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At least 181 records · Page 10

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DOE OSTI2020

Evaluations of Silicon Carbide Temperature Monitors

The effect of irradiation on materials properties is an important field of study for materials usage in both fission and fusion systems for energy production. Neutron flux and energy spectrums are well understood; however, the irradiation temperature can be more difficult to determine. Since the early 1960s, Silicon Carbide (SiC) has been used as a passive post-irradiation temperature monitor because the irradiation defects anneal out above the irradiation temperature. Irradiation temperature is determined by measuring a property change after isochronal annealing or during a continuously monitored annealing process. There are many properties that may be measured including electrical resistivity, bulk density, dimensions, thermal diffusivity, or lattice spacing. Electrical resistivity is accepted as a robust measurement technique; however, such method is time-consuming since the steps involved must be performed in a serial manner. The use of thermal expansion from continuous dilatometry is an automated process requiring minimal setup time. As part of a Nuclear Science User Facilities (NSUF) project, low dose silicon carbide monitors were irradiated in the BR2 material test reactor at SCK, Belgium, and were then evaluated at the Idaho National Laboratory (INL) High Temperature Test Laboratory (HTTL) to determine their peak temperature achieved during irradiation. The technical significance of this work was that the monitors were irradiated to a dose that was significantly less than recommended in published literature. This paper will discuss the evaluation processes available at HTTL and the performance of the low dose silicon carbide temperature monitors.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

Designing Monte Carlo Simulation and an Optimal Machine Learning to Optimize and Model Space Missions

This paper investigates applying artificial intelligence (AI) algorithms to attitude control system of satellites to optimally tune the controller using high performance computing. This methodology is applied to the Virtual Telescope for X-ray Observation mission, which is a precise formation of two separate spacecraft observing multiple objects in the space in the X-ray domain. The mission is divided into phases based on the instrumentation and the mission goal. To reach an stable precise formation robust to stochastic slew and slew rate (i.e., Euler angles and angular velocities) in a minimal constrained time T , consumed energy of the attitude control system, denoted as E, and root-mean-square state error of attitude control system, denoted as e, are minimized. Monte-Carlo simulation is used for the sensitivity analysis of optimization and designing a controller. Deep neural networks (DNN), Gaussian processes (GP), and support vector regression (SVR) learn this optimization as a surrogate model, while their hyperparameters are optimized in a novel approach. THETA supercomputer at Argonne Leadership Computing Facility (ALCF) is used for optimizing the hyperparameters of DNN. The surrogate model meets the requirements of the mission, and it shows a better performance over the optimization and Monte-Carlo. The optimal DNN can satisfy the mission requirements e and T while reducing E for 90% compared to the other given methods.

42 ENGINEERING↗

DOE OSTI2021

Using occupant interaction with advanced lighting systems to understand opportunities for energy optimization: Control data from a hospital NICU

For this study, lighting system control data from five patient rooms in a neonatal intensive care unit (NICU) over a 25-week monitoring period were analyzed to better understand how the NICU staff and families interacted with an automatic tunable lighting system (variable spectrum and intensity) with manual override options. The data were analyzed to determine the amount of time spent in available lighting modes, estimate energy consumption, and to observe any patterns in how the lighting system was being used by occupants. Further analysis revealed opportunities for optimizing the system for occupant needs and energy savings. The patient room occupants were clearly engaged in using the lighting system throughout the 24-hour day to meet needs and preferences. Giving occupants control over the lighting system did not result in a considerable increase in energy use. Implementing a time-out would minimize the extended periods of time when the lights were left on without need at the highest brightness level, further decreasing energy use. This analysis reveals the potential of lighting control data to contribute to energy savings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS1990

Implementing real-time robotic systems using CHIMERA II

A description is given of the CHIMERA II programming environment and operating system, which was developed for implementing real-time robotic systems. Sensor-based robotic systems contain both general- and special-purpose hardware, and thus the development of applications tends to be a time-consuming task. The CHIMERA II environment is designed to reduce the development time by providing a convenient software interface between the hardware and the user. CHIMERA II supports flexible hardware configurations which are based on one or more VME-backplanes. All communication across multiple processors is transparent to the user through an extensive set of interprocessor communication primitives. CHIMERA II also provides a high-performance real-time kernel which supports both deadline and highest-priority-first scheduling. The flexibility of CHIMERA II allows hierarchical models for robot control, such as NASREM, to be implemented with minimal programming time and effort.

