Physics-based Prediction Research Articles

Assessing the impact of regional geology on the ground motion model variability at the Kashiwazaki-Kariwa Nuclear Power Plant (Japan) via physics-based numerical simulation

This study investigates the sensitivity of physics-based earthquake prediction to the choice of the regional geological model. The generally poor information on the mechanical properties of the Earth’s crust induces large uncertainty margins on the synthetic seismic response. Therefore, uncertainty quantification must be associated with high-fidelity numerical simulation, especially when verging on broad-band simulations (i.e., numerical theoretical accuracy higher than the conventional 1 Hz limit). In this paper, the synthetic seismic response of the Niigata region (Japan), during the Chūetsu offshore earthquake (MW6.6) serves as representative case study. Three plausible three-dimensional (3-D) geological structures are compared for two point-wise aftershock numerical simulations, duly validated. The framework of comparison targets the Ground Motion Prediction Equation (GMPE) for pseudo-spectral acceleration at the surface. The impact of the geology on the variability of between-event and the within-event residuals is inferred from synthetic simulations and related to common parameters representing site-specific conditions.

Physics-based data-driven interpretation and prediction of rolling contact fatigue damage on high-speed train wheels

Rolling contact fatigue (RCF) of high-speed train wheels is detrimental to reliability and entails huge maintenance costs. Accurate prediction of RCF damage is important for train operators, however, physics-based methods are not suitable for practical applications. Based on accumulated RCF data, this paper develops a physics-based data-driven method for the interpretation and prediction of RCF damage risk. First, based on RCF mechanism, it is found that many relevant parameters can be used for RCF prediction. Then, eight predictors are extracted from RCF-related data through dimensionality reduction methods, including linear discriminant analysis and kernel Fisher discriminant analysis. It shows that greater wheel out-of-roundness or smaller wheel diameters leads to higher RCF risks; previously defective wheels are more likely to reencounter RCF damage; the RCF risk varies in different seasons; the effect of wear on RCF damage is significantly nonlinear. Afterwards, a predictive model is established based on logistic regression. Validation results yield an average accuracy of 75% and also demonstrate its easy interpretability, fast computational speed and stable performance. Further, test result shows an accuracy of 85% to distinguish high-risk wheelsets prior to RCF inspections. The proposed method can be applied to the risk-based maintenance of high-speed train wheels.

Lifetime Benefit Analysis of Intelligent Maintenance: Simulation Modeling Approach and Industrial Case Study

The introduction of Industry 4.0 is expected to revolutionize current maintenance practices by reaching new levels of predictive (detection, diagnosis, and prognosis processes) and prescriptive maintenance analytics. In general, the new maintenance paradigms (predictive and prescriptive) are often difficult to justify because of their multiple inherent trade-offs and hidden systems causalities. The prediction models, in the literature, can be considered as a “black box” that is missing the links between input data, analysis, and final predictions, which makes the industrial adaptability to such models almost impossible. It is also missing enable modeling deterioration based on loading, or considering technical specifications related to detection, diagnosis, and prognosis, which are all decisive for intelligent maintenance purposes. The purpose and scientific contribution of this paper is to present a novel simulation model that enables estimating the lifetime benefits of an industrial asset when an intelligent maintenance management system is utilized as mixed maintenance strategies and the predictive maintenance (PdM) is leveraged into opportunistic intervals. The multi-method simulation modeling approach combining agent-based modeling with system dynamics is applied with a purposefully selected case study to conceptualize and validate the simulation model. Three maintenance strategies (preventive, corrective, and intelligent) and five different scenarios (case study data, manipulated case study data, offshore and onshore reliability data handbook (OREDA) database, physics-based data, and hybrid) are modeled and simulated for a time period of 20 years (175,200 h). Intelligent maintenance is defined as PdM leveraged in opportunistic maintenance intervals. The results clearly demonstrate the possible lifetime benefits of implementing an intelligent maintenance system into the case study as it enhanced the operational availability by 0.268% and reduced corrective maintenance workload by 459 h or 11%. The multi-method simulation model leverages and shows the effect of the physics-based data (deterioration curves), loading profiles, and detection and prediction levels. It is concluded that implementing intelligent maintenance without an effective predictive horizon of the associated PdM and effective frequency of opportunistic maintenance intervals, does not guarantee the gain of its lifetime benefits. Moreover, the case study maintenance data shall be collected in a complete (no missing data) and more accurate manner (use hours instead of date only) and used to continuously upgrade the failure rates and maintenance times.

