Results for “Validation Network” — Page 39

DOE OSTI2024

Statistical and Neural Network for Real Sensor-Data-Driven Anomaly Detection in Nuclear Applications

Anomaly detection (AD) in sensor data is critical to ensure uninterrupted functionality of nuclear power plants (NPPs). Consequently, validation of AD models through real-world sensor data is important for their application in nuclear facilities. In this paper, we propose an Autoencoder (AE)— a multi-layered neural network, for AD in sensor data from an operational NPP testbed. Since the dataset lacks labels for irregularities, we introduce random noise and label them to effectively train our model. The proposed AE model assigns a higher reconstruction error to the abnormal samples that deviate from those encountered during the training phase and uses the reconstruction loss to detect anomalies in a representative imbalanced dataset. We also introduce an analytical solution—seasonal trend decomposition (STD) — as another AD scheme for identifying irregularities within the same time-series dataset. In contrast to the AE model which relies on reconstruction loss, the STD scheme decomposes the entire dataset into its trend, seasonality, and residual components to pinpoint irregularities. Our findings indicate that the proposed AE and STD models individually achieve recall scores of 97% and 92%, respectively. We also validate the performance of the two models on both balanced and imbalanced data. We further solidify the results by picking the combined selected anomalies of the two solutions with an "AND" operator for more reliable predictions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

Tensor Network Quantum Virtual Machine for Simulating Quantum Circuits at Exascale

The numerical simulation of quantum circuits is an indispensable tool for development, verification, and validation of hybrid quantum-classical algorithms intended for near-term quantum co-processors. The emergence of exascale high-performance computing (HPC) platforms presents new opportunities for pushing the boundaries of quantum circuit simulation. Here, we present a modernized version of the Tensor Network Quantum Virtual Machine (TNQVM) that serves as the quantum circuit simulation backend in the eXtreme-scale ACCelerator (XACC) framework. The new version is based on the scalable tensor network processing library ExaTN (Exascale Tensor Networks). It provides multiple configurable quantum circuit simulators that perform either an exact quantum circuit simulation via the full tensor network contraction or an approximate simulation via a suitably chosen tensor factorization scheme. Upon necessity, stochastic noise modeling from real quantum processors is incorporated into the simulations by modeling quantum channels with Kraus tensors. By combining the portable XACC quantum programming frontend and the scalable ExaTN numerical processing backend, we introduce an end-to-end virtual quantum development environment that can scale from laptops to future exascale platforms. We report initial benchmarks of our framework, which include a demonstration of the distributed execution, incorporation of quantum decoherence models, and simulation of the random quantum circuits used for the certification of quantum supremacy on Google’s Sycamore superconducting architecture.

Nguyen, Thien↗

DOE OSTI2024

Inverse Design of Photonic Surfaces via High throughput Femtosecond Laser Processing and Tandem Neural Networks

Abstract This work demonstrates a method to design photonic surfaces by combining femtosecond laser processing with the inverse design capabilities of tandem neural networks that directly link laser fabrication parameters to their resulting textured substrate optical properties. High throughput fabrication and characterization platforms are developed that generate a dataset comprising 35280 unique microtextured surfaces on stainless steel with corresponding measured spectral emissivities. The trained model utilizes the nonlinear one‐to‐many mapping between spectral emissivity and laser parameters. Consequently, it generates predominantly novel designs, which reproduce the full range of spectral emissivities (average root‐mean‐squared‐error < 2.5%) using only a compact region of laser parameter space 25 times smaller than what is represented in the training data. Finally, the inverse design model is experimentally validated on a thermophotovoltaic emitter design application. By synergizing laser‐matter interactions with neural network capabilities, the approach offers insights into accelerating the discovery of photonic surfaces, advancing energy harvesting technologies.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Multistage economic MPC for systems with a cyclic steady state: A gas network case study

Multistage model predictive control (MPC) provides a robust control strategy for dynamic systems with uncertainties and a setpoint tracking objective. Moreover, extending MPC to minimize an economic cost instead of tracking a pre-calculated optimal setpoint improves controller performance. This paper presents a novel multistage economic nonlinear model predictive control (E-NMPC) framework for dynamic systems operating under uncertainty, with specific application to natural gas transmission networks. A key innovation lies in the integration of cyclic steady-state (CSS) constraints within the multistage MPC formulation, enabling the controller to manage periodic operating conditions commonly observed in energy systems. A Lyapunov-based descent condition is enforced to ensure robust stability of the controller. The multistage economic MPC framework is validated on two gas pipeline case studies, where it successfully minimizes net energy consumption, respects operational constraints under uncertain demand profiles, and guides the network to optimal cyclic operation. The Lyapunov function remains bounded in both case studies, validating the robust stability of multistage E-NMPC.

