection of potential faults in the system. The established model provides theoretical support for designing, optimizing, and operating offshore wind-hydrogen systems, reducing reliance on actual experiments and lowering project risks and costs. Introducing artificial faults can evaluate the system's performance in different fault scenarios. This paper proposes a CNN-BiLSTM fault detection method that achieves automatic feature extraction and high-precision classification of time-series fault data, confirming the practicality and feasibility of offshore wind-hydrogen systems. This investigation realizes the integration of the offshore wind-hydrogen system model and deep learning, as well as a fully automated learning process from data to fault detection. By calculating and comparing results, it is verified that the framework has a high accuracy of 96.9%. This innovative research offers new methods and perspectives for enhancing the reliability and efficiency of fault detection in offshore wind-hydrogen systems.

Numerical treatment for radiative hybrid nanofluid flow over a stretching sheet

Artificial neural networks (ANNs) have brought a huge transformation to the machine learning regime by furnishing unparalleled abilities for modeling intricate phenomena and coping with a variety of challenges. In the oneiric field of ANNs, the essential approach for training ANNs is backpropagation. However, optimization is crucial when handling the complex fluid flow phenomenon through backpropagation. The current article implements the Levenberg Marquardt Technique with ANN backpropagation (LMT-BP-ANN) to investigate the radiative flow of a hybrid nanofluid (RHNF) addressing a Cattaneo-Christov flux system past a stretching sheet (SS). Fe2O3 with single-walled carbon nanotubes (SWCNT) and multi-walled carbon nanotubes (MWCNT) hybrid nanofluid is considered. The base fluid is ethylene glycol. Physical parameters of nanofluidic system, including the magnetic parameter M, the solid volume fractions φ1 and φ2, the velocity slip parameter λ, the relaxation time parameter Ω, the Biot number ϒ, and the thermal radiation parameter Gr are evaluated through appropriate variations that effectively accomplish the dynamics of the fluid model. These are utilized to create the dataset for LMT-BP-ANNs using the renowned deterministic Adam's numerical technique in Mathematica software. In MATLAB software, 70 % of data is kept for preparing, 15 % for checking, and 15 % for validating the neural network. The performance plot, regression graphs, and error histograms are constructed to demonstrate the suggested LMT-BP neural network scheme's high efficiency, efficacy, and exactness. When ANN performs, a mean squared error (MSE) of order 10−11 is observed. The observed R squared value is 1. As the magnetic parameter estimate grows, the fluid velocity declines, although the thermal profile shows increasing behavior. As the velocity slip parameter increases, the velocity of the fluid drops. Key findings of this study are anticipated to have a noteworthy bearing on sectors that require local refrigeration and heating by contravention.

Toward robust and high-throughput detection of seed defects in X-ray images via deep learning

BackgroundThe detection of internal defects in seeds via non-destructive imaging techniques is a topic of high interest to optimize the quality of seed lots. In this context, X-ray imaging is especially suited. Recent studies have shown the feasibility of defect detection via deep learning models in 3D tomography images. We demonstrate the possibility of performing such deep learning-based analysis on 2D X-ray radiography for a faster yet robust method via the X-Robustifier pipeline proposed in this article.Results2D X-ray images of both defective and defect-free seeds were acquired. A deep learning model based on state-of-the-art object detection neural networks is proposed. Specific data augmentation techniques are introduced to compensate for the low ratio of defects and increase the robustness to variation of the physical parameters of the X-ray imaging systems. The seed defects were accurately detected (F1-score >90%), surpassing human performance in computation time and error rates. The robustness of these models against the principal distortions commonly found in actual agro-industrial conditions is demonstrated, in particular, the robustness to physical noise, dimensionality reduction and the presence of seed coating.ConclusionThis work provides a full pipeline to automatically detect common defects in seeds via 2D X-ray imaging. The method is illustrated on sugar beet and faba bean and could be efficiently extended to other species via the proposed generic X-ray data processing approach (X-Robustifier). Beyond a simple proof of feasibility, this constitutes important results toward the effective use in the routine of deep learning-based automatic detection of seed defects.