An empirical model for the drilling performance prediction for roller-cone drill bits

The drilling process is a challenge in the oil and gas industry. These challenges are usually associated with the high costs involved in this process. Technological advances in the past few years, specifically in the drilling process, have allowed the exploitation of oil and gas fields that were uneconomical before. The economic feasibility of drilling a well is directly associated with the accuracy of the plan before the drilling starts, called “well planning”. Therefore, an accurate drilling performance prediction is crucial. Usually, the approaches to drilling performance prediction are divided into two categories: physics-based and data-driven models. Physics-based prediction models, or traditional models, are based on laboratory experiments, being empirical models designed for specific types of drilling parameters. Data-driven models are based purely on data, incorporating either machine learning or the implementation of neural networks to predict the rate of penetration. de Moura et al. (de Moura et al., 2020) developed a new drilling performance prediction model that includes the founder point location for fixed cutter drill bit operations. This model was based on the physics-based prediction approach because it usually presents low complexities and good accuracy. Based on several drilling datasets, the applicability and accuracy of the new model for roller-cone bit operations is discussed. Based on an analysis of the drilling data-sets from drill-off tests and field operations, the linear relationship between the “drillability constant” of the Maurer model and the weight on bit (WOB) was confirmed. The results of this study show that the new model presented high accuracy to predict the drilling performance in 25 distinct scenarios that included roller-cone bits with diameters that varied between 63.50 and 444.50 mm and in different rock specimens with rock strength varying between 30 and 317.16 MPa. • The development of a drilling performance prediction model that includes the location of the founding point is essential for the planning of wells. • Physics-based prediction models have low complexity and good accuracy. • This model showed high accuracy for roller-cone drill bits. • The Maurer's model is restricted to predicting drilling performance before the founding point. • The drillability constant has a linear dependence on the WOB.

Transition mechanisms in cross-flow-dominated hypersonic flows with free-stream acoustic noise

Transition to turbulence in high-speed flows is determined by multiple parameters, many of which are not fully understood, leading to problems in developing physics-based prediction methods. In this contribution, we compare transition mechanisms in configurations with unswept and swept leading edges that are exposed to free-stream acoustic disturbances. Direct numerical simulations are run at a Mach number of six with the same free-stream noise, consisting of either fast or slow acoustic disturbances, with two different amplitudes to explore the linear and nonlinear aspects of receptivity and transition. For the unswept configuration, receptivity follows an established mechanism involving synchronisation of fast acoustic disturbances with boundary-layer modes. At high forcing amplitudes, transition proceeds via the formation of streaks and their eventual breakdown. In the swept case, the process of streak-induced transition is modified by the presence of a cross-flow instability in the leading-edge region. Linear stability analysis confirms the presence of a cross-flow mode as well as weaker first and second mode waves. Both fast and slow types of forcing independently stimulate an unusual transition mechanism involving significantly narrower streaks than those arising from the cross-flow instability behind the swept leading edge or those induced nonlinearly in the unswept case. In the observed transition process, the cross-flow mode leads to a thin layer of streamwise vorticity that breaks up under the influence of high spanwise wavenumber disturbances. These disturbances first appear in the leading-edge region.

An anisotropic mesoscale model of fatigue failure in a titanium alloy containing duplex microstructure and hard α inclusions

Fatigue life estimation on hard α inclusion induced failure in titanium alloy remains in long-term interest, due to the local stress distribution induced by the interaction between inclusion and surface as well as the scatter in randomly oriented grains. In this paper, an anisotropic mesoscale model is established to describe the fatigue failure of typical bi-phase titanium alloy with hard α inclusion. The stress-life criterion is formulated by dislocation stress field elaborated from anisotropic fracture mechanics, taking in random variables to represent the scatter in grain size and orientation. Parameters include microstructures characterized by microscope, dislocation slip properties evaluated by molecular dynamics (MD) simulation, and information of activated slip system via crystal plasticity finite element method (CPFEM) simulation. By using the data of specimens with and without inclusion, the proposed model exhibits excellent capability in fatigue life prediction for a wide range of stress ratio. This work draws insights on physics-based life prediction for inclusion induced fatigue failure.