03 NATURAL GAS↗

DOE OSTI2022

Wavelet and Deep-Learning-Based Approach for Generation System Problematic Parameters Identification and Calibration

Accurate models of generation systems are critical for maintaining reliable and secure grid operations. In this paper, a novel and systematic approach is proposed to identify and calibrate the generation system problematic parameters using continuous wavelet transform (CWT) and advanced deep-learning technology. The phasor measurement unit (PMU) data are used through “event playback” to check whether the parameter calibration is required, and if yes, a group of suspicious parameters will be identified as the primary problematic parameter candidates (PPCs). These primary PPCs are randomly perturbed to generate the event playback simulation data, which are used by the CWT and convolutional neural networks (CNNs) to further narrow down the primary PPCs into a smaller set of candidates. Then, the identified candidates are perturbed again to generate massive event playback simulation data for training a parameter calibration neural network. Here, we designed a multi-output neural network structure to find the mappings between the perturbed parameters and the simulation data using both CNN and long short-term memory (LSTM) models. Finally, the well-trained and tested CNN-LSTM model is used to estimate the accurate value of the suspicious parameters with actual PMU measurements. The proposed CNN-LSTM network can accurately and reliably estimate the generation-system problematic parameters, and has better performance when compared to other machine-learning methods, such as the multilayer perceptron network and the conditional variational autoencoder method. The accuracy and effectiveness of the proposed approach have been validated through simulation and real-world data.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRSDate not supplied

Development of a Ground Multi-Mission Low-Cost Optical Terminal (LCOT) for Free-Space Optical Communications

Once confined to the realm of laboratory experiments and theoretical papers, space-based laser communications (lasercomm) are on the verge of achieving mainstream status. Organizations from Facebook to NASA, and missions from cubesats to Orion are employing lasercomm to achieve gigabit communication speeds at mass and power requirements lower than that of traditional radio frequency (RF) methods. Since first demonstrating free-space optical communications services with Lunar Laser Communications Demonstration (LLCD) in 2013, NASA has invested in developing optical communications technologies and capabilities to enhancing its space communications networks. Along with evolving optical space terminals, NASA is also developing lasercomm grounds stations capable of meeting the rapidly increasing data volume demands of upcoming missions, from low-earth to lunar orbits and beyond and integrating these advanced capabilities into its Near-Space and Deep Space Networks. To meet this emerging need, the Low-Cost Optical Terminal (LCOT) project at NASA’s Goddard Space Flight Center (GSFC) is designing, building and validating a prototype for a flexible, multi-mission, and economical optical ground terminal that could be used as a blueprint for a global network of optical ground stations, capable of supporting a wide variety of missions. To date a major impediment to widespread adoption of laser communication has been the lack of an existing ground network infrastructure. A mission that wishes to take advantage of laser communication not only needs to invest in an optical space terminal, but it must also finance the creation of ground terminals to receive the downlink signal. This adds significant additional cost. Missions that do decide to incur the cost of financing a network of ground terminals end up building highly specialized optical receivers that are operable as receivers for that specific mission only. Significant Non-Recurring Engineering (NRE) cost is invested to build highly specialized one-of-a-kind ground terminals that go into storage after that particular mission is over. This is not an economical approach and does nothing to grow the number of optical ground stations available to future missions. In essence each mission that wants to take advantage of the benefits of lasercom has to start from scratch to provide a ground terminal network to support it. As long as this is the case, the cost for using laser communications will be too high for most missions to consider. LCOT intends to close this gap in technology by designing and developing a standard optical ground terminal design that is flexible enough to serve as a receiver for a wide range of future missions – a ground terminal that can be quickly reconfigured to receive downlinks at different wavelengths using different signal formats. Not only does LCOT have the industry-building objectives of utilizing commercial-off-the-shelf (COTS) components to the maximum extent possible, but also spurs the commercial development of other necessary lasercomm components not currently offered by industry. Finally, LCOT will give NASA scientists and engineers a facility where they can gain real-world experience with optical communications. It will give engineers a cost-effective way to try out new concepts and processes by providing the infrastructure for such testing. In this way it is hoped LCOT will serve as a stimulus for innovation in optical communications and speed its widespread adoption by future missions. The LCOT is comprised of five subsystems: Free-Space Optical, Transceiver, Amplifier, Monitor and Control, and Observatory Infrastructure. In August 2021, the LCOT team installed a 70 cm telescope, developed by Planewave Instruments that was optimized for optical communications. Free-Space Optical subsystem comprises of the telescope and its associated hardware, including a transmitter optical assembly, wide field cameras, two optical benches, and an adaptive optics subsystem. The transmit optical assembly, a unique concept design, is a cluster of four functionally independent transmit subassemblies located on the receive telescope. In addition to receiving optical signals and directing the expanded beam with high precision to the space terminal, it also performs tracking functions. The transmit optical assembly will support operations from Low Earth Orbit (LEO) through lunar and will be used as a template for industry manufacturing. The Optical Infrastructure subsystem is responsible for providing environmentally controlled shelters for LCOT equipment and various other systems. To maintain the safety and proper functionality of the telescope, a 16 ft Astrohaven clamshell dome procured which provides all-sky coverage without the need to rotate the dome. Additionally, Atmospheric Monitoring Assembly (AMA) will be part of optical infrastructure subsystem to ensure accurate performance of the LCOT. Like existing optical ground stations, LCOT will measure standard weather station parameters, infrared all sky image of cloud cover, and cloud height. LCOT, however, adds requirements for measuring night time seeing and, in the future, daytime seeing. Unlike other optical ground terminals, the LCOT is transceiver agnostic; user transceivers may be duplex transceivers, standalone receivers, or standalone transmitters with or without acquisition beacon functionality. As such, the LCOT project accommodates testing with external customer transceivers in a flexible manor, further complimenting its intended multi-mission goals. Another unique component of LCOT is the use of a new amplifier technology – the Very Large Mode Area (VLMA) amplifiers. This new technology allows LCOT to avoid the issues faced by previous laser communications ground terminals, gives users more flexibility and modular capability, and is capable of reaching an order of magnitude higher peak power than traditional High Power Optical Amplifiers (HPOA). One drawback of the VLMA HPOA approach is that the amplified light is output into free-space. The solution developed by LCOT is an optics train that couples the output of the VLMA amplifier into a short fiber for transport to the transmit telescopes with high efficiency. Like many of the LCOT components, a set of detailed manufacturing drawings have been created for the optics train to allow any machine shop with a multi-axis Computer Numerical Control (CNC) machine to fabricate the piece parts from commonly available materials. In line with the goals of LCOT, the monitor and control functions are developed as a modular and flexible system with the ability to support future hardware or algorithm changes, minimizing disruptions. A main priority of development in the Monitor and Control Subsystem (MCS) is the safety monitor system.

