Dynamic Physical Model Research Articles

Physics-informed data assimilation model for displacement prediction of hydrodynamic pressure-driven landslide

Due to the complex and non-linear characteristics of landslide evolution, the deformation law of landslides is always hard to be predicted. Considering the dynamical evolution process of landslide physical mechanism, a Physics-Informed Data Assimilation (PIDA) method is first proposed for landslide displacement forecasting. It can enhance the prediction ability of the physical forecasting model, and solves the problem of ignoring the physical significance of landslides in mathematical prediction model. Additionally, the superiority of Particle Filter (PF) in nonlinear and non-Gaussian system determines its application in complex problem. Aiming at the hydrodynamic pressure-driven landslide, the Sliding Zone Deterioration (SZD) model is constructed as the state equation of the PF algorithm. And the observation equation is formed by the relationship between landslide deformation and monitoring datasets. The physics-informed landslide displacement prediction model serves as a bridge, consisting of the shear strength parameter dynamic updated model and physical mechanism analysis model, for key parameters feedback, modification, and update. Comparing the results to traditional Long Short-Term Memory (LSTM) and Back Propagation Neural Network (BPNN) methods, the PIDA method combining triggering and instability mechanisms performs better in accuracy. Moreover, it enables dynamic and reliable landslide deformation prediction.

Semi-physical simulation and coordinated control of SOFC-PV/T-HP system

Due to the differences in the dynamic characteristics of various energy processes, the operation and control of multi-energy coupled integrated energy systems (IES) pose significant challenges. This paper focuses on the semi-physical simulation and coordinated control of SOFC-PV/T-HP thermoelectric system, which includes solid oxide fuel cells, a solar photovoltaic/thermal panel and a heat pump. In order to enhance the experimental safety and reliability of the research findings, a semi-physical simulation platform is developed by integrating physical equipment and dynamic models. The operational characteristics of the cogeneration system are investigated and controlled using an extended state-space based model predictive algorithm. The experimental results demonstrate that the designed centralized model predictive controller effectively stabilizes the demand power of the battery. The standard deviation of the battery demand power is reduced from 2.39 W to 0.66 W within 500 s, which alleviates the regulation burden of the battery. At the same time, the hot water temperature is also well controlled. The fluctuation of hot water supply temperature does not exceed 0.11℃ after stability. The system can maintain stable operation under external disturbance, and maintain the balance between electricity/heat supply and demand.

The effects of vocal tract constrictions on aerodynamic measures in a synthetic vocal fold model.

According to nonlinear source-filter theory, as the strength of the coupling between the source and filter increases, typically by a decrease in the vocal tract cross-sectional area, the resultant increase in the inertance of the vocal tract yields an increase in the interactions between acoustic pressures within the vocal tract and the changing glottal airflow and/or the vibratory pattern of the vocal folds as noted in Titze [(2008). J. Acoust. Soc. Am. 123(4), 1902-1915]. The purpose of the current research was to examine the effects of parametric vocal tract constrictions mimicking epilaryngeal tube and lip narrowing on aerodynamic measures in a dynamic self-oscillating physical model of the vocal folds and vocal tract. Multilayered silicone vocal fold models were created based on Murray and Thomson [(2011). J. Visualized Exp. 58, e3498] and Murray and Thomson [(2012). J. Acoust. Soc. Am. 132(5), 3428-3438] and mounted to a simple synthetic trachea and supraglottal vocal tract model. Four constriction cross-sectional areas were examined at two locations (i.e., at the epilarynx and lip regions). Phonation threshold pressure and flow were measured at phonation onset and offset using four M5-CONV vocal fold models. Results indicated that both constriction magnitude and location are relevant factors in determining glottal aerodynamics. In general, a narrow epilarynx tube or lip constriction resulted in the lowest onset pressures and airflows while the no vocal tract condition resulted in the highest onset pressures and airflows.

