Estimation of white-box model parameters via artificial data generation: a two-stage approach

2008 ◽  
Vol 41 (2) ◽  
pp. 11409-11414 ◽  
Author(s):  
Simone Garatti ◽  
Sergio Bittanti
Keyword(s):  
Box Model ◽  
Model Parameters ◽  
Data Generation ◽  
Artificial Data ◽  
Two Stage

scholarly journals Two-stage Bayesian system identification using Gaussian discrepancy model

2020 ◽  
pp. 147592172093352
Author(s):  
Feng-Liang Zhang ◽  
Siu-Kui Au ◽  
Yan-Chun Ni
Keyword(s):  
System Identification ◽  
Structural Model ◽  
Structural Parameters ◽  
Stage Ii ◽  
Mode Shapes ◽  
Modal Parameters ◽  
Model Parameters ◽  
Modeling Error ◽  
Two Stage ◽  
Classical Statistics

System identification aims at updating the model parameters (e.g. mass and stiffness) associated with the mathematical model of a structure based on measured structural response. In this process, a two-stage approach is commonly adopted. In Stage I, modal parameters including natural frequencies and mode shapes are identified. In Stage II, the modal parameters are used to update structural parameters such as those related to stiffness, mass, and boundary conditions. A recent Bayesian formulation allows the identification results in the first stage to be incorporated in the second stage directly via Bayes’ rule without using a heuristic model (often based on classical statistics) that transfers the information from Stages I to II. This opens up opportunities for explicitly accounting for modeling error in the structural model (Stage II) through the conditional distribution of modal parameters given structural model parameters. Following this approach, this article investigates a methodology where the modeling error between the two stages is incorporated with Gaussian distributions whose statistical parameters are also updated with available data. Leveraging on special mathematical structure induced by the model, computational issues are resolved and an analytical investigation is performed that yields insights on the role of modeling error and whether its statistics can be distinguished from those of identification uncertainty (defined for given structural model). The proposed methodology is verified using synthetic data and applied to a laboratory-scale structure.

scholarly journals Secondary organic aerosol formation from smoldering and flaming combustion of biomass: a box model parametrization based on volatility basis set

2019 ◽  
Author(s):  
Giulia Stefenelli ◽  
Jianhui Jiang ◽  
Amelie Bertrand ◽  
Emily A. Bruns ◽  
Simone M. Pieber ◽  
...  
Keyword(s):  
Mass Spectrometer ◽  
Secondary Organic Aerosol ◽  
Time Of Flight ◽  
Organic Aerosol ◽  
Box Model ◽  
Basis Set ◽  
Model Parameters ◽  
Oh Radicals ◽  
Smog Chamber ◽  
Aerosol Formation

Abstract. Box model simulations based on the volatility basis set (VBS) approach were used to assess secondary organic aerosol (SOA) precursors and volatility distributions from residential wood combustion. Emissions were sampled from three different residential stoves at different combustion conditions (flaming vs. smoldering-dominated), aging temperatures (−10 °C, 2 °C and 15 °C), and emission loads, then exposed to hydroxyl (OH) radicals in a smog chamber. Primary emissions of SOA precursor compounds, organic aerosol and their evolution during aging in the smog chamber were monitored by a comprehensive suite of gas and particle instrumentation, including a proton transfer reaction time-of-flight mass spectrometer (PTR-TOF-MS) and a high resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS). SOA precursors were classified according to their chemical composition and the identification of the nature of the precursors revealed useful to better constrain model parameters, in particular SOA production rates and molecular characteristics of the condensable gases formed. The general aim of the model was the determination of the parameters describing the volatility distributions of the oxidation products from the different chemical classes considered and their temperature dependence. Novel parameterization methods based on a genetic algorithm (GA) approach allowed estimation of precursor class contributions to SOA and evaluation of the effect of emission variability on SOA yield predictions. Significant differences were observed in the gas-phase composition between smoldering and flaming emissions. Smoldering phase emissions were dominated by oxidized VOCs with less than six carbon atoms family (OVOCc 

MEMS Gyro Random Drift Model Parameter Identification Based on Two-Stage Recursive Least Squares Method

2012 ◽  
Vol 220-223 ◽  
pp. 1044-1047 ◽  
Author(s):  
Zhao Hua Liu ◽  
Jia Bin Chen ◽  
Yu Liang Mao ◽  
Chun Lei Song
Keyword(s):  
Least Squares ◽  
Least Squares Method ◽  
Moving Average ◽  
Recursive Least Squares ◽  
Model Parameters ◽  
Random Drift ◽  
Two Stage ◽  
Autoregressive Moving Average Model ◽  
Drift Model ◽  
Recursive Least Squares Method

Autoregressive moving average model (ARMA) was usually used for gyro random drift modeling. Because gyro random drift was a non-stationary, weak non-linear and time-variant random signal, model parameters were random and time-variant, too. For improving precision of gyro and reducing effects of random drift, this paper adopted two-stage recursive least squares method for ARMA parameter estimation. This method overcame the shortcomings of the conventional recursive extended least squares (RELS) algorithm. At the same time, the forgetting factor was introduced to adapt the model parameters change. The simulation experimental results showed that this method is effective.

