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All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Metrics for Offline Evaluation of Prognostic Performance

Prognostic performance evaluation has gained significant attention in the past few years. Currently, prognostics concepts lack standard definitions and suffer from ambiguous and inconsistent interpretations. This lack of standards is in part due to the varied end-user requirements for different applications, time scales, available information, domain dynamics, etc. to name a few. The research community has used a variety of metrics largely based on convenience and their respective requirements. Very little attention has been focused on establishing a standardized approach to compare different efforts. This paper presents several new evaluation metrics tailored for prognostics that were recently introduced and were shown to effectively evaluate various algorithms as compared to other conventional metrics. Specifically, this paper presents a detailed discussion on how these metrics should be interpreted and used. These metrics have the capability of incorporating probabilistic uncertainty estimates from prognostic algorithms. In addition to quantitative assessment they also offer a comprehensive visual perspective that can be used in designing the prognostic system. Several methods are suggested to customize these metrics for different applications. Guidelines are provided to help choose one method over another based on distribution characteristics. Various issues faced by prognostics and its performance evaluation are discussed followed by a formal notational framework to help standardize subsequent developments.

Saxena, Abhinav↗
NASA NTRS2012

NASA Tech Briefs, October 2012

Topics discussed include: Detection of Chemical Precursors of Explosives; Detecting Methane From Leaking Pipelines and as Greenhouse Gas in the Atmosphere; Onboard Sensor Data Qualification in Human-Rated Launch Vehicles; Rugged, Portable, Real-Time Optical Gaseous Analyzer for Hydrogen Fluoride; A Probabilistic Mass Estimation Algorithm for a Novel 7-Channel Capacitive Sample Verification Sensor; Low-Power Architecture for an Optical Life Gas Analyzer; Online Cable Tester and Rerouter; A Three-Frequency Feed for Millimeter-Wave Radiometry; Capacitance Probe Resonator for Multichannel Electrometer; Inverted Three-Junction Tandem Thermophotovoltaic Modules; Fabrication of Single Crystal MgO Capsules; Inflatable Hangar for Assembly of Large Structures in Space; Mars Aqueous Processing System; Hybrid Filter Membrane; Design for the Structure and the Mechanics of Moballs; Pressure Dome for High-Pressure Electrolyzer; Cascading Tesla Oscillating Flow Diode for Stirling Engine Gas Bearings; Compact, Low-Force, Low-Noise Linear Actuator; Ultra-Compact Motor Controller; Extreme Ionizing-Radiation-Resistant Bacterium; Wideband Single-Crystal Transducer for Bone Characterization; Fluorescence-Activated Cell Sorting of Live Versus Dead Bacterial Cells and Spores; Nonhazardous Urine Pretreatment Method; Laser-Ranging Transponders for Science Investigations of the Moon and Mars; Ka-Band Waveguide Three-Way Serial Combiner for MMIC Amplifiers; Structural Health Monitoring with Fiber Bragg Grating and Piezo Arrays; Low-Gain Circularly Polarized Antenna with Torus-Shaped Pattern; Stereo and IMU- Assisted Visual Odometry for Small Robots; Global Swath and Gridded Data Tiling; GOES-R: Satellite Insight; Aquarius iPhone Application; Monitoring of International Space Station Telemetry Using Shewhart Control Charts; Theory of a Traveling Wave Feed for a Planar Slot Array Antenna; Time Manager Software for a Flight Processor; Simulation of Oxygen Disintegration and Mixing With Hydrogen or Helium at Supercritical Pressure; A Superfluid Pulse Tube Refrigerator Without Moving Parts for Sub-Kelvin Cooling; Sapphire Viewports for a Venus Probe; The Mobile Chamber; Electric Propulsion Induced Secondary Mass Spectroscopy; and Radiation-Tolerant DC-DC Converters.

