Numerical Simulation Data Research Articles

Control Synthesis for Hypersonic Vehicle Flight Testing with Input–Output-Sampled Nonlinearities

The flight testing of hypersonic vehicles is challenging due to the nonlinear, uncertain, and possibly unstable dynamics of these vehicles. This paper presents a control synthesis method for a hypersonic vehicle with the purpose of guaranteeing robust stability during flight testing when accurate analytic vehicle models are unavailable. Conventional model-based approaches typically rely on curve fitting of numerical simulation and test data, which can introduce inaccuracies. Our proposed approach assumes knowledge of the linear form of the ordinary differential equation and uses quadratic constraints to bound sampled data that accounts for the nonlinear and uncertain portion of the model for controller synthesis by iteratively solving convex semidefinite programs. A numerical example demonstrating the effectiveness of the proposed method is performed before applying the technique to a nonlinear hypersonic vehicle found in the literature. Using the synthesized stabilizing controller, we demonstrate that the vehicle states remain within a certified bound given a known harmonic excitation when initiated from a quantified set of allowable initial conditions. Based on these simulation results, our proposed control synthesis method shows promise in ensuring robustness when performing hypersonic vehicle flight testing.

Energy thickness in turbulent boundary layer flows

In this study, we investigate the properties of energy thickness $\delta _3$ in turbulent boundary layer (TBL) flows, a parameter derived solely from the mean streamwise velocity ( $U$ ) profile. Through an analysis of the energy integral equation for zero pressure gradient TBLs, we establish a close relationship between turbulent kinetic energy (TKE) production and $\delta _3$ , offering a practical method to estimate TKE production, which is particularly useful in physical experiments where direct measurements are challenging. The significance of $\delta _3$ becomes even more pronounced in TBLs under pressure gradient. Through extensive analysis of numerical and experimental data, we show that the ratio between $\delta _3$ and the momentum thickness $\delta _2$ is a promising criterion for predicting flow separation. Moreover, we derive a new energy integral equation for TBLs under arbitrary pressure gradients, and provide approximations for TKE productions terms by $R_{uv}\,\partial U/\partial y$ and $R_{uu}\,\partial U/\partial x$ , and dissipation term by the mean shear. Here, $x, y$ represent the streamwise and wall-normal directions, respectively, and $R_{uu}$ and $R_{uv}$ are the Reynolds normal and shear stresses. The accuracy and robustness of the new energy integral equation and the approximation equations are validated using direct numerical simulations data. Our results show that the TKE production by $R_{uv}\,\partial U/\partial y$ and the overall productions consistently remain positive, reflecting a continuous conversion of mean kinetic energy into TKE across all TBLs. However, under strong favourable pressure gradients, TKE production by $R_{uu}\,\partial U/\partial x$ becomes negative, indicating a reverse energy transfer from TKE to mean kinetic energy.

A Data-Driven Approach to Refine the Partially Stirred Reactor Closure for Turbulent Premixed Flames

AbstractAccurately predicting turbulent combustion processes is fundamental for optimizing efficiency, reducing pollutant emissions, and ensuring operational safety in combustion systems. To this purpose, computational fluid dynamics (CFD) simulations are widely employed. In particular, large eddy simulations (LES) balance prediction accuracy with computational efficiency by resolving only the most energy-containing scales of turbulence and rely on modeling the turbulence-chemistry interactions (TCI) occurring at the smallest scales. Among the existing closures, the partially stirred reactor (PaSR) model incorporates finite-rate chemistry and estimates a cell reacting fraction based on the local Damköhler number to account for the subfilter-scale TCI. Although widely validated in CFD computations, the PaSR model was found limited by the way it computes the cell reacting fraction. To tackle this point, our study proposes a machine learning (ML) enhanced partially stirred reactor model for LES. A fully connected neural network is trained on direct numerical simulation (DNS) data of turbulent premixed jet flames to compute a correction coefficient for the cell reacting fraction. Maintaining the original model shape, this ML-enhanced closure aims at bridging the gap between physics-based models and advanced data-driven techniques. The proposed formulation not only improves the prediction accuracy of quantities of interest such as the heat release rate but also features computational feasibility and generalisation capabilities over a large range of LES grid refinement. This demonstrates the significant potential of ML-aided TCI closures in future applications of combustion engineering.

