Deterministic Simulation Research Articles

Phenalenyl growth reactions and implications for prenucleation chemistry of aromatics in flames.

The energetics and kinetics of phenalene and phenalenyl growth reactions were studied theoretically. Rate constants of phenalene and phenalenyl H-abstraction and C2H2 addition to the formed radicals were evaluated through quantum-chemical and rate-theory calculations. The obtained values, assigned to all π radicals, were tested in deterministic and kinetic Monte Carlo simulations of aromatics growth under conditions of laminar premixed flames. Kekulé and non-Kekulé structures of the polycyclic aromatic hydrocarbons (PAHs) evolving in the stochastic simulations were identified by on-the-fly constrained optimization. The numerical results demonstrated an increased PAH growth and qualitatively reproduced experimental observations of Homann and co-workers of non-decaying PAH concentrations with nearly equal abundances of even and odd carbon-atom PAHs. The analysis revealed that the PAH growth proceeds via alternating and sterically diverse acetylene and methyl HACA additions. The rapid and diverse spreading in the PAH population supports a nucleation model as PAH dimerization, assisted by the non-equilibrium phenomena, forming planar aromatics first and then transitioning to the PAH-PAH stacking with size.

Multi-output gaussian processes for the reconstruction of homogenized cross-sections

Deterministic nuclear reactor simulators employing the prevalent two-step scheme often generate a substantial amount of intermediate data at the interface of their two subcodes, which can impede the overall performance of the software. The bulk of this data comprises “few-groups homogenized cross-sections” or HXS, which are stored as tabulated multivariate functions and interpolated inside the core simulator. A number of mathematical tools have been studied for this interpolation purpose over the years, but few meet all the challenging requirements of neutronics computation chains: extreme accuracy, low memory footprint, fast predictions… We here present a new framework to tackle this task, based on multi-outputs gaussian processes (MOGP). This machine learning model enables us to interpolate HXS’s with improved accuracy compared to the current multilinear standard, using only a fraction of its training data – meaning that the amount of required precomputation is reduced by a factor of several dozens. It also necessitates an even smaller fraction of its storage requirements, preserves its reconstruction speed, and unlocks new functionalities such as adaptive sampling and facilitated uncertainty quantification. We demonstrate the efficiency of this approach on a rich test case reproducing the VERA benchmark, proving in particular its scalability to datasets of millions of HXS.

Unlocking the potential of stochastic simulation through Bluecat: Enhancing runoff predictions in arid and high‐altitude regions

AbstractRunoff prediction is crucial for effective water resource management and risk mitigation. However, predicting these catchment responses is challenging due to their unique characteristics and the randomness of hydrological processes. This manuscript explores two different types of modelling frameworks (deterministic and stochastic) and aims to answer questions regarding the reliance on stochastic simulation based on deterministic simulations, the suitability of simple deterministic models, and the influence of catchment characteristics on the results. A simple deterministic rainfall‐runoff model (with only one model parameter) was used to feed the Brisk Local Uncertainty Estimator for Hydrological Simulations and Predictions (Bluecat) framework, exploring the whole range of values of the model parameter. Our findings showed that Bluecat enhanced the Kling‐Gupta Efficiency (KGE) outcomes in arid and semi‐arid regions as well as high‐altitude catchments. Additionally, using the mean of the confidence band of the stochastic simulation as the simulated discharge, rather than the median, resulted in improved KGE values for all catchments. Hysteresis between S‐KGE (stochastic KGE) and D‐KGE (deterministic KGE) was observed, indicating a non‐monotonic relationship between the two variables, and therefore, S‐KGE optimisation can be achieved even when D‐KGE is not optimal. Bluecat showed exceptional performance extended to arid and semi‐arid regions, as well as high‐altitude areas, making it a promising alternative for rainfall‐runoff simulations in these challenging locations.

Application of the FSV Method in EMC Uncertainty Simulation Results

The uncertainty analysis method has been a research hotspot in the electromagnetic compatibility field in recent years, and its results are presented in the form of a probability density curve. Currently, there is no reasonable method to evaluate the validity of uncertain results. It is not conducive to the improvement and popularization of new uncertainty analysis methods or models. Feature selective validation method is a mature validity evaluation method for deterministic electromagnetic simulation results, and this paper extends it to electromagnetic compatibility uncertainty simulation results. On this basis, the convergence determination of the partial uncertainty analysis method is realized. The purpose is to further promote the application of the uncertainty analysis method in the electromagnetic compatibility field.

