scholarly journals Sodar Measurements of Wing Vortex Strength and Position

2007 ◽  
Vol 24 (2) ◽  
pp. 141-155 ◽  
Author(s):  
Stuart Bradley ◽  
Erich Mursch-Radlgruber ◽  
Sabine von Hünerbein
Keyword(s):  
Real Time ◽  
Measurement Data ◽  
Ground Surface ◽  
Vortex Pair ◽  
Performance Criteria ◽  
Model Parameters ◽  
Parameter Uncertainties ◽  
And Performance ◽  
Sodar Measurements ◽  
Flow Vortex

Abstract A method is developed for robust real-time visualization of aircraft vortex spatial and temporal development based on measurement data from a line array of sodars. The method relies on using a potential-flow vortex model, with spatial averaging according to the along-beam and transverse spatial resolution of the sodar. The model comprises the wing vortex pair, together with two image vortices below ground such that there is no flow through the ground surface. An analytic solution for the temporal–spatial evolution of this four-vortex system is obtained as an aid to establishing relevant scales and performance criteria for any sodar. Field results from an array of four sodars are used on an individual profile basis (every 2 s of real time) to fit the model parameters of vortex circulation, position, and spacing. This method gives vortex trajectories and strength as a function of real time without dependence on assumptions regarding interactions with the atmosphere. Estimates of parameter uncertainties are also produced in real time, and it is found that estimates of position and spacing can be obtained to around ±4 m and of vortex circulation to ±50 m2 s−1. Recommendations are given for optimizing sodars for vortex measurements using practical technology.

Dynamic Data-Driven Spatiotemporal System Behavior Prediction With Simulations and Sensor Measurement Data

2018 ◽  
Author(s):  
Xiangxue Zhao ◽  
Shapour Azarm ◽  
Balakumar Balachandran
Keyword(s):  
Dynamical System ◽  
Real Time ◽  
Measurement Data ◽  
Data Driven ◽  
Sensor Data ◽  
Model Parameters ◽  
High Fidelity ◽  
System Behavior ◽  
Sensor Measurement

Online prediction of dynamical system behavior based on a combination of simulation data and sensor measurement data has numerous applications. Examples include predicting safe flight configurations, forecasting storms and wildfire spread, estimating railway track and pipeline health conditions. In such applications, high-fidelity simulations may be used to accurately predict a system’s dynamical behavior offline (“non-real time”). However, due to the computational expense, these simulations have limited usage for online (“real-time”) prediction of a system’s behavior. To remedy this, one possible approach is to allocate a significant portion of the computational effort to obtain data through offline simulations. The obtained offline data can then be combined with online sensor measurements for online estimation of the system’s behavior with comparable accuracy as the off-line, high-fidelity simulation. The main contribution of this paper is in the construction of a fast data-driven spatiotemporal prediction framework that can be used to estimate general parametric dynamical system behavior. This is achieved through three steps. First, high-order singular value decomposition is applied to map high-dimensional offline simulation datasets into a subspace. Second, Gaussian processes are constructed to approximate model parameters in the subspace. Finally, reduced-order particle filtering is used to assimilate sparsely located sensor data to further improve the prediction. The effectiveness of the proposed approach is demonstrated through a case study. In this case study, aeroelastic response data obtained for an aircraft through simulations is integrated with measurement data obtained from a few sparsely located sensors. Through this case study, the authors show that along with dynamic enhancement of the state estimates, one can also realize a reduction in uncertainty of the estimates.

scholarly journals Robust Process Design in Pharmaceutical Manufacturing under Batch-to-Batch Variation

Processes ◽  
2019 ◽  
Vol 7 (8) ◽  
pp. 509 ◽  
Author(s):  
Xiangzhong Xie ◽  
René Schenkendorf
Keyword(s):  
Process Design ◽  
Measurement Data ◽  
Pharmaceutical Manufacturing ◽  
Chamber Pressure ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Parameter Uncertainties ◽  
Model Based ◽  
Point Estimate Method ◽  
Robust Process

Model-based concepts have been proven to be beneficial in pharmaceutical manufacturing, thus contributing to low costs and high quality standards. However, model parameters are derived from imperfect, noisy measurement data, which result in uncertain parameter estimates and sub-optimal process design concepts. In the last two decades, various methods have been proposed for dealing with parameter uncertainties in model-based process design. Most concepts for robustification, however, ignore the batch-to-batch variations that are common in pharmaceutical manufacturing processes. In this work, a probability-box robust process design concept is proposed. Batch-to-batch variations were considered to be imprecise parameter uncertainties, and modeled as probability-boxes accordingly. The point estimate method was combined with the back-off approach for efficient uncertainty propagation and robust process design. The novel robustification concept was applied to a freeze-drying process. Optimal shelf temperature and chamber pressure profiles are presented for the robust process design under batch-to-batch variation.