Preliminary analysis of earthquake probability based on the synthetic seismic catalog

The analysis of seismic hazards relies on the statistical analysis of historical seismic data and the instrumental seismic catalog to obtain the regional earthquake recurrence interval and earthquake probability. The accuracy of analysis thus depends strongly on the completeness of the seismic data used. However, available seismic catalogs are too short or incomplete for the reliable analysis of the statistical characteristics of earthquakes. If a long-term synthetic seismic catalog can be generated using a physics-based numerical simulation, and the simulation results match the crustal deformation, seismicity, and other observations, then such a synthetic catalog helps us to further understand the characteristics of seismic activity and analyze the regional seismic hazard. In this paper, taking the northeastern Tibetan Plateau as a case study, we establish a three-dimensional visco-elastoplastic finite-element model to simulate earthquake cycles and the spatiotemporal evolution of earthquakes on the model fault system and obtain a seismic catalog on a time scale of tens of thousands of years. On the basis that the model satisfies the regional geodynamics of the northeastern Tibetan Plateau, we analyze seismicity on the northeastern Tibetan Plateau using the simulated synthetic earthquake catalog. The characteristics of earthquake recurrence at different locations and different magnitudes, and the long-term average probability of earthquake occurrence within the fault system on the northeastern Tibetan plateau are studied. The results are a reference for regional seismic hazard assessment and provide a basis for the physics-based numerical prediction of earthquakes.

Machine Learning-Enriched Lamb Wave Approaches for Automated Damage Detection.

Lamb wave approaches have been accepted as efficiently non-destructive evaluations in structural health monitoring for identifying damage in different states. Despite significant efforts in signal process of Lamb waves, physics-based prediction is still a big challenge due to complexity nature of the Lamb wave when it propagates, scatters and disperses. Machine learning in recent years has created transformative opportunities for accelerating knowledge discovery and accurately disseminating information where conventional Lamb wave approaches cannot work. Therefore, the learning framework was proposed with a workflow from dataset generation, to sensitive feature extraction, to prediction model for lamb-wave-based damage detection. A total of 17 damage states in terms of different damage type, sizes and orientations were designed to train the feature extraction and sensitive feature selection. A machine learning method, support vector machine (SVM), was employed for the learning model. A grid searching (GS) technique was adopted to optimize the parameters of the SVM model. The results show that the machine learning-enriched Lamb wave-based damage detection method is an efficient and accuracy wave to identify the damage severity and orientation. Results demonstrated that different features generated from different domains had certain levels of sensitivity to damage, while the feature selection method revealed that time-frequency features and wavelet coefficients exhibited the highest damage-sensitivity. These features were also much more robust to noise. With increase of noise, the accuracy of the classification dramatically dropped.

Building thermal load prediction through shallow machine learning and deep learning

Building thermal load prediction informs the optimization of cooling plant and thermal energy storage. Physics-based prediction models of building thermal load are constrained by the model and input complexity. In this study, we developed 12 data-driven models (7 shallow learning, 2 deep learning, and 3 heuristic methods) to predict building thermal load and compared shallow machine learning and deep learning. The 12 prediction models were compared with the measured cooling demand. It was found XGBoost (Extreme Gradient Boost) and LSTM (Long Short Term Memory) provided the most accurate load prediction in the shallow and deep learning category, and both outperformed the best baseline model, which uses the previous day’s data for prediction. Then, we discussed how the prediction horizon and input uncertainty would influence the load prediction accuracy. Major conclusions are twofold: first, LSTM performs well in short-term prediction (1 h ahead) but not in long term prediction (24 h ahead), because the sequential information becomes less relevant and accordingly not so useful when the prediction horizon is long. Second, the presence of weather forecast uncertainty deteriorates XGBoost’s accuracy and favors LSTM, because the sequential information makes the model more robust to input uncertainty. Training the model with the uncertain rather than accurate weather data could enhance the model’s robustness. Our findings have two implications for practice. First, LSTM is recommended for short-term load prediction given that weather forecast uncertainty is unavoidable. Second, XGBoost is recommended for long term prediction, and the model should be trained with the presence of input uncertainty.