Assessment of algebraic anisotropic turbulent heat flux models using Reynolds stress modeling for simulating film cooling flows

This study evaluates the performance of various algebraic turbulent heat flux models in predicting film cooling flows using different Reynolds stress turbulence models. a priori analysis using direct numerical simulation data was used to assess the models' performance. Additionally, Reynolds-averaged Navier–Stokes (RANS) simulations based on Reynolds stress turbulence models were performed to investigate the models' practical application in film cooling. Results demonstrate that the enhanced explicit algebraic turbulent heat flux model significantly outperforms existing models. It accurately predicts both streamwise and wall-normal turbulent heat fluxes based on direct numerical simulation data in a channel flow, and exhibits superior performance in film cooling simulations respect to known turbulent heat flux models. The recommended turbulent heat flux model decreased the error norm more than 90% in channel flow. Furthermore, the study highlights the importance of selecting appropriate Reynolds stress models in conjunction with turbulent heat flux models for accurate film cooling predictions. The right choice of Reynolds stress model according to the turbulent heat flux model can significantly influence the overall accuracy of the RANS simulation.

Effect of voltage polarity on reaction mechanism of air atmospheric surface dielectric barrier discharge: A numerical study

This study establishes a two-dimensional fluid model of nanosecond surface dielectric barrier discharge (nSDBD) at atmospheric air to investigate the effects of positive and negative sinusoidal nanosecond pulsed voltages on the discharge characteristics. Key discharge parameters are studied, including discharge current, distribution of major active particles, surface charge distribution on the dielectric, energy deposition density distribution, and gas temperature. The numerical simulation results indicate that the plasma streamers excited by positive and negative bipolar pulses exhibit markedly different discharge characteristics, with the discharge characteristics in the first half-cycle largely determining those of the entire cycle. Positive bipolar pulsed streamer discharges exhibit greater discharge currents and stronger local electric fields, with faster propagation speeds but also more pronounced declines. The energy deposition of positive bipolar pulse is higher than that of negative bipolar pulse. The discharges driven by negative bipolar pulses exhibit a more pronounced temperature rise effect, primarily due to their higher efficiency in converting electrical energy into thermal energy, leading to stronger localized thermal release. Consequently, the pressure waves generated by negative bipolar pulsed discharges are more intense. These numerical simulation data provide theoretical explanations and references for understanding and optimizing the physical mechanisms of nSDBD.

БАЙЕСОВСКАЯ ОПТИМИЗАЦИЯ МЕТОДА ИДЕНТИФИКАЦИИ МЕЗОМАСШТАБНЫХ ОКЕАНСКИХ ВИХРЕЙ В МОРЕ ЛАБРАДОР В ДАННЫХ ВИХРЕРАЗРЕШАЮЩЕГО МОДЕЛИРОВАНИЯ

Deep convection in the Labrador Sea has a significant role in the formation of the Northern Hemisphere climate. The eddy activity in the Labrador Sea, represented by Irminger rings, influences the spatial and temporal heterogeneity of the mixed layer depth. Automated eddy identification methods are widely used as a tool to study eddy activity in statistically significant samples. However, the most commonly used method for finding local extrema is highly dependent on a variety of parameters chosen by the author or the user of the method. In this paper, a new algorithm for the identification of mesoscale anticyclonic eddies in numerical simulation data is developed. Using Bayesian optimisation method, the optimal values of hyperparameters of the developed eddy identification algorithm were selected. The identification quality in the F1-score measure is improved to 0.232 compared to 0.352 in the baseline configuration.

