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DOE OSTI2023

Grayscale Digital Light Processing Gradient Printing for Stress Concentration Reduction and Material Toughness Enhancement

Avoiding stress concentrations is essential to achieve robust parts since failure tends to originate at such concentrations. With recent advances in multimaterial additive manufacturing, it is possible to alter the stress (or strain) distribution by adjusting the material properties in selected locations. Here, we investigate the use of grayscale digital light processing (g-DLP) 3D printing to create modulus gradients around areas of high stress. These gradients prevent failure by redistributing high stresses (or strains) to the neighboring material. The improved material distributions are calculated using finite element analysis. The much-enhanced properties are demonstrated experimentally for thin plates with circular, triangular, and elliptical holes. In conclusion, this work suggests that multimaterial additive manufacturing techniques like g-DLP printing provide a unique opportunity to create tougher engineering materials and parts.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Conceptual Machine Learning Strategy for Maintenance of Molten Salt Heat Exchanger

This report addresses the requirements of Milestone M3.2 Define maintenance scenarios and benchmarks. Development of digital twins (DT) for molten salt reactor (MSR) components is crucial for reducing operating and maintenance costs (O&M) and ensuring commercial viability of these reactors. Our focus is on development of DT for MSR primary system heat exchanger (HX), a critical component, the fault in which can reduce operating efficiency and force reactor shutdown. We are investigating feasibility of a conceptual DT of HX consisting of internal distributed temperature sensing with fiber optics and machine learning (ML) algorithms to detect and localize faults.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

Advanced Reactor Control Systems Authentication Methods and Recommendations

In the dynamic landscape of Operational Technology (OT), and specifically the emerging landscape for Advanced Reactors, the establishment of trust between digital assets emerges as a challenge for cybersecurity modernization. This report reviews existing approaches to authentication in Enterprise environments, and proposed methods for authentication in OT, and analyzes each for its applicability to future Advanced Reactor digital networks. Principles of authentication ranging from underlying cryptographic mechanisms to trust authorities are evaluated through the lens of OT. These facets emphasize the importance of mutual authentication in real-time environments, enabling a paradigm shift from the current approach of strong boundaries to a more malleable network that allows for flexible operation. This work finds that there is a need for evaluation and decision making by industry stakeholders, but current technologies and approaches can be adapted to fit needs and risk tolerances.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2026

Work With Us to Advance Cybersecurity for the Current and Future Grid

The energy sector is undergoing rapid change. Advanced technologies and controls, digitally connected devices, and a mix of generation sources offer increased energy reliability and security, reduced energy costs, and enhanced grid operations. But they can also carry physical and cybersecurity risks that are not yet fully understood. With deep expertise in advanced energy technologies, the National Laboratory of the Rockies is examining the cybersecurity implications of evolving systems and developing cutting-edge tools, novel approaches, and innovative research capabilities to address sector-wide challenges. Together with our partners, we are working to achieve a more secure, defensible, and reliable grid.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2020

5G Enabled Energy Innovation: Advanced Wireless Networks for Science

Digital wireless communication has become a foundational technology for the nation. The U.S. Department of Energy’s Office of Science (DOE-SC) is the Nation’s largest supporter of basic research in the physical sciences discovering new materials, designing advanced microelectronics, and understanding the physics of radio frequency signaling. The expanding national rollout of a new fifth-generation (5G) mobile network, coupled with the torrent of scientific data generated by next-generation devices such as battery-powered Internet of Things (IoT) sensors, has created an urgent need to enhance cutting-edge wireless technology. Breakthroughs in the deployment, integration, security, and operational range of wireless networking can provide new scientific capabilities for the next decade - from autonomous mobile instruments for scientific user facilities to intelligent sensors networks distributed over thousands of kilometers to study environmental processes. To realize this promise, however, we must continue to drive innovations in computing, artificial intelligence (AI), advanced materials, high-speed networking, and microelectronics. In March 2020, the DOE-SC convened a workshop to identify the potential opportunities and explore the scientific challenges of advanced wireless technologies.