Multi-physics Coupled Simulation and Model App Development for Lithium Slurry Batteries

Lithium slurry batteries, as an electrochemical energy storage technology, have the advantages of high operating voltage, large energy density and flexible configuration, and have broad application prospects. Due to the high cost of experiment time, materials traditional experimental methods have low R&D efficiency and the internal reactions and physical fields of batteries are very complex, research on battery performance is difficult and the threshold is high. To overcome this challenge, A new dynamic three-dimensional multi physical field coupling model for lithium slurry flow batteries has been proposed. This model can study the fluid dynamics of slurry batteries, the electrochemistry of lithium electrode reactions, the transport of lithium in solid particles, and the mass balance in storage tanks. Based on this model, we develop model applications using Comsol’s built-in interface. This app has a simple interface and simple operation to reduce the threshold for researchers and improve their research efficiency.

Multi-Sensor Graph Transfer Network for Health Assessment of High-Speed Rail Suspension Systems

Suspension systems are significant for safe and comfort operation of high-speed trains. Health assessment is a useful tool to schedule maintenance plans of suspension systems, and furthermore ensure safety operation of high-speed railway transportation. In real operating condition, two problems, i.e. data imbalance and shortage of labelled data, result in difficult for health assessment of the suspension system using deep learning. In this work, a multi-sensor information fusion method, called as multi-sensor graph transfer network (MSGTN), is proposed in basis of deep transfer learning and graph neural network. In the proposed method, a domain-share multi-sensor graph neural network (MSGNN) is firstly proposed to extract features from vibration signals collected from three different positions in train vehicles. A graph-based fusion layer in MSGNN is proposed to fuse multi-sensor information by combining frequency response curves of the suspension system. The MSGTN mainly includes two parts in source and target domains respectively. In the source domain, a simple physical dynamic model of high-speed rail suspension system is built to generate labelled simulation datasets to pre-train MSGNN. In the target domain, the initial hyper-parameters of MSGNN are that of the pre-train model in the source domain. The labelled data in the target domain is fed to fine-tune MSGNN and then the final model for health assessment can be obtained by minimizing the loss function. The effectiveness of the proposed method was verified using real-work operation data.

Data worth analysis within a model-free data assimilation framework for soil moisture flow

Abstract. Conventional data worth (DW) analysis for soil water problems depends on physical dynamic models. The widespread occurrence of model structural errors and the strong nonlinearity of soil water flow may lead to biased or wrong worth assessment. By introducing the nonparametric data worth analysis (NP-DWA) framework coupled with the ensemble Kalman filter (EnKF), this real-world case study attempts to assess the worth of potential soil moisture observations regarding the reconstruction of fully data-driven soil water flow models prior to data gathering. The DW of real-time soil moisture observations after Gaussian process training and Kalman update was quantified with three representative information metrics, including the trace, Shannon entropy difference and relative entropy. The sequential NP-DWA framework was examined by a number of cases in terms of the variable of interest, spatial location, observation error, and prior data content. Our results indicated that, similarly to the traditional DW analysis based on physical models, the overall increasing trend of the DW from the sequential augmentation of additional observations within the NP-DWA framework was also susceptible to interruptions by localized surges due to never-experienced atmospheric conditions (i.e., rainfall events). The difference is that this biased DW in the former is caused by model structural errors triggered by contrasting scenarios, which is difficult to be compensated for by assimilating more prior data, while this performance degradation in the NP-DWA can be effectively alleviated by enriching training scenarios or the appropriate amplification of observational noise under extreme meteorological conditions. Nevertheless, a substantial expansion of the prior data content may cause an unexpected increase in the DW of future potential observations due to the possible introduction of ensuing observation noises. Hence, high-quality and representative small data may be a better choice than unfiltered big data. Compared with the observations in the surface layer with the strongest time variability, the soil water content in the middle layer robustly exhibited remarkable superiority in the construction of model-free soil moisture models. We also demonstrated that the DW assessment performance was jointly determined by 3C, i.e., the capacity of potential observation realizations to capture actual observations, the correlation of potential observations with the variables of interest and the choice of DW indicators. Direct mapping from regular meteorological data to soil water content within the NP-DWA mitigated the adverse effects of nonlinearity-related interference, which thus facilitated the identification of the soil moisture covariance matrix, especially the cross-covariance.

