Instantaneous Characteristics Research Articles

A study of particle motion in Kwerk-type twisted blades: Effect of configuration on particle mixing performance by CFD–DEM

Static mixers could promote the particle mixing in industries. Experimental and numerical investigations of particle mixing and flow characteristics in the riser with static mixers are carried out. The instantaneous microscopic characteristics and energy loss in Kwerk-type static mixer (K-KSM) are evaluated by Computational Fluid Dynamics coupled with Discrete Element Method (CFD–DEM). A more suitable calculation method is proposed, which utilizes particle coordinates and the coordination number of central particles to calculate the instantaneous mixing degree of particles (PK). The PK, Residence Time Distribution (RTD) and collision correlation are analyzed at different superficial gas velocities (Ug) and mass flow rates (Ms). The results show that the performance of K-KSM is superior to that of KSM under current conditions. Back mixing and aggregation phenomena are eliminated at Ug > 5 m/s and Ms < 1.73 g/s, and the recommended regimes are Ug ≥ 6.0 m/s and Ms < 1.5 g/s.

Adaptive Cruise Control System: A Literature Survey

Adaptive cruise control (ACC) assists automobiles in preserving a safe following distance and adhering to speed limits. This advanced driver-assistance system (ADAS) modifies the car's speed to keep a safe gap from oncoming traffic. All vehicle types include combustion engines, pure electric vehicles, hybrid electric vehicles, and methods of operation; controllers are designed to react to cruise control signals and provide an efficient route profile according to the surrounding environment and instantaneous vehicle performance characteristics. ACC uses a perception system to measure the forward vehicle's current distance, speed, and acceleration relative to the host vehicle. Some of these systems use lasers, radar, cameras, or a combination of these sensors to determine the distance and speed of the leading vehicle. Other systems even use wireless communication to collect data from surrounding vehicles. ACC can help reduce stress on long drives, increase road safety, prevent accidents, and enhance traffic flow energy efficiency. This paper aims to introduce a comprehensive study of the research on ACC and mention different controlling techniques used to deal with the problem. Furthermore, a discussion of each method with its cons and pros is mentioned too. First, an introduction to the ACC system and control approaches with a brief discussion of their main principle is presented. Next, various application cases of ACC are presented. These applications include lateral dynamics, wireless technology, energy vehicles, navigation data, and practical experimental tests. At last, future guidance and challenges are discussed.

Unveiling Turbulent Flow Dynamics in Blind-Tee Pipelines: Enhancing Fluid Mixing in Subsea Pipeline Systems

Blind tees, as important junctions, are widely used in offshore oil and gas transportation systems to improve mixing flow conditions and measurement accuracies in curved pipes. Despite the significance of blind tees, their unsteady flow characteristics and mixing mechanisms in turbulent flow regimes are not clearly established. Therefore, in this study, Unsteady Reynolds-Averaged Navier–Stokes (URANS) simulations, coupled with Explicit Algebraic Reynolds Stress Model (EARSM), are employed to explore the complex turbulent flow characteristics within blind-tee pipes. Firstly, the statistical flow features are investigated based on the time-averaged results, and the swirl dissipation analysis reveals an intense dissipative process occurring within blind tees, surpassing conventional elbows in swirling intensity. Then, the instantaneous flow characteristics are investigated through time and frequency domain analysis, uncovering the oscillatory patterns and elucidating the mechanisms behind unsteady secondary flow motions. In a 2D-length blind tee, a nondimensional dominant frequency of oscillation (Stbt = 0.0361) is identified, highlighting the significant correlation between dominant frequencies inside and downstream of the plugged section, which emphasizes the critical role of the plugged structure in these unsteady motions. Finally, a power spectra analysis is conducted to explore the influence of blind-tee structures, indicating that the blind-tee length of lbt = 2D enhances the flow-mixing conditions by amplifying the oscillation intensities of secondary flow motions.

