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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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DOE OSTI2022

EFCOG Best Practice HPI for Knowledge Workers ISM-HPI-22-02

This document is a collection of these best practices as determined by team members. This best practice attempts to: Realize opportunities to break the myth where people believe that HPI does not apply to them as they perform no physical work. Recommend options to create an environment that promotes intellectual collaboration and trust, enabling candor and vulnerability. Explain how errors manifest differently from the same human fallibility. Knowledge workers (KW) have errors that take different perspectives to find and mitigate the unique manifestation of these conditions. Help KW identify the critical steps (or risk important steps) in their processes. Reduce risk/consequence from KW errors (limit latent errors as well as finding latent conditions), building resiliency into KW tasks. Mitigation strategies may be different.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2016

Exploring versioned distributed arrays for resilience in scientific applications: Global view resilience

Exascale studies project reliability challenges for future HPC systems. We present the Global View Resilience (GVR) system, a library for portable resilience. GVR begins with a subset of the Global Arrays interface, and adds new capabilities to create versions, name versions, and compute on version data. Applications can focus versioning where and when it is most productive, and customize for each application structure independently. This control is portable, and its embedding in application source makes it natural to express and easy to maintain. The ability to name multiple versions and “partially materialize” them efficiently makes ambitious forward-recovery based on “data slices” across versions or data structures both easy to express and efficient. Using several large applications (OpenMC, preconditioned conjugate gradient (PCG) solver, ddcMD, and Chombo), we evaluate the programming effort to add resilience. The required changes are small (< 2% lines of code (LOC)), localized and machine-independent, and perhaps most important, require no software architecture changes. We also measure the overhead of adding GVR versioning and show that overheads < 2% are generally achieved. This overhead suggests that GVR can be implemented in large-scale codes and support portable error recovery with modest investment and runtime impact. Our results are drawn from both IBM BG/Q and Cray XC30 experiments, demonstrating portability. We also present two case studies of flexible error recovery, illustrating how GVR can be used for multi-version rollback recovery, and several different forward-recovery schemes. GVR’s multi-version enables applications to survive latent errors (silent data corruption) with significant detection latency, and forward recovery can make that recovery extremely efficient. Lastly, our results suggest that GVR is scalable, portable, and efficient. GVR interfaces are flexible, supporting a variety of recovery schemes, and altogether GVR embodies a gentle-slope path to tolerate growing error rates in future extreme-scale systems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

A Review of Human Reliability Needs in the U.S. Nuclear Industry

In this survey, 34 subject matter experts from the U.S. nuclear industry were interviewed to determine specific needs for human reliability analysis (HRA). Conclusions from the interviews are detailed in this article. A summary of the findings includes: (1) The need for improved guidance on the use of HRA methods generally and for specific applications. (2) The need for additional training in HRA to provide more hands-on experience in the application of HRA methods. (3) Thedevelopment of HRA approaches suitable for advanced reactors, severe accident situations, and low-power and shutdown applications. (4) The refinement of HRA methods to account forfactors such as crew variability, latent errors, more sophisticated dependency modeling, and errors of commission. (5) The continued need for simplified HRA methods appropriate for field applications. (6) The need for tighter coupling of HRA and human factors. (7) The need for improvements in the quantitative basis of HRA methods. These findings suggest the field of HRA is mature but still benefits from refinements.

human reliability analysis↗

DOE OSTI2014

Task Decomposition in Human Reliability Analysis

In the probabilistic safety assessments (PSAs) used in the nuclear industry, human failure events (HFEs) are determined as a subset of hardware failures, namely those hardware failures that could be triggered by human action or inaction. This approach is top-down, starting with hardware faults and deducing human contributions to those faults. Elsewhere, more traditionally human factors driven approaches would tend to look at opportunities for human errors first in a task analysis and then identify which of those errors is risk significant. The intersection of top-down and bottom-up approaches to defining HFEs has not been carefully studied. Ideally, both approaches should arrive at the same set of HFEs. This question remains central as human reliability analysis (HRA) methods are generalized to new domains like oil and gas. The HFEs used in nuclear PSAs tend to be top-down— defined as a subset of the PSA—whereas the HFEs used in petroleum quantitative risk assessments (QRAs) are more likely to be bottom-up—derived from a task analysis conducted by human factors experts. The marriage of these approaches is necessary in order to ensure that HRA methods developed for top-down HFEs are also sufficient for bottom-up applications.