Magnetic Circuit Optimization and Physical Modeling of Giant Magnetostrictive Actuator

Due to the excellent performance, giant magnetostrictive actuator is used in the active vibration isolation control system. However, its hysteresis nonlinear dynamic model is too complex for engineering practical applications, so it is necessary to establish an accurate and easy-to-use model. Based on Simulink/Simcape, a magnetic circuit model and a nonlinear dynamic physical model of the giant magnetostrictive actuator are developed. In the optimization of the magnetic circuit, the uniform distribution and the magnetic energy utilization of the giant magnetostrictive actuator are taken as the optimization objective, and the design criteria of the magnetic circuit are given. The hysteresis performance between the current and the magnetization is analyzed by simulating the magnetic circuit model. From the perspective of energy conservation, a linear magnetostrictive model which can reflect the effects of the frequency doubling and preload is established. Finally, the accuracy of the nonlinear dynamic physical model for the giant magnetostrictive actuator is verified by an experiment. The results show that the physical model agrees well with the experiment results not only under the quasistatic operating conditions but also under dynamic operating conditions. The error of the output displacement of the GMA under step response is less than 0.6 μm.

Reduced order modeling of a pressure column of an air separation unit using the Dynamic Edmister Method

Accurate physical dynamic models applied as digital twins are vital for developing advanced control strategies and optimizing dynamic plant operation. First principle models are often large-scale, complex, computationally expensive, and not real-time capable. As a result, they need to be reduced for application in optimization and control. This article introduces the Dynamic Edmister Method, which is a generic numerically robust model reduction approach used for dynamic distillation columns. In illustrative case studies, the Dynamic Edmister Method is applied to a digital twin of the pressure column of an air separation unit. The method is validated by simulations of a start-up procedure and a step test. Both case studies show that the reduced models accurately reproduce the dynamic and steady-state behavior of the underlying rigorous model while reducing the calculation effort by 70%. The model reduction approach presented is a significant first step towards using digital twins for dynamic optimization.

Adaptive unknown input observer-based detection and identification method for intelligent transportation under malicious attack

This paper aims at developing a novel detection and identification method against malicious attacks in intelligent transportation. Due to the development and applications of communication and advanced sensor technologies, intelligent transportation has faced new safety risks. In particular, the emerging malicious attacks, such as false data injection attack, can mask the destruction of physical dynamic by tampering with information in layer to fool the current detection methods. Because of this reason, an adaptive unknown input observer-based detection and identification method is developed. Firstly, a physical dynamics model of vehicle networking system is established by considering the actual physical state. Considering the spoofing characteristics of false data injection attack, an unknown input observer-based detection method is proposed. Through the design of adaptive unknown input observer parameters, the detection performance, can be improved by cutting down the state estimation error. Compared with the UIO-based detection method, simulations demonstrate that the false positive rate can be reduced 0.1%. Based on the feature of state residuals that is not sensitive to the attacked ith residual, but sensitive to other residuals, a novel identification criterion is developed. At last, simulation experiments on the Matlab verify the performance of the proposed detection and identification algorithm in intelligent transportation system.

Detection-based active defense of biased injection attack based on robust adaptive controller

As the promising technology, the cooperative cyber-physical system can enhance the operating efficiency and reliability of smart grids. Meanwhile, the characteristics that deep integration of cyber-physical system can make smart grid face new security problems caused by false data injection attack. To maintain a safe and stable operation of smart grids, timely detection and defense of the emerging false data injection attacks, such as biased injection attack, is crucial. For this reason, this paper aims at developing a detection-based active defense mechanisms against biased injection attacks via robust adaptive controller. Through the established physical dynamic power model, an improved adaptive observer-based detection algorithm is proposed. Through the design of observer parameters, the proposed adaptive observer can enhance the accuracy of estimation state. In contrast to well-known attack detection methods for smart grids, the performance of attack detection under the developed detection algorithm can be effectively improved, such as the accuracy of state estimation and false positive rate. Through the above results provided by the attack detection, a robust adaptive controller-based active defense method is further developed. The proposed method can offset the impact of biased injection attack to maintain the stable running of power system. Simulation studies demonstrate the reliable response of the developed active defense method against biased injection attacks.

