Calibration Of Dynamic Models Research Articles

Towards Zero Energy Buildings: The UniZEB case study

Buildings are among the primary contributors to global energy consumption, and reducing their demand is one of the challenges that need to be considered for a sustainable future. Zero Energy Buildings (ZEB) represent one of the most promising strategies in this endeavor, and in this sense, the UniZEB project addresses this issue with an innovative approach. It is a Zero Energy Building Laboratory of the University of Padova, built up from a collaboration between local companies, students, and researchers, featuring high performance HVAC and envelope technologies integrated with renewable energy sources. The present work offers an overview of the project, showing some of the already-faced challenges, as well as future opportunities of research and study. The purpose of the laboratory is indeed to offer the students the possibility to put in practice the knowledge they gain through university, as well as exploring new subjects through recent research topics, e.g., the development of a sensor network for the monitoring of the building, the dynamic model calibration, able to compare expected and current energy demand. The paper aims also to demonstrate the potential of a project like UniZEB, proving how research, innovation, and collaboration can shape the future of sustainable construction.

Real-Time Diagnosis and Fault-Tolerant Control of a Sensor Single Fault Based on a Data-Driven Feedforward-Feedback Control System

Fault diagnosis is studied based on the system type, which facilitates the realization of the engineering configuration and improves the diagnosis efficiency. The fault-tolerant control method is unified based on the concept of fault compensation. According to the dynamic characteristics of the system, the method takes the boundary value of no-fault signal fluctuation as the basis for fault detection, then takes the changing intensity of the solenoid valve control signal after the fault occurs as the fault location basis. Finally, it takes the difference or ratio of the signals before and after the fault occurs as the fault estimation. For the basis of fault separation, the integral value of the fitting equation between the fault signal and time is used as the Eigenvalue of fault type separation to comprehend fault separation. A program is written in C++ and combined with MATLAB/S-Fun function to realize fault tolerance. At the same time, the dynamic model calibration and real-time fault diagnosis, and fault-tolerant control process of sensor fault diagnosis are provided, which makes it suitable for general engineering feedforward-feedback systems and has a certain suppression effect on noise. The simulation results verify that the method is not only viable and it is exact.

Design and Demonstration of Eigenstructure Configuration Control Law based on Free Flight Model

According to the needs of advanced flight control law demonstration, a flight control law demonstration platform based on free flight model is constructed, a flight test system for flight control law demonstration is designed, the dynamic model calibration is completed and the control law based on eigenstructure configuration is designed, finally, the flight test is carried out. The test results show that the scaled free flight model can effectively complete the aerodynamic model calibration and control law demonstration. what’s more, it has strong engineering application value; Compared with the PID control law, the flight control law based on eigenstructure configuration has good control effect and strong robustness, which greatly reduces the flight test parameter adjustment and improves the flight test efficiency.

Dynamic modelling of N2 O emissions from a full-scale granular sludge partial nitritation-anammox reactor.

Partial nitration-anammox is a resource-efficient pathway for nitrogen removal from wastewater. However, the advantages of this nitrogen removal technology may be counter-acted by the emission of N2 O, a potent greenhouse gas. In this study, mathematical modelling was applied to analyse N2 O formation and emission dynamics and to develop N2 O mitigation strategies for a one-stage partial nitritation-anammox granular sludge reactor. Dynamic model calibration for such a full-scale reactor was performed, applying a one-dimensional biofilm model and including several N2 O formation pathways. Simultaneous calibration of liquid phase concentrations and N2 O emissions leads to improved model fit compared to their consecutive calibration. The model could quantitatively predict the average N2 O emissions and qualitatively characterize the N2 O dynamics, adjusting only seven parameter values. The model was validated with N2 O data from an independent data set at different aeration conditions. Nitrifier nitrification was identified as the dominating N2 O formation pathway. Off-gas recirculation as a potential N2 O emission reduction strategy was tested by simulation and showed indeed some improvement, be it at the cost of higher aeration energy consumption.

A protocol for dynamic model calibration.

