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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.

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At least 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

Transportable Payload Operations Control Center reusable software: Building blocks for quality ground data systems

The Mission Operations Division (MOD) at Goddard Space Flight Center builds Mission Operations Centers which are used by Flight Operations Teams to monitor and control satellites. Reducing system life cycle costs through software reuse has always been a priority of the MOD. The MOD's Transportable Payload Operations Control Center development team established an extensive library of 14 subsystems with over 100,000 delivered source instructions of reusable, generic software components. Nine TPOCC-based control centers to date support 11 satellites and achieved an average software reuse level of more than 75 percent. This paper shares experiences of how the TPOCC building blocks were developed and how building block developer's, mission development teams, and users are all part of the process.

Mahmot, Ron↗
NASA NTRS1989

Advanced power system protection and incipient fault detection and protection of spaceborne power systems

This research concentrated on the application of advanced signal processing, expert system, and digital technologies for the detection and control of low grade, incipient faults on spaceborne power systems. The researchers have considerable experience in the application of advanced digital technologies and the protection of terrestrial power systems. This experience was used in the current contracts to develop new approaches for protecting the electrical distribution system in spaceborne applications. The project was divided into three distinct areas: (1) investigate the applicability of fault detection algorithms developed for terrestrial power systems to the detection of faults in spaceborne systems; (2) investigate the digital hardware and architectures required to monitor and control spaceborne power systems with full capability to implement new detection and diagnostic algorithms; and (3) develop a real-time expert operating system for implementing diagnostic and protection algorithms. Significant progress has been made in each of the above areas. Several terrestrial fault detection algorithms were modified to better adapt to spaceborne power system environments. Several digital architectures were developed and evaluated in light of the fault detection algorithms.

Russell, B. Don↗
NASA NTRS1991

Geostationary Operational Environmental Satellite (GOES-N report). Volume 1: Technical

The GOES-N study consisted of five distinct tasks including: determining replication costs of GOES I-M and GOES-7 in the GOES-N time frame; defining and evaluating modifications to GOES I-M to improve efficiency or reduce costs; and defining evolutionary changes to the GOES I-M design to satisfy National Weather Service (NWS) 1983 and NOAA 1989 requirements. The categorization and disposition of NOAA requirements is reported in volume 1 section 4. Results of the GOES I-M efficiency/cost improvement modifications study are described in Section 7.1. The system concept options 1, 2, and 3 that generally represent the results of the Task 2, 3A, and 3B studies are summarized in Section 7.2. Another result of the GOES-N study, the determination of which NWS 1983 and NOAA 1989 requirements can be met with the three options, is contained in volume 1 section 7. Conclusions and recommendations are covered in volume 1 section 8. Imager, sounder, control system, space environment monitor, search and rescue, weather facsimile, data collection system, and products/process/communications recommendations were extracted from sections 9, 10, and 11. Section 8 also contains conclusions pertaining to programmatic operational satellite issues (prerequisite development strategies, the direct procurement of instruments by the government, protoflight mission, etc.). Sections 9, 10, and 11 address instrument, control system, image/navigation/registration, and other system design considerations and surveys. These sections are supported by the appendices in volume 2.

Source record↗
DOE OSTI2022

Reinforcement Learning for Intelligent Building Energy Management System Control *

A building energy management system (BEMS) is a computer-based system designed to monitor and control a building's energy needs. Modern BEMS rely on the sensing and connectivity capabilities of Internet of Things (IoT) technology to intelligently adjust the energy consumption to reduce cost while respecting the consumers' preferences. Increasingly, control decisions are made based on predictions by models trained using supervised machine learning methods, which still requires control policies to be formulated in a rule-based fashion. When using reinforcement learning (RL) instead, control policies are learned by observing the utility in terms of cost and comfort associated with actions such as a change in the heating system's setpoint. The resulting RL-based controllers can capture not only the dynamics of the building and the associated electrical devices, but also fluctuations in electricity prices and user demand, avoiding the need to combine multiple predictive models with tailored control policies. This chapter will provide an overview of RL-based approaches for BEMS. After sketching the taxonomy of general RL methods, we discuss the implications of relying on the individual methods in a BEMS context. Existing work applying RL is presented along the key devices controlled by BEMS systems. Finally, we summarize the state-of-the-art and sketch limitations and open research directions.