error of commission↗

DOE OSTI2022

Estimation of Corn Latent Heat Flux from High Resolution Thermal Imagery

Crop evapotranspiration (ET), which is directly related to latent heat flux, is also a key indicator in determining the water status of crops. In order to estimate the latent heat flux, two-source energy balance (TSEB) models have been developed for thermal imagery from satellite platforms. However, because of the coarse resolution of thermal sensors on the satellite, distinguishing soil and vegetation is difficult which complicates the calculation process and introduces errors in latent heat estimates. In this research, high-resolution thermal datasets (0.05 m) and corresponding RGB datasets (0.03 m) were used for calculating crop latent heat flux using an adapted TSEB model. The RGB datasets were used for supervised classification of soil and vegetation, and the classification results were then used to filter the thermal mosaics to separate vegetation and soil temperatures. The vegetation temperature is used for calculating latent heat flux and the results are validated against the ground reference measurements of latent heat using a handheld porometer. The objective of this research is to introduce a workflow including an adapted TSEB model which is customized for high resolution thermal images from unmanned aircraft systems (UAS) to estimate the latent heat flux of row crops in agricultural fields. Nine dates of data collection in 2018 and 2020 have been evaluated and the root mean square error (RMSE) varies between 16 to 106 W/m2 depending on the days after planting (DAP) and the time of measurement for each day. The results indicate that the workflow introduced here is able to provide estimates of instantaneous latent heat flux (evapotranspiration) measurements for row crops in agricultural fields which will enable people to make reliable decisions related to irrigation scheduling.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

Investigation of a “Sharps” Incident

Special nuclear material research, process development, technology demonstration, and manufacturing capabilities are provided at the Los Alamos National Laboratory Plutonium Facility. Engineered barriers provide the most effective protection from radioactive and hazardous materials. The Worker Safety Security Team augments these passive safety feature by investigating incidents to identify appropriate prevention and mitigation measures. “Learning Teams” facilitate employee feedback loop and integration toward process improvement. Here, this article reports an investigation of a “Sharps” incident and reviews a case study of a technician that cuts his left thumb while making a gasket. Causal analysis of the sharps incident uncovered contributing factors that created the environment in which the incident occurred. Finally, latent organizational conditions that created error-likely situations or weakened defenses were identified and controlled. Effective improvements that reduce the probability or consequence of similar sharps incidents were implemented.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2025

Kernel Manifolds: Nonlinear‐Augmentation Dimensionality Reduction Using Reproducing Kernel Hilbert Spaces

This paper generalizes recent advances on quadratic manifold (QM) dimensionality reduction by developing kernel methods-based nonlinear-augmentation dimensionality reduction. QMs, and more generally feature map-based nonlinear corrections, augment linear dimensionality reduction with a nonlinear correction term in the reconstruction map to overcome approximation accuracy limitations of purely linear approaches. While feature map-based approaches typically learn a least squares optimal polynomial correction term, we generalize this approach by learning an optimal nonlinear correction from a user-defined reproducing kernel Hilbert space. Our approach allows one to impose arbitrary nonlinear structure on the correction term, including polynomial structure, and includes feature map and radial basis function-based corrections as special cases. Furthermore, our method has relatively low training cost and has monotonically decreasing error as the latent space dimension increases. In conclusion, we compare our approach to proper orthogonal decomposition and several recent QM approaches on data from several example problems.

kernel methods↗

DOE OSTI2020

On Neural Architectures for Astronomical Time-series Classification with Application to Variable Stars

Despite the utility of neural networks (NNs) for astronomical time-series classification, the proliferation of learning architectures applied to diverse data sets has thus far hampered a direct intercomparison of different approaches. Here we perform the first comprehensive study of variants of NN-based learning and inference for astronomical time series, aiming to provide the community with an overview on relative performance and, hopefully, a set of best-in-class choices for practical implementations. In both supervised and self-supervised contexts, we study the effects of different time-series-compatible layer choices, namely the dilated temporal convolutional neural network (dTCNs), long-short term memory NNs, gated recurrent units and temporal convolutional NNs (tCNNs). Additionally, we also study the efficacy and performance of encoder-decoder (i.e., autoencoder) networks compared to direct classification networks, different pathways to include auxiliary (non-time-series) metadata, and different approaches to incorporate multi-passband data (i.e., multiple time series per source). Performance—applied to a sample of 17,604 variable stars (VSs) from the MAssive Compact Halo Objects (MACHO) survey across 10 imbalanced classes—is measured in training convergence time, classification accuracy, reconstruction error, and generated latent variables. We find that networks with recurrent NNs generally outperform dTCNs and, in many scenarios, yield to similar accuracy as tCNNs. In learning time and memory requirements, convolution-based layers perform better. We conclude by discussing the advantages and limitations of deep architectures for VS classification, with a particular eye toward next-generation surveys such as the Legacy Survey of Space and Time, the Roman Space Telescope, and Zwicky Transient Facility.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

