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DOE OSTI2023

Time-lapse VSP integration and calibration of subsurface stress field utilizing machine learning approaches: A case study of the morrow B formation, FWU

This study aims to develop a methodology for calibrating subsurface stress changes through time-lapse vertical seismic profiling (VSP) integration. The selected study site is a region around the injector well located within Farnsworth field unit (FWU), where there is an ongoing CO 2 -enhanced oil recovery (EOR) operation. In our study, a site-specific rock physics model was created from extensive geological, geophysical, and geomechanical characterization through 3D seismic data, well logs, and core assessed as part of the 1D MEM conducted on the characterization well within the study area. Here, the Biot-Gassmann workflow was utilized to combine the rock physics and reservoir simulation outputs to determine the seismic velocity change due to fluid substitution. Modeled seismic velocities attributed to mean effective stress were determined from the geomechanical simulation outputs, and the stress-velocity relationship developed from ultrasonic seismic velocity measurements. A machine learning-assisted workflow comprised of an artificial neural network and a particle swarm optimizer (PSO) was utilized to minimize a penalty function created between the modeled seismic velocities and the observed time-lapse VSP dataset. The successful execution of this workflow has affirmed the suitability of acoustic time-lapse measurements for 4D-VSP geomechanical stress calibration pending measurable stress sensitivities within the anticipated effective stress changes and the availability of suitable and reliable datasets for petroelastic modeling. © 2023 Society of Chemical Industry and John Wiley & Sons, Ltd.

58 GEOSCIENCES↗

NASA NTRS1997

Fast Context Switching in Real-Time Propositional Reasoning

The trend to increasingly capable and affordable control processors has generated an explosion of embedded real-time gadgets that serve almost every function imaginable. The daunting task of programming these gadgets is greatly alleviated with real-time deductive engines that perform all execution and monitoring functions from a single core model, Fast response times are achieved using an incremental propositional deductive database (an LTMS). Ideally the cost of an LTMS's incremental update should be linear in the number of labels that change between successive contexts. Unfortunately an LTMS can expend a significant percentage of its time working on labels that remain constant between contexts. This is caused by the LTMS's conservative approach: a context switch first removes all consequences of deleted clauses, whether or not those consequences hold in the new context. This paper presents a more aggressive incremental TMS, called the ITMS, that avoids processing a significant number of these consequences that are unchanged. Our empirical evaluation for spacecraft control shows that the overhead of processing unchanged consequences can be reduced by a factor of seven.

Nayak, P. Pandurang↗

DOE OSTI2015

Stand-Alone Dynamic System Simulation of a Fissile Solution System

Dynamic system simulations (DSS) of fissile solution systems (FSS) have been performed at Los Alamos National Laboratory (LANL) for several years. DSS have been designed and executed using the DESIRE (Direct Executing Simulation in Real Time) system, a platform ideal for expert system modelers, as it allows for direct mathematical specification of a model, and facilities for performing experiments and analyses with the model. Models built in DESIRE have been compared to historical data from several historic aqueous homogeneous reactors (AHR) showing close agreement between experimental data and model estimates. Historical systems modeled include: SUPO (Super Power), an AHR that operated at Los Alamos National Laboratory from 1951 to 1974; SILENE, which was operated by CEA at Valduc, France until 2011; KEWB (Kinetics Experiment Water Boiler) A-2 and B-5 cores, operated by North American Atomics during the 1960’s and 1970’s. SUPO is considered a benchmark for steady-state operations, SILENE for pulse, and KEWB as confirmatory for both modes. However, there are some simulation tasks that are not well-suited to DESIRE model implementation. These tasks include engineering design and analysis and providing a hardware simulator backend. It has been determined that executable simulation models would better suit these purposes, hence a process for converting DESIRE simulation models to executable simulation models was designed and implemented, and several models have been converted. DESIRE simulation models are converted to C++ executable models by an automated process. The conversion process is quick, accurate and repeatable, giving confidence that converted models will perform as well as the original DESIRE models. Extensive testing on the SUPO and accelerator driven subcritical system models has shown that the executable models perform almost identically to the DESIRE models. All major trends and events are simulated with excellent agreement and the steady-state results typically agree within a small fraction of a percent (always better than 0.1% and generally better than 0.01% agreement).

