scholarly journals Short-term fire forecast based on air state gain recurrence and zero-order brown model

2021 ◽  
Vol 3 (10(111)) ◽  
pp. 27-33
Author(s):  
Boris Pospelov ◽  
Evgenіy Rybka ◽  
Ruslan Meleshchenko ◽  
Olekcii Krainiukov ◽  
Igor Biryukov ◽  
...  
Keyword(s):  
Indoor Air ◽  
Forecast Error ◽  
Sharp Decrease ◽  
Zero Order ◽  
Smoothing Parameter ◽  
Model Parameters ◽  
Forecast Errors ◽  
Limit Set ◽  
Short Term ◽  
Model Parameter

Possibilities of parameterization of the zero-order Brown model for indoor air forecasting based on the current measure of air state gain recurrence are considered. The key to the zero-order parametric Brown forecasting model is the selection of the smoothing parameter, which characterizes forecast adaptability to the current air state gain recurrence measure. It is shown that for effective short-term indoor fire forecast, the Brown model parameter must be selected from the out-of-limit set defined by 1 and 2. The out-of-limit set for the Brown model parameter is an area of effective fire forecasting based on the measure of current indoor air state gain recurrence. Errors of fire forecast based on the parameterized zero-order Brown model in the case of the classical and out-of-limit sets of the model parameters are investigated using the example of ignition of various materials in a laboratory chamber. As quantitative indicators of forecast quality, the absolute and mean forecast errors exponentially smoothed with a parameter of 0.4 are investigated. It was found that for alcohol, the smoothed absolute and mean forecast errors for the classical smoothing parameter in the no-ignition interval do not exceed 20 %. At the same time, for the out-of-limit case, the indicated forecast errors are, on average, an order of magnitude smaller. Similar ratios for forecast errors remain in paper, wood and textile ignition. However, for the transition zone corresponding to the time of material ignition, a sharp decrease in the current measure of chamber air state gain recurrence is observed. It was found that for this zone, the smoothed absolute forecast error for alcohol is about 2 % if the model parameter is selected from the classical set. If the model parameter is selected from the out-of-limit set, the forecast error is about 0.2 %. The results generally demonstrate significant advantages of using the zero-order Brown parametric model with out-of-limit model parameters for indoor fire forecasting

scholarly journals Devising a self-adjusting zero-order Brown’s model for predicting irreversible processes and phenomena

2021 ◽  
Vol 5 (10 (113)) ◽  
pp. 40-47
Author(s):  
Boris Pospelov ◽  
Vladimir Andronov ◽  
Evgenіy Rybka ◽  
Olekcii Krainiukov ◽  
Nadiya Maksymenko ◽  
...  
Keyword(s):  
Time Series ◽  
Forecast Error ◽  
Time Moment ◽  
Zero Order ◽  
Smoothing Parameter ◽  
The Self ◽  
Irreversible Processes ◽  
Limit Set ◽  
Experimental Time Series ◽  
Chaotic Nature

A self-adjusting zero-order Brown’s model has been devised. This model makes it possible to predict with high accuracy not only fires in the premises but also irreversible processes and phenomena of a random and chaotic nature under actual conditions. The essence of the self-adjusting model is that, based on Kalman’s approach, it is proposed to set the smoothing parameter for each time moment. Such a parameter is determined depending on the resulting current forecast error, taking into consideration the real and unknown dynamics of the studied series and noise. That does not require the selection of the smoothing parameter characteristic of known models. In addition, the proposed Brown’s model, unlike the known modifications, does not require setting a dynamics model of the level of the examined time series. The self-adjusting model provides negligible errors and efficiency of the forecast. The operability of the devised model was checked using an example of the experimental time series for the current measure of the recurrence of the increments of the state of the air medium in the laboratory chamber during alcohol combustion. As quantitative indicators of the quality of the forecast error, the current values for the square and absolute values were considered. It has been established that the current square of the forecast error is more than six orders of magnitude smaller compared to the case of a fixed smoothing parameter from a beyond-the-limit set. However, the current square of the forecast error for abrupt changes in the dynamics of the series level is half that of the fixed parameter of the beyond-the-limit set. It is noted that the results confirm the feasibility of the proposed self-adjusting Brown’s model

