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NASA NTRS2012

Seeking Counterparts to Advanced LIGO/Virgo Transients with Swift

Binary neutron star (NS) mergers are among the most promising astrophysical sources of gravitational wave emission for Advanced LIGO and Advanced Virgo, expected to be operational in 2015 . Finding electromagnetic counterparts to these signals will be essential to placing them in an astronomical context. The Swift satellite carries a sensitive X-ray telescope (XRT), and can respond to target-of-opportunity requests within 1-2 hours, and so is uniquely poised to find the X-ray counterparts to LIGO / Virgo triggers. Assuming NS mergers are the progenitors of short gamma-ray bursts (GRBs), some percentage of LIGO/Virgo triggers will be accompanied by X-ray band afterglows that are brighter than 10(exp -12) ergs/s/sq cm in the XRT band one day after the trigger time. We find that a soft X-ray transient of this flux is bright enough to be extremely rare, and so could be confidently associated with even a moderately localized GW signal. We examine two possible search strategies with the Swift XRT to find bright transients in LIGO/Virgo error boxes. In the first strategy, XRT could search a volume of space with a approx.100 Mpc radius by observing approx 30 galaxies over the course of a day, with sufficient depth to observe the expected X-ray afterglow. For an extended LIGO / Virgo horizon distance, the XRT could employ very short 100 s exposures to cover an area of approx 35 square degrees in about a day, and still be sensitive enough to image GW discovered GRB afterglows. These strategies demonstrate that the high X-ray luminosity of short GRBs and the relatively low X-ray transient background combine to make high confidence discoveries of X-ray band counterparts to GW triggers possible, though challenging, with current satellite facilities.

Kanner, Jonah↗

NASA NTRS2010

Virtualization in the Operations Environments

Virtualization provides the opportunity to continue to do "more with less"---more computing power with fewer physical boxes, thus reducing the overall hardware footprint, power and cooling requirements, software licenses, and their associated costs. This paper explores the tremendous advantages and any disadvantages of virtualization in all of the environments associated with software and systems development to operations flow. It includes the use and benefits of the Intelligent Platform Management Interface (IPMI) specification, and identifies lessons learned concerning hardware and network configurations. Using the Huntsville Operations Support Center (HOSC) at NASA Marshall Space Flight Center as an example, we demonstrate that deploying virtualized servers as a means of managing computing resources is applicable and beneficial to many areas of application, up to and including flight operations.

Pitts, Lee↗

NASA NTRS2010

Utilization of Virtual Server Technology in Mission Operations

Felton, Larry↗

DOE OSTI2023

Explicit model predictive control through robust optimization

A strategy that calculates an explicit state feedback policy to regulate constrained uncertain discrete‐time uncertain linear systems is presented. We consider uncertain processes, affected by box‐bounded multiplicative uncertainty as well as bounded additive uncertainty with linear state and inputs constraints. The proposed method includes (i) the calculation of a terminal set constraint and (ii) the robust reformulation of state constraints in the prediction horizon. These features allow the derivation of the desired policy by solving a single multiparametric quadratic programming problem that guarantees feasible operation in the presence of uncertainty. Additionally, we employ variable and constraint elimination approaches to enhance the computational performance of the strategy. We demonstrate the steps and benefits of these developments with a numerical example and a chemical engineering case study.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Time-dependent SOLPS-ITER simulations of the tokamak plasma boundary for model predictive control using SINDy *

Abstract Time-dependent SOLPS-ITER simulations have been used to identify reduced models with the sparse identification of nonlinear dynamics (SINDy) method and develop model-predictive control of the boundary plasma state using main ion gas puff actuation. A series of gas actuation sequences are input into SOLPS-ITER to produce a dynamic response in upstream and divertor plasma quantities. The SINDy method is applied to identify reduced linear and nonlinear models for the electron density at the outboard midplane n e , s e p O M P and the electron temperature at the outer divertor T e , s e p d i v . Note that T e , s e p d i v is not necessarily the peak value of T e along the divertor. The identified reduced models are interpretable by construction (i.e. not black box), and have the form of coupled ordinary differential equations. Despite significant noise in T e , s e p d i v , the reduced models can be used to predict the response over a range of actuation levels to a maximum deviation of 0.5% in n e , s e p O M P and 5%–10% in T e , s e p d i v for the cases considered. Model retraining using time history data triggered by a preset error threshold is also demonstrated. A model predictive control strategy for nonlinear models is developed and used to perform feedback control of a SOLPS-ITER simulation to produce a setpoint trajectory in n e , s e p O M P using the integrated plasma simulator framework. The developed techniques are general and can be applied to time-dependent data from other boundary simulations or experimental data. Ongoing work is extending the approach to model identification and control for divertor detachment, which will present transient nonlinear behavior from impurity seeding, including realistic latency and synthetic diagnostic signals derived from the full SOLPS-ITER output.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Time Dilated Bundt Cake Analysis of PV Output [Poster]

