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DOE OSTI2021

Evaluation and projection of long period return values of extreme daily precipitation in the CMIP5 and CMIP6 models

Using a non-stationary Generalized Extreme Value statistical method, we calculate selected extreme daily precipitation indices and their 20 year return values from the CMIP5 and CMIP6 climate models over the historical and future periods. We evaluate model performance of these indices and their return values in replicating similar quantities calculated from multiple gridded observational products. Difficulties in interpreting model quality in the context of observational uncertainties are discussed. Projections are framed in terms of specified global warming target temperatures rather than at specific times and under specific emissions scenarios. The change in framing shifts projection uncertainty due to differences in model climate sensitivity from the values of the projections to the timing of the global warming target. At their standard resolutions, we find there are no meaningful differences between the two generations of models in their quality or projections of simulated extreme daily precipitation.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Robust global detection of forced changes in mean and extreme precipitation despite observational disagreement on the magnitude of change

Detection and attribution (D&A) of forced precipitation change are challenging due to internal variability, limited spatial, and temporal coverage of observational records and model uncertainty. These factors result in a low signal-to-noise ratio of potential regional and even global trends. Here, we use a statistical method – ridge regression – to create physically interpretable fingerprints for the detection of forced changes in mean and extreme precipitation with a high signal-to-noise ratio. The fingerprints are constructed using Coupled Model Intercomparison Project phase 6 (CMIP6) multi-model output masked to match coverage of three gridded precipitation observational datasets – GHCNDEX, HadEX3, and GPCC – and are then applied to these observational datasets to assess the degree of forced change detectable in the real-world climate in the period 1951–2020. We show that the signature of forced change is detected in all three observational datasets for global metrics of mean and extreme precipitation. Forced changes are still detectable from changes in the spatial patterns of precipitation even if the global mean trend is removed from the data. This shows the detection of forced change in mean and extreme precipitation beyond a global mean trend is robust and increases confidence in the detection method's power as well as in climate models' ability to capture the relevant processes that contribute to large-scale patterns of change. We also find, however, that detectability depends on the observational dataset used. Not only coverage differences but also observational uncertainty contribute to dataset disagreement, exemplified by the times of emergence of forced change from internal variability ranging from 1998 to 2004 among datasets. Furthermore, different choices for the period over which the forced trend is computed result in different levels of agreement between observations and model projections. These sensitivities may explain apparent contradictions in recent studies on whether models under- or overestimate the observed forced increase in mean and extreme precipitation. Lastly, the detection fingerprints are found to rely primarily on the signal in the extratropical Northern Hemisphere, which is at least partly due to observational coverage but potentially also due to the presence of a more robust signal in the Northern Hemisphere in general.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Development of observation-based global multilayer soil moisture products for 1970 to 2016

Abstract. Soil moisture (SM) datasets are critical to understanding the global water, energy, and biogeochemical cycles and benefit extensive societal applications. However, individual sources of SM data (e.g., in situ and satellite observations, reanalysis, offline land surface model simulations, Earth system model – ESM – simulations) have source-specific limitations and biases related to the spatiotemporal continuity, resolutions, and modeling and retrieval assumptions. Here, we developed seven global, gap-free, long-term (1970–2016), multilayer (0–10, 10–30, 30–50, and 50–100 cm) SM products at monthly 0.5∘ resolution (available at https://doi.org/10.6084/m9.figshare.13661312.v1; Wang and Mao, 2021) by synthesizing a wide range of SM datasets using three statistical methods (unweighted averaging, optimal linear combination, and emergent constraint). The merged products outperformed their source datasets when evaluated with in situ observations (mean bias from −0.044 to 0.033 m3 m−3, root mean square errors from 0.076 to 0.104 m3 m−3, Pearson correlations from 0.35 to 0.67) and multiple gridded datasets that did not enter merging because of insufficient spatial, temporal, or soil layer coverage. Three of the new SM products, which were produced by applying any of the three merging methods to the source datasets excluding the ESMs, had lower bias and root mean square errors and higher correlations than the ESM-dependent merged products. The ESM-independent products also showed a better ability to capture historical large-scale drought events than the ESM-dependent products. The merged products generally showed reasonable temporal homogeneity and physically plausible global sensitivities to observed meteorological factors, except that the ESM-dependent products underestimated the low-frequency temporal variability in SM and overestimated the high-frequency variability for the 50–100 cm depth. Based on these evaluation results, the three ESM-independent products were finally recommended for future applications because of their better performances than the ESM-dependent ones. Despite uncertainties in the raw SM datasets and fusion methods, these hybrid products create added value over existing SM datasets because of the performance improvement and harmonized spatial, temporal, and vertical coverages, and they provide a new foundation for scientific investigation and resource management.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

