Source Term Estimation Research Articles

Source term estimation using an adjoint model: a comparison of two different algorithms

The location of an unknown source and its pollutant emissions are estimated from concentration observations by means of two approaches, both making use of the adjoint of a Lagrangian particle dispersion model. In the first approach, plausible source locations are estimated by identifying areas with maximum spatial and temporal consistency among backward trajectories from each sensor. In the second approach, a variational method is used to minimise the objective function and to estimate emissions at each grid-box reached by backward trajectories: the resulting map provides information on the source location and on its uncertainty. The two methods are compared using a hierarchy of test cases, starting from a controlled field experiment up to real world operational cases. Both methods give acceptable results with regard to the source location, and the variational method gives a more accurate estimation of the emitted masses. Main sources of uncertainty are discussed.

Source term estimation using an adjoint model: a comparison of two different algorithms

Source term estimation using an adjoint model: a comparison of two different algorithms

An improved simplified method of evaluating severe accident source term in the containment of AP1000

This paper focuses on the source term estimation in AP1000 Nuclear Power Plant (NPP) under severe accident condition. Methods that utilize MELCOR code can evaluate the source term, but the evaluation process is time-consuming. Also, the reference values in NUREG-1465 can be directly used as the severe accident source term; however, it was initially proposed for second–generation reactors and it has difficulty in simulating changes in release fraction along with accident process. In this paper, the Improved Simplified Source Term (ISST) code was developed based on the IAEA’s Simplified Source Term model (SST) presented in IAEA-TECDOC-1127. In addition, the models for core uncovery stage and adiabatic heating-up stage were optimized in this work; taking into account the Accumulator (ACC) and the Core Makeup Tank (CMT) of the emergency core cooling system. Moreover, four fission products release evaluation models were integrated into ISST, making it applicable for the source term evaluation of AP1000 reactor under severe accident initiated by LBLOCA condition. To verify the developed ISST code, the IAEA’s SST, MELCOR and the reference source term in NUREG-1465 were differently used to evaluate the source term released into the containment in a LBLOCA of the AP1000 nuclear power plant. The results obtained from the models in the ISST code were analyzed and compared with each other, and with those obtained from NUREG-1465, IAEA’s SST and MELCOR. Compared with results calculated with the IAEA’s SST, the results calculated with ISST are closer to those calculated using MELCOR. ISST is computationally faster than MELCOR, hence it gives the source term calculation results quicker. Compared with results calculated with MELCOR, the results calculated with ISST are more conservative for Cs and I while the result derived from NUREG-1465 for noble gases is more conservative.

An optimized inverse modelling method for determining the location and strength of a point source releasing airborne material in urban environment

An improved inverse modelling method to estimate the location and the emission rate of an unknown point stationary source of passive atmospheric pollutant in a complex urban geometry is incorporated in the Computational Fluid Dynamics code ADREA-HF and presented in this paper. The key improvement in relation to the previous version of the method lies in a two-step segregated approach. At first only the source coordinates are analysed using a correlation function of measured and calculated concentrations. In the second step the source rate is identified by minimizing a quadratic cost function. The validation of the new algorithm is performed by simulating the MUST wind tunnel experiment. A grid-independent flow field solution is firstly attained by applying successive refinements of the computational mesh and the final wind flow is validated against the measurements quantitatively and qualitatively. The old and new versions of the source term estimation method are tested on a coarse and a fine mesh. The new method appeared to be more robust, giving satisfactory estimations of source location and emission rate on both grids. The performance of the old version of the method varied between failure and success and appeared to be sensitive to the selection of model error magnitude that needs to be inserted in its quadratic cost function. The performance of the method depends also on the number and the placement of sensors constituting the measurement network. Of significant interest for the practical application of the method in urban settings is the number of concentration sensors required to obtain a “satisfactory” determination of the source. The probability of obtaining a satisfactory solution – according to specified criteria –by the new method has been assessed as function of the number of sensors that constitute the measurement network.

