A Mechanism-Data Codriven Hybrid Modeling Framework for WWTPs to Achieve Reliable Simulation at System-Level

In this paper, within the framework of a self-consistent multilevel hybrid model, the kinetics of electrons in the negative glow region in similar glow discharges in helium at low and medium pressures is considered. The model is based on solving a two-dimensional kinetic equation for the electron distribution function written in the Fokker-Planck form and one-dimensional balance equations for the densities of charged and excited particles, the Poisson equation for an electric field. Within the framework of the model, the experimentally observed distributions of plasma parameters obtained using probe diagnostics are reproduced. The results are compared with the results of calculations obtained on the basis of an extended hydrodynamic model. Within the framework of the hybrid model, the formation of the spectrum of Penning electrons from impurities of complex molecules with an energy above the temperature of the main group of electrons is shown.

Accurate prediction of atmospheric optical turbulence in localized environments is essential for estimating the performance of free-space optical systems. Macro-meteorological models developed to predict turbulent effects in one environment may fail when applied in new environments. However, existing macro-meteorological models are expected to offer some predictive power. Building a new model from locally measured macro-meteorology and scintillometer readings can require significant time and resources, as well as a large number of observations. These challenges motivate the development of a machine-learning informed hybrid model framework. By combining a baseline macro-meteorological model with local observations, hybrid models were trained to improve upon the predictive power of each baseline model. Comparisons between the performance of the hybrid models, selected baseline macro-meteorological models, and machine-learning models trained only on local observations, highlight potential use cases for the hybrid model framework when local data are expensive to collect. Both the hybrid and data-only models were trained using the gradient boosted decision tree architecture with a variable number of in situ meteorological observations. The hybrid and data-only models were found to outperform three baseline macro-meteorological models, even for low numbers of observations, in some cases as little as one day. For the first baseline macro-meteorological model investigated, the hybrid model achieves an estimated 29% reduction in the mean absolute error using only one day-equivalent of observation, growing to 41% after only two days, and 68% after 180days-equivalent training data. The data-only model generally showed similar, but slightly lower performance, as compared to the hybrid model. Notably, the hybrid model's performance advantage over the data-only model dropped below 2% near the 24 days-equivalent observation mark and trended towards 0% thereafter. The number of days-equivalent training data required by both the hybrid model and the data-only model is potentially indicative of the seasonal variation in the local microclimate and its propagation environment.

Assessing uncertainty propagation in hybrid models for daily streamflow simulation based on arbitrary polynomial chaos expansion

New trends in modeling and control of hybrid systems

Nonlinear decay of a localized perturbation into ion-acoustic solitons in the presence of both trapped and super-thermal electrons (Abbasi et al 2008 Plasma Phys. Control. Fusion 50 095007) is revisited. The motivation is to present a benchmark by introducing a more precise model, the hybrid (Vlasov-fluid) model. In this way, it is possible to address the restrictions that a simple modified Korteweg de–Vries (mKdV) model has. For instance, the lack of vital information in phase space associated with the evolution of electron velocity distribution, its perturbative nature which limits it to weak nonlinear case, and special spatio-temporal scaling based on which the mKdV is derived. From these points of view, the hybrid model is a more convenient model that can be extended to more complex situations. Remarkable differences between the results of the two models lead us to conclude that the mKdV model can only monitor the general aspects of the dynamics, and the precise picture, including the correct spatio-temporal scales and the properties of solitons, should be studied within the framework of hybrid model.