Code Comparison Study Fosters Confidence in the Numerical Simulation of Enhanced Geothermal Systems

Numerical simulation has become a standard analytical tool for scientists and engineers to evaluate the potential and performance of enhanced geothermal systems. A variety of numerical simulators developed by industry, universities, and national laboratories are currently available and being applied to better understand enhanced geothermal systems at the field scale. To yield credible predictions and be of value to site operators, numerical simulators must be able to accurately represent the complex coupled processes induced by producing geothermal systems, such as fracture aperture changes due to thermal stimulation, fracture shear displacement with fluid injection, rate of thermal depletion of reservoir rocks, and permeability alteration with mineral precipitation or dissolution. A suite of numerical simulators was exercised on a series of test problems that considered coupled thermal, hydraulic, geomechanical, and geochemical (THMC) processes. Problems were selected and designed to isolate selected coupled processes, to be executed on workstation class computers, and have simple but illustrative metrics for result comparisons. This paper summarizes the initial suite of seven benchmark problems, describes the code comparison activities, provides example results for problems and documents the capabilities of currently available numerical simulation codes to represent coupled processes that occur during the production of geothermal resources. Code comparisons described in this paper use the ISO (International Organization for Standardization) standard ISO-13538 for proficiency testing of numerical simulators. This approach was adopted for a recent code comparison study within the radiation transfer-modeling field of atmospheric sciences, which was focused on canopy reflectance models. This standard specifies statistical methods for analyzing laboratory data from proficiency testing schemes to demonstrate that the measurement results do not exhibit evidence of an unacceptable level of bias.

benchmark problems↗

DOE OSTI2015

Quantifying Uncertainties through Advanced Theoretical Analysis for Image and Signal Reconstruction NLV-02-15, Year 1 of 2

Presentation describes project to (1) adapt accelerated matrix-free linear system solvers for applying new statistical approaches to very large–scale problems, (2) characterize the degree to which new statistical methods are superior to classical methods in respect to the sensitivity to “tuning parameters,” and (3) develop a new mathematical theory and associated numerical methods for computing the point spread function of an x-ray imaging system.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Quantifying Uncertainties Through Advanced Theoretical Analysis for Image and Signal Reconstruction, NLV-02-15, Year 2 of 2

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

Discovering Geothermal Systems in the Great Basin Region: An Integrated Geologic, Geochemical, and Geophysical Approach for Establishing Geothermal Play Fairways