laser communication↗

NASA NTRSDate not supplied

Development of a Ground Multi-mission Low Cost Optical Terminal(LCOT) for Free-Space Optical Communication

Once confined to the realm of laboratory experiments and theoretical papers, space-based laser communications (lasercomm) are on the verge of achieving mainstream status. Organizations from Facebook to NASA, and missions from cubesats to Orion are employing lasercomm to achieve gigabit communication speeds at mass and power requirements lower than that of traditional radio frequency (RF) methods. Since first demonstrating free-space optical communications services with Lunar Laser Communications Demonstration (LLCD) in 2013, NASA has invested in developing optical communications technologies and capabilities to enhancing its space communications networks. Along with evolving optical space terminals, NASA is also developing lasercomm grounds stations capable of meeting the rapidly increasing data volume demands of upcoming missions, from low-earth to lunar orbits and beyond and integrating these advanced capabilities into its Near-Space and Deep Space Networks. To meet this emerging need, the Low-Cost Optical Terminal (LCOT) project at NASA’s Goddard Space Flight Center (GSFC) is designing, building and validating a prototype for a flexible, multi-mission, and economical optical ground terminal that could be used as a blueprint for a global network of optical ground stations, capable of supporting a wide variety of missions. To date a major impediment to widespread adoption of laser communication has been the lack of an existing ground network infrastructure. A mission that wishes to take advantage of laser communication not only needs to invest in an optical space terminal, but it must also finance the creation of ground terminals to receive the downlink signal. This adds significant additional cost. Missions that do decide to incur the cost of financing a network of ground terminals end up building highly specialized optical receivers that are operable as receivers for that specific mission only. Significant Non-Recurring Engineering (NRE) cost is invested to build highly specialized one-of-a-kind ground terminals that go into storage after that particular mission is over. This is not an economical approach and does nothing to grow the number of optical ground stations available to future missions. In essence each mission that wants to take advantage of the benefits of lasercom has to start from scratch to provide a ground terminal network to support it. As long as this is the case, the cost for using laser communications will be too high for most missions to consider. LCOT intends to close this gap in technology by designing and developing a standard optical ground terminal design that is flexible enough to serve as a receiver for a wide range of future missions – a ground terminal that can be quickly reconfigured to receive downlinks at different wavelengths using different signal formats. Not only does LCOT have the industry-building objectives of utilizing commercial-off-the-shelf (COTS) components to the maximum extent possible, but also spurs the commercial development of other necessary lasercomm components not currently offered by industry. Finally, LCOT will give NASA scientists and engineers a facility where they can gain real-world experience with optical communications. It will give engineers a cost-effective way to try out new concepts and processes by providing the infrastructure for such testing. In this way it is hoped LCOT will serve as a stimulus for innovation in optical communications and speed its widespread adoption by future missions. The LCOT is comprised of five subsystems: Free-Space Optical, Transceiver, Amplifier, Monitor and Control, and Observatory Infrastructure. In August 2021, the LCOT team installed a 70 cm telescope, developed by Planewave Instruments that was optimized for optical communications. Free-Space Optical subsystem comprises of the telescope and its associated hardware, including a transmitter optical assembly, wide field cameras, two optical benches, and an adaptive optics subsystem. The transmit optical assembly, a unique concept design, is a cluster of four functionally independent transmit subassemblies located on the receive telescope. In addition to receiving optical signals and directing the expanded beam with high precision to the space terminal, it also performs tracking functions. The transmit optical assembly will support operations from Low Earth Orbit (LEO) through lunar and will be used as a template for industry manufacturing. The Optical Infrastructure subsystem is responsible