Kotevska, Olivera↗
DOE OSTI2014

Fukushima Daiichi—A Case Study for BWR Instrumentation and Control Systems Performance During a Severe Accident, Rev 1

This report provides an assessment of the performance of various instrumentation and control (I&C) and monitoring systems, with a focus on the first few days of the Fukushima Daiichi accident starting on March 11, 2011. Similar to what occurred at the Three Mile Island Unit 2 (TMI-2) accident in 1979, operator responses were challenged when information on key parameters was lost, erroneous, or misleading. While numerous improvements in instrumentation capabilities have been made since the TMI-2 event, the accident at Fukushima Daiichi suggests the potential for further improvement. The objective of this research is to review the performance of the Fukushima Daiichi Units 1–3 instrumentation systems that could have helped ensure that plant operators and emergency responders had relevant, accurate, and timely knowledge of plant conditions as they managed the accident response. As was the case with TMI-2, forensic analyses of plant instrumentation failures will be a long-term effort. As an introduction to this long-term research, this report focuses on the information available in the control room during the accident and illustrates the challenges to operators in their understanding of plant conditions during the progression of the accident. I&C systems monitor the success or failure of achievement of light water reactor safety principles in which the protection of the reactor fuel, reactor coolant system, and reactor containment is accomplished. This report (1) provides an overview of the safety principles; (2) notes parameters associated with the boiling water reactor (BWR)/3 and BWR/4 protection, actuation, and monitoring systems as they relate to the achievement of these principles; (3) presents data from past research on predicted instrumentation performance during severe accidents, and (4) assesses performance of the key I&C and monitoring systems associated with BWR/3 and BWR/4 reactors like those at Fukushima Daiichi. Lessons learned or potential improvements associated with the performance of these systems in the context of severe accidents are delineated. As background information, an overview of key BWR/3- and BWR/4-Mark I reactor designs (BWR/3 and BWR/4 with a Mark I containment) reactor protection, engineered safety features actuation, and accident monitoring systems is provided. A brief description of plant safety systems used to prevent or mitigate reactor accidents is also provided. This background information provides context for the brief discussion of previous research that has been conducted on BWR severe accidents and for the discussion of the accident sequences, which led to core damage at Fukushima Daiichi Units 1–3. The progression of the accident sequences at Fukushima Daiichi Units 1–3 occurred very much as predicted in prior severe accident research conducted in the United States and internationally, given the complete station blackout that occurred when the tsunami generated by one of the most powerful earthquakes ever recorded rolled onshore.

Accident Tolerant Equipment↗
NASA NTRS1993

Toward an embedded training tool for Deep Space Network operations

There are three issues to consider when building an embedded training system for a task domain involving the operation of complex equipment: (1) how skill is acquired in the task domain; (2) how the training system should be designed to assist in the acquisition of the skill, and more specifically, how an intelligent tutor could aid in learning; and (3) whether it is feasible to incorporate the resulting training system into the operational environment. This paper describes how these issues have been addressed in a prototype training system that was developed for operations in NASA's Deep Space Network (DSN). The first two issues were addressed by building an executable cognitive model of problem solving and skill acquisition of the task domain and then using the model to design an intelligent tutor. The cognitive model was developed in Soar for the DSN's Link Monitor and Control (LMC) system; it led to several insights about learning in the task domain that were used to design an intelligent tutor called REACT that implements a method called 'impasse-driven tutoring'. REACT is one component of the LMC training system, which also includes a communications link simulator and a graphical user interface. A pilot study of the LMC training system indicates that REACT shows promise as an effective way for helping operators to quickly acquire expert skills.