Transport characteristic evaluation of runaway electrons in an ITER disruption simulation

In previous studies, it has been observed that the transport of energetic electrons decreases with increasing energy. This observation is a global and long-time-scale result, attributed to the space-averaged perturbations. In this work, we focus on the local and instantaneous transport characteristics of runaway electrons (REs) during the phase of tokamak disruption, with REs speeds close to the speed of light. To simulate the dynamics of REs, we utilize a particle tracing code called PTC. By coupling PTC with the MHD code JOREK, we are able to study the energy and spatial dependence of RE transport. Our investigations reveal that the finite orbit width (FOW) effect plays an important role in RE transport. This effect is influenced by the relative direction of the electron drift and the magnetic field line. Specifically, the FOW effect strengthens the transport when the drift direction aligns with the deflecting direction of the field line. And we compare the transport profiles among three time slices: at the beginning of the thermal quench, during the thermal quench, and at the beginning of the current quench. In this ITER disruption simulation, the perturbation scale is strong and δB/B is up to 0.05 at developed thermal quench stage. It is reasonable that the influence of FOW effect on transport is less than that of magnetic perturbation even if the energy of REs is about hundreds MeV and the orbit width is equal to or greater than the perturbation length. These analyses provide insights into the mechanisms of RE transport based on magnetic perturbations.

Prediction of the cutting forces in milling operation based on multi-objective optimization of the time series analysis parameters

Accurate simulation of the cutting forces in the milling process is extremely complicated because of variations in cutting process parameters, including the change in modal characteristics of the workpiece along the machining path and the high-temperature gradient around the cutting zone, especially for the modeling of milling forces in flexible thin-walled workpieces. Therefore, proposing effective methods for prediction and control of cutting forces is highly important to ensure the high performance and stability of the cutting process as well as the quality of the machined surface. In this article, a novel method is presented for rapid prediction of cutting force signals based on the combination of the multi-variable time series analysis, the Imperialist Competitive Algorithm (ICA) and Multi-View Embedding (MVE) algorithm. A state-space model of the dynamic system is presented in order to quickly predict the instantaneous dynamic characteristics of the cutting process. The maximum error between the amplitude of experimental and predicted force signals (peak-to-peak) in stable and unstable cutting conditions is less than 8% and 17%, respectively. Therefore, it is illustrated that the presented method has remarkable computational accuracy and convergence speed for predicting the future of the cutting forces under both stable and unstable conditions.

Experimental and numerical investigations to the aeroelastic response of flexible thin airfoil

The paper investigates the phenomenon of the aeroelastic response of flexible thin airfoils under various angles of attack (AOAs) and flow velocities through wind tunnel experiments and numerical simulations. The vibration modal characteristics are explored, including vibration frequencies, amplitudes, modal transition, and instantaneous characteristics. Vibration is directly measured using non-contact laser sensors, and the numerical model is appropriately configured to simulate the fluid–structure interaction (FSI) problem under large deformation. Experiments cover a range of AOAs (1°–20°) and incoming velocities (from 10 to 73 m/s), with dynamic responses measured using four laser sensors. Both average and instantaneous modal response features are analyzed, revealing multi-modal characteristics as velocity and AOA increase. The vibration mode transitions from pure bending to higher-order modes as incoming velocity increases. Specifically, at higher velocities and increased AOA, the high-order vibration component shifts from bending-torsional coupled mode to pure-torsional mode. Comparison of vibration frequencies between experimental measurements and finite element method simulations highlights significant shifts, particularly in the pure-torsional mode. Furthermore, employing commercial software ANSYS CFX and ANSYS Mechanical, a two-way three-dimensional FSI model successfully replicates flutter boundaries observed experimentally at 1° AOA and approximately incoming velocity 73 m/s. This FSI model is extended to simulate the multi-modal vibrations at 15° AOA, yielding insights into flow phenomena contributing to multi-modal vibration at this AOA. An explanation is provided for the multi-modal vibration phenomenon observed in the experiments based on the above insight. Finally, the differences between the experimental and numerical simulations are speculated upon.

Temporal contrast enhancement of a Ti:Sapphire laser by nonlinear Fourier filtering

Temporal cleaning of high-power infrared (IR) pulses generated by a Ti:Sapphire system is demonstrated by the use of the Nonlinear Fourier Filtering (NFF) method. In a proof-of-principle experiment suppression of up to 1000 is achieved for the temporal pedestal prior to the main pulse, with a moderate (20-25%) overall throughput. This includes the same suppression ratio for the picosecond coherent pedestal in the direct vicinity of the main pulse. Based on the instantaneous, intensity-dependent and high-order switching characteristics of NFF, excellent pulse cleaning performance is observed. The efficient, high-contrast removal of the coherent pedestal from the foot of the main pulse even if its duration is shorter than 100 fs is compared with the capability of the plasma mirror technique. Calculations are also performed, supporting the experimentally observed sharp intensity dependence of the switching process, pointing out the dominant role of the ionization-based refractive index change.

