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All sources NASA NTRS DOE OSTI

DOE OSTI2015

Radiation Tolerant Electronics and Digital Processing for the Phase-1 Read-out Upgrade of the ATLAS Liquid Argon Calorimeters

The ATLAS Liquid Argon calorimeters are designed and built to study proton-proton collisions produced at the LHC at centre-of-mass energies up to 14 TeV. Liquid argon (LAr) sampling calorimeters are employed for all electromagnetic calorimetry in the pseudorapidity region |η|<3.2, and for hadronic calorimetry in the region from |η|=1.5 to |η|=4.9. Although the nominal LHC experimental programme is still in progress, an upgrade of the read-out electronics is being launched to cope with luminosities of up to 3x10{sup 34} cm{sup -2}s{sup -1}, which are beyond the original design by a factor of 3. An improved spatial granularity of the trigger primitives is therefore proposed in order to improve the identification performance for trigger signatures, like electrons, photons, tau leptons, jets, total and missing energy, at high background rejection rates. For the upgrade Phase-1 in 2018, new LAr Trigger Digitizer Boards (LTDB) are being designed to receive higher granularity signals, digitize them on detector and send them via fast optical links to a new LAr digital processing system (LDPS). The LDPS applies a digital filtering and identifies significant energy depositions in each trigger channel. The refined trigger primitives are then transmitted to the first level trigger system to extract improved trigger signatures. The read-out of the trigger signals will process 34000 so-called Super Cells at every LHC bunch-crossing at a frequency of 40 MHz. The new LTDB on-detector electronics is designed to be radiation tolerant in order to be operated for the remaining live-time of the ATLAS detector up to a total luminosity of 3000 fb{sup -1}. For the analog-to-digital conversion (12-bit ADC at 40 MSPS), the data serialization and the fast optical link (5.44 Gb/s) custom components have been developed. They have been qualified for the expected radiation environment of a total ionization dose of 1.3 kGy and a hadron fluence of 6 x 10{sup 13} h/cm{sup 2} with energies above 20 MeV. For the digital components like the ADC, cross-sections for single event effects have been determined. This talk will present R and D results from tests of the radiation tolerant components, the fast data processing electronics and prototypes of the LTDB and LDPS boards. First experience from a Demonstrator setup will be reported, in which about 1/10 of the full Super Cell readout will be equipped with prototype versions of the LTDB and LDPS boards. The Demonstrator will be operated in parallel to the regular ATLAS trigger read-out during the upcoming LHC run. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

A Fast and Scalable Genetic Algorithm-Based Approach for Planning of Microgrids in Distribution Networks

As a result of climate change, extreme weather events are occurring more frequently and with increasing impact. This trend poses a significant challenge for distribution utilities and system operators to ensure that there is uninterrupted power supply to critical loads in their networks; thus, the level of proactive preparation of the distribution system to be able to handle severe impacts of extreme weather events represents the system's resilience. One method that distribution system planners can use to prepare for future extreme events is to plan multiple microgrids which can use local generation as much as possible to supply critical loads. But partitioning an existing distribution system such that multiple feasible islands are planned and which are capable of supporting critical loads is still challenging for distribution systems - first, because of the size of the network graph partitioning problem and, second, because of the difficulty in properly formulating the desired attributes of such islands or microgrids. Therefore, this paper presents a genetic algorithm based approach that facilitates incorporating multiple objectives for grid partitioning by formulating two types of problems - node allocation and edge elimination - and it considers multiple topological and resilience-enhancing objectives. The performance of the proposed genetic algorithm-based approach is numerically evaluated on multiple test systems as well as on a real distribution feeder in Colorado, United States.

genetic algorithm↗

DOE OSTI2014

Design and Stability of Load-Side Primary Frequency Control in Power Systems

We present a systematic method to design ubiquitous continuous fast-acting distributed load control for primary frequency regulation in power networks, by formulating an optimal load control (OLC) problem where the objective is to minimize the aggregate cost of tracking an operating point subject to power balance over the network. We prove that the swing dynamics and the branch power flows, coupled with frequency-based load control, serve as a distributed primal-dual algorithm to solve OLC. We establish the global asymptotic stability of a multimachine network under such type of load-side primary frequency control. These results imply that the local frequency deviations on each bus convey exactly the right information about the global power imbalance for the loads to make individual decisions that turn out to be globally optimal. Simulations confirm that the proposed algorithm can rebalance power and resynchronize bus frequencies after a disturbance with significantly improved transient performance.

