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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

zPerf: A Statistical Gray-Box Approach to Performance Modeling and Extrapolation for Scientific Lossy Compression

With the scaling up of simulation-based scientific discovery on high-performance computing systems, the disparity between compute and I/O has increased, forcing domain scientists to save only a small amount of simulation data to persistent storage. This can result in the loss of essential physics fields that are needed for data analysis. While error-bounded lossy compression has made tremendous progress in bridging the gap between compute and I/O, the lack of understanding of compression performance remains a key hurdle to its wide adoption. Here, in this work, we present zPerf, a statistical gray-box performance modeling approach for scientific lossy compression. Our contributions are threefold: 1) We develop zPerf to estimate the performance of lossy compression techniques, based on in-depth understanding and statistical modeling for data features and core compression metrics; 2) We demonstrate the in-detailed implementation of zPerf using two case studies, where we derive the performance modeling for SZ and ZFP, two leading lossy compressors; 3) We evaluate the effectiveness of zPerf on real-world datasets across various domains. Based on the evaluation, we demonstrate the efficacy of the zPerf performance model; 4) We further discuss three case studies where zPerf is applied to extrapolate the compression ratio of SZ and ZFP with alternative encoding schemes as well as ZFP with an alternative transform scheme. Through the case studies, we demonstrate the potential of zPerf for exploring the design space of lossy compression, which has hardly been studied in the literature.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

X-ray and Neutron Radiography for Quantitative Material Reconstructions

Radiography is a powerful tool to determine the interior structure of objects. X-ray radiography is widely used and provides high-resolution images, though X-rays have limited transmission through materials of high atomic number (Z) and density. In contrast, neutrons can penetrate many materials that are heavily attenuating to X-rays, such as metals, providing contrast in the inner layers of highly attenuating items. Past work has shown the value in using both X-ray and neutron radiography for estimating material thicknesses, though that work was limited to simulated data. Here, we demonstrate quantitative material reconstructions using experimental X-ray and neutron radiography data from lab-based systems, accurately modeling radiography system responses to within a few percent to enable quantitative measures of material thickness. We demonstrate the utility of neutron radiography and X-ray radiography for these quantitative reconstructions and introduce methods for using their complementarity to improve image quality and optimize experimental design.

Gilbert, Andrew J.↗
DOE OSTI2018

Electromechanical Mode Estimation in the Presence of Periodic Forced Oscillations

Electromechanical modes are inherent to any inter-connected power systems which provide a measure of the small-signal stability margin of the system. A number of algorithms have been developed for the estimation of these modes using synchrophasor measurements. However, most of these algorithms are not designed to operate in the presence of forced oscillations (FO). These FOs are results of periodic rogue input driving the system. When FOs are present, estimates of system modes can be biased depending on the frequency and the amplitude of the FOs. To tackle this problem, a new algorithm is proposed in this paper to estimate system modes in the presence of FOs. In the proposed method, the over-determined modified Yule-Walker method which is used to estimate AR coefficients of an ARMA (Auto-Regressive Moving-Average) signal model is extended to an ARMA with exogenous input (ARMAX) model that incorporates the presence of FOs. Two versions of the proposed method are included in this paper based on the requirement of the information of the duration of FOs in the signal. Furthermore, the results obtained by implementing the proposed algorithm on simulated data and real-world data validate the effectiveness of both versions of the proposed method.

42 ENGINEERING↗
DOE OSTI2023

Modified Eigen-Decomposition-based Interval Analysis (MEDIA) for Power System Dynamic State Estimation

The Bayesian approach has been used for the dynamic state estimation (DSE) of a power system. However, due to the complexity of noise resources, it is difficult to quantify measurement and process noise using probability density functions (PDFs). To overcome the difficulty, the authors of this paper propose a modified eigen-decomposition-based interval analysis (MEDIA) method, which employs bounds instead of PDFs to quantify the noise, and uses the eigen decomposition method to reduce the negative impact of the overestimation problem. Using the simulation data generated from IEEE 16-machine and IEEE 10-machine systems, it is shown that the proposed MEDIA method can estimate the hard boundaries of dynamic states in real time. Furthermore, comparison with the forward-backward propagation method and the extended set-membership filter also shows that the proposed MEDIA method performs better by providing narrower boundaries in the DSE.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Forced Oscillation Source Location in Power Systems Using MVMD-assisted DEF in TF Plane