Simulation model of a patient with type 1 diabetes using fuzzification

Type 1 diabetes is one of the most common diseases. The disease is caused by a lack of insulin secretion from the beta cells of the pancreas, which leads to improper regulation of blood glucose levels. The article presents a simulation model for determining changes in glucose-insulin levels using fuzzy logic techniques. The work concerns a quite simple deterministic simulation model of a digital twin of a type 1 diabetes patient, and fuzzification can significantly improve the efficiency of this model. A series of numerical experiments showed that enriching a simple deterministic patient model with a fuzzy approach gives much more accurate results than the simple deterministic model. The use of fuzzy sets opens up a number of possibilities and is a completely natural approach, resulting from, among others, the specificity of the simulated phenomenon - vital parameters of people with type 1 diabetes.

Uncertainty Analysis in Airfoil–Turbulence Interaction Noise Using Polynomial Chaos Expansion

Airfoil–turbulence interaction noise is a known source of environmental disturbance and acoustic performance loss in aeroacoustics and hydroacoustics. This noise can be predicted using semi-analytical models that require input measurements of the incoming turbulent flow parameters. However, the turbulence parameters are inherently difficult to measure accurately. These parameters, which include the turbulence kinetic energy and its dissipation rate, have a stochastic nature. This study aims to investigate how small variations in the measurements of turbulence parameters affect the uncertainty of the predicted airfoil–turbulence interaction noise. This is achieved by applying polynomial chaos expansion (PCE) to the semi-analytical model of Amiet’s theory for airfoil-interaction noise. The validity of the deterministic and stochastic simulations is ensured by comparisons against available experimental data from the literature, and Monte Carlo simulations, respectively. Uncertainty quantification is then performed using a stochastic collocation technique, where the aerodynamic noise is evaluated at specific collocation points to estimate the coefficients required for PCE. Both the individual and combined effects of varying the uncertain input turbulence parameters are simulated to quantify the uncertainty of the output aerodynamic noise. The insights gained from the results suggest it is important to incorporate the stochastic behavior of the incoming turbulent flow in operational models for airfoil–turbulence interaction noise predictions.

What Matters in the Effectiveness of Airborne Collision Avoidance Systems? Monte Carlo Simulation of Uncertainties for TCAS II and ACAS Xa

TCAS II is a rule-based airborne collision avoidance system (ACAS) that is used in current commercial air transport operations, and ACAS Xa is a new optimization-based system. Operational validation studies have mainly used deterministic simulations of ACAS performance using various sets of encounters. Recently a new approach was developed, which employs Monte Carlo (MC) simulation of agent-based models to evaluate the impact of sensor errors and pilot response variability. This paper contrasts the results of both approaches in a comparison of TCAS II and ACAS Xa for various types of synthetic encounters. It was found that conventional estimates of near mid-air collision (NMAC) probabilities are often lower than the estimates achieved using MC simulation, and that the biases in the P(NMAC) estimates are consistently larger for ACAS Xa than for TCAS II. Contributions to unresolved risk are largest for pilot performance, then for encounter types, and finally for sensor errors. The contribution of non-responding pilots is much larger than the differences between TCAS II and ACAS Xa. It is concluded that the agent-based MC simulation overcomes the limitations in traditional evaluation of altimetry errors and pilot response, providing an independent means to effectively analyze the robustness of ACASs.

