SEARCH · Search NASA
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Operation of check valve improved by redesigning poppet and adding two new components: dashpot and dashpot ring between dashpot and poppet. Modifications eliminate self-sustaining poppet oscillations that cause premature wear and jamming of poppet mechanism.
A self-consistent empirical model for the major plasma parameters in a solar-minimum coronal hole was developed using the ultraviolet coronagraph spectrometer (UVCS) operating onboard the Solar and Heliospheric Observatory (SOHO). The radial and latitudinal distribution of density, velocity, and kinetic temperature for electrons, neutral hydrogen, and ionized oxygen was obtained. The data were acquired during November 1996 and April 1997. The model provides experimental values which can be used to constrain theoretical models of the fast solar wind. The implications on various models of coronal heating and acceleration are discussed.
NASA and the U.S. Department of Defense are conducting programs which support the future vision of "intelligent" aircraft engines for enhancing the affordability, performance, operability, safety, and reliability of aircraft propulsion systems. Intelligent engines will have advanced control and health management capabilities enabling these engines to be self-diagnostic, self-prognostic, and adaptive to optimize performance based upon the current condition of the engine or the current mission of the vehicle. Sensors are a critical technology necessary to enable the intelligent engine vision as they are relied upon to accurately collect the data required for engine control and health management. This paper reviews the anticipated sensor requirements to support the future vision of intelligent engines from a control and health management perspective. Propulsion control and health management technologies are discussed in the broad areas of active component controls, propulsion health management and distributed controls. In each of these three areas individual technologies will be described, input parameters necessary for control feedback or health management will be discussed, and sensor performance specifications for measuring these parameters will be summarized.
During 2015, as part of our ongoing research on crew autonomy, our team adapted and extended the plan viewing tool, Playbook, into a mobile tool for exploring crew autonomy onboard the International Space Station (ISS). This work enables crewmembers to intuitively re-plan and schedule on their own with limited input from mission control. Designed for ease of use, re-planning is composed of simple drag and drop interactions and learning the tool requires little to no training. Playbook was successfully uplinked onto ISS as a technology demonstration and completed its onboard ground and crew checkouts in August and September 2015, respectively. We will discuss and describe how Playbook was able to integrate with actual ISS operational plans and operational procedures without disrupting real mission operations. This work will also outline the design and technical constraints required to build a collaborative tool that allows multiple users to simultaneously self-schedule and synchronize during communication dropouts in the restricted networking and computing environment onboard ISS. Finally, we will explore the aspects of crew autonomy that this technology enables as well as discuss our next steps and possible extensions.
Apparatus and methods are disclosed for coordination of a population of Thermostatically Controlled Loads (TCLs) with unknown parameters to achieve group objectives including bidding and market clearing strategies designed to motivate self-interested users to realize efficient energy allocation subject to a peak power constraint. In one examples of the disclosed technology, a method of operating a load includes estimating a set of values for unmeasured parameters of the load's thermal environment based on output measurements of the thermal environment, determining an energy response based on the estimated set of values for the unmeasured parameters, and transmitting a bid for power for a finite time period based on the determined energy response to the coordinator. A clearing price is received from the coordinator responsive to the transmitted bid and power is sent to the load responsive to the received clearing price.
To establish a technical basis for self-regulating microreactors, a model predictive control (MPC) system is investigated to proactively respond to anomalies and disturbances in anticipation of potential deviations from operating setpoints. Due to the difficulty of developing a physics-based surrogate model that can accurately match plant data in various operating conditions, machine learning algorithms are used in MPC, which allow for learning from both simulation and operation data, thus efficiently describing the targeted transient with arbitrary accuracy. However, one of the biggest concerns in applying ML algorithms like artificial neural networks (ANNs) is that the predictive capabilities of ANN are limited by training data. If there are gaps between the training and target domain, the accuracy of an ANN can degrade significantly when it is used to predict unseen data. To improve the predictive capability of ANN and enable a confident use of data-driven MPCs outside the training data, this study proposes an adaptive data-driven MPC framework. The system will monitor the discrepancy between plant responses and surrogate predictions, fine-tune the ANN-based surrogate when a large discrepancy is detected, and continue MPC operation with updated surrogates. The framework is demonstrated on a point kinetic model for microreactors. The hyperparameters of the update strategy, including layers to update, error thresholds, learning rate discount, and number of data points used for fine-tuning, are optimized so the simulated microreactor is able to follow changes in setpoint with the smallest of deviations.
