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All sources NASA NTRS DOE OSTI

DOE OSTI2024

An Approach to Dynamic Human Reliability Analysis and Its Data Collection Framework

Human reliability analysis (HRA) is a method for evaluating human errors in a variety of complex systems such as nuclear power plants, military systems, aircraft, and chemical plants. Most HRA methods currently used by regulatory institutes or utilities are called static HRA and are carried out by simple worksheets or simple calculators. To date, there are many unsolved or intrinsic challenges in static HRA. For example, existing static HRA does not realistically model and evaluate human actions as they would be performed at actual systems. There is no method with HRA to objectively estimate the time required for human actions despite being essential to HRA processes. In addition, many HRA methods still rely on a dataset generated prior to the 1980s, from unrelated industry experience or simply from expert judgment. Accordingly, this study attempted to research how to overcome the challenges of existing HRA via dynamic risk assessment (a.k.a., simulation-based or computation-based risk assessment) techniques. First, this study developed a dynamic HRA method, named as PRocedure-based Investigation Method of EMRALD Risk Assessment – HRA (PRIMERA-HRA). The PRIMERA-HRA mainly concentrates on providing HRA analysts with specific guidelines on how to reasonably model human actions, assign human reliability data and evaluate output of simulation within a dynamic probabilistic risk assessment tool, called as Event Modeling Risk Assessment using Linked Diagrams (EMRALD). Second, this study also developed a module for performance shaping factors (i.e., the key concept in HRA quantification) applicable to dynamic HRA, then implemented it based on PRIMERA-HRA within the EMRALD tool. Third, this study developed an HRA data collection framework to support dynamic HRA, called as Simplified Human Error Experimental Program (SHEEP). Originally, the SHEEP study aimed to support static HRA and its data collection, but recently extended the scope to the new technologies such as dynamic HRA or HRA for advanced reactors. SHEEP focuses on the use of data collected from simplified simulators to complement—but not replace—data collection studies using full-scope simulators and actual operators. To date, many experiments were conducted under the SHEEP framework. Multiple analyses, such as human performance analysis, human error analysis, task complexity analysis, learning effect analysis and time distribution analysis, were also carried out using the collected data. Then, based on the major insights, an approach to inferring full-scope data based on simplified simulator data was proposed. The PRIMERA-HRA and SHEEP research are expected to evaluate human actions more realistically than existing static HRA, provide an opportunity to collect more HRA data with reasonable cost and labor, then contribute to enhance the quality of HRA.

99 - GENERAL AND MISCELLANEOUS↗

DOE OSTI2024

An Approach to Dynamic Human Reliability Analysis and Its Data Collection Framework

99 - GENERAL AND MISCELLANEOUS↗

DOE OSTI2020

Fluorescent amplification for next generation sequencing (FA-NGS) library preparation

BACKGROUND: Next generation sequencing (NGS) has become a universal practice in modern molecular biology. As the throughput of sequencing experiments increases, the preparation of conventional multiplexed libraries becomes more labor intensive. Conventional library preparation typically requires quality control (QC) testing for individual libraries such as amplification success evaluation and quantification, none of which occur until the end of the library preparation process. RESULTS: In this study, we address the need for a more streamlined high-throughput NGS workflow by tethering real-time quantitative PCR (qPCR) to conventional workflows to save time and implement single tube and single reagent QC. We modified two distinct library preparation workflows by replacing PCR and quantification with qPCR using SYBR Green I. qPCR enabled individual library quantification for pooling in a single tube without the need for additional reagents. Additionally, a melting curve analysis was implemented as an intermediate QC test to confirm successful amplification. Sequencing analysis showed comparable percent reads for each indexed library, demonstrating that pooling calculations based on qPCR allow for an even representation of sequencing reads. To aid the modified workflow, a software toolkit was developed and used to generate pooling instructions and analyze qPCR and melting curve data. CONCLUSIONS: We successfully applied fluorescent amplification for next generation sequencing (FA-NGS) library preparation to both plasmids and bacterial genomes. As a result of using qPCR for quantification and proceeding directly to library pooling, the modified library preparation workflow has fewer overall steps. Therefore, we speculate that the FA-NGS workflow has less risk of user error. The melting curve analysis provides the necessary QC test to identify and troubleshoot library failures prior to sequencing. While this study demonstrates the value of FA-NGS for plasmid or gDNA libraries, we speculate that its versatility could lead to successful application across other library types.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

(Doublon) Benchmarking of Different Inverse Point Kinetics Implementations for an Autocorrected Reactimeter Algorithm