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

NASA NTRS2010

Antenna Controller Replacement Software

The Antenna Controller Replacement (ACR) software accurately points and monitors the Deep Space Network (DSN) 70-m and 34-m high-efficiency (HEF) ground-based antennas that are used to track primarily spacecraft and, periodically, celestial targets. To track a spacecraft, or other targets, the antenna must be accurately pointed at the spacecraft, which can be very far away with very weak signals. ACR s conical scanning capability collects the signal in a circular pattern around the target, calculates the location of the strongest signal, and adjusts the antenna pointing to point directly at the spacecraft. A real-time, closed-loop servo control algorithm performed every 0.02 second allows accurate positioning of the antenna in order to track these distant spacecraft. Additionally, this advanced servo control algorithm provides better antenna pointing performance in windy conditions. The ACR software provides high-level commands that provide a very easy user interface for the DSN operator. The operator only needs to enter two commands to start the antenna and subreflector, and Master Equatorial tracking. The most accurate antenna pointing is accomplished by aligning the antenna to the Master Equatorial, which because of its small size and sheltered location, has the most stable pointing. The antenna has hundreds of digital and analog monitor points. The ACR software provides compact displays to summarize the status of the antenna, subreflector, and the Master Equatorial. The ACR software has two major functions. First, it performs all of the steps required to accurately point the antenna (and subreflector and Master Equatorial) at the spacecraft (or celestial target). This involves controlling the antenna/ subreflector/Master-Equatorial hardware, initiating and monitoring the correct sequence of operations, calculating the position of the spacecraft relative to the antenna, executing the real-time servo control algorithm to maintain the correct position, and monitoring tracking performance.

Chao, Roger Y.↗

DOE OSTI2017

Machine Learning-Based Temperature Prediction for Runtime Thermal Management Across System Components

Elevated temperatures limit the peak performance of systems because of frequent interventions by thermal throttling. Non-uniform thermal states across system nodes also cause performance variation within seemingly equivalent nodes leading to significant degradation of overall performance. In this paper we present a framework for creating a lightweight thermal prediction system suitable for run-time management decisions. We pursue two avenues to explore optimized lightweight thermal predictors. First, we use feature selection algorithms to improve the performance of previously designed machine learning methods. Second, we develop alternative methods using neural network and linear regression-based methods to perform a comprehensive comparative study of prediction methods. We show that our optimized models achieve improved performance with better prediction accuracy and lower overhead as compared with the Gaussian process model proposed previously. Specifically we present a reduced version of the Gaussian process model, a neural network-based model, and a linear regression-based model. Using the optimization methods, we are able to reduce the average prediction errors in the Gaussian process from 4: 2 degrees C to 2: 9 degrees C. We also show that the newly developed models using neural network and Lasso linear regression have average prediction errors of 2: 9 degrees C and 3: 8 degrees C respectively. The prediction overheads are 0.22, 0.097, and 0.026 ms per prediction for reduced Gaussian process, neural network, and Lasso linear regression models, respectively, compared with 0.57 ms per prediction for the previous Gaussian process model. We have implemented our proposed thermal prediction models on a two-node system configuration to help identify the optimal task placement. The task placement identified by the models reduces the average system temperature by up to 11: 9 degrees C without any performance degradation. Furthermore, these models respectively achieve 75, 82.5, and 74.17 percent success rates in correctly pointing to those task placements with better thermal response, compared with 72.5 percent success for the original model in achieving the same objective. Lastly, we extended our analysis to a 16-node system and we were able to train models and execute them in real time to guide task migration and achieve on average 17 percent reduction in the overall system cooling power.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1990

Remote mission specialist - A study in real-time, adaptive planning

A high-level planning architecture for robotic operations is presented. The remote mission specialist integrates high-level directives with low-level primitives executable by a run-time controller for command of autonomous servicing activities. The planner has been designed to address such issues as adaptive plan generation, real-time performance, and operator intervention.

Rokey, Mark J.↗

DOE OSTI2023

AI ATAC 1: An Evaluation of Prominent Commercial Malware Detectors

This work presents an evaluation of six prominent commercial endpoint malware detectors, a network malware detector, and a file-conviction algorithm from a cyber technology vendor. The evaluation was administered as the first of the Artificial I ntelligence Applications t o Autonomous Cybersecurity (AI ATAC) prize challenges, funded by / completed in service of the US Navy. The experiment employed 100K files (50/50% benign/malicious) with a stratified distribution of file types, including ~1K zero-day program executables (increasing experiment size two orders of magnitude over previous work). We present an evaluation process of delivering a file to a fresh virtual machine donning the detection technology, waiting 90s to allow static detection, then executing the file and waiting another period for dynamic detection; this allows greater fidelity in the observational data than previous experiments, in particular, resource and time-to-detection statistics. To execute all 800K trials (100K files × 8 tools), a software framework is designed to choreograph the experiment into an automated, time-synced, and reproducible workflow with substantial parallelization. Software with base classes for this framework are provided. A cost-benefit model was configured to integrate the tools’ detection statistics into a comparable quantity by simulating costs of use. This provides a ranking methodology for cyber competitions and a lens for reasoning about the varied statistical results. The results provide insights on state of commercial malware detection.