Source record↗
NASA NTRS2013

Evaluating NMME Seasonal Forecast Skill for use in NASA SERVIR Hub Regions

The U.S. National Multi-Model Ensemble seasonal forecasting system is providing hindcast and real-time data streams to be used in assessing and improving seasonal predictive capacity. The coupled forecasts have numerous potential applications, both national and international in scope. The NASA / USAID SERVIR project, which leverages satellite and modeling-based resources for environmental decision making in developing nations, is focusing on the evaluation of NMME forecasts specifically for use in driving applications models in hub regions including East Africa, the Hindu Kush- Himalayan (HKH) region and Mesoamerica. A prerequisite for seasonal forecast use in application modeling (e.g. hydrology, agriculture) is bias correction and skill assessment. Efforts to address systematic biases and multi-model combination in support of NASA SERVIR impact modeling requirements will be highlighted. Specifically, quantilequantile mapping for bias correction has been implemented for all archived NMME hindcasts. Both deterministic and probabilistic skill estimates for raw, bias-corrected, and multi-model ensemble forecasts as a function of forecast lead will be presented for temperature and precipitation. Complementing this statistical assessment will be case studies of significant events, for example, the ability of the NMME forecasts suite to anticipate the 2010/2011 drought in the Horn of Africa and its relationship to evolving SST patterns.

Roberts, J. Brent↗
NASA NTRS2015

On-Line Safe Flight Envelope Determination for Impaired Aircraft

The design and simulation of an on-line algorithm which estimates the safe maneuvering envelope of aircraft is discussed in this paper. The trim envelope is estimated using probabilistic methods and efficient high-fidelity model based computations of attainable equilibrium sets. From this trim envelope, a robust reachability analysis provides the maneuverability limitations of the aircraft through an optimal control formulation. Both envelope limits are presented to the flight crew on the primary flight display. In the results section, scenarios are considered where this adaptive algorithm is capable of computing online changes to the maneuvering envelope due to impairment. Furthermore, corresponding updates to display features on the primary flight display are provided to potentially inform the flight crew of safety critical envelope alterations caused by the impairment.

algorithms↗
NASA NTRS2014

Latest NASA Instrument Cost Model (NICM): Version VI

The NASA Instrument Cost Model, NICM, is a suite of tools which allow for probabilistic cost estimation of NASA's space-flight instruments at both the system and subsystem level. NICM also includes the ability to perform cost by analogy as well as joint confidence level (JCL) analysis. The latest version of NICM, Version VI, was released in Spring 2014. This paper will focus on the new features released with NICM VI, which include: 1) The NICM-E cost estimating relationship, which is applicable for instruments flying on Explorer-like class missions; 2) The new cluster analysis ability which, alongside the results of the parametric cost estimation for the user's instrument, also provides a visualization of the user's instrument's similarity to previously flown instruments; and 3) includes new cost estimating relationships for in-situ instruments.

Mrozinski, Joe↗
DOE OSTI2022

Natural Language Processing-Enhanced Nuclear Industry Operating Experience Data Analysis to Support Risk Model Parameter Estimations

This set of slides has been prepared for a talk at the INL AI/ML Symposium held on September 8, 2022. Presentation outline: Background - Nuclear power plant operating experience data sources • Research focus and motivation - Analyzing free-text operating experience data: present and future • Research method - Input - Methodological steps - Output • Conclusions and next steps

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2018

Long-term Prediction of Spent Fuel Storage Canisters - Issues and Approaches - 18112

The considerations involved in aging management of stainless steel canisters used in dry cask storage of spent fuel are discussed in this paper in a probabilistic framework. The two important modes of degradation in the long-term performance of the casks are localized corrosion and stress corrosion cracking initiating from the external surface. These degradation modes are determined by the evolution of the electrochemical behavior and the strain rate of the stainless steel. Laboratory data suggest that Type 304L/316L stainless steels should suffer from localized corrosion and SCC under salt deposits. However, field inspection of the canisters has not indicated extensive pitting or stress corrosion cracking. This discrepancy can be understood in terms of the joint probabilities of various factors that must come together to cause these failure modes. A Bayesian network approach is suggested as the framework for modeling the probability of failure of canisters and long-term management of aging. The gaps in knowledge to conduct a Bayesian network analysis are discussed. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Self-Navigating Robotic Assistant for Long-term Wide Area Floor Contamination Monitoring - 18213