High-order iterative rearrangement transform for time–frequency characterization of bearing fault impact

Time–frequency analysis (TFA) can effectively characterize features of non-stationary signals. Traditional TFA algorithms construct signal models in the time domain and make the assumption that the instantaneous characteristics of each component are continuous. However, the instantaneous frequency (IF) of the transient signal is discontinuous in the time domain and exhibits a multifaceted relationship with time, such as shock, vibration wave, damped sound wave, etc. Additionally, in most existing TFA methods, low-order group delay (GD) is used to describe transient signals, which leads to unsatisfactory energy concentration and calculation accuracy. To address about issues, a novel TFA technique, termed high-order iterative rearrangement transform (HOIRT), is developed in this research. First, the signal model is defined within the frequency domain, and the frequency ridge of the transient signal is described by a high-order GD (HOGD), which is similar to the IF. Second, a HOGD-based iterative synchrosqueezing operator is defined to reassign time–frequency coefficients into the GD trajectories along the time direction. Finally, the HOGD-based frequency extraction operator is constructed to only retain the target time–frequency information of the transient signal from the rearranged results, such that the noise interference is eliminated and the energy-concentrated TFR is obtained. A simulation signal with nonlinear GDs is employed to illustrate the effectiveness of the HOIRT. Compared with the other seven typical TFA algorithms, the developed technique has the smallest calculation error and Rényi entropy, showing that the HOIRT has the highest accuracy and energy concentration. Analysis result of the bearing fault impact signal shows that the proposed HOIRT can display the time when pulses occur while ensuring high time–frequency resolution, making it suitable for detecting bearing faults.

Dynamic coil switching strategies for significant efficiency increases in electromagnetic energy generators

The development of self-powering systems has been recognized as critical such that innovative stand-alone emerging technologies can operate sustainably from scavenged ambient energy. Electromagnetic generators (EMGs) using magnetic levitation architectures for mechanical vibration energy harvesting are a promising technology that can be tailored to specific needs and provide low-cost electric powering for both small-scale and large-scale devices. They also present non-complex design, with low maintenance requirements and can operate with stable performance for long periods of time. Despite these prominent features, their complex non-linear and hysteresis-based resonant characteristics makes performance optimization hard to achieve and still needs to be addressed as a function of the input excitation. Numerical and experimental results are here provided to demonstrate the effectiveness of a new concept of EMG that aims to dynamically adapt the coil-array architecture throughout its operation to ensure maximum harvested power and to optimize the transduction mechanism efficiency. The self-adaptive motion-driven levitation-based autonomously rearranges each coil independently as a function of the instantaneous time-varying characteristics of the levitating magnet (LM) position. The mechanism features two dynamic coil switching strategies: (i) on/off switching, by short circuiting, with transmission gate switches, the coils without influence on the electromotive force; and (ii) reversing polarity switching, to avoid the sum of electromotive forces cancels each other. Average output powers of 635 mW (up to ∼4.1 W of peak power) were obtained with only the 4-centre (out of 14) permanently active coils, while only 292 mW (up to 833 mW of peak power) were achieved with the 14-coils permanently connected under optimal load conditions and harmonic translational input excitations with 15 Hz frequency and 20 mm amplitude. However, the adaptive generator was able to provide an impressive average power output of 3 W under the same conditions. Up to 14-fold larger output average power and 5.5-fold larger electric efficiency demonstrate the potential of the proposed coil switching self-adaptation system for enhancing the total energy conversion from general widespread mechanical vibrations.

A theoretical model for predicting the startup performance of pumps as turbines

Using the unsteady Bernoulli equation for the piping system and the angular momentum equation for the rotor, derives here a theoretical model to predict the startup performance of a pump as turbine (PAT). This model is effective for predicting the instantaneous evolution characteristics of the main performance parameters of PAT during startup, and these changings are initially faster and then slowly as a whole. The effect of the rotor moment of inertia and the final stabilized rotational speed of PAT on evolution characteristics of parameters is opposite. The rotational speed, head, hydraulic power, and conversion efficiency show a upward rising trend with the startup time, whereas the flow rate and hydraulic head loss display a downward trend.