Improved representations of coupled soil–canopy processes in the CABLE land surface model (Subversion revision 3432)

CABLE is a global land surface model, which has been used extensively in offline and coupled simulations. While CABLE performs well in comparison with other land surface models, results are impacted by decoupling of transpiration and photosynthesis fluxes under drying soil conditions, often leading to implausibly high water use efficiencies. Here, we present a solution to this problem, ensuring that modelled transpiration is always consistent with modelled photosynthesis, while introducing a parsimonious single-parameter drought response function which is coupled to root water uptake. We further improve CABLE's simulation of coupled soil–canopy processes by introducing an alternative hydrology model with a physically accurate representation of coupled energy and water fluxes at the soil–air interface, including a more realistic formulation of transfer under atmospherically stable conditions within the canopy and in the presence of leaf litter. The effects of these model developments are assessed using data from 18 stations from the global eddy covariance FLUXNET database, selected to span a large climatic range. Here, marked improvements are demonstrated, with root mean squared errors for monthly latent heat fluxes and water use efficiencies being reduced by 40 %. Results highlight the important roles of deep soil moisture in mediating drought response and litter in dampening soil evaporation.

58 GEOSCIENCES↗

DOE OSTI2023

Q-BEEP: Quantum Bayesian Error Mitigation Employing Poisson Modeling over the Hamming Spectrum

Quantum computing technology has grown rapidly in recent years, with new technologies being explored, error rates being reduced, and quantum processor’s qubit capacity growing. However, near-term quantum algorithms are still unable to be induced without compounding consequential levels of noise, leading to non-trivial erroneous results. Quantum Error Correction (in-situ error mitigation) and Quantum Error Mitigation (post-induction error mitigation) are promising fields of research within the quantum algorithm scene, aiming to alleviate quantum errors, increasing the overall fidelity and hence the overall quality of circuit induction. Earlier this year, a pioneering work, namely HAMMER, published in ASPLOS-22 demonstrated the existence of a latent structure regarding post-circuit induction errors when mapping to the Hamming spectrum. However, they intuitively assumed that errors occur in local clusters, and that at higher average Hamming distances this structure falls away. In this work, we show that such a correlation structure is not only local but extends certain non-local clustering patterns which can be precisely described by a Poisson distribution model taking the input circuit, the device run time status (i.e., calibration statistics) and qubit topology into consideration. Using this quantum error characterizing model, we developed an iterative algorithm over the generated Bayesian network state-graph for post-induction error mitigation. Thanks to more precise modeling of the error distribution latent structure and the new iterative method, our Q-Beep approach provides state of the art performance and can boost circuit execution fidelity by up to 234.6% on Bernstein-Vazirani circuits and on average 71.0% on QAOA solution quality, using 16 practical IBMQ quantum processors. For other benchmarks such as those in QASMBench, the fidelity improvement is up to 17.8%. Q-Beep is a light-weight post-processing technique that can be performed offline and remotely, making it a useful tool for quantum vendors to integrate and provide more reliable circuit induction results.

Stein, Samuel A.↗

DOE OSTI2016

Sources of errors in the simulation of south Asian summer monsoon in the CMIP5 GCMs

Accurate simulation of the South Asian summer monsoon (SAM) is still an unresolved challenge. There has not been a benchmark effort to decipher the origin of undesired yet virtually invariable unsuccessfulness of general circulation models (GCMs) over this region. This study analyzes a large ensemble of CMIP5 GCMs to show that most of the simulation errors in the precipitation distribution and their driving mechanisms are systematic and of similar nature across the GCMs, with biases in meridional differential heating playing a critical role in determining the timing of monsoon onset over land, the magnitude of seasonal precipitation distribution and the trajectories of monsoon depressions. Errors in the pre-monsoon heat low over the lower latitudes and atmospheric latent heating over the slopes of Himalayas and Karakoram Range induce significant errors in the atmospheric circulations and meridional differential heating. Lack of timely precipitation further exacerbates such errors by limiting local moisture recycling and latent heating aloft from convection. Most of the summer monsoon errors and their sources are reproducible in the land–atmosphere configuration of a GCM when it is configured at horizontal grid spacing comparable to the CMIP5 GCMs. While an increase in resolution overcomes many modeling challenges, coarse resolution is not necessarily the primary driver in the exhibition of errors over South Asia. These results highlight the importance of previously less well known pre-monsoon mechanisms that critically influence the strength of SAM in the GCMs and highlight the importance of land–atmosphere interactions in the development and maintenance of SAM.

