scholarly journals Improved Parametric Models for Explosion Pressure Signals Derived From Large Datasets

2020 ◽  
Vol 91 (3) ◽  
pp. 1752-1762
Author(s):  
Julie Schnurr ◽  
Keehoon Kim ◽  
Milton A. Garces ◽  
Arthur Rodgers
Keyword(s):  
Forensic Analysis ◽  
Parametric Models ◽  
Model Parameters ◽  
Large Set ◽  
Least Squares Regression ◽  
Explosion Pressure ◽  
Atmospheric Propagation ◽  
Dependent Model ◽  
Fitting In

Abstract Accurate recording and characterization of explosion-induced pressure signals are key components of the forensic analysis of explosion events in the atmosphere. Parametric overpressure models based on several key waveform features (peak overpressure, positive pulse duration, and impulse) are widely used to estimate explosion energy in terms of trinitrotoluene equivalent yield. However, those models are often developed by a limited dataset, including only a few events or recordings at relatively short propagation distances. Here, we develop empirical waveform-parameter models based on a regression analysis of a large set of data curated from four chemical explosion experiments including 16 detonations. We measured peak overpressure and impulse for positive and negative phases from ∼1000 pressure signals recorded at local ranges (<20  km) with scaled distance up to 8000  m/kg1/3. The measured waveform parameters showed large variation with respect to observing distances indicating the effects of atmospheric propagation. In this study, a second-order polynomial model was used in a least-squares regression to account for those propagation effects and to improve data fitting. In addition to model parameters for waveform features, we also determined range-dependent model uncertainties based on data variance. The model uncertainty represents the prediction error of our models and can be critical to evaluating the uncertainty of yield estimate.

scholarly journals Nonlinear Characterization of the MRE Isolator Using Binary-Coded Discrete CSO and ELM

2018 ◽  
Vol 18 (08) ◽  
pp. 1840007 ◽  
Author(s):  
Yang Yu ◽  
Yancheng Li ◽  
Jianchun Li ◽  
Xiaoyu Gu ◽  
Sayed Royel
Keyword(s):  
Engineering Application ◽  
Experimental Results ◽  
Parametric Models ◽  
Structural Vibration ◽  
Nonparametric Model ◽  
Model Parameters ◽  
Nonlinear Responses ◽  
Feed Forward Network ◽  
Hidden Layer

Magnetorheological elastomer (MRE) isolator has been proved as a promising semi-active control device for structural vibration control. For its engineering application, developing an accurate and robust model is definitely necessary and also a challenging task. Most of the present models, belonging to parametric models, need to identify various model parameters and sometimes are not capable of perfectly capturing the unique characteristics of the device. In this work, a novel nonparametric model is proposed to characterize the inherent dynamics of the MRE isolator with the features of hysteresis and nonlinearity. Initially, dynamic tests are conducted to evaluate the performance of the isolator under various loading conditions, including harmonic, random, and seismic excitations. Then, on the basis of the captured experimental results, a hybrid learning method is designed to forecast the nonlinear responses of the device with known external inputs. In this method, a type of single hidden layer feed-forward network, called extreme learning machine (ELM), is developed to forecast the nonlinear responses (shear force) of the device with captured velocity, displacement, and current level. To obtain optimal performance of the developed model, an improved binary-coded discrete cat swarm optimization (BCDCSO) method is adopted to select optimal inputs and neuron number in the hidden layer for the network development. The performance of the proposed method is verified through the comparison between experimental results and model predictions. Due to the noise influence in the practical condition, the robustness of the proposed method is also validated via adding noise disturbance into the supplying currents. The results show that the proposed method outperforms the standard ELM in terms of characterization of the MRE isolator, even though the captured responses are polluted with external measurement noises.

scholarly journals Industrial Plume Properties Retrieved by Optimal Estimation Using Combined Hyperspectral and Sentinel-2 Data