Bridges, Robert↗

DOE OSTI2025

The Dynamic Networks Experiments: Virtual Experiments to Quantify Gains in Nuclear Explosion Monitoring

We describe an ongoing series of virtual experiments conducted collaboratively by four United States National Laboratories: Sandia National Laboratories, Los Alamos National Laboratory, Lawrence Livermore National Laboratory, and Pacific Northwest National Laboratory. These Dynamic Network Experiments (DNEs) provide an experimental framework to evaluate the potential impact of new research tools on nuclear explosion monitoring. The second DNE (DNE2), completed in 2024, exploited waveform data (seismic, infrasound, and electromagnetic) that was recorded by multi-modal sensors within and near the Nevada National Security Site and synthetic radionuclide signatures over multiple time periods. During the execution of DNE2, we processed and analyzed data through a multi-stage event processing pipeline that ingested raw data, performed quality control, detected signals, built events from these signals, located these events, and characterized the events’ source types and sizes. For each stage and over the entire event processing pipeline, we evaluated performance changes by comparing the performance of new data processing methods, models, and algorithms against a baseline. We also performed an additional execution phase to assess event processing pipeline function, speed, and efficiency against that of an expert analyst, including computational and manual efforts. Finally, we assessed the impact and effort of modern computing infrastructure on the monitoring pipeline. This paper describes key elements of the DNEs, from formulation through execution, as demonstrated in DNE2. The DNEs introduce several novel concepts to quantitatively measure the potential impact of new methods on explosion monitoring, including the collaborative design of multi-modal datasets, performance and logistical metrics, and integrated analyses.

42 ENGINEERING↗

DOE OSTI2022

Adaptive workflow for simulation of RF heaters

Accurate RF (Radio Frequency) simulations of fusion systems like ITER require the definition of high-fidelity analysis geometries that include detailed antenna, reactor wall, and physics regions. Here, this paper will describe a workflow for the execution of adaptive high-performance simulations of RF fusion systems. In this workflow, the simulation input consists of a CAD model attributed with the needed analysis attributes. The analysis mesh is automatically generated and the analysis steps are executed using the time-harmonic Maxwell's equations solved using high-order Nédélec finite elements. A patch recovery-based error estimator is used to drive a conforming mesh adaptation procedure.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Approximation of refrigerant thermophysical properties using neural networks to speed up transient thermofluid simulations

Accurate and efficient evaluations of refrigerant thermophysical properties and their partial derivatives are essential for transient simulations of thermofluid systems, where several computations need to be executed at each integration time step. Since the utilization of an Equation of State for retrieving properties based on a pair of independent inputs typically involves numerical iterations in solution procedures, when the input variables differ from the refrigerant state variables employed in dynamic models, a variety of approaches including lookup table interpolation and curve fitting have been developed to explicitly approximate these properties based on the state variables, and consequently eliminate internal iterations. This paper presents an alternative method that exploits derivative-informed neural networks to model refrigerant properties explicitly from inputs of pressure and enthalpy, while ensuring consistent partial derivatives generated by differentiating the neural networks. Computational speed and accuracy of the proposed approach are demonstrated via transient simulations of a discretized heat exchanger model in Modelica, and comparisons against other property evaluation routines. Simulation results indicate that the proposed approach can realize a significant speedup with negligible discrepancies in predicted transients. The method is implemented in an open-source Modelica library.

Ma, Jiacheng↗

DOE OSTI2018

Coping with silent and fail-stop errors at scale by combining replication and checkpointing

This paper provides a model and an analytical study of replication as a technique to cope with silent errors, as well as a mixture of both silent and fail-stop errors on large-scale platforms. Compared with fail-stop errors that are immediately detected when they occur, silent errors require a detection mechanism. To detect silent errors, many application-specific techniques are available, either based on algorithms (e.g., ABFT), invariant preservation or data analytics, but replication remains the most transparent and least intrusive technique. We explore the right level (duplication, triplication or more) of replication for two frameworks: (i) when the platform is subject to only silent errors, and (ii) when the platform is subject to both silent and fail-stop errors. A higher level of replication is more expensive in terms of resource usage but enables to tolerate more errors and to even correct some errors, hence there is a trade-off to be found. Replication is combined with checkpointing and comes with two flavors: process replication and group replication. Process replication applies to message-passing applications with communicating processes. Each process is replicated, and the platform is composed of process pairs, or triplets. Group replication applies to black-box applications, whose parallel execution is replicated several times. The platform is partitioned into two halves (or three thirds). In both scenarios, results are compared before each checkpoint, which is taken only when both results (duplication) or two out of three results (triplication) coincide. Otherwise, one or more silent errors have been detected, and the application rolls back to the last checkpoint, as well as when fail-stop errors have struck. We provide a detailed analytical study for all of these scenarios, with formulas to decide, for each scenario, the optimal parameters as a function of the error rate, checkpoint cost, and platform size. We also report a set of extensive simulation results that nicely corroborates the analytical model.

