Dynamic Technique Research Articles

An assessment of the impact of boundary conditions in dynamical downscaling techniques for fetch-limited waves

ABSTRACT The handling of boundary conditions (BCs) is a key issue for the development of dynamical downscaling techniques in which nearshore wave models require the definition of the sea conditions at the offshore boundaries. This study addresses the implications of using simplified BCs represented by bulk or partitioned wave parameters with respect to using the complete information from directional wave spectra. As a large-scale model, we used WAVEWATCH III (WW3) running over the Mediterranean Sea providing the wave BCs for the small-scale SWAN model. One year of measured directional wave spectra from three deepwater buoys allowed the assessment of the WW3 model accuracy. The nested SWAN model simulations propagated the wave field over three nearshore areas. The accuracy of SWAN simulations was assessed through the comparison of computed results with wave measurements from nearshore buoys. Being the wave conditions dominated by unimodal seas, modest differences of error metrics are found between the datasets forced by different types of BCs. Conversely, focusing the attention on multimodal seas, an accurate definition of wave BCs achieved by the use of the directional wave spectra or, in alternative, by the reconstruction of wave spectra from partitioned wave parameters becomes crucial for nearshore wave predictions.

Design and synthesis of novel main protease inhibitors of COVID-19: quinoxalino[2,1-b]quinazolin-12-ones.

The COVID-19 pandemic represents a substantial global challenge, being a significant cause of mortality in numerous countries. Thus, it is imperative to conduct research to develop effective therapies to combat COVID-19. The primary aim of this study is to employ a two-step tandem reaction involving 2,3-dichloroquinoxaline and 2-amino-N-substituted benzamides in alkaline media/DMF at an elevated temperature to design and synthesize a series of polycyclic derivatives endowed with quinoxalino[2,1-b]quinazolin-12-one framework. Following synthesis, the newly synthesized heterocycles were evaluated for their potential as inhibitors of the main protease of SARS-CoV-2 by means of molecular docking and dynamic simulation techniques. The in silico investigation demonstrated that all tested compounds effectively establish stable binding interactions, primarily through multiple hydrogen bonding and hydrophobic interactions, at the active site of the enzyme. These findings offer crucial structural insights that can be employed in future endeavors toward designing potent inhibitors targeting the main protease (Mpro). Among the investigated compounds, the p-tolylamino-substituted quinoxalino[2,1-b]quinazolinone derivative exhibited the most promise as an inhibitor of the main protease in COVID-19. Consequently, it warrants further investigation both in vitro and in vivo to identify it as a prospective candidate for anti-SARS-CoV-2 drug development.

DIO-SLAM: A Dynamic RGB-D SLAM Method Combining Instance Segmentation and Optical Flow.

Feature points from moving objects can negatively impact the accuracy of Visual Simultaneous Localization and Mapping (VSLAM) algorithms, while detection or semantic segmentation-based VSLAM approaches often fail to accurately determine the true motion state of objects. To address this challenge, this paper introduces DIO-SLAM: Dynamic Instance Optical Flow SLAM, a VSLAM system specifically designed for dynamic environments. Initially, the detection thread employs YOLACT (You Only Look At CoefficienTs) to distinguish between rigid and non-rigid objects within the scene. Subsequently, the optical flow thread estimates optical flow and introduces a novel approach to capture the optical flow of moving objects by leveraging optical flow residuals. Following this, an optical flow consistency method is implemented to assess the dynamic nature of rigid object mask regions, classifying them as either moving or stationary rigid objects. To mitigate errors caused by missed detections or motion blur, a motion frame propagation method is employed. Lastly, a dense mapping thread is incorporated to filter out non-rigid objects using semantic information, track the point clouds of rigid objects, reconstruct the static background, and store the resulting map in an octree format. Experimental results demonstrate that the proposed method surpasses current mainstream dynamic VSLAM techniques in both localization accuracy and real-time performance.