Zhao, CH↗

DOE OSTI2015

A new apparatus design for high temperature (up to 950 °C) quasi-elastic neutron scattering in a controlled gaseous environment

A design for a sample cell system suitable for high temperature Quasi-Elastic Neutron Scattering (QENS) experiments is presented. The apparatus was developed at the Spallation Neutron Source in Oak Ridge National Lab where it is currently in use. The design provides a special sample cell environment under controlled humid or dry gas flow over a wide range of temperature up to 950 °C. Using such a cell, chemical, dynamical, and physical changes can be studied in situ under various operating conditions. While the cell combined with portable automated gas environment system is especially useful for in situ studies of microscopic dynamics under operational conditions that are similar to those of solid oxide fuel cells, it can additionally be used to study a wide variety of materials, such as high temperature proton conductors. The cell can also be used in many different neutron experiments when a suitable sample holder material is selected. The sample cell system has recently been used to reveal fast dynamic processes in quasi-elastic neutron scattering experiments, which standard probes (such as electrochemical impedance spectroscopy) could not detect. In this work, we outline the design of the sample cell system and present results demonstrating its abilities in high temperature QENS experiments.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2018

Field Testing of Telemetry for Demand Response Control of Small Loads

The electricity system in California, from generation through loads, must be prepared for high renewable penetration and increased electrification of end uses while providing increased resilience and lower operating cost. California has an aggressive renewable portfolio standard that is complemented by world-leading greenhouse gas goals. The goal of this project was to evaluate methods of enabling fast demand response (DR) signaling to small loads for low-cost site enablement. We used OpenADR 2.0 to meet telemetry requirements for providing ancillary services, and we used a variety of low-cost devices coupled with open-source software to enable an end-to-end fast DR. The devices, architecture, implementation, and testing of the system is discussed in this report. We demonstrate that the emerging Internet of Things (IoT) and Smart Home movements provide an opportunity for diverse small loads to provide fast, low-cost demand response. We used Internet-connected lights, thermostats, load interruption devices, and water heaters to demonstrate an ecosystem of controllable devices. The system demonstrated is capable of providing fast load shed for between 20 dollars and $300 per kilowatt (kW) of available load. The wide range results from some loads may have very low cost but also very little shed capability (a 10 watt [W] LED light can only shed a maximum of 10 W) while some loads (e.g., water heaters or air conditioners) can shed several kilowatts but have a higher initial cost. These costs, however, compare well with other fast demand response costs, with typically are over $100/kilowatt of shed. We contend these loads are even more attractive than their price suggests because many of them will be installed for energy efficiency or non-energy benefits (e.g., improved lighting quality or controllability), and the ability to use them for fast DR is a secondary benefit. Therefore the cost of enabling them for DR may approach zero if a software-only solution can be deployed to enable fast DR after devices are installed for other reasons. We recommend that the DR research community continue to engage with the IoT community to encourage the use of documented and open development interfaces. A library of device drivers and machine-readable interface specifications would significantly reduce the burden on users or system integrators for deploying systems in large numbers of buildings in California.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS1990

Delivery and application of precise timing for a traveling wave powerline fault locator system

The Bonneville Power Administration (BPA) has successfully operated an in-house developed powerline fault locator system since 1986. The BPA fault locator system consists of remotes installed at cardinal power transmission line system nodes and a central master which polls the remotes for traveling wave time-of-arrival data. A power line fault produces a fast rise-time traveling wave which emanates from the fault point and propagates throughout the power grid. The remotes time-tag the traveling wave leading edge as it passes through the power system cardinal substation nodes. A synchronizing pulse transmitted via the BPA analog microwave system on a wideband channel sychronizes the time-tagging counters in the remote units to a different accuracy of better than one microsecond. The remote units correct the raw time tags for synchronizing pulse propagation delay and return these corrected values to the fault locator master. The master then calculates the power system disturbance source using the collected time tags. The system design objective is a fault location accuracy of 300 meters. BPA's fault locator system operation, error producing phenomena, and method of distributing precise timing are described.