Stochastic simulation of geological cross-sections from boreholes: A random field approach with Markov Chain Monte Carlo method

A reliable geological cross-section is essential to the design and risk assessment of underground structures. Random fields are commonly employed to model geological uncertainty. However, determining the covariance parameters can be a challenging task. In this study, a Bayesian approach is introduced for the simulation of geological formations utilizing available boreholes from a specific site. The cross-section and its associated covariance parameters are learned from geological formations observed in boreholes, using the Markov Chain Monte Carlo-based Bayesian method. A cross-section consisting of seven boreholes in Botanic Gardens, Singapore is used to demonstrate the effectiveness of the proposed method. The results show that the stochastic simulation of geological formations has a sound performance. For the training boreholes, most geological formations have true positive rates and positive predictive values larger than 0.89. For the validation boreholes, almost all geological formations in boreholes are accurately identified. When plenty of boreholes are available, a uniform prior distribution is a better choice to reduce geological uncertainty. Alternatively, when borehole data is limited, a log-normal prior distribution can be used to ensure a more deterministic simulation. For a length of 300-m cross-section of four ordered geological formations, when the number of boreholes is greater than three, the posterior standard deviation of the geological model is less than 10% of the prior, and the effective range of a single borehole is found to be between 21 and 27 m.

Modeling the long-term transport and fate of oil spilled from the 2021 A Symphony tanker collision in the Yellow Sea, China: Reliability of the stochastic simulation

Coupled with a 3D hydrodynamic model, a well-established 3D oil spill model is used to simulate the transport and fate of oil spilled from the A Symphony oil tanker collision in the Yellow Sea on April 27, 2021. The model is first validated by airborne mini-SAR and shipborne X-band radar observation in a 30-day simulation. Subsequently, the model prediction capabilities are investigated over a longer period when hydrodynamic data may not be available. In these cases, hydrodynamic data in earlier years are used for stochastic simulation, and the reliability of a multi-year stochastic simulation is tested. To evaluate the impact of hydrodynamics and meteorological conditions, indexes including oil sweeping area for surface oil, as well as oil centroid trajectory and oil fate for both the surface and subsurface oil, are statistically analyzed for a period up to 90 days after the spill. Over time, the deviation in the stochastic simulation relative to the deterministic simulation increased in the modeled oil transport, indicating an error accumulation. However, in the modeled oil fate, the deviation showed an interesting variation of first increasing and then gradually decreasing. This phenomenon can be attributed to three contributing factors: (1) limitations in empirical equations used in the model; (2) limited environmental impact of the spill; (3) differential role of small-scale ocean processes in short- and long-term simulations. The uncertainty in stochastic members may serve as an indicator of forecast accuracy.

Interview With Adrian Raftery

SummaryProfessor Adrian E. Raftery is the Boeing International Professor of Statistics and Sociology and an adjunct professor of Atmospheric Sciences at the University of Washington in Seattle. He was born in Dublin, Ireland, and obtained a BA in Mathematics (1976) and an MSc in Statistics and Operations Research (1977) at Trinity College Dublin. He obtained a doctorate in mathematical statistics in 1980 from the Université Pierre et Marie Curie in Paris, France, under the supervision of Paul Deheuvels. He was a lecturer in statistics at Trinity College Dublin from 1980 to 1986, and then an associate (1986–1990) and full (1990‐present) professor of statistics and sociology at the University of Washington. He was the founding Director of the Center for Statistics and Social Sciences (1999–2009). Professor Raftery has published over 200 articles in peer‐reviewed statistical, sociological and other journals. His research focuses on Bayesian model selection and Bayesian model averaging, model‐based clustering, inference for deterministic simulation models, and the development of new statistical methods for demography, sociology, and the environmental and health sciences. He is a member of the United States National Academy of Sciences, a Fellow of the American Academy of Arts and Sciences, an Honorary Member of the Royal Irish Academy, a member of the Washington State Academy of Sciences, a Fellow of the American Statistical Association, a Fellow of the Institute of Mathematical Statistics, and an elected Member of the Sociological Research Association. He has won the Population Association of America's Clifford C. Clogg Award, the American Sociological Association's Paul F. Lazarsfeld Award for Distinguished Contribution to Knowledge, the Jerome Sacks Award for Outstanding Cross‐Disciplinary Research from the National Institute of Statistical Sciences, the Parzen Prize for Statistical Innovation, and the Science Foundation Ireland St. Patrick's Day Medal. He is also a former Coordinating and Applications Editor of the Journal of the American Statistical Association and a former Editor of Sociological Methodology. He was identified as the world's most cited researcher in mathematics for the decade 1995–2005 by Thomson‐ISI. Thirty‐three students have obtained PhD's working under Raftery's supervision, of whom 21 hold or have held tenure‐track university faculty positions. He has over 150 academic descendants. This interview took place over two sessions in March 2023.