Stewart, David B.↗

DOE OSTI2023

Blood treatment device priming devices, methods, and systems

Simple-to-use systems, methods, and devices for priming replacement blood treatment devices, for swapping the blood treatment devices out, for replacing swapped-out blood treatment devices, and other related operations are described. In embodiments, a blood treatment device can be primed while a therapy is still running. When the replacement blood treatment device is needed, the therapy can be stopped momentarily (less than a minute) for the rapid and safe swap of the blood treatment device. Blood loss can be minimized. The down time from therapy can be minimized.

Brugger, James M.↗

DOE OSTI2025

Optimization of a Mixed Fleet of Aerial Drones for Medical Supplies: A Case Study of Blood Delivery Logistics

Aerial drones have emerged as an innovative solution for faster transportation of time-sensitive items (e.g., emergency medical supplies), potentially reducing the transmission of contagious diseases and enhancing healthcare availability through contactless autonomous delivery. We study fleet sizing and efficient scheduling of a mixed fleet of drones for delivering time-sensitive medical items having distinct release and due times to minimize the required fleet size and fleet composition, the required number of additional batteries, and the total energy consumption. We continuously track the remaining battery energy of drones to determine the optimal timing for battery replacement, rather than replacing the battery at each node. Using actual drone flight test data, we employed a machine learning (ML) method to estimate the energy consumption of different drone types during flight segments for different operating parameters. We present a novel mixed-integer programming model to efficiently formulate the problem that integrates the estimated energy consumption functions from ML. We propose a new greedy heuristic (GH) algorithm and a customized genetic algorithm (GA) for solving large-scale instances of this problem faster. Results demonstrate that the GH algorithm is substantially faster than the accelerated CPLEX and the GA, while sacrificing the solution quality by a small amount. Results based on an actual blood sample delivery case study from Pendleton, Oregon, United States, show that using a mixed fleet of drones reduces the total cost and total energy consumption up to 18.18% and 28.7%, respectively, compared to using a homogeneous fleet.

29 - ENERGY PLANNING, POLICY AND ECONOMY↗

NASA NTRS1984

Analysis of cure in composites processing

Finite element analysis is a general numerical tool for solving the field equations of engineering practice, and this paper demonstrates its use in modeling the nonisothermal cure of pultruded composite material. A very simple grid is used in this case to model a narrow strip of material, and this grid is then solved using a time-stepping transient algorithm to simulate the passage of the strip along the pultruder die. As time proceeds, heat is conducted into the strip from the heated boundaries at the die walls, and cure proceeds at a rate dependent on the local temperature. The computer model can be used to minimize the time needed for sufficient cure, and helps avoid such processing errors as undercure or thermal degradation.

Aylward, L.↗

DOE OSTI2021

Smart Methane Emission Detection System Development Final Report

03 NATURAL GAS↗

DOE OSTI2021

Smart Methane Emission Detection System Development (Final Report)