Large eddy simulation of non-premixed flame behavior and NOx emission characteristics in a staged combustor with multi-swirling flows

This study utilizes Large Eddy Simulation (LES) to investigate flame characteristics and NOx emission generation in a concentric staged combustor with three distinct pilot swirler arrangements. We examine the effects of multi-swirling flow on the instantaneous flow field and flame characteristics using the Proper Orthogonal Decomposition (POD) method, and assess how variations in swirler structures influence thermoacoustic instability. Furthermore, a comparison of NOx emissions between two pilot swirler configurations, with similar mean flow field and flame characteristics, highlights the precessing vortex core's role in NOx emissions. Compared to a single-swirler setup, a double-swirler configuration produces a more distinct single precessing vortex core, thereby amplifying thermoacoustic instability. The extended venturi structure in the double-swirler setup mitigates the development of the swirl-induced vortex core, reducing thermoacoustic instability. However, the expansion section in this setup impedes initial fuel and air mixing, leading to an extended flame length, which in turn results in higher NOx emissions due to a more concentrated temperature distribution. With similar mean flow field and flame structure, the double-swirler combustor proves more effective in reducing NO emissions than the triple-swirler combustor.

Prediction of instantaneous flow characteristics of hydrocyclone with long short-term memory network based on computational fluid dynamics data

Accelerating the prediction time of separation performance and flow field characteristics in industrial hydrocyclones holds paramount importance for real-time control. Machine learning methods exhibit significant advantages in this particular aspect. This study presents a network model that integrates a long short-term memory (LSTM) layer with a fully connected layer to accurately forecast the flow field and separation performance under various operational conditions, leveraging CFD data. The study evaluates the effect of factors such as the number of LSTM layers and neurons per layer on the model's performance, aiming to identify the optimal network structure. The results demonstrate that the predicted flow field and performance of the hydrocyclones using LSTM closely align with the outcomes from CFD simulations. The average absolute percentage error is constrained to within 6%, which significantly enhances the prediction efficiency. The findings contribute to a more efficient and automated separation process in industrial environments.

MODELING OF RELAY PROTECTION OF ELECTRIC POWER SYSTEM USING MATLAB/SIMULINK

This article presents the modeling of overcurrent and differential relays in MATLAB/Simulink. The overcurrent relay has the features of instantaneous, time-definite, and inverse definite minimum time (IDMT) characteristics according to IEC 60255 and IEEE C37.112-1996 standards. The 220 kV km transmission systems are selected as examples for fault simulation and relay testing for each relay model and the coordination protection. The simulation results are given and a description of how this case study can be used to compare protection schemes.

The improved backward compatible physics-informed neural networks for reducing error accumulation and applications in data-driven higher-order rogue waves.

Due to the dynamic characteristics of instantaneity and steepness, employing domain decomposition techniques for simulating rogue wave solutions is highly appropriate. Wherein, the backward compatible physics-informed neural network (bc-PINN) is a temporally sequential scheme to solve PDEs over successive time segments while satisfying all previously obtained solutions. In this work, we propose improvements to the original bc-PINN algorithm in two aspects based on the characteristics of error propagation. One is to modify the loss term for ensuring backward compatibility by selecting the earliest learned solution for each sub-domain as pseudo-reference solution. The other is to adopt the concatenation of solutions obtained from individual subnetworks as the final form of the predicted solution. The improved backward compatible PINN (Ibc-PINN) is applied to study data-driven higher-order rogue waves for the nonlinear Schrödinger (NLS) equation and the AB system to demonstrate the effectiveness and advantages. Transfer learning and initial condition guided learning (ICGL) techniques are also utilized to accelerate the training. Moreover, the error analysis is conducted on each sub-domain, and it turns out that the slowdown of Ibc-PINN in error accumulation speed can yield greater advantages in accuracy. In short, numerical results fully indicate that Ibc-PINN significantly outperforms bc-PINN in terms of accuracy and stability without sacrificing efficiency.