In November 2017, the Transient Reactor Test Facility returned to operation. Since that time, many transient test series have been completed, such as the Transient Heatsink Overpower Response capsule (THOR), the Transient Water Irradiation System for TREAT (TWIST), and Sirius. Each has provided valuable data for materials performance and reactor safety that can be applied in future designs. During each experimental series, detector count rates provided important information on the core behavior during transients. However, a limitation of these data is that variations in the neutron distribution during experiments can cause errors when attempting to infer reactivity evolution from detector signals. Neutron physics codes can be used to compute the flux shape variations. However, this is a poor solution when the experimental data is used for code verification, validation and uncertainty quantification. Indeed, if the output of the code is used both as a reference and to correct what the reference is compared to, the circular dependency limits the quality of the verification, validation and uncertainty quantification approach. To overcome this problem, the autocorrected reactimeter algorithm (ACRA) has been developed. This approach infers a time-dependent reactivity evolution by testing different spatial corrections and selecting the one that minimizes reactivity variations when the core is in a frozen configuration (i.e., when there is no variation in parameters affecting reactivity). However, the scope of this method was limited to transients where there were negligible thermal feedback. Indeed, the core is never in a frozen configuration when the fuel temperature varies during the whole transient. This is our motivation for developing an improved version of the ACRA that does not require frozen configurations. To develop this new algorithm, we need a precise and unbiased implementation of the inverse point kinetic equations (IPKEs) as any error in the reactivity evaluation will be propagated into the choice of the optimal spatial correction. Indeed, the previous reactimeter algorithm would use approximations, such as a negligible flux amplitude derivative, to focus on rapidity. For the numerical validation of ACRA, we aim at absolute error under for reactivity derived from signals similar to the one of this study. In this summary, we test eight different IPKE implementations. Each will process a mockup signal built for this study, similar to those that the future ACRA will process. Each reactivity output will be compared to the reference reactivity that has been used to generate the mockup signal. The implementation minimizing the difference with the reference reactivity will be used in the development of a new ACRA formulation.

73 - NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2020

Analytic insights on the information content of new observables

Uncertainty quantification has emerged as a rapidly growing field in nuclear science. Theoretical predictions of physical observables often involve extrapolations to regions that are poorly constrained by laboratory experi- ments and astrophysical observations. Without properly quantified theoretical errors, such model predictions are of limited value. Also, one often deals with theoretical constructs that involve fundamental quantities that are not accessible to experiment or observation. Particularly relevant in this context is the pressure of pure neutron matter. Furthermore, in this Rapid Communication, we develop an analytic framework to answer the question of “How can new data reduce uncertainties of current theoretical models?” Simple and insightful expressions are obtained to quantify the impact of one or two new observables on theoretical uncertainties in two critical quantities: The slope of the symmetry energy at saturation density and the pressure of pure neutron matter at twice nuclear matter saturation density.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

Thinking Bayesian for plasma physicists

Bayesian statistics offers a powerful technique for plasma physicists to infer knowledge from the heterogeneous data types encountered. To explain this power, a simple example, Gaussian Process Regression, and the application of Bayesian statistics to inverse problems are explained. The likelihood is the key distribution because it contains the data model, or theoretic predictions, of the desired quantities. By using prior knowledge, the distribution of the inferred quantities of interest based on the data given can be inferred. Because it is a distribution of inferred quantities given the data and not a single prediction, uncertainty quantification is a natural consequence of Bayesian statistics. The benefits of machine learning in developing surrogate models for solving inverse problems are discussed, as well as progress in quantitatively understanding the errors that such a model introduces.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

Rossi-alpha Uncertainty Quantification by Analytic, Bootstrap, and Sample Methods to Inform Fitting Best Practices

The prompt neutron period (the negative reciprocal of the prompt neutron decay constant) can be estimated using the Rossi-alpha technique that is predicated on fitting Rossi alpha histograms and of interest in nuclear criticality safety and nonproliferation. The histograms are traditionally fit with a one-exponential model; however, recent work has proposed a two-exponential model to account for reflector induced phenomenon. Until recently, the uncertainty quantification for either model was inadequate (inaccurate and demanded large measurement times). Measurement uncertainty quantification by sample and analytic methods was developed and validated in Ref. The purpose of this transaction is to (i) validate a new bootstrap method by comparing bin-by-bin error bar estimates and (ii) demonstrate how to choose bin widths and reset times to optimize precision and accuracy.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

