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Studies indicate that the current NCC mode of operation needs to be enhanced to meet the needs of the mid to late 19990's. There is a need to simplify the request interface for Space Network services. This is because there are more complex missions, more flexible spacecraft operations, more scheduling data volume, and more events per spacecraft and scheduling period.
The toughening of the PMR-15 and LARC RP-46 high temperature thermosetting polyimides is presently attempted through the construction of a semiinterpenetrating network at ply interfaces through the use of the Matrimid 5218 thermoplastic polyimide powder, whose 315-320 glass transition temperature is compatible with the PMR matrices. The 60 vol pct fiber composites thus prepared for the two resins, with and without toughening, were comprehensively characterized in flexure, tension, intralaminar and short beam shear, compression and quasi-isotropic short-block compression, as well as modes I and II interlaminar fracture toughness and compression after impact.
Increasing the processor performance and capability has not only become a wish list item for the Space Station Freedom (SSF) Data Management System (DMS), but also a necessity. There are many commercially available processors which have superior performance compared to the 386, but we cannot only consider performance when selecting a processor for the DMS. The processor is the 'foundation' of the DMS. It will affect the local bus, system bus, interface unit, global network, etc., that have been selected for the DMS. Besides, once the processor instruction set architecture (ISA) is selected, all of the important system software will be implemented based on this ISA. Selecting an ISA that has a strong commercial support and can be upgraded in the 30-year life cycle of the Space Station Freedom is one of the most important items for the DMS.
NASA's Space Network (SN) provides telecommunications and tracking services to low earth orbiting spacecraft. One proposal for improving resource allocation and automating conflict resolution for the SN is the concept of flexible scheduling. In this concept, each Payload Operations Control Center (POCC) will possess a Space Network User POCC Interface (SNUPI) to support the development and management of flexible requests. Flexible requests express the flexibility, constraints, and repetitious nature of the user's communications requirements. Flexible scheduling is expected to improve SN resource utilization and user satisfaction, as well as reduce the effort to produce and maintain a schedule. A prototype testbed has been developed to better understand flexible scheduling as it applies to the SN. This testbed consists of a SNUPI workstation, an SN scheduler, and a flexible request language that conveys information between the two systems. All three are being evaluated by operations personnel. Benchmark testing is being conducted on the scheduler to quantify the productivity improvements achieved with flexible requests.
We present results on an InP monolithic millimeter- wave integrated circuit (MMIC) amplifier having 10-dB gain at 235 GHz. We designed this circuit and fabricated the chip in Northrop Grumman Space Technology's (NGST) 0.07- m InP high electron mobility transistor (HEMT) process. Using a WR3 (220-325 GHz) waveguide vector network analyzer system interfaced to waveguide wafer probes, we measured this chip on-wafer for -parameters. To our knowledge, this is the first time a WR3 waveguide on-wafer measurement system has been used to measure gain in a MMIC amplifier above 230 GHz.
Big data and artificial intelligence/machine learning are transforming the global business environment. Data is now the most valuable asset for enterprises in every industry. Companies are using data-driven insights for competitive advantage. With that, the adoption of machine learning-based data analytics is rapidly taking hold across various industries, producing autonomous systems that support human decision-making. This work explored the application of machine learning to aircraft engine conceptual design. Supervised machine-learning algorithms for regression and classification were employed to study patterns in an existing, open-source database of production and research turbofan engines, and resulting in predictive analytics for use in predicting performance of new turbofan designs. Specifically, the author developed machine learning-based analytics to predict cruise thrust specific fuel consumption (TSFC) and core sizes of high-efficiency turbofan engines, using engine design parameters as the input. The predictive analytics were trained and deployed in Keras, an open-source neural networks application program interface (API) written in Python, with Google’s TensorFlow (an open source library for numerical computation) serving as the backend engine. The promising results of the predictive analytics show that machine-learning techniques merit further exploration for application in aircraft engine conceptual design.