scholarly journals Identifying the connective strength between model parameters and performance criteria

2017 ◽  
Vol 21 (11) ◽  
pp. 5663-5679 ◽  
Author(s):  
Björn Guse ◽  
Matthias Pfannerstill ◽  
Abror Gafurov ◽  
Jens Kiesel ◽  
Christian Lehr ◽  
...  
Keyword(s):  
Performance Criterion ◽  
Performance Criteria ◽  
Joint Analysis ◽  
Model Parameters ◽  
Flow Conditions ◽  
Target Variable ◽  
Model Parameter ◽  
And Performance ◽  
Parameter Values ◽  
Three Components

Abstract. In hydrological models, parameters are used to represent the time-invariant characteristics of catchments and to capture different aspects of hydrological response. Hence, model parameters need to be identified based on their role in controlling the hydrological behaviour. For the identification of meaningful parameter values, multiple and complementary performance criteria are used that compare modelled and measured discharge time series. The reliability of the identification of hydrologically meaningful model parameter values depends on how distinctly a model parameter can be assigned to one of the performance criteria. To investigate this, we introduce the new concept of connective strength between model parameters and performance criteria. The connective strength assesses the intensity in the interrelationship between model parameters and performance criteria in a bijective way. In our analysis of connective strength, model simulations are carried out based on a latin hypercube sampling. Ten performance criteria including Nash–Sutcliffe efficiency (NSE), Kling–Gupta efficiency (KGE) and its three components (alpha, beta and r) as well as RSR (the ratio of the root mean square error to the standard deviation) for different segments of the flow duration curve (FDC) are calculated. With a joint analysis of two regression tree (RT) approaches, we derive how a model parameter is connected to different performance criteria. At first, RTs are constructed using each performance criterion as the target variable to detect the most relevant model parameters for each performance criterion. Secondly, RTs are constructed using each parameter as the target variable to detect which performance criteria are impacted by changes in the values of one distinct model parameter. Based on this, appropriate performance criteria are identified for each model parameter. In this study, a high bijective connective strength between model parameters and performance criteria is found for low- and mid-flow conditions. Moreover, the RT analyses emphasise the benefit of an individual analysis of the three components of KGE and of the FDC segments. Furthermore, the RT analyses highlight under which conditions these performance criteria provide insights into precise parameter identification. Our results show that separate performance criteria are required to identify dominant parameters on low- and mid-flow conditions, whilst the number of required performance criteria for high flows increases with increasing process complexity in the catchment. Overall, the analysis of the connective strength between model parameters and performance criteria using RTs contribute to a more realistic handling of parameters and performance criteria in hydrological modelling.

scholarly journals Identifying the connective strength between model parameters and performance criteria

2017 ◽  
Author(s):  
Björn Guse ◽  
Matthias Pfannerstill ◽  
Abror Gafurov ◽  
Jens Kiesel ◽  
Christian Lehr ◽  
...  
Keyword(s):  
Parameter Identification ◽  
Latin Hypercube Sampling ◽  
Performance Criteria ◽  
Joint Analysis ◽  
Model Parameters ◽  
Flow Conditions ◽  
Target Variable ◽  
Model Parameter ◽  
And Performance ◽  
Three Components

Abstract. In hydrological models, parameters are used to adapt the model to the conditions of the catchments. Hereby, the parameters need to be identified based on their role in controlling the hydrological behaviour in the model. For parameter identification, multiple and complementary performance criteria are used, which have to capture the different aspects of hydrological response of catchments. A reliable parameter identification depends on how distinctly a model parameter can be assigned to one of the performance criteria. We introduce an analysis that reveals the connective strength between model parameters and performance criteria. The connective strength assesses the intensity in the inter-relationship between model parameters and performance criteria. In our analysis of connective strength, model simulations are carried out based on a Latin Hypercube sampling. Ten performance criteria in cluding the NSE, the KGE and its three components (alpha, beta and r) as well as the RSR for different segments of the flow duration curve (FDC) are calculated. With a joint analysis of two regression trees (RT), it is derived how a model parameter is connected to the different performance criteria. At first, RTs are constructed using each performance criteria as target variable to detect the most relevant model parameters for each performance criteria. A second RT approach using each parameter as target variable detects which performance criterion is impacted by changes in parameter values. Based on this, appropriate performance criteria are identified for each model parameter. A high bijective connective strength is calculated for low and mid flow conditions. Moreover, the RT analyses emphasise the benefit of an individual analysis of the three components of the KGE and of the FDC segments. It is emphasised under which conditions these performance criteria provide insights into a precise parameter identification. Separate performance criteria are required to identify dominant parameters on low and mid flow conditions, whilst the number of required performance criteria for high flows increases with the process complexity in the catchment. Overall, the analysis of the connective strength using RTs contribute towards a better handling of parameters and performance criteria in hydrological modelling.

Sign in / Sign up

Export Citation Format

Share Document