Turbulent Schmidt number for source term estimation using Bayesian inference

Source term estimation (STE) addresses the retrieval of emission source information, including location and strength, based on available information. STE can be viewed as an assimilation process of the observed concentration data measured by a sensor network and the predicted concentration data provided by a dispersion model. When considering emissions in complex urban areas, computational fluid dynamics (CFD) approaches are generally used to provide building-resolving results; however, the value of a key parameter, the turbulent Schmidt number Sct, has remained an arbitrary choice. Therefore, it is important to investigate the role of Sct in STE problems and determine its optimum value for the purpose of obtaining better estimation results. In this paper, the impact of Sct on STE problems is examined, and Bayesian inference is used to improve estimation accuracy by treating Sct as an extra unknown parameter. A wind tunnel experiment with a constant point tracer source in an urban-like geometry is used for demonstration. The results show that Sct has a major impact on estimation. Larger Sct values shift the estimated location towards the upwind direction and decrease the estimated strength. Compared with a conventional estimation method performed by using a pre-assigned value of Sct=0.7, treating Sct as an unknown improves point estimates, while the uncertainty increases since the proposed method introduces an extra unknown parameter. For source strength, more improvement in point estimates and a larger increase in uncertainty are shown due to its greater sensitivity of Sct compared with the source location.

Gas emission source term estimation with 1-step nonlinear partial swarm optimization–Tikhonov regularization hybrid method

Abstract Source term identification is very important for the contaminant gas emission event. Thus, it is necessary to study the source parameter estimation method with high computation efficiency, high estimation accuracy and reasonable confidence interval. Tikhonov regularization method is a potential good tool to identify the source parameters. However, it is invalid for nonlinear inverse problem like gas emission process. 2-step nonlinear and linear PSO (partial swarm optimization)–Tikhonov regularization method proposed previously have estimated the emission source parameters successfully. But there are still some problems in computation efficiency and confidence interval. Hence, a new 1-step nonlinear method combined Tikhonov regularization and PSO algorithm with nonlinear forward dispersion model was proposed. First, the method was tested with simulation and experiment cases. The test results showed that 1-step nonlinear hybrid method is able to estimate multiple source parameters with reasonable confidence interval. Then, the estimation performances of different methods were compared with different cases. The estimation values with 1-step nonlinear method were close to that with 2-step nonlinear and linear PSO–Tikhonov regularization method. 1-step nonlinear method even performs better than other two methods in some cases, especially for source strength and downwind distance estimation. Compared with 2-step nonlinear method, 1-step method has higher computation efficiency. On the other hand, the confidence intervals with the method proposed in this paper seem more reasonable than that with other two methods. Finally, single PSO algorithm was compared with 1-step nonlinear PSO–Tikhonov hybrid regularization method. The results showed that the skill scores of 1-step nonlinear hybrid method to estimate source parameters were close to that of single PSO method and even better in some cases. One more important property of 1-step nonlinear PSO–Tikhonov regularization method is its reasonable confidence interval, which is not obtained by single PSO algorithm. Therefore, 1-step nonlinear hybrid regularization method proposed in this paper is a potential good method to estimate contaminant gas emission source term.

Toward quantitative forecasts of volcanic ash dispersal: Using satellite retrievals for optimal estimation of source terms

AbstractAirborne volcanic ash is a hazard to aviation. There is an increasing demand for quantitative forecasts of ash properties such as ash mass load to allow airline operators to better manage the risks of flying through airspace likely to be contaminated by ash. In this paper we show how satellite‐derived mass load information at times prior to the issuance of the latest forecast can be used to estimate various model parameters that are not easily obtained by other means such as the distribution of mass of the ash column at the volcano. This in turn leads to better forecasts of ash mass load. We demonstrate the efficacy of this approach using several case studies.

Source term assessment using inverse modeling and environmental radiation measurements for nuclear emergency response

A simple model ‘Assessment of Source Term for Emergency Response (ASTER)’ for estimation of time-dependent atmospheric source term based on real-time environmental gamma dose rate measurements is developed for application in Online Nuclear Emergency Response System (ONERS). The model inverts the measured dose rates to the airborne radioactive release rate using the dilution factors predicted by a random-walk particle atmospheric dispersion model called System for Prediction of Environmental Emergency Dose Information (SPEEDI) taking into account the real-time meteorological observations and location coordinates of the gamma radiation detectors. A point-kernel method for estimating the cloud-gamma dose rates at receptor locations is implemented in SPEEDI for this purpose. ASTER is validated against Ar-41 routine release data from a pressurized heavy-water reactor (PHWR) at Kalpakkam for over 100 days covering different seasons during 2014–2015. Comparison of ASTER-simulated overhead plume gamma dose rates with the measured dose rates at detector points gives a correlation of R = 0.59, bias of 89 nGy/h, and RMSE of 104.09 nGy/h indicating a slight overestimation of gamma dose rates at monitor points. Comparison of ASTER-predicted source term with Ar-41 release rates indicated that about 90% of the calculations fall within a factor of 5 of the actual source term. A correlation of 0.584, bias of 0.152 TBq/day, and RMSE of 0.192 TBq/day are found between the computed and actual daily source term values indicating reasonable agreement. The slight underestimation in the computed source term is due to the overestimation of the dose rate by the dispersion model, location of few monitors near buildings and tree canopies, uneven distribution of detectors in various wind direction sectors, and the use of daily source term values for comparison.