Working with the Department of Energy's National Energy Technology Laboratory, Southwest Research Institute® (SwRI®) developed a system to identify methane leaks reliably, accurately, and autonomously at critical midstream sections of the natural gas distribution network in real-time for the purpose of mitigating methane emissions using Optical Gas Imaging (OGI) cameras. SwRI's Smart Leak Detection – Methane (SLED/M) adds a high degree of automation to the process of methane leak detection to minimize sources of human error, minimize response time to a leak event, and maximize midstream visibility. Furthermore, SwRI has been working towards integrating Quantitative OGI (QOGI) capabilities into this existing technology. By leveraging Deep Learning, SwRI now has the capability to estimate fugitive emission leak rates quickly and reliably, which allows operators to detect emissions, quantify leak rate, prioritize repairs, and validate the repairs in a single instrument. The next generation QOGI technology leverages the same cameras used in Leak Detection and Repair (LDAR) programs, with improvements in safety and speed for traditional quantification-based repairs, ultimately leading to less overhead cost for the operators. The goals for this research were to develop two types of models with the following goals: Run in real-time on the edge (≥ 12 Hz), Classification: Achieve less than 5% false positive detection, Classification: Achieve ≥ 95% methane plume detection rate, Regression: achieve ≤ 10 standard cubic feet per hour (scfh) prediction > 70% of the time. In order to achieve these results, multiple infrared (IR) and other sensors were investigated in tandem with the midwave IR (MWIR) OGI to provide additional information to train the underlying models. Information on atmospheric conditions including humidity, temperature, pressure, and solar radiation was provided by a weather station. Several machine learning and deep learning architectures and methods, including looking at quantized classification networks and regressions networks, were explored. As further data was collected, curated, and labeled, it allowed for more refined regressive networks to be adequately trained, leading to better insight into the true flow rates being observed. An important valuable deliverable of this research effort was the development of an advanced network which underwent multiple iterations capable of giving a continuous output. The current network has a predicted mean average percentage error (MAPE) of 12.3% just outside our target goal of 10.00%, but an accuracy of 97.78% at ±50 scfh, well within the overall goal for the Department of Energy (DOE) program. Upon closer inspection, it was observed that more than 10% of datapoints contributing to the MAPE predictions were the result of low flow rate predictions and are beyond the sensitivity of instrument measurement as a result of normal operational variation and noise.

03 NATURAL GAS↗

DOE OSTI2021

Smart Methane Emission Detection System Development (Final Report)

Working with the Department of Energy’s National Energy Technology Laboratory, Southwest Research Institute® (SwRI®) developed a system to identify methane leaks reliably, accurately, and autonomously at critical midstream sections of the natural gas distribution network in real- time for the purpose of mitigating methane emissions using Optical Gas Imaging (OGI) cameras. SwRI’s Smart Leak Detection – Methane (SLED/M) adds a high degree of automation to the process of methane leak detection to minimize sources of human error, minimize response time to a leak event, and maximize midstream visibility. Furthermore, SwRI has been working towards integrating Quantitative OGI (QOGI) capabilities into this existing technology. By leveraging Deep Learning, SwRI now has the capability to estimate fugitive emission leak rates quickly and reliably, which allows operators to detect emissions, quantify leak rate, prioritize repairs, and validate the repairs in a single instrument. The next generation QOGI technology leverages the same cameras used in Leak Detection and Repair (LDAR) programs, with improvements in safety and speed for traditional quantification-based repairs, ultimately leading to less overhead cost for the operators. The goals for this research were to develop two types of models with the following goals: 1. Run in real-time on the edge (≥ 12 Hz) 2. Classification: Achieve less than 5% false positive detection 3. Classification: Achieve ≥ 95% methane plume detection rate 4. Regression: achieve ≤ 10 standard cubic feet per hour (scfh) prediction > 70% of the time In order to achieve these results, multiple infrared (IR) and other sensors were investigated in tandem with the midwave IR (MWIR) OGI to provide additional information to train the underlying models. Information on atmospheric conditions including humidity, temperature, pressure, and solar radiation was provided by a weather station. Several machine learning and deep learning architectures and methods, including looking at quantized classification networks and regressions networks, were explored. As further data was collected, curated, and labeled, it allowed for more refined regressive networks to be adequately trained, leading to better insight into the true flow rates being observed. An important valuable deliverable of this research effort was the development of an advanced network which underwent multiple iterations capable of giving a continuous output. The current network has a predicted mean average percentage error (MAPE) of 12.3% just outside our target goal of 10.00%, but an accuracy of 97.78% at ±50 scfh, well within the overall goal for the Department of Energy (DOE) program. Upon closer inspection, it was observed that more than 10% of datapoints contributing to the MAPE predictions were the result of low flow rate predictions and are beyond the sensitivity of instrument measurement as a result of normal operational variation and noise.

03 NATURAL GAS↗

NASA NTRS1990

Assignment Of Finite Elements To Parallel Processors

Elements assigned approximately optimally to subdomains. Mapping algorithm based on simulated-annealing concept used to minimize approximate time required to perform finite-element computation on hypercube computer or other network of parallel data processors. Mapping algorithm needed when shape of domain complicated or otherwise not obvious what allocation of elements to subdomains minimizes cost of computation.