2021 ◽  
Vol 13 (10) ◽  
pp. 1865
Author(s):  
Gabriel Calassou ◽  
Pierre-Yves Foucher ◽  
Jean-François Léon
Keyword(s):  
Matched Filter ◽  
Estimation Method ◽  
Optimal Estimation ◽  
Model Parameters ◽  
Surface Reflectance ◽  
Sequential Method ◽  
Modal Radius ◽  
Sentinel 2

Stack emissions from the industrial sector are a subject of concern for air quality. However, the characterization of the stack emission plume properties from in situ observations remains a challenging task. This paper focuses on the characterization of the aerosol properties of a steel plant stack plume through the use of hyperspectral (HS) airborne remote sensing imagery. We propose a new method, based on the combination of HS airborne acquisition and surface reflectance imagery derived from the Sentinel-2 Multi-Spectral Instrument (MSI). The proposed method detects the plume footprint and estimates the surface reflectance under the plume, the aerosol optical thickness (AOT), and the modal radius of the plume. Hyperspectral surface reflectances are estimated using the coupled non-negative matrix factorization (CNMF) method combining HS and MSI data. The CNMF reduces the error associated with estimating the surface reflectance below the plume, particularly for heterogeneous classes. The AOT and modal radius are retrieved using an optimal estimation method (OEM), based on the forward model and allowing for uncertainties in the observations and in the model parameters. The a priori state vector is provided by a sequential method using the root mean square error (RMSE) metric, which outperforms the previously used cluster tuned matched filter (CTMF). The OEM degrees of freedom are then analysed, in order to refine the mask plume and to enhance the quality of the retrieval. The retrieved mean radii of aerosol particles in the plume is 0.125 μμm, with an uncertainty of 0.05 μμm. These results are close to the ultra-fine mode (modal radius around 0.1 μμm) observed from in situ measurements within metallurgical plant plumes from previous studies. The retrieved AOT values vary between 0.07 (near the source point) and 0.01, with uncertainties of 0.005 for the darkest surfaces and above 0.010 for the brightest surfaces.

scholarly journals Characterization of Moisture Diffusion in Cured Concrete Slabs at Early Ages

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Xiao Zhang ◽  
Hongduo Zhao
Keyword(s):  
Process Model ◽  
Model Simulation ◽  
Moisture Diffusion ◽  
Experimental Results ◽  
Concrete Slabs ◽  
Model Parameters ◽  
Early Age ◽  
Water Curing ◽  
Early Age Concrete

The objective of this paper is to investigate the characterization of moisture diffusion inside early-age concrete slabs subjected to curing. Time-dependent relative humidity (RH) distributions of three mixture proportions subjected to three different curing methods (i.e., air curing, water curing, and membrane-forming compounds curing) and sealed condition were measured for 28 days. A one-dimensional nonlinear moisture diffusion partial differential equation (PDE) based on Fick’s second law, which incorporates the effect of curing in the Dirichlet boundary condition using a concept of curing factor, is developed to simulate the diffusion process. Model parameters are calibrated by a genetic algorithm (GA). Experimental results show that the RH reducing rate inside concrete under air curing is greater than the rates under membrane-forming compound curing and water curing. It is shown that the effect of water-to-cement (w/c) ratio on self-desiccation is significant. Lower w/c ratio tends to result in larger RH reduction. RH reduction considering both effect of diffusion and self-desiccation in early-age concrete is not sensitive to w/c ratio, but to curing method. Comparison between model simulation and experimental results indicates that the improved model is able to reflect the effect of curing on moisture diffusion in early-age concrete slabs.