Reconciliation of Train-Run data and Train consumption data for a better traction energy consumption estimation, a new Tool

The present work addresses the estimation of the traction energy using train-run data. The proper quantification of electrical traction energy by Railway Undertaking is necessary for energy settlement and billing by the different energy stakeholders such as energy suppliers as well as the infrastructure managers. The system thus needs a fair estimation of the train consumption most notably in case of missing data. Train-run data are used to compute an estimation at the train level and then aggregated at the Railway Undertaking level. We used an aggregate tree-based model for the estimation models using a set of different physical variables extrapolated from a train dynamic physical model. In turn, it has imposed another tool for the reconciliation between train-run data and consumption data at the vehicle level because often train-run data and consumption data are siloed.This methodology has allowed the development of ORES, a new tool dedicated to the train-run data and consumption data reconciliation as well as train-run electrical traction energy estimation. Some additional approaches have also been tried using consumption at substations. The tools have been successfully used on various types of mission and rolling stocks and have enabled to better understand the limits of such systems.

Latent heat storage system integrating phase change material in spherical capsules: A comparative study between three dynamic physical models

The main objective of this investigation is to carry out a detailed comparative study between three different thermal dynamic models for simulating a latent heat storage system using spherical capsules. These developed dynamic models are based on the apparent heat capacity method (model 1), on the heat source approach (model 2) and on the radial diffusion method (model 3). The main difference between the three models lies in the treatment of heat transfer inside the capsules. Obtained results from these models are first validated with experimental measurements from the literature and then used to investigate the thermal performance of the storage system under realistic exploitation conditions. Results show that all models are able to simulate the dynamic behavior of the storage system. The parametric study indicated that increasing the mass flow rate of the heat transfer fluid or reducing the capsule diameter can improve the energy performance of the thermal storage system and significantly reduce the discharge time.

Modeling and control of ball-ramp electromechanical clutch actuator for in-wheel AMT of electric vehicles

A novel ball-ramp electromechanical clutch actuator (BR-ECA) based on the in-wheel two-speed transmission topology for electric vehicles is proposed in this study. It is compact, lightweight, and installed within the wheel to achieve accurate gear shifting. Moreover, the actuator motor is parallel to the transmission central axis and mounted on the knuckle, thus freeing the limited arrangement space. A ball-ramp self-energizing mechanism is designed to enhance the clutch normal force, which allows for a lower-torque motor. In this study, the physical structure and dynamic model of the BR-ECA are described in detail. Furthermore, considering the strong nonlinear characteristics in the system, a triple-step nonlinear controller is designed for clutch position tracking. Finally, the good continuous controllability and tracking accuracy of the actuator are validated through simulations and experiments. The complete engagement/disengagement time is less than 0.3 s, and the steady-state error is less than 0.05 mm. Compared with the PID, the tracking error of the continuous step response is reduced by 6.4% and the sinusoidal response at 1 Hz is reduced by 10.09% under the triple-step nonlinear controller.

Preheating time estimation in intermittent heating with hot-water radiators by considering model uncertainties

Intermittent heating refers to heating buildings only during occupied periods. Owing to the thermal inertia of buildings, a preheating time is necessary to bring the indoor temperature into a comfortable range at the start of occupied periods. This paper proposes a new method to estimate the preheating time in intermittent heating with hot-water radiators by considering model uncertainties. First, a dynamic physical model is established based on the thermal equilibrium of buildings, and is approximated as a first-order continuous-time model by ignoring the influence of ambient conditions on the indoor temperature. The ignored part is represented as model uncertainties. Second, the continuous-time model is identified from collected data of indoor temperature and supply water temperature, and the model uncertainty is obtained based on the identified model. Third, the preheating time is determined with a credible probability to ensure that the indoor temperature meet thermal comfortable requirements. The proposed method has two novel features: (i) it can be applied to buildings with unknown physical parameters and unmeasurable ambient conditions, and (ii) it can yield reliable estimates of preheating time based on the model uncertainties to ensure the success of preheating under a given probability. Experimental examples demonstrate the effectiveness of the proposed method.