Hill, Randall W., Jr.↗
NASA NTRS1998

Flight Tests on a Fiber Optic Temperature Sensor

For aircraft engine control, one key parameter to detect on an airplane is the exhaust gas temperature (EGT). Presently, thermocouples are used to perform this measurement. These electrical sensors perform adequately; however, fully utilizing the benefits of optical sensors requires replacing electrical architectures with optical architectures. Part of this requires replacing electrical sensors with optical sensors, such as the EGT sensor chosen for these tests. The objective of the development and testing of this prototype sensor system was to determine the feasibility of operating an optical sensor in a hostile aircraft environment. The fiber optic sensor system was developed to measure temperatures from 20C to 600C in an aircraft environment and was utilized to monitor the EGT of an OV-10D aircraft engine. The sensor has successfully flown over 50 hours and proven to be immune to surface deterioration of the optical element (located inside the sensor head) and able to withstand and operate in normal and sustained severe flight conditions where forces on the airplane exceeded 4 g's. Potential commercial uses for this sensor include monitoring temperature for aeropropulsion system control, military vehicle and naval engine control, conventional and nuclear power plant monitoring and industrial plan monitoring where EMI issues are critical.

Tuma, Margaret L.↗
NASA NTRS2014

Use of Semi-Autonomous Tools for ISS Commanding and Monitoring

As the International Space Station (ISS) has moved into a utilization phase, operations have shifted to become more ground-based with fewer mission control personnel monitoring and commanding multiple ISS systems. This shift to fewer people monitoring more systems has prompted use of semi-autonomous console tools in the ISS Mission Control Center (MCC) to help flight controllers command and monitor the ISS. These console tools perform routine operational procedures while keeping the human operator "in the loop" to monitor and intervene when off-nominal events arise. Two such tools, the Pre-positioned Load (PPL) Loader and Automatic Operators Recorder Manager (AutoORM), are used by the ISS Communications RF Onboard Networks Utilization Specialist (CRONUS) flight control position. CRONUS is responsible for simultaneously commanding and monitoring the ISS Command & Data Handling (C&DH) and Communications and Tracking (C&T) systems. PPL Loader is used to uplink small pieces of frequently changed software data tables, called PPLs, to ISS computers to support different ISS operations. In order to uplink a PPL, a data load command must be built that contains multiple user-input fields. Next, a multiple step commanding and verification procedure must be performed to enable an onboard computer for software uplink, uplink the PPL, verify the PPL has incorporated correctly, and disable the computer for software uplink. PPL Loader provides different levels of automation in both building and uplinking these commands. In its manual mode, PPL Loader automatically builds the PPL data load commands but allows the flight controller to verify and save the commands for future uplink. In its auto mode, PPL Loader automatically builds the PPL data load commands for flight controller verification, but automatically performs the PPL uplink procedure by sending commands and performing verification checks while notifying CRONUS of procedure step completion. If an off-nominal condition occurs during procedure execution, PPL Loader notifies CRONUS through popup messages, allowing CRONUS to examine the situation and choose an option of how PPL loader should proceed with the procedure. The use of PPL Loader to perform frequent, routine PPL uplinks offloads CRONUS to better monitor two ISS systems. It also reduces procedure performance time and decreases risk of command errors. AutoORM identifies ISS communication outage periods and builds commands to lock, playback, and unlock ISS Operations Recorder files. Operation Recorder files are circular buffer files of continually recorded ISS telemetry data. Sections of these files can be locked from further writing, be played back to capture telemetry data that occurred during an ISS loss of signal (LOS) period, and then be unlocked for future recording use. Downlinked Operation Recorder files are used by mission support teams for data analysis, especially if failures occur during LOS. The commands to lock, playback, and unlock Operations Recorder files are encompassed in three different operational procedures and contain multiple user-input fields. AutoORM provides different levels of automation for building and uplinking the commands to lock, playback, and unlock Operations Recorder files. In its automatic mode, AutoORM automatically detects ISS LOS periods, then generates and uplinks the commands to lock, playback, and unlock Operations Recorder files when MCC regains signal with ISS. AutoORM also features semi-autonomous and manual modes which integrate CRONUS more into the command verification and uplink process. AutoORMs ability to automatically detect ISS LOS periods and build the necessary commands to preserve, playback, and release recorded telemetry data greatly offloads CRONUS to perform more high-level cognitive tasks, such as mission planning and anomaly troubleshooting. Additionally, since Operations Recorder commands contain numerical time input fields which are tedious for a human to manually build, AutoORM's ability to automatically build commands reduces operational command errors. PPL Loader and AutoORM demonstrate principles of semi-autonomous operational tools that will benefit future space mission operations. Both tools employ different levels of automation to perform simple and routine procedures, thereby offloading human operators to perform higher-level cognitive tasks. Because both tools provide procedure execution status and highlight off-nominal indications, the flight controller is able to intervene during procedure execution if needed. Semi-autonomous tools and systems that can perform routine procedures, yet keep human operators informed of execution, will be essential in future long-duration missions where the onboard crew will be solely responsible for spacecraft monitoring and control.