Checkpointing↗

DOE OSTI2018

Analyzing the Impact of System Reliability Events on Applications in the Titan Supercomputer

Extreme-scale computing systems employ Reliability, Availability and Serviceability (RAS) mechanisms and infrastructure to log events from multiple system components. In this paper, we analyze RAS logs in conjunction with the application placement and scheduling database, in order to understand the impact of common RAS events on application performance. This study conducted on the records of about 2 million applications executed on Titan supercomputer provides important insights for system users, operators and computer science researchers. Specifically, we investigate the impact of RAS events on application performance and its variability by comparing cases where events are recorded with corresponding cases where no events are recorded. Such a statistical investigation is possible since we observed that system users tend to execute their applications multiple times. Our analysis reveals that most RAS events do impact application performance, although not always. We also find that different system components affect application performance differently. In particular, our investigation includes the following components: parallel file system processor, memory, graphics processing units, system and user software issues. Our work establishes the importance of providing feedback to system users for increasing operational efficiency of extreme-scale systems.

Ashraf, Rizwan↗

DOE OSTI2018

Enforcing End-to-end I/O Policies for Scientific Workflows using Software-Defined Storage Resource Enclaves

Data-intensive knowledge discovery requires scientific applications to run concurrently with analytics and visualization codes executing in situ for timely output inspection and knowledge extraction. Consequently, I/O pipelines of scientific workflows can be long and complex because they comprise many stages of analytics across different layers of the I/O stack of high-performance computing systems. Performance limitations at any I/O layer or stage can cause an I/O bottleneck resulting in greater than expected end-to-end I/O latency. In this paper, we present the design and implementation of a novel data management infrastructure called Software-Defined Storage Resource Enclaves (SIREN) at system level to enforce end-to-end policies that dictate an I/O pipeline's performance. SIREN provides an I/O performance interface for users to specify the desired storage resources in the context of in-situ analytics. If suboptimal performance of analytics is caused by an I/O bottleneck when data are transferred between simulations and analytics, schedulers in different layers of the I/O stack automatically provide the guaranteed lower bounds on I/O throughput. Lastly, our experimental results demonstrate that SIREN provides performance isolation among scientific workflows sharing multiple storage servers across two I/O layers (burst buffer and parallel file systems) while maintaining high system scalability and resource utilization.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

SU-D-BRD-06: Creating a Safety Net for a Fully Automated, Script Driven Electronic Medical Record

Purpose: Demonstrate the effectiveness of in-house software in ensuring EMR workflow efficiency and safety. Methods: A web-based dashboard system (WBDS) was developed to monitor clinical workflow in real time using web technology (WAMP) through ODBC (Open Database Connectivity). Within Mosaiq (Elekta Inc), operational workflow is driven and indicated by Quality Check Lists (QCLs), which is triggered by automation software IQ Scripts (Elekta Inc); QCLs rely on user completion to propagate. The WBDS retrieves data directly from the Mosaig SQL database and tracks clinical events in real time. For example, the necessity of a physics initial chart check can be determined by screening all patients on treatment who have received their first fraction and who have not yet had their first chart check. Monitoring similar “real” events with our in-house software creates a safety net as its propagation does not rely on individual users input. Results: The WBDS monitors the following: patient care workflow (initial consult to end of treatment), daily treatment consistency (scheduling, technique, charges), physics chart checks (initial, EOT, weekly), new starts, missing treatments (>3 warning/>5 fractions, action required), and machine overrides. The WBDS can be launched from any web browser which allows the end user complete transparency and timely information. Since the creation of the dashboards, workflow interruptions due to accidental deletion or completion of QCLs were eliminated. Additionally, all physics chart checks were completed timely. Prompt notifications of treatment record inconsistency and machine overrides have decreased the amount of time between occurrence and execution of corrective action. Conclusion: Our clinical workflow relies primarily on QCLs and IQ Scripts; however, this functionality is not the panacea of safety and efficiency. The WBDS creates a more thorough system of checks to provide a safer and near error-less working environment.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2018