Dispersion of artificial tracers in ventilated caves

Artificial CO2 was used as a tracer along ventilated karst conduits to infer airflow and investigate tracer dispersion. In the karst vadose zone, cave ventilation is an efficient mode of transport for heat, gases and aerosols and thus drives the spatial distribution of airborne particles. Modelling this airborne transport requires geometrical and physical parameters of the conduit system, including the cross-sectional areas, the airflow and average air speed, as well as the longitudinal dispersion coefficient which describes the spreading of a solute. Four gauging tests were carried out in one mine (artificial conduit) and two ventilated caves (natural conduits). In this paper, we demonstrate that it is possible to gain reliable airflow rates and geometric information of ventilated karst conduits using CO2 as a tracer. Airflow was gauged along two caves and one mine and compared with punctual measurements made with a hot-wire anemometer. Cross-sectional areas estimated with CO2 tests were compared with those measured in situ. Moreover, breakthrough curve (BTC) analysis displayed an accentuated tailing along the investigated natural conduits due to the presence of dispersive singularities which possibly enable aerosol deposition. The long tailing observed in Milandre and Longeaigue Caves is probably due to cross-section variations. A 1-D advection-dispersion model tested for these sites was unable to fit BTC tailing in natural conduits. In Baulmes artificial conduit, where long tailing is not observed, the dispersion coefficient has been estimated using Chatwin’s method, and compared with the prediction of Taylor’s theory. Despite the regular geometry of Baulmes Mine, Taylor’s correlation significantly underestimates the dispersion coefficient deduced from field data, showing the need for more theoretical work on turbulent dispersion in mines. This paper gives a first insight into air motion and matter dispersion along ventilated karst conduits, preparing for proper aerosol dispersion modelling.

Stacked ensemble learning approach for PCM-based double-pipe latent heat thermal energy storage prediction towards flexible building energy

Dynamic performance prediction of the PCM-based double-pipe latent heat thermal energy storage (LHTES) system plays an important role for building peak load shifting. This study presents a stacked ensemble learning framework for the phase change performance prediction of LHTES systems. The proposed model has a two-layer structure involving base and meta models: Regression Tree, Support Vector Regression and Linear Regression. Sensitivity analysis has been utilized for the feature extraction considering possible geometrical and physical parameters of LHTES systems with a fixed PCM volume that can store 0.94 kWh of energy within 255 min. Case studies show that the proposed model outperforms the base models in terms of both prediction accuracy and robustness. Compared with base models, the proposed model has a maximum 7.82% (charging) enhancement in Mean Absolute Percentage Error (MAPE) over all-range data sets and a maximum 16.43% enhancement in MAPE for the initial discharging stage, and has more stable predictive results within the high accuracy zone (accuracy > 95%) with a maximum 7.3% (charging) increase in the distribution density over all-range data sets and a maximum interquartile range reduction of 25.6% (middle discharging stage) in Mean Absolute Error. Application of the proposed model achieved more building peak load reduction.

Three sub-Jovian-mass microlensing planets: MOA-2022-BLG-563Lb, KMT-2023-BLG-0469Lb, and KMT-2023-BLG-0735Lb

Aims. We analyze the anomalies appearing in the light curves of the three microlensing events MOA-2022-BLG-563, KMT-2023-BLG-0469, and KMT-2023-BLG-0735. The anomalies exhibit common short-term dip features that appear near the peak. Methods. From the detailed analyses of the light curves, we find that the anomalies were produced by planets accompanied by the lenses of the events. For all three events, the estimated mass ratios between the planet and host are on the order of 10−4: q ~ 8 × 10−4 for MOA-2022-BLG-563L, q ~ 2.5 × 10−4 for KMT-2023-BLG-0469L, and q ~ 1.9 × 10−4 for KMT-2023-BLG-0735L. The interpretations of the anomalies are subject to a common inner-outer degeneracy, which causes ambiguity when estimating the projected planet-host separation. Results. We estimated the planet mass, Mp, host mass, Mh, and distance, DL, to the planetary system by conducting Bayesian analyses using the observables of the events. The estimated physical parameters of the planetary systems are (Mh/M⊙, Mp/MJ, DL/kpc) = (0.48−0.30+0.36, 0.40−0.25+0.31, 6.53−1.57+1.12) for MOA-2022-BLG-563L, (0.47−0.26+0.35, 0.124−0.067+0.092, 7.07−1.19+1.03) for KMT-2023-BLG-0469L, and (0.62−0.35+0.34, 0.125−0.070+0.068, 6.26−1.67+1.27) for KMT-2023-BLG-0735L. According to the estimated parameters, all planets are cold planets with projected separations that are greater than the snow lines of the planetary systems, they have masses that lie between the masses of Uranus and Jupiter of the Solar System, and the hosts of the planets are main-sequence stars that are less massive than the Sun. In all cases, the planetary systems are more likely to be in the bulge with probabilities Pbulge = 64%, 73%, and 56% for MOA-2022-BLG-563, KMT-2023-BLG-0469, and KMT-2023-BLG-0735, respectively.