Heat transfer enhancement associated with electrostatic suppression of Leidenfrost droplets

A liquid drop levitates on its own vapor on a sufficiently hot surface (the Leidenfrost effect). Application of an electrical potential difference across the vapor gap suppresses the Leidenfrost state via electrostatic attraction of liquid towards the surface. The resultant transient solid–liquid contacts significantly reduce the thermal resistance to heat transfer across the insulating vapor gap. This work is an indepth study on the heat transfer enhancement accompanying electrostatic suppression of the Leidenfrost state. A purely analytical model is developed to predict the heat transfer enhancement based on the transient wetting dynamics observed during suppression. This model is based on semi-infinite conduction heat transfer coupled with a physics-based prediction of the voltage-dependent wetted fraction. The model is strongly validated by comparison with inhouse experiments and related experimental data from literature. These experiments measure the heat transfer enhancement associated with electrostatic suppression of the Leidenfrost state for three fluids, at various applied voltages and superheats. The experimentally-validated model predicts that heat transfer enhancement scales as the square of the wetted fraction. Results show a 4X enhancement in heat transfer by electrostatic suppression of the Leidenfrost state of water droplets; higher enhancements are measured for organic solvents.

Analysis of BLEVE overpressure using spherical shock theory

The near-field hazards from BLEVE including blast, ground force, drag loading from the rapid liquid phase change and projectiles. There are several correlations available in the literature for the far field blast overpressure from a BLEVE, usually requiring the calculation of the available expansion energy and the application of correction factors. However, there is very little information available for near-field effects and how this is affected by the details of vessel failure.This work presents near-field blast overpressure data and prediction models to fill in this gap. First, experimental measurements of overpressure in the near-field of a small scale cylindrical controlled BLEVE experiments with propane (V=0.6L, d =50mm, L=300mm) were performed. Then, this work establishes a prediction model based solely on the vapour phase properties at failure, using shock tube overpressure prediction and spherical shock propagation models.The model predicts well the strongest tests and is conservative with all the others. Scaling the model up to larger scale experimental data from literature shows that it is transposable, proposing a simple physics-based prediction model for BLEVE overpressure.

Time-variant reliability prediction for dynamic systems using partial information

Efficient time-variant reliability prediction for dynamic systems is a challenging problem to reduce the risk because large amounts of information is needed for the prediction. In this paper, a physics-based reliability prediction method is presented with partial information. The cumulative probabilities of failure are first estimated for the given time intervals with complete information based on the subset simulation with splitting and time-variant copula function. An appropriate probability distribution is then selected for fitting the estimated cumulative probabilities of failure. The partial information, which can be collected from mathematical models or the physical experiments during the later time intervals, is used to effectively update the distribution parameters to improve the prediction accuracy. A case study of a vibratory system representing the quarter car model is employed to testify the proposed method.

Progress in disruption prevention for ITER

Key plasma physics and real-time control elements needed for robustly stable operation of high fusion power discharges in ITER have been demonstrated in recent research worldwide. Recent analysis has identified the current density profile as the main drive for disruptive instabilities in discharges simulating ITER’s baseline scenario with high and low external torque. Ongoing development of model-based profile control and active control of magnetohydrodynamic instabilities is improving the stability of multiple scenarios. Significant advances have been made toward real-time physics-based prediction of instabilities, including path-oriented analysis, active sensing, and machine learning techniques for prediction that are beginning to go beyond simple disruption mitigation trigger applications. Active intervention contributes to prevention of disruptions, including forced rotation of magnetic islands to prevent wall locking, and localized heating/current drive to shrink the islands. Stable discharge rampdowns have been achieved with the fastest ITER-like scaled current ramp rates, while maintaining an X-point configuration. These elements are being integrated into stable operating scenarios and new event-handling systems for off-normal events in order to develop the physics basis and techniques for robust control in ITER.

Template-Based Method for Conformation Generation and Scoring for Congeneric Series of Ligands.

