Source Term Estimation Research Articles

MODELLING THE SOURCE TERM FOR POLLUTANTS GENERATED FROM GALVANIC CELLS

Being able to estimate the quantity of pollutants that can be spread in the environment is a factor of paramount importance in the industrial panorama. In order to accomplish such aim, a crucial step is the estimation of the so-called source term, that is the rate of generation (or release) of a substance in a generic process. A proper estimation requires to consider both the machinery and the unit operations present into the analysed system. For such reasons, ad hoc considerations and models should be developed for each specific process, in order to represent the reality in the most consistent way. In this paper, a process of the galvanic industry has been taken into account. Specifically, we propose a possible theoretical method for estimating the term of generation from a group of electrolytic deposition tanks. The model is based upon modelling the rupture of bubbles which are formed during the process. The model was applied in order to estimate the pollutants concentration inside the extraction line the and inside the facility. Results have been compared to experimental measurements. Particularly, it has been observed a good agreement between experimental and predicted concentrations for what concerns sulphuric acid and nickel; some deviations can be observed for compounds as nitric acid. This is mainly due to the particular nature of this substance. In fact, nitric acid is subjected to some decomposition reactions which have not be taken into account in the evaporation model. This aspect could be analyzed in future works.

Weak-constraint inverse modeling using HYSPLIT-4 Lagrangian dispersion model and Cross-Appalachian Tracer Experiment (CAPTEX) observations – effect of including model uncertainties on source term estimation

Abstract. A Hybrid Single-Particle Lagrangian Integrated Trajectory version 4 (HYSPLIT-4) inverse system that is based on variational data assimilation and a Lagrangian dispersion transfer coefficient matrix (TCM) is evaluated using the Cross-Appalachian Tracer Experiment (CAPTEX) data collected from six controlled releases. For simplicity, the initial tests are applied to release 2, for which the HYSPLIT has the best performance. Before introducing model uncertainty terms that will change with source estimates, the tests using concentration differences in the cost function result in severe underestimation, while those using logarithm concentration differences result in overestimation of the release rate. Adding model uncertainty terms improves results for both choices of the metric variables in the cost function. A cost function normalization scheme is later introduced to avoid spurious minimal source term solutions when using logarithm concentration differences. The scheme is effective in eliminating the spurious solutions and it also helps to improve the release estimates for both choices of the metric variables. The tests also show that calculating logarithm concentration differences generally yields better results than calculating concentration differences, and the estimates are more robust for a reasonable range of model uncertainty parameters. This is further confirmed with nine ensemble HYSPLIT runs in which meteorological fields were generated with varying planetary boundary layer (PBL) schemes. In addition, it is found that the emission estimate using a combined TCM by taking the average or median values of the nine TCMs is similar to the median of the nine estimates using each of the TCMs individually. The inverse system is then applied to the other CAPTEX releases with a fixed set of observational and model uncertainty parameters, and the largest relative error among the six releases is 53.3 %. At last, the system is tested for its capability to find a single source location as well as its source strength. In these tests, the location and strength that yield the best match between the predicted and the observed concentrations are considered as the inverse modeling results. The estimated release rates are mostly not as good as the cases in which the exact release locations are assumed known, but they are all within a factor of 3 for all six releases. However, the estimated location may have large errors.

An Optimization‐Based Approach for Source Term Estimations of Atmospheric Releases

AbstractThis article highlights an optimization‐based approach to estimate the main characteristics (i.e., location and intensity) that describe the source of a release of a hazardous material (e.g., a chemical or biological agent) in the atmosphere. The method, which uses, as input data, concentration measurements provided by a network of sensors (and some additional meteorological data), is first formalized in the general framework of parametric source term estimation inverse problems. Some simple and new geometric interpretations of the method are proposed. Next, it is shown that the approach can also provide a realistic understanding of the uncertainties in the estimation of the characteristics of the source. Then, a strategy for an optimal deployment of mobile sensors in hazardous materials operations is proposed. To conclude, the source estimation approach and the network design strategy are evaluated with data from a wind tunnel experiment conducted at the Environmental Flow Research Center at the University of Surrey, UK.