NASA NTRS2004

Precipitation and Latent Heating Distributions from Satellite Passive Microwave Radiometry: Method and Uncertainties - Part 1

A revised Bayesian algorithm for estimating surface rain rate, convective rain proportion, and latent heating/drying profiles from satellite-borne passive microwave radiometer observations over ocean backgrounds is described. The algorithm searches a large database of cloud-radiative model simulations to find cloud profiles that are radiatively consistent with a given set of microwave radiance measurements. The properties of these radiatively consistent profiles are then composited to obtain best estimates of the observed properties. The revised algorithm is supported by an expanded and more physically consistent database of cloud-radiative model simulations. The algorithm also features a better quantification of the convective and non-convective contributions to total rainfall, a new geographic database, and an improved representation of background radiances in rain-free regions. Bias and random error estimates are derived from applications of the algorithm to synthetic radiance data, based upon a subset of cloud resolving model simulations, and from the Bayesian formulation itself. Synthetic rain rate and latent heating estimates exhibit a trend of high (low) bias for low (high) retrieved values. The Bayesian estimates of random error are propagated to represent errors at coarser time and space resolutions, based upon applications of the algorithm to TRMM Microwave Imager (TMI) data. Errors in instantaneous rain rate estimates at 0.5 deg resolution range from approximately 50% at 1 mm/h to 20% at 14 mm/h. These errors represent about 70-90% of the mean random deviation between collocated passive microwave and spaceborne radar rain rate estimates. The cumulative algorithm error in TMI estimates at monthly, 2.5 deg resolution is relatively small (less than 6% at 5 mm/day) compared to the random error due to infrequent satellite temporal sampling (8-35% at the same rain rate).

Olson, William S.↗

DOE OSTI2025

Real-time elemental analysis of liquids for process monitoring using laser-induced breakdown spectroscopy with a liquid wheel sampling approach

This article presents an engineered sampling system that used a rotating wheel to form a thin liquid layer, permitting the use of laser-induced breakdown spectroscopy (LIBS) for in situ, real-time elemental impurity quantification during liquid processing. The sampling approach was demonstrated on eight elements from across the periodic table (Na, Al, K, Ca, Ti, Sr, Mo, and Yb). Univariate and multivariate calibrations were presented for each element. The average value for percent root mean square errors of cross-validation for the multivariate models was 3.64%, highlighting the method's strong prediction accuracy. Additionally, the limits of detection for each analyte were estimated from their univariate models: Na = 0.0532, Al = 18.5, K = 0.105, Ca = 0.273, Ti = 67.7, Sr = 0.640, Mo = 22.4, and Yb = 22.9 μg mL –1 . Finally, a test in which multivariate models were used to monitor a liquid system for 80 min was performed to investigate the real-time monitoring capabilities of this liquid LIBS sampling approach. Rigorous measurements were performed to effectively predict the absence and concentrations of multiple analytes as they were spiked and diluted. This demonstration showed the feasibility of using LIBS for real-time liquid quantification models with estimated precision ≤ 8.1%. Finally, the limitations of this approach and potential future improvements are discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

An Interactive Spectral Analysis Tool for Chemical Identification and Quantification of Gas-Phase Species in Complex Spectra

A spectral analysis tool has been developed to interactively identify and quantify individual gas-phase species from complex infrared absorbance spectra obtained from laboratory or field data. The SpecQuant program has an intuitive graphical interface that accommodates both reference and experimental data with varying resolution and instrumental lineshape, as well as algorithms to readily align the wavenumber axis of a sample spectrum with the raster of a reference spectrum. Using a classical least squares model in conjunction with reference spectra such as those from the Pacific Northwest National Laboratory (PNNL) gas-phase infrared database or simulated spectra derived from the HITRAN line-by-line database, the mixing ratio of each identified species is determined along with its associated estimation error. After correcting the wavelength and intensity of the field data, SpecQuant displays the calculated mixing ratio versus the experimental data for each analyte along with the residual spectrum with any or all analyte fits subtracted for visual inspection of the fit and residuals. The software performance for multianalyte quantification was demonstrated using moderate resolution (0.5 cm –1 ) infrared spectra that were collected during the time-resolved infrared photolysis of methyl iodide.