Conditioning Model Ensembles to Various Observed Data (Field and Regional Level) by Applying Machine-Learning-Augmented Workflows to a Mature Field with 70 Years of Production History

2021 ◽  
pp. 1-18
Author(s):  
Gisela Vanegas ◽  
John Nejedlik ◽  
Pascale Neff ◽  
Torsten Clemens
Keyword(s):  
Machine Learning ◽  
Oil Recovery ◽  
Numerical Models ◽  
Operating Conditions ◽  
Model Parameters ◽  
Large Set ◽  
Model Parameter ◽  
Production History ◽  
Hydrocarbon Fields ◽  
Parameter Distributions

Summary Forecasting production from hydrocarbon fields is challenging because of the large number of uncertain model parameters and the multitude of observed data that are measured. The large number of model parameters leads to uncertainty in the production forecast from hydrocarbon fields. Changing operating conditions [e.g., implementation of improved oil recovery or enhanced oil recovery (EOR)] results in model parameters becoming sensitive in the forecast that were not sensitive during the production history. Hence, simulation approaches need to be able to address uncertainty in model parameters as well as conditioning numerical models to a multitude of different observed data. Sampling from distributions of various geological and dynamic parameters allows for the generation of an ensemble of numerical models that could be falsified using principal-component analysis (PCA) for different observed data. If the numerical models are not falsified, machine-learning (ML) approaches can be used to generate a large set of parameter combinations that can be conditioned to the different observed data. The data conditioning is followed by a final step ensuring that parameter interactions are covered. The methodology was applied to a sandstone oil reservoir with more than 70 years of production history containing dozens of wells. The resulting ensemble of numerical models is conditioned to all observed data. Furthermore, the resulting posterior-model parameter distributions are only modified from the prior-model parameter distributions if the observed data are informative for the model parameters. Hence, changes in operating conditions can be forecast under uncertainty, which is essential if nonsensitive parameters in the history are sensitive in the forecast.

Strategies to Enhance the Performance of Gaussian Mixture Model Fitting for Uncertainty Quantification by Conditioning to Production Data

2021 ◽  
Author(s):  
Guohua Gao ◽  
Jeroen Vink ◽  
Fredrik Saaf ◽  
Terence Wells
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
History Matching ◽  
Gaussian Mixture ◽  
Model Parameters ◽  
Gaussian Component ◽  
Large Set ◽  
Cpu Time ◽  
Fitting Method ◽  
New Strategies

Abstract When formulating history matching within the Bayesian framework, we may quantify the uncertainty of model parameters and production forecasts using conditional realizations sampled from the posterior probability density function (PDF). It is quite challenging to sample such a posterior PDF. Some methods e.g., Markov chain Monte Carlo (MCMC), are very expensive (e.g., MCMC) while others are cheaper but may generate biased samples. In this paper, we propose an unconstrained Gaussian Mixture Model (GMM) fitting method to approximate the posterior PDF and investigate new strategies to further enhance its performance. To reduce the CPU time of handling bound constraints, we reformulate the GMM fitting formulation such that an unconstrained optimization algorithm can be applied to find the optimal solution of unknown GMM parameters. To obtain a sufficiently accurate GMM approximation with the lowest number of Gaussian components, we generate random initial guesses, remove components with very small or very large mixture weights after each GMM fitting iteration and prevent their reappearance using a dedicated filter. To prevent overfitting, we only add a new Gaussian component if the quality of the GMM approximation on a (large) set of blind-test data sufficiently improves. The unconstrained GMM fitting method with the new strategies proposed in this paper is validated using nonlinear toy problems and then applied to a synthetic history matching example. It can construct a GMM approximation of the posterior PDF that is comparable to the MCMC method, and it is significantly more efficient than the constrained GMM fitting formulation, e.g., reducing the CPU time by a factor of 800 to 7300 for problems we tested, which makes it quite attractive for large scale history matching problems.

scholarly journals Are streamflow recession characteristics really characteristic?