For Routine Contamination Testing (RCT) of an operational nuclear facility or one undergoing Decontamination and Decommissioning (D and D), workers utilize specified travel corridors (enclosed hallways or areas designated with tape or rope in open spaces) to minimize the risk of spreading contamination. Radiation health technicians must continually monitor these areas for contamination to minimize worker dose and prevent its spread to uncontrolled areas. For many sites, it is impractical for the technician to adequately survey such areas. The Nuclear Robotics Group (NRG) at UT Austin has developed a self-navigating robotic platform equipped with radiation detectors to augment the technician to improve both survey coverage and frequency. While maintaining a controlled velocity and floor-to-sensor distance, the system takes radiation measurements to identify and/or localize contamination sources. Readings above background levels are reported to a remote workstation to allow technicians to then respond accordingly. The completion of these mundane tasks by a robot allows the trained technicians to focus instead on operational tasks, D and D, or responding to alarms. The robot generates a map using a Lidar range sensor with established Simultaneous Localization and Mapping (SLAM) techniques. Range data is then compared to previously generated maps for robot localization and autonomous navigation. To maximize coverage, the robot executes a Complete Coverage Path Planning (CCPP) algorithm using a static map that is discretized into cells to assure comprehensive yet efficient coverage. As the system executes the generated plan, it builds a radiation map of the area for the technician to validate and verify the results. If contamination is detected, the robot will minimize the spread of contamination by stopping, sounding an alarm, and alerting the remote technician. The proposed system allows for long-term deployment with minimal human intervention. It is robust to both highly dynamic obstacles (i.e. personnel) and low dynamic changes (i.e. temporally stored cart) in the environment. This is accomplished through continuous spatiotemporal updating of the static map, and the utilization of a supervisory controller that allows the system to reliably respond to unexpected events or states (i.e. low battery, closed door, etc.). While the supervisory controller can respond to temporary complications, the system also recognizes the environment can change slowly over time necessitating that the CCPP automatically be updated to account for observed 'permanent' changes to the environment. To do this, the robot uses intermediate probabilistic data to make intelligent planning decisions offline while still maintaining local reactive obstacle avoidance and safety features. The self-updating of the map allows the robot to run longer without the need to remake the map, leading to more consistent monitoring data and less maintenance from the human technician. This paper will review the current system hardware, the CCPP algorithm, the supervisory controller, the probabilistic algorithm for identifying permanent changes, and recent experiments at UT Austin to experimentally validate the reported sensor coverage. (authors)

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DOE OSTI2018

Subsidence Risk Mitigation in LLW Final Covers: A Case History - 18286

Typical waste packaging and disposal practices at low-level radioactive waste (LLW) disposal facilities, while adequate for safe transportation and facility operations, introduce significant subsidence risk for the facilities' final cover systems. Subsidence is often caused by the collapse of voids in the waste packaging and surrounding cover soils that eventually propagate to the ground surface. This subsidence could result in the loss of final cover system integrity, stormwater ponding, concentrated flow of surface water and/or gas, increased leachate generation, transport of contaminants from the landfill, and potential adverse chemical reactions within the waste mass. For new LLW facilities, steps can be taken to minimize the subsidence potential of newly placed waste. However, there is a significant inventory of existing disposal facilities (trenches, pits, shafts, vaults, etc.), tank farms, and historic, subsurface burial grounds that require final covers - all of which present real subsidence risks. Final covers for these existing LLW disposal sites can be engineered to mitigate these risks. A case history of a 60-acre LLW final cover system design and construction project illustrates an innovative approach to this problem that offers superior risk mitigation compared to typical final cover design practice. This approach combines engineering simulations of the projected final cover mechanical performance, specific probabilistic performance targets, and targeted cover system improvements to manage the subsidence risk in the final design. The result is a successful final cover project that meets the needs of the community, state and federal regulators, and facility owner. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Explicitly Accounting for Multiple Spatial Scales in Data Informing Kd Distribution Development - 18414