Thompson, Christopher J.↗

DOE OSTI2022

Human reliability analysis studies from simulator experiments using Bayesian inference

Probabilistic Safety Assessment (PSA) of complex facilities is performed to arrive at the risk posed by them. PSA also accounts for the contribution of the human errors towards the overall risk through Human Reliability Analysis (HRA) in terms of Human Error Probability (HEP). Human operators are part of the system and do not work in isolation. Their performance is influenced by the context in which the actions are performed. As a result, quantification of HEP requires operator performance data under the given context. Some good sources of operator performance data are plant‘s operation data, simulator data and expert judgement. The plant operation data pertaining to HRA is generally sparse. In this situation, a full scope plant simulator provides a good alternative for operator performance data generation. Many of the currently practiced HRA methods have been developed by combining the empirical evidence with expert judgement and contain a lot of uncertainty in their estimates. Bayesian inference is suitable for updating the prior HRA estimates with the simulator evidence to obtain the posterior HEP. Here, posterior HEP has been calculated for postulated accident scenarios in advanced reactor (first of its kind) at design stage, using plant simulator.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Model Calibration with Markov Chain Monte Carlo Tutorial

The purpose of this tutorial is to demonstrate how to use Markov chain Monte Carlo (MCMC) to calibrate a model. By calibration, we mean the selection of model parameters (and, when relevant, structures). A common goal in model development and diagnostics is calibration, or the identification of model structures and parameters which are consistent with data. While models can be calibrated through hand-tuning parameters or minimizing simple error metrics such as root-mean-square-error (RMSE), these approaches can underrepresent the probabilistic nature of the data-generating process, as well as the potential for multiple model configurations to be consistent with the data. Probabilistic uncertainty quantification, which is the topic of this notebook, can address these concerns. This tutorial is presented as an appendix to the e-book: Addressing Uncertainty in MultiSector Dynamics Research.

Markov chain Monte Carlo↗

NASA NTRS2006

Multilayered Clouds Identification and Retrieval for CERES Using MODIS

Traditionally, analyses of satellite data have been limited to interpreting the radiances in terms of single layer clouds. Generally, this results in significant errors in the retrieved properties for multilayered cloud systems. Two techniques for detecting overlapped clouds and retrieving the cloud properties using satellite data are explored to help address the need for better quantification of cloud vertical structure. The first technique was developed using multispectral imager data with secondary imager products (infrared brightness temperature differences, BTD). The other method uses microwave (MWR) data. The use of BTD, the 11-12 micrometer brightness temperature difference, in conjunction with tau, the retrieved visible optical depth, was suggested by Kawamoto et al. (2001) and used by Pavlonis et al. (2004) as a means to detect multilayered clouds. Combining visible (VIS; 0.65 micrometer) and infrared (IR) retrievals of cloud properties with microwave (MW) retrievals of cloud water temperature Tw and liquid water path LWP retrieved from satellite microwave imagers appears to be a fruitful approach for detecting and retrieving overlapped clouds (Lin et al., 1998, Ho et al., 2003, Huang et al., 2005). The BTD method is limited to optically thin cirrus over low clouds, while the MWR method is limited to ocean areas only. With the availability of VIS and IR data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and MW data from the Advanced Microwave Scanning Radiometer EOS (AMSR-E), both on Aqua, it is now possible to examine both approaches simultaneously. This paper explores the use of the BTD method as applied to MODIS and AMSR-E data taken from the Aqua satellite over non-polar ocean surfaces.

Sun-Mack, Sunny↗

NASA NTRS2018

Realistic Covariance Generation for the GPM Spacecraft

A covariance realism process for NASA's Global Precipitation Measurement (GPM) spacecraft is detailed. The GPM spacecraft is in a low earth orbit, and performs collision avoidance maneuvers few times a year. Currently GPM is below the International Space Station (ISS). So, in addition to cataloged debris objects, GPM must contend with smallsat/cubesat objects that are deployed from the ISS. Both operational scenarios require complete knowledge of the expected GPM prediction errors as a function of time. In this study, we present a method for generating realistic predicted covariance that uses linear propagation of the covariance with the addition of process noise. Further analyses are presented for the process noise ''tuning'' that generates an inflation factor based on the observed error statistics of the predictive satellite trajectories when compared to the definitive ones. Different tuning strategies are considered and compared via a Goodness-of-Fit testing for the Gaussian properties of the scaled covariance. SpaceNav's realistic covariance generation approach takes into account the contribution of predicted maneuver errors in the increased propagation uncertainty. Corresponding maneuver uncertainty is injected into the state uncertainty, and is used within the collision avoidance process to determine the collision risk for close approach events that follow a maneuver. This is a critical step in the maneuver planning process that provides the satellite operator with an accurate quantification of the collision probability for planned maneuvers. Using this information, an informed decision can be made to proceed with a maneuver if the collision risk is acceptable. This approach is validated by Monte-Carlo simulations and results are presented.