АВТОМАТИЧЕСКАЯ ИДЕНТИФИКАЦИЯ НОВОЗЕМЕЛЬСКОЙ БОРЫ

The paper examines a type of downslope windstorm – the Novaya Zemlya bora. This is a insufficiently studied mesoscale phenomenon characterized by the presence of high wind speeds on the western slope of the archipelago, threatening the safety of port buildings and maritime navigation. The paper proposes an approach for automatic identification of bora, which will allow to make a climatic picture of this phenomenon and to identify its characteristic features. The developed approach was applied to long-term (2015–2023) high-resolution numerical simulation data obtained using the WRF atmospheric model, and to lower-resolution data – atmospheric reanalysis ERA5. Over a 9-year period, about 220 bora events were analyzed, and this was done not only for the entire archipelago (which is typical for most studies), but also for individual regions of Novaya Zemlya. It was shown that the qualitative climatic characteristics of bora do not depend on spatial resolution, which potentially allows us to apply the developed method to identify the bora on longer time periods, for example, the entire period covered by the ERA5 reanalysis data. However, on a quantitative level, high resolution showed a greater intensity and duration of the phenomenon, as expected.

Breaking the cosmological invariance of the dark-matter halo shape as a new probe of modified gravity

In a recent paper, we highlighted in $w$CDM models derived from general relativity (GR) (with Dark Energy Universe numerical simulation data), a cosmological invariance of the distribution of dark-matter (DM) halo shapes when expressed in terms of the nonlinear fluctuations of the cosmic matter field. This paper shows that this invariance persists when tested on numerical simulations performed with a different N-body solver, and that it is also robust to adding massive neutrinos to the cold DM component. Furthermore, this discovery raises crucial questions about the validity of this invariance in MG models. Thus, we examined whether it remains robust in the case of Hu Sawicki model using numerical simulations. By comparing the results of advanced numerical simulations in these different theoretical frameworks, we found significant deviations from the invariance observed in the framework of $w$CDM models of GR. These deviations suggest that the gravitation's nature significantly influences the DM halos' shape. We then interpreted this departure from the GR models' invariance as a manifestation of the scalar-field screening effect corresponding to such $f(R)$-type theories. This one modifies the sphericization process of DM halos during their formation, precisely because the critical mass at which this scalar field becomes non-negligible is the mass at which the deviation appears. To this extent, the departure from cosmological invariance in DM halos' shape is a cosmological probe of the nature of gravity, and the mass scale at which it appears can be used to estimate the R0 $ parameter of such theories.

Support vector regression model for the prediction of buildings’ maximum seismic response based on real monitoring data

Maximum drift ratio (MDR), one of the engineering demand parameters (EDPs), provides fundamental physical value for predicting building damage. Existing machine learning based prediction models mainly rely on numerical simulation data or structural experiments and are not appropriate for prediction of seismic response of real structures. The New Earthquake Data (NDE1.0) is the most comprehensive publicly available dataset of actual structural seismic response observations. Currently the prediction models using NDE1.0 are mainly based on linear or log-linear regression. In this study, based on the NDE1.0 flatfile, we develop a full-feature support vector regression (SVR) based MDR prediction model (SVR-MDR), treating all the available 41 characteristic parameters including structural information as input feature. To improve the model’s efficiency and practical applicability, we also establish a reduced-feature SVR model (RSVR-MDR) by selecting 10 fundamental parameters based on SHapley Additive exPlanations (SHAP) values and the accessibility of features. Our results demonstrate that SVR-MDR model outperform other machine learning models such as kernel ridge regression and decision tree models, and SVR-MDR and RSVR-MDR models outperform conventional loglinear regression and multinomial models, because SVR can map the complex nonlinear function of multiple variables and consider the available information of buildings especially the fundamental frequency. The proposed RSVR-MDR model have promising potential application for post-event seismic damage assessment and post-event emergency response in near real time.

On the accuracy of compressibility transformations

This study highlights the importance of satisfying the eddy viscosity equivalence below the logarithmic layer, to deriving accurate compressibility transformations. First, we analyze the ability of known transformations to satisfy the eddy viscosity equivalence and show that the accuracy of these transformations is strongly dependent on this ability. Second, in a step-by-step manner, we devise new transformations that satisfy this hypothesis. An approach based on curve fitting of the incompressible Direct Numerical Simulation data for eddy viscosity profiles below the logarithmic layer provides an extremely accurate transformation, which motivates self-contained methods, making use of mixing length formulas in the inner region. It is shown that the accuracy of existing transformations can be significantly improved by applying these ideas, below the logarithmic layer. Motivated by the effectiveness of the formulations derived from eddy viscosity equivalence, we introduce a new integral transformation based on Reynolds number equivalence between compressible and incompressible flows. This approach is based on defining a new compressible velocity scale, which affects the accuracy of transformations. Several choices for the velocity scale are tested, and in each attempt, it is shown that the eddy viscosity equivalence plays a very important role for the accuracy of compressibility transformations.