This work provides an example of incorporating information about spatial scale into input parameter distribution development for probabilistic performance assessment (PA) models and highlights the importance of having an interdisciplinary team working together in the distribution development process. The novel approach is demonstrated for contaminant partitioning coefficients for water/rock (Kds), providing a specific context for discussing the approach and challenges, but the strategy is general and applies to many other PA model inputs. In this example, a PA model of a radioactive waste disposal site is assumed to have large cells representing spatial scales of up to thousands of square meters in area and several to tens of meters in depth. Spatial variability in Kds is expected to occur at scales much smaller than the units in the model domain over which values are applied, and therefore values are intended to represent possible averages over the modeled volume (i.e., spatial scale represented by a PA model cell). Information about possible Kd values for each material category comes from a variety of literature sources, and the reported results used to inform the distribution are available at a wide range of scales (e.g., individual batch experiment results from small-scale samples, averages based on samples collected over site-scales, averages based on samples from large regions, etc.). Effectively addressing the mismatch in spatial scale between available information and the PA Model is a challenge within PA distribution development and it takes effort from database managers, subject matter experts, modelers, and statisticians. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

A Decision-Based Iterative Modeling Framework for Probabilistic Performance Assessments - 18441

Radiological probabilistic performance assessments (PPAs) employ highly complex computer models to simulate the release of radionuclides from wastes in an engineered disposal facility, migration of these radionuclides over time, and resulting radiological dose to potentially exposed individuals. An iterative approach to PPA modeling is described to help define the appropriate complexity of the computer model. An initial model is developed that addresses all relevant processes and media, but that employs preliminary statistical parameter distributions to minimize the initial effort expended. Where appropriate, transport and exposure submodels with simplifying assumptions are also used in the initial model. Different assumptions and boundaries may be explored with the initial model to assess their importance in constraining model results. Exploration of the results of the initial model are used to determine the benefits of model refinements, and to guide such refinements to focus on specific areas of the model that most influence the model outputs of interest. This approach promotes efficiency and model parsimony, but does not compromise defensibility when performed in a transparent manner. Key model boundaries for a PPA include the time frame of the modeling (temporal boundaries), where future human receptors may be located (spatial boundaries), and what types of land uses and activities may exist in the future (exposure scenarios). Simplified transport and exposure models can be useful in helping decision makers and stakeholders establish such boundaries by analyzing the consequences of selecting different bounds and understanding which assumptions and boundaries have the greatest influence on the magnitude and distribution of model results. Pathway analysis and parameter sensitivity analysis performed on PPA model results are key components of the iterative modeling approach. The objective of pathway analysis is to identify the critical radionuclides and pathways contributing to radiation dose during the modeling performance period. An environmental 'pathway' is described by the connections from a waste package or cell in the disposal facility to a future human exposure event. These connections are commonly visualized using graphical means. A simple example of a potentially complete pathway for radon-222 is: radium-containing waste → radon emanation → upwards diffusion in cap material → diffusion/advection into a future building → inhalation exposure to a building occupant Parameter sensitivity analysis is conducted on the results of a PPA to identify those specific model input parameters whose distribution of values contributes the most to the distribution of target model output, such as annual radiation dose. Pathway and sensitivity analyses on initial modeling results provide information for guiding subsequent model development, and possibly interim decisions, by informing questions tied to the modeling objectives and performance criteria. For example, questions related to radiological doses from existing disposals may include: 1. Is radiation dose from radiological exposures either obviously negligible or obviously unacceptable based on initial modeling results? 2. If dose is not obviously negligible or unacceptable relative to applicable performance criteria, what model refinements would reduce uncertainty in the results to better inform decisions? (authors)

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DOE OSTI2019

Performance Assessment Results for the Hanford Integrated Disposal Facility - 19134