Fast and accurate forced oscillation source location (FOSL) is critical for mitigating forced oscillations in power systems. Here, to overcome the shortcomings of traditional dissipating energy flow (DEF) in decomposing the forced oscillation components from the measurement responses, a multivariate variational mode decomposition (MVMD)-assisted FOSL is proposed in this paper to locate the forced oscillation sources from measurements in time-frequency (TF) plane. The multi-channel measurement matrix of each generator is formed, then the multi-channel modes are decomposed from the formed multi-channel measurement matrix using the MVMD. Further, the intrinsic mode functions (IMFs) associated with the forced oscillation mode are distinguished from the decomposed modes according to the relative energy weights. With the distinguished IMFs, the MVMD-assisted DEF calculation method is developed to locate the forced oscillation source. Finally, the performance of the proposed method is evaluated using the simulation data of the WECC 179-bus test system and IEEE-NASPI Oscillation Source Location Contest as well as the actual PMU data of ISO New England. The results validated the effectiveness and accuracy of the proposed method in the FOSL.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

FIRE: A Failure-Adaptive RL Framework for Edge Computing Migrations

In edge computing, users' service profiles are migrated between edge servers due to user mobility. Reinforcement Learning (RL) frameworks have been proposed to do so, often trained on simulated data. However, existing RL frameworks overlook occasional server failures, which although rare, impact latency-sensitive applications like AR/VR and real- time obstacle detection. These rare failures, being not adequately represented in historical training data, pose a challenge for data-driven RL algorithms. We introduce FIRE, a framework that adapts to rare events by training a RL policy in an edge computing digital twin environment. We propose FIRE-ImRE, an importance sampling-based Q-learning algorithm, which samples rare events proportionally to their impact on the value function. FIRE considers delay, migration, failure, and backup placement costs across individual and shared service profiles. We prove FIRE-ImRE's boundedness and convergence to optimality. Next, we introduce novel deep Q-learning (FIRE-ImDQL) and actor critic (FIRE-ImACRE) versions of our algorithm to enhance scalability. Here, we extend our framework to accommodate users with varying risk tolerances of rare failure events. Through trace-driven experiments, we show that FIRE reduces edge computing costs compared to vanilla RL and the greedy baseline in the event of failures.

Edge computing↗
DOE OSTI2019

Detection and Characterization of Intrusions to Network Parameter Data in Electric Power Systems

Combating cyberattacks is an emerging challenge in maintaining the reliable and economic operation of electric power systems. Possible cyberattacks include intrusions to the parameter data at a control center. In this class of attacks, algorithms at the control center are correctly executed, but the attacker's modification of the associated parameter data yields improper results. This paper proposes an algorithm for detecting and characterizing cyberattacks to network parameter data, with specific application to optimal power flow problems. The proposed algorithm evaluates whether historical operating point data are consistent with the network parameters. Inconsistencies indicating potential cyberattacks are characterized using historical operational data (power injections and voltage phasors) along with network parameter data. Simulated test cases illustrate the proposed algorithm's detection and characterization capabilities.

cybersecurity↗
DOE OSTI2019

Learning Behavior of Distribution System Discrete Control Devices for Cyber-Physical Security

Conventional cyber-security intrusion detection systems monitor network traffic for malicious activity and indications that an adversary has gained access to the system. The approach discussed here expands the idea of a traditional intrusion detection system within electrical power systems, specifically power distribution networks, by monitoring the physical behavior of the grid. This is achieved through the use of high-rate distribution Phasor Measurement Units (PMUs), alongside SCADA packets analysis, for the purpose of monitoring the behavior of discrete control devices. In this work we present a set of algorithms for passively learning the control logic of voltage regulators and switched capacitor banks. Upon detection of an abnormal operation, the operator is alerted and further action can be taken. Finally, the proposed learning algorithms are validated on both simulated data and on measured PMU data from a utility pilot deployment site.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

eFESTA: Ensemble Feature Exploration with Surface Density Estimates

We propose surface density estimate (SDE) to model the spatial distribution of surface features-isosurfaces, ridge surfaces, and streamsurfaces-in 3D ensemble simulation data. The inputs of SDE computation are surface features represented as polygon meshes, and no field datasets are required (e.g., scalar fields or vector fields). The SDE is defined as the kernel density estimate of the infinite set of points on the input surfaces and is approximated by accumulating the surface densities of triangular patches. We also propose an algorithm to guide the selection of a proper kernel bandwidth for SDE computation. An ensemble Feature Exploration method based on Surface densiTy EstimAtes (eFESTA) is then proposed to extract and visualize the major trends of ensemble surface features. For an ensemble of surface features, each surface is first transformed into a density field based on its contribution to the SDE, and the resulting density fields are organized into a hierarchical representation based on the pairwise distances between them. The hierarchical representation is then used to guide visual exploration of the density fields as well as the underlying surface features. We demonstrate the application of our method using isosurface in ensemble scalar fields, Lagrangian coherent structures in uncertain unsteady flows, and streamsurfaces in ensemble fluid flows.