The Great Basin currently hosts ~24 geothermal power plants with more than 600 MW of capacity, but estimates suggest that the region is capable of producing much greater amounts of geothermal energy. Recent studies indicate that a combination of geological, geochemical, and geophysical features characterize the most robust geothermal systems. Considering that most of the geothermal resources in this region are blind, it is imperative that the favorable characteristics for geothermal activity be synthesized and techniques perfected for the discovery of new viable systems. We therefore utilized a multi-disciplinary approach and synthesized 9 geologic, geochemical, and geophysical parameters to produce a new detailed geothermal play fairway map of a large transect across the Great Basin region (96,000 km2) from west-central to eastern Nevada, with the primary objective of facilitating discovery of commercial-grade, blind geothermal fields (i.e. systems with no surface hot springs or fumaroles). Our analysis included: 1) structural settings (i.e., patterns of faulting), 2) age of recent faulting, 3) slip rates on recent faults, 4) regional-scale strain rates, 5) the tendency of faults to slip or dilate based on their orientation in the regional stress field, 6) earthquake density, 7) gravity data, 8) temperature at 3 km depth, and 9) geochemistry from springs and wells. These parameters were grouped into subsets to delineate rankings for local permeability, regional permeability, and heat, which collectively defined the geothermal play fairways (i.e., most likely locations for significant geothermal fluid flow). A major challenge was developing appropriate weightings of individual data types to best predict permeability and overall geothermal potential. Rigorous statistical methods, utilizing 34 benchmarks of known relatively high-temperature (>130°C) geothermal systems in the region, were employed to determine the hierarchal weights of each parameter. The new geothermal potential maps will help to reduce the risks in prospecting for new geothermal systems throughout the region.

15 GEOTHERMAL ENERGY↗

DOE OSTI2021

Identification of Life Models for Li-Ion Batteries Using Penalized Regression and Bilevel Optimization

Reduced-order physics-based life models are extremely useful for rapidly predicting battery state-of-health and for simulating battery lifetime in arbitrary aging conditions. However, identification of well-parameterized models is difficult. This is because, for maximum usefulness in predicting lifetime under a variety of conditions, aging test data exhibits many degradation mechanisms, which all need to be accurately modeled. However, because aging tests are time-consuming and expensive, especially for large-format batteries, a minimum of tests are conducted while probing many stress factors. Building a well-parameterized model is then very challenging: an under-parameterized model will neglect critical degradation modes, and an over-parameterized model will extrapolate poorly to new testing conditions. To complicate this matter, the functional form of the model for any individual degradation rate can be very difficult to identify. In this work, the statistical tools of penalized regression and bilevel optimization are used to help identify both the functional forms of and optimize the parameters of reduced-order life models, accelerating identification of robust models. Model robustness is demonstrated through traditional statistics methods of cross-validation and sensitivity analysis, uncertainty quantification through bootstrap resampling and Monte-Carlo simulation, and simulation of real-world use cases.

47 OTHER INSTRUMENTATION↗

DOE OSTI2021

Visual HPC Workflows for the Analysis of System Dynamics Models

Visual analytics supported by high performance computing (HPC) accelerates and enhances the discovery, exploration, and analysis of causal patterns in complex system dynamics (SD) models. We present a suite of visualization-assisted ensemble-based techniques for hypothesis generation and testing, and for sensitivity analysis. By employing HPC to provide parallel, on-demand simulation of SD models, one can “steer” an ensemble of simulated scenarios in real time as one first formulates and then informally tests those hypotheses: this provides rapid feedback for analysts to refine their understanding of the causal relationships emergent from a model. Such understandings can be followed and augmented by rigorous application of statistical methods, namely global variance-based sensitivity analysis, Monte-Carlo filtering, adaptive regional sensitivity analysis, and self-organized maps: here timely computation relies on HPC, while effective presentation emphasizes high-dimensional multivariate data visualization. Immersive visualization in virtual 3D environments provides an excellent adjunct to the traditional 2D graphics typically used for SD models, as it generates an embodied understanding of model behavior and facilitates an active, collaborative critique of model structure and output. Finally, we summarize prospects for HPC-enabled visual analytics applied to SD modeling.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

Real-Time Health Monitoring for Gas Turbine Components Using Online Learning and High-Dimensional Data (Final Report)