Deterministic full-scenario analysis for maximum credible earthquake hazards

Great earthquakes are one of the major threats to modern society due to their great destructive power and unpredictability. The maximum credible earthquake (MCE) for a specific fault, i.e., the largest magnitude earthquake that may occur there, has numerous potential scenarios with different source processes, making the future seismic hazard highly uncertain. We propose a full-scenario analysis method to evaluate the MCE hazards with deterministic broadband simulations of numerous scenarios. The full-scenario analysis is achieved by considering all uncertainties of potential future earthquakes with sufficient scenarios. Here we show an application of this method in the seismic hazard analysis for the Xiluodu dam in China by simulating 22,000,000 MCE scenarios in 0–10 Hz. The proposed method can provide arbitrary intensity measures, ground-motion time series, and spatial ground-motion fields for all hazard levels, which enables more realistic and accurate MCE hazard evaluations, and thus has great application potential in earthquake engineering.

Exploring the stability of unsaturated soil slope under rainfall infiltration conditions: A study based on multivariate interrelated random fields using R-vine copula

Estimating the instability of soil slopes due to rainfall in highly heterogeneous materials poses a considerable challenge. In this study, a general framework for coupled hydro-mechanical modeling of rainfall-induced instability in unsaturated slopes with multivariate random fields is developed. The R-vine copula is introduced to simulate the intricate non-Gaussian dependencies between soil parameters. UMAP is utilized for visualizing these dependencies. The study compares the fitting performances of R-vine and Gaussian copulas using LOWESS Regression. Subsequently, deterministic model computations are conducted in Abaqus, along with batch random field model analysis based on the R-vine copula. The instability probability of soil slopes under rainfall infiltration conditions is evaluated through direct Monte Carlo simulations, and statistically investigate the relationships between groundwater level, sliding volume, plastic zone volume, and safety factor. The findings indicate that: 1) The R-vine copula model proficiently captures the non-Gaussian dependencies in soil data, demonstrating superior fitting performance over the Gaussian copula; 2) deterministic simulations might overestimate the safety factor at certain instances; 3) as rainfall progresses, a growing negative correlation is observed between groundwater levels and slope instability; 4) continuous rainfall leads to a re-equilibration of the sliding mass, heightening the influence of the sliding volume on stability.

A Data-Driven Variance Reduction Technique for Efficiently Modelling Astronaut Radiation Doses in Spacecraft in High-Energy Isotropic Radiation Fields.

The ionizing radiation exposure to crew on current and future space missions can significantly increase their health risks for cancers, degenerative diseases, and other acute and late effects. A common approach for estimating risk to crew is by completing stochastic (e.g., Monte Carlo) or deterministic particle transport simulations. Within the simulated environment, a small fraction of the particle histories tracked will interact with the astronaut or detector, particularly for larger spacecraft such as the International Space Station, Tiangong Space Station or Lunar Gateway. These simulations can be computationally intensive as they require a very large number of particle histories to achieve a low statistical uncertainty. Variance reduction techniques are applied to simulations to reduce the computational time of the simulation while maintaining the same (or less) statistical uncertainty. The variance reduction technique developed herein involves applying a directional source bias to an isotropic radiation field, such as galactic cosmic rays, to reduce the quantity of particles that have a low probability of interacting with the astronaut or detector. A custom application has been developed utilizing the Geant4 Toolkit that computes the trajectories and energies of particles in three dimensions in the International Space Station using the Monte Carlo method. The results demonstrate the impact of our variance reduction technique on effective dose equivalence depending on: primary and secondary particle type (proton, neutron, photon, heavy ion, etc.), geometric volumes and spacecraft materials. Our variance reduction technique can be tuned by the user to optimize the simulation time depending on their objectives and enables rapid testing of different shield configurations and materials. This variance reduction technique is implemented easily using several input parameters for boundary conditions. Recommended values are presented for rapid implementation in simulations.