In 2014, the US DOE Office of River Protection and its contractors began to develop a performance assessment for the near-surface disposal of low-level and mixed low-level waste at the Hanford Integrated Disposal Facility. The Hanford Integrated Disposal Facility is a doubly-lined landfill that was constructed between 2004 and 2006 to be the disposal facility for the vitrified low-activity waste containers that will be produced at the Hanford Waste Treatment and Immobilization Plant. The disposal facility is also expected to receive solid secondary waste produced at the Hanford Waste Treatment and Immobilization Plant and other solid wastes from site activities. The constructed facility is awaiting authorization to receive waste. Although no waste destined for the facility is currently being generated, operations cannot begin until the facility has received a Disposal Authorization Statement from US DOE and a permit modification from the State of Washington Department of Ecology. Both the Disposal Authorization Statement and permit modification require a demonstration that the system of engineered and natural features of the disposal facility will limit releases from the facility and be protective of human health and the environment for the next 1,000 to 10,000 years. Based on the analyses presented in the Integrated Disposal Facility Performance Assessment, US DOE issued a conditional Disposal Authorization Statement for the facility in June 2018. Demonstrating the long-term performance of the facility to be protective of human health and the environment was evaluated under the requirements of US DOE Order 435.1, Radioactive Waste Management. Computer simulations were performed to evaluate whether or not the disposal facility would comply with the performance objectives specified in the order. The evaluation was performed using numerical models to simulate source term releases from vitrified and cement-based waste forms. Source term releases were subsequently transported through an air pathway and a groundwater pathway to a point of compliance 100 meters from the disposed of waste. In addition, an analysis evaluating the consequences of an inadvertent intrusion into the waste was also performed. In accordance with US DoE's technical standard for performance assessments, deterministic and probabilistic analyses were performed to demonstrate that the Hanford Integrated Disposal Facility, together with the engineered features of the waste forms and natural features of the selected site, provides a reasonable expectation that the low-level waste and mixed low-level waste disposal will meet the radiological performance objectives established by US DOE. Post-closure doses to future members of the public were evaluated after the time of closure and included a 100-year period of institutional controls that limited exposure immediately following closure of the facility. Calculated doses to a future member of the public from the air pathway were below the 100 μSv/yr dose performance objective. Calculated doses to a future member of the public from all exposure pathways, including the air pathway and the groundwater pathway, were below the 250 μSv/yr dose performance objective. Doses following an inadvertent intrusion into the facility after the institutional control period were below the 5 mSv acute dose performance measure and the 1 mSv/yr chronic dose performance measure. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

A Sensitivity-informed, Process Model Abstraction Implementation in GoldSim - 19276

System-level Performance Assessment (PA) models rely on specific process-level models for some inputs, such as infiltration. For probabilistic PA, the value of the process model output depends on the values of input parameters that also vary in the PA model. The simplest conceptual solution is to draw values of the input parameters from their distributions, run the process level model at these values, and then use the output where it is needed in the PA model. However, this solution is unsatisfactory for multiple reasons: (1) the PA Model should be set up such that new runs can be made and distributions can change without having to go back and run the process-level model; (2) calling the process model from the system model is possible, but technical difficulties remain in coupling the two; and (3) there is no uncertainty in the process-level model results incorporated into the PA model. Therefore, it is desirable to develop a method that maps the values of PA Model inputs to process level model outputs, in real time within the PA Model set up and using as simple of a map as possible without losing key features of the relationship. This has been termed 'model abstraction' because information about process model runs is 'abstracted' to be used with the PA model. A simple 'nearest neighbor' abstraction approach is presented to overcome these problems, in which a sensitivity-informed distance metric is used to map a system-level model realization to the most appropriate process-level model realization. (authors)

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Development of a Stochastic Infiltration Model for PPA Implementation - 19293