CMIP5↗

DOE OSTI2022

DeepAdversaries: examining the robustness of deep learning models for galaxy morphology classification

With increased adoption of supervised deep learning methods for work with cosmological survey data, the assessment of data perturbation effects (that can naturally occur in the data processing and analysis pipelines) and the development of methods that increase model robustness are increasingly important. In the context of morphological classification of galaxies, we study the effects of perturbations in imaging data. In particular, we examine the consequences of using neural networks when training on baseline data and testing on perturbed data. We consider perturbations associated with two primary sources: (a) increased observational noise as represented by higher levels of Poisson noise and (b) data processing noise incurred by steps such as image compression or telescope errors as represented by one-pixel adversarial attacks. We also test the efficacy of domain adaptation techniques in mitigating the perturbation-driven errors. We use classification accuracy, latent space visualizations, and latent space distance to assess model robustness in the face of these perturbations. For deep learning models without domain adaptation, we find that processing pixel-level errors easily flip the classification into an incorrect class and that higher observational noise makes the model trained on low-noise data unable to classify galaxy morphologies. On the other hand, we show that training with domain adaptation improves model robustness and mitigates the effects of these perturbations, improving the classification accuracy up to 23% on data with higher observational noise. Domain adaptation also increases up to a factor of ${\approx}2.3$ the latent space distance between the baseline and the incorrectly classified one-pixel perturbed image, making the model more robust to inadvertent perturbations. Successful development and implementation of methods that increase model robustness in astronomical survey pipelines will help pave the way for many more uses of deep learning for astronomy.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Nonnegative canonical tensor decomposition with linear constraints: nnCANDELINC

Abstract There is an emerging interest for tensor factorization applications in big‐data analytics and machine learning. To speed up the factorization of extra‐large datasets, organized in multidimensional arrays (also known as tensors), easy to compute compression‐based tensor representations, such as, Tucker and tensor train formats, are used to approximate the initial large‐tensor. Further, tensor factorization is used to extract latent features that can facilitate discoveries of new mechanisms and signatures hidden in the data, where the explainability of the latent features is of principal importance. Nonnegative tensor factorization extracts latent features that are naturally sparse and parts of the data, which makes them easily interpretable. However, to take into account available domain knowledge and subject matter expertise, often additional constraints need to be imposed, which lead us to canonical decomposition with linear constraints (CANDELINC), a canonical polyadic decomposition with rank deficient factors. In CANDELINC, Tucker compression is used as a preprocessing step, which lead to a larger residual error but to more explainable latent features. Here, we propose a nonnegative CANDELINC (nnCANDELINC) accomplished via a specific nonnegative Tucker decomposition; we refer to as minimal or canonical nonnegative Tucker. We derive several results required to understand the specificity of nnCANDELINC, focusing on the difficulties of preserving the nonnegative rank of a tensor to its Tucker core and comparing the real valued to nonnegative case. Finally, we demonstrate nnCANDELINC performance on synthetic and real‐world examples.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Analyzing count data with measurement error

In this article, we analyze observed count data such as the number of defects in a steel product where the observed counts are the true counts measured with errors. We account for the measurement error by using a measurement error model based on a latent lognormal (LLN) distribution. We consider making inference about a single population (e.g., from samples of a production lot) and a regression model (e.g., from runs of a designed experiment), where the measurement system properties are known, that is, the parameters of the LLN distribution are known. Then, we consider simultaneous inference for the single population and regression model as well as the measurement system. We demonstrate the proposed methodology with both simulated and real observed counts.