Automatic deep sparse clustering with a dynamic population-based evolutionary algorithm using reinforcement learning and transfer learning

Clustering data effectively remains a significant challenge in machine learning, particularly when the optimal number of clusters is unknown. Traditional deep clustering methods often struggle with balancing local and global search, leading to premature convergence and inefficiency. To address these issues, we introduce ADSC-DPE-RT (Automatic Deep Sparse Clustering with a Dynamic Population-based Evolutionary Algorithm using Reinforcement Learning and Transfer Learning), a novel deep clustering approach. ADSC-DPE-RT builds on Multi-Trial Vector-based Differential Evolution (MTDE), an algorithm that integrates sparse auto-encoding and manifold learning to enable automatic clustering without prior knowledge of cluster count. However, MTDE's fixed population size can lead to either prolonged computation or premature convergence. Our approach introduces a dynamic population generation technique guided by Reinforcement Learning (RL) and Markov Decision Process (MDP) principles. This allows for flexible adjustment of population size, preventing premature convergence and reducing computation time. Additionally, we incorporate Generative Adversarial Networks (GANs) to facilitate dynamic knowledge transfer between MTDE strategies, enhancing diversity and accelerating convergence towards the global optimum. This is the first work to address the dynamic population issue in deep clustering through RL, combined with Transfer Learning to optimize evolutionary algorithms. Our results demonstrate significant improvements in clustering performance, positioning ADSC-DPE-RT as a competitive alternative to state-of-the-art deep clustering methods.

Hydro-abrasive erosion in Kaplan turbines: a case study

ABSTRACT Sediment in the flowing water causes hydro-abrasive erosion of hydraulic turbines and other underwater components. In the present study, erosion quantification in the Kaplan turbine of a low-head hydropower plant (HPP) on the Upper Ganga Canal, is considered in the study. The study uses the guidelines of the International Electrotechnical Commission IEC 62364: 2019 for quantifying erosion in the Kaplan turbine. This work extends the research of Rai and Kumar (2016) and uses their primary details of some suspended sediment and operating parameters. The shape and mineral composition of the suspended sediment were quantified using dynamic imaging and X-ray diffraction techniques, respectively. The annual average particle load at the study HPP is obtained to be 356.87 kg·h/m3. Based on the analysis, it is estimated that the erosion depth varies from 0.88 to 1.37 mm/year at the blade tip, whereas from 2.40 to 3.74 mm/year at the blade outlet. At the runner chamber, the erosion depth varies from 1.27 to 1.98 mm/year. The erosion depth varies from 0.01 to 1.53 mm/year in the guide vanes. The calibration factor corresponding to the maximum erosion in guide vanes is estimated to be 1.75 × 10−5.

Development and Comparison of InSAR-Based Land Subsidence Prediction Models

Land subsidence caused by human engineering activities is a serious problem worldwide. We selected Qian’an County as the study area to explore the evolution of land subsidence and predict its deformation trend. This study utilized synthetic aperture radar interferometry (InSAR) technology to process 64 Sentinel-1 data covering the area, and high-precision and high-resolution surface deformation data from January 2017 to December 2021 were obtained to analyze the deformation characteristics and evolution of land subsidence. Then, land subsidence was predicted using the intelligence neural network theory, machine learning methods, time-series prediction models, dynamic data processing techniques, and engineering geology of ground subsidence. This study developed three time-series prediction models: a support vector regression (SVR), a Holt Exponential Smoothing (Holt) model, and multi-layer perceptron (MLP) models. A time-series prediction analysis was conducted using the surface deformation data of the subsidence funnel area of Zhouzi Village, Qian’an County. In addition, the advantages and disadvantages of the three models were compared and analyzed. The results show that the three developed time-series data prediction models can effectively capture the time-series-related characteristics of surface deformation in the study area. The SVR and Holt models are suitable for analyzing fewer external interference factors and shorter periods, while the MLP model has high accuracy and universality, making it suitable for predicting both short-term and long-term surface deformation. Ultimately, our results are valuable for further research on land subsidence prediction.