42 ENGINEERING↗

DOE OSTI2016

Super-transient scaling in time-delay autonomous Boolean network motifs

Autonomous Boolean networks are commonly used to model the dynamics of gene regulatory networks and allow for the prediction of stable dynamical attractors. However, most models do not account for time delays along the network links and noise, which are crucial features of real biological systems. Concentrating on two paradigmatic motifs, the toggle switch and the repressilator, we develop an experimental testbed that explicitly includes both inter-node time delays and noise using digital logic elements on field-programmable gate arrays. We observe transients that last millions to billions of characteristic time scales and scale exponentially with the amount of time delays between nodes, a phenomenon known as super-transient scaling. We develop a hybrid model that includes time delays along network links and allows for stochastic variation in the delays. Using this model, we explain the observed super-transient scaling of both motifs and recreate the experimentally measured transient distributions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2023

Laboratory Instrument Software Controlled Spread Spectrum Time Domain Reflectometry for Electrical Cable Testing

This research discusses development of a software-controlled laboratory instrument based spread spectrum time domain reflectometry system (SSTDR). This constitutes one task within PNNL’s Light Water Sustainability Program (LWRS) whose mission includes advancing nondestructive examination (NDE) techniques for off-line and on-line in-situ cable condition monitoring. In 2022, PNNL evaluated SSTDR for detection and characterization of a number of cable anomalies (Glass et al. 2022). The review included comparison of SSTDR to Frequency Domain Reflectometry (FDR) techniques which have enjoyed encouraging feedback and are starting to be used in nuclear power plants for periodic cable condition monitoring of cable systems as part of the plant’s overall cable aging management program. The FDR test introduces a broad-band chirp onto the cable at the cable end then listens for any reflection from a change of impedance along the cable caused by a damaged conductor or insulation, splices, contact with moisture, or other cable anomalies. The signal is captured in the frequency domain then transformed back to the time domain using an inverse Fourier transform (IFT). Based on the velocity of propagation, the impedance response signal is plotted against distance along the cable. Peak locations along the X-axis indicate the distance along the cable where a portion of the signal has been reflected back to the instrument as a result of a cable anomaly. The FDR test is considered the gold standard of reflectometry however it does require the cable to be de-energized to perform the test. The LIVEWIRE commercial SSTDR produces a similar plot to the FDR however all processing is in the time domain. A pseudo-random noise code (PN code) is input onto the cable conductor and the instrument listens for any reflected response from cable anomalies. The SSTDR processes the signal as an autocorrelation comparing the input PN code to any reflected signal detected. The autocorrelation analysis for thermal aging, water and water ingress detection, ground fault and phase-to-phase fault detection at various locations along the cable and with the cable attached and detached from a motor load, and on both energized and un-energized conditions were performed. These results were contrasted to Frequency Domain Reflectometry (FDR) measurements of the un-energized cable. Results were encouraging but indicated more work was warranted – particularly with the SSTDR, it seemed that the insulation damage would likely be better evaluated with multiple bandwidth cable tests particularly including larger bandwidths than were possible with the current commercial instrument. The commercial instrument’s bandwidth was set at 6, 12, 24, and 48MHz but note that SSTDR and FDR definitions of bandwidth trend similarly but are not the same. The FDR response could be more broadly adjusted, and the bandwidth of 100 to 500 MHz produced the best responses. FDR responses to anomalies were clearer than SSTDR responses and indications were that a broader bandwidth SSTDR may lead to improved SSTDR detection capability. This project used a laboratory instrument based SSTDR (primarily using an Arbitrary Waveform Generator (AWG) and a digital oscilloscope plus Python in-house software) that allowed software adjustment of the SSTDR bandwidth, window functions applied to the exciting Pseudo-random Noise (PN) code plus and other aspects of the SSTDR signal processing. Hereafter, this will be referred to as the PNNL SSTDR. Evaluating specific performance of the PNNL SSTDR is left to a separate report. This report documents hardware and software development to produce the SSTDR cable test system.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2020

Scalable Wind Turbine Generator Bearing Fault Prediction Using Machine Learning: A Case Study

Operation and maintenance (O&M) costs for wind turbines pose a risk to competitiveness and asset owners. With machine-learning technologies and digitalization rapidly maturing, the wind industry is actively investigating these new technologies to optimize O&M practices and reduce costs. This paper reviews recent work on machine-learning approaches to generator bearing failure prediction and presents a relevant real-world case study through a collaboration between the National Renewable Energy Laboratory and Envision Digital Corporation. In the case study, we evaluate the performance of representative machine-learning algorithms for predicting wind turbine generator bearing failures. Operational supervisory control and data acquisition data from one wind power plant was used to train and test the machine-learning models. The investigated data channels are chosen based on whether physically they reflect the failed generator bearing conditions and the component historical usage, including both environmental and operational conditions. Benefits and drawbacks of different methods are identified.