Street, Michael A.↗

NASA NTRS2004

Control of a High Speed Flywheel System for Energy Storage in Space Applications

A novel control algorithm for the charge and discharge modes of operation of a flywheel energy storage system for space applications is presented. The motor control portion of the algorithm uses sensorless field oriented control with position and speed estimates determined from a signal injection technique at low speeds and a back EMF technique at higher speeds. The charge and discharge portion of the algorithm use command feed-forward and disturbance decoupling, respectively, to achieve fast response with low gains. Simulation and experimental results are presented demonstrating the successful operation of the flywheel control up to the rated speed of 60,000 rpm.

Kenny, Barbara H.↗

DOE OSTI2022

Development and assessment of a reactor system prognosis model with physics-guided machine learning

Autonomous control systems provide recommendations to help operators in decision-making during plant operations ranging from normal operation to accident management. An important step of autonomous control is prognosis. In nuclear engineering domain, prognosis is the process of predicting future conditions of a system or equipment based on present signs and symptoms of a fault. The prognosis model allows predicting future reactor states for possible candidate control strategies so that the outcomes can be evaluated to determine the best control strategy. The prognosis model requires representing direct relationships between the symptoms and the predictions. In nuclear engineering, computational simulations are approximate representations of the operation of the real system. However, prognosis with computational simulations requires high computation power and time due to possible large number of scenarios. Necessary computation resources can be reduced with machine learning (ML) approach for fast predictions by building a surrogate function using the simulation data. A critical issue is, ML models are ignorant of physical knowledge, and these models approximate statistical relationships between the system variables. This ignorance can produce results that are inconsistent with physical laws, even if an optimal result is achieved from a mathematical point of view. Physics-guided machine learning (PGML) is an approach to tackle this issue. Here, this work formulates and illustrates a framework to guide development and assessment of the ML-based prognosis model for autonomous control systems. The development of the prognosis model considers the training of a ML model which consists of optimizing many aspects of the ML approach. The assessment of the prognosis model considers training data limitations and uncertainties of the ML approach. Prognosis models with standalone ML and PGML are developed and assessed on the loss-of-flow scenario of Experimental Breeder Reactor II. The results indicate that PGML based prognosis model has the best performance compared to other prognosis models.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2015

Data acquisition for the new muon g-2 experiment at Fermilab

A new measurement of the anomalous magnetic moment of the muon, a μ ≡ (g - 2)/2, will be performed at the Fermi National Accelerator Laboratory. The most recent measurement, performed at Brookhaven National Laboratory and completed in 2001, shows a 3.3-3.6 standard deviation discrepancy with the Standard Model predictions for a μ . The new measurement will accumulate 21 times those statistics, measuring a μ to 140 ppb and reducing the uncertainty by a factor of 4. The data acquisition system for this experiment must have the ability to record deadtime-free records from 700 μs muon spills at a raw data rate of 18 GB per second. Data will be collected using 1296 channels of μTCA-based 800 MSPS, 12 bit waveform digitizers and processed in a layered array of networked commodity processors with 24 GPUs working in parallel to perform a fast recording and processing of detector signals during the spill. The system will be controlled using the MIDAS data acquisition software package. Lastly, the described data acquisition system is currently being constructed, and will be fully operational before the start of the experiment in 2017.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2016

A domain-specific compiler for a parallel multiresolution adaptive numerical simulation environment