Reducing Forecast Errors Due to Model Imperfections Using Ensemble Kalman Filtering

2010 ◽  
Vol 138 (8) ◽  
pp. 3316-3332 ◽  
Author(s):  
Hiroshi Koyama ◽  
Masahiro Watanabe
Keyword(s):  
Parameter Estimation ◽  
Time Scale ◽  
Lead Time ◽  
General Circulation ◽  
Forecast Error ◽  
Small Scale ◽  
Model Parameters ◽  
Forecast Errors ◽  
Model Ensemble ◽  
Forecast Lead Time

Abstract A method is introduced for reducing forecast errors in an extended-range to one-month forecast based on an ensemble Kalman filter (EnKF). The prediction skill in such a forecast is typically affected not only by the accuracy of initial conditions but also by the model imperfections. Hence, to improve the forecast in imperfect models, the framework of EnKF is modified by using a state augmentation method. The method includes an adaptive parameter estimation that optimizes mismatched model parameters and a model ensemble initialized with the perturbed model parameter. The main features are the combined ensemble forecast of the initial condition and the parameter, and the assimilation for time-varying parameters with a theoretical basis. First, the method is validated in the imperfect Lorenz ’96 model constructed by parameterizing the small-scale variable of the perfect model. The results indicate a reduction in the ensemble-mean forecast error and the optimization of the ensemble spread. It is found that the time-dependent parameter estimation contributes to reduce the forecast error with a lead time shorter than one week, whereas the model ensemble is effective for improving a forecast with a longer lead time. Moreover, the parameter assimilation is useful when model imperfections have a longer time scale than the forecast lead time, and the model ensemble appears to be relevant in any time scale. Preliminary results using a low-resolution atmospheric general circulation model that implements this method support some of the above findings.

Effect of Short-Term Temperature Increase on the Performance of a Mesophilic UASB Reactor

1990 ◽  
Vol 22 (9) ◽  
pp. 183-190 ◽  
Author(s):  
J. B. van Lier ◽  
J. Rintala ◽  
J. L. Sanz Martin ◽  
G. Lettinga
Keyword(s):  
Sharp Decrease ◽  
Methanogenic Bacteria ◽  
Decay Rates ◽  
Uasb Reactor ◽  
Short Term ◽  
Retention Capacity ◽  
Temperature Shock ◽  
Biomass Retention ◽  
Propionate Oxidation ◽  
Ph Decrease

A study was carried out to assess the effects of short-term temperature increments on the treatment efficiency and methane production of UASB reactors at a working temperature of 37-39°C. Two different substrates were used to determine the effects on the several bacterial groups involved in the digestion process. One reactor was fed with defined synthetic acidified wastewater the other with unacidified wastewaler from a distillery process. Shocks of 5-24 hrs were applied at temperatures in the range of 45 to 61°C. Up to 45°C no detrimental effects were noticeable. Higher temperatures led to a sharp decrease of the activity of the different microbial populations as a result of elevated decay rates. Propionate oxidation turned out to be the most sensitive for temperature increments, whereas the acidogenic bacteria were least affected. Temperature shocks of 55 and 61°C led to a decrease of 50% of the overall efficiency after 10 and 3 hrs, respectively. By means of batch experiments decay rates of 0.44 and > 10 hr −1 of the methanogenic bacteria were estimated at 55 and 65°C respectively. As temporary inactivation of the mesophilic bacteria during a temperature shock was found to be unlikely, reactor recovery is dependent on the bacterial growth and the biomass retention capacity of the reactor. When unacidified wastewater is treated, a pH decrease has to be considered during a temperature shock.

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.