Optimal design of Luenberger reduced-order observer with low sensitivity for linear multivariable systems

For the linear multivariable systems, by combining both merits of orthogonal-function approach and evolutionary optimization, in this paper, a new method is presented for designing a Luenberger reduced-order observer to solve the low-sensitivity design issue for physical system parameter deviation and simultaneously to minimize a measurement of the quadratic performance for reducing state transient estimation error. Two given examples illustrate the effectiveness of the presented new low-sensitivity design approach on state estimation performance. From the given examples, it shows that the estimated state errors are not sensitive to system parameter deviation and have the asymptotical convergence property. Besides, the performances are apparently superior to those without considering low-sensitivity design means.

Critical behavior of a mixed-spin Ising model on a square–octagon lattice

The magnetic behavior and phase diagrams of a mixed spin-5/2 and spin-3/2 Ising system on a two-dimensional square–octagon lattice were investigated using the mean-field approach based on the statistical Bogoliubov inequality. Two ground-state phase diagrams were plotted in two different planes. We also examined the effects of exchange interactions, crystal field, and external magnetic field on the magnetizations and phase diagrams of the system. First- and second-order phase transitions and isolated critical points were obtained for certain physical conditions. The influence of various physical system parameters on the blocking temperature was also clarified.

Simultaneous multicolour transit photometry of hot Jupiters HAT-P-19b, HAT-P-51b, HAT-P-55b, and HAT-P-65b

ABSTRACT Accurate physical parameters of exoplanet systems are essential for further exploration of planetary internal structure, atmospheres, and formation history. We aim to use simultaneous multicolour transit photometry to improve the estimation of transit parameters, to search for transit timing variations (TTVs), and to establish which of our targets should be prioritized for follow-up transmission spectroscopy. We performed time series photometric observations of 12 transits for the hot Jupiters HAT-P-19b, HAT-P-51b, HAT-P-55b, and HAT-P-65b using the simultaneous four-colour camera MuSCAT2 on the Telescopio Carlos Sánchez. We collected 56 additional transit light curves from TESS photometry. To derive transit parameters, we modelled the MuSCAT2 light curves with Gaussian processes to account for correlated noise. To derive physical parameters, we performed EXOFASTv2 global fits to the available transit and radial velocity data sets, together with the Gaia DR3 parallax, isochrones, and spectral energy distributions. To assess the potential for atmospheric characterization, we compared the multicolour transit depths with a flat line and a clear atmosphere model. We consistently refined the transit and physical parameters. We improved the orbital period and ephemeris estimates, and found no evidence for TTVs or orbital decay. The MuSCAT2 broad-band transmission spectra of HAT-P-19b and HAT-P-65b are consistent with previously published low-resolution transmission spectra. We also found that, except for HAT-P-65b, the assumption of a planetary atmosphere can improve the fit to the MuSCAT2 data. In particular, we identified HAT-P-55b as a priority target among these four planets for further atmospheric studies using transmission spectroscopy.