for providing environmentally controlled shelters for LCOT equipment and various other systems. To maintain the safety and proper functionality of the telescope, a 16 ft Astrohaven clamshell dome procured which provides all-sky coverage without the need to rotate the dome. Additionally, Atmospheric Monitoring Assembly (AMA) will be part of optical infrastructure subsystem to ensure accurate performance of the LCOT. Like existing optical ground stations, LCOT will measure standard weather station parameters, infrared all sky image of cloud cover, and cloud height. LCOT, however, adds requirements for measuring night time seeing and, in the future, daytime seeing. Unlike other optical ground terminals, the LCOT is transceiver agnostic; user transceivers may be duplex transceivers, standalone receivers, or standalone transmitters with or without acquisition beacon functionality. As such, the LCOT project accommodates testing with external customer transceivers in a flexible manor, further complimenting its intended multi-mission goals. Another unique component of LCOT is the use of a new amplifier technology – the Very Large Mode Area (VLMA) amplifiers. This new technology allows LCOT to avoid the issues faced by previous laser communications ground terminals, gives users more flexibility and modular capability, and is capable of reaching an order of magnitude higher peak power than traditional High Power Optical Amplifiers (HPOA). One drawback of the VLMA HPOA approach is that the amplified light is output into free-space. The solution developed by LCOT is an optics train that couples the output of the VLMA amplifier into a short fiber for transport to the transmit telescopes with high efficiency. Like many of the LCOT components, a set of detailed manufacturing drawings have been created for the optics train to allow any machine shop with a multi-axis Computer Numerical Control (CNC) machine to fabricate the piece parts from commonly available materials. In line with the goals of LCOT, the monitor and control functions are developed as a modular and flexible system with the ability to support future hardware or algorithm changes, minimizing disruptions. A main priority of development in the Monitor and Control Subsystem (MCS) is the safety monitor system.

Haleh Safavi↗

NASA NTRS2018

Enriching the Twitter Stream Increasing Data Mining Yield and Quality Using Machine Learning

Social media data streams are important sources of real-time and historical global information for science applications. At the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), we are exploring the Twitter data stream for its potential in augmenting the validation program of NASA Earth science missions, specifically the Global Precipitation Measurement (GPM) mission. We have implemented a tweet processing infrastructure that outputs classified precipitation tweets. Inputs are "passive" tweets, along with a smaller number of tweets from "active" participants, i.e., those knowingly contributing to our effort. The "active" tweets, presumably of higher quality, enrich the Twitter stream. "Active" sources include data scraped from other social media (e.g., public Facebook posts) and data from existing crowdsourcing programs (e.g., mPING reports). In addition, there is likely relevant precipitation information in images and documents that are the end points of links often included in tweets. Information derived from these "active" sources could then be tweeted into the Twitter stream, thus enriching its quality. The objective of our current work is to mine these tweetÂ linked images and documents, using neural networks, to increase the information content and quality related to precipitation. For images, we classified them as either precipitation-related or not. For training and validation, we used images obtained via the Google custom search API. We created two models: (1) by training a simple Convolutional Neural Network and (2) by using transfer learning principles to adapt a pre-trained object recognition model. For documents, both those linked to tweets and the tweet contents, we trained Hierarchical Attention Networks to determine precipitation occurrence, type, and intensity. For training and validation, we used a keyword-filtered tweet data set labelled with ground truth data from Dark Sky (an API to retrieve weather-related labels) and the National Severe Storms Laboratory's MultiÂ Radar/Multi-Sensor (MRMS) system. Our results demonstrated the efficacy of our machine learning approaches for enriching the Twitter stream, to derive information potentially useful for validation of earth science satellite data.