This paper describes the design and implementation of a layered domain-specific compiler to support MADNESS---Multiresolution ADaptive Numerical Environment for Scientific Simulation. MADNESS is a high-level software environment for the solution of integral and differential equations in many dimensions, using adaptive and fast harmonic analysis methods with guaranteed precision. MADNESS uses k-d trees to represent spatial functions and implements operators like addition, multiplication, differentiation, and integration on the numerical representation of functions. The MADNESS runtime system provides global namespace support and a task-based execution model including futures. MADNESS is currently deployed on massively parallel supercomputers and has enabled many science advances. Due to the highly irregular and statically unpredictable structure of the k-d trees representing the spatial functions encountered in MADNESS applications, only purely runtime approaches to optimization have previously been implemented in the MADNESS framework. This paper describes a layered domain-specific compiler developed to address some performance bottlenecks in MADNESS. The newly developed static compile-time optimizations, in conjunction with the MADNESS runtime support, enable significant performance improvement for the MADNESS framework.

Rajbhandari, Samyam↗

NASA NTRS1986

Problems associated with operations and measurement in cryogenic wind tunnels

Cryogenic wind tunnel T'3 under continuous blower operation has been the object of improvements and the installation of auxiliary equipment, dealing in particular with the enlargement of the liquid nitrogen injection reservoir and the hook-up to a fast data acquisition system. Following a brief description of the installation and its functioning, we present the main experimental techniques and the instrumentation used in the cryogenic environment.

Blanchard, A.↗

NASA NTRS1993

Infrared sensors and systems for enhanced vision/autonomous landing applications

There exists a large body of data spanning more than two decades, regarding the ability of infrared imagers to 'see' through fog, i.e., in Category III weather conditions. Much of this data is anecdotal, highly specialized, and/or proprietary. In order to determine the efficacy and cost effectiveness of these sensors under a variety of climatic/weather conditions, there is a need for systematic data spanning a significant range of slant-path scenarios. These data should include simultaneous video recordings at visible, midwave (3-5 microns), and longwave (8-12 microns) wavelengths, with airborne weather pods that include the capability of determining the fog droplet size distributions. Existing data tend to show that infrared is more effective than would be expected from analysis and modeling. It is particularly more effective for inland (radiation) fog as compared to coastal (advection) fog, although both of these archetypes are oversimplifications. In addition, as would be expected from droplet size vs wavelength considerations, longwave outperforms midwave, in many cases by very substantial margins. Longwave also benefits from the higher level of available thermal energy at ambient temperatures. The principal attraction of midwave sensors is that staring focal plane technology is available at attractive cost-performance levels. However, longwave technology such as that developed at FLIR Systems, Inc. (FSI), has achieved high performance in small, economical, reliable imagers utilizing serial-parallel scanning techniques. In addition, FSI has developed dual-waveband systems particularly suited for enhanced vision flight testing. These systems include a substantial, embedded processing capability which can perform video-rate image enhancement and multisensor fusion. This is achieved with proprietary algorithms and includes such operations as real-time histograms, convolutions, and fast Fourier transforms.

Kerr, J. Richard↗

NASA NTRS2015

Film-Evaporation MEMS Tunable Array for Picosat Propulsion and Thermal Control

The Film-Evaporation MEMS Tunable Array (FEMTA) concept for propulsion and thermal control of picosats exploits microscale surface tension effect in conjunction with temperature- dependent vapor pressure to realize compact, tunable and low-power thermal valving system. The FEMTA is intended to be a self-contained propulsion unit requiring only a low-voltage DC power source to operate. The microfabricated thermal valving and very-high-integration level enables fast high-capacity cooling and high-resolution, low-power micropropulsion for picosats that is superior to existing smallsat micropropulsion and thermal management alternatives.