Ordinary differential equation models are nowadays widely used for the mechanistic description of biological processes and their temporal evolution. These models typically have many unknown and nonmeasurable parameters, which have to be determined by fitting the model to experimental data. In order to perform this task, known as parameter estimation or model calibration, the modeller faces challenges such as poor parameter identifiability, lack of sufficiently informative experimental data and the existence of local minima in the objective function landscape. These issues tend to worsen with larger model sizes, increasing the computational complexity and the number of unknown parameters. An incorrectly calibrated model is problematic because it may result in inaccurate predictions and misleading conclusions. For nonexpert users, there are a large number of potential pitfalls. Here, we provide a protocol that guides the user through all the steps involved in the calibration of dynamic models. We illustrate the methodology with two models and provide all the code required to reproduce the results and perform the same analysis on new models. Our protocol provides practitioners and researchers in biological modelling with a one-stop guide that is at the same time compact and sufficiently comprehensive to cover all aspects of the problem.

Global Sensitivity Analysis and Calibration by Differential Evolution Algorithm of HORTSYST Crop Model for Fertigation Management

Sensitivity analysis is the first step in elucidating how the uncertainties in model parameters affect the uncertainty in model outputs. Calibration of dynamic models is another issue of considerable interest, which is usually carried out by optimizing an objective function. The first aim of this research was to perform a global sensitivity analysis (GSA) with Sobol’s method for the 16 parameters of the new HORTSYST nonlinear model that simulates photo–thermal time (PTI), daily dry matter production (DMP), nitrogen uptake (Nup), leaf area index (LAI), and crop transpiration (ETc). The second objective was to carry out the calibration of the HORTSYST model by applying a differential evolution (DE) algorithm as the global optimization method. Two tomato (Solanum lycopersicum L.) crops were established during the autumn–winter and spring–summer seasons under greenhouse and soilless culture conditions. Plants were distributed with a density of 3.5 plants m−2. Air temperature and relative humidity were measured with an S-THB-M008 model sensor. Global solar radiation was measured with an S-LIB-M003 sensor connected to a U-30-NRC datalogger. In the sensitivity analysis run in the two growth stages, it was observed that a greater number of parameters were more important at the beginning of fructification than at the end of crop growth for 10% and 20% of the variation of the parameters. The sensitivity analysis came up with nine parameters (RUE, a, b, c1 , c2, A, Bd, Bn, and PTIini) as the most important of the HORTSYST model, which were included in the calibration process with the DE algorithm. The best fit, according to RMSE, was for LAI, followed by Nup, DMP, and ETc for both crop seasons; the RMSE was close to zero, indicating a good prediction of the model’s performance.

Use of measurement theory for operationalization and quantification of psychological constructs in systems dynamics modelling

The analytical tools available to social scientists have traditionally been adapted from tools originally designed for analysis of natural science phenomena. This article discusses the applicability of systems dynamics - a qualitative based modelling approach, as a possible analysis and simulation tool that bridges the gap between social and natural sciences. After a brief overview of the systems dynamics modelling methodology, the advantages as well as limiting factors of systems dynamics to the potential applications in the field of social sciences and human interactions are discussed. The issues arise with regards to operationalization and quantification of latent constructs at the simulation building stage of the systems dynamics methodology and measurement theory is proposed as a ready and waiting solution to the problem of dynamic model calibration, with a view of improving simulation model reliability and validity and encouraging the development of standardised, modular system dynamics models that can be used in social science research.

Continental European WAMs System Analysis, Protection and Dynamics Applications

In the last ten years, Wide Area Monitoring (WAM) systems have become important tools for detailed event analysis, dynamic system monitoring and enhanced protection system and dynamic model calibration support. Current efforts are directed towards solutions which allow for the creation of a link between dynamic WAM information and the steady-state SCADA/EMS man-machine interface.

Calibration of Dynamic Building Energy Models with Multiple Responses Using Bayesian Inference and Linear Regression Models

This paper proposes a lightweight Bayesian calibration of dynamic models that accounts for model parameter uncertainties. A regression model was built to represent the dynamic model using parameter screening and multiple linear regression. Given this regression model and prior probability distributions of its input parameters, a Bayesian calibration method is developed to provide their posterior distributions. A case study is presented and the result shows considerable alignment between model prediction and measurement after calibration. This indicates its capability to perform fast risk-conscious calibration for most current retrofit practice where only monthly consumption and demand data are available.

Calibration of Dynamic Models through Adaptive Optimal Input Designs

AbstractDie optimale Versuchsplanung ist ein methodischer Ansatz zur Kalibrierung und Validierung von Modellen mit experimentellen Daten. Sie besitzt ein großes Potenzial zur Reduktion des experimentellen Aufwands und zur Maximierung der Modellgenauigkeit. In dieser Arbeit werden jüngste Entwicklungen zur Verbesserung der Adaptivität der Methodik diskutiert, die den Anwendungsbereich stark erweitern. Neben illustrativen Beispielen wird die experimentelle Anwendung der voll‐automatisierten Echtzeit‐Kalibrierung eines Chromatographiemodells präsentiert.