Determination of Citronellal-geraniol Phase Equilibrium Data and Distillation Simulation Study based on Aspen Plus Software

Aspen Plus is an efficient, reliable, and scalable large-scale process simulation software suitable for a variety of applications. It boasts excellent computational efficiency, high-quality physical property data, and comprehensive system integration, earning consistent acclaim within the industry. The separation of citronella oil components is increasingly expensive due to their thermal sensitivity and high separation difficulty. This study utilizes gas chromatography to analyze the volatile oil components and investigates the phase equilibrium relationship of citronellal -geraniol system in citronella oil. Experimental phase equilibrium data for the citronellal-geraniol system in citronella oil are obtained to provide the thermodynamic basis for the separation of the citronellal-geraniol system in citronella oil. Aspen Plus process simulation software is used to estimate the physical property parameters of the citronellal-geraniol system and optimize the distillation separation, providing support for the industrial production of high-purity separation of the citronellal-geraniol system.

A Review on Internet of Things (IoT) Based Water Monitoring System

Current advancements in sensor networks are crucial for environmental remediation. The Internet of Things (IoT) connects numerous devices that may share and collect data. IoT expands its capabilities to include environmental concerns in addition to the automation sector by utilizing industry 4.0. Polluted water has emerged as one of the most serious dangers in latest days since drinking water has been contaminated and polluted. Contaminated water may trigger a variety of illnesses in humans and animals, affecting the ecosystem’s life cycle. This means, there is an increased demand for water which might affect the available sources of water at large. Therefore, effective management and monitoring of available resources of water is the need of the hour. This review paper includes the use of Internet of Things (IoT) technology for physical parameters of the water monitoring systems to manage and save scarce water resources. The traditional method to monitor water quality and quantity has been a quite difficult task involving a large amount of labour and cash. But the IoT application slashes human involvement and most of the procedural decisions are made by algorithms. Thus, smart methods for investigating water contamination are becoming increasingly significant with advancements in sensors, communication, and IoT technologies. A water monitoring system (WMS) is a low-cost and feasible system that uses IoT technology to analyse the quality of drinking water. use of IoT tools for water monitoring will be a step in the right direction. Even though there are many great technological advancements for the surveillance of water quality, the research field is still challenging. This article aims to unveil the recent research for developing intelligent, energy-efficient, and highly effective water monitoring systems that will allow for continuous monitoring of water in less privileged areas of Pakistan. This article will assist researchers and administrative organizations in starting pertinent investigations of water in rural areas by utilizing a WMS.

Quantifying the resilience of ICT-enabled grid services in cyber-physical energy system

Information and Communication Technology (ICT) is vital for the operation of modern power systems, giving rise to Cyber-Physical Energy Systems (CPESs). ICT enables the grid services (GSs) needed for monitoring and controlling the physical parameters of the power system, especially for remedying the impact of disturbances. But the ICT integration makes the overall system more complex, leading to new and unforeseen disturbances. This motivates the need for a resilient system design capable of absorbing and recovering from such disturbances. The current state of the art lacks a comprehensive resilience assessment of ICT-enabled GSs in CPESs. To address this, a novel method and metrics to assess the resilience of GSs in CPESs are presented in this paper. An operational state model of a GS, with three states, i.e., normal, limited and failed, is used to capture its performance, which is essential for quantifying its resilience. Sequential Monte Carlo simulations are performed with the model to capture the behaviour of ICT components to compute the operational state trajectory of the GSs. Metrics are then derived to quantify the resilience and its constituting phases. The method is demonstrated using two ICT system designs for the CIGRE MV benchmark grid, considering the state estimation as an exemplary GS. The simulation results show that the proposed method can capture the differences between ICT system designs with regard to resilience metrics. The contribution can, therefore, be used to analyse, compare and potentially improve the resilience of ICT system designs for CPES.

Impact of charge on the complexity of static cylindrical system

This work examines the complexity of charged static cylindrical object with anisotropic matter configuration in the background of energy–momentum squared gravity. For this purpose, we construct the field equations, the non-conservation equation and investigate the celestial structure. We evaluate the mass functions through both C-energy as well as Tolman mass formulae. Numerous factors, such as anisotropic pressure, inhomogeneous energy density and charge, etc have an impact on the complexity of the structure under consideration. The orthogonal splitting of the Riemann tensor yields two structure scalars. Among these both, the factor incorporating all the physical parameters of the self-gravitating system is named as the complexity factor. The complexity-free constraint is employed to compute charged static solutions for the Gokhroo–Mehra model as well as the polytropic equation of state. We conclude that the inclusion of charge reduces the complexity of anisotropic system.