Anti-resonance in the systems of nonlinear oscillators

The anti-resonance phenomenon analogous to the Fano resonance in the classical system of two oscillators with nonlinearity is observed. The focus of the work is on the effects of nonlinearity in the system. The explicit form of the solution for the full nonlinear problem is obtained for the arbitrary type of the nonlinearity and in a wide range of the parameters. The results of the analytical study show very good agreement with the data of the direct numerical simulations. The peculiarities resulted from the multiplicity of the solutions are discussed. The paper was presented at PhysCon2024.

Microseismic Electronic Fencing for Monitoring of Transboundary Mining in Mines

Mine transboundary mining has been occurring frequently in recent years, and this illegal behavior has brought great potential danger to mine safety while also causing greater losses of state-owned assets. However, the current method of monitoring transboundary mining is still mainly based on underground verification by supervisors, which is far from meeting the demand for supervision. Microseismic monitoring technology is effective for monitoring transboundary mining due to its ability to locate vibration signals. For mine transboundary mining monitoring, this paper proposes a microseismic electronic fence method focusing on mine boundary locating, which differs from the routine microseismic monitoring used in mining operations. This method focuses its key monitoring area on the mine boundary. The deployment mode, number of sensors, and localization theory are analyzed, and numerical simulation and field measurement data analysis results show that the microseismic electronic fence method can achieve a localization accuracy of 15–20 m for underground microseismic events in the vicinity of mine boundaries, which can be effectively applied to the monitoring of transboundary mining activities.

Optimising Physics-Informed Neural Network Solvers for Turbulence Modelling: A Study on Solver Constraints Against a Data-Driven Approach

Physics-informed neural networks (PINNs) have emerged as a promising approach for simulating nonlinear physical systems, particularly in the field of fluid dynamics and turbulence modelling. Traditional turbulence models often rely on simplifying assumptions or closed numerical models, which simplify the flow, leading to inaccurate flow predictions or long solve times. This study examines solver constraints in a PINNs solver, aiming to generate an understanding of an optimal PINNs solver with reduced constraints compared with the numerically closed models used in traditional computational fluid dynamics (CFD). PINNs were implemented in a periodic hill flow case and compared with a simple data-driven approach to neural network modelling to show the limitations of a data-driven model on a small dataset (as is common in engineering design). A standard full equation PINNs model with predicted first-order stress terms was compared against reduced-boundary models and reduced-order models, with different levels of assumptions made about the flow to monitor the effect on the flow field predictions. The results in all cases showed good agreement against direct numerical simulation (DNS) data, with only boundary conditions provided for training as in numerical modelling. The efficacy of reduced-order models was shown using a continuity only model to accurately predict the flow fields within 0.147 and 2.6 percentage errors for streamwise and transverse velocities, respectively, and a modified mixing length model was used to show the effect of poor assumptions on the model, including poor convergence at the flow boundaries, despite a reduced solve time compared with a numerically closed equation set. The results agree with contemporary literature, indicating that physics-informed neural networks are a significant improvement in solve time compared with a data-driven approach, with a novel proposition of numerically derived unclosed equation sets being a good representation of a turbulent system. In conclusion, it is shown that numerically unclosed systems can be efficiently solved using reduced-order equation sets, potentially leading to a reduced compute requirement compared with traditional solver methods.