Capital-intensive turbomachinery, such as gas turbines and combined cycle plants, are constantly being monitored for performance anomalies, faults, and physical degradation. Although these power-generating assets are equipped with hundreds of sensors, existing monitoring tools can only handle moderate-sized data. As a result, only a handful of aggregate metrics are used to monitor machine health. At the same time, developing advanced tools suitable for large datasets have been restricted by the lack of appropriate data. The objective of this proposal was to demonstrate a Big Data analytics framework for fault detection and diagnosis in gas turbine applications. We develop a predictive analytics framework methodology guided by these experimental data, industrial data from our collaborators, and physics-based models with engineering domain knowledge. Our analytics framework consists of four key components: (1) a data curation process that addresses data storage, data quality assessments, and integrity checks, (2) a feature engineering component that utilizes statistical methods and transformation algorithms guided by physics-based models to extract high-fidelity fault features that can be leveraged for fault detection and classifying fault severities, (3) a Machine Learning-based fault detection and diagnostics algorithms for detecting operational and hardware faults in the combustion and the turbines section. We utilize two industry-class gas turbine component test rigs to generate first of its kind data for critical gas turbine faults with varying severity levels. Advanced gas turbine test facilities will be interrogated using state-of-the-art instrumentation techniques to build fault signatures and data trends for key combustor and turbine faults. Data generated from a combustor test rig (Georgia Tech) and a turbine test rig (Penn State) during both normal operation and with seeded faults serve as the basis for the Big Data sets. The test conditions in the two test facilities include common, critical events that occur in the operation. Utilizing the combustor test rig, we examine two common combustor faults: lean blowout and centerbody degradation. For the turbine section we develop analytic models for monitoring cooling faults in the gas turbine.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2016

Investigation of methodology for uncertainty quantification of model parameters

Quantifying the uncertainty contributors of best-estimate thermal hydraulic code is getting more and more attention in safety analysis of nuclear industry during recent decades. Yet for uncertainties of certain model parameters which may not be directly measured, the quantification process is usually subjective and inaccurate. This paper investigates the statistical methodologies in order to get the probability density function (pdf) of model parameters more objectively based on observed experimental responses. The simplification of mathematical model is described for the parameter estimation, and solutions based on Bayesian theorem and Maximum Likelihood Estimation (MLE) are classified and demonstrated for both deterministic approach and stochastic approach. As an application, the uncertainties of model parameters related to reflood phenomena implemented in RELAP5 code are quantified. It is indicated that the statistical method which is independent of best-estimate code is feasible and easy to apply. And through the check of uncertainty propagation, it demonstrates that the uncertainty bands can envelope most of the experiment measurement with advantage of accuracy. (authors)

42 ENGINEERING↗

DOE OSTI2019

A Thermal Model for the Performance Analysis of Large Scale Spent Fuel Cooling Pond - 19173

This paper presents a thermal model for the performance analysis of a large scale cooling pond for the long term storage of spent nuclear fuel. The model has been developed using the knowledge gained from fundamental investigation using CFD and has been validated by comparing against reliable experimental and/or numerical data. It was then applied in the context of operation of a large scale pond to ascertain the sensitivity of various operational and environmental conditions. To develop the model, the water body along with the heating elements was considered as one zone and humid air inside the building as another zone, both of which are considered as well-mixed zones. Various input conditions namely, total heat load from the heat source, environmental temperature and relative humidity, ventilation rate, efficiency of the cooling tower, recirculation and make-up water flow rates have been programmed in the model. The statistical method AVONA has been used to assess the influence of the above variables. The results reveal that the efficiency of the cooling tower is the most influential parameter on the cooling performance of cooling pond within the considered values of heat load. It has also been found that the majority of the heat is taken by the recirculating water for very high heat load while the other modes of heat transfer are small but not insignificant. The model is capable of simulating the scenario of loss of cooling water and suggests that it can be used for managing a large pond efficiently as well as recovery from an accident situation. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