A Fully Integrated Approach for Better Determination of Fracture Parameters Using Streamline Simulation; A gas condensate reservoir case study in Iran

Many large oil and gas fields in the most productive world regions happen to be fractured. The exploration and development of these reservoirs is a true challenge for many operators. These difficulties are due to uncertainties in geological fracture properties such as aperture, length, connectivity and intensity distribution. To successfully address these challenges, it is paramount to improve the approach of characterization and simulation of fractured reservoirs.In this study, a fully integration of all available data and methods have been used for generating stochastic discrete fracture network (DFN)such as outcrop study, core description, petrophysical and image logs and also for better result validation, streamline simulation has been conducted. In this comprehensive process a real gas condensate fractured carbonate reservoir has been used.Firstly, three main fracture sets were defined that have fold-related fractures, then the fracture intensity and DFN model using fracture drivers correlation were generated. After that, permeability of the developed DFNs was calibrated with available well test permeability. Then, a streamline simulation was used because of its high computational speed, high accuracy and good visualization for the repeated nature of history matching of a dual porosity model in the gas condensate reservoir. So, with running streamline simulation, three realizations (High, Medium and Low) ranked based on the objective function values. These three realizations are common realization that are well known with optimistic, most likely and pessimistic scenarios. Finally, comprehensive history matching was done for all the three-selected realizations.The overall goal is to develop a representative fluid flow simulation model for improving gas cycling procedure in gas condensate reservoir. This method has great application in the high resolution fractured reservoir modeling due to using actual fracture parameters. Also, it can be used for model ranking, screening and optimum dynamic model calibration for reduction of the history matching complexity without being manipulated by reservoir engineer.

A Generalized Polynomial Chaos-Based Method for Efficient Bayesian Calibration of Uncertain Computational Models

This paper addresses the Bayesian calibration of dynamic models with parametric and structural uncertainties, in particular where the uncertain parameters are unknown/poorly known spatio-temporally varying subsystem models. Independent stationary Gaussian processes with uncertain hyper-parameters describe uncertainties of the model structure and parameters, while Karhunen-Loeve expansion is adopted to spectrally represent these Gaussian processes. The Karhunen-Loeve expansion of a prior Gaussian process is projected on a generalized Polynomial Chaos basis, whereas intrusive Galerkin projection is utilized to calculate the associated coefficients of the simulator output. Bayesian inference is used to update the prior probability distribution of the generalized Polynomial Chaos basis, which along with the chaos expansion coefficients represent the posterior probability distribution. The proposed method is demonstrated for calibration of a simulator of quasi-one-dimensional flow through a divergent nozzle with uncertain nozzle area profile. The posterior distribution of the nozzle area profile and the hyper-parameters obtained using the proposed method are found to match closely with the direct Markov Chain Monte Carlo-based implementation of the Bayesian framework. Efficacy of the proposed method is demonstrated for various choices of prior. Posterior hyper-parameters of the model structural uncertainty are shown to quantify acceptability of the simulator model.

Improving Reservoir Characterization and Simulation With Near-Wellbore Modeling

SummaryNew reservoir characterization methods are needed to integrate multiscale exploration and development data, particularly at the interface between well and field models. In this paper, we illustrate a novel workflow involving high-resolution near-wellbore modeling (NWM), which allows us to accurately include seismic, wireline data, image logs, and well core logs from highly heterogeneous reservoirs in field-scale reservoir simulations. We demonstrate that an NWM-enhanced geoengineering workflow has the potential to improve reservoir characterization by applying it to a realistic clastic reservoir with high variance at small scale. We have performed a number of sensitivities comparing conventional local grid refinement (LGR) in the near-wellbore region with that involving NWM, and we obtained a significant increase in the accuracy of reservoir characterization and the calibration of dynamic models. Centimeter-scale models, containing several million cells, representing the fine geological details of the near-wellbore region, were constructed with available data from core and openhole well-log suits. The resulting well models were upscaled into regular grids with the highest resolution possible through the NWM software and incorporated into a field-scale simulation model to evaluate the dynamic behavior of the reservoir with a static-model transient test. Our results show that the use of NWM tools for reservoir modeling yields more precise flow calculations and improves our fundamental understanding of the interactions between the reservoir and the wellbore.