2013 ◽  
Vol 17 (2) ◽  
pp. 817-828 ◽  
Author(s):  
M. Stoelzle ◽  
K. Stahl ◽  
M. Weiler
Keyword(s):  
Extraction Method ◽  
Storage Capacity ◽  
Extraction Methods ◽  
Model Parameters ◽  
Catchment Scale ◽  
Model Parameterization ◽  
Recession Curves ◽  
Recession Analysis ◽  
Meso Scale

Abstract. Streamflow recession has been investigated by a variety of methods, often involving the fit of a model to empirical recession plots to parameterize a non-linear storage–outflow relationship based on the dQ/dt−Q method. Such recession analysis methods (RAMs) are used to estimate hydraulic conductivity, storage capacity, or aquifer thickness and to model streamflow recession curves for regionalization and prediction at the catchment scale. Numerous RAMs have been published, but little is known about how comparably the resulting recession models distinguish characteristic catchment behavior. In this study we combined three established recession extraction methods with three different parameter-fitting methods to the power-law storage–outflow model to compare the range of recession characteristics that result from the application of these different RAMs. Resulting recession characteristics including recession time and corresponding storage depletion were evaluated for 20 meso-scale catchments in Germany. We found plausible ranges for model parameterization; however, calculated recession characteristics varied over two orders of magnitude. While recession characteristics of the 20 catchments derived with the different methods correlate strongly, particularly for the RAMs that use the same extraction method, not all rank the catchments consistently, and the differences among some of the methods are larger than among the catchments. To elucidate this variability we discuss the ambiguous roles of recession extraction procedures and the parameterization of the storage–outflow model and the limitations of the presented recession plots. The results suggest strong limitations to the comparability of recession characteristics derived with different methods, not only in the model parameters but also in the relative characterization of different catchments. A multiple-methods approach to investigating streamflow recession characteristics should be considered for applications whenever possible.

scholarly journals 31 Characterization of feeding-behavior patterns and application of chemometrics to predict residual feed intake based on feeding-behavior traits in growing Holstein heifers

2019 ◽  
Vol 97 (Supplement_1) ◽  
pp. 23-23
Author(s):  
Jocelyn R Johnson ◽  
Ira L Parsons ◽  
Gordon E Carstens ◽  
Luis Orlindo Tedeschi ◽  
Claas Heuer ◽  
...  
Keyword(s):  
Feeding Behavior ◽  
Mixed Model ◽  
Corn Silage ◽  
Event Data ◽  
Least Squares Regression ◽  
Dairy Heifers ◽  
Leave One Out ◽  
Plsr Model ◽  
Time Eating

Abstract Objectives of this study were to characterize feeding-behavior (FB) patterns in growing dairy heifers with divergent RFI phenotypes (±0.50 SD) and to evaluate the accuracy of partial least squares regression (PLSR) models to predict RFI based on FB traits. Performance, DMI, and FB traits were measured for 70 to 100 d in 15 trials with Holstein heifers (n = 611) fed a corn-silage based ration. Seventeen FB traits were evaluated: frequency and duration of bunk visit (BV) and meal events, head-down duration (HDD), meal length, maximum non-feeding interval, corresponding day-today variation (SD) of these traits, and ratios of HDD per BV duration and meal duration, HDD per meal duration, and BV events per meal event. Data was analyzed using a mixed model that included RFI group and trial. The PLSR model for RFI was developed using cross-validation procedures (Leave-One-Out) in JMP (SAS), with FB traits as independent variables. LowRFI heifers consumed 24% less (P < 0.01) DMI and had lower (P < 0.01) day-to-day DMI variation than high-RFI heifers. Distinct differences were observed in FB patterns between low- and high-RFI heifers (Table 1). Eight of 17 FB traits were included [selected based on variable of importance (VIP) score > 0.80] in the PLSR model that explained 33% of the variation in RFI. Head-down duration had the highest VIP score; accordingly, low-RFI animals had 44% lower HDD and 30 and 40% lower ratios of HDD per BV duration and meal duration, respectively. Additionally, low-RFI animals had 20 and 18% fewer BV and meal events per day, spent 21% less time eating during BV events, and had reduced day-to-day variation in HDD and meal frequency. For this study, distinctive differences were observed in the FB patterns of Holstein heifers with divergent RFI, which explained 33% of the between-animal variation in RFI.