Performance Assessment models often require inputs representing specific hydrologic processes and properties such as infiltration, water content, groundwater flow rate, streamflow, and sediment flux. This paper details the development of a process-level infiltration model and its use in a Probabilistic Performance Assessment (PPA) model for the West Valley Site in western New York. Additionally, this paper demonstrates a method for obtaining distributions of the long-term average hydrologic processes and properties for a PA model. For this PPA, an infiltration model was developed using HELP (Hydrologic Evaluation of Landfill Performance) to evaluate the rates of infiltration, percolation, and drainage through several site soil and proposed decommissioning cover systems, as well as the volumetric water contents of each of the unsaturated layers in a modeled soil column. A particular challenge of probabilistic modeling over long periods of time is scaling parameters appropriately so that outcomes are not grossly over- or underestimated. For these models, distributions of climatic parameters such as average precipitation rate, temperature, wind speed, humidity, and solar radiation were scaled to 1000-year averages and were used to force steady-state models that predict 1000-year average infiltration rates, drainage rates, and water contents. For the HELP model, distributions were developed for the physical and hydrologic properties of each material in a given soil column or cover system, and these properties were allowed to vary stochastically along with climatic inputs. The HELP model was run 5000 times for each of seven different soil and cover systems representing various existing and proposed conditions (i.e. native soil, backfill, existing covers, and a proposed close-in-place cover). The results of the HELP model provide ranges of 1000-year average infiltration rates, drainage rates, and water contents through and within the various layers of each of the different soil and cover conditions. The model outcomes also demonstrate the effectiveness of a proposed close-in-place cover, which essentially eliminates infiltration during its operational lifetime. Following model simulation, a sensitivity analysis was performed to establish the relative importance of each stochastic input with respect to each output. The outcomes of the model simulations and the sensitivity analysis were implemented into a system-level GoldSim model via a novel sensitivity-informed nearest neighbor approach. These infiltration rates, drainage rates, and water contents are used to parameterize mixing cells within the GoldSim Contaminant Transport module, which is the primary system-level model used in the PPA. The infiltration rates are also used as boundary conditions for a stochastic groundwater flow model that is also utilized in the PPA, but not discussed in this paper. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

A Preliminary Probabilistic Performance Assessment Model for Radiological and Chemical Contamination at the West Valley Site, New York - 19295

The New York State Energy Research and Development Authority (NYSERDA) is the owner of the Western New York Nuclear Service Center (WNYNSC), a 1,351-ha site located approximately 48 km south of Buffalo, New York. In 1962, Nuclear Fuel Services, Inc. (NFS) entered into agreements with the Atomic Energy Commission and New York State to construct the first commercial reprocessing plant of nuclear fuel in the United States at the WNYNSC. NFS built and operated the spent fuel reprocessing plant and waste disposal facilities, processing 640 Mg (640 metric tons) of spent nuclear fuel from 1966 to 1972 under an Atomic Energy Commission license. Nuclear fuel reprocessing operations halted in 1972 and never restarted, leaving behind radioactive and chemical wastes. The reprocessing wastes include high-level waste (HLW), so U.S. Department of Energy (DOE) was compelled to complete certain waste management activities under the West Valley Demonstration Project (WVDP) Act of 1980, including decommissioning of WVDP facilities. As collaborating agencies, NYSERDA and the DOE are tasked with making decisions about decommissioning and risk reduction for the West Valley Site. Neptune and Company, Inc. (Neptune) was contracted to develop a probabilistic performance assessment (PPA) model to assist the agencies in their decision making process for decommissioning the WVDP and WNYNSC. One important tool that is needed in order to inform the decision-making process is a science-based model of the contamination at the West Valley Site and its potential implications for human health and the environment. The West Valley PPA Model (the Model), developed using the GoldSim modeling software, is a tool intended to provide support for decision making with uncertainty, in a manner that is transparent, defensible, and robust. The PPA Model includes contaminant transport and health effects components, and is organized around geographically-organized contaminated facilities. These include the waste disposal areas licensed by the U.S. Nuclear Regulatory Commission and the State of New York, a waste tank farm for storage of HLW resulting from reprocessing operations, and several areas contaminated with radioactive and chemical constituents. The Model evaluates contaminant transport from these sources to points of exposure across the site and into receiving surface waters and sediments. Hypothetical people and wildlife are exposed to contamination at these locations, and the effects of these exposures are evaluated. Contaminant transport processes to be evaluated in the Model include groundwater and surface water transport, contaminant translocation by plants and animals, diffusion in subsurface air and water, and erosion of the Site. The evaluation of exposures to people in this preliminary model is limited to a resident farmer scenario, and ecological assessment is performed at the level of a screening analysis. The results of these preliminary evaluations inform future model developments. Model results are subjected to sensitivity analysis in order to determine those pathways and parameters that are most significant in influencing the results. This information allows analysts and decision makers to focus on those aspects of Site behavior and processes that contribute most to overall uncertainty. With this information, the Supplemental Environmental Impact Statement (SEIS) can inform defensible decisions regarding remediation of the Site. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Scaling Input Distributions for Probabilistic Models - 19472