Albayrak, Arif↗

DOE OSTI2026

SuperLab 2.0 Showcase: Connecting Five Labs to Tackle Grid Complexity and Unlock Unique Grid Asset Potential

SuperLab 2.0 (5-Lab Demo) is a collaborative, national-scale experiment showcasing the coordination of geographically distributed energy assets in real time. The demonstration integrates 25 physical and digital assets, spanning wind, PV, batteries, electrolyzers, DC fast chargers, microgrid controllers, building automation systems, small modular reactor (SMR), control centers, and gas turbines, across five DOE national laboratories-NLR, INL, NETL, LBNL, and SNL. These assets are unified using Energy Sciences Network (ESnet), a low-latency, high-performance U.S. Department of Energy's (DOE) network, and controlled via a centralized energy controller hosted at NLR's ARIES facility. The demonstration validates the ability to stress-test hybrid energy systems under dynamic scenarios to de-risk advanced control strategies for greater resilience and flexibility. SuperLab 2.0 (5-Lab Demo) showcased a major advancement in federated national laboratory collaboration, enabling real-time, cross-laboratory experimentation to coordinate geographically dispersed distributed energy resources (DERs) using various communication protocols and networks. SuperLab 2.0 (5-Lab Demo) built on previous demonstrations conducted between NLR-PNNL and NLR-INL connecting diverse assets including distant protection devices, a SMR simulator, and a high temperature electrolyzer (HTE). Previous demos were based on a single connection between two labs with minimal coordination challenges. The 5-Lab demo with a centralized controller, distributed testbeds across different geographical locations, and use of protocols-based communication represents a scenario closer to real-world grid operations that coordinate resources across a region to meet system needs. This experiment studied how local DER controllers interact with a centralized energy controller during normal and abnormal events to maintain reliability. The SuperLab team across the five labs implemented a notional power system model equivalent of transmission and distribution lines, represented by the data networks interconnecting the labs. Each lab continuously exchanged local parameters (such as P and Q) from its Hardware-In-Loop (CHIL) and Power Hardware-In-Loop (PHIL) assets through centralized energy controller at NLR, enabling real-time interaction and coordination across sites. By leveraging ESnet as the communication backbone, the team successfully operated the distributed assets as a unified power system, with each bus represented by a different laboratory. This setup mirrors how assets interact in real-world power systems across dispersed locations with various protocols and latencies. At each lab site, assets were operated using their own local controllers which were coordinated through an overarching operation and control layer of centralized energy controller, equivalent to how an energy management system (EMS) orchestrates assets across a regional or national grid. SuperLab's federated connectivity utilized a Digital Real-Time Simulators (DRTS)-type gateway to connect Controller Hardware-In-Loop (CHIL) and PHIL assets between labs. To enable this federated connection through ESnet, a deterministic network was established where latency variations were consistent. This consistency allowed the development of digital filters for the power system assets across CHIL and PHIL interfaces to avoid unstable and unreliable grid conditions. This report provides an overview of the cross-laboratory configuration and offers insights into interconnecting geographically distributed research assets to test them as if they were co-located. This experiment represents a step toward linking nine DOE national laboratories, enabling nation-wide simulations that can address utility-driven challenges with grid resilience, flexibility, and modernization.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS2011

In Situ Validation of the Soil Moisture Active Passive (SMAP) Satellite Mission

SMAP is a new NASA mission proposed for 2014 that would provide a number of soil moisture and freeze/thaw products. The soil moisture products span spatial resolutions from 3 to 40 km. In situ soil moisture observations will be one of the key elements of the validation program for SMAP. Data from the currently available set of soil moisture observing sites and networks need improvement if they are to be useful. Problems include a lack of standardization of instrumentation and installation and the disparity in spatial scale between the point-scale in situ data (a few centimeters) and the coarser satellite products. SMAP has initiated activities to resolve these issues for some of the existing resources. The other challenge to soil moisture validation is the need to expand the number of sites and their geographic distribution. SMAP is attempting to increase the number of sites and their value in validation through collaboration. The issues and solutions involving in situ validation being investigated will be described along with recent results from SMAP validation projects.