Simulating compound flooding (CF) at the river-ocean interface within large-scale Earth System Models (ESMs) presents significant challenges due to complex interactions between river discharge, storm surge, and tides. This study assesses the comparative advantages of physics-informed and data-driven machine learning (ML) approaches for enhancing local ESM performance. We systematically compare data-driven neural network models (i.e., CNNs, U-Net, Long Short-Term Memory (LSTM), Gated Recurrent Unit), and physics-informed neural network (PINN) models, including vanilla PINN and a finite-difference-based PINN (FD-PINN). Specifically, FD-PINN is introduced to enhance computational efficiency, accelerating vanilla PINNs by ∼6.5 times while improving accuracy. To enhance data-driven model training, a new data-generation approach is developed to sample historical fluvial and coastal flood events, which ensures a robust data set for extreme event prediction. The models are evaluated using a realistic one-dimensional river domain extracted from an ESM's river mesh and the Hurricane Irene event as an independent test case. Results show that FD-PINN achieves accurate predictions with significantly reduced computational costs relative to vanilla PINNs. Among data-driven models, the best overall performance is achieved by a CNN-LSTM hybrid, which balances accuracy and efficiency. While a fully connected CNN (CNN-FC) provides the best accuracy, it incurs high computational cost. Architectures lacking strong temporal modeling tend to underperform on unseen events. These findings highlight the importance of sequence-aware designs for robust generalization. This study reveals the trade-offs between physics-informed and data-driven models and proposes an adaptive hybrid framework for integrating ML into ESMs to enhance local flood simulations.
A detailed Lunar Ultraviolet Telescope Experiment (LUTE) program plan representing major decisions and tasks leading to those decisions for program execution are presented. The purpose of this task was to develop an integrated plan of project activities for the LUTE project, and to display the plan as an integrated network that shows the project activities, all critical interfaces, and schedules. The integrated network will provide the project manager with a frame work for strategic planning and risk management throughout the life of the project.
The Spaceport Command and Control System (SCCS) is being built at the Kennedy Space Center in order to successfully launch NASA’s revolutionary vehicle that allows humans to explore further into space than ever before. During my internship, I worked with the Network, Firewall, and Hardware teams that are all contributing to the huge SCCS network project effort. I learned the SCCS network design and the several concepts that are running in the background. I also updated and designed documentation for physical networks that are part of SCCS. This includes being able to assist and build physical installations as well as configurations. I worked with the network design for vehicle telemetry interfaces to the Launch Control System (LCS); this allows the interface to interact with other systems at other NASA locations. This network design includes the Space Launch System (SLS), Interim Cryogenic Propulsion Stage (ICPS), and the Orion Multipurpose Crew Vehicle (MPCV). I worked on the network design and implementation in the Customer Avionics Interface Development and Analysis (CAIDA) lab.
Telecommunication networks of time division switches, interconnected by digital transmission are being put into place. At each switch, each incoming bit stream is brought into its own buffer. Then the clock in the switch reads each buffer to re-establish phase. Care must be taken to keep frequency differences between various clocks from becoming too large. Based on empirically defined data transmission requirements, one major network has determined that fractional frequency inequality between switches should be no worse than 1.7 X 10 to the minus 9 power. A network needs near frequency equality between its own switches, and also between its switches and other networks with which it interfaces. As a practical matter, the best way to achieve needed frequency equality is for each network to have master clock with an accuracy which is at least as good as 1 X 10 to the minus 10 power. The relationship between the master and the secondary clock is discussed. The questions of master clock accuracy and precision and the free-running capability of the secondary clocks are examined.
The technical challenges, engineering solutions, and results of the NOCC computer-human interface design are presented. The use-centered design process was as follows: determine the design criteria for user concerns; assess the impact of design decisions on the users; and determine the technical aspects of the implementation (tools, platforms, etc.). The NOCC hardware architecture is illustrated. A graphical model of the DSN that represented the hierarchical structure of the data was constructed. The DSN spacecraft summary display is shown. Navigation from top to bottom is accomplished by clicking the appropriate button for the element about which the user desires more detail. The telemetry summary display and the antenna color decision table are also shown.
No abstract available
We model transient diffusion in heterogeneous materials using a novel physics-informed neural networks framework (PINNs) termed Adaptive interface physics-informed neural networks or AdaI-PINNs (Roy et al. arXiv preprint arXiv:2406.04626, 2024). AdaI-PINNs utilize different activation functions with trainable slopes tailored to each material region within the computational domain, allowing for a fully automated and adaptive PINNs approach to model interface problems with strongly and weakly discontinuous solutions. To enhance its performance in highly heterogeneous transient diffusion systems, we prescribe a suite of robust practices, including appropriate non-dimensionalization of equations, a biased sampling method, Glorot initialization, and the hard enforcement of boundary and initial conditions. Here we evaluate the efficacy of the proposed method on several benchmark forward and inverse problems. Comparative studies on one-dimensional and two-dimensional benchmark problems reveal that the modified AdaI-PINNs outperform its unmodified counterpart, achieving root-mean-square errors that are at least two orders of magnitude better in forward problems. For inverse problems, the maximum errors in the approximated diffusion coefficients by modified AdaI-PINNs are four orders of magnitude better than those of the unmodified version. Additionally, modified AdaI-PINNs demonstrate improved stability in problems with large material mismatches.