Physics-based prediction of protein-ligand binding affinities for a congeneric series of ligands in lead optimization requires their geometries as a first step. In this paper, we report a method that uses the 3D conformation of a lead compound in complex with a protein as a template to generate conformations of a series of related analog compounds. The method uses the Maximal Common Substructure (MCS) computed between lead and analog ligands to assign coordinates for the atoms shared between the ligands. For the differing atoms, a conformation generation procedure is implemented that results in a diversity of conformations. The generated conformations are sorted using a score based on the Molecular Mechanics and Generalized Born with Solvent Accessible Surface Area contribution (MM-GBSA) method. The accuracy of the generated conformations is tested retrospectively using a cross-validation approach applied to four data sets obtained from the Drug Design Data Resource (D3R) by measuring the RMSD of the top scored conformation with respect to the crystallographic pose. The scoring ability of the method is independently assessed using data for the same protein targets to test the rank ordering ability and separating active and inactive ligands. We tested the effect of protein flexibility during structural optimization and scoring approaches with and without strain energies. Retrospective validation on data sets comprising 4 targets shows that the method outperforms random selection for all targets and outperforms a molecular weight-based null model in 3 out of 4 targets in separating active and inactive compounds. Therefore, the presented method is expected to be of utility in lead optimization for rapidly screening analog ligands and generating initial conformations for use in more detailed physics-based binding affinity prediction methods.

Physics-Based Prediction of Atmospheric Transfer Characteristics at Terahertz Frequencies

The amplitude and phase distortions of radar echo signal will cause the emergence of undesirable ghost scattering points, which degrade the quality of terahertz (THz) radar images. In this paper, a physics-based procedure is presented to predict the atmospheric attenuation and dispersion characteristics at THz frequencies, which is mainly based on the line-by-line calculation method with a specific modification in phase-shift prediction. The line-by-line parameters provided by the high-resolution transmission spectroscopic database and atmospheric condition parameters obtained from the Air Force Geophysics Laboratory reference atmospheric constituent profiles are adopted to predict the atmospheric transmittance and the phase shift for specific transfer paths. The results are compared with measured data to demonstrate the accuracy, while the proposed procedure is used to analyze the impacts of atmospheric transfer characteristics on radar imaging via the high-resolution range profile simulation. The signal distortion is interpreted in terms of paired echoes to illustrate the importance of frequency band selection for high-resolution imagery at THz frequencies.

Multiscale crystal-plasticity phase field and extended finite element methods for fatigue crack initiation and propagation modeling

This paper presents a physics-based prediction of crack initiation at the microstructure level using the phase field (PF) model without finite element discretization, coupled with an efficient and accurate modeling of crack propagation at macro-scale based on extended finite element method (XFEM). Although the macro-scale model assumes linear elastic material behavior, at micro-scale the behavior of plastically deforming heterogeneous polycrystals is taken into account by coupling the PF model and a crystal plasticity model in the fast Fourier transform computational framework. A sequential coupling has been established for the multiscale modeling where the macro-scale finite element (FE) model determines the hot spots at each cyclic loading increment and passes the associated stress/strain values to the unit-cell phase-field model for accurate physics-based microstructure characterization and prediction of plasticity induced crack initiation. The PF model predicts the number of cycles for the crack initiation and the phenomenological crack growth models are employed to propagate the initiated crack by the appropriate length to be inserted in the FE mesh. Finally, the XFEM solution module is activated to perform mesh independent crack propagation from its initial crack size to the final size for the total life prediction. The effectiveness of the proposed multiscale method is demonstrated through numerical examples.

On the simulation of the flow around a circular cylinder at [formula omitted

The flow around a circular cylinder at Reynolds number of 1.4 × 105 is examined with Reynolds-Averaged Navier–Stokes equations (RANS) and Scale-Resolving Simulation (SRS) methods. Such problem is in the upper limit of the flow regime where turbulent transition occurs in the free shear-layers and so the flow dynamics is dominated by the spatial development of vortex-shedding structure, and in particular by the Kelvin–Helmholtz rollers and turbulence onset. The objectives of this investigation are threefold: (i) determine the aptitude of distinct RANS and SRS models to simulate the correct flow regime; (ii) compare the predictions of selected methods with available experimental measurements; and (iii) examine key modelling and flow features that contribute to the observed results. The evaluated models range from RANS supplemented with linear, transition, and non-linear turbulent viscosity closures, to hybrid and bridging SRS methods. Bridging computations are conducted at various constant degrees of physical resolution (range of resolved scales). The results illustrate the complexity of predicting the present flow problem. It is shown that RANS and SRS formulations modelling turbulence in boundary-layers with the selected linear turbulent viscosity closures lead to a premature onset of turbulence which alters the flow regime of the simulations. Although the transition and non-linear RANS closures can predict the correct flow regime, the outcome of this study indicates that solely the bridging model at constant physical resolution is able to achieve an accurate and physics-based prediction of the flow dynamics. Nonetheless, the necessary degree of physical resolution makes the numerical requisites of such computations demanding.