Approach to Quality Assurance for Complex Environmental Modeling - 20407

A strong Quality Assurance (QA) program for complex environmental modeling is essential for regulatory and public acceptance and trust, but it does not have to be onerous. Neptune and Company, Inc. (Neptune) has developed a strong QA program that improves transparency, traceability, reproducibility, and therefore, defensibility and trust. Neptune's QA program has evolved over the past 27 years, transitioning from an ad hoc QA program, to a program that is currently Nuclear Quality Assurance-1 (NQA-1) compliant, and will soon be NQA-1/DOE approved for DOE EM modeling work. In addition, Neptune is a qualified laboratory assessor and qualified auditor for reference materials, a proficiency testing provider, and fully complies with the Environmental Protection Agency (EPA) QA program. Neptune's President and CEO, Kelly Black, was recently named the Chairperson of the International Organization for Standardization (ISO) Technical Committee 69, Application of Statistical Methods. A strong QA program has been developed for Neptune's radiological performance assessment (PA) program. Although pieces of their QA program are currently in development, their current program includes document control using Subversion, issue tracking and work flow tracking using JIRA, and transparency and traceability via a system of 'calc sheets' for documentation of all data analysis and modeling combined with 'check print' documentation of QA checking, and rigorous model testing and configuration control. All work is reviewed by an independent subject matter expert who is not associated with the collection and assembly of information, for an internal peer review. The program is enforced with a handful of Standard Operation Procedures (SOPs), Work Instructions, QA Project Plans (QAPPs), and Quality Management Plans (QMPs) that are updated frequently, with required annual training and acknowledgment. In addition, effective communication of modeling approaches and results to clients and stakeholders are integral to their QA program. Many of their models are built using the GoldSim modeling platform, for which their models are well-known for their level of transparency, documentation, and QA traceability. This level of QA is also applied to their process-level models. Neptune has taken some lessons learned from the extremely rigorous QA program required for the Yucca Mountain Project (YMP), and imposed the YMP requirements of complete traceability, transparency, and reproducibility, but avoided the inflexibility of a QA program that likely contributed to the suspect e-mails in 2005 that resulted in loss of trust in the YMP, and in nuclear waste disposal (and nuclear power) in general. There are far too many examples of loss of public trust due to poor QA that could have been easily avoided with a simple and straight-forward QA program combined with stakeholder engagement. The purpose of this paper is to share features of Neptune's Radiological Performance Assessment program QA program, and some of their QA related lessons learned over the past 27 years of QA for complex environmental modeling. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

Probabilistic Groundwater Modeling of the RDX Plume at Los Alamos National Laboratory to Support Risk Assessment - 20359

A plume of the contaminant hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) with concentrations greater than the New Mexico tap water drinking standard (9.66 ppb) is present in the regional aquifer near the southwestern boundary of Los Alamos National Laboratory (LANL). A risk assessment is performed for exposure to regional aquifer groundwater, with long-term predictions of RDX concentrations provided by a calibrated, probabilistic, numerical fate and transport model. The structure of the model is hierarchical, with the RDX Regional Aquifer (RA) groundwater model acting as the primary tool for analysis of downgradient RDX concentrations. The RA model is deeply informed by the conceptual site model (CSM) and is calibrated using site RDX concentration data and hydraulic head measurements, along with other analyses. The model is calibrated using data through December 2019. Where data are scarce other lines of evidence are used to inform inputs, including the multiphase RDX Vadose Zone (VZ) model and Pipe and Disk analytical screening tool. Model inputs are described with informative prior distributions using a robust approach to development that incorporates all available lines of evidence for every parameter used as an input to the RA model. Calibration is performed using a classical nonlinear optimization routine, which is then used to initialize a Bayesian calibration. The Bayesian calibration constrains the uncertainty in the classical calibration, ultimately providing posterior distributions for all model parameters. The challenges of the calibration include high-dimensional parameter space, including spatially heterogeneous hydraulic conductivities, comparatively sparse data, and low RDX concentrations. Posterior parameter distributions developed in the calibration process are then used for stochastic predictive model runs into the future. The result of the forward runs is spatially and temporally explicit estimates of head and concentration with uncertainty at all points in space and time. The probabilistic modeling approach presented here includes innovative computational and statistical methods that leverage high-performance computing (HPC) resources. It makes use of prior modeling work performed at the chromium plume site in the central LANL area, with extensive updates. The risk assessment will ultimately be used to support decision-making at the site, using multiple appropriately weighted sources of information, as well as uncertainty, sensitivity, and value-of-information analyses. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