It is important to address scaling issues when building models to support risk-based decisions for radioactive waste disposal sites. Currently, probabilistic risk assessment and probabilistic performance assessment models are run for multiple realizations where each realization uses one value from each input distribution over the entire modeled time. The input distributions represent the current state of knowledge in the mean of the input variable over the spatial region of interest and over the time period of the model run. This paper focuses on the time factor in input distribution scaling because it presents more serious conceptual and distributional challenges than the spatial factor. One of the challenges in temporal scaling is the effect of non-linearity on propagation of distribution uncertainty through to uncertainty in results. Radioactive waste disposal models tend to have both linear and non-linear components in their models. Dose assessment calculations are typically represented by linear models, while contaminant transport models have linear and non-linear components. In principle, temporal scaling of input parameter distributions for a linear model is straightforward, but scaling of input distributions for non-linear models is not. This paper uses examples to investigate the effects of input distribution temporal scaling on simple linear and non-linear models. Different scaling scenarios were investigated by comparing three analysis tools: analytical mathematical representations, statistical software programming, and GoldSim Monte Carlo simulation software. R statistical software is an open source statistical programming language often used for statistical analyses and graphical representation of data. GoldSim is a robust probabilistic dynamic modeling platform used in performance assessment modeling of radioactive waste sites. A simple linear model, a quadratic model and a multiplicative model were the cases explored for each of these tools. For the three analysis tools considered and with each of the scaling cases, 1,000 realizations were implemented from distributions of the random variables of interest. This is a sufficient number of realizations for simple models to capture the model space. An annual temporal resolution of a 100-year simulation period was chosen for all models and analysis tools. The effects of temporal scaling were demonstrated by investigating two different options for time scaling. Option 1 was set up so that the input distributions represented the expected value on an annual time scale, and a new value from that distribution was chosen every year (at every time step which equaled a year) for 100 years. In Option 2, the input distributions were scaled to represent the expected mean value of the input variable over the 100 years of simulation, and one value from that distribution was chosen for the entire elapsed time. Results show agreement across the three analysis tools. As expected, the linear models have identical results for the two different scaling Options, and the non-linear models diverge in the results for different time scale implementation. The implications of this work point to the need for careful consideration of input distribution temporal scaling. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

A Comparison of GoldSim, RESRAD-Onsite, and NORMALYSA Modeling Tools at Radioactive Waste Sites - 19480