validation↗

DOE OSTI2021

Gauge-invariant perturbation expansion in powers of electric charge for the density-of-states of a network model for charged-particle motion in a uniform background magnetic flux density

An explicitly gauge-invariant expansion in powers of e/$\hbar$ times the magnetic flux density is formally obtained for the density of states [as characterized by the trace of the resolvent $\hat{G}$ = (ω-$\hat{ℎ}$)-1] of a charged particle moving on a Hermitian quantum network that is embedded in a Euclidean background that supports a uniform magnetic flux density. The explicit expressions, given here up to third order in the flux density, are also valid for the “local trace” (the trace of $\hat{P}_i\hat{G}$ , where $\hat{P}_i$ is the projector on a network node) and do not appear to have been previously given.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

NASA NTRS2000

Wireless Telemetry and Command (T and C) Program

The Wireless Telemetry and Command (T&C) program is to investigate methods of using commercial telecommunications service providers to support command and telemetry services between a remote user and a base station. While the initial development is based on ground networks, the development is being done with an eye towards future space communications needs. Both NASA and the Air Force have indicated a plan to consider the use of commercial telecommunications providers to support their space missions. To do this, there will need to be an understanding of the requirements and limitations of interfacing with the commercial providers. The eventual payoff will be the reduced operations cost and the ability to tap into commercial services being developed by the commercial networks. This should enable easier realization of EP services to the end points, commercial routing of data, and quicker integration of new services into the space mission operations. Therefore, the ultimate goal of this program is not just to provide wireless radio communications for T&C services but to enhance those services through wireless networking and provider enhancements that come with the networks. In the following chapters, the detailed technical procedure will be showed step by step. Chapter 2 will talk about the general idea of simulation as well as the implementation of data acquisition including sensor array data and GPS data. Chapter 3 will talk about how to use LabVEEW and Component Works to do wireless communication simulation and how to distribute the real-time information over the Internet by using Visual Basic and ActiveX controls. Also talk about the test configuration and validation. Chapter 4 will show the test results both from In-Lab test and Networking Test. Chapter 5 will summarize the whole procedure and give the perspective for the future consideration.

Jiang, Hui↗

DOE OSTI2020

Efficient bifurcation and tabulation of multi-dimensional combustion manifolds using deep mixture of experts: An a priori study

This work describes and validates an approach for autonomously bifurcating turbulent combustion man-ifolds to divide regression tasks amongst specialized artificial neural networks (ANNs). This approach relies on the mixture of experts (MoE) framework, where each neural network is trained to be specialized in a given portion of the input space. The assignment of different input regions to the experts is determined by a gating network, which is a neural network classifier. In some previous studies, it has been demonstrated that bifurcation of a complex combustion manifold and fitting different ANNs for each part leads to better fits or faster inference speeds. However, the manner of bifurcation in these studies was based on heuristic approaches or clustering techniques. In contrast, the proposed technique enables automatic bifurcation using non-linear planes in high-dimensional turbulent combustion manifolds that are often associated with complex behavior due to different dominating physics in various zones. Furthermore, the proposed concept is validated using 4-dimensional (4D) and 5D flamelet tables, showing that the errors obtained with a given network size, or conversely the network size required to achieve a given accuracy, is considerably reduced. The effect of the number of experts on inference speed is also investigated, showing that by increasing the number of experts from 1 to 8, the inference time can be approximately reduced by a factor of two. Moreover, it is shown that the MoE approach divides the input manifold in a physically intuitive manner, suggesting that the MoE framework can elucidate high-dimensional datasets in a physically meaningful way.