Whose Gas is it anyway? Differentiating the Source of a Large Soil Vapor Plume beneath Two Adjacent Waste Sites - 20487

DOE contractor CH2M Hill Plateau Remediation Company is currently responsible for conducting groundwater contamination monitoring at several RCRA treatment, storage, and disposal units located on the Hanford Site in Richland, Washington State. The Nonradioactive Dangerous Waste Landfill treatment, storage, and disposal unit presents a distinct groundwater monitoring problem because of a large multi-contaminant soil vapor plume beneath it that is a likely source of low-level volatile organic compound groundwater contamination. Adjacent to Nonradioactive Dangerous Waste Landfill is the Solid Waste Landfill. Volatile organic compounds are inventory components of both the Nonradioactive Dangerous Waste Landfill and the Solid Waste Landfill. Therefore, it is possible that both sites could be contributing to the soil vapor plume. For regulatory purposes, it is important to differentiate which site is the primary contributor of volatile organic compounds to the plume. An approach was developed to identify the primary volatile organic compound source of the soil vapor plume beneath Nonradioactive Dangerous Waste Landfill and Solid Waste Landfill. The site conceptual model hypothesis of vapor-phase volatile organic compound transport to the dissolved phase in groundwater was tested by a simple mathematical model of vapor/liquid equilibrium concentrations at the groundwater/air interface. Once it was shown that vapor-phase volatile organic compound transport to groundwater was a valid conceptual model for Nonradioactive Dangerous Waste Landfill and Solid Waste Landfill, spatial and statistical methods were used to determine the primary site contributing to the majority of volatile organic compounds to the soil vapor plume. Average groundwater chloroform, tetrachloroethene, and trichloroethene concentrations from Nonradioactive Dangerous Waste Landfill and Solid Waste Landfill monitoring network wells were plotted on maps of the facilities and immediate vicinities and compared to soil vapor sampling probe locations. Principal component analysis and mixing ratios were used to identify source contributions of each treatment, storage, and disposal unit to the plume. Results of the vapor/liquid equilibrium concentrations mathematical model showed that transport phenomena outweigh steady-state equilibria. Estimated vapor/liquid equilibrium concentrations were considerably lower than soil vapor measurements. The results indicate that dynamic vadose zone and groundwater factors such as decreased vapor concentrations with depth, vapor dilution from dispersion in the vadose zone, and advective and diffusional volatile organic compound dilution in groundwater result in groundwater volatile organic compound concentrations much less than would be measured under steady-state equilibrium conditions. Site source contribution differentiation by principal component analysis and mixing ratios was inconclusive using actual soil gas data because of the similarity in concentration values in both datasets for Nonradioactive Dangerous Waste Landfill and Solid Waste Landfill. Similar data populations suggest mixing of the vapor contributions from both sites by dispersion through the soil matrix pore spaces. However, when groundwater volatile organic compound data were compared between the Nonradioactive Dangerous Waste Landfill and Solid Waste Landfill monitoring networks, Solid Waste Landfill mean concentrations were higher, suggesting more vapor-phase volatile organic compound transport to groundwater at those locations. Simulated volatile organic compound soil vapor and groundwater datasets created to test the methods developed for this study show that the method can be successful in source differentiation when significantly different datasets are compared. This paper will describe a method of testing a conceptual model for vapor-phase contaminant transport to groundwater and for differentiating site sources of contaminants comprising a mixed-constituent soil vapor plume. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2016