Propulsion↗

DOE OSTI2012

AVESTAR Center for Operational Excellence of Electricity Generation Plants

To address industry challenges in attaining operational excellence for electricity generation plants, the U.S. Department of Energy’s (DOE) National Energy Technology Laboratory (NETL) has launched a world-class facility for Advanced Virtual Energy Simulation Training and Research (AVESTARTM). This presentation will highlight the AVESTARTM Center simulators, facilities, and comprehensive training, education, and research programs focused on the operation and control of high-efficiency, near-zero-emission electricity generation plants. The AVESTAR Center brings together state-of-the-art, real-time, high-fidelity dynamic simulators with full-scope operator training systems (OTSs) and 3D virtual immersive training systems (ITSs) into an integrated energy plant and control room environment. AVESTAR’s initial offering combines--for the first time--a “gasification with CO2 capture” process simulator with a “combined-cycle” power simulator together in a single OTS/ITS solution for an integrated gasification combined cycle (IGCC) power plant with carbon dioxide (CO2) capture. IGCC systems are an attractive technology option for power generation, especially when capturing and storing CO2 is necessary to satisfy emission targets. The AVESTAR training program offers a variety of courses that merge classroom learning, simulator-based OTS learning in a control-room operations environment, and immersive learning in the interactive 3D virtual plant environment or ITS. All of the courses introduce trainees to base-load plant operation, control, startups, and shutdowns. Advanced courses require participants to become familiar with coordinated control, fuel switching, power-demand load shedding, and load following, as well as to problem solve equipment and process malfunctions. Designed to ensure work force development, training is offered for control room and plant field operators, as well as engineers and managers. Such comprehensive simulator-based instruction allows for realistic training without compromising worker, equipment, and environmental safety. It also better prepares operators and engineers to manage the plant closer to economic constraints while minimizing or avoiding the impact of any potentially harmful, wasteful, or inefficient events. The AVESTAR Center is also used to augment graduate and undergraduate engineering education in the areas of process simulation, dynamics, control, and safety. Students and researchers gain hands-on simulator-based training experience and learn how the commercial-scale power plants respond dynamically to changes in manipulated inputs, such as coal feed flow rate and power demand. Students also analyze how the regulatory control system impacts power plant performance and stability. In addition, students practice start-up, shutdown, and malfunction scenarios. The 3D virtual ITSs are used for plant familiarization, walk-through, equipment animations, and safety scenarios. To further leverage the AVESTAR facilities and simulators, NETL and its university partners are pursuing an innovative and collaborative R&D program. In the area of process control, AVESTAR researchers are developing enhanced strategies for regulatory control and coordinated plant-wide control, including gasifier and gas turbine lead, as well as advanced process control using model predictive control (MPC) techniques. Other AVESTAR R&D focus areas include high-fidelity equipment modeling using partial differential equations, dynamic reduced order modeling, optimal sensor placement, 3D virtual plant simulation, and modern grid. NETL and its partners plan to continue building the AVESTAR portfolio of dynamic simulators, immersive training systems, and advanced research capabilities to satisfy industry’s growing need for training and experience with the operation and control of clean energy plants. Future dynamic simulators under development include natural gas combined cycle (NGCC) and supercritical pulverized coal (SCPC) plants with post-combustion CO2 capture. These dynamic simulators are targeted for use in establishing a Virtual Carbon Capture Center (VCCC), similar in concept to the DOE’s National Carbon Capture Center for slipstream testing. The VCCC will enable developers of CO2 capture technologies to integrate, test, and optimize the operation of their dynamic capture models within the context of baseline power plant dynamic models. The objective is to provide hands-on, simulator-based “learn-by-operating” test platforms to accelerate the scale-up and deployment of CO2 capture technologies. Future AVESTAR plans also include pursuing R&D on the dynamics, operation, and control of integrated electricity generation and storage systems for the modern grid era. Special emphasis will be given to combining load-following energy plants with renewable and distributed generating supplies and fast-ramping energy storage systems to provide near constant baseload power.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

A Targeted Attack For Enhancing Resiliency of Intelligent Intrusion Detection Modules in Energy Cyber Physical Systems

Abstract— Secure high-speed communication is required to ensure proper operation of complex power grid systems and prevent malicious tampering activities. In this paper, artificial neural networks with temporal dependency are introduced for false data identification and mitigation for broadcasted IEC 61850 SMV messages. The fast responses of such intelligent modules in intrusion detection make them suitable for time- critical applications, such as protection. However, care must be taken in selecting the appropriate intelligence model and decision criteria. As such, this paper presents a customizable malware script to sniff and manipulate SMV messages and demonstrates the ability of the malware to trigger false positives in the neural network’s response. The malware developed is intended to be as a vaccine to harden the intrusion detection system against data manipulation attacks by enhancing the neural network’s ability to learn and adapt to these attacks.