42 ENGINEERING↗

DOE OSTI2022

Integrating Reanalysis and Satellite Cloud Information to Estimate Surface Downward Long-Wave Radiation

The estimation of downward long-wave radiation (DLR) at the surface is very important for the understanding of the Earth’s radiative budget with implications in surface–atmosphere exchanges, climate variability, and global warming. Theoretical radiative transfer and observationally based studies identify the crucial role of clouds in modulating the temporal and spatial variability of DLR. In this study, a new machine learning algorithm that uses multivariate adaptive regression splines (MARS) and the combination of near-surface meteorological data with satellite cloud information is proposed. The new algorithm is compared with the current operational formulation used by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Land Surface Analysis (LSA-SAF). Both algorithms use near-surface temperature and dewpoint temperature along with total column water vapor from the latest European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis ERA5 and satellite cloud information from the Meteosat Second Generation. The algorithms are trained and validated using both ECMWF-ERA5 and DLR acquired from 23 ground stations as part of the Baseline Surface Radiation Network (BSRN) and the Atmospheric Radiation Measurement (ARM) user facility. Results show that the MARS algorithm generally improves DLR estimation in comparison with other model estimates, particularly when trained with observations. When considering all the validation data, root mean square errors (RMSEs) of 18.76, 23.55, and 22.08 W·m –2 are obtained for MARS, operational LSA-SAF, and ERA5, respectively. The added value of using the satellite cloud information is accessed by comparing with estimates driven by ERA5 total cloud cover, showing an increase of 17% of the RMSE. The consistency of MARS estimate is also tested against an independent dataset of 52 ground stations (from FLUXNET2015), further supporting the good performance of the proposed model.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2019

Transitioning the NASA SLR Network to Event Timing Mode for Reduced Systematics, Improved Stability and Data Precision

NASA's legacy Satellite Laser Ranging (SLR) network produces about one-third of the global SLR data to support spacegeodesy. This network of globally distributed stations has been using Time Interval Units (TIU) for range measurements for thelast 25 + years. To improve the reliability of the SLR network and satisfy the need for stable millimeter precision data, a phasedreplacement of the TIUs in the network with picosecond-precise Event Timer Modules was initiated in 2015. This schemeallowed the time of flight and laser transmit epoch measurement to one picosecond resolution. For a network with globalscientific impact, transitioning to a new data generation metrological scheme requires significant data scrutiny and long-termscience data validation. Any long-term testing/measurement has the potential to interrupt the station's daily operational dataflow to the International Laser Ranging Service (ILRS) as the station under test will have to put its test data into quarantine.We have demonstrated a very effective way to test and implement the new device without removing the old hardware andwithout the need for the orbit analysis. This operationally noninvasive scheme performed concurrent test measurements enablinguninterrupted operational data flow to the users, while allowing simultaneous test data capture for short- and long-termsystematics and stability analysis. Extensive analysis of the test data was performed by the NASA SLR engineering team andthe ILRS Analysis Standing Committee, to uncover biases and any dependencies on the satellite ranges (for nonlinear scaleissues). Multi-ETM comparison was also performed at two of the SLR stations through the interchange of hardware to establishthe inter-device range biases and stability. Such benchmarked hardware was subsequently sent to the remaining stationsto allow traceability and normalize the network performance. The range bias intercomparison performed using the multiyearSLR data analysis agreed well with the engineering changes, thus validating the approach to flush out station-specific rangingsystematics affecting precise orbit determination. Such an improvement and rebalancing of the current network will allowan orderly transition of the current NASA SLR network operating at a maximum rate of 10 Hz to the NASA next generationSpace Geodesy Satellite Laser Ranging (SGSLR) network operating at 2 kHz (McGarry et al. in J Geod, 2018. https ://doi.org/10.1007/s0019 0-018-1191-6; Merkowitz et al. in J Geod, 2018. https ://doi.org/10.1007/s0019 0-018-1204-5).

Varghese, Thomas↗

NASA NTRS2022

Optimal Estimation Framework for Ocean Color Atmospheric Correction and Pixel-level Uncertainty Quantification

Ocean color remote sensing requires compensation for atmospheric scattering and absorption (aerosol, Rayleigh, and trace gases), referred to as atmospheric correction (AC). AC allows inference of parameters such as spectrally resolved remote sensing reflectance ( R rs )(λ) ; sr 1 ) at the ocean surface from the top-of-atmosphere reflectance. Often, the uncertainty of this process is not fully explored. Bayesian inference techniques provide a simultaneous AC and uncertainty assessment via a full posterior distribution of the relevant variables, given the prior distribution of those variables and the radiative transfer (RT) likelihood function. Given uncertainties in the algorithm inputs, the Bayesian framework enables better constraints on the AC process by using the complete spectral information compared to traditional approaches that use only a subset of bands for AC. This paper investigates a Bayesian inference research method (Optimal Estimation, OE) for ocean color AC by simultaneously retrieving atmospheric and ocean properties using all visible and near-infrared spectral bands. The OE algorithm analytically approximates the posterior distribution of parameters based on normality assumptions and provides a potentially viable operational algorithm with a reduced computational expense. We developed a Neural Network (NN) RT forward model look-up-table-based emulator to increase algorithm efficiency further and thus speed up the likelihood computations. We then applied the OE algorithm to synthetic data and observations from the MODerate resolution Imaging Spectroradiometer (MODIS) on NASA’s Aqua spacecraft. We compared the R rs )(λ) retrieval and its uncertainty estimates from the OE method with in-situ validation data from the SeaWiFS Bio-optical Archive and Storage System (SeaBASS) and Aerosol Robotic Network Ocean Color (AERONET-OC) datasets. The OE algorithm improved R rs )(λ) estimates relative to the NASA standard operational algorithm by improving all statistical metrics at 443, 555, and 667 nm. Unphysical negative R rs )(λ) , which often appear in complex water conditions, was reduced by a factor of 3. The OE-derived pixel-level R rs )(λ) uncertainty estimates were also assessed relative to in-situ data and were shown to have skill.