Efficient calibration for imperfect epidemic models with applications to the analysis of COVID-19

Abstract The estimation of unknown parameters in simulations, also known as calibration, is crucial for practical management of epidemics and prediction of pandemic risk. A simple yet widely used approach is to estimate the parameters by minimising the sum of the squared distances between actual observations and simulation outputs. It is shown in this paper that this method is inefficient, particularly when the epidemic models are developed based on certain simplifications of reality, also known as imperfect models which are commonly used in practice. To address this issue, a new estimator is introduced that is asymptotically consistent, has a smaller estimation variance than the least-squares estimator, and achieves the semiparametric efficiency. Numerical studies are performed to examine the finite sample performance. The proposed method is applied to the analysis of the COVID-19 pandemic for 20 countries based on the susceptible-exposed-infectious-recovered model with both deterministic and stochastic simulations. The estimation of the parameters, including the basic reproduction number and the average incubation period, reveal the risk of disease outbreaks in each country and provide insights to the design of public health interventions.

Seismic response of high-speed railway system under actions of ground motions from deterministic physics-based simulation

In order to investigate the response of a high-speed railway bridge under seismic excitations in the near-fault area, a deterministic physics-based method is used to construct the ground motion field in the near-fault area of a scenario earthquake. A finite element model of an assumed high-speed railway system is established, which consists of a finite length CRTS III ballastless track laid on a 15-span simply-supported girder bridge. The simulated ground motions and a group of three-component as-recorded accelerograms are used to provide non-uniform and uniform excitations at bridge piers, respectively. The seismic response of the rail-track-bridge system is calculated, and the effects of using different types of seismic excitations for the seismic analysis of the high-speed railway bridge in the near-fault area are analyzed. The results show that two end piers of a multi-span simply supported girder bridge could encounter remarkably different excitations in terms of waveform and frequency content for different bridge alignments in the near-fault area. The vertical response of the railway bridge system is more sensitive to the pattern of the excitation than the longitudinal response. Considering the spatial difference of ground motions among piers could increase the difference of the rail vertical displacement between the beam joint and mid-point of span, forming a remarkable periodic vertical irregularity of the rail. Using non-uniform excitation could lead to a reduction in DPeak and APeak which is most significant for DPeak in the vertical direction.

Sample-wised probabilistic stability analysis of circular shafts using the Atom Search Optimization-based Artificial Neural Network

Circular shafts are widely concerned in the construction of urban underground infrastructures and their stability is essential. Uncertainties and randomness in soil properties always exist, therefore probabilistic analyses will allow to obtain more reliable results than deterministic ones. In this study, a sample-wised probabilistic approach SPAA based on the Atom Search Optimization (ASO)-Artificial Neural Network (ANN) model, is proposed to analyze the circular shafts stability considering the soil parameters variabilities. The deterministic analysis is carried out by a Finite Element Limit Analysis. The ASO-ANN surrogate model with self-adaptive convergence has high efficiency. It is able to replace time-consuming numerical simulations. The initial samples are generated by the Latin Hypercube Sampling method and the iterative samples enrichment allowed searching the most representative points for the ASO-ANN model construction. Monte-Carlo Simulations and a Global Sensitivity Analysis are then performed to provide several valuable results, which include the failure probability, probability density function, cumulative distribution function, statistical moments of the system response and sensitivity index of each random variable. Two low-dimensional random variable cases and a high-dimensional random field problem are then considered and discussed based on the proposed hybrid SPAA approach. The results indicate that the proposed SPAA outperforms the existing methods since it requires fewer deterministic simulations with guaranteed results accuracy, particularly for the high-dimensional case.

Stacked revenues for energy storage participating in energy and reserve markets with an optimal frequency regulation modeling

This paper investigates the opportunity for a Battery Energy Storage System (BESS) to participate in multiple energy markets. The study proposes an offline assessment to calculate the maximum annual revenues to reach the optimum stack of services through deterministic simulations. The markets include wholesale energy markets (day-ahead and intraday), ancillary services (frequency regulation and reserve), and the capacity mechanism. The study case performed on the French markets shows that frequency services overperform the other markets, where the long-term capacity market has the least potential. Moreover, providing multiple services maximizes the battery's revenues, for example, participating in joint energy and reserve markets showed a 76% increase in annual profits. Furthermore, a novel operation approach was proposed to enhance the performance of these joint markets, by indirectly utilizing energy products as frequency reserves. The results demonstrate that the proposed formulation allows a revenue increase of ∼23% compared to the conventional framework for the provision of frequency regulation with BESSs. Additionally, the joint markets have been shown to be economically viable with 6.2 years payback period after considering battery degradation and depreciation cost.