Diffusivity of solvents in semi-crystalline polyethylene using the Vrentas-Duda free-volume theory

2018 ◽  
Vol 38 (10) ◽  
pp. 925-931 ◽  
Author(s):  
Derek R. Sturm ◽  
Kevin J. Caputo ◽  
Siyang Liu ◽  
Ronald P. Danner
Keyword(s):  
Free Volume ◽  
Weight Fraction ◽  
Amorphous Region ◽  
Model Parameters ◽  
Crystalline Region ◽  
Free Volume Theory ◽  
Melt Temperature ◽  
Tortuosity Factor ◽  
Crystalline Polyethylene

Abstract Diffusion of penetrants in polyethylene below the melt temperature is heavily dependent on the crystallinity of the polyethylene, the temperature of the experiment, and the concentration of solvent in the polymer. As the crystallinity of the polyethylene increases, there is an increase in the path that the solvent must travel as the solvent cannot penetrate the tightly packed chains in the crystalline domain. This effect is typically accounted for by a tortuosity factor. In this work, a simple and effective characterization of the tortuosity factor based simply on the crystal weight fraction has been developed. Data have been collected for six polyethylenes having densities ranging from 0.912 to 0.961 g/cm3 and for three solvents – isopentane, cyclohexane, and 1-hexene. Diffusivity predictions have been obtained using the free-volume theory of Vrentas and Duda in conjunction with the new tortuosity factor. The polyethylenes had crystallinities varying from 40% to 82% effecting an approximately 60% change in the diffusivity. The decrease resulting from ignoring the crystallinity altogether was in some cases essentially a factor of 5. The error in the predicted diffusivities over all the systems was 25%. For cyclohexane, it is shown that the same model parameters characterize data below the melt temperature (in the semi-crystalline region) as well as above the melt temperature (in the amorphous region).

scholarly journals Analytical and Numerical Connections between Fractional Fickian and Intravoxel Incoherent Motion Models of Diffusion MRI

Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 1963
Author(s):  
Jingting Yao ◽  
Muhammad Ali Raza Anjum ◽  
Anshuman Swain ◽  
David A. Reiter
Keyword(s):  
Skeletal Muscle ◽  
Goodness Of Fit ◽  
Numerical Models ◽  
Tissue Perfusion ◽  
Parametric Models ◽  
Intravoxel Incoherent Motion ◽  
Fickian Diffusion ◽  
Model Parameters ◽  
Mathematical Framework ◽  
Stretched Exponential

Impaired tissue perfusion underlies many chronic disease states and aging. Diffusion-weighted imaging (DWI) is a noninvasive MRI technique that has been widely used to characterize tissue perfusion. Parametric models based on DWI measurements can characterize microvascular perfusion modulated by functional and microstructural alterations in the skeletal muscle. The intravoxel incoherent motion (IVIM) model uses a biexponential form to quantify the incoherent motion of water molecules in the microvasculature at low b-values of DWI measurements. The fractional Fickian diffusion (FFD) model is a parsimonious representation of anomalous superdiffusion that uses the stretched exponential form and can be used to quantify the microvascular volume of skeletal muscle. Both models are established measures of perfusion based on DWI, and the prognostic value of model parameters for identifying pathophysiological processes has been studied. Although the mathematical properties of individual models have been previously reported, quantitative connections between IVIM and FFD models have not been examined. This work provides a mathematical framework for obtaining a direct, one-way transformation of the parameters of the stretched exponential model to those of the biexponential model. Numerical simulations are implemented, and the results corroborate analytical results. Additionally, analysis of in vivo DWI measurements in skeletal muscle using both biexponential and stretched exponential models is shown and compared with analytical and numerical models. These results demonstrate the difficulty of model selection based on goodness of fit to experimental data. This analysis provides a framework for better interpreting and harmonizing perfusion parameters from experimental results using these two different models.

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