Numerical Simulation of the Unsteady Airwake of the Liaoning Carrier Based on the DDES Model Coupled with Overset Grid

The wake behind an aircraft carrier under heavy wind condition is a key concern in ship design. The Chinese Liaoning ship’s upturned bow and the island on the deck could cause serious flow separation in the landing and take-off area. The flow separation induces strong velocity gradients and intense pulsations in the flow field. In addition, the sway of the aircraft carrier caused by waves could also intensify the flow separation. The complex flow field poses a significant risk to the shipboard aircraft take-off and landing operation. Therefore, accurately predicting the wake of an aircraft carrier during wave action motion is of great interest for design optimization and recovery aircraft control. In this research, the aerodynamic around an aircraft carrier (i.e., Liaoning) was analyzed using the computational fluid dynamics technique. The validity of two turbulence models was verified through comparison with the existing data from the literature. The upturned bow take-off deck and the right-hand island were the main areas where flow separation occurred. Delayed detached eddy simulation (DDES), which combines the advantages of LES and RANS, was adopted to capture the full-scale spatial and temporal flow information. The DDES was also coupled with the overset grid to calculate the flow field characteristics under the effect of hull sway. The downwash area at 15° starboard wind became shorter when the hull was stationary, while the upwash area and turbulence intensity increased. The respective characteristics of the wake flow field in the stationary and swaying state of the ship were investigated, and the flow separation showed a clear periodic when the ship was swaying. Comprehensive analysis of the time-dependent flow characteristic of the approach line for fixed-wing naval aircraft is also presented.

Enhanced channel estimation with atomic norm minimization and reconfigurable intelligent surfaces in mmWave MIMO systems

SummaryThe performance of millimeter‐wave (mmWave) multiple‐input multiple‐output (MIMO) systems has been significantly enhanced by the incorporation of dynamic reconfigurable intelligent surfaces (RIS). This paper proposes a novel dynamic channel estimation technique that combines dynamic atomic norm minimization with dynamic RIS to optimize RIS‐aided mmWave MIMO systems. Leveraging the dynamic nature of both atomic norm minimization and RIS, the proposed approach efficiently adapts to changing environmental conditions, providing robust and accurate channel estimation. By dynamically optimizing the RIS configuration, the system achieves improved spectral and energy efficiency, enabling high‐speed and reliable communication in challenging mmWave environments. Theoretical analysis and simulation results demonstrate the effectiveness of the proposed dynamic channel estimation technique, highlighting its potential for enhancing the performance of future wireless communication systems.

Sub-attosecond-precision optical-waveform stability measurements using electro-optic sampling

The generation of laser pulses with controlled optical waveforms, and their measurement, lie at the heart of both time-domain and frequency-domain precision metrology. Here, we obtain mid-infrared waves via intra-pulse difference-frequency generation (IPDFG) driven by 16-femtosecond near-infrared pulses, and characterise the jitter of sub-cycle fractions of these waves relative to the gate pulses using electro-optic sampling (EOS). We demonstrate sub-attosecond temporal jitter at individual zero-crossings and sub-0.1%-level relative amplitude fluctuations in the 10-kHz–0.625-MHz band. Chirping the nearly-octave-spanning mid-infrared pulses uncovers wavelength-dependent attosecond-scale waveform jitter. Our study validates EOS as a broadband (both in the radio-frequency and the optical domains), highly sensitive measurement technique for the jitter dynamics of optical waveforms. This sensitivity reveals outstanding stability of the waveforms obtained via IPDFG and EOS, directly benefiting precision measurements including linear and nonlinear (infrared) field-resolved spectroscopy. Furthermore, these results form the basis toward EOS-based active waveform stabilisation and sub-attosecond multi-oscillator synchronisation/delay tracking.

Diagnostic Strategy in Idiopathic Diaphragmatic Paralysis

Unilateral diaphragmatic paralysis is a medical condition characterized by the impairment of either the right or left hemidiaphragm, resulting in an inability to contract effectively during inspiration. This condition may arise due to intrinsic diaphragmatic muscle dysfunction or a disruption in the neural input from the phrenic nerve to the affected hemidiaphragm. The etiology is diverse, with clinical manifestations varying from an absence of symptoms to severe respiratory compromise. This case study aims to review the assessment and management of unilateral diaphragmatic paralysis, highlighting the role of dynamic imaging techniques, such as diaphragmatic ultrasound, in the diagnosis, evaluation, and ongoing monitoring of patients with this condition.

