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NASA NTRS1988

Design consideration in constructing high performance embedded Knowledge-Based Systems (KBS)

As the hardware trends for artificial intelligence (AI) involve more and more complexity, the process of optimizing the computer system design for a particular problem will also increase in complexity. Space applications of knowledge based systems (KBS) will often require an ability to perform both numerically intensive vector computations and real time symbolic computations. Although parallel machines can theoretically achieve the speeds necessary for most of these problems, if the application itself is not highly parallel, the machine's power cannot be utilized. A scheme is presented which will provide the computer systems engineer with a tool for analyzing machines with various configurations of array, symbolic, scaler, and multiprocessors. High speed networks and interconnections make customized, distributed, intelligent systems feasible for the application of AI in space. The method presented can be used to optimize such AI system configurations and to make comparisons between existing computer systems. It is an open question whether or not, for a given mission requirement, a suitable computer system design can be constructed for any amount of money.

Dalton, Shelly D.↗

NASA NTRS2019

Prototyping Operational Autonomy for Space Traffic Management

Current state of the art in Space Traffic Management (STM) relies on a handful of providers for surveillance and collision prediction, and manual coordination between operators. Neither is scalable to support the expected 10x increase in spacecraft population in less than 10 years, nor does it support automated manuever planning. We present a software prototype of an STM architecture based on open Application Programming Interfaces (APIs), drawing on previous work by NASA to develop an architecture for low-altitude Unmanned Aerial System Traffic Management. The STM architecture is designed to provide structure to the interactions between spacecraft operators, various regulatory bodies, and service suppliers, while maintaining flexibility of these interactions and the ability for new market participants to enter easily. Autonomy is an indispensable part of the proposed architecture in enabling efficient data sharing, coordination between STM participants and safe flight operations. Examples of autonomy within STM include syncing multiple non-authoritative catalogs of resident space objects, or determining which spacecraft maneuvers when preventing impending conjunctions between multiple spacecraft. The STM prototype is based on modern micro-service architecture adhering to OpenAPI standards and deployed in industry standard Docker containers, facilitating easy communication between different participants or services. The system architecture is designed to facilitate adding and replacing services with minimal disruption. We have implemented some example participant services (e.g. a space situational awareness provider/SSA, a conjunction assessment supplier/CAS, an automated maneuver advisor/AMA) within the prototype. Different services, with creative algorithms folded into then, can fulfil similar functional roles within the STM architecture by flexibly connecting to it using pre-defined APIs and data models, thereby lowering the barrier to entry of new players in the STM marketplace. We demonstrate the STM prototype on a multiple conjunction scenario with multiple maneuverable spacecraft, where an example CAS and AMA can recommend optimal maneuvers to the spacecraft operators, based on a predefined reward function. Such tools can intelligently search the space of potential collision avoidance maneuvers with varying parameters like lead time and propellant usage, optimize a customized reward function, and be implemented as a scheduling service within the STM architecture. The case study shows an example of autonomous maneuver planning is possible using the API-based framework. As satellite populations and predicted conjunctions increase, an STM architecture can facilitate seamless information exchange related to collision prediction and mitigation among various service applications on different platforms and servers. The availability of such an STM network also opens up new research topics on satellite maneuver planning, scheduling and negotiation across disjoint entities.

space traffic management↗

NASA NTRS1995

Intelligent automated surface grid generation

The goal of our research is to produce a flexible, general grid generator for automated use by other programs, such as numerical optimizers. The current trend in the gridding field is toward interactive gridding. Interactive gridding more readily taps into the spatial reasoning abilities of the human user through the use of a graphical interface with a mouse. However, a sometimes fruitful approach to generating new designs is to apply an optimizer with shape modification operators to improve an initial design. In order for this approach to be useful, the optimizer must be able to automatically grid and evaluate the candidate designs. This paper describes and intelligent gridder that is capable of analyzing the topology of the spatial domain and predicting approximate physical behaviors based on the geometry of the spatial domain to automatically generate grids for computational fluid dynamics simulators. Typically gridding programs are given a partitioning of the spatial domain to assist the gridder. Our gridder is capable of performing this partitioning. This enables the gridder to automatically grid spatial domains of wide range of configurations.