Computational modeling↗
DOE OSTI2024

Localized Evaluation for Constructing Discrete Vector Fields

Topological abstractions offer a method to summarize the behavior of vector fields, but computing them robustly can be challenging due to numerical precision issues. One alternative is to represent the vector field using a discrete approach, which constructs a collection of pairs of simplices in the input mesh that satisfies criteria introduced by Forman's discrete Morse theory. While numerous approaches exist to compute pairs in the restricted case of the gradient of a scalar field, state-of-the-art algorithms for the general case of vector fields require expensive optimization procedures. This paper introduces a fast, novel approach for pairing simplices of two-dimensional, triangulated vector fields that do not vary in time. The key insight of our approach is that we can employ a local evaluation, inspired by the approach used to construct a discrete gradient field, where every simplex in a mesh is considered by no more than one of its vertices. Specifically, we observe that for any edge in the input mesh, we can uniquely assign an outward direction of flow. We can further expand this consistent notion of outward flow at each vertex, which corresponds to the concept of a downhill flow in the case of scalar fields. Working with outward flow enables a linear-time algorithm that processes the (outward) neighborhoods of each vertex one-by-one, similar to the approach used for scalar fields. Here, we couple our approach to constructing discrete vector fields with a method to extract, simplify, and visualize topological features. Empirical results on analytic and simulation data demonstrate drastic improvements in running time, produce features similar to the current state-of-the-art, and show the application of simplification to large, complex flows.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Efficient species identification for Pacific salmon genetic monitoring programs

Abstract Genetic monitoring of Pacific salmon in the Columbia River basin provides crucial information to fisheries managers that is otherwise challenging to obtain using traditional methods. Monitoring programs such as genetic stock identification (GSI) and parentage‐based tagging (PBT) involve genotyping tens of thousands of individuals annually. Although rare, these large sample collections inevitably include misidentified species, which exhibit low genotyping success on species‐specific Genotyping‐in‐Thousands by sequencing (GT‐seq) panels. For laboratories involved in large‐scale genotyping efforts, diagnosing non‐target species and reassigning them to the appropriate monitoring program can be costly and time‐consuming. To address this problem, we identified 19 primer pairs that exhibit consistent cross‐species amplification among salmonids and contain 51 species informative variants. These genetic markers reliably discriminate among 11 salmonid species and two subspecies of Cutthroat Trout and have been included in species‐specific GT‐seq panels for Chinook Salmon, Coho Salmon, Sockeye Salmon, and Rainbow Trout commonly used for Pacific salmon genetic monitoring. The majority of species‐informative amplicons (16) were newly identified from the four existing GT‐seq panels, thus demonstrating a low‐cost approach to species identification when using targeted sequencing methods. A species‐calling script was developed that is tailored for routine GT‐seq genotyping pipelines and automates the identification of non‐target species. Following extensive testing with empirical and simulated data, we demonstrated that the genetic markers and accompanying script accurately identified species and are robust to missing genotypic data and low‐frequency, shared polymorphisms among species. Finally, we used these tools to identify Coho Salmon incidentally caught in the Columbia River Chinook Salmon sport fishery and used PBT to determine their hatchery of origin. These molecular and computing resources provide a valuable tool for Pacific salmon conservation in the Columbia River basin and demonstrate a cost‐effective approach to species identification for genetic monitoring programs.

Robinson, Zachary L.↗
DOE OSTI2020

Control-Oriented Modeling of Cycle-to-Cycle Combustion Variability at the Misfire Limit in SI Engines

A control-oriented model is presented that can capture the prior-cycle correlation of combustion cycles during conditions with high levels of exhaust gas recirculation (EGR). Combustion events are modeled in discrete time and the dynamic evolution is captured by the residual air, fuel, and inert gas trapped in the combustion chamber. The mathematical formulation of the model is presented together with the calibration procedure to emulate a particular engine operating condition. A cycle-to-cycle system identification methodology is described which allows regressing model parameters from experimental data. Simulations are presented and compared to real engine measurements to show the modeling potential for analysis and control of combustion events.