Metalearning Generalizable Dynamics from Trajectories.

We present the interpretable meta neural ordinary differential equation (iMODE) method to rapidly learn generalizable (i.e., not parameter-specific) dynamics from trajectories of multiple dynamical systems that vary in their physical parameters. The iMODE method learns metaknowledge, the functional variations of the force field of dynamical system instances without knowing the physical parameters, by adopting a bilevel optimization framework: an outer level capturing the common force field form among studied dynamical system instances and an inner level adapting to individual system instances. Apriori physical knowledge can be conveniently embedded in the neural network architecture as inductive bias, such as conservative force field and Euclidean symmetry. With the learned metaknowledge, iMODE can model an unseen system within seconds, and inversely reveal knowledge on the physical parameters of a system, or as a neural gauge to "measure" the physical parameters of an unseen system with observed trajectories. iMODE can be generally applied to a dynamical system of an arbitrary type or number of physical parameters and is validated on bistable, double pendulum, Van der Pol, Slinky, and reaction-diffusion systems.

Novel low energy mixed amine biphasic solvent for carbon dioxide capture in biogas upgrading

A low energy consumption aqueous mixed amine biphasic solvent consisting of monoethanolamine, N-methyldiethanolamine and 1-butyl-3-methylimidazolium tetrafluoroborate (BF) was prepared for CO2 capture in biogas upgrading process. The solvent could be divided into two phases after biogas upgrading: rich liquid phase and lean liquid phase, the rich liquid phase would enter the regeneration tower for regeneration, and the lean liquid phase could directly enter the absorber tower for biogas upgrading without regeneration, the methane purity of the upgraded biogas could reach 100 %, the energy consumption of regeneration would be lower than that of 30 wt% MEA by 44.07 %, and the carbon enrichment rate of the rich liquid phase could reach more than 98 % after the phase separation. A comprehensive study of the influence of the BF addition on CO2 capture and mass transfer was carried out Amplified by analyzing the physical parameters of mixed system and a beneficial conclusion was reached. In addition, the added BF could effectively reduce the free energy during the reaction and improve the efficiency of the reaction. The higher dipole moment of the BF also provided an effective impetus for the phase separation process to take place. Finally, an amplified phase separator was used to simulate the whole process of phase separation in real conditions and it was found that high CO2 loadings were effective in accelerating the rate of phase separation. This study provided a theoretical basis for the subsequent research of biphasic solvents in the field of biogas upgrading, as well as technical guidance for the wide application of biogas upgrading in industry, which had a broad research prospect.

Data-driven boundary regression for equivalent identification of external parameters in steady-state models of power systems

External Network Modeling (ENM) is an important method to simplify and accelerate power system control, protection, and optimization. Traditional model-based methods obtain accurate external parameters by analyzing the physical properties of the whole system. In the case of incomplete physical information, the use of data-driven models has become an effective means to analyze the operating state of power systems. In this paper, a data-driven approach is used to propose an external equivalent parameter identification framework for steady-state models of power systems to accurately estimate the operating states of power systems of interest. Linear correction coefficients are used to deal with the challenges brought by the nonconvexities of the power flow equation to the regression. Based on the linearly mapped power flow equation, we derive the Multivariate Linear Regression (MLR) model of the external power system. The Box-Cox transformation (BCT) and ridge regression algorithm (RRA) algorithms are designed to address data normality, collinearity, and overfitting. The proposed framework is tested on a series of IEEE standard cases, which include both meshed transmission grids and radial distribution grids, with both Monte Carlo simulated data and public testing data. The results show that the proposed method can obtain a higher calculation accuracy than model-based methods can. The results also demonstrate that the proposed data-driven equivalent framework can obtain accurate regression parameter matrices under the condition of non-normal data, and accurately reflect the physical parameters of the external power system.