Local precursors to anomalous dissipation in Navier-Stokes turbulence: Burgers vortex-type models and simulation analysis

Anomalous dissipation is a mechanism to dissipate kinetic energy which is established by a sufficiently spatially rough velocity field. It implies that the rescaled mean kinetic energy dissipation rate becomes constant with respect to Reynolds number Re, the dimensionless parameter that characterizes the strength of turbulence, given that Re≫1. The present study aims at bridging this statistical behavior of high-Reynolds-number turbulence to one specific structural building block of fluid turbulence—local vortex stretching configurations which take in the simplest case the form of Burgers's classical vortex stretching model from 1948. We discuss the anomalous dissipation in the framework of Duchon and Robert for this analytical solution of the Navier-Stokes equations, apply the same analysis subsequently to a generalized model of randomly oriented Burgers vortices by Kambe and Hatakeyama, and analyze finally direct numerical simulation data of three-dimensional homogeneous, isotropic box turbulence in this respect. We identify local high-vorticity events in fully developed Navier-Stokes turbulence that approximate the analytical models of strong vortex stretching well. They correspond to precursors of enhanced anomalous dissipation. Published by the American Physical Society 2024

Multi-condition hawser tension prediction of offshore offloading system based on long and short-term memory network and transfer learning

Real-time prediction of hawser tension of the side-by-side oil offloading system of Floating Production Storage and Offloading (FPSO) can provide early warnings for hawser breakages and ship collision risk, thus improving structural, property, and environmental security. Although offline numerical models and Long Short-Term Memory (LSTM) could offer substantial precision, they confront intensive time costs in recalibrating or retraining models. This paper proposes an integration method of LSTM networks and transfer learning for real-time tension prediction considering inputs of actual remote wave elevations. We use short-term environmental data and numerical simulation data of correlated hawser tensions during offloading operations to train a pre-trained benchmark model for transfer learning. Then, a highly generalized and efficient transferred model is constructed by using a small sample to realize short-term tension predictions in time-varying environments. The results show the occurrence time and value of extreme tensions predicted by transfer learning nearly match the reference data, and their maximum errors are 3 s and 0.11, respectively, superior to LSTM direct training. Therefore, it provides sufficient demand for real-time prediction and early collision risk prevention in dynamically changing ocean environment. The research results could provide an alternative framework for intelligent monitoring of large-scale marine structures.

Evaluation method of carbon-fiber-reinforced polymer material anti-radiation performance based on synergistic effect model

Aiming at the high computational costs of the current multi-source X-ray radiation numerical simulations, a multi-source X-ray evaluation method based on a synergistic effect model is studied. First, based on the advantage of the low computational cost of single-source X-ray radiation numerical simulations, a hierarchical Gaussian process model is used to construct a superimposed single-source numerical simulation data fusion model. Second, by constructing composite correlation functions, the data fusion ability of the Gaussian process model is improved. The unknown parameters in the data fusion evaluation model are solved with the help of the Markov chain Monte Carlo method and a nonlinear optimization algorithm. Based on the obtained sampling values of the unknown parameters, an approximate solution method for the estimated value of the superimposed single-source X-ray radiation numerical simulations with unknown input conditions is given. Then a synergistic effect model is constructed based on the established linear regression surrogate model. The synergistic evaluation of multi-source X-ray radiation tests based on single-source X-ray radiation numerical simulation superimposed test data is studied. Finally, carbon-fiber-reinforced polymer experiments show the proposed method in better than existing Kriging methods, for prediction of multi-source X-ray radiation data.

A conditional deep learning model for super-resolution reconstruction of small-scale turbulent structures in particle-Laden flows

Super-resolution reconstruction of turbulent flows using deep learning has gained significant attention, yet challenges remain in accurately capturing physical small-scale structures. This study introduces the Conditional Enhanced Super-Resolution Generative Adversarial Network (CESRGAN) for reconstructing high-resolution turbulent velocity fields from low-resolution inputs. CESRGAN consists of a conditional discriminator and a conditional generator, the latter being called CoGEN. CoGEN incorporates subgrid-scale (SGS) turbulence kinetic energy as conditional information, improving the recovery of small-scale turbulent structures with the desired level of energy. By being aware of SGS turbulence kinetic energy, CoGEN is relatively insensitive to the degree of detail in the input. As shown in the paper, its advantages become more pronounced when the model is applied to heavily filtered input. We evaluate the model using direct numerical simulation (DNS) data of forced homogeneous isotropic turbulence. The analysis of Q-criterion isosurfaces, energy spectra, and probability density functions shows that the proposed CoGEN reconstructs fine-scale vortical structures more precisely and captures turbulent intermittency better compared to the traditional generator. Particle-pair dispersion simulations validate the physical fidelity of CoGEN-reconstructed fields, closely matching DNS results across various Stokes numbers and filtering levels. This paper demonstrates how incorporating available physical information enhances super-resolution models for turbulent flows.