Vertical structure of tropical cyclone precipitation over the North Indian Ocean: a spaceborne precipitation radar perspective

ABSTRACT Spaceborne precipitation radars provide an unprecedented opportunity to study three-dimensional structure of precipitation, particularly over the open ocean where in-situ observations are rather meagre. In this study, instantaneous surface precipitation characteristics and their vertical structures during the tropical cyclones (TCs) over the North Indian Ocean (NIO) between 2014 and 2022 have been analysed using the dual-frequency precipitation radar onboard the Global Precipitation Measurement Core Observatory. Although stratiform precipitation accounts for more than 70% of total TC surface precipitation area, convective precipitation contributes about half of the total TC surface precipitation amount over the NIO. About 90% of stratiform TC precipitation area yields surface precipitation of less than 10 mm/hour. The vertical structures of stratiform and convective TC precipitation vary with surface precipitation intensity and have nearly similar characteristics over both basins of the NIO. This preliminary quantitative TC precipitation analysis would be useful for better understanding of precipitation processes during TCs over the NIO, and for further advancement in numerical models through improved parameterization schemes for TC precipitation forecasting.

Multi-temporal dependency handling in video smoke recognition: A holistic approach spanning spatial, short-term, and long-term perspectives

Accurately recognizing video-based smoke is still a profoundly challenging task due to the special characteristics of smoke, such as non-rigid morphology, semi-transparent representations, serious interferences (namely non-smoky white steams and clouds etc.), and so on. In this study, we present the Spatio-Temporal Local-Enhanced Network (STENet), which is meticulously designed to handle the spatio-temporal dependencies of smoke video in multi-temporal views, effectively synthesizing spatial, long-term, and short-term smoke features. To account for the semi-translucent nature of smoke, we introduce a short-term temporal branch within STENet that models the transient temporal dependencies between adjacent frames, thereby distinguishing smoke from visually similar interferences such as clouds and water steam. Furthermore, the representation of spatial and short-term temporal features, which capture the instantaneous spatio-temporal characteristics of a single smoke plume, can be fine-grained aligned and mutually guided, which is accomplished through our Collaborative Alignment(CoAM). Moreover, to address the unique drifting characteristics and dynamic variations of non-rigid objects across different temporal scales, we propose a new Prioritized Local-enhanced Transformer (PLT) that selectively focuses on key frame subsets for capturing long-term temporal dependencies. Experiments conducted on benchmarks demonstrate that the proposed STENet achieves state-of-the-art results on large-scale video dataset for recognizing industrial smoke emissions (RISE) and forest fire surveillance video (FSmoke). In particular, our STENet achieves 0.893 F-score on RISE dataset with 6.9M parameters and 3.86G FLOPs, being 4×-18× smaller and using 5×-51× fewer FLOPs than previous baseline models.

Numerical Study of the Boundary Layer Separation of a Low‐Pressure Turbine Cascade at Different Incidence Angles

Modern low‐pressure turbine (LPT) pursues high blade loading to improve its efficiency while the possibility of boundary layer separation on the blades is still a challenging problem. The incidence angle is one of the main factors affecting the flow separation and laminar‐turbulent transition, which is of key importance for the design of LPT. In this work, based on the initial simulation results obtained using the Reynolds‐averaged Navier–Stokes (RANS) method, the flow past a single LPT cascade at three representative incident angles (i = −10°, 0°, +10°) is investigated using large‐eddy simulation (LES) method with approximately 26 million structured grids. The simulations are carried out with Open Source Field Operation and Manipulation (OpenFOAM) for an off‐design low Reynolds number condition Re = 40,000 and Ma = 0.5. The time‐averaged aerodynamic performances and three‐dimensional instantaneous flow characteristics of the LPT cascade are discussed in detail, and the results show that the blade loading and the transverse pressure gradient (TPG) from the pressure side to the suction side of leading edge (LE) increase with increasing incidence angle. At the incidence angle of +10°, the LPT cascade undergoes separation and reattachment from the LE to around 20% Cax on the suction surface, which weakens the separation phenomenon at the rear half of the suction surface and the total pressure loss downstream of the trailing edge (TE). It is worth noting that the RANS method underpredicts the blade boundary layer separation region compared with LES under these extreme conditions. These results may offer off‐design condition data for the future improvement of RANS models or experimental studies.