PurposeThe purpose of this paper is to promote a system dynamics-discrete event simulation (SD-DES) hybrid modelling framework, one that is useful for investigating problems comprising multifaceted elements which interact and evolve over time, such as is found in TPM.Design/methodology/approachThe hybrid modelling framework commences with system observation using field notes which culminate in model conceptualization to structure the problem. Thereafter, an SD-DEShybrid model is designed for the system, and simulated to proffer improvement programmes. The hybrid model emphasises the interactions between key constructs relating to the system, feedback structures and process flow concepts that are the hallmarks of many problems in production. The modelling framework is applied to the TPM operations of a bottling plant where sub-optimal TPM performance was affecting throughput performance.FindingsSimulation results for the case study show that intangible human factors such as worker motivation do not significantly affect TPM performance. What is most critical is ensuring full compliance to routine and scheduled maintenance tasks and coordinating the latter to align with rate of machine defect creation.Research limitations/implicationsThe framework was developed with completeness, generality and reuse in view. It remains to be applied to a wide variety of TPM and non-TPM-related problems.Practical implicationsThe developed hybrid model is scalable and can fit into an existing discrete event simulation model of a production system. The case study findings indicate where TPM managers should focus their efforts.Originality/valueThe investigation of TPM using SD-DES hybrid modelling is a novelty.

The advancement of an (in vitro/ex vivo) hybrid model framework for forecasting polyviral lung disease outcomes addresses the pressing need for sophisticated tools to understand the complexities of these infections. The primary objective of this study is to advance the development and application of an (in vitro/ex vivo) hybrid model framework for forecasting polyviral lung disease outcomes. This phase of data collection for lung infections, involving single and co-infecting viruses, utilizes ex vivo models (perfused lung tissue slices) and in vitro models (lung cell cultures). Before employing Local Binary Patterns for image analysis, data pre-processing, including Weighted Local Gabor Binary Pattern, is essential. Feature extraction is a critical initial step in enhancing the dataset for developing a hybrid model framework (in vitro/ex vivo) to predict polyviral lung disease outcomes. By employing VGG16 and CBRACDC algorithms, a hybrid model framework (in vitro/ex vivo) is created to forecast polyviral lung disease outcomes. Incorporating the Random Survival Forest algorithm into the hybrid model framework brings numerous benefits for polyviral lung disease prognosis. Python was utilized extensively throughout the development and analysis phases, contributing to the framework's robustness and versatility. The observed minimum cost value of 1.079 indicates the algorithm's optimal performance based on the defined objective. Future research avenues could focus on integrating advanced computational techniques, such as deep learning and artificial intelligence, to improve the predictive accuracy and scalability of hybrid models for forecasting polyviral lung disease outcomes. This could enable personalized medicine approaches and more targeted therapeutic interventions.

An optimal control method for nonlinear systems based on the framework of hybrid model is proposed to improve the whole performance of the systems. Firstly, a number of linear models which are produced by nonlinear model at specified operating points are synthesized under the framework of hybrid model. Secondly, the method of collocation on finite elements is used to lower the dimension and the optimal control problem is transformed to MIQP problem over the whole space. The model mismatch produced by discretization is avoided by using the strategy of receding-horizon optimization. Simulation results show that a satisfactory performance can be obtained by using the presented approach.

A reliable hybrid modeling and simulation methodology is developed to predict the progressive damage evolution and ultimate strength in multidirectional fiber-reinforced polymer (FRP) composite laminates. The integrated modeling approach combines continuum damage modeling (CDM), the extended finite element method (X-FEM), and the cohesive zone modeling (CZM) technique, to capture fiber breakage, polymer matrix major cracking, composite ply interlaminar delamination, and the interactions of these failure modes. The Schapery theory is incorporated into the finite element model to accurately simulate the pre-peak nonlinearity of the load-bearing response caused by matrix micro-cracking. Multidirectional composite laminates with open-hole tension (OHT), open-hole compression (OHC), filled-hole tension (FHT), and filled-hole compression (FHC) configurations are examined as case studies. It is demonstrated that this hybrid modeling framework and methodology can effectively and efficiently capture the complex composite damage progression and properly predict the residual strengths of damaged composite laminates.