generator bearing failures↗

DOE OSTI2024

Entropy Analysis of FPGA Interconnect and Switch Matrices for Physical Unclonable Functions

Random variations in microelectronic circuit structures represent the source of entropy for physical unclonable functions (PUFs). In this paper, we investigate delay variations that occur through the routing network and switch matrices of a field-programmable gate array (FPGA). The delay variations are isolated from other components of the programmable logic, e.g., look-up tables (LUTs), flip-flops (FFs), etc., using a feature of Xilinx FPGAs called dynamic partial reconfiguration (DPR). A set of partial designs is created to fix the placement of a time-to-digital converter (TDC) and supporting infrastructure to enable the path delays through the target interconnect and switch matrices to be extracted by subtracting out common-mode delay components. Delay variations are analyzed in the different levels of routing resources available within FPGAs, i.e., local routing and across-chip routing. Data are collected from a set of Xilinx Zynq 7010 devices, and a statistical analysis of within-die variations in delay through a set of the randomly-generated and hand-crafted interconnects is presented.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

On the convergence of statistics in simulations of stationary incompressible turbulent flows

When reporting statistics from simulations of statistically stationary chaotic phenomenon, it is important to verify that the simulations are time-converged. This condition is connected with the statistical error or number of digits with which statistics can be reliably reported. In this work we consider numerical experiments of low Reynolds number incompressible homogeneous and isotropic turbulence as a model problem to investigate statistical convergence over finite simulation times. Specifically, we investigate the time integration requirements that allow meaningful reporting of the statistical error associated with finiteness of the temporal domain. We address two key questions: (1) How long should a simulation be performed in terms of large eddy time, and (2) How should the simulation time be divided among temporal windows over which a quantity of interest is estimated so that its statistical error could be reliably reported? We find that reliable reporting of statistical errors requires simulations on the order of 10 4 large eddy times, which is orders of magnitude longer than typically performed. Additionally, data post-processing should employ windows of at least ten times the large eddy time scale, with the most robust computation of statistical error of the mean requiring window sizes of an additional factor of ten. For practical simulations, we demonstrate that it is possible to estimate the statistical error within a factor of two under a less stringent condition in which a minimum of four windows with size at least ten large eddy times are used. In conclusion, our observations for homogeneous isotropic turbulence are also shown to hold in turbulent channel flow.

42 ENGINEERING↗

DOE OSTI2016

Technical Basis for Evaluating Software-Related Common-Cause Failures

The instrumentation and control (I&C) system architecture at a nuclear power plant (NPP) incorporates protections against common-cause failures (CCFs) through the use of diversity and defense-in-depth. Even for well-established analog-based I&C system designs, the potential for CCFs of multiple systems (or redundancies within a system) constitutes a credible threat to defeating the defense-in-depth provisions within the I&C system architectures. The integration of digital technologies into the I&C systems provides many advantages compared to the aging analog systems with respect to reliability, maintenance, operability, and cost effectiveness. However, maintaining the diversity and defense-in-depth for both the hardware and software within the digital system is challenging. In fact, the introduction of digital technologies may actually increase the potential for CCF vulnerabilities because of the introduction of undetected systematic faults. These systematic faults are defined as a “design fault located in a software component” and at a high level, are predominately the result of (1) errors in the requirement specification, (2) inadequate provisions to account for design limits (e.g., environmental stress), or (3) technical faults incorporated in the internal system (or architectural) design or implementation. Other technology-neutral CCF concerns include hardware design errors, equipment qualification deficiencies, installation or maintenance errors, instrument loop scaling and setpoint mistakes.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

STREAM II-V7: Revision for STREAM II-V6 to include outflow from all Savannah River Site tributaries

STREAM II is the aqueous transport model of the Weather Information Display (WIND) emergency response system at Savannah River Site. It is used to calculate transport in the event of a chemical or radiological spill into the waterways on the Savannah River Site. Improvements were made to the code (STREAM II V7) to include flow from all site tributaries to the Savannah River total flow and utilize a 4 digit year input. The predicted downstream concentrations using V7 were generally on the same order of magnitude as V6 with slightly lower concentrations and quicker arrival times when all onsite stream flows are contributing to the Savannah River flow. The downstream arrival time at the Savannah River Water Plant ranges from no change to an increase of 8.77%, with minimum changes typically in March/April and maximum changes typically in October/November. The downstream concentrations are generally no more than 15% lower using V7 with the maximum percent change in January through April and minimum changes in June/July.