Kernel Regression Residual Decomposition-Based Polynomial Frequency Modulation Integral Algorithm to Identify Physical Parameters of Time-Varying Systems under Random Excitation

The physical parameters (stiffness, damping) of time-varying (TV) systems under random excitation provide valuable information for their working condition but they are often overwhelmed by noise interference. To overcome this problem, this paper presents a novel multi-level kernel regression residual decomposition method, which can not only effectively separate each modal component from the raw vibration acceleration signal, but also eliminate noise interference. Additionally, the multiple degree-of-freedom (DOF) parameter identification problem is transformed into a single DOF parameter identification problem. Combined with the derived polynomial frequency modulation integral algorithm and the cross-correlation theory based on the fractional Fourier ambiguity function, a physical parameter identification method is proposed. The method provides a new idea in modeling TV systems and identifying physical parameters under random excitation. To demonstrate the effectiveness of the proposed method, numerical simulations are conducted with three different cases of variation (variation, quadratic variation, and periodic variation) in time. Moreover, its robustness is evaluated by adding different signal-to-noise ratio levels of noise (20 dB, 50 dB, 100 dB) to the input vibration acceleration signal. The analysis results confirm the performance of the proposed method for the parameter identification of TV systems under random excitation.

Entanglement enhancement from single-port and two-port feedback optical parametric amplifiers

Coherent feedback control has been proved to be an effective approach in entanglement enhancement of the entangled states produced by a nondegenerate optical parametric amplifier (NOPA). However, the present research mainly focuses on a two-port feedback NOPA (TFPA). In this study, a scheme of single-port feedback NOPA (SFPA) is proposed and investigated. For the ideal case, both SFPA and TFPA have the potential to realize an infinite degree of entanglement. For the practical case with feasible physical parameters of realistic systems, the features of entanglement enhancement for two schemes are compared. The effect of entanglement enhancement for SFPA is relatively weaker than that of TFPA, but has a loose phase-locking accuracy requirement. Our results may provide useful references on the control of nonclassical light fields.

Dynamical analysis of nonlinear fractional order Lorenz system with a novel design of intelligent solution predictive radial base networks

In this research work, the convective flow of Lorenz attractor in the fractional domain is modeled with a nonlinear flexible structure of radial basis neural network (RBNN). The physical parameters of the complex fractional-order system are initially computed with fractional order Runge–Kutta solver for different stochastic scenarios of control constraints and designed its parametric model with RBFN under various initial conditions of the chaotic Lorenz system. Multiple chaos of the Lorenz system with the dynamical neural network structure are computed for different fractional order solutions to analyze the sensitivity of chaotic behavior by means of Lyapunov exponents. Phase diagrams of the chaotic pattern show the fractional-order Lorenz system has compact dynamical behavior and has the potential for the design application of a real dynamical system. Embedded dimension and time delay of the fractional Lorenz system are computed using the average mutual information (AMI) technique to evaluate the suitability of the time delay chaotic pattern in the fractional domain, which is affected by the numerical algorithm and fluctuated on minute time step size. The proposed design of RBFN has been confirmed as an exceptionally high-performance tool for soft computing and dynamics analysis of chaotic systems in the fractional order domain.

Statistical Analysis of Physical Parameters of Pulsars

Thanks to the excellent performance of FAST (Five-hundred-meter Aperture Spherical radio Telescope), the number of pulsars has increased rapidly. It is very important to analyze the physical parameters of known pulsars. The overall properties of pulsars are studied by analyzing the related physical parameters such as spatial position, period, surface magnetic flux density and so on. A large number of pulsars were detected by FAST near the galactic disk, which reflect the superiority of its detection ability. The diagram of the relationship between the period and the time derivative of period of pulsars has been updated. At present, 57 pulsars have crossed the classical “death line”, and five were discovered by FAST. Finally, the physical parameters of the binary pulsar systems are statistically analyzed, the binary pulsar systems are evolving towards the direction of low eccentricity and the decrease mass of the companion star. Moreover, 9 are located above the “spin-up line”. FAST is making China into the golden age of pulsar discovery, which will further promote the rapid development of pulsar physics.