Source term estimation of a hazardous airborne release using an unmanned aerial vehicle

AbstractGaining information about an unknown gas source is a task of great importance with applications in several areas, including responding to gas leaks or suspicious smells, quantifying sources of emissions, or in an emergency response to an industrial accident or act of terrorism. In this paper, a method to estimate the source term of a gaseous release using measurements of concentration obtained from an unmanned aerial vehicle (UAV) is described. The source term parameters estimated include the three‐dimensional location of the release, its emission rate and other important variables needed to forecast the spread of the gas using an atmospheric transport and dispersion model. The parameters of the source are estimated by fusing concentration observations from a gas detector on‐board the aircraft, with meteorological data and an appropriate model of dispersion. Two models are compared in this paper, both derived from analytical solutions to the advection–diffusion equation. Bayes’ theorem, implemented using a sequential Monte Carlo algorithm, is used to estimate the source parameters to take into account the large uncertainties in the observations and formulated models. The system is verified with novel, outdoor, fully automated experiments, where observations from the UAV are used to estimate the parameters of a diffusive source. The estimation performance of the algorithm is assessed subject to various flight path configurations and wind speeds. Observations and lessons learned during these unique experiments are discussed, and areas for future research are identified.

Source term estimation of multi‐specie atmospheric release of radiation from gamma dose rates

Determination of a source term of an accidental release of radioactive material into the atmosphere is very important for evaluating emergency situations and their consequences. However, knowledge of the source term and its composition is typically vague and uncertain. One possible way to obtain the source term is inverse modeling in which an atmospheric transport model is combined with field measurements. The most accessible measurements are those from gamma dose rate (GDR) detectors. However, GDR measurements represent a sum of contribution from all nuclides from both plume and deposition which makes the problem particularly difficult. The same difficulty arises when the measurements can not distinguish contribution from another species in the release, such as nuclides attached to different particle sizes. We propose a Bayesian method for recovery of the source term from GDR measurements where a priori knowledge on ratios of different species is given in the form of bounds. This knowledge is incorporated into the model of covariance matrix of the source term. The Bayesian methodology allows to handle uncertain knowledge on the nuclide ratios as well as unknown temporal correlations of the source term. We evaluate and compare the proposed method with other state‐of‐the‐art methods on a twin experiment of a non‐stationary release of 16 nuclides from the Czech nuclear power plant Temelin being registered by the Austrian GDR monitoring network. Real‐world validation of the approach is performed on the latest measurements of concentration and deposition of caesium‐137 from the Chernobyl accident, where we estimate composition of the source term from different particle sizes (species). The estimated source term is in very good agreement with previously reported results and the calculated species ratios are supported by the available observations.

Source term estimation of hazardous material releases using hybrid genetic algorithm with composite cost functions

Source term estimation (STE) of atmospheric dispersion plays an important role in public safety, environmental protection and many other application fields. In this paper, several new composite cost functions for STE using hybrid genetic algorithm are proposed and compared using Nemenyi test based on 68 STE tasks from Prairie Grass field experiment. Results show one of the new composite cost functions, named as WSD, has outstanding performance in estimating both source location and emission rate. Then the patterns in STE results using different cost functions are analyzed based on the 68 tasks mentioned above, which provides further insights into what to expect from STE. At last, the relationship between composite cost functions and multi-objective optimization is analyzed to facilitate the understanding of composite cost functions. To summarize, composite cost functions such as WSD has the potential to achieve a better balance between sensitivity and robustness of cost functions applied in STE, providing the most accurate estimates. Statistical algorithm comparison techniques like Nemenyi test can help us better understand the characteristics and performance of specific settings in STE methods.

Evaluating Methods To Estimate Methane Emissions from Oil and Gas Production Facilities Using LES Simulations.

Large-eddy simulations (LES) coupled to a model that simulates methane emissions from oil and gas production facilities are used to generate realistic distributions of meteorological variables and methane concentrations. These are sampled to obtain simulated observations used to develop and evaluate source term estimation (STE) methods. A widely used EPA STE method (OTM33A) is found to provide emission estimates with little bias when averaged over six time periods and seven well pads. Sixty-four percent of the emissions estimated with OTM33A are within ±30% of the simulated emissions, showing a slightly larger spread than the 72% found previously using controlled release experiments. A newly developed method adopts the OTM33A sampling strategy and uses a variational or a stochastic STE approach coupled to an LES to obtain a better fit to the sampled meteorological conditions and to account for multiple sources within the well pad. This method can considerably reduce the spread of the emissions estimates compared to OTM33A (92-95% within ±30% percent error), but it is associated with a substantial increase in computational cost due to the LES. It thus provides an alternative when the additional costs can be afforded to obtain more precise emission estimates.