Thermodynamic model of Ni(II) solubility, hydrolysis and complex formation with ISA

AbstractThe solubility of β–Ni(OH)2(cr) was investigated atT=(22±2)°C in the absence and presence of α-isosaccharinic acid (ISA), the main degradation product of cellulose under alkaline pH conditions. Batch solubility experiments were performed from undersaturation conditions under inert gas (Ar) atmosphere. Solubility experiments in the absence of ISA were conducted in 0.5 and 3.0 M NaCl–NaOH solutions at 7.5≤ pHm≤13 (with pHm=–log10[H+]). XRD analyses of selected solid phases collected after completing the solubility experiments (≈300 days) confirmed that β–Ni(OH)2(cr) remains as solid phase controlling the solubility of Ni(II) in all investigated conditions. Based on the slope analysis (log10[Ni] vs. pHm) of the solubility data and solid phase characterization, the equilibrium reactions β–Ni(OH)2(cr)+2 H+⇔Ni2++2 H2O(l) and β–Ni(OH)2(cr)⇔Ni(OH)2(aq) were identified as controlling the solubility of Ni(II) within the investigated pHmregion. The conditional equilibrium constants determined from the solubility experiments at different ionic strengths were evaluated with the specific ion interaction theory (SIT). In contrast to the current thermodynamic selection in the NEA–TDB, solubility data collected in the present work does not support the formation of the anionic hydrolysis species Ni(OH)3−up to pHm≤13.0. Solubility experiments in the presence of ISA were conducted in 0.5 M NaCl–NaOH–NaISA solutions with 0.01 M≤[NaISA] ≤0.2 M and 9≤ pHm≤13. XRD analyses confirmed that β–Ni(OH)2(cr) is also the solid phase controlling the solubility of Ni(II) in the presence of ISA. Solubility data of all investigated systems can be properly explained with chemical and thermodynamic models including the formation of the complexes NiOHISA(aq), Ni(OH)2ISA−and Ni(OH)3ISA2−. The reported data confirm the low solubility (<10−7M) of Ni(II) in hyperalkaline pH conditions representative of cementitious environments (10≤ pH ≤13), which increases to up to 10−5M in the presence of 0.2 M NaISA. These results significantly improve source term estimations for Ni(II) in environments relevant for the disposal of low and intermediate level radioactive waste (L/ILW). The chemical and thermodynamic models derived in this work can be implemented in geochemical models/calculations, and provide further confidence in the safety analysis of repositories for the disposal of L/ILW.

A Conditional Moment Closure Study of Chemical Reaction Source Terms in SCCI Combustion

The objective of this study is to evaluate conditional moment closure (CMC) approaches to model chemical reaction rates in compositionally stratified, autoigniting mixtures, in thermochemical conditions relevant to stratified charge compression ignition (SCCI) engines. First-order closure, second-order closure and double conditioning are evaluated and contrasted as options in comparison to a series of direct numerical simulations (DNSs). The two-dimensional (2D) DNS cases simulate ignitions in SCCI-like thermochemical conditions with compositionally stratified n-heptane/air mixtures in a constant volume. The cases feature two different levels of stratification with three mean temperatures in the negative-temperature coefficient (NTC) regime of ignition delay times. The first-order closure approach for reaction rates is first assessed using hybrid DNS-CMC a posteriori tests when implemented in an open source computational fluid dynamics (CFD) package known as OpenFOAM $^{{\circledR }}$ . The hybrid DNS-CMC a posteriori tests are not a full CMC but a DNS-CMC hybrid in that they compute the scalar and velocity fields at the DNS resolution, thus isolating the first-order reaction rate closure model as the main source of modelling error (as opposed to turbulence model, scalar probability density function model, and scalar dissipation rate model). The hybrid DNS-CMC a posteriori test reveals an excellent agreement between the model and DNS for the cases with low levels of stratification, whereas deviations from the DNS are observed in cases which exhibit high level of stratifications. The a priori analysis reveals that the reason for disagreement is failure of the first-order closure hypothesis in the model due to the high level of conditional fluctuations. Second-order and double conditioning approaches are then evaluated in a priori tests to determine the most promising path forwards in addressing higher levels of stratification. The a priori tests use the DNS data to compute the model terms, thus directly evaluating the model assumptions. It is shown that in the cases with a high level of stratification, even the second-order estimation of the reaction rate source term cannot provide a reasonably accurate closure. Double conditioning using mixture-fraction and sensible enthalpy, however, provides an accurate first-order closure to the reaction rate source term.