To establish a technical basis for self-regulating microreactors, a model predictive control (MPC) system is investigated to proactively respond to anomalies and disturbances in anticipation of potential deviations from operating setpoints. Due to the difficulty of developing a physics-based surrogate model that can accurately match plant data in various operating conditions, machine learning algorithms are used in MPC, which allow for learning from both simulation and operation data, thus efficiently describing the targeted transient with arbitrary accuracy. However, one of the biggest concerns in applying ML algorithms like artificial neural networks (ANNs) is that the predictive capabilities of ANN are limited by training data. If there are gaps between the training and target domain, the accuracy of an ANN can degrade significantly when it is used to predict unseen data. To improve the predictive capability of ANN and enable a confident use of data-driven MPCs outside the training data, this study proposes an adaptive data-driven MPC framework. The system will monitor the discrepancy between plant responses and surrogate predictions, fine-tune the ANN-based surrogate when a large discrepancy is detected, and continue MPC operation with updated surrogates. The framework is demonstrated on a point kinetic model for microreactors. The hyperparameters of the update strategy, including layers to update, error thresholds, learning rate discount, and number of data points used for fine-tuning, are optimized so the simulated microreactor is able to follow changes in setpoint with the smallest of deviations.
As part of the efforts to understand the unexpected 'self-regulating' mode of the RCIC (Reactor Core Isolation Cooling) systems in Fukushima accidents and extend BWR RCIC and PWR AFW (Auxiliary Feed Water) operational range and flexibility, mechanistic models for the Terry turbine should be developed and implemented in reactor system analysis or severe accident codes. The Terry turbine has been used for all the BWR RCIC systems and for PWR AFW (Auxiliary Feed Water) systems. The Terry turbine is essentially a solid cylindrical wheel with multiple machined semi-circular 'buckets' that are shaped into the body of the wheel. Fixed nozzles and reversing chambers surround the wheel inside the turbine casing. High pressure steam is accelerated to supersonic flow inside the turbine nozzle. The kinetic energy is then converted to shaft work by the impulse force on the turbine buckets. A Fully implicit and strongly coupled RCIC system model has been developed in the RELAP-7 code and used for simplified BWR SBO (Station Black-Out) simulations in the past. In this model, a generic turbine model was used to conserve mass and energy while turbine operation characteristic curves were used to obtain non-dimensional mass flow rate and thermal efficiency. This model could be used for simulating RCIC off-design behavior if off-design operation characteristic curves were available. However, No such curves currently exist for the Terry turbine system due to its unique pure impulse design. A set of mechanistic Terry turbine models suitable for system code implementation has recently been developed by Sandia National Labs. In the Sandia model, the turbine bucket inlet velocity is provided according to a reduced-order model which was obtained from a large number of CFD simulations. In this paper, we propose an alternative method, using an under-expanded jet model to obtain the velocity and thermodynamic conditions for the turbine bucket inlet. These sets of analytical models are simple and generic, and suitable for use in system analysis codes. (authors)
Explore the source record for details and available documents.
The Self-Excited Loop (SEL) architecture, used in some continuous-wave (CW) superconducting linacs, relies on a positive feedback mechanism that requires carefully defined operating limits to ensure stable operation. These limits are typically derived from amplifier calibration, which characterizes the relationship between forward power and DAC drive. However, amplifier non-linearity often prevents a simple linear fit of this characteristic, introducing errors that can compromise stability. To address this, we present a modified calibration procedure that incorporates amplifier non-linearity into the SEL framework. The approach is validated with test data from a 32 kW solid-state amplifier (SSA) and a cavity emulator developed for the Fermilab PIP-II linac.