Recent Research in Singing

Recent Research in Singing Donald Simonson (bio) RECENT RESEARCH IN SINGING The following list of recent research in singing is a brief sampling of dissertations/theses published during the last year. It is by no means comprehensive and reflects only a small fraction of the available documents. If you have published recent research in singing, voice pedagogy, voice science, vocal repertoire, pedagogic methodology, or other topics of interest to the membership of NATS, please send citations and abstracts to Donald Simonson at drs@iastate.edu for review and possible inclusion in future columns. ________ Bidstrup Graham, Kelly. "From Broadway to Community, the Job of a Music Director for Musical Theatre." M.A. Thesis, University of Colorado at Boulder, 2022, 67 pages; ProQuest 2681016185. "What in the world do music directors do? Despite musical theater being a billion dollar industry, the backbone of these productions, the music director, is often unknown and even less so understood within the theatre industry. In this study I interviewed nineteen music directors from across the professional and educational spectrum in search of crystallizing a common set of best practices that can be utilized by new musical directors, but more importantly, can be used by non-music directors in the industry to improve their working relationships with music directors, in order to create more musical theater in meaningful and creative ways." (ProQuest/Author Abstract) ________ May, Nicholas A. "The Effect of Nonlinear Source-Filter Interaction on Aerodynamic Measures in a Synthetic Model of the Vocal Folds and Vocal Tract." Ph.D. Dissertation 2022, Bowling Green State University, 321 pages; ProQuest 2684505746. "According to nonlinear source-filter theory, as the strength of the coupling between the source and filter increases, typically by a decrease in the vocal tract cross sectional area, the resultant increase in the inertance of the vocal tract yields an increase in the interactions between acoustic pressures within the vocal tract and the changing glottal airflow and/or the vibratory pattern of the vocal folds. The purpose of the current research was to examine the effects of parametric vocal tract constrictions mimicking epilaryngeal tube and lip narrowing on aerodynamic and projected glottal area measures in a dynamic self-oscillating physical model of the vocal folds and vocal tract. "Multilayered silicone vocal fold models were created (after Murray & Thomson, 2011, 2012) and mounted to a simple synthetic trachea and supraglottal vocal tract model. Four constriction cross sectional areas were examined at two locations yielding 16 possible experimental vocal tract conditions. Vocal fold model adduction and tension were held constant across all experimental conditions; three constant subglottal pressures were examined. "Results indicate that: 1) mean phonation threshold pressure (PTP) at onset and offset, phonation instability pressure (PIP) onset, phonation pressure range (PPR) onset, phonation threshold flow (PTF) at onset and offset, phonation instability flow (PIF) onset and offset, and phonation flow [End Page 263] range (PFR) onset and offset were all found to generally decrease given a constriction in the epilarynx tube region; 2) mean PIP offset, and PPR offset were all found to generally increase given a constriction in the epilarynx tube region; 3) mean PTP at onset and offset and PTF at onset and offset were all found to generally decrease given a constriction in the lip region; and 4) mean PIP onset and offset, PPR onset and offset, PIF onset and offset, and PFR onset and offset were all found to generally increase given a constriction in the lip region. Thus, results indicate that both constriction magnitude and location are relevant factors in determining glottal aerodynamics and projected glottal area measures." (ProQuest/Author Abstract) ________ Moreno, Amanda Marie. "An Examination of the Effects of Body Mapping Instruction on Singers' Static Standing Posture and Posture While Singing." Ph.D. Dissertation, The University of North Dakota, 2022, 159 pages; ProQuest 2682732045. "Physical posture is one of the fundamental aspects of vocal technique, and voice pedagogues suggest that effective alignment is necessary for healthy vocal production. Body Mapping (BMG) is a somatic method that focuses on the understanding and correction of errors in a person's body map to facilitate effective movement for musical activity. Because this method has balance, physical alignment, and posture as...

Incremental residual learning-based dynamic modeling and stability analysis for multipower underwater vehicles

Dynamical modeling is the basis of state prediction, motion control, and performance analysis for underwater vehicles. Existing dynamic modeling methods were derived from physical principles, less considering the modeling reality gap caused by environmental uncertainty. Therefore, an incremental residual learning-based dynamic model is proposed for a novel multipower underwater vehicle with strengths of the water-jet pump and buoyancy drives. First, the multimodal physical dynamic model is derived from multibody systems, including driving forces and hydrodynamics. Then, the continuous residuals caused by the environmental uncertainty are compensated by the incremental residual learning. After that, multimodal stability criteria are established based on the hydrodynamic equilibrium equation. Finally, experiments on Computational Fluid Dynamics and lake trials are conducted to demonstrate the effectiveness of the proposed method.