A novel method to improve dose assessment due to severe NPP accidents based on field measurements and particle swarm optimization

Severe nuclear power plant (NPP) accidents are those which involve significant core degradation and lead the plant to conditions more severe than a design basis accident. Under such conditions the accident progression might become unpredictable and the source term estimation, imprecise by orders of magnitude. The consequence is a dose assessment very far from the reality and a deficient decision making support. This work presents a novel approach to improve accuracy of dose estimation, based on field measurements and particle swarm optimization (PSO) algorithm. The main idea is to determine a correction matrix, which once applied to the originally estimated (incorrect) dose distribution map, generates a corrected one, which better fits to the field measurements. The proposed correction matrix is the result of a concatenation of geometric transformations and an amplification/attenuation factor, aimed to fit the shape of the original map and radiation intensities in order to match the field measurements. Finding the optimum transformations (correction matrix) is, however a complex nonlinear optimization problem, which has been successfully solved by using a PSO algorithm. Results demonstrate that PSO was able to find good correction transformations, which can be used to better project future dose distributions and, consequently, improve decision making support.

Bayesian identification of a single tracer source in an urban-like environment using a deterministic approach

This paper presents a two-step deterministic approach for identifying an unknown point source with a constant emission rate in built-up urban areas. The analytic form of the marginal posterior probability density function of the source location is derived to estimate the source location. The emission rate is then estimated using the conditional posterior distribution. Such a procedure deconstructs the calculation of the joint posterior distribution of the source parameters into calculations of two separate distributions and can thus be easily calculated directly and accurately without stochastic sampling. The proposed method is tested using real data obtained in two wind tunnel scenarios of contaminant dispersion in typical urban geometries represented by block arrays. Computational fluid dynamics (CFD) modeling and the adjoint equations are used to calculate the building-resolving source-receptor relationship required in the identification. The estimated source parameters in both cases are close to true values. In both cases, the source locations are identified with errors less than half of the block size, and the emission rates are well estimated, with only slight overestimation. Moreover, in this paper, we test two potential performance indicators for a posteriori evaluation of the credibility of a certain estimation. One indicator is the size of the highest probability density region, and the other is the angle between the observed and predicted concentration vectors, which is derived from the analytic form of the marginal posterior distribution of the source location. Synthetic concentration data are generated to test the validity of both indicators. It is found that the former is not appropriate for denoting the credibility of estimations but that the latter shows a strong correlation with estimation performance and is likely to be an effective performance indicator for Bayesian source term estimation.

Methodology of the source term estimation for DEMO reactor

The problem of source term qualification is one of the most important topics in order to predict possible releases of the Activation Products (APs) and Tritium from the DEMO Fusion reactor. The prevention of any possible consequence, which can affect the environment and the population, is the mission of Fusion technology. In the frame of the EUROfusion Work Package of Safety and Environment (WPSAE) a methodology to scale and evaluate the source terms assessed for ITER and the Fusion Power Plant Conceptual Study (PPCS) has been studied.This paper refers to the activity currently conducted for the DEMO source terms assessment and the preliminary results obtained. During activities in the task, the methodology was developed for the evaluation of Tritium and APs concentration inventory. The methodology is explained in detail for the prediction of the Tritium and APs concentration in Vacuum Vessel (VV) and in the Breeding Blanket (BB) starting from the DEMO current design data and the inventories assumed in ITER, PPCS and SEAFP programs. These results refer to the Helium Cooled Lead Liquid (HCLL) and the Helium Cooled Pebble Bed (HCPB) concepts. The approach is based on the foundations, set in the fission technology safety analysis of the Design Basis Accidents (DBA), Design Extension Conditions (DEC) and Beyond Design Basis Accident (BDBA).