Computational source term estimation of the Gaussian puff dispersion

The hazardous or toxic chemical releases have a detrimental impact on public safety. Estimating source parameters is of particular importance in aiding emergency response and post-assessment. Source term estimation from sensor measurements with a given Gaussian puff dispersion model is a typical inverse problem, which can be transformed into an optimization problem. In this paper, we employed the particle swarm optimization, the Nelder–Mead method, and their hybrid method to solve the optimization problem. Furthermore, we proposed a three-dimensional neighborhood topology which considerably improves performance of the particle swarm optimization. We implemented all these algorithms in JAVA on an embedded system to make a preliminary estimation of the accidental puff release. Numerical experiments with synthetic datasets show that the particle swarm optimization maintains a balance between computation time, accuracy, robustness, and implementation complexity. In contrast, the hybrid algorithm has an advantage in computation time at the expense of more sophisticated implementation.

Information-Based Search for an Atmospheric Release Using a Mobile Robot: Algorithm and Experiments

Finding the location and strength of an unknown hazardous release is of paramount importance in emergency response and environmental monitoring; thus, it has been an active research area for several years known as source term estimation (STE). This paper presents a joint Bayesian estimation and planning algorithm to guide a mobile robot to collect informative measurements, allowing the source parameters to be estimated quickly and accurately. The estimation is performed recursively using Bayes’ theorem, where uncertainties in the meteorological and dispersion parameters are considered and the intermittent readings from a low-cost gas sensor are addressed by a novel likelihood function. The planning strategy is designed to maximize the expected utility function based on the estimated information gain of the source parameters. Subsequently, this paper presents the first experimental result of such a system in turbulent, diffusive conditions, in which a ground robot equipped with the low-cost gas sensor responds to the hazardous source simulated by incense sticks. The experimental results demonstrate the effectiveness of the proposed estimation and search algorithm for STE based on the mobile robot and the low-cost sensor.

Potential Source Apportionment and Meteorological Conditions Involved in Airborne 131I Detections in January/February 2017 in Europe

Traces of particulate radioactive iodine (131I) were detected in the European atmosphere in January/February 2017. Concentrations of this nuclear fission product were very low, ranging 0.1 to 10 μBq m-3 except at one location in western Russia where they reached up to several mBq m-3. Detections have been reported continuously over an 8-week period by about 30 monitoring stations. We examine possible emission source apportionments and rank them considering their expected contribution in terms of orders of magnitude from typical routine releases: radiopharmaceutical production units > sewage sludge incinerators > nuclear power plants > spontaneous fission of uranium in soil. Inverse modeling simulations indicate that the widespread detections of 131I resulted from the combination of multiple source releases. Among them, those from radiopharmaceutical production units remain the most likely. One of them is located in Western Russia and its estimated source term complies with authorized limits. Other existing sources related to 131I use (medical purposes or sewage sludge incineration) can explain detections on a rather local scale. As an enhancing factor, the prevailing wintertime meteorological situations marked by strong temperature inversions led to poor dispersion conditions that resulted in higher concentrations exceeding usual detection limits in use within the informal Ring of Five (Ro5) monitoring network.

Evaluation of an inverse modelling methodology for the prediction of a stationary point pollutant source in complex urban environments

The estimation of a hazardous contaminant unknown source characteristics (i.e., rate and location) in a complex urban environment using efficient inverse modelling techniques is a challenging problem that involves advanced computational fluid dynamics combined with appropriate mathematical algorithms. In this paper we further assess our recently proposed inverse source term estimation method (Efthimiou et al., 2017, Atmos. Environ., 170, 118–129) by applying it in two wind tunnel experiments simulating atmospheric flow and tracer dispersion following a stationary release in realistic urban settings, namely Michelstadt and Complex Urban Terrain Experiment (CUTE). The method appears to be robust and to predict with encouraging accuracy the source location and emission rate for both wind tunnel experiments.