Study on aerodynamic and coupled motion response characteristics of floating vertical axis wind turbine

ABSTRACT To improve the efficiency of a floating VAWT (FVAWT), the aerodynamic and coupled motion response characteristics of 5MW Φ-type FVAWT are studied in fully coupled system. Based on the computational fluid dynamics (CFD) method, the dynamic fluid body interaction (DFBI), overlapping grid and dynamic mesh technique are adopted for numerical simulations. Under wind-wave coupling conditions, the power coefficient (Cp) and motion response of FVAWTs with different tip speed ratios (TSR) and height-todiameter ratios (η) are analysed. The mechanism of platform heave motion on torque Q generation of VAWT is explored. The results show that the FVAWT with height-to-diameter ratio of 1.2 and tip speed ratio of 5 has optimal aerodynamic and motion performance. Under the optimal tip speed ratio, the Cp of the offshore FVAWT is 5.21% higher than that of the land VAWT. As the floating platform rises, the torque of wind turbine decreased correspondingly, and vice versa.

CFD Modelling and Optimization of Solid Waste Combustion in a 1 MW Fixed Bed Combustion Chamber

The global population and energy consumption grow each year. Therefore, the tendency to use biofuels instead of fossil fuels assist the global requirement. Also, enhancing of the combustion efficiency and minimizing pollutant emissions during the combustion of municipal solid waste (MSW). The present study employed a CFD model to simulate solid fuel combustion in a grate combustion chamber which was commonly used in small-capacity boilers. computational fluid dynamics technique was used to simulate complex solid fuel reactions where 1 MW boiler was supposed for wood and coal combustion. The achievements found that 10%-dried MSW was used as fuel. The results of the study were related to the average gas flow temperatures and composition in the furnace outflow section, as well as the high combustion temperatures of fossil fuels. The results for the average temperature and average chemical molecular fractions (CO, O2, CO2, H2O, and N2) generated at the exit of the combustion process were positive. Thus, it was found that the thermal efficiency of the boiler was significantly affected by the amount of air supplied to the system at a temperature of 273 K and atmospheric pressure. We observed variations in flame morphology, temperature profiles, and corresponding numerical values.

A second life for the crystallographic structure of Berenil-dodecanucleotide complex: a computational revisitation thirty years after its publication

AbstractThis study revisits the pioneering work of Professor Neidle, and co-workers, on the crystal structure of complexes formed between groove binders and DNA sequences. The original research revealed a DNA-ligand complex consisting of a dodecanucleotide bound with Berenil [1,3-bis(4′-amidinophenyl)-triazene] an anti-trypanocidal drug. This article aims to delve deeper into the structural dynamics of this system, showcasing the role played by water molecules in stabilizing the interaction between the ligand and the DNA. With this work, we reevaluate the findings from the original crystallographic study by employing modern molecular dynamics techniques, including Supervised Molecular Dynamics (SuMD) for generating binding trajectories, Thermal Titration Molecular Dynamics for assessing unbinding events, and AquaMMapS to identify regions occupied by stationary water molecules. The study addresses a minor and a major groove binding mode and assesses their strength and specificity using TTMD simulations, generating unbinding trajectories. This comprehensive approach integrates the understanding of the interaction of this DNA-ligand complex, which originated with the valuable work of Professor Neidle, resulting in an in-depth insight into the pivotal role of water molecules with this DNA, a behavior detected and extendable even to other nucleic acid complexes.