Radioactive waste disposal and associated decision making is a challenging problem around the world. A strong performance assessment model plays a crucial role in making good management and remediation decisions and in optimizing the performance of a site. The International Atomic Energy Agency (IAEA) MODARIA II Working Group 1 (WG1) is comparing the performance of various modeling platforms used at Naturally Occurring Radioactive Material (NORM) and legacy waste sites to support risk-informed decision making. Neptune and Company, Inc. (Neptune) has developed probabilistic performance assessment models using the GoldSim system modeling software at a variety of radioactive waste sites. This paper presents a comparison between GoldSim and two other tools used to evaluate radiation exposure and their associated risks: the NORM And Legacy Site Assessment (NORMALYSA) tool used to assess risk at the Zapadnoe mill tailings site, and the residual radioactivity (RESRAD-Onsite) computer program used to calculate Authorized Limits at the Los Alamos National Laboratory (LANL). The International Atomic Energy Agency (IAEA) supported development of NORMALYSA based off of Ecolego 6.0, in which the user connects several universal modules, representing various environmental transport and exposure pathways, and enters in site-specific data. Further work with NORMALYSA has continued under the MODARIA II WG 1. A radionuclide transport and dose calculation model of the Zapadnoe uranium mill tailings site at the Pridneprovsky Chemical Plant in the Ukraine has been configured by MODARIA II WG1 to test the capabilities of NORMALYSA. Under this test case, contaminant transport and effective dose calculations were performed for two exposure scenarios. The onsite worker is exposed via external ground irradiation and radon and radioactive dust inhalation. The offsite resident defines a residence established 300 years after institutional control has ended, 800 meters downstream from the site with exposure pathways including: ingestion of garden vegetables irrigated by contaminated ground water, ingestion of fish from the Konoplyanka River and agricultural activities in the contaminated garden. GoldSim modeling software is a dynamic probabilistic modeling platform that evaluates uncertainty in a robust and transparent manner. Although GoldSim provides contaminant and radionuclide transport modules to facilitate integrated modeling of radionuclide migration in the environment, these features are not embedded in an existing modeling framework, except as created by the user. Neptune developed a GoldSim model for the two exposure scenarios of the Zapadnoe test case. Neptune's Generic PA Model was used as a starting point for the Zapadnoe site. Input parameters for the GoldSim model include site-specific parameters from the MODARIA Working Group as well as parameters from the Generic PA Model and other PA models created by Neptune. For the other comparison, Version 7.0 of RESRAD-Onsite, developed by Argonne National Laboratory for the U.S. Department of Energy (DOE), was used to calculate Authorized Limits for LANL. Authorized Limits are concentrations of radionuclides in soil that support the evaluation process to clear land for release under different uses. The RESRAD-Onsite computer code was used to calculate Authorized Limits because this program was specifically designed to derive radionuclide soil guidelines corresponding to a specific dose criterion. The scope of the RESRAD dose or risk evaluation is constrained to the transport and exposure pathways that have been included in the computer code. To assist in illustrating the capability of GoldSim modeling software, a GoldSim model was created to replicate the calculation of LANL's Authorized Limits for soil. The main exposure pathways in this model are inhalation of dust and gas, plant ingestion, soil ingestion, and external ground irradiation. All of the processes and outputs of RESRAD-Onsite and NORMALYSA can be created in a GoldSim model, but the value of GoldSim lies in its flexibility for application to any of a wide variety of environmental modeling problems. GoldSim allows development of site-specific inventory, transport mechanisms and risk assessment scenarios. If all potentially complete release, transport, and exposure pathways are addressed in an existing dose and risk assessment computer code, and if the code's capabilities fulfill the project modeling objectives, then expending resources to create a GoldSim model may not be necessary. Site optimization and good decision making about site management are often best when supported with the flexibility of GoldSim as a modeling tool. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Development of Concentration Ratios and Transfer Factors for Human and Ecological Probabilistic Performance Assessments - 19781

Human and ecological risk assessments utilize concentration ratios and transfer factors to calculate contaminant concentrations in plant and animal tissues in equilibrium with soil or water (concentration ratios) or based on an animal's contaminant intake from various media (transfer factors). These ratios and factors are used to evaluate human dose via food ingestion pathways and to evaluate ecological dose to an organism. In a probabilistic performance assessment, concentration ratio and transfer factor distributions are developed for every element, and these distributions are applied across the spatial and temporal domain of the assessment. As a result, it is necessary to scale the underlying data distributions by applying the central limit theorem to develop appropriate distributions of the mean for the temporal and spatial scale of the model. Herein we discuss the methodologies for identifying and ranking references, and how to develop statistical distributions of concentration ratios and transfer factors for probabilistic performance assessment models. We also discuss factors that could be pertinent to differences when selecting references and developing distributions for human versus ecological risk assessment scenarios. (authors)

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