Generation of Low Fidelity Experimental Covariance Matrices for ICSBEP Cases

In validation, statistical methods routinely incorporate information from integral experiments to improve estimations of code biases and uncertainties. One source of high quality integral experiments is the ICSBEP Handbook. While uncertainties for criticality benchmarks are well characterized in the Handbook, information pertaining to the shared uncertainty/covariance between the cases is sparse, yet required for the aforementioned methods; correlations can result from using the same fuel for example. To remedy the lack of available covariance data in the Handbook, recent efforts have been undertaken, notably, within the framework of the NEA Expert Group on Uncertainty Analysis in Criticality Safety Assessment (UACSA) under the Working Party on Nuclear Criticality Safety (WPNCS) . The UACSA activities have focused primarily on comparing methods used to generate high quality covariance matrices for a small number of evaluations. Despite these efforts, within the Database for ICSBEP DICE only about 2% of the nearly 5000 experiments in the ICSBEP Handbook have correlation coefficients, so the amount of work required to generate the missing data appears daunting. And so, to fill in the gaps where no correlations are available, it is proposed to generate low fidelity correlations by isolating the largest components of uncertainty and determining the fraction shared between cases within an evaluation. Uncertainties can be shared when they are not independently measured, or the source of uncertainty is unchanged between cases. The degree to which an uncertainty is shared is linked to the experimental procedure; an approximation is made that in the absence of additional information, similar experimental procedures will lead to a similar fraction of shared uncertainty. Between experimental procedures there is some commonality. One method, proposed here, to help elucidate similar procedures is to encode each procedure into a decision tree grouped by uncertainty type. End nodes of the tree require judgment to assign the fraction of shared uncertainty between cases associated with the node. This can also be thought of as a set of rules, such that if experimental procedures and conditions occurred, then assign the shared uncertainty as some number between 0.0 and 1.0. One advantage of such a procedure is the formation of a transparent logical link between the judgment applied and the amount of shared uncertainty. This makes the results both more reproducible and easy to update. Generating correlation coefficients is reduced to developing decision trees that categorize how the experiments were done, and apply judgment of the degree of shared uncertainty for the end node of the tree. Trees need to be constructed after reading section 1 and 2 of the evaluation and some evaluations certainly do not contain enough information to create informative decision trees nor to assign an amount of shared uncertainty. These evaluations will not be considered here. (authors)

42 ENGINEERING↗

DOE OSTI2025

An R shiny graphical user interface for highprecision mass spectrometric data analysis

• There is currently a lack of software that meets the needs for the analysis of raw data produced by modern isotope ratio mass spectrometers for both R&D and routine use at SRNL and other US national labs • Needs to accommodate multiple isotope systems, instruments, and manufacturers • Include modern statistical methods and handling/visualization of uncertainty • Flexible software with transparent (no “black box”) and reproducible methods • This project is inspired by existing discipline-specific data analysis software (e.g., Tripoli1 , ET_Redux2, IsoplotR3) used in the geochemical community • Our goal is to build an open source data analysis software package that focuses on flexibility, transparency, and reproducibility

LABONE, ELIZABETH↗

DOE OSTI2025

Contrasting Time-Frequency Representations for Unknown Waveform Detection

Identifying unseen electromagnetic waveforms is critical for many applications, like interference management, electronic warfare and spectrum management. Traditionally this is done using statistical methods for anomaly detection, which has evolved to deep learning models for identifying the unseen data, formally termed as open set recognition. Some prior methods use a generative model to emulate open set data, which face challenges in generating synthetic samples for open set while simultaneously selecting an optimal discriminator for accurate classification. To alleviate this issue, we propose a discriminative model that effectively combines time and frequency domain features of communication signals for accurate predictions. We further introduce a cosine similarity loss that makes the domain specific features unique to enhance the prediction rate. Additionally, our model avoids generic feature vectors by extracting class-specific features during training, resulting in improved class representation. The experiment results show that this combined feature approach with cosine loss outperforms single-domain models and improves accuracy by 10% over models without cosine loss.

99 - GENERAL AND MISCELLANEOUS↗