Robust speech command recognition in challenging industrial environments

Speech is among the main forms of communication between humans and robots in industrial settings, being the most natural way for a human worker to issue commands. However, the presence of pervasive and loud environmental noise poses significant challenges to the adoption of Speech-Command Recognition systems onboard manufacturing robots; indeed, they are expected to perform in real time on hardware with limited computational capabilities and also to be robust and accurate in such complex environments. In this paper, we propose an innovative system based on an End-to-End architecture with a Conformer backbone. Our system is specifically designed to achieve high accuracy in noisy industrial environments and to guarantee a minimal computational burden to meet stringent real-time requirements while running on computing devices that are embedded in robots. In order to increase the generalization capability of the system, the training procedure is driven by a Curriculum Learning strategy combined with dynamic data augmentation techniques, that progressively increase the complexity of input samples by increasing the noise during the training phase. We have conducted extensive experimentation to assess the effectiveness of our system, using a dataset composed of more than 50,000 samples, of which about 2,000 have been acquired during the daily operations of a Stellantis Italian factory. The results confirm the suitability of the proposed approach to be adopted in a real industrial environment; indeed, it is able to achieve, on both English and Italian commands, an accuracy higher than 90%, maintaining a compact model size (the network is 1.81 MB) and running in real-time on an industrial embedded device (namely 41ms over an NVIDIA Xavier NX).

Adaptive neural network dynamic surface optimal saturation control for single‐phase grid‐connected photovoltaic systems

AbstractAn adaptive neural network (NN) based optimal saturation control scheme is investigated for single‐phase grid‐connected photovoltaic (PV) systems by incorporating dynamic surface control (DSC) and adaptive dynamic programming (ADP) based on the backstepping control design framework. For each backstepping step, a critic‐actor architecture is constructed via reinforcement learning (RL), and the PV system is optimized according to the cost function in the architecture. Due to the nonlinearity, it is difficult to solve the Hamilton–Jacobi–Bellman (HJB) equation. The neural networks (NNs) are employed to approximate the solution of the HJB equation such that the optimal virtual control and the actual controller are obtained. By considering control input symmetric saturation nonlinearity link, constraints on pulse width modulation (PWM) are ensured. On this basis, the combination of backstepping control design and dynamic surface technique is used to overcome the shortcomings of “differential explosion” and simplify calculations. Based on the Lyapunov method, the stability analysis proves that all signals of the closed‐loop PV systems are semiglobally uniformly ultimately bounded (SGUUB). Simulation experiments and comparative results are given to verify the efficacy of the studied control strategy.

Mechanical properties assessment of composite solid propellant based on different aziridine bonding agents

Abstract The employment of the dynamic mechanical analyzer (DMA) represents a modern methodology for the monitoring of the thermo-mechanical characteristics and also the thermomechanical loads of polyurethane-based composite solid rocket propellants during combustion. A meticulous preparation of composite propellant samples based on different bonding agents was carried out to examine the influence of various bonding agents on mechanical properties. To simulate the effects of natural aging, an accelerated aging program was implemented and subjected to the propellant samples for 5-day exposure at 90°C, equivalent to 5 years of natural aging. The dynamic mechanical modern analysis technique was used to assess the viscoelastic behavior and degree of viscoelasticity of both fresh and aged propellant samples. It is concluded that M4 based on MT-X bonding agent is the most valuable formulation of CSRP with tan δ of 0.183591, also with mechanical results; implying that M4 formulation based on MT-X bonding agent has the highest degree of viscoelasticity.

Dynamic spectroscopic and optical characterization and modeling of bovine serum albumin corona during interaction with N-hydroxysulfo-succinimide-covalently functionalized gold nanourchins.