Lightweight Detection of Cache Conflicts

In memory hierarchies, caches perform an important role in reducing average memory access latency. Minimizing cache misses can yield significant performance gains. As set-associative caches are widely used in modern architectures, capacity and conflict cache misses co-exist. These two types of cache misses require different optimization strategies. While cache misses are commonly studied using cache simulators, state-of-the-art simulators usually incur hundreds to thousands of times a program’s execution runtime. Moreover, a simulator has difficulty in simulating complex real hardware. To overcome these limitations, measurement methods are proposed to directly monitor program execution on real hardware via performance monitoring units. However, existing measurement-based tools either focus on capacity cache misses or do not distinguish capacity and conflict cache misses. In this paper, we design and implement CCProf, a lightweight measurement-based profiler that identifies conflict cache misses and associates them with program source code and data structures. CCProf incurs moderate runtime overhead that is at least an order of magnitude lower than simulators. With the evaluation on a number of representative programs, CCProf is able to guide optimizations on cache conflict misses and obtain nontrivial speedups.

Roy, Probir↗

DOE OSTI2024

DQL (Django Query Logger) [SWR-24-96]

The Django Query Logger (DQL) is a tool that allows developers of Django web applications to stream the raw database queries that are being executed in real-time from any Django application for review, profiling, filtering, formatting, and analysis. Reference herein to any specific commercial products, process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or Alliance for Sustainable Energy, LLC. The views and opinions of authors expressed in the available or referenced documents do not necessarily state or reflect those of the United States Government or Alliance.

Swindler, Alexander↗

DOE OSTI2016

FutureGen 2.0 Pipeline and Regional Carbon Capture Storage Project - Final Report

The U.S. Department of Energy’s (DOE) FutureGen 2.0 Program involves two projects: (1) the Oxy-Combustion Power Plant Project and (2) the CO2 Pipeline and Storage Project. This Final Technical Report is focused on the CO2 Pipeline and Storage Project. The FutureGen 2.0 CO2 Pipeline and Storage Project evolved from an initial siting and project definition effort in Phase I, into the Phase II activity consisting permitting, design development, the acquisition of land rights, facility design, and licensing and regulatory approvals. Phase II also progressed into construction packaging, construction procurement, and targeted early preparatory activities in the field. The CO2 Pipeline and Storage Project accomplishments were significant, and in some cases unprecedented. The engineering, permitting, legal, stakeholder, and commercial learnings substantially advance the nation’s understanding of commercial-scale CO2 storage in deep saline aquifers. Voluminous and significant information was obtained from the drilling and the testing program of the subsurface, and sophisticated modeling was performed that held up to a wide range of scrutiny. All designs progressed to the point of securing construction contracts or comfort letters attesting to successful negotiation of all contract terms and willing execution at the appropriate time all major project elements – pipeline, surface facilities, and subsurface – as well as operations. While the physical installation of the planned facilities did not proceed in part due to insufficient time to complete the project prior to the expiration of federal funding, the project met significant objectives prior to DOE’s closeout decision. Had additional time been available, there were no known, insurmountable obstacles that would have precluded successful construction and operation of the project. Due to the suspension of the project, site restoration activities were developed and the work was accomplished. The site restoration efforts are also documented in this report. All permit applications had been submitted to all agencies for those permits or approvals required prior to the start of project construction. Most of the requisite permits were received during Phase II. This report includes information on each permitting effort. Successes and lessons learned are included in this report that will add value to the next generation of carbon storage efforts.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

Staghorn: An Automated Large-Scale Distributed System Analysis Platform

Conducting experiments on large-scale distributed computing systems is becoming significantly easier with the assistance of emulation. Researchers can now create a model of a distributed computing environment and then generate a virtual, laboratory copy of the entire system composed of potentially thousands of virtual machines, switches, and software. The use of real software, running at clock rate in full virtual machines, allows experiments to produce meaningful results without necessitating a full understanding of all model components. However, the ability to inspect and modify elements within these models is bound by the limitation that such modifications must compete with the model, either running in or alongside it. This inhibits entire classes of analyses from being conducted upon these models. We developed a mechanism to snapshot an entire emulation-based model as it is running. This allows us to \freeze time" and subsequently fork execution, replay execution, modify arbitrary parts of the model, or deeply explore the model. This snapshot includes capturing packets in transit and other input/output state along with the running virtual machines. We were able to build this system in Linux using Open vSwitch and Kernel Virtual Machines on top of Sandia's emulation platform Firewheel. This primitive opens the door to numerous subsequent analyses on models, including state space exploration, debugging distributed systems, performance optimizations, improved training environments, and improved experiment repeatability.

97 MATHEMATICS AND COMPUTING↗