42 ENGINEERING↗

DOE OSTI2014

Multiple indicators, multiple causes measurement error models

Multiple indicators, multiple causes (MIMIC) models are often employed by researchers studying the effects of an unobservable latent variable on a set of outcomes, when causes of the latent variable are observed. There are times, however, when the causes of the latent variable are not observed because measurements of the causal variable are contaminated by measurement error. The objectives of this study are as follows: (i) to develop a novel model by extending the classical linear MIMIC model to allow both Berkson and classical measurement errors, defining the MIMIC measurement error (MIMIC ME) model; (ii) to develop likelihood-based estimation methods for the MIMIC ME model; and (iii) to apply the newly defined MIMIC ME model to atomic bomb survivor data to study the impact of dyslipidemia and radiation dose on the physical manifestations of dyslipidemia. Finally, as a by-product of our work, we also obtain a data-driven estimate of the variance of the classical measurement error associated with an estimate of the amount of radiation dose received by atomic bomb survivors at the time of their exposure.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Long–short-term memory encoder–decoder with regularized hidden dynamics for fault detection in industrial processes

The ability of recurrent neural networks (RNN) to model nonlinear dynamics of high dimensional process data has enabled data-driven RNN-based fault detection algorithms. Previous studies have focused on detecting faults by identifying the discrepancies in data distribution between the faulty and normal data, as reflected in prediction errors generated by RNN models. However, in industrial processes, variations in data distribution can also result from changes in normal control setpoints and compensatory control adjustments in response to disturbances, making it hard to differentiate between normal and faulty conditions. This paper proposes a fault detection method utilizing a long short-term memory (LSTM) encoder–decoder structure with regularized hidden dynamics and reversible instance normalization (RevIN) to compactly represent high-dimensional measurements for effective monitoring. During training, the hidden states of the model are regularized to form a low-dimensional latent space representation of the original multivariate time series data. As a result, the prediction errors of the latent states can be used to monitor the abnormal dynamic variations, while the reconstruction errors of the measured variables are used to monitor the abnormal static variations. Furthermore, the proposed indices can reflect operating conditions, even when the distribution of test data changes, which helps distinguish faults from normal adjustments and disturbances that controllers can settle. Here, data from numerical simulation and the Tennessee Eastman process are used to illustrate the effectiveness of the proposed fault detection method.

42 ENGINEERING↗

DOE OSTI2025

Mesh-based super-resolution of fluid flows with multiscale graph neural networks

A graph neural network (GNN) approach is introduced in this work which enables mesh-based three-dimensional super-resolution of fluid flows. In this framework, the GNN is designed to operate not on the full mesh-based field at once, but on localized meshes of elements (or cells) directly. To facilitate mesh-based GNN representations in a manner similar to spectral (or finite) element discretizations, a baseline GNN layer (termed a message passing layer, which updates local node properties) is modified to account for synchronization of coincident graph nodes, rendering compatibility with commonly used element-based mesh connectivities. Furthermore, the architecture is multiscale in nature, and is comprised of a combination of coarse-scale and fine-scale message passing layer sequences (termed processors) separated by a graph unpooling layer. The coarse-scale processor embeds a query element (alongside a set number of neighboring coarse elements) into a single latent graph representation using coarse-scale synchronized message passing over the element neighborhood, and the fine-scale processor leverages additional message passing operations on this latent graph to correct for interpolation errors. Demonstration studies are performed using hexahedral mesh-based data from Taylor–Green Vortex and backward-facing step flow simulations at Reynolds numbers of 1600 and 3200. Through analysis of both global and local errors, the results ultimately show how the GNN is able to produce accurate super-resolved fields compared to targets in both coarse-scale and multiscale model configurations. Reconstruction errors for fixed architectures were found to increase in proportion to the Reynolds number. Geometry extrapolation studies on a separate cavity flow configuration show promising cross-mesh capabilities of the super-resolution strategy.

Backward-facing step↗

DOE OSTI2016

Investigation of a light fixture fire

Metal-halide lamps produce light by discharging an electric arc through a gaseous mixture of vaporized mercury and metal halides. Metal-halide lamps for use in spaces with lower mounting heights can produce excessive visual glare in the normal, higher field-of-view unless they are equipped with prismatic lenses. Should the bulb fail, high internal operating pressure of the arc tube can launch fragments of arc tube at high velocity in all directions, striking the outer bulb of the lamp with enough force to cause the outer bulb to break. This article reports an investigation of a light fixture fire and reviews a case study of a metal-halide lamp fire. We reported on causal analysis of the metal-halide lamp fire uncovered contributing factors that created the environment in which the incident occurred. Latent organizational conditions that created error-likely situations or weakened defenses were identified and controlled. Lastly, effective improvements that reduce the probability or consequence of similar metal-halide lamp fire incidents were implemented.

36 MATERIALS SCIENCE↗