Enforcing conservation at Monte Carlo level in a coupled finite volume—Monte Carlo simulation

Plasma edge simulations generally require coupling a finite‐volume (FV) fluid plasma discretization with a kinetic neutral Monte Carlo (MC) model. From the MC neutral simulation, source terms are estimated and inserted into the FV plasma simulation, transferring MC stochasticity to the FV simulation. While the FV simulation is based on the physical principle of conservation, most common MC source term estimators do not strictly conserve mass, momentum, and energy. Through the coupling, this also breaks the conservation in the FV simulation. Losing conservation can lead to numerical instability on the one hand and the inability to fulfil global mass, momentum, and energy balances on the other. Presently, the SOLPS‐ITER code rescales the neutral flux launched from the target to mitigate the instability. In this paper, we propose to instead independently rescale each of the source term estimations. We show that rescaling introduces a bias that depends on the number of MC particles and is negligible compared to the statistical error in the simulation. The resulting method restores all conservation properties in the FV/MC code, which will allow monitoring of the convergence through stabilization of the global quantities.

Estimation of external plume dose for a coastal site

Estimation of external exposure due to atmospheric releases of photon emitting radionuclides from nuclear facilities is important during normal and emergency conditions since their contribution to the total public dose is relatively significant. Gaussian plume model coupled with either semi-infinite cloud model or finite plume integration method is normally used to estimate this dose component for inland sites. However, for coastal sites, shoreline dispersion model that handles local sea-land breeze circulation should be coupled with the external plume dose-computing module. In the present study, plume external photon dose is calculated for coastal sites considering appropriate combination of these two dispersion models and finite plume integration model. Based on this methodology, a simple and fast calculation tool is developed to estimate external plume doses under normal operating (sector average plume) and accidental conditions (single plume). This numerical program can also be used for source term estimation from field measurements, thereby assisting decision support system during emergency situations.

Spatial gradients of conditional averages in turbulent flames

Data from the two different methane/air jet experimental databases is examined to understand the effects of spatial variations in the conditional averages of reactive scalars in the context of the Conditional Source-term Estimation (CSE) model, with particular emphasis on the double-conditional version, DCSE. Data from all spatial locations and from the four turbulent flames in the Sandia/TUD database are averaged together to generate a global, two-condition (mixture fraction and a species-based reaction progress variable) conditional average; it is found that the conditional fluctuations around this new, two-condition conditional average do not exhibit any significant dependence on either spatial location or even on Reynolds number. Furthermore, the normalized RMS of the conditional fluctuations are found to be dramatically lower than those seen using only one condition. These results indicate that the two-condition conditional averages in the Sandia/TUD piloted methane/air flame series do not vary in space, nor vary with Reynolds number. In the Sydney Swirl burner data, the two-condition conditional averages exhibit modest gradients in space which are attributed to heat transfer with the bluff body in the flow; when a third condition is added – based on the total enthalpy – the spatial gradients in the conditional average of temperature a dramatically reduced. In both of the cases tested, the normalized RMS of the conditional fluctuations around both the two- and three-condition conditional averages are relatively small. These results imply that, in a simulation of these flames using DCSE, one would be better off ignoring spatial variations in the conditional averages and instead collecting all of the spatial data together for the integral equation inversion using either two or three conditioning variables in a single, global ensemble species involved in relatively slow chemistry, such as NO. The results also imply that the underlying assumptions in DCSE – that the conditional variances and the spatial gradients in the conditional averages are both negligible – are valid when sufficient conditioning variables are used, at least for the flames considered.

Uncertainty analysis of containment dose rate for core damage assessment in nuclear power plants

Abstract One of the most widely used methods to estimate core damage during a nuclear power plant accident is containment radiation measurement. The evolution of severe accidents is extremely complex, leading to uncertainty in the containment dose rate (CDR). Therefore, it is difficult to accurately determine core damage. This study proposes to conduct uncertainty analysis of CDR for core damage assessment. First, based on source term estimation, the Monte Carlo (MC) and point-kernel integration methods were used to estimate the probability density function of the CDR under different extents of core damage in accident scenarios with late containment failure. Second, the results were verified by comparing the results of both methods. The point-kernel integration method results were more dispersed than the MC results, and the MC method was used for both quantitative and qualitative analyses. Quantitative analysis indicated a linear relationship, rather than the expected proportional relationship, between the CDR and core damage fraction. The CDR distribution obeyed a logarithmic normal distribution in accidents with a small break in containment, but not in accidents with a large break in containment. A possible application of our analysis is a real-time core damage estimation program based on the CDR.