Carbon Brake Friction Model Parameter Identification Using Genetic Algorithms

1995 ◽  
Author(s):  
Roger C. von Doenhoff ◽  
Robert J. Streifel ◽  
Robert J. Marks
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Surface Temperature ◽  
Parameter Identification ◽  
Friction Model ◽  
Carbon Surface ◽  
Model Parameters ◽  
Model Parameter ◽  
Brake Pressure ◽  
Model Parameter Identification

Abstract A model of the friction characteristics of carbon brakes is proposed to aid in the understanding of the causes of brake vibration. The model parameters are determined by a genetic algorithm in an attempt to identify differences in friction properties between brake applications during which vibration occurs and those during which there is no vibration. The model computes the brake torque as a function of wheelspeed, brake pressure, and the carbon surface temperature. The surface temperature is computed using a five node temperature model. The genetic algorithm chooses the model parameters to minimize the error between the model output and the torque measured during a dynamometer test. The basics of genetic algorithms and results of the model parameter identification process are presented.

FULL-WAVEFORM INVERSION WITH SOURCE AND RECEIVER COUPLING EFFECTS CORRECTION

Geophysics ◽  
2021 ◽  
pp. 1-37
Author(s):  
Xinhai Hu ◽  
Wei Guoqi ◽  
Jianyong Song ◽  
Zhifang Yang ◽  
Minghui Lu ◽  
...  
Keyword(s):  
Waveform Inversion ◽  
Nonlinear Least Squares ◽  
Real Data ◽  
Model Parameters ◽  
Full Waveform Inversion ◽  
Linear Inversion ◽  
Near Surface ◽  
Model Parameter ◽  
Full Waveform ◽  
Coupling Factors

Coupling factors of sources and receivers vary dramatically due to the strong heterogeneity of near surface, which are as important as the model parameters for the inversion success. We propose a full waveform inversion (FWI) scheme that corrects for variable coupling factors while updating the model parameter. A linear inversion is embedded into the scheme to estimate the source and receiver factors and compute the amplitude weights according to the acquisition geometry. After the weights are introduced in the objective function, the inversion falls into the category of separable nonlinear least-squares problems. Hence, we could use the variable projection technique widely used in source estimation problem to invert the model parameter without the knowledge of source and receiver factors. The efficacy of the inversion scheme is demonstrated with two synthetic examples and one real data test.

scholarly journals Uncertainty analysis in model parameters regionalization: a case study involving the SWAT model in Mediterranean catchments (Southern France)

2014 ◽  
Vol 18 (6) ◽  
pp. 2393-2413 ◽  
Author(s):  
H. Sellami ◽  
I. La Jeunesse ◽  
S. Benabdallah ◽  
N. Baghdadi ◽  
M. Vanclooster
Keyword(s):  
Model Prediction ◽  
Hydrological Model ◽  
Swat Model ◽  
Behavioral Model ◽  
Model Parameters ◽  
Ungauged Catchments ◽  
Southern France ◽  
Model Parameter ◽  
Ungauged Catchment ◽  
Prediction Uncertainty

Abstract. In this study a method for propagating the hydrological model uncertainty in discharge predictions of ungauged Mediterranean catchments using a model parameter regionalization approach is presented. The method is developed and tested for the Thau catchment located in Southern France using the SWAT hydrological model. Regionalization of model parameters, based on physical similarity measured between gauged and ungauged catchment attributes, is a popular methodology for discharge prediction in ungauged basins, but it is often confronted with an arbitrary criterion for selecting the "behavioral" model parameter sets (Mps) at the gauged catchment. A more objective method is provided in this paper where the transferrable Mps are selected based on the similarity between the donor and the receptor catchments. In addition, the method allows propagating the modeling uncertainty while transferring the Mps to the ungauged catchments. Results indicate that physically similar catchments located within the same geographic and climatic region may exhibit similar hydrological behavior and can also be affected by similar model prediction uncertainty. Furthermore, the results suggest that model prediction uncertainty at the ungauged catchment increases as the dissimilarity between the donor and the receptor catchments increases. The methodology presented in this paper can be replicated and used in regionalization of any hydrological model parameters for estimating streamflow at ungauged catchment.

scholarly journals Southern High-Latitude Ensemble Data Assimilation in the Antarctic Mesoscale Prediction System