We present a novel method for modeling time-dependent statistics in the power signal generated by a photovoltaic (PV) system. Our white-box machine learning method is interpretable and auditable, based on principles of multiperiodic basis functions and convex optimization. Our proposed method of time dilating the daily signal to remove night time values results in a novel representation of PV power signals, evocative of a ‘Bundt cake’. The proposed model describes the marginal distribution of power output as a function of date and time. The resulting probabilistic model of a PV system can be used to perform a variety of tasks, and here, we demonstrate the application of clear sky detection.

14 SOLAR ENERGY↗

DOE OSTI2024

Enabling Ultra-Compact, Lightweight, Efficient, and Reliable 6.6 kW On-Board Bi-Directional Electric Vehicle Charger with Advanced Topology and Control

The research explored new topologies, control methods, mechanical integration, and thermal management methods for electric vehicle (EV) on-board chargers. The team investigated capacitor-based power conversion, leveraging the high energy densities inherent to capacitive energy storage compared to inductive methods. The proposed topologies simultaneously enabled high power density and high efficiency of the design. The proposed architecture was demonstrated in a 6.6 kW bi-directional charger prototype. The research pursued several directions to improve system performance. Innovative topologies were studied for both the main power conversion stage as well as the single-phase twice-line-frequency energy buffer. To ensure robust and efficient operation, new control methods were developed to integrate these two subsystems. To achieve high power density in the full system solution, the mechanical structure of the charger is highly optimized to maximally fill the converter box volume. In parallel with the mechanical design effort, the converter was packaged with high-performance cooling methods which removed heat from key areas of power dissipation in the converter. The thermal management system was optimized to minimize its weight and volume, ultimately motivating the design of a custom additively manufactured cold-plate. The full system achieves a peak power of 7 kW with less than 0.3% total harmonic distortion (THD) and greater than 0.994 power factor in power factor correction (PFC) operation, corresponding to a total box-volume power density of 47.9 kW/L and gravimetric power density of 24.6 W/g. The system achieves a peak efficiency of 98.9%, with 97.9% efficiency at maximum power.

33 ADVANCED PROPULSION SYSTEMS↗

NASA NTRSDate not supplied

On the Probabilistic Analysis of Neural Networks

Neural networks are powerful tools for automated decision-making,seeing increased application in safety-critical domains, such as autonomous driving. Due to their black-box nature and large scale,reasoning about their behavior is challenging. Statistical analysis is often used to infer probabilistic properties of a network, such as its robustness to noise and inaccurate inputs. While scalable, statistical methods can only provide probabilistic guarantees on the quality of their results and may underestimate the impact of low probability inputs leading to undesired behavior of the network.We investigate here the use of symbolic analysis and constraint solution space quantification to precisely quantify probabilistic properties in neural networks. We demonstrate the potential of the proposed technique in a case study involving the analysis of ACAS-Xu, a collision avoidance system for unmanned aircraft control.

neural nets↗

DOE OSTI2025

Benefits Of Automated Construction And Energy Efficiency Measures In Modular Homes