58 GEOSCIENCES↗

DOE OSTI2021

Integration of Dynamical System Scaling to RAVEN and Facility Application

As part of the design optimization and model validation effort within the hybrid energy systems program, the research to implement dynamical system scaling (DSS) code to RAVEN is funded under the Integrated Energy System program in collaboration with the Digital Reactor Technology & Development department. The DSS data processing algorithm has been coded within the RAVEN framework along with other metrics and postprocessing models. The implemented code was tested using a gravity-driven draining tank draining case modeled in RELAP5-3D and generated data was successfully postprocessed based on the scaling analyses defined by DSS. Postprocessed data concluded less agreement for the tank exit velocity and indicated that the modeled input deck for RELAP5-3D requires modification to enforce minimal pressure differential effects to the draining process.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Evaluation of a Variable-Aperture Full-Ring SPECT System using Large-Area Pixelated CZT Modules: A Simulation Study for Brain SPECT Applications

Single photon emission computed tomography (SPECT) scanner using cadmium zinc telluride (CZT) offers improved imaging capability over conventional NaI (Tl)-based SPECT scanner. We aim to demonstrate a full-ring SPECT system design with eight large-area CZT detectors that can be used for a broad spectrum of SPECT radiopharmaceuticals and provides higher sensitivity and better spatial resolution than those of conventional NaI (Tl)-based gamma cameras. A newly-designed full-ring SPECT system is composed of 8 large-area CZT cameras (128 mm × 179.2 mm effective area) that can be independently swiveled around their own axes of rotation independently and can have radial motion for varying aperture sizes that can be adapted to different sizes of imaging volume. Extended projection data were generated by conjoining projections of two adjacent detectors to overcome the limited field-of-view (FOV) by each CZT camera. Using Monte Carlo simulations, we evaluated this new system design with digital phantoms including a Derenzo hot-rod phantom and a Zubal brain phantom. Comparison of performance metrics such as spatial resolution, sensitivity, contrast-to-noise ratio (CNR), and contrast-recovery ratio were made between our design and a conventional SPECT scanner. The proposed scanner could result in up to about 3 times faster in acquisition time over conventional scan time at same acquisition time per step. The spatial resolution of our proposed scanner was similar or better to that of the conventional scanner, and there was significant performance improvement over the conventional scanner particularly in sensitivity (approximately 4 times). Overall, we successfully reconstructed the phantom image for both 99 mTc-based perfusion and 123 I-based dopamine transporter (DaT) brain studies simulated for our new design. In particular, the striatal/background contrast-recovery ratio in 3-to-1 reference ratio was over the 0.8 for the 123 I-based DaT study. In conclusion, we demonstrated the potential of our new full-ring CZT SPECT design, showing improved performance metrics such as improved system sensitivity and CNR while maintaining other important imaging parameters such as spatial resolution.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Creation of a Robust and Generalizable Machine Learning Classifier for Patient Ventilator Asynchrony

Background: As healthcare increasingly digitizes, streaming waveform data is being made available from an variety of sources, but there still remains a paucity of performant clinical decision support systems. For example, in the intensive care unit (ICU) existing automated alarm systems typically rely on simple thresholding that result in frequent false positives. Recurrent false positive alerts create distrust of alarm mechanisms that can be directly detrimental to patient health. To improve patient care in the ICU, we need alert systems that are both pervasive, and accurate so as to be informative and trusted by providers. Objective: We aimed to develop a machine learning-based classifier to detect abnormal waveform events using the use case of mechanical ventilation waveform analysis, and the detection of harmful forms of ventilation delivery to patients. We specifically focused on detecting injurious subtypes of patient-ventilator asynchrony (PVA).Methods: Using a dataset of breaths recorded from 35 different patients, we used machine learning to create computational models to automatically detect, and classify two types of injurious PVA, double trigger asynchrony (DTA), breath stacking asynchrony (BSA). We examined the use of synthetic minority over-sampling technique (SMOTE) to overcome class imbalance problems, varied methods for feature selection, and use of ensemble methods to optimize the performance of our model. Results: We created an ensemble classifier that is able to accurately detect DTA at a sensitivity/specificity of 0.960/0.975, BSA at sensitivity/specificity of 0.944/0.987, and non-PVA events at sensitivity/specificity of .967/.980. Conclusions: Our results suggest that it is possible to create a high-performing machine learning-based model for detecting PVA in mechanical ventilator waveform data in spite of both intra-patient, and inter-patient variability in waveform patterns, and the presence of clinical artifacts like cough and suction procedures. Our work highlights the importance of addressing class imbalance in clinical data sets, and the combined use of statistical methods and expert knowledge in feature selection.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