Comparative study between Conditional Moment Closure (CMC) and Conditional Source-term Estimation (CSE) applied to piloted jet flames

Conditional Moment Closure (CMC) and Conditional Source-term Estimation (CSE) are investigated in a direct side-by-side comparison of the Sandia flames. The objective of the present study is to determine the strengths and weaknesses of each modelling approach given the same initial conditions and computational set-up. The predictions from both CMC and CSE are compared to detailed experimental data. In case of Sandia flame D, the CMC and CSE turbulent flow and mixing fields are similar near the nozzle exit, in agreement with the experimental data, with some differences farther downstream. The CMC and CSE conditional mass fractions for the major species are in good agreement with the experimental data downstream of the nozzle for lean mixtures. For fuel rich mixtures, an underprediction of the conditional mass fractions of methane and corresponding overestimation of the conditional mass fraction of water are observed at some axial locations. The CMC and CSE conditional mass fractions of the minor species and conditional temperature reproduce the general characteristics of the experimental profiles. However, major differences are observed for Sandia flame E. The discrepancies are attributed to the use of RANS and the boundary conditions set in CMC and some approximations made in the chemistry tables in CSE. Favre-averaged profiles are comparable in CMC and CSE. The computational time for each model is investigated and CSE appears to be faster. Further discussion on some of the strengths and weaknesses of each combustion model is also presented. Overall, both CMC and CSE are shown to produce results of comparable quality given the same numerical schemes, mesh, and boundary conditions.

Fast source term estimation using the PGA-NM hybrid method

There are significant challenges related to estimating the source term of the atmospheric release. Urged on by robots in performing emergency responding tasks, a fast and accurate algorithm for this inversion problem is indispensable. Sometimes the NM simplex algorithm is efficient in the optimization problem, but sometimes the quality of convergence is unacceptable as a numerical breakdown, even for smooth and well-behaved functions. In contrast, full convergence might be seen in parallel genetic algorithms with a comparative slower convergence. In this paper we combine the PGA and the NM simplex algorithm by initializing simplex from the final individual of PGA results and obtaining the best vertex through simplex algorithm thereafter. A numerical simulation of the proposed algorithm shows noteworthy improvement of efficiency and robustness, compared with the PGA or the NM algorithm only.

Evaluation of mixture-fraction-based turbulent-reaction-rate model assumptions for high-pressure reactive flows

Several assumptions of atmospheric-pressure (atmospheric-p) single-phase turbulent reaction rate models are examined for high-p reactive flows having turbulent characteristics. The study uses a Direct Numerical Simulation (DNS) database described elsewhere (Bellan, 2017). This database was obtained with a model combining multi-species mixing under high- p conditions, a real-gas equation of state (EOS) and a single-step chemical reaction. The database, created in the configuration of a temporal mixing layer, probes the effect of the initial Reynolds number, Re0, of the initial pressure, p0, and of the initial composition of the two mixing-layer streams. The reaction is initiated in a turbulent flow and in each simulation the computations are pursued past a time when a maximum average-volumetric p is attained, tpp*. The examination of the vorticity and enstrophy equations at a time before reaction initiation and also at tpp* highlights the pivotal role of the high density-gradient magnitude regions (Bellan, 2017) in producing turbulence and the crucial role at tpp* of the baroclinic effect representing the misalignments of gradients of thermodynamic variables. Compared to the classical mixture fraction equation, the results show that the mixture fraction obeys an equation in which there is an additional diffusion term having a larger r.m.s. magnitude than that of the mixture-fraction typical diffusion term. An assessment of the Conditional Source-term Estimation (CSE) model assumptions found that, using an accurate mixture fraction probability density function (PDF), one can obtain a very good representation of the turbulent reaction rate in the most intense reaction regions, however the quality of the predictions of CSE deteriorated significantly when a common model for the PDF, the β -PDF, was used despite the β-PDF being constructed with the accurate moments extracted from the DNS. In the regions of minimal reaction, the CSE model using the exact PDF extracted from the DNS does not provide an accurate representation of the turbulent reaction rate, a fact which is attributed to the combined effect of lack of correlation between fluctuations of the reaction rate and of the mixture fraction, and to the strong correlation of the thermodynamic variables through the real-gas EOS in regions of colder and denser fluid.