An undergraduate research experience in earth science education that benefits pre-service teachers and in-service earth science teachers

Three cohorts of six pre-service Earth Science teachers (undergraduate majors in Earth Science Education) participated in summer research experiences focused on developing dynamic physical models of Earth processes to help middle and high school students understand complex concepts and confront misconceptions. The pre-service teachers used published criteria for evaluating models. Participants deepened their understanding of specific Earth Science concepts and broadened their perceptions of effective, student-centered, constructivist pedagogical practices through the use of models and model-based learning. Our pre-service Earth Science teachers achieved the same benefits that STEM majors report from their undergraduate research experiences, including better understanding of the nature of science, gains in problem-solving and communication skills, increased confidence, collaborative skills and comfort in working independently. Evaluation of the research experience via the Undergraduate Research Student Self-Assessment indicated that pre-service teachers reported higher gains than STEM majors in nearly all categories. The pre-service teachers presented the results of their projects to in-service teachers in professional development workshops at a science teachers’ conference. In-service teachers’ responses to these workshops were uniformly positive (98.2%; n = 57). Unlike most professional development activities in which participants benefit, but presenters may not, these professional development activities benefited participants and presenters alike.

Modelling Dynamic Recrystallization of A356 Aluminum Alloy during Hot Deformation

The flow stress and dynamic recrystallization behavior of A356 aluminum alloy was studied, with a strain rate ranging from 0.001 s−1 to 1 s−1 and temperature ranging from 300 to 500 °C. Both the true stress–strain curves and microstructure examination of A356 aluminum alloy indicated that dynamic recrystallization occurred during the isothermal compression. A physical dynamic recrystallization model based on the Arrhenius equation was developed, and this model can accurately predict the dynamic recrystallization fraction of A356 aluminum alloy during the isothermal compression. Finally, this model was implemented in FEM software Forge, and the microstructure evolution was simulated well.

Dynamic physical model of Japanese hydrogen refueling stations for quantitative trade-off study between benefit and risk

As hydrogen refueling stations come into increasing use and being common, both benefit and safety are required. To promote its use, an optimized trade-off must be reached for hydrogen station configurations. We constructed a dynamic physical model of the station that enables quantitative evaluation of benefit and risk for a trade-off study, aiming to formulate a technical standard for hydrogen refueling stations that would realize high-level safety and rationalize the equipment configuration and operation. The proposed model enables a quantitative evaluation of both the hydrogen refueling time during normal operation and external leakage under several accident scenarios. This paper discusses the modeling concept and approach of the proposed model and reports quantitative verification results of the proposed model based on past experimental test results. In this study, capability of the proposed model for quantitative evaluation of dynamic behavior of hydrogen during refueling operation at the station reflecting its configuration and operation condition was demonstrated. This feature of the proposed model also provides a physical and realistic dynamic leakage rate for calculating individual risks compared to the conventional risk analysis method.

Modified Dynamic Physical Model of Valence Change Mechanism Memristors.

Valence change-type resistance switching behaviors in oxides can be understood by well-established physical models describing the field-driven oxygen vacancy distribution change. In those models, electroformed residual oxygen vacancy filaments are crucial as they work as an electric field concentrator and limit the oxygen vacancy movement along the vertical direction. Therefore, their movement outward by diffusion is negligible. However, this situation may not be applicable in the electroforming-free system, where the field-driven movement is less prominent, and the isotropic oxygen vacancy diffusion by concentration gradient is more significant, which has not been given much consideration in the conventional model. Here, we propose a modified physical model that considers the change in the oxygen vacancies' charged state depending on their concentrations and the resulting change in diffusivity during switching to interpret the electroforming-free device behaviors. The model suggests formation of an hourglass-shaped filament constituting a lower concentration of oxygen vacancies due to the fluid oxygen diffusion in the thin oxide. Consequently, the proposed model can explain the electroforming-free device behaviors, including the retention failure mechanism, and suggest an optimized filament configuration for improved retention characteristics. The proposed model can plausibly explain both the electroformed and the electroforming-free devices. Therefore, it can be a standard model for valence change memristors.