Dynamic reliability analysis of three-dimensional slopes considering the spatial variability in soil parameters

Slopes are commonly a significant aspect of geotechnical engineering projects. The incorrect evaluation and prediction of slope stability can cause serious disasters when it experiences seismic activity, and reliability is the key index a slope’s dynamic state. Spatial variability is an inherent soil property and has an essential influence on the dynamic response of a slope body. However, in previous studies, soil was often described deterministically, and the interaction between the uncertainty and spatial dimensions was ignored. To fill this gap, in this paper, practical expressions for the cohesion and friction angle in each working condition are given to simulate spatial variability by a lognormal random field. Then, the finite element method and effective dynamic reliability calculation method are used to obtain the safety factor of a 3D slope. Finally, the generalized probability density evolution method is performed to obtained the probability density information of the dynamic slope response and judge the influence of the soil spatial variability probabilistically. Due to the spatial variability in soil, the probability density distribution under each condition is distributed in a large range and thus may not provide results that are much more dangerous than those of the deterministic simulation. Meanwhile, the friction angle is more sensitive than cohesion and shows stronger discreteness in the spatial variability. The change in the cross-correlation coefficient has a greater influence on reliability than the autocorrelation length. Consequently, the simplification of model slopes in dimension and variability could deviate the reliability results from actual dynamic responses, either overestimating or underestimating, resulting in incorrect designs and evaluations.

Improvements in Classical Proportional Navigation Guidance Using Fuzzy Logic

The performance of classical Proportional Navigation guidance is limited against highly maneuvering targets and in presence of measurements noise resulting into large miss distances and lower hit probability. Also, the advanced Augmented PN guidance technique requires target state estimation. In this paper, fuzzy logic has been used to implement PN guidance and Membership Functions of fuzzy logic has been tuned using genetic algorithm. The proposed Fuzzy Based PN (FBPN) guidance gives superior performance than PN and APN in terms of lower miss distance, higher hit ratio and better ability to handle measurement noise at all target attack aspects. Deterministic simulations have proved the superior performance and robustness of proposed scheme for various conditions such as step target maneuver upto 9g, weaving maneuver and presence of measurement noise. Further, Monte Carlo simulations have proved that proposed scheme enhances interception performance in realistic scenarios by improving the hit ratio upto 48% and rms miss distance upto 41%. Limitations of APN scheme in terms of target state estimation is also mitigated in the proposed guidance scheme. Finally, global asymptotic stability of fuzzy homing guidance loop is proved using circle criterion.

Epidemiological impacts of attenuated African swine fever virus circulating in wild boar populations

African swine fever virus (ASFV) genotype II has been present in wild boar in the European Union since 2014. Control measures have reduced the incidence of the ASF, but highly virulent as well as attenuated ASFV strains continue to circulate. We present the intraherd epidemiological parameters of low and highly virulent ASFV in wild boar from experimental data, and for the first time, evaluate the impact of attenuated strain circulation through unique deterministic compartmental model simulations under various potential scenarios and hypotheses.Using an estimated PCR infectious threshold of TPCR = 36.4, we obtained several transmission parameters, like an Rx (experimental intraherd R0) value of 4.5. We also introduce two novel epidemiological parameters: infectious power and resistance power, which indicate the ability of animals to transmit the infection and the reduction in infectiousness after successive exposures to varying virulence strains, respectively.The presence of ASFV attenuated strains results in 4–17% of animals either remaining in a carrier state or becoming susceptible again when exposed to highly virulent ASFV for more than two years. The timing between exposures to viruses of different virulence also influences the percentage of animals that die or remain susceptible.The findings of this study can be utilized in epidemiological modelling and provide insight into important risk situations that should be considered for surveillance and future potential ASF vaccination strategies in wild boar.