25 ENERGY STORAGE↗

DOE OSTI2021

FedFleet 2021: Federal Automotive Statistical Tool - Federal Vehicle Fleet Data Collection

This presentation presents a brief overview of the collection of information about the US government's fleet of motor vehicles using the Federal Automotive Statistical Tool (FAST), discusses the makeup and operation of the vehicle fleet during FY 2020, discusses challenges associated with quality of the submitted data, and touches on future aspects of fleet data collection and reporting. FAST is a web-based information system sponsored by GSA's Office of Government-wide Policy and DOE's Federal Energy Management Program to collect information about the US federal government's fleet of motor vehicles; FAST is developed, maintained, and supported by DOE's Idaho National Laboratory (INL).

99 GENERAL AND MISCELLANEOUS↗

NASA NTRS2014

GPU Lossless Hyperspectral Data Compression System

Hyperspectral imaging systems onboard aircraft or spacecraft can acquire large amounts of data, putting a strain on limited downlink and storage resources. Onboard data compression can mitigate this problem but may require a system capable of a high throughput. In order to achieve a high throughput with a software compressor, a graphics processing unit (GPU) implementation of a compressor was developed targeting the current state-of-the-art GPUs from NVIDIA(R). The implementation is based on the fast lossless (FL) compression algorithm reported in "Fast Lossless Compression of Multispectral-Image Data" (NPO- 42517), NASA Tech Briefs, Vol. 30, No. 8 (August 2006), page 26, which operates on hyperspectral data and achieves excellent compression performance while having low complexity. The FL compressor uses an adaptive filtering method and achieves state-of-the-art performance in both compression effectiveness and low complexity. The new Consultative Committee for Space Data Systems (CCSDS) Standard for Lossless Multispectral & Hyperspectral image compression (CCSDS 123) is based on the FL compressor. The software makes use of the highly-parallel processing capability of GPUs to achieve a throughput at least six times higher than that of a software implementation running on a single-core CPU. This implementation provides a practical real-time solution for compression of data from airborne hyperspectral instruments.

Aranki, Nazeeh I.↗

DOE OSTI2019

Process intensification of biomass fast pyrolysis through autothermal operation of a fluidized bed reactor

Heat transfer is the bottleneck to fast pyrolysis of biomass. Although the enthalpy for pyrolysis of biomass is relatively small operation at temperatures around 500 °C constrains heat carrier selection to inert gases and granular media that can sustain only modest thermal fluxes in practical pyrolysis systems. With heat transfer controlling the rate of pyrolysis, reactor capacity only scales as the square of reactor diameter and does not benefit from economies of scale in building larger reactors. We have eliminated this heat transfer bottleneck by replacing it with partial oxidation of pyrolysis products to provide the enthalpy for pyrolysis in a fluidized bed reactor, a process that can be described as autothermal pyrolysis. Here, the oxygen-to-biomass equivalence ratio depends upon the kind of biomass being pyrolyzed and the level of parasitic heat losses from the reactor, but under conditions that simulate adiabatic operation, equivalence ratios are around 0.10, compared to 0.20 or higher for autothermal gasifiers. At this low equivalence ratio, there was no significant loss in bio-oil yield when operating the reactor autothermally (64.8 wt%) as compared to conventional pyrolysis (64.4 wt%). Carbon balances indicate that less valuable pyrolysis products (char and aqueous, bio-oil light ends) are consumed via partial oxidative reactions to provide the enthalpy for pyrolysis. While the carbon yields of char and bio-oil light ends decreased by 25.0% and 21.3%, respectively, the most valuable pyrolysis product (bio-oil heavy ends) only decreased 8.0%.

09 BIOMASS FUELS↗