Physics-based prediction of biopolymer degradation.

In the natural environment, insoluble biomatter provides a preeminent source of carbon for bacteria. Its degradation by microbial communities thus plays a major role in the global carbon-cycle. The prediction of degradation processes and their sensitivity to changes in environmental conditions can therefore provide critical insights into globally occurring environmental adaptations. To elucidate and quantify this macro-scale phenomenon, we conduct micro-scale experiments that examine the degradation of isolated biopolymer particles and observe highly nonlinear degradation kinetics. Since conventional scaling arguments fail to explain these observations, it is inferred that the coupled influence of both the physical and biochemical processes must be considered. Hence, we develop a theoretical model that accounts for the bio-chemo-mechanically coupled kinetics of polymer degradation, by considering the production of bio-degraders and their ability to both dissociate the material from its external boundaries and to penetrate it to degrade its internal mechanical properties. This change in mechanical properties combined with the intake of solvent or moisture from the environment leads to chemo-mechanically coupled swelling of the material and, in-turn, influences the degradation kinetics. We show that the model quantitatively captures our experimental results and reveals distinct signatures of different bacteria that are independent of the specific experimental conditions (i.e. particle volume and initial concentrations). Finally, after validating our model against the experimental data we extend our predictions for degradation processes across various length and time scales that are inaccessible in a laboratory setting.

The Influence of Thermal Transient Rates on Coated Turbine Parts' Life Expectancy

During rapid engine throttling operations, turbine airfoils can experience very rapid heating and cooling, particularly at take-off conditions. These rapid transient events lead to the generation of high thermal gradients and nonuniform stress distributions through the thermal barrier coating (TBC), environmental barrier/bond coating, and substrate. This, in turn, can lead to coating delamination, overheat of the substrate materials, creep, and thermo-mechanical fatigue of the part. We present the process and computer modeling methodology for a physics-based prediction of deformation, damage, crack propagation and local failure modes in coated turbine airfoils and other parts operating at hot section turbine environment conditions as a function of engine operational regimes, with a particular emphasis on rapid transient events. The overall goal is to predict the effects and severity of the cooling and heating thermal rates on transient thermal mechanical fatigue life of coated hot parts (turbine airfoils, blade outer air seals, and combustor liners). The computational analysis incorporates time-accurate, coupled aerothermodynamics with nonlinear deformation thermal-structural finite element modeling, and fracture mechanics modeling for high-rate thermal transient events. TBC thermal failure and spallation are introduced by the use of interface fracture toughness and interface property evolution as well as dissipated energy rate. The spallation model allows estimations of the part remaining life as a function of the heating/cooling rates. Applicability of the developed model is verified using experimental coupons and calibrated against burner rig test data for high-flux thermal cycles. Our results show a decrease in TBC spall life due to high-rate transient events.

Hybrid data-driven physics-based model fusion framework for tool wear prediction

An integral part of modern manufacturing process management is to acquire useful information from machining processes to monitor machine and tool condition. Various models have been introduced to detect, classify, and predict tool wear, as a key parameter of the machining process. In more recent developments, sensor-based approaches have been attempted to infer the tool wear condition from real-time processing of the measurement data. Experiments show that the physics-based prediction models can include large uncertainties. Likewise, the measurement-based (or sensor-based) inference techniques are affected by sensor noise and measurement model uncertainties. To manage uncertainties and noise of both methods, a hybrid framework is proposed to fuse together the results of the prediction model and the measurement-based inference data in a stepwise manner. The fusion framework is an extension to the regularized particle filtering technique, used to facilitate updating the state prediction with a numerical inference model, when measurement models alone are not satisfactory. The results show significant improvement in tool wear state estimation, reducing the prediction errors by almost half, compared to the prediction model and sensor-based monitoring method used independently.