Atmospheric correction↗

DOE OSTI2024

Upsampling Monte Carlo Reactor Simulation Tallies in Depleted Sodium-Cooled Fast Reactor Assemblies Using a Convolutional Neural Network

The computational demand of neutron Monte Carlo transport simulations can increase rapidly with the spatial and energy resolution of tallied physical quantities. Convolutional neural networks have been used to increase the resolution of Monte Carlo simulations of light water reactor assemblies while preserving accuracy with negligible additional computational cost. Here, we show that a convolutional neural network can also be used to upsample tally results from Monte Carlo simulations of sodium-cooled fast reactor assemblies, thereby extending the applicability beyond thermal systems. The convolutional neural network model is trained using neutron flux tallies from 300 procedurally generated nuclear reactor assemblies simulated using OpenMC. Validation and test datasets included 16 simulations of procedurally generated assemblies, and a realistic simulation of a European sodium-cooled fast reactor assembly was included in the test dataset. We show the residuals between the high-resolution flux tallies predicted by the neural network and high-resolution Monte Carlo tallies on relative and absolute bases. The network can upsample tallies from simulations of fast reactor assemblies with diverse and heterogeneous materials and geometries by a factor of two in each spatial and energy dimension. The network’s predictions are within the statistical uncertainty of the Monte Carlo tallies in almost all cases. This includes test assemblies for which burnup values and geometric parameters were well outside the ranges of those in assemblies used to train the network.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

NASA NTRSDate not supplied

The Surface Longwave Downward Fluxes of the NASA GEWEX SRB Release 4.0 IP Products: Validation Against the Surface-Based BSRN and PMEL Observed Data

Since the NASA Global Energy Water Exchanges (GEWEX) Surface Radiation Budget (SRB) project released its 3rd version of products in 2010, the GEWEX Data Assessments Panel (GDAP) has been working on integrating various data products to address issues in the closing of the global energy and water cycles. The 4th version of the SRB products, Rel. 4.0-IP, has integrated data products from the cloud, aerosol, atmosphere, ocean surface, and land surface projects, coordinating within GDAP, to produce a long-term time series of TOA and surface radiative estimates. The Rel. 4-IP shortwave products span 34 years continuously from July 1983 to June 2017 on a quasi-equal-area 1degree longitude by 1degree latitude grid system. The longwave products are for land only from 1983-07 to 1987-12; for both land and ocean from 1988-01 to 2009-12; and ocean only from 2010-01 to 2017-06. The data are provided at 3 hourly, 3-hourly-monthly, daily and monthly means. The ISCCP HXS clouds and radiances are the key cloud input of the current GEWEX SRB algorithms. In addition, the longwave algorithm has also made changes in cloud microphysical property, surface skin temperature input, surface emissivity, atmospheric profile, adding longwave aerosol optical properties, revising cloud overlap procedure, and so on. Details of changes in both inputs and algorithms are documented in a NASA Algorithm Theoretical Basis Document (ATBD). We have validated the surface longwave downward fluxes against the surface-based Baseline Surface Radiation Network (BSRN) and the Pacific Marine Environmental Laboratory (PMEL) buoy data. As of 2020, the BSRN archive has 12,116 site-months of observed records from 73 stations on all seven continents, and as of 2017, PMEL archive has 4389 buoy months of observed records from 64 buoys deployed in the tropics of Pacific, Atlantic and Indian Oceans. This paper presents how the SRB Rel. 4.0-IP surface longwave downward fluxes compare with these surface-based measurements and how the comparison statistics differ from that of Rel. 3.0.

GEWEX SRB↗