In this study, bovine serum albumin (BSA) is used as a globular protein model to examine the conformational changes that occur during the interaction of BSA with N-hydroxysulfo-succinimide (sodium salt)-functionalized gold nanourchins (GNUs), for which dynamic spectroscopic techniques are employed. The results showed that the absorbance of phosphate-buffered saline-BSA at 278 nm decreased when a GNU was added to the solution due to adsorption, and it decreased further when the GNU was increased. The intensity and width of the peak of local surface plasmon resonance increased, indicating the effect of corona formation. Dynamic UV-vis spectroscopy and scattering revealed a nonlinear behavior of BSA-GNU interaction. The bioplasmonic solution resulted in higher transmission and scattering than the BSA solution. Fourier transform-near-infrared spectra exhibited several bands due to overtones and combinations of the amide group and different proportions of α-helix and β-sheet components in BSA before and after the addition of the GNU. Time-resolved fluorescence spectroscopy demonstrated an initial increase in blueshifted emission, followed by a redshifted quenching of two major peaks of Tyr and tryptophan (Trp). The binding and dissociation constants were determined as Kb = 2.17 × 1010 M-1 and Kd = 4.6 × 10-11, respectively, using the Stern-Volmer relation. Both the dynamic CMOS-based imaging and the cadmium sulfide sensors demonstrated a nonlinear response of bioplasmonic solution. By increasing the GNU, the resistance of the solution decreased in the order of A > S1 > S3, where S3 exhibited the highest initial transmission with a longer desorption time. MATLAB modeling showed 80% surface coverage by the protein in 15 s at 0.05M, equivalent to a thickness of 1.7 nm, which was in agreement with the value determined by using the Stokes-Einstein relation.

Temporal-spatial and energy dissipation characteristics of vortex evolutions in Francis turbine during load reduction

This study numerically investigates the load reduction effects, on a model Francis turbine, combining cavitation model and structured dynamic grid technique. The results indicate that the vapor volume in the draft tube undergoes two rapid increases and decreases until cavitation ceases. The precessing vortex rope transitions from a strong helical structure to axial contraction as ellipticity increases, ultimately forming a discrete band before disappearing. Initially, vapor volume in the runner increases gradually and linearly, followed by continued growth with a consistent pulsation amplitude. The inter-blade vortex (IBV) first appears at the blade trailing edge and then develops into a complete structure extending from the runner crown to the blade trailing edge, driven by pulsating vapor volume growth. Axial force extracted by the runner changes significantly and correlates closely with variations in the vapor volume in the runner. Flow separation in the runner occurs near the runner crown, forming dual separation lines that enhance IBV formation, which highlights the significant influence of crown-proximal flow separation on IBV development. Regarding energy loss, initial decreases followed by increases are observed in both the draft tube and runner, with draft tube losses consistently exceeding 57.4% and runner losses exceeding 27.1%. Turbulent kinetic energy generation and Reynolds stress are the primary forms of energy dissipation, with high-value regions corresponding to vortex locations, underscoring the substantial role of vortices in energy dissipation. This study provides new insights into the evolution of vortices and energy dissipation characteristics during load reduction in Francis turbines.

Optimized Backstepping Cooperative Control for Output-Constrained Stochastic Nonlinear Network Systems via a Multibridge-Hole Function.

In this article, a new leader-following tracking control approach is investigated for stochastic multiagent systems with multibridge-hole output constraints. The multibridge-hole output constraints mean that the output of the system is constrained in some intervals and unconstrained in other intervals. The constrained and unconstrained intervals can be set arbitrarily. By designing a new shift function to construct the barrier Lyapunov function, the optimal controller is constructed by combining the backstepping technique with the adaptive dynamic programming technique. The model network is used to estimate the unknown disturbances and uncertainty terms in the system. The critic network and the actor network are constructed such that the designed controller adheres to the Bellman optimality principle and gives the optimal solution of the system. The proposed control method is versatile and compatible with various types of output constrained control problems, such as unconstrained control problems, constrained control problems, and delay constrained problems without changing the structure of the controller. Finally, some simulation results are given to verify the effectiveness of the method.

Adaptive Neural Cooperative Control of Multirobot Systems With Input Quantization.

This article develops the adaptive neural cooperative control scheme for a group of mobile robots with a limited sensing range in presence of input quantization by a dynamic surface control technique. First, to make the controller design feasible, the original robotic system is transformed into a new fully actuated system using a transverse function. Then, taking into consideration the effects of a hysteresis quantizer, an adaptive neural cooperative controller is developed based on the universal approximation property of the radial basis function neural networks and the connectivity preservation strategy. Furthermore, the proposed control scheme can guarantee that all closed-loop signals are semi-globally uniformly ultimately bounded. Meanwhile, desired constraints are not breached and tracking errors are within the predefined domains. Finally, several simulation results are carried out to testify the feasibility and efficiency of the theoretical findings revealed in this article.