The INTERSECT Open Federated Architecture for the Laboratory of the Future

A federated instrument-to-edge-to-center architecture is needed to autonomously collect, transfer, store, process, curate, and archive scientific data and reduce human-in-the-loop needs with (a) common interfaces to leverage community and custom software, (b) pluggability to permit adaptable solutions, reuse, and digital twins, and (c) an open standard to enable adoption by science facilities world-wide. The Selfdriven Experiments for Science/Interconnected Science Ecosystem (INTERSECT) Open Architecture enables science breakthroughs using intelligent networked systems, instruments and facilities with autonomous experiments, “self-driving” laboratories, smart manufacturing and artificial intelligence (AI) driven design, discovery and evaluation. It creates an open federated architecture for the laboratory of the future using a novel approach, consisting of (1) science use case design patterns, (2) a system of systems architecture, and (3) a microservice architecture.

Engelmann, Christian↗

DOE OSTI2011

A SPECTROSCOPIC SURVEY AND ANALYSIS OF BRIGHT, HYDROGEN-RICH WHITE DWARFS

We have conducted a spectroscopic survey of over 1300 bright (V {<=} 17.5), hydrogen-rich white dwarfs based largely on the last published version of the McCook and Sion catalog. The complete results from our survey, including the spectroscopic analysis of over 1100 DA white dwarfs, are presented. High signal-to-noise ratio optical spectra were obtained for each star and were subsequently analyzed using our standard spectroscopic technique where the observed Balmer line profiles are compared to synthetic spectra computed from the latest generation of model atmospheres appropriate for these stars. First, we present the spectroscopic content of our sample, which includes many misclassifications as well as several DAB, DAZ, and magnetic white dwarfs. Next, we look at how the new Stark broadening profiles affect the determination of the atmospheric parameters. When necessary, specific models and analysis techniques are used to derive the most accurate atmospheric parameters possible. In particular, we employ M dwarf templates to obtain better estimates of the atmospheric parameters for those white dwarfs that are in DA+dM binary systems. Certain unique white dwarfs and double-degenerate binary systems are also analyzed in greater detail. We then examine the global properties of our sample including the mass distribution and their distribution as a function of temperature. We then proceed to test the accuracy and robustness of our method by comparing our results to those of other surveys such as SPY and Sloan Digital Sky Survey. Finally, we revisit the ZZ Ceti instability strip and examine how the determination of its empirical boundaries is affected by the latest line profile calculations.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2013

Obtaining identical results with double precision global accuracy on different numbers of processors in parallel particle Monte Carlo simulations

We describe and compare different approaches for achieving numerical reproducibility in photon Monte Carlo simulations. Reproducibility is desirable for code verification, testing, and debugging. Parallelism creates a unique problem for achieving reproducibility in Monte Carlo simulations because it changes the order in which values are summed. This is a numerical problem because double precision arithmetic is not associative. Parallel Monte Carlo, both domain replicated and decomposed simulations, will run their particles in a different order during different runs of the same simulation because the non-reproducibility of communication between processors. In addition, runs of the same simulation using different domain decompositions will also result in particles being simulated in a different order. In [1], a way of eliminating non-associative accumulations using integer tallies was described. This approach successfully achieves reproducibility at the cost of lost accuracy by rounding double precision numbers to fewer significant digits. This integer approach, and other extended and reduced precision reproducibility techniques, are described and compared in this work. Increased precision alone is not enough to ensure reproducibility of photon Monte Carlo simulations. Non-arbitrary precision approaches require a varying degree of rounding to achieve reproducibility. For the problems investigated in this work double precision global accuracy was achievable by using 100 bits of precision or greater on all unordered sums which where subsequently rounded to double precision at the end of every time-step.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