Improved calibration of a solid substrate fermentation model

Background: Calibration of dynamic models in biotechnology is challenging. Kinetic models are usually complex and differential equations are highly coupled involving a large number of parameters. In addition, available measurements are scarce and infrequent, and some key variables are often non-measurable. Therefore, effective optimization and statistical analysis methods are crucial to achieve meaningful results. In this research, we apply a metaheuristic scatter search algorithm to calibrate a solid substrate cultivation model. Results: Even though scatter search has shown to be effective for calibrating difficult nonlinear models, we show here that a posteriori analysis can significantly improve the accuracy and reliability of the estimation. Conclusions: Sensibility and correlation analysis helped us detect reliability problems and provided suggestions to improve the design of future experiments.

Impact of Air Flow Rate and Frequency of Influent Data on Full-Scale Dynamic Model Calibration

Impact of Air Flow Rate and Frequency of Influent Data on Full-Scale Dynamic Model Calibration

Calibration of dynamic models of biological systems with KInfer

Methods for parameter estimation that are robust to experimental uncertainties and to stochastic and biological noise and that require a minimum of a priori input knowledge are of key importance in computational systems biology. The new method presented in this paper aims to ensure an inference model that deduces the rate constants of a system of biochemical reactions from experimentally measured time courses of reactants. This new method was applied to some challenging parameter estimation problems of nonlinear dynamic biological systems and was tested both on synthetic and real data. The synthetic case studies are the 12-state model of the SERCA pump and a model of a genetic network containing feedback loops of interaction between regulator and effector genes. The real case studies consist of a model of the reaction between the inhibitor kappaB kinase enzyme and its substrate in the signal transduction pathway of NF-kappaB, and a stiff model of the fermentation pathway of Lactococcus lactis.

A Prelaunch Diffusion Model for Evaluating Market Defense Strategies

This paper describes the development and application of a marketing model to help set an incumbent's defensive marketing strategy prior to a new competitor's launch. The management problem addressed is to assess the market share impact of a new entrant in the residential Australian long distance telephone call market and determine the factors that would influence its dynamics and ultimate market appeal. The paper uses probability flow models to provide a framework to generate forecasts and assess the determinants of share loss. We develop models at two levels of complexity to give both simple, robust forecasts and more detailed diagnostic analysis of the effect of marketing actions. The models are calibrated prior to the new entrant's launch, enabling preemptive marketing strategies to be put in place by the defending company. The equilibrium level of consideration of the new entrant was driven by respondents' strength of relationship with the defender and inertia, while trial was more price-based. Continued use of the defender depends on both service factors and price. The rate at which share loss eventuates is negatively related to the defender's perceived responsiveness, saving money being the only reason to switch, and risk aversion. Prelaunch model forecasts, validated six months after launch using both aggregate monthly sales data and detailed tracking surveys, are shown to closely follow the actual evolution of the market. The paper provides a closed-form multistate model of the new entrant's diffusion, a methodology for the prelaunch calibration of dynamic models in practice, and insights into defensive strategies for existing companies facing new entrants.

A Calibration Procedure of Dynamic CGE Models for Non-Steady State Situations Using GEMPACK

This paper is concerned with the development of a calibration procedure for dynamic CGE models for non-steady state situations. While the literature on calibration of dynamic models mainly concentrates on the process of calibration in the context of a steady state, this essay centers upon the calibration procedure for the case that the model‘s state variables are not initially at their steady state values. Though this is the more realistic interpretation of a base year‘s data set it raises a number of additional difficulties including intertemporal feedback and the requirement that the basic data are consistent with the intertemporal equations of the model. These difficulties are discussed and a procedure is presented which can cope with them. Finally, the procedure is applied to an overlapping generations growth model with foresight and the steps involved in calibration are illustrated in detail.

Subtle 14C Signals: The Influence of Atmospheric Mixing, Growing Season and In-Situ Production

Atmospheric 14C concentrations vary with time and latitude. These variations, measured directly on atmospheric samples, or in independently-dated organic material such as tree rings, supply data essential for the calibration of dynamic models of the global carbon cycle. Short variations in the production rate of atmospheric 14C are strongly attenuated in the relatively large atmospheric CO2 reservoir. In-situ production of 14C should be negligible for ages up to 80 ka bp. Background problems in AMS dating are more likely attributable to contamination of very small samples.