Brzezinski, Amy S.↗
NASA NTRS1993

Model-based reasoning for power system management using KATE and the SSM/PMAD

The overall goal of this research effort has been the development of a software system which automates tasks related to monitoring and controlling electrical power distribution in spacecraft electrical power systems. The resulting software system is called the Intelligent Power Controller (IPC). The specific tasks performed by the IPC include continuous monitoring of the flow of power from a source to a set of loads, fast detection of anomalous behavior indicating a fault to one of the components of the distribution systems, generation of diagnosis (explanation) of anomalous behavior, isolation of faulty object from remainder of system, and maintenance of flow of power to critical loads and systems (e.g. life-support) despite fault conditions being present (recovery). The IPC system has evolved out of KATE (Knowledge-based Autonomous Test Engineer), developed at NASA-KSC. KATE consists of a set of software tools for developing and applying structure and behavior models to monitoring, diagnostic, and control applications.

Morris, Robert A.↗
NASA NTRS2004

Stroboscope Controller for Imaging Helicopter Rotors

A versatile electronic timing-and-control unit, denoted a rotorcraft strobe controller, has been developed for use in controlling stroboscopes, lasers, video cameras, and other instruments for capturing still images of rotating machine parts especially helicopter rotors. This unit is designed to be compatible with a variety of sources of input shaftangle or timing signals and to be capable of generating a variety of output signals suitable for triggering instruments characterized by different input-signal specifications. It is also designed to be flexible and reconfigurable in that it can be modified and updated through changes in its control software, without need to change its hardware. Figure 1 is a block diagram of the rotorcraft strobe controller. The control processor is a high-density complementary metal oxide semiconductor, singlechip 8-bit microcontroller. It is connected to a 32K x 8 nonvolatile static random-access memory (RAM) module. Also connected to the control processor is a 32K 8 electrically programmable read-only-memory (EPROM) module, which is used to store the control software. Digital logic support circuitry is implemented in a field-programmable gate array (FPGA). A 240 x 128-dot, 40- character 16-line liquid-crystal display (LCD) module serves as a graphical user interface; the user provides input through a 16-key keypad mounted next to the LCD. A 12-bit digital-to-analog converter (DAC) generates a 0-to-10-V ramp output signal used as part of a rotor-blade monitoring system, while the control processor generates all the appropriate strobing signals. Optocouplers are used to isolate all input and output digital signals, and optoisolators are used to isolate all analog signals. The unit is designed to fit inside a 19-in. (.48-cm) rack-mount enclosure. Electronic components are mounted on a custom printed-circuit board (see Figure 2). Two power-conversion modules on the printedcircuit board convert AC power to +5 VDC and 15 VDC, respectively.

Jensen, Scott↗
DOE OSTI2025

Human Factors Considerations for Anticipated Uses of Autonomy in Advanced Reactor Technologies

The objective of this report is examine anticipated advanced reactor designs that incorporate high levels of autonomy in operation of advanced reactor systems or facilities, and to begin identifying the human factors engineering (HFE) considerations relevant to those designs. The insights gained will help identify areas where U.S. Nuclear Regulatory Commission (NRC) may need to develop or update review guidance to adequately address HFE considerations essential for safe operation. The adequacy of existing guidance, along with recommendations for updates or new guidance, may be addressed in future reports. We use the term “landscape analysis” to describe the review, analysis, and characterization of publicly available information on the anticipated future state of the industry regarding particular technologies or operational concepts. This landscape analysis considers the potential range of advanced reactor automation concepts that may be used for monitoring and controlling nuclear systems and facilities, including the potential for highly automated, near autonomous, or fully autonomous operation of advanced nuclear reactors.