This article builds on and adds to a Buildings XV publication that introduced the Transformative Efficiency and Automation in Modular Homes (TEAMH) project. The TEAMH project sought to develop a scalable solution for producing modular homes with 20-50% energy savings and similar cost relative to site-fabricated single-family home construction. A key aspect of the project was assessing the potential for labor cost reductions through automation-assisted construction using light gauge steel (LGS). To quantify the advantages of this approach, side-by-side comparisons were made between traditional wood-framed construction and automation-assisted LGS construction. This demonstration involved constructing one wood-framed wall and several LGS test walls, accompanied by a time-and-motion study. The results indicated that automation assistance could decrease construction time and associated labor costs by as much as 46%. High-performance envelope technologies for exterior insulation and air sealing were evaluated to compare modular homes with site-built homes that meet the International Energy Conservation Code (IECC). A key technology considered was vacuum insulation panels (VIPs) with fiberglass cores. Guarded hot box testing of multiple full-scale wall assemblies containing different combinations of exterior continuous insulation systems containing phenolic foam and VIPs. Testing on various full-scale wall assemblies revealed that, with LGS construction, cavity insulation had minimal impact on exterior wall performance. Omitting cavity insulation can reduce labor and material costs while streamlining manufacturing, as its installation is labor-intensive and not easily automated due to the need for precise placement around wiring and other internal components. Guarded hot box tests of multiple LGS test walls with foam and VIP-based exterior insulation systems achieved R-values of up to 31 hr-ft2-°F/Btu. Finally, building energy modeling of multiple modular home designs indicated that the upgraded envelope assemblies can yield heating energy savings of up to 50% and cooling energy savings of up to 30% compared to IECC 2018 standards.

Shrestha, Som [ORNL] (ORCID:0000000183993797)↗

DOE OSTI2022

Water Imbibition and Oil Recovery in Shale: Dynamics and Mechanisms Using Integrated Centimeter-to-Nanometer-Scale Imaging

Water imbibition, and the associated oil displacement, is an important process in shale oil reservoirs after hydraulic fracturing and in water-based enhanced oil recovery (EOR). Current techniques for water imbibition measurement are mostly “black-box”-type methods. A more explicit understanding of the water imbibition/oil recovery dynamics and geological controls is in demand. In this paper, a multiscale imaging technique that covers centimeter to nanometer scale (i.e., core to pore scale), integrating neutron radiography, microcomputed tomography (micro-CT), and scanning electron microscope (SEM) is applied to investigate the water imbibition depth and rate and the cause of heterogeneity of imbibition in shale samples. The dynamic processes of water imbibition in the 1-in. (25.4-mm) core sample were explicitly demonstrated, and the imbibition along the matrix and imbibition through microfractures are distinguished through neutron radiography image analysis. The causes of observed imbibition heterogeneity were further investigated through micro-CT and SEM image analysis for 1.5-mm diameter miniplug samples from different laminas of the 1-in. core samples. Imbibition depth and rate were calculated on the basis of image analysis as well. Estimation of oil recovery through water imbibition in shale matrix was performed for an example shale field. Here this innovative and integrated multiscale imaging technique provides a “white/gray-box” method to understand water imbibition and water-oil displacement in shale. The wide span of the length scale (from centimeter to nanometer) of this technique enables a more comprehensive, accurate, and specific understanding of both the core-scale dynamics and pore-scale mechanisms of water imbibition, oil recovery, and matrix-fracture interaction.

04 OIL SHALES AND TAR SANDS↗

NASA NTRS1997

Characterizing the Relationships Among Lightning and Storm Parameters: Lightning as a Proxy Variable

We have gained important insights from prior studies that have suggested relationships between lightning and storm growth, decay, convective rain flux, vertical distribution of storm mass and echo volume in the region, and storm energetics. A study was initiated in the Summer of 1996 to determine how total (in-cloud plus ground) lightning observations might provide added knowledge to the forecaster in the determination and identification of severe thunderstorms and weather hazards in real-time. The Melbourne Weather Office was selected as a primary site to conduct this study because Melbourne is the only site in the world with continuous and open access to total lightning (LDAR) data and a Doppler (WSR-88D) radar. A Lightning Imaging Sensor Data Applications Demonstration (LISDAD) system was integrated into the forecaster's workstation during the Summer 1996 to allow the forecaster to interact in real-time with the multi-sensor data being displayed. LISDAD currently ingests LDAR data, the cloud-to-ground National Lightning Detection Network (NLDN) data, and the Melbourne radar data in f real-time. The interactive features provide the duty forecaster the ability to perform quick diagnostics on storm cells of interest. Upon selection of a storm cell, a pop-up box appears displaying the time-history of various storm parameters (e.g., maximum radar reflectivity, height of maximum reflectivity, echo-top height, NLDN and LDAR lightning flash rates, storm-based vertically integrated liquid water content). This product is archived to aid on detailed post-analysis.