Yao, Ke-Thia↗

NASA NTRS1988

STRUTEX: A prototype knowledge-based system for initially configuring a structure to support point loads in two dimensions

The purpose of this research effort is to investigate the benefits that might be derived from applying artificial intelligence tools in the area of conceptual design. Therefore, the emphasis is on the artificial intelligence aspects of conceptual design rather than structural and optimization aspects. A prototype knowledge-based system, called STRUTEX, was developed to initially configure a structure to support point loads in two dimensions. This system combines numerical and symbolic processing by the computer with interactive problem solving aided by the vision of the user by integrating a knowledge base interface and inference engine, a data base interface, and graphics while keeping the knowledge base and data base files separate. The system writes a file which can be input into a structural synthesis system, which combines structural analysis and optimization.

Rogers, James L.↗

NASA NTRS1997

Data Partitioning and Load Balancing in Parallel Disk Systems

Parallel disk systems provide opportunities for exploiting I/O parallelism in two possible waves, namely via inter-request and intra-request parallelism. In this paper we discuss the main issues in performance tuning of such systems, namely striping and load balancing, and show their relationship to response time and throughput. We outline the main components of an intelligent, self-reliant file system that aims to optimize striping by taking into account the requirements of the applications and performs load balancing by judicious file allocation and dynamic redistributions of the data when access patterns change. Our system uses simple but effective heuristics that incur only little overhead. We present performance experiments based on synthetic workloads and real-life traces.

Scheuermann, Peter↗

NASA NTRS2001

Neutron Environment Calculations for Low Earth Orbit

The long term exposure of astronauts on the developing International Space Station (ISS) requires an accurate knowledge of the internal exposure environment for human risk assessment and other onboard processes. The natural environment is moderated by the solar wind, which varies over the solar cycle. The HZETRN high charge and energy transport code developed at NASA Langley Research Center can be used to evaluate the neutron environment on ISS. A time dependent model for the ambient environment in low earth orbit is used. This model includes GCR radiation moderated by the Earth's magnetic field, trapped protons, and a recently completed model of the albedo neutron environment formed through the interaction of galactic cosmic rays with the Earth's atmosphere. Using this code, the neutron environments for space shuttle missions were calculated and comparisons were made to measurements by the Johnson Space Center with onboard detectors. The models discussed herein are being developed to evaluate the natural and induced environment data for the Intelligence Synthesis Environment Project and eventual use in spacecraft optimization.

Clowdsley, M. S.↗

NASA NTRS2021

Automated Uas Measurements of Reflectance and Solar Induced Florescence (SIF) for Assessment Of the Dinamics in Photosynthetic Function, Application for Maze (Zea Mays L.) in Greenbelt, Maryland, Us

For accurate assessment of vegetation function there is a strong need for advancement in the remote sensing methods and instruments, to enable monitoring of the parameters governing photosynthesis at the temporal frequency relevant to their dynamics and at a spatial scale that allows practical assessment and management. Currently, no single sensor can provide data at the desired high temporal, spectral and spatial resolutions. Our field measurements obtained using the integrated UAS Piccolo system during the summers of2017 and 2018 demonstrate that science quality reflectance and solar induced fluorescence (SIF) data can be retrieved with high temporal frequency using small Unmanned Aerial Systems (UAS). The implemented approach facilitates data comparisons through space and time, and the integration with other spectral satellite and airborne data. This investigation contributes for bridging the gap in Earth observation between field and airborne measurements by implementing autonomous methods for obtaining high temporal frequency spectroscopy measurements from an Unmanned Aerial Systems (UAS) at various illumination conditions. An advancement in the Intelligent Payload Module (IPM) facilitated the implementation of an optimization workflow to collect spectral data for characterizing vegetation reflectance and solar induced fluorescence (SIF).

Petya Campbell↗

NASA NTRSDate not supplied

Trajectory Planning and Online Performance Model Estimation for Advanced Air Mobility

We propose a framework for adaptive guidance based on optimal control and estimation using closed-loop performance models. In the proposed approach, parameters of a reduced-order performance model are estimated in real time so that trajectories and guidance commands are replanned with more accurate knowledge of the system's response and performance capabilities. