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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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A cryogenic liquid distribution system suitable for the reaction control system on space shuttles is described. The system thermodynamics, operation, performance and weight analysis are discussed along with the design, maintenance and integration concepts.
Supervisory control systems must deal with various types of intelligence distributed throughout the layers of control. Typical layers are real-time servo control, off-line planning and reasoning subsystems and finally, the human operator. Design methodologies must account for the fact that the majority of the intelligence will reside with the human operator. Hierarchical decompositions and feedback loops as conceptual building blocks that provide a common ground for man-machine interaction are discussed. Examples of types of parallelism and parallel implementation on several classes of computer architecture are also discussed.
The distributed processing concept is defined in terms of control primitives, variables, and structures and their use in performing a decomposed discrete Fourier transform (DET) application function. The design assumes interprocessor communications to be anonymous. In this scheme, all processors can access an entire common database by employing control primitives. Access to selected areas within the common database is random, enforced by a hardware lock, and determined by task and subtask pointers. This enables the number of processors to be varied in the configuration without any modifications to the control structure. Decompositional elements of the DFT application function in terms of tasks and subtasks are also described. The experimental hardware configuration consists of IMSAI 8080 chassis which are independent, 8 bit microcomputer units. These chassis are linked together to form a multiple processing system by means of a shared memory facility. This facility consists of hardware which provides a bus structure to enable up to six microcomputers to be interconnected. It provides polling and arbitration logic so that only one processor has access to shared memory at any one time.
The temporal and spatial mismatch between PV generation and electric vehicle (EV) charging and discharging may cause voltage violations in active distribution networks. Despite the widespread use of deep reinforcement learning (DRL) in power system optimization and control, it lacks guarantees on constraint satisfaction during both training and deployment. This paper proposes a Lagrangian-based safe DRL approach for model predictive control (MPC) of active distribution systems with large-scale integration of PVs, EVs, and energy storage systems (ESSs). A Transformer-LSTM time-series model is proposed to forecast EV charging demand, which is then formulated as a constraint to ensure charging requirements are met. Using this prediction, a Lagrangian-based safe soft actor-critic (SAC) framework is developed for real-time control in a three-phase unbalanced distribution system, enforcing voltage safety constraints while optimizing the cumulative net reward. By integrating the forecasting model with multi-period constraints, the proposed framework jointly coordinates PV systems, EV charging and discharging, and ESS scheduling within the MPC horizon. Numerical experiments on a modified IEEE 123-bus system with real-world data show that, under a high PV penetration scenario, the proposed method increases the net reward by 30.74% and reduces average voltage violations from 0.0011 p.u. to 0.0002 p.u. compared with standard SAC. Compared with the optimal power flow (OPF) approach, it achieves similar voltage security while yielding lower line losses. It also maintains real-time control capability, reducing operation latency to 53.21 ms per 15-minute control interval. The proposed method remains effective under varying PV/EV penetrations and load conditions.
A distributed system of proprietary engineering-class workstations is incorporated into NASA's Space Shuttle Mission-Control Center to increase the automation of mission control. The Real-Time Data System (RTDS) allows the operator to utilize expert knowledge in the display program for system modeling and evaluation. RTDS applications are reviewed including: (1) telemetry-animated communications schematics; (2) workstation displays of systems such as the Space Shuttle remote manipulator; and (3) a workstation emulation of shuttle flight instrumentation. The hard and soft real-time constraints are described including computer data acquisition, and the support techniques for the real-time expert systems include major frame buffers for logging and distribution as well as noise filtering. The incorporation of the workstations allows smaller programming teams to implement real-time telemetry systems that can improve operations and flight testing.
The Machine Learning Data Platform (MLDP) is a product providing full-stack support for data science, Machine Learning, and Artificial Intelligence (ML/AI) applications at particle accelerator and large experimental physics facilities. It supports ML/AI applications from front-end, high-speed acquisition of heterogeneous, time-series data, through data archiving and management, to back-end analysis. The MLDP embodies a “data-science ready” platform for data analysis and ML/AI applications in diagnosis, modelling, control, and optimization of these facilities. It provides data scientists and applications a consistent, datacentric interface to archive data standardizing implementation and deployment of ML/AI algorithms to different operations configurations within the same facility, or between facilities. Being an open-source, public-domain project, the MLDP is intended for broadest possible impact by increasing accessibility and minimizing the required expertise for installation and operation. The MLDP can also be deployed at user facilities for experimental data collection, archiving, and analysis. It is capable of acquisition and archiving of heterogeneous data from experimental equipment (e.g., images, arrays, structures, etc.) along with system hardware configurations (e.g., scalars, tables), control system process variables, and any metadata required for provenance. Thus, the MLDP can manage experimental data through its entire lifecycle, from acquisition and archiving, through analysis and investigation, to release and final publication.
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To support instrument and experiment operations effectively in the Space Station era, planning, scheduling, and control must allow for: (1) interactive real-time remote operations; (2) responsive scheduling and rescheduling; (3) support of the full range of distributed science, application and commercial users; (4) interaction and cooperation among distributed users; and (5) efficient use of often onboard, communications, and ground-based resources. We suggest conceptual and managerial approaches that address these needs. Specifically, we describe approaches to distributed planning, scheduling and control functions that are based on resources and on a distributed knowledge hierarchy. We describe the scheduling functions as the component of the integrated space-ground Operations Management System. We include the integration of the planning and scheduling functions with the real-time operations control system, and we discuss automated scheduling assistants. These suggested approaches, taken from the users' point-of-view, have resulted in two prototype systems: Operations and Science Instrument Support package and Science User Resource Planning and Scheduling System.
The chapter starts with a brief overview of the state-of-the-art of the sensing technologies. Principles of detection and the stage of integration in conjunction with the need are discussed. Based on the projected sensor requirements to accomplish propulsion systems with distributed active control, the existent gaps and selected types of sensor technologies are addressed, which are foreseen to deliver the required sensitivity, the resolution, the range, and the bandwidth in classes of sensors and which will allow operation under harsh environment conditions. By changing the packaging and/or design of the current sensors, operation environments of 750°C may be possible, which would meet requirements for sensors located towards the engine intake, compressor and in some cases low-pressure turbine. However, the majority of sensors for locations close to the engine combustion chamber or afterburner (with operation temperatures up to 1700°C) do not exist. There is also a need for smart sensors, which would enable future distributed control architecture. In addition a number of sensors, which are of interest for more intelligent gas turbine engines, are being explored or do not yet exist, for example turbine emission species sensors, burning patter factor sensors, fuel property sensors, and exhaust gas composition sensors. Meanwhile, rapid emerging technologies were made available during the three years duration of the Task Group. These include tip clearance measurement technologies such as eddy current, and microwave methods. To meet future sensor requirements, new fabrication and material technologies (MEMS, other), advanced sensing principles (spectroscopy, other), and their potential applications to new sensor types need to be explored. The status of current R&D for new sensors and future expectations are summarized in a comprehensive table. Sensors and associated electronics that have to operate in high temperature are foreseen to be largely available by 2015 (SiC) and 2020 (SiCN). Both of these technologies come with the potential of embedding such high temperature sensors within the structure. The information presented in the chapter represents the state-of-the-art of the sensing technology of the years 2005-2007 to the best knowledge of the authors. The authors are also fully aware of the fact that there may be many ongoing research programs as well as individual research that are not publicly supported and public information is not available. Therefore, the report might miss that section of knowledge. Also, progress in research and development is made every day such that often, the recent information may be fast outdated. The reader should bear in mind these facts.
Presently, hybrid Remote Power Controllers (RPC's) are in production and prototype units are available for systems utilizing 28VDC, 120VDC, 115VAC/400 Hz and 230VAC/400 Hz. This paper describes RPC development in a new area of application: HVDC distribution systems utilizing 270/300VDC. Two RPC current ratings, 1 amp and 2 amps, were selected for development as they are adequate to control 90% of projected system loads. The various aspects and trade-offs encountered in circuit development are discussed with special focus placed on the circuits that see the duress of the high dc potentials. The comprehensive evaluation tests are summarized which confirmed the RPC compliance with the specification and with system/load compatibility requirements. In addition, present technology status and new applications are summarized.
Conventional phased array antennas using waveguide or coax for signal distribution are impractical for large scale implementation on satellites or spacecraft because they exhibit prohibitively large system size, heavy weight, high attenuation loss, limited bandwidth, sensitivity to electromagnetic interference (EMI) temperature drifts and phase instability. However, optical beam forming systems are smaller, lighter, and more flexible. Three optical beam forming techniques are identified as applicable to large spaceborne phased array antennas. They are (1) the optical fiber replacement of conventional RF phased array distribution and control components, (2) spatial beam forming, and (3) optical beam splitting with integrated quasi-optical components. The optical fiber replacement and the spatial beam forming approaches were pursued by many organizations. Two new optical beam forming architectures are presented. Both architectures involve monolithic integration of the antenna radiating elements with quasi-optical grid detector arrays. The advantages of the grid detector array in the optical process are the higher power handling capability and the dynamic range. One architecture involves a modified version of the original spatial beam forming approach. The basic difference is the spatial light modulator (SLM) device for controlling the aperture field distribution. The original liquid crystal light valve SLM is replaced by an optical shuffling SLM, which was demonstrated for the 'smart pixel' technology. The advantages are the capability of generating the agile beams of a phased array antenna and to provide simultaneous transmit and receive functions. The second architecture considered is the optical beam splitting approach. This architecture involves an alternative amplitude control for each antenna element with an optical beam power divider comprised of mirrors and beam splitters. It also implements the quasi-optical grid phase shifter for phase control and grid amplifier for RF power. The advantages are no SLM is required for this approach, and the complete antenna system is capable of full monolithic integration.
It is technically feasible to design a satellite communication system to serve the United States electric utility industry's needs relative to load management, real-time operations management, remote meter reading and to determine the costs of various elements of the system. The functions associated with distribution automation and control and communication system requirements are defined. Factors related to formulating viable communication concepts, the relationship of various design factors to utility operating practices, and the results of the cost analysis are discussed The system concept and several ways in which the concept could be integrated into the utility industry are described.
An inverse design method calculates the blade shape that produces a prescribed input pressure distribution. By controlling this input pressure distribution the aerodynamic design objectives can easily be met. Because of the intrinsic relationship between pressure distribution and airfoil physical properties, a Neural Network can be trained to choose the optimal pressure distribution that would meet a set of physical requirements. Neural network systems have been attempted in the context of direct design methods. From properties ascribed to a set of blades the neural network is trained to infer the properties of an 'interpolated' blade shape. The problem is that, especially in transonic regimes where we deal with intrinsically non linear and ill posed problems, small perturbations of the blade shape can produce very large variations of the flow parameters. It is very unlikely that, under these circumstances, a neural network will be able to find the proper solution. The unique situation in the present method is that the neural network can be trained to extract the required input pressure distribution from a database of pressure distributions while the inverse method will still compute the exact blade shape that corresponds to this 'interpolated' input pressure distribution. In other words, the interpolation process is transferred to a smoother problem, namely, finding what pressure distribution would produce the required flow conditions and, once this is done, the inverse method will compute the exact solution for this problem. The use of neural network is, in this context, highly related to the use of proper optimization techniques. The optimization is used essentially as an automation procedure to force the input pressure distributions to achieve the required aero and structural design parameters. A multilayered feed forward network with back-propagation is used to train the system for pattern association and classification.
An inverse design method calculates the blade shape that produces a prescribed input pressure distribution. By controlling this input pressure distribution the aerodynamic design objectives can easily be met. Because of the intrinsic relationship between pressure distribution and airfoil physical properties, a neural network can be trained to choose the optimal pressure distribution that would meet a set of physical requirements. The neural network technique works well not only as an interpolating device but also as an extrapolating device to achieve blade designs from a given database. Two validating test cases are discussed.
An L-band frequencies satellite link providing the medium for direct control of electrical loads at individual customer sites from remote central locations is described. All loads supplied under interruptible-service contracts are likely condidates for such control, and they can be cycled or switched off to reduce system loads. For every kW of load eliminated or deferred to off-peak hours, the power company reduces its need for additional generating capacity. In addition, the satellite could switch meter registers so that their readings automatically reflected the time of consumption. The system would perform load-shedding operations during emergencies, disconnecting large blocks of load according to predetermined priorities. Among the distribution operations conducted by the satellite in real time would be: load reconfiguration, voltage regulation, fault isolation, and capacitor and feeder load control.
A robotic system for performing an autonomous task includes a humanoid robot having a plurality of compliant robotic joints, actuators, and other integrated system devices that are controllable in response to control data from various control points, and having sensors for measuring feedback data at the control points. The system includes a multi-level distributed control framework (DCF) for controlling the integrated system components over multiple high-speed communication networks. The DCF has a plurality of first controllers each embedded in a respective one of the integrated system components, e.g., the robotic joints, a second controller coordinating the components via the first controllers, and a third controller for transmitting a signal commanding performance of the autonomous task to the second controller. The DCF virtually centralizes all of the control data and the feedback data in a single location to facilitate control of the robot across the multiple communication networks.
Pressure distributions and photographs of oil flow patterns are presented for a circulation control wing. The model was an aspect ratio four semispan wing mounted on the side wall of the NASA Ames Transonic Wind Tunnel. The airfoil was a 20 percent thick ellipse, modified with circular leading and trailing edges of 4 percent radius, and had a 25.4 cm constant chord. This configuration does not represent a specific wing design, but is generic. A full span, tangetial, rearward blowing, circulation control slot was incorporated ahead of the trailing edge on the upper surface. The wing was tested at Mach numbers from 0.3 to 0.75 at sweep angle of 0 to 45 deg with internal to external pressure ratios of 1.0 to 3.0. Lift and pitching momemt coefficients were obtained from measured pressure distributions at five span stations. When the conventional corrections resulting from sweep angle are applied to the lift and moment of circulation control sections, no additional corrections are necessary to account for changes in blowing efficiency. This is demonstrated for an aft sweep angle of 45 deg. An empirical technique for estimating the downwash distribution of a swept wing was validated.
A distributed microprocessor network has been developed to position a laser-based velocity measurement package. Designed primarily for use inside the Ames 40 x 80-foot Subsonic Wind Tunnel, the instrument assembly is remotely operated. The mechanical system is reconfigurable for other test environments which necessitates an adaptive control approach. Three stepper motor drives provide two-dimensional positioning of the sampling point over 200/sq m of the test section, with a 5-mm resolution. Real-time multiprocessing provides fast response, fault detection, and parametric data acquisition. Manual tests are run from a dedicated control panel and automated tests are accomplished via an I.E.E.E 488 interface. The design approach and the progress to date are discussed.