Data-driven method for estimating emission factors of multiple pollutants from excavators based on portable emission measurement system and online driving characteristic identification

This study aims to explore the factors that influence the emission characteristics of multiple pollutants from non-road mobile machinery (NRMM) under real-world conditions and to establish a data-driven method for calculating accurate emission factors. This research focused on NRMM excavators meeting the third-stage emission standards and identified the actual work characteristics of 108 excavators in different scenarios based on a self-developed testing system for 368,000 h. Additionally, a portable emission testing system (PEMS) was used to study the instantaneous emission characteristics under different driving styles and modes for 10 EC210 excavators with the largest engineering construction inventory. The results showed that the average time proportions of idling, working, and moving modes for excavators were 21 %, 66 %, and 13 %, respectively. The results also revealed that the instantaneous emission rates of multiple pollutants varied significantly under different driving styles and modes. Driving style affected the hydraulic pump power change rate through hydraulic pilot pressure, and engine load surge caused turbocharger response delay and in-cylinder combustion deterioration, which affected pollutant emissions. Driving mode affected the emission characteristics of idling, high-speed idling, moving, and working modes of excavators through the external characteristics corresponding to the engine speed gear set. The data-driven method for calculating emission factors differed from the traditional method for most indicators to varying degrees. In terms of fuel-based emission factors (EFfs), except for the EFfNOx indicator, which was 7.859 % higher than the traditional method, the other three indicators were significantly lower than the traditional method. In terms of power-based emission factors (EFps), except for EFpPM and EFpPN, the other two indicators were much higher than the traditional method. EFpCO and EFpNOx were 7.93 % and 20.332 % higher than the traditional method, respectively. It is recommended to use the data-driven method based on the actual driving data distribution to provide scientific support for accurately establishing the emission inventory.

(Invited) Comparison of Empirical Models for Broadband Insertion Losses in TSV-Interconnects

The reliability of through silicon via (TSV)-interconnects depends on various factors including integration schemes, manufacturing process (construction materials, wafer-scale homogeneity, thermal budget) and operating conditions. The magnitude of the frequency-dependent $S_{21}$ microwave scattering parameter quantifies insertion losses in TSV-interconnects. The insertion loss depends on the instantaneous electrical characteristics of the device under test. We attribute insertion loss (which affects device reliability) to reorientation of electrically active defects, cracks, voids, dielectric constant variation, and water molecules affected by heat. We model insertion losses with equivalent circuit models of varying complexity. We compare results determined with global and local models fit to low-frequency and high-frequency subsets of the full spectrum. We estimate model parameters with a stochastic optimization implementation of the Levenberg-Marquadt method. To illustrate the relevance of this methodology, for TSV-interconnects from two different providers, we quantify how estimated model parameters vary with thermal cycles for TSV-interconnects fabricated at particular wafer locations.

Gaussian process metamodel and Markov chain Monte Carlo-based Bayesian inference framework for stochastic nonlinear model updating with uncertainties

The estimation of the posterior probability density function (PDF) of unknown parameters remains a challenge in stochastic nonlinear model updating with uncertainties; thus, a novel Bayesian inference framework based on the Gaussian process metamodel (GPM) and the advanced Markov chain Monte Carlo (MCMC) method is proposed in this paper. The instantaneous characteristics (ICs) of the decomposed measurement response, calculated using the Hilbert transform and the discrete analytical mode decomposition methodology, are extracted as nonlinear indices and further used to construct the likelihood function. Then, the posterior PDFs of structural nonlinear model parameters are derived based on the Bayesian theorem. To precisely calculate the posterior PDF, an advanced MCMC approach, i.e., delayed rejection adaptive Metropolis-Hastings (DRAM) algorithm, is adopted with the advantages of a high acceptance ratio and strong robustness. However, as a common shortage in most MCMC methods, the resampling technology is still applied, and numerous iterations of nonlinear simulations are conducted to ensure accuracy, thus directly reducing the computational efficiency of the DRAM. To address the abovementioned issue, a mathematical regression metamodel of the GPM with a polynomial kernel function is adopted in this paper instead of the traditional finite element model (FEM) to simulate a nonlinear response for the reduction of computational cost, and the hyperparameters are further estimated using the conjugate gradient optimization methodology. Finally, numerical simulations concerning a Giuffré–Menegotto–Pinto modeled steel-frame structure and a seven-storey base-isolated structure are conducted. Furthermore, a shake-table experimental test of a nonlinear steel framework is investigated to validate the accuracy of the Bayesian inference method. Both simulations and experiment demonstrate that the proposed GPM and DRAM-based Bayesian method effectively estimates the posterior PDF of unknown parameters and is appropriate for stochastic nonlinear model updating even with multisource uncertainties.