<p><span lang="EN-US">Most real-world dynamical systems are often involving continuous behaviors and discrete events, in this case, they are called hybrid dynamical systems (HDSs). To properly model this kind of systems, it is necessary to consider both the continuous and the discrete aspects of its dynamics. In this paper, a modeling framework based on the hybrid automata (HA) approach is proposed. This hybrid modeling framework allows combining the multi-state models of the system, described by nonlinear differential equations, with the system’s discrete dynamics described by finite state machines. To attest to the efficiency of the proposed modeling framework, its application to a two-tank hybrid system (TTHS) is presented. The TTHS studied is a typical benchmark for HDSs with four operating modes. The MATLAB Simulink and Stateflow tools are used to implement and simulate the hybrid model of the TTHS. Different simulations results demonstrate the efficiency of the proposed modeling framework, which allows us to appropriately have a complete model of an HDS.</span></p>

This paper introduces a hybrid modeling and optimal control framework for a class of layer-by-layer manufacturing processes. Specifically, a stepped-concurrent layer-by-layer process is offered as a solution for overcoming the challenge of maintaining through-cure during thick-part fabrication using Ultraviolet (UV) radiation inputs that are subject to in-domain attenuation. The layering and curing sequence is modeled as a hybrid system, where the layering steps constitute discrete events on otherwise continuous curing kinetics and thermal processes. It is shown that the UV intensity as well as the inter-layer hold times can be selected optimally by posing an optimal control problem with the objective of minimizing the overall cure deviation in the thick multi-layer part. The necessary conditions for optimality are explicitly derived by adjoining the coupled PDE and ODE constraints of the process model. The potential benefit of the proposed optimization scheme is demonstrated considering simulations of a composite laminate curing process. It is found that, compared to traditional equal-interval layering, optimal layering time control gives significantly improved performance in terms of minimizing cure-level deviation, for comparable total energy usage. There is also some added benefit to optimizing the inter-layer UV input as well.

Disintegration of a Gaussian profile into ion-acoustic solitons in the presence of trapped electrons [H. Hakimi Pajouh and H. Abbasi, Phys. Plasmas 15, 082105 (2008)] is revisited. Through a hybrid (Vlasov-Fluid) model, the restrictions associated with the simple modified Korteweg de-Vries (mKdV) model are studied. For instance, the lack of vital information in the phase space associated with the evolution of electron velocity distribution, the perturbative nature of mKdV model which limits it to the weak nonlinear cases, and the special spatio-temporal scaling based on which the mKdV is derived. Remarkable differences between the results of the two models lead us to conclude that the mKdV model can only monitor the general aspects of the dynamics, and the precise picture including the correct spatio-temporal scales and the properties of solitons should be studied within the framework of hybrid model.

In this short note, we observe that the Serre functor on the residual category of a complete intersection can be easily described in the framework of hybrid models. Using this description, we recover some recent results of Kuznetsov and Perry.

We present a univariate hybrid machine learning framework to predict daily high resolution Sea Surface Temperature (SST) near the Gulf of Kutch region at a resolution of ~ 5.5 km. The hybrid model integrates Intrinsic Mode Functions (IMF) derived from variational mode decomposition with a Long Short-Term Memory (LSTM) network to augment predictive skill. The predicted SST demonstrates impressive performance up to lead times of 7 days. Using statistical metrics like Kullback-Leibler divergence and mutual information, we show that the SST predicted from the hybrid model with a lead time of 3 days decisively outperforms the high-resolution GLORYS SST reanalysis let alone forecast skills of a data-assimilated dynamical model. Using conditional probability, we show that the SST forecasts from the hybrid model are quite reliable over the entire range of SST observations in the study domain. In contrast, the reliability of GLORYS falters in the lower range of SST observations. Also, the hybrid model excels in capturing fine-scale SST features, such as SST fronts, and detecting Marine Heatwaves (MHWs) up to 3 days in advance. These capabilities hold significant applications for Potential Fishing Zone identification and coral bleaching alerts. The hybrid model framework is also adept at forecasting location specific high frequency (3 hourly) SST with a lead time of a day.

Combining driving simulator and physiological sensor data in a latent variable model to incorporate the effect of stress in car-following behaviour