The Automated Reconfigurable Mission Adaptive Digital Assembly Systems (ARMADAS) project, under development at NASA Ames Research Center, has demonstrated on-ground autonomous robotic assembly of extensive digital structures, and it is now moving forward towards in-space demonstration. The ARMADAS system comprises of the operation software, the operation user interface (opsUI), and a swarm of robots. The robotic system consists of a multitude of collaborative agents specifically designed to transport, place and bolt the building blocks, called voxels (volumetric pixels). This paper focuses on the bolting robot, referred to as Mobile Metamaterial Internal Co-Integrator (MMIC-I). MMIC-I is a battery-powered crawling robot. It navigates the structure through extension, contraction and gripping. Two distinct controller boards operate the robot's two symmetric modules, referred to as module A and B. Board A is the master board: it coordinates motion planning and motion primitives execution, hosts the WiFi client, performs periodic self-assessment and system idle check and triggers faults if anomalies are detected. Board B periodically sends a heartbeat to board A, through a wired communication channel that uses the Serial protocol. Additionally, board A's WiFi client receives heartbeat packet requests or motion/bolting commands from a dedicated server board, and acknowledges reception sending back a response heartbeat packet containing information about the overall robot status, e. g. electrical current and voltage values, target and actual angles, operating mode, fault status. Whenever a motion command is sent, the motion planning section of the firmware determines the current robot configuration, using Inertial Measurement Unit readings and the motors Pulse Width Modulation values. Afterwards, it calculates the list of primitives needed to reach the target state, and controls their execution in the proper order. MMIC-I can receive and execute motion and bolting commands only when it is in operational mode. MMIC-I has three operating modes: standby, operational and safed. Standby mode is automatically entered upon startup. While in standby mode, all motors are powered off, and the only accepted commands are the ones relative to a change of mode and heartbeat packet request. Fault detection causes the robot to automatically enter safed or standby mode. Whenever the detected fault occurs within a motion and requires immediate intervention, e. g. an over-current situation, the robot enters safed mode. Safed mode powers off all motors except for the locomotion module, thus preventing the robot from collapsing. Conversely, when the detected fault doesn't require immediate intervention (low battery warning, for instance), the robot enters standby mode after completing the ongoing motion. This paper provides a detailed discussion of MMIC-I's firmware architecture. It accurately describes the implementation approach for each module: sensor data reading, motor control and actuation, WiFi server-client communication, intra-boards Serial communication, operating modes and autonomous fault detection, motion planning, coordination and execution, etc. Moreover, in support of the software description, this paper includes a thorough characterization of MMIC-I's hardware and avionics.
Abstract Riming is a key process of precipitation formation in ice‐containing clouds, but quantifying riming from observations is challenging, limiting our ability to evaluate the riming process in numerical weather models. One challenge for radar observations is that riming changes both the physical properties (mass, area cross‐section) and scattering properties of ice particles. These changes need to be implemented consistently as a function of riming in radar forward operators, which are required for retrievals and model evaluation in observation space. In this study, mass–size, cross‐section area–size, and backscattering cross‐section relations are developed as a function of the normalized rime mass for aggregates composed of various monomer types (columns, dendrites, needles, plates, and rosettes). The proposed framework allows us to simulate scattering properties of aggregated ice particles consistently as a function of riming in retrievals and radar forward operators. The parameterizations are developed from a large data set of simulated rimed aggregates of different sizes and monomer crystal types. The backscattering cross‐section parameterization (the “riming‐dependent parameterization”) is evaluated for radar frequencies of 35.6 and 94.0 GHz and is based on the Self‐Similar Rayleigh–Gans approximation (SSRGA), which is increasingly used to calculate microwave scattering of ice crystals and snowflakes. Compared with parameterizations from the literature that do not consider riming, the riming‐dependent parameterization leads to significantly smaller biases in terms of backscattering cross‐section. When using the particle masses and scattering properties of the individual particles simulated by the aggregation and riming model as a reference, the bias of our parameterization is below 1 dB when integrating over an exponential particle size distribution with sizes from 0.1–10 mm.
The common vulnerabilities and exposures (CVE) database was created with a mission to ``identify, define, and catalog publicly disclosed cybersecurity vulnerabilities''. This rich body of information can be used to enable rapid and efficient response to secure and defend cyber operations and protect critical cyber infrastructure. The main goal of this paper is to develop a visual analytics tool to enable deep analysis of CVEs using unsupervised clustering techniques. We enhance our analysis by first mapping CVEs to hierarchical-classes in Common Weakness Enumeration (CWE) using information in the National Vulnerability Database (NVD). Both the mapping and the numerical representation of CVEs are enabled by V2W-BERT, which uses natural language processing of the extensive information in NVD to generate a large tabular database of 137,226 CVE entries from 1999 to 2020, where each CVE is represented by a vector of 768 numerical features. The vectorized data is processed by Self-Organizing Maps (SOM), which is an unsupervised machine learning technique for dimensionality reduction, visual representation and clustering. Using a Torus map of 6417 units, we achieve ~10-fold data compression of ~140k CVEs using SOM. The trained map is further clustered using standard K-means clustering into 138 clusters of CVEs. We conducted a brief investigation of the rich mapping of CVEs to best-matching-units to K-means clusters, as well as CVEs to CWEs. For example, this novel mapping provided insight into the role of CWE-59 and CWE-264 in several CVEs that is otherwise hard to explore in the original data. We conclude that our this novel approach will not only enable deep analysis of the complex relationships between CVEs and CWEs, but also a mechanism to quickly respond to and design mitigation actions for rapidly evolving vulnerabilities that have not been mapped to existing CWEs.