Salama, Moktar A.↗

DOE OSTI2021

An automated approach to the alignment of compound refractive lenses

Compound refractive lenses (CRLs) are established X-ray focusing optics, and are thus a key element of many beamlines at X-ray facilities, used to either to focus a beam or image a sample. While CRLs are quite established, the stack of single lens elements affords a very small numerical aperture because of the thick lens profile, making them far more difficult to align than classical optical lenses that obey the thin-lens approximation. This means that the alignment must be very precise and is highly sensitive to changes to the incident beam; aligning CRLs is thus typically treated as a time-consuming manual process. Many groups circumvent day-to-day realignment by using engineering controls (e.g. mounting optics) that sacrifice some of the beam’s focusing characteristics, i.e. spot size, or resolution. While these choices minimize setup time, there are clear disadvantages. This work presents a new automated approach to align CRLs using a simple alignment apparatus that is easy to adapt and install at different types of X-ray experiments or facilities. Our approach builds on recent CRL modeling efforts, using an approach based on the Stochastic Nelder-Mead (SNM) simplex method. We demonstrate the efficacy of this method by numerical simulation and then extend this test to real experiments conducted at the Advanced Photon Source to confirm its performance with a synchrotron X-ray beam. This work provides an opportunity to automate key instrumentation at X-ray facilities.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

GPU-based optical simulation of the DARWIN detector

Abstract Understanding propagation of scintillation light is critical for maximizing the discovery potential of next-generation liquid xenon detectors that use dual-phase time projection chamber technology. This work describes a detailed optical simulation of the DARWIN detector implemented using Chroma, a GPU-based photon tracking framework. To evaluate the framework and to explore ways of maximizing efficiency and minimizing the time of light collection, we simulate several variations of the conventional detector design. Results of these selected studies are presented. More generally, we conclude that the approach used in this work allows one to investigate alternative designs faster and in more detail than using conventional Geant4 optical simulations, making it an attractive tool to guide the development of the ultimate liquid xenon observatory.

Instruments & Instrumentation↗

DOE OSTI2023

Network-Wide Traffic Signal Control Using Bilinear System Modeling and Adaptive Optimization

This study proposes a new multi-input multi-output optimal bilinear signal control method in which a bilinear dynamic model approximation is used to capture the nonlinear dynamics of the urban traffic networks. With signal green time splits as the control input and traffic delay changes as the output for each intersections in the network, a bilinear system model was developed, which, on the basis of linear system modeling, takes interactions among traffic delays and signal timing splits into consideration. Based on the bilinear system modeling framework, we conducted two steps in each time interval to derive traffic control strategies: (1) we used the normalized least-squared algorithm to estimate system parameters; and (2) we solved an online optimization problem to obtain the updated traffic control inputs for the signal timing that minimizes future traffic delays. We evaluated the proposed method in a microscopic traffic simulation environment (VISSIM) with a 35-intersection network of Bellevue city in Washington. Two different traffic demand patterns: (1) normal traffic demands; and (2) time-varying traffic demands were simulated to compare the performance of different control strategies. Experimental results show that (1) the proposed bilinear system model can better describe traffic system dynamics than linear-model based methods, such as our previously developed linear-quadratic regulator control; and (2) the proposed method outperforms the state-of-the-art signal control strategies, namely the max-pressure and the self-organizing traffic light control methods. We have also shown that the proposed method is applicable to all other possible network layouts and signal controller phasing structures.

42 ENGINEERING↗

NASA NTRS2000

Longitudinal-Mode Combustion Instabilities: Modeling and Experiments

Combustion instabilities can lead to increased development time and cost for aeroengine gas turbines. This problem has been evident in the development of very-low emissions stationary gas turbines, and will likely be encountered in the newer, more aggressive aeroengine designs. In order to minimize development time and cost, it is imperative that potential combustion dynamics issues be resolved using analyses and smaller-scale experimentation. This paper discusses a methodology through which a problem in a full-scale engine was replicated in a single-nozzle laboratory combustor. Specifically, this approach is valid for longitudinal and "bulk" mode combustion instabilities. An explanation and partial validation of the acoustic analyses that were used to achieve this replication are also included. This approach yields a testbed for the diagnosis of combustion dynamics problems and for their solution through passive and active control techniques.