The Xpress Transfer Protocol (XTP) is a reliable, light weight transfer layer protocol. Current transport layer protocols such as DoD's Transmission Control Protocol (TCP) and ISO's Transport Protocol (TP) were not designed for the next generation of high speed, interconnected reliable networks such as fiber distributed data interface (FDDI) and the gigabit/second wide area networks. Unlike all previous transport layer protocols, XTP is being designed to be implemented in hardware as a VLSI chip set. By streamlining the protocol, combining the transport and network layers, and utilizing the increased speed and parallelization possible with a VLSI implementation, XTP will be able to provide the end-to-end data transmission rates demanded in the high speed networks without compromising reliability and functionality. This tutorial briefly describes the operation of the XTP protocol and in particular, its error, flow and rate control; inter-networking addressing mechanisms; and multicast support features, as defined in the XTP Protocol Definition Revision 3.4.
The Xpress Transfer Protocol (XTP) is a reliable, real-time, light weight transfer layer protocol. Current transport layer protocols such as DoD's Transmission Control Protocol (TCP) and ISO's Transport Protocol (TP) were not designed for the next generation of high speed, interconnected reliable networks such as fiber distributed data interface (FDDI) and the gigabit/second wide area networks. Unlike all previous transport layer protocols, XTP is being designed to be implemented in hardware as a VLSI chip set. By streamlining the protocol, combining the transport and network layers and utilizing the increased speed and parallelization possible with a VLSI implementation, XTP will be able to provide the end-to-end data transmission rates demanded in high speed networks without compromising reliability and functionality. This paper describes the operation of the XTP protocol and in particular, its error, flow and rate control; inter-networking addressing mechanisms; and multicast support features, as defined in the XTP Protocol Definition Revision 3.4.
The following tasks were prioritized: software acquisition management plan; space station flight data system architectural study; space station user data system interface; automation of software development process; automation of software testing; distributed data base management; ADA (automated data acquisition) evaluation and transition and planning; network operating system software; fault tolerant computer validation methodology for onboard data management system; systems integration; artificial intelligence/expert systems; space station data network concept; space station standard interface protocols; space station data networks systems; integrated software development facility; and language trade studies.
This paper outlines various mission requirements and technical approaches that support the potential use of portable computers in several defined activities within the Space Station Freedom (SSF) program. Specifically, the use of portable computers as consoles for both spacecraft control and payload applications is presented. Various issues and proposed solutions regarding the incorporation of portable computers within the program are presented. The primary issues presented regard architecture (standard interface for expansion, advanced processors and displays), integration (methods of high-speed data communication, peripheral interfaces, and interconnectivity within various support networks), and evolution (wireless communications and multimedia data interface methods).
A system with multimodality instrument for tissue identification includes a computer-controlled motor driven heuristic probe with a multisensory tip is discussed. For neurosurgical applications, the instrument is mounted on a stereotactic frame for the probe to penetrate the brain in a precisely controlled fashion. The resistance of the brain tissue being penetrated is continually monitored by a miniaturized strain gauge attached to the probe tip. Other modality sensors may be mounted near the probe tip to provide real-time tissue characterizations and the ability to detect the proximity of blood vessels, thus eliminating errors normally associated with registration of pre-operative scans, tissue swelling, elastic tissue deformation, human judgement, etc., and rendering surgical procedures safer, more accurate, and efficient. A neural network, program adaptively learns the information on resistance and other characteristic features of normal brain tissue during the surgery and provides near real-time modeling. A fuzzy logic interface to the neural network program incorporates expert medical knowledge in the learning process. Identification of abnormal brain tissue is determined by the detection of change and comparison with previously learned models of abnormal brain tissues. The operation of the instrument is controlled through a user friendly graphical interface. Patient data is presented in a 3D stereographics display. Acoustic feedback of selected information may optionally be provided. Upon detection of the close proximity to blood vessels or abnormal brain tissue, the computer-controlled motor immediately stops probe penetration.