Application of an Improved Streamline Curvature Approach to a Modern, Two-Stage Transonic Fan: Comparison With Data and CFD

2002 ◽  
Author(s):  
Keith M. Boyer ◽  
Walter F. O’Brien
Keyword(s):  
Three Dimensional ◽  
Operating Conditions ◽  
Navier Stokes ◽  
Model Parameters ◽  
Two Stage ◽  
Streamline Curvature ◽  
Wide Range ◽  
Improved Model ◽  
Transonic Fan ◽  
Streamline Curvature Method

A streamline curvature method with improvements to key loss models is applied to a two-stage, low aspect ratio, transonic fan with design tip relative Mach number of approximately 1.65. Central to the improvements is the incorporation of a physics-based shock model. The attempt here is to capture the effects of key flow phenomena relative to the off-design performance of the fan. A quantitative analysis regarding solution sensitivities to model parameters that influence the key phenomena over a wide range of operating conditions is presented. Predictions are compared to performance determined from overall and interstage measurements, as well as from a three-dimensional, steady, Reynolds-averaged Navier-Stokes method applied across the first rotor. Overall and spanwise comparisons demonstrate that the improved model gives reasonable performance trending and generally accurate results. The method can be used to provide boundary conditions to higher-order solvers, or implemented within novel approaches using the streamline curvature method to explore complex engine-inlet integration issues, such as time-variant distortion.

scholarly journals A two-stage biomass random effects model for stock assessment without catches: What can be estimated using only biomass survey indices?

2008 ◽  
Vol 65 (6) ◽  
pp. 1024-1035 ◽  
Author(s):  
Verena M. Trenkel
Keyword(s):  
Random Effects ◽  
Stock Assessment ◽  
Random Effect ◽  
Simulated Data ◽  
Fish Stock ◽  
Model Parameters ◽  
Biomass Growth ◽  
Two Stage ◽  
Commercial Catch ◽  
Nested Models

A simple two-stage biomass random effects population dynamics model is presented for carrying out fish stock assessments based on survey indices using no commercial catch information. Recruitment and biomass growth are modelled as random effects, reducing the number of model parameters while maintaining model flexibility. No assumptions regarding natural mortality rates are required. The performance of the method was evaluated using simulated data with emphasis on identifying parameter redundancy, which showed that the variance of the biomass growth random effect might only be estimable if large (>0.2). The full and two nested models were fitted to European anchovy ( Engraulis encrasicolus ) in the Bay of Biscay using two survey series. The best-fitting model had fixed biomass growth and random recruitment following a lognormal distribution.

scholarly journals Two-stage battery recharge scheduling and vehicle-charger assignment policy for dynamic electric dial-a-ride services

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0251582
Author(s):  
Tai-Yu Ma
Keyword(s):  
Large Scale ◽  
Waiting Times ◽  
Model Parameters ◽  
Two Stage ◽  
Charging Infrastructure ◽  
Solution Approach ◽  
Charging Station ◽  
Total Vehicle ◽  
Vehicle Charging ◽  
The Impact

Coordinating the charging scheduling of electric vehicles for dynamic dial-a-ride services is challenging considering charging queuing delays and stochastic customer demand. We propose a new two-stage solution approach to handle dynamic vehicle charging scheduling to minimize the costs of daily charging operations of the fleet. The approach comprises two components: daily vehicle charging scheduling and online vehicle–charger assignment. A new battery replenishment model is proposed to obtain the vehicle charging schedules by minimizing the costs of vehicle daily charging operations while satisfying vehicle driving needs to serve customers. In the second stage, an online vehicle–charger assignment model is developed to minimize the total vehicle idle time for charges by considering queuing delays at the level of chargers. An efficient Lagrangian relaxation algorithm is proposed to solve the large-scale vehicle-charger assignment problem with small optimality gaps. The approach is applied to a realistic dynamic dial-a-ride service case study in Luxembourg and compared with the nearest charging station charging policy and first-come-first-served minimum charging delay policy under different charging infrastructure scenarios. Our computational results show that the approach can achieve significant savings for the operator in terms of charging waiting times (–74.9%), charging times (–38.6%), and charged energy costs (–27.4%). A sensitivity analysis is conducted to evaluate the impact of the different model parameters, showing the scalability and robustness of the approach in a stochastic environment.

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