Maldonado Puente, Bryan↗
DOE OSTI2013

Dose rate profile surrounding a repository

The focus of this research was to analyze the dose rate profile around a waste repository using Monte Carlo techniques. Dose rates at various heights and distances were analyzed outside of the waste repository using MCNPX [1]. The heights measured extended the height of the building and the distances varied between 0 and 22 m away from the waste repository. The simulation data were fitted by smooth analytical functions for different height levels and distances, such that vertical and horizontal dose rates as functions of source-detector distance were achieved. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

Subsampling-based compression and flow visualization

As computational capabilities increasingly outpace disk speeds on leading supercomputers, scientists will, in turn, be increasingly unable to save their simulation data at its native resolution. One solution to this problem is to compress these data sets as they are generated and visualize the compressed results afterwards. We explore this approach, specifically subsampling velocity data and the resulting errors for particle advection-based flow visualization. We compare three techniques: random selection of subsamples, selection at regular locations corresponding to multi-resolution reduction, and introduce a novel technique for informed selection of subsamples. Furthermore, we explore an adaptive system which exchanges the subsampling budget over parallel tasks, to ensure that subsampling occurs at the highest rate in the areas that need it most. We perform supercomputing runs to measure the effectiveness of the selection and adaptation techniques. Overall, we find that adaptation is very effective, and, among selection techniques, our informed selection provides the most accurate results, followed by the multi-resolution selection, and with the worst accuracy coming from random subsamples.

Agranovsky, Alexy↗
DOE OSTI2012

Statistical CT noise reduction with multiscale decomposition and penalized weighted least squares in the projection domain

Purposes: The suppression of noise in x-ray computed tomography (CT) imaging is of clinical relevance for diagnostic image quality and the potential for radiation dose saving. Toward this purpose, statistical noise reduction methods in either the image or projection domain have been proposed, which employ a multiscale decomposition to enhance the performance of noise suppression while maintaining image sharpness. Recognizing the advantages of noise suppression in the projection domain, the authors propose a projection domain multiscale penalized weighted least squares (PWLS) method, in which the angular sampling rate is explicitly taken into consideration to account for the possible variation of interview sampling rate in advanced clinical or preclinical applications. Methods: The projection domain multiscale PWLS method is derived by converting an isotropic diffusion partial differential equation in the image domain into the projection domain, wherein a multiscale decomposition is carried out. With adoption of the Markov random field or soft thresholding objective function, the projection domain multiscale PWLS method deals with noise at each scale. To compensate for the degradation in image sharpness caused by the projection domain multiscale PWLS method, an edge enhancement is carried out following the noise reduction. The performance of the proposed method is experimentally evaluated and verified using the projection data simulated by computer and acquired by a CT scanner. Results: The preliminary results show that the proposed projection domain multiscale PWLS method outperforms the projection domain single-scale PWLS method and the image domain multiscale anisotropic diffusion method in noise reduction. In addition, the proposed method can preserve image sharpness very well while the occurrence of 'salt-and-pepper' noise and mosaic artifacts can be avoided. Conclusions: Since the interview sampling rate is taken into account in the projection domain multiscale decomposition, the proposed method is anticipated to be useful in advanced clinical and preclinical applications where the interview sampling rate varies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

SU-E-T-221: Investigation of Lower Energy (< 6 MV) Photon Beams for Cancer Radiotherapy