2005 ◽  
Vol 133 (12) ◽  
pp. 3431-3449 ◽  
Author(s):  
D. M. Barker
Keyword(s):  
Data Assimilation ◽  
Sampling Error ◽  
Forecast Error ◽  
Weather Prediction ◽  
Forecast Errors ◽  
Prediction System ◽  
Spatial Covariance ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
The Antarctic

Abstract Ensemble data assimilation systems incorporate observations into numerical models via solution of the Kalman filter update equations, and estimates of forecast error covariances derived from ensembles of model integrations. In this paper, a particular algorithm, the ensemble square root filter (EnSRF), is tested in a limited-area, polar numerical weather prediction (NWP) model: the Antarctic Mesoscale Prediction System (AMPS). For application in the real-time AMPS, the number of model integrations that can be run to provide forecast error covariances is limited, resulting in an ensemble sampling error that degrades the analysis fit to observations. In this work, multivariate, climatologically plausible forecast error covariances are specified via averaged forecast difference statistics. Ensemble representations of the “true” forecast errors, created using randomized control variables of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) three-dimensional variational (3DVAR) data assimilation system, are then used to assess the dependence of sampling error on ensemble size, data density, and localization of covariances using simulated observation networks. Results highlight the detrimental impact of ensemble sampling error on the analysis increment structure of correlated, but unobserved fields—an issue not addressed by the spatial covariance localization techniques used to date. A 12-hourly cycling EnSRF/AMPS assimilation/forecast system is tested for a two-week period in December 2002 using real, conventional (surface, rawinsonde, satellite retrieval) observations. The dependence of forecast scores on methods used to maintain ensemble spread and the inclusion of perturbations to lateral boundary conditions are studied.

Effect of Assimilating Himawari-8 Atmospheric Motion Vectors on Forecast Errors over East Asia

2018 ◽  
Vol 35 (9) ◽  
pp. 1737-1752 ◽  
Author(s):  
Dae-Hui Kim ◽  
Hyun Mee Kim
Keyword(s):  
East Asia ◽  
Geopotential Height ◽  
Forecast Error ◽  
Experimental Period ◽  
Energy Norm ◽  
Observation System ◽  
Forecast Errors ◽  
Motion Vectors ◽  
Atmospheric Motion ◽  
Atmospheric Motion Vectors

AbstractIn this study, the effect of assimilating Himawari-8 (HIMA-8) atmospheric motion vectors (AMVs) on forecast errors in East Asia is evaluated using observation system experiments based on the Weather Research and Forecasting Model and three-dimensional variational data assimilation system. The experimental period is from 1 August to 30 September 2015, during which both HIMA-8 and Multifunctional Transport Satellite-2 (MTSAT-2) AMVs exist. The energy-norm forecast error based on the analysis of each experiment as reference was reduced more by replacing MTSAT-2 AMVs with HIMA-8 AMVs than by adding HIMA-8 AMVs to the MTSAT-2 AMVs. When the HIMA-8 AMVs replaced or were added to MTSAT-2 AMVs, the observation impact was reduced, which implies the analysis–forecast system was improved by assimilating HIMA-8 AMVs. The root-mean-square error (RMSE) of the 500-hPa geopotential height forecasts based on the analysis of each experiment decreases more effectively when the region lacking in upper-air wind observations is reduced by assimilating both MTSAT-2 and HIMA-8 AMVs. When the upper-air radiosonde (SOUND) observations are used as reference, assimilating more HIMA-8 AMVs decreases the forecast error. Based on various measures, the assimilation of HIMA-8 AMVs has a positive effect on the reduction of forecast errors. The effects on the energy-norm forecast error and the RMSE based on SOUND observations are greater when HIMA-8 AMVs replaced MTSAT-2 AMVs. However, the effects on the RMSE of the 500-hPa geopotential height forecasts are greater when both HIMA-8 and MTSAT-2 AMVs were assimilated, which implies potential benefits of assimilating AMVs from several satellites for forecasts over East Asia depending on the choice of measurement.

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