A forward-backward coupled source term estimation for nuclear power plant accident: A case study of loss of coolant accident scenario

Source term information of radioactive release in a nuclear accident is important for emergency response. Two major categories of source term estimation techniques are forward method based on the status data of nuclear power plant and backward method based on environmental monitoring data. Both data contain considerable uncertainties which can propagate through estimation and result in a biased estimate. To solve the problem, a coupled estimation method is proposed in this study, which combines forward method (Response Technical Manual, RTM-96) and backward method (ensemble Kalman filter). The coupled method builds an evolution model based on a forward estimate and uses it to constrain the temporal correlation of estimates in the backward part, so that the propagation of uncertainties is reduced. Numerical experiments and sensitivity analysis are performed to verify the proposed method, based on a hypocritical loss-of-coolant (LOCA) accident process and the records of a tracer field experiment for Sanmen nuclear power plant. The results demonstrate that the coupled method provides the most accurate estimate in all tests and is more robust to uncertainties in various parameters than both RTM-96 and ensemble Kalman filter.

Evaluation of Bayesian source estimation methods with Prairie Grass observations and Gaussian plume model: A comparison of likelihood functions and distance measures

Source term estimation for atmospheric dispersion deals with estimation of the emission strength and location of an emitting source using all available information, including site description, meteorological data, concentration observations and prior information. In this paper, Bayesian methods for source term estimation are evaluated using Prairie Grass field observations. The methods include those that require the specification of the likelihood function and those which are likelihood free, also known as approximate Bayesian computation (ABC) methods. The performances of five different likelihood functions in the former and six different distance measures in the latter case are compared for each component of the source parameter vector based on Nemenyi test over all the 68 data sets available in the Prairie Grass field experiment. Several likelihood functions and distance measures are introduced to source term estimation for the first time. Also, ABC method is improved in many aspects. Results show that discrepancy measures which refer to likelihood functions and distance measures collectively have significant influence on source estimation. There is no single winning algorithm, but these methods can be used collectively to provide more robust estimates.

Lesson Learned from the February 2014 Fire and Radiation Release Events at the Waste Isolation Pilot Plant in New Mexico, USA

The recently reopened Waste Isolation Pilot Plant (WIPP) is expected to resume accepting new shipments of nuclear waste from across the DOE (Department of Energy) complex by April, 2017. There was no road map for cleaning up the nation’s only deep geologic repository for defense nuclear waste when it was contaminated three years ago after two accidents in February 2014 shutdown the facility and contaminated 22 workers with very low level of radiation as some radioactive material escaped above ground. The dominant radionuclides released were americium and plutonium, in a ratio that matches the content of the breached drum. According to the source term estimation, the actual amount of radioactivity released from the WIPP site was less than 1.5 millicurie. From the modeling, monitoring, and air filter analyses, DOE calculated public doses from this radiation release event to be less than 0.01 mSv (<1 mrem/year), which is well below the 0.1mSv/year (10mrem/year ) regulatory limit. The cumulative effect of inadequacies in ventilation system design and operability, compounded by a slow erosion of safety culture and an atmosphere of complacency, resulted in the release of radioactive material from the WIPP underground. The clean-up is expected to cost DOE almost a billion, and with radiological contamination and reduced air flow underground, the regular work of running WIPP is going to be slow and costly. Nevertheless, the lesson learned as a result of these two accidents at the WIPP is worth documenting as it brought renewed focus on risk, oversight, and emergency planning surrounding the nuclear facility. At some point in the future, the changes made in response to these two events will be seen as a valuable lesson learned on behalf of future repository program.

Development of Northeast Asia Nuclear Power Plant Accident Simulator.

A conclusion from the lessons learned after the March 2011 Fukushima Daiichi accident was that Korea needs a tool to estimate consequences from a major accident that could occur at a nuclear power plant located in a neighboring country. This paper describes a suite of computer-based codes to be used by Korea's nuclear emergency response staff for training and potentially operational support in Korea's national emergency preparedness and response program. The systems of codes, Northeast Asia Nuclear Accident Simulator (NANAS), consist of three modules: source-term estimation, atmospheric dispersion prediction and dose assessment. To quickly assess potential doses to the public in Korea, NANAS includes specific reactor data from the nuclear power plants in China, Japan and Taiwan. The completed simulator is demonstrated using data for a hypothetical release.