Key feature identification of internal kink mode using machine learning

The internal kink mode is one of the crucial factors affecting the stability of magnetically confined fusion devices. This paper explores the key features influencing the growth rate of internal kink modes using machine learning techniques such as Random Forest, Extreme Gradient Boosting (XGboost), Permutation, and SHapley Additive exPlanations (SHAP). We conduct an in-depth analysis of the significant physical mechanisms by which these key features impact the growth rate of internal kink modes. Numerical simulation data were used to train high-precision machine learning models, namely Random Forest and XGBoost, which achieved coefficients of determination values of 95.07% and 94.57%, respectively, demonstrating their capability to accurately predict the growth rate of internal kink modes. Based on these models, key feature analysis was systematically performed with Permutation and SHAP methods. The results indicate that resistance, pressure at the magnetic axis, viscosity, and plasma rotation are the primary features influencing the growth rate of internal kink modes. Specifically, resistance affects the evolution of internal kink modes by altering current distribution and magnetic field structure; pressure at the magnetic axis impacts the driving force of internal kink modes through the pressure gradient directly related to plasma stability; viscosity modifies the dynamic behavior of internal kink modes by regulating plasma flow; and plasma rotation introduces additional shear forces, affecting the stability and growth rate of internal kink modes. This paper describes the mechanisms by which these four key features influence the growth rate of internal kink modes, providing essential theoretical insights into the behavior of internal kink modes in magnetically confined fusion devices.

Numerical simulation and intelligent prediction of a 500 t/d municipal solid waste incinerator

After classifying municipal solid waste (MSW), understanding how changes in its characteristics affect the incineration process becomes essential. Optimizing and adjusting operating parameters is crucial to ensure the smooth operation of MSW incinerators. In this study, we developed a three-dimensional numerical simulation model for a 500 t/d MSW incinerator, based on a two-fluid model that accounts for grate motion. We examined the effects of load and moisture on the combustion process. Using the results from the numerical simulation and on-site operational data, we established a machine learning model to predict incinerator conditions based on load, waste particle size, and moisture content. The average relative errors for the training and test sets were approximately 0.33 % and 0.44 %, respectively. Finally, we compared the influence of different operating parameters on flue gas temperature. Our numerical simulation model and intelligent prediction method offer valuable guidance for optimizing existing MSW incinerators and designing new ones.

A response surface equation for the drag polar for an airplane flying in the vicinity of wavy sea surface

The drag polar equation is the key issue to calculate the performance-based parameters during various flight phases and determine the optimum conditions. This is an essential feature to be included in airplane flight control system to manage the flight. The aircraft under certain emergency conditions are instructed to land on sea water. This underlines the combinational role of ground effect and the surface wave shape on airplane aerodynamic responses. Up to now, many investigations have described the airfoil and wing aerodynamic behaviors near the ground but none was devoted to an airplane flying near a wavy surface. In this paper, intensive numerical simulations have been carried out on a general aviation airplane flying near the water wavy surface. The effects of ground proximity as well as the wave amplitude have been investigated on airplane 2D longitudinal forces and moment. Some limited wind tunnel tests have also been performed on the same model, in the vicinity of water surface to validate the numerical calculations. A statistical quadratic equation based on Response Surface Model, RSM, has been proposed for the airplane flying in the vicinity of the wavy water in which, the wave shape deterioration due to both the airplane flowfield and the viscous dissipation has been taken into account. The statistical measures approve the model quality and accuracy in comparison to the numerical simulation data. The results show that the oscillations in aerodynamic forces are strong functions of the wave amplitude. The time variations of both CL and CD follow the shape of the surface wave while it does not have any remarkable effect on their mean values.