Spatial-aware source estimation in building downwash environments

This paper introduces a concept called ”spatial-aware” source-term estimation (STE), which utilizes the acquired knowledge of how the forward model (FM) predictions compare against observations spatially to enhance the overall quality of source estimation. We evaluated the effectiveness of the proposed method against a wind tunnel experiment simulating low-stack plume dispersion in a building downwash environment with known source locations and varying building aspect ratios and building angles relative to wind direction, selected to achieve the balance of physical complexity and environmental controllability. Specifically, we adopted AERMOD as the FM, constructed spatially-resolved error models by comparing the FM predictions against measured concentrations for one configuration, referred to as the training case. Then the error models were applied to the rest of the configurations, referred to as test cases, for estimating emission rates. The results show that the spatial-aware STE can improve the estimation results, due to closer agreement between the error models and error distribution in terms of shapes and/or magnitudes. For example, using the vertical measurements around the H/W = 1/2 building with wind direction perpendicular to the building as the training case and applying the spatially-resolved error models, the estimated emission rates for all test cases with same building aspect ratio differ from the true emission rate by less than 30%, compared to 63.2%–236% without applying any error models. We argue that parallel efforts, i.e., improving the accuracy of FMs and quantifying the FM performance are needed to further advance the science and applications of source estimation.

Post-operation radiological source term and dose rate estimates for the Scalable LIquid Metal-cooled small Modular Reactor

The Scalable LIquid Metal cooled small Modular (SLIMM-1.2) reactor has recently been developed at the University of New Mexico’s Institute for Space and Nuclear Power Studies, for generating 10–100 MWth continuously without refueling for ∼66–6.3 full-power years, respectively. The SLIMM reactor is cooled by natural circulation of in-vessel liquid sodium during nominal operation and after shutdown. It is assembled, fueled, and sealed in the factory, and transported by rail, a heavy-duty truck, or a barge to the site and installed below ground on seismic isolation bearings. Following end of life (EOL) shutdown, the SLIMM reactor remains temporarily on site until the external dose rate decreases to an acceptable level for safe handling. The conducted analyses calculate the post-operation radiological source term, the photon and neutron emission rates, and the biological dose rates in the reactor and outside the guard vessel at EOL and as a function of time after shutdown. When operating at 100 MWth before shutdown, the calculated biological dose rate outside the SLIMM reactor guard vessel ∼65 days after EOL shutdown is slightly lower than the US federal transportation limit of 0.2 rem/h. It decreases to 0.069 and 0.036 rem/h, 6 months and 1 year after EOL shutdown, respectively. These rates ensure safe handling, removal and transportation of the post-operation SLIMM-1.2 reactor back to the factory or a processing facility, and subsequent replacement by another unit loaded with fresh fuel. Results also show that for the same thermal power of 100 MWth and operation life of 6.3 full power years, the SLIMM reactor generates ∼179% more 239Pu and ∼72–100% less minor actinides than a comparable PWR. The former decreases the fissile depletion during reactor operation, while the latter decrease the toxicity of used fuel at EOL.

Bayesian source term estimation of atmospheric releases in urban areas using LES approach

The estimation of source information from limited measurements of a sensor network is a challenging inverse problem, which can be viewed as an assimilation process of the observed concentration data and the predicted concentration data. When dealing with releases in built-up areas, the predicted data are generally obtained by the Reynolds-averaged Navier-Stokes (RANS) equations, which yields building-resolving results; however, RANS-based models are outperformed by large-eddy simulation (LES) in the predictions of both airflow and dispersion. Therefore, it is important to explore the possibility of improving the estimation of the source parameters by using the LES approach. In this paper, a novel source term estimation method is proposed based on LES approach using Bayesian inference. The source-receptor relationship is obtained by solving the adjoint equations constructed using the time-averaged flow field simulated by the LES approach based on the gradient diffusion hypothesis. A wind tunnel experiment with a constant point source downwind of a single building model is used to evaluate the performance of the proposed method, which is compared with that of the existing method using a RANS model. The results show that the proposed method reduces the errors of source location and releasing strength by 77% and 28%, respectively.