Purpose: To study the potential applications of the lower energy (< 6MV) photon beams in the radiotherapeutic management of pediatric cancer and lung cancer patients. Methods: Photon beams of 2, 3, 4, 5 and 6MV were first simulated with EGS4/BEAM and then used for Monte-Carlo dose calculations. For four pediatric patients with abdominal and brain lesions, six 3D-conformal radiotherapy (3DCRT) plans were generated using single photon energy (2 to 6MV) or mixed energies (3 and 6MV). Furthermore, a virtual machine of 3 and 6MV was commissioned in a treatment planning system (TPS) based on Monte-Carlo simulated data. Three IMRT plans of a lung cancer patient were generated on this virtual machine. All plans were normalized to D95% of target dose for 6MV plan and then compared in terms of integral dose and OAR sparing. Results: For the four pediatric patients, the integral dose for the 2, 3, 4 and 5MV plans increased by 9%, 5%, 3.5%, 1.7%, respectively as compared to 6MV. Almost all OARs in the 2MV plan received more than 10% more doses than 6MV. Mixed energy 3DCRT plans were of the same quality as 6MV plans. For the lung IMRT plans, both the 3MV plan and the mixed beam plan showed better OAR sparing in comparison to 6MV plan. Specifically, the maximum and mean doses to the spinal cord in the mixed energy plan were lower by 21% and 16%, respectively. Conclusion: Single lower energy photon beam was found to be inferior to 6MV in the radiotherapy of pediatric patients and lung cancer patients when the integral doses and the doses to the OARs were considered. However, mixed energy plans combining low with high energy beams showed significant OAR sparing while maintaining the same PTV coverage. Investigation with more patient data is ongoing for further confirmation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

WE-G-18A-06: Sinogram Restoration in Helical Cone-Beam CT

Purpose: To extend CT sinogram restoration, which has been shown in 2D to reduce noise and to correct for geometric effects and other degradations at a low computational cost, from 2D to a 3D helical cone-beam geometry. Methods: A method for calculating sinogram degradation coefficients for a helical cone-beam geometry was proposed. These values were used to perform penalized-likelihood sinogram restoration on simulated data that were generated from the FORBILD thorax phantom. Sinogram restorations were performed using both a quadratic penalty and the edge-preserving Huber penalty. After sinogram restoration, Fourier-based analytical methods were used to obtain reconstructions. Resolution-variance trade-offs were investigated for several locations within the reconstructions for the purpose of comparing sinogram restoration to no restoration. In order to compare potential differences, reconstructions were performed using different groups of neighbors in the penalty, two analytical reconstruction methods (Katsevich and single-slice rebinning), and differing helical pitches. Results: The resolution-variance properties of reconstructions restored using sinogram restoration with a Huber penalty outperformed those of reconstructions with no restoration. However, the use of a quadratic sinogram restoration penalty did not lead to an improvement over performing no restoration at the outer regions of the phantom. Application of the Huber penalty to neighbors both within a view and across views did not perform as well as only applying the penalty to neighbors within a view. General improvements in resolution-variance properties using sinogram restoration with the Huber penalty were not dependent on the reconstruction method used or the magnitude of the helical pitch. Conclusion: Sinogram restoration for noise and degradation effects for helical cone-beam CT is feasible and should be able to be applied to clinical data. When applied with the edge-preserving Huber penalty, sinogram restoration leads to an improvement in resolution-variance tradeoffs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

MO-D-213-02: Quality Improvement Through a Failure Mode and Effects Analysis of Pediatric External Beam Radiotherapy

Purpose: To conduct a failure mode and effects analysis (FMEA) as per AAPM Task Group 100 on clinical processes associated with teletherapy, and the development of mitigations for processes with identified high risk. Methods: A FMEA was conducted on clinical processes relating to teletherapy treatment plan development and delivery. Nine major processes were identified for analysis. These steps included CT simulation, data transfer, image registration and segmentation, treatment planning, plan approval and preparation, and initial and subsequent treatments. Process tree mapping was utilized to identify the steps contained within each process. Failure modes (FM) were identified and evaluated with a scale of 1–10 based upon three metrics: the severity of the effect, the probability of occurrence, and the detectability of the cause. The analyzed metrics were scored as follows: severity – no harm = 1, lethal = 10; probability – not likely = 1, certainty = 10; detectability – always detected = 1, undetectable = 10. The three metrics were combined multiplicatively to determine the risk priority number (RPN) which defined the overall score for each FM and the order in which process modifications should be deployed. Results: Eighty-nine procedural steps were identified with 186 FM accompanied by 193 failure effects with 213 potential causes. Eighty-one of the FM were scored with a RPN > 10, and mitigations were developed for FM with RPN values exceeding ten. The initial treatment had the most FM (16) requiring mitigation development followed closely by treatment planning, segmentation, and plan preparation with fourteen each. The maximum RPN was 400 and involved target delineation. Conclusion: The FMEA process proved extremely useful in identifying previously unforeseen risks. New methods were developed and implemented for risk mitigation and error prevention. Similar to findings reported for adult patients, the process leading to the initial treatment has an associated high risk.

60 APPLIED LIFE SCIENCES↗