spacecraft uncertainty propagation↗

DOE OSTI2021

Uncertainty quantification for deep learning in particle accelerator applications

With the advent of increased computational resources and improved algorithms, machine learning-based models are being increasingly applied to complex problems in particle accelerators. However, such data-driven models may provide overly confident predictions with unknown errors and uncertainties. For reliable deployment of machine learning models in high-regret and safety-critical systems such as particle accelerators, estimates of prediction uncertainty are needed along with accurate point predictions. In this investigation, we evaluate Bayesian neural networks (BNN) as an approach that can provide accurate predictions along with reliably quantified uncertainties for particle accelerator problems, and compare their performance with bootstrapped ensembles of neural networks. We select three accelerator setups for this evaluation: a storage ring, a photoinjector, and a linac. The problems span different data volumes and dimensionalities (e.g., scalar predictions as well as image outputs). It is found that BNN provide accurate predictions of the mean along with reliable estimates of predictive uncertainty across the test cases. In this vein, BNN may offer an attractive alternative to deterministic deep learning tools to generate accurate predictions with quantified uncertainties in particle accelerator applications.

43 PARTICLE ACCELERATORS↗

DOE OSTI2020

An Approach to Dependence Assessment in Human Reliability Analysis: Application of Lag and Linger Effects

Dependence assessment refers to an approach used in human reliability analysis (HRA) to adjust a human error probability (HEP) for the following action by considering the impact of the preceding action. It has been known to significantly affect the overall results of probabilistic safety assessment (PSA). If the dependence assessment is not adequate, the result could be unconvincing for explaining the operator failures in the context of PSA. To date, several methods and some recent research have identified suggestions for treating dependence issues in HRA; however, these are still exclusively based on the intrinsic approach of the Technique for Human Error Rate Prediction (THERP), an HRA method. THERP inevitably has a challenge with the subjectivity of expert evaluation as well as the requirement for PSA and HRA expertise with resource-intensive and time-consuming processes. This paper suggests an approach to dependence assessment that could not only minimize the influence of expert judgment, but also saves time to perform the analysis with reasonable manpower. It modifies existing HRA methods with considering lag and linger effects to apply dependence effects for them. Based on a representative HRA method, i.e., Standardized Plant Analysis Risk - HRA (SPAR-H), guidance for how to apply lag and linger effects for the HRA method is suggested. Then, an investigation is carried out to compare quantification results of the revised HRA method with that of the original approach based on experimental data.

99 GENERAL AND MISCELLANEOUS↗

NASA NTRS2017

Sensitivity Analysis and Uncertainty Quantification of a Mars Ascent Vehicle Concept

The design of a conceptual Mars ascent vehicle is a challenging problem. In order to aid the vehicle and mission concept design it is important to understand the driving design parameters and the expected performance in the presence of model errors and uncertainties. An existing six degree of freedom simulation model is analyzed on a statistical basis using the methods available in the Design Analysis Kit for Optimization and Terascale Applications toolkit. The methods utilized include conventional Monte Carlo techniques, metamodeling via polynomial chaos expansions, and global variance-based sensitivity analyses. Two additional analysis methods referred to as “Monte Carlo filtering” and ”Classification trees” are used to determine which uncertain parameters are driving the performance of the vehicle. Monte Carlo filtering provides a methodology to determine which parameters cause qualitatively different behavior while the classification trees use heuristics to partition the input space and assign probabilities to each partition. These methods serve as qualitative descriptors of model sensitivity while variance-based global sensitivity analysis seeks a quantitative mapping from total output variance to the variance of individual inputs. Application of these techniques to several outputs of a Mars ascent vehicle concept simulation indicates that only a select few input factors dominate their variance.

Noyes, Connor↗

DOE OSTI2025

Accelerating Discovery of Atomistic Defects via Machine Learning

The quantification of defects such as vacancies in crystalline structures is a cornerstone of materials science research. Traditional efforts often rely on manual detection, a process that is time-intensive, prone to human error, and challenging to scale. Here we leverage machine learning (ML) methods to identify and quantify vacancies within a crystalline lattice, aiming to expedite detection while improving accuracy. Additionally, we explore the transferability of these ML techniques, identifying characteristics of atomistic imaging data that complicate this task. We show how the integration of ML can drive innovation, providing a powerful tool that will play an increasingly crucial role in the future of materials science.

2D materials↗