Goodman, S. J.↗

DOE OSTI2022

Recent Advances toward Efficient Calculation of Higher Nuclear Derivatives in Quantum Chemistry

In this article, we provide an overview of state-of-the-art techniques that are being developed for efficient calculation of second and higher nuclear derivatives of quantum mechanical (QM) energy. Calculations of nuclear Hessians and anharmonic terms incur high costs and memory and scale poorly with system size. Three emerging classes of methods—machine learning (ML), automatic differentiation (AD), and matrix completion (MC)—have demonstrated promise in overcoming these challenges. We illustrate studies that employ unsupervised ML methods to reduce the need for multiple Hessian calculations in dynamics simulations and those that utilize supervised ML to construct approximate potential energy surfaces and estimate Hessians and anharmonic terms at reduced cost. By extension, if electronic structure operations could be written in a manner similar to functions underlying ML methods, rapid differentiation or AD routines can be employed to inexpensively calculate higher arbitrary-order derivatives. While ML approaches are typically black-box, we describe methods such as compressed sensing (CS) and MC, which explicitly leverage problem-specific mathematical properties of higher derivatives such as sparsity and low-rank, to complete higher derivative information using only a small, incomplete sample. The three classes of methods facilitate reliable predictions of observables ranging from infrared spectra to thermal conductivity and constitute a promising way forward in accurately capturing otherwise intractable higher-order responses of QM energy to nuclear perturbations.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

NASA NTRS2002

Digital Distortion Caused by Traveling- Wave-Tube Amplifiers Simulated

Future NASA missions demand increased data rates in satellite communications for near real-time transmission of large volumes of remote data. Increased data rates necessitate higher order digital modulation schemes and larger system bandwidth, which place stricter requirements on the allowable distortion caused by the high-power amplifier, or the traveling-wave-tube amplifier (TWTA). In particular, intersymbol interference caused by the TWTA becomes a major consideration for accurate data detection at the receiver. Experimentally investigating the effects of the physical TWTA on intersymbol interference would be prohibitively expensive, as it would require manufacturing numerous amplifiers in addition to acquiring the required digital hardware. Thus, an accurate computational model is essential to predict the effects of the TWTA on system-level performance when a communication system is being designed with adequate digital integrity for high data rates. A fully three-dimensional, time-dependent, TWT interaction model has been developed using the electromagnetic particle-in-cell code MAFIA (Solution of Maxwell's equations by the Finite-Integration-Algorithm). It comprehensively takes into account the effects of frequency-dependent AM (amplitude modulation)/AM and AM/PM (phase modulation) conversion, gain and phase ripple due to reflections, drive-induced oscillations, harmonic generation, intermodulation products, and backward waves. This physics-based TWT model can be used to give a direct description of the effects of the nonlinear TWT on the operational signal as a function of the physical device. Users can define arbitrary excitation functions so that higher order modulated digital signals can be used as input and that computations can directly correlate intersymbol interference with TWT parameters. Standard practice involves using communication-system-level software packages, such as SPW, to predict if adequate signal detection will be achieved. These models use a nonlinear, black-box model to represent the TWTA. The models vary in complexity, but most make several assumptions regarding the operation of the high-power amplifier. When the MAFIA TWT interaction model was used, these assumptions were found to be in significant error. In addition, digital signal performance, including intersymbol interference, was compared using direct data input into the MAFIA model and using the system-level analysis tool SPW for several higher order modulation schemes. Results show significant differences in predicted degradation between SPW and MAFIA simulations, demonstrating the significance of the TWTA approximations made in the SPW model on digital signal performance. For example, a comparison of the SPW and MAFIA output constellation diagrams for a 16-ary quadrature amplitude modulation (16-QAM) signal (data shown only for second and fourth quadrants) is shown. The upper-bound degradation was calculated from the corresponding eye diagrams. In comparison to SPW simulations, the MAFIA data resulted in a 3.6-dB larger degradation.