99 - GENERAL AND MISCELLANEOUS↗
NASA NTRS2009

Monitoring and Controlling an Underwater Robotic Arm

The SSRMS Module 1 software is part of a system for monitoring an adaptive, closed-loop control of the motions of a robotic arm in NASA s Neutral Buoyancy Laboratory, where buoyancy in a pool of water is used to simulate the weightlessness of outer space. This software is so named because the robot arm is a replica of the Space Shuttle Remote Manipulator System (SSRMS). This software is distributed, running on remote joint processors (RJPs), each of which is mounted in a hydraulic actuator comprising the joint of the robotic arm and communicating with a poolside processor denoted the Direct Control Rack (DCR). Each RJP executes the feedback joint-motion control algorithm for its joint and communicates with the DCR. The DCR receives joint-angular-velocity commands either locally from an operator or remotely from computers that simulate the flight like SSRMS and perform coordinated motion calculations based on hand-controller inputs. The received commands are checked for validity before they are transmitted to the RJPs. The DCR software generates a display of the statuses of the RJPs for the DCR operator and can shut down the hydraulic pump when excessive joint-angle error or failure of a RJP is detected.

Haas, John↗
DOE OSTI2019

Automated control of microfluidic devices based on machine learning

A system is provided to automatically monitor and control the operation of a microfluidic device using machine learning technology. The system receives images of a channel of a microfluidic device collected by a camera during operation of the microfluidic device. Upon receiving an image, the system applies a classifier to the image to classify the operation of the microfluidic device as normal, in which no adjustment to the operation is needed, or as abnormal, in which an adjustment to the operation is needed. When an image is classified as normal, the system may make no adjustment to the microfluidic device. If, however, an image is classified as abnormal, the system may output an indication that the operation is abnormal, output an indication of a needed adjustment, or control the microfluidic device to make the needed adjustment.

42 ENGINEERING↗
DOE OSTI2021

Automated control of microfluidic devices based on machine learning

A system is provided to automatically monitor and control the operation of a microfluidic device using machine learning technology. The system receives images of a channel of a microfluidic device collected by a camera during operation of the microfluidic device. Upon receiving an image, the system applies a classifier to the image to classify the operation of the microfluidic device as normal, in which no adjustment to the operation is needed, or as abnormal, in which an adjustment to the operation is needed. When an image is classified as normal, the system may make no adjustment to the microfluidic device. If, however, an image is classified as abnormal, the system may output an indication that the operation is abnormal, output an indication of a needed adjustment, or control the microfluidic device to make the needed adjustment.

Giera, Brian↗
DOE OSTI2015

The Machine Protection System for the Fermilab Accelerator Science and Technology Facility

The Machine Protection System (MPS) for the Fermilab Accelerator Science and Technology Facility (FAST) has been implemented and tested. The system receives signals from several subsystems and devices which conveys the relevant status needed to the safely operate the accelerator. Logic decisions are made based on these inputs and some predefined user settings which in turn controls the gate signal to the laser of the photo injector. The inputs of the system have a wide variety of signal types, encoding methods and urgencies for which the system is designed to accommodate. The MPS receives fast shutdown (FSD) signals generated by the beam loss system and inhibits the beam or reduces the beam intensity within a macropulse when the beam losses at several places along the accelerator beam line are higher than acceptable values. TTL or relay contact signals from the vacuum system, toroids, magnet systems etc., are chosen with polarities that ensure safe operation of the accelerator from unintended events such as cable disconnection in the harsh industrial environment of the experimental hall. A RS422 serial communication scheme is used to interface the operation permit generator module and a large number of movable devices each reporting multi-bit status. The system also supports operations at user defined lower beam levels for system conunissioning. The machine protection system is implemented with two commercially available off-the-shelf VMEbus based modules with on board FPGA devices. The system is monitored and controlled via the VMEbus by a single board CPU

Wu, Jinyuan↗
NASA NTRS1988

Feasibility of vibration monitoring of small rotating machines for the environmental control and life support systems (ECLSS) of the NASA advanced space craft

The application of vibration monitoring to the rotating machinery typical of ECLSS components in advanced NASA spacecraft was studied. It is found that the weighted summation of the accelerometer power spectrum is the most successful detection scheme for a majority of problem types. Other detection schemes studied included high-frequency demodulation, cepstrum, clustering, and amplitude processing.

Milner, G. Martin↗