Solubility and hydrolysis of U(VI) in 0.5 mol/kg NaCl solutions at T = 22 and 80 °C

Abstract In this study, the solubility and hydrolysis of U(VI) was investigated at elevated temperature. Experiments were performed in solutions of NaCl 0.51 mol/kg of water in the pHm range 4–13.4 (with pHm = –log[H+]) at T = 22 ± 3 and 80 ± 5 °C under N2/Ar atmosphere. Two solid phases were used as starting U(VI) materials: UO3·2H2O(cr) equilibrated in solutions with 4 ≤ pHm ≤ 5 and Na2U2O7·H2O(cr) (a synthetic clarkeite-like phase) equilibrated in solutions with 7 ≤ pHm ≤ 13.4. The solubility of UO3·2H2O(cr) at T = 80 °C in the acidic pHm-range is decreased by a factor of ten with respect to the U(VI)-phase studied at room temperature. Solid phase characterization by XRD, SEM–EDS and quantitative chemical analysis indicate that this decrease is correlated to the transformation of UO3·2H2O(cr) into a sodium-containing U(VI) solid phase. The solubility of Na2U2O7·H2O(cr) in the alkaline pHm-range is drastically enhanced at T = 80 °C. The main contribution to this effect results from the increased acidity of water at elevated temperature, although an enhanced hydrolytic activity of U(VI) or a decreased stability of Na2U2O7·H2O(cr) are also considered, in order to explain the solubility data under these experimental conditions. Solid phase characterization after completing the solubility experiments confirms that Na2U2O7·H2O(cr) retains its stoichiometry and structure at T = 80 °C within the timeframe of the study (t ≤ 290 days). A thermodynamic model, comprising conditional stability constants and enthalpy data has been developed, describing reliably the processes of hydrolytic dissolution of Na2U2O7·H2O(cr) in alkaline NaCl solutions up to 0.51 mol/kg and their temperature dependence in the range 22–80 °C. This work represents the first comprehensive study on U(VI) solubility and hydrolysis at elevated temperatures extending to alkaline pHm conditions relevant in the context of nuclear waste disposal. It provides robust solubility upper limits to be considered in source term estimations, but also thermodynamic data for both solid phases and aqueous species that can be implemented in thermodynamic databases and related geochemical calculations.

Comparisons between a new point kernel-based scheme and the infinite plane source assumption method for radiation calculation of deposited airborne radionuclides from nuclear power plants

Radiation from the deposited radionuclides is indispensable information for environmental impact assessment of nuclear power plants and emergency management during nuclear accidents. Ground shine estimation is related to multiple physical processes, including atmospheric dispersion, deposition, soil and air radiation shielding. It still remains unclear that whether the normally adopted “infinite plane” source assumption for the ground shine calculation is accurate enough, especially for the area with highly heterogeneous deposition distribution near the release point. In this study, a new ground shine calculation scheme, which accounts for both the spatial deposition distribution and the properties of air and soil layers, is developed based on point kernel method. Two sets of “detector-centered” grids are proposed and optimized for both the deposition and radiation calculations to better simulate the results measured by the detectors, which will be beneficial for the applications such as source term estimation. The evaluation against the available data of Monte Carlo methods in the literature indicates that the errors of the new scheme are within 5% for the key radionuclides in nuclear accidents. The comparisons between the new scheme and “infinite plane” assumption indicate that the assumption is tenable (relative errors within 20%) for the area located 1 km away from the release source. Within 1 km range, the assumption mainly causes errors for wet deposition and the errors are independent of rain intensities. The results suggest that the new scheme should be adopted if the detectors are within 1 km from the source under the stable atmosphere (classes E and F), or the detectors are within 500 m under slightly unstable (class C) or neutral (class D) atmosphere. Otherwise, the infinite plane assumption is reasonable since the relative errors induced by this assumption are within 20%. The results here are only based on theoretical investigations. They should be further thoroughly evaluated with real measurements in the future.