An integrated control and electromagnetic/antenna formulation is presented for evaluating the performance of a distributed antenna system as a function of formation geometry. A distributed and self-organizing control law for the control law for the control of multiple antennas in Low Earth Orbit (LEO) is presented. The control system provides collaborative commanding and performance optimization to configure and operate the distributed formation system. A large aperture antenna is thereby realized by a collection of miniature sparse antennas in formation. A case study consisting of a simulation of four antennas in Low Earth orbit (LEO)is presented to demonstrate the concept.
A diagnostic system, which has a design goal of high-portability, has been designed at Oak Ridge National Laboratory (ORNL). This project aims at providing measurements of key plasma parameters (ne, Te, ni, Ti) for fusion-relevant devices, utilizing Thomson scattering (TS) and optical emission spectroscopy (OES). The innovative design employs mostly commercial off-the-shelf instrumentation and a traveling team of researchers to conduct measurements at various magnetic-confinement plasma devices. The TS diagnostic uses a Quantel Q-smart 1500 Nd:YAG laser with a 2ω harmonic generator to produce up to 850 mJ of 532 nm laser pulses at 10 Hz. Collection optics placed at the detection port consists of an 11 × 3 optical fiber bundle, where the TS diagnostic uses an 11 × 1 subset array of the fibers, the OES diagnostic uses another 11 fibers, and the remaining fibers are available to the host institution. The detection system is comprised of two separate IsoPlane-320 spectrometers with triple-grating turrets of various line spacing and two PI-MAX 4 intensified CCD detectors, used simultaneously to measure a broad range of ion, impurity, and electron parameters. The self-contained diagnostic package also includes a data processing and storage system. The design and initial implementation of the TS-OES diagnostic system are described. The experiments from the proof-of-principle operation of the portable package on a high density (∼2.5 × 1022 m−3) and low-temperature (∼5 eV) electrothermal arc source at ORNL are also discussed.
Recently, the maximally-helicity-violating four-point form factor for the chiral stress-energy tensor in planar $\mathcal{N}$ = 4 super Yang-Mills was computed to three loops at the level of the symbol associated with multiple polylogarithms. It exhibits antipodal self-duality, or invariance under the combined action of a kinematic map and reversing the ordering of letters in the symbol. Here we lift the two-loop form factor from symbol level to function level. We provide an iterated representation of the function’s derivatives (coproducts). In order to do so, we find a three-parameter limit of the five-parameter phase space where the symbol’s letters are all rational. We also use function-level information about dihedral symmetries and the soft, collinear, and factorization limits, as well as limits governed by the form-factor operator product expansion (FFOPE). We provide plots of the remainder function on several kinematic slices, and show that the result is compatible with the FFOPE data. We further verify that antipodal self-duality is valid at two loops beyond the level of the symbol.
We demonstrate an efficient extreme ultraviolet (EUV) source for operation at {lambda} = 6.7 nm by optimizing the optical thickness of gadolinium (Gd) plasmas. Using low initial density Gd targets and dual laser pulse irradiation, we observed a maximum EUV conversion efficiency (CE) of 0.54% for 0.6% bandwidth (BW) (1.8% for 2% BW), which is 1.6 times larger than the 0.33% (0.6% BW) CE produced from a solid density target. Enhancement of the EUV CE by use of a low-density plasma is attributed to the reduction of self-absorption effects.
Proposed two-arm robotic manipulator capable of changing its mechanical structure to fit given task. No reassembly or replacement in conventional sense: instead, arms commanded during operation to assume any of number of alternative configurations, described briefly in "Dynamic Coordination of a Two-Arm Robotic Manipulator" (NPO-18816), NASA Tech Briefs.