Cohen, J. M.↗

NASA NTRS2014

5.8kV SiC PiN Diode for Switching of High-Efficiency Inductive Pulsed Plasma Thruster Circuits

Inductive Pulsed Plasma Thruster (IPPT) pulse circuits, such as those needed to operate the Pulsed Inductive Thruster (PIT), are required to quickly switch capacitor banks operating at a period of μs while conducting current at levels on the order of at least 10 kA. [1,2] For all iterations of the PIT to date, spark gaps have been used to discharge the capacitor bank through an inductive coil. Recent availability of fast, high‐power solid state switching devices makes it possible to consider the use of semiconductor switches in modern IPPTs. In addition, novel pre‐ionization schemes have led to a reduction in discharge energy per pulse for electric thrusters of this type, relaxing the switching requirements for these thrusters. [3,4] Solid state switches offer the advantage of greater controllability and reliability, as well as decreased drive circuit dimensions and mass relative to spark gap switches. The use of solid state devices such as Integrated Gate Bipolar Transistors (IGBTs), Gate Turn‐off Thyristors (GTOs) and Silicon‐Controlled Rectifiers (SCRs) often involves the use of power diodes. These semiconductor devices may be connected antiparallel to the switch for protection from reverse current, or used to reduce power loss in a circuit by clamping off current ringing. In each case, higher circuit efficiency may be achieved by using a diode that is able to transition, or 'switch,' from the forward conducting state ('on' state) to the reverse blocking state ('off' state) in the shortest amount of time, thereby minimizing current ringing and switching losses. Silicon Carbide (SiC) PiN diodes offer significant advantages to conventional fast‐switching Silicon (Si) diodes for high power and fast switching applications. A wider band gap results in a breakdown voltage 10 times that of Si, so that a SiC device may have a thinner drift region for a given blocking voltage. [5] This leads to smaller, lighter devices for high voltage applications, as well as reduced forward conduction losses, faster reverse recovery time (faster turn‐off), and lower‐magnitude reverse recovery current. In addition, SiC devices have lower leakage current as compared to their Si counterparts, and a high thermal conductivity, potentially allowing the former to operate at higher temperatures with a smaller, lighter heatsink (or no heatsink at all).

Toftul, Alexandra↗

NASA NTRS1990

Multiple Access Trade Study

The Personal Access Satellite System (PASS) strawman design uses a hybrid Time Division Multiple Access (TDMA)/Frequency Division Multiple Access (FDMA) implementation. TDMA is used for the forward direction (from Suppliers to Users), and FDMA for the return direction (from Users to Suppliers). An alternative architecture is proposed that will require minimal real time coordination and yet provide a fast access method by using random access Code Division Multiple Access (CDMA). The CDMA system issues are addressed such as connecting suppliers and users, both of whom may be located anywhere in the CONUS, when the user terminals are constrained in size and weight; and providing efficient traffic routing under highly variable traffic requirements. It is assumed that bandwidth efficiency is not of paramount importance. CDMA or Spread Spectrum Multiple Access (SSMA) communication is a method in which a group of carriers operate at the same nominal center frequency but are separable from each other by the low cross correlation of the spreading codes used. Interference and multipath rejection capability, ease of selective addressing and message screening, low density power spectra for signal hiding and security, and high resolution ranging are among the benefits of spread spectrum communications.

Motamedi, Masoud↗

NASA NTRS1972

V/STOL tilt rotor aircraft study. Volume 1: Conceptual design of useful military and/or commercial aircraft

The conceptual designs of four useful tilt-rotor aircraft for the 1975 to 1980 time period are presented. Parametric studies leading to design point selection are described, and the characteristics and capabilities of each configuration are presented. An assessment is made of current technology status, and additional tilt-rotor research programs are recommended to minimize the time, cost, and risk of development of these vehicles.

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