Kory, Carol L.↗

DOE OSTI2022

Improving convergence of the Matrix Power Control Algorithm for random vibration testing

Herein, this paper describes modifications to the Matrix Power Control Algorithm (MPCA) to improve convergence for Random Vibration Control (RVC) testing. In particular, this paper presents Multiple-Input Multiple-Output (MIMO) implementations of MPCA in simulation and experiment. An Euler–Bernoulli beam model was simulated with applied base excitations and the Box Assembly with Removable Component (BARC) was used in experiment to validate results. The Bayesian optimization package Dragonfly was used to optimize control parameters. Additionally, a moving-average was employed and optimized to improve the measured response feedback for MPCA, reduce the number of averages needed to be taken between control updates, and further improve convergence. The key results of this paper show that the performance of MPCA can be improved by tuning the control parameters and by applying an optimized moving-average. Furthermore, it is demonstrated that convergence can be achieved within 12 drive-frames, which greatly enhances vibration control capability.

42 ENGINEERING↗

DOE OSTI2024

NRIC DOME Crane Trade Study and Recommendation

The National Reactor Innovation Center (NRIC) is a national program that was established as part of the Nuclear Energy Innovation Capabilities Act (NEICA). NRIC’s mission is to accelerate the demonstration and deployment of advanced nuclear energy through its mission to inspire stakeholders and the public, empower innovators, and deliver successful outcomes through efficient coordination of partners and resources. NRIC is designed to bridge the gap between research, development, and the marketplace to help convert some of the nation’s most promising advanced nuclear reactors into commercial applications. The NRIC Demonstration of Microreactor Experiments (DOME) facility, formerly known as Experimental Breeder Reactor II (EBR II), located at the Materials and Fuels Complex (MFC) at the Idaho National Laboratory (INL) is intended to allow industrial and other partners the opportunity to test Advanced Microreactors up to 20MW thermal power. The 75-ton capacity polar crane located in the EBR II facility was rendered inoperable to support planned facility demolition in 2015; small holes were flame cut in the girders; hoists and cables were removed; oil was drained from gear boxes; trolley drive, and then filled with absorbent; and the electrical and control umbilical’s were disconnected, removed and disposed. Subsequently, the decision was made to convert the EBR II facility into the DOME test bed.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗

DOE OSTI2024

Machine tool cross beam design, fabrication, and testing using metal big area additive manufacturing

This paper describes the application of metal Big Area Additive Manufacturing (mBAAM) to the fabrication of a machine tool cross beam. The replacement of a traditional box design weldment with a new design printed by wire arc additive manufacturing using the MedUSA system at Oak Ridge National Laboratory (ORNL) is detailed. This requires a new design strategy based on the unique mBAAM capabilities. The intent of the new design is to reduce mass, while maintaining the dynamic stiffness. To compare the two designs, the natural frequencies and mode shapes are measured using impact testing and predicted using finite element analysis. It is confirmed that the printed structure dynamics agreed with the numerical model predictions, which demonstrates that it is feasible to model a large-scale mBAAM part and understand its behavior prior to printing. Another notable outcome of this study is that the significant residual stress and distortion in the print indicate that knowledge gaps remain for widespread implementation of mBAAM.

42 ENGINEERING↗

DOE OSTI2024

Using Knowledge-Guided Machine Learning To Assess Patterns of Areal Change in Waterbodies across the Contiguous United States

Lake and reservoir surface areas are an important proxy for freshwater availability. Advancements in machine learning (ML) techniques and increased accessibility of remote sensing data products have enabled the analysis of waterbody surface area dynamics on broad spatial scales. However, interpreting the ML results remains a challenge. While ML provides important tools for identifying patterns, the resultant models do not include mechanisms. Thus, the “black-box” nature of ML techniques often lacks ecological meaning. Using ML, we characterized temporal patterns in lake and reservoir surface area change from 1984 to 2016 for 103,930 waterbodies in the contiguous United States. We then employed knowledge-guided machine learning (KGML) to classify all waterbodies into seven ecologically interpretable groups representing distinct patterns of surface area change over time. Many waterbodies were classified as having “no change” (43%), whereas the remaining 57% of waterbodies fell into other groups representing both linear and nonlinear patterns. This analysis demonstrates the potential of KGML not only for identifying ecologically relevant patterns of change across time but also for unraveling complex processes that underpin those changes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Using Knowledge-Guided Machine Learning To Assess Patterns of Areal Change in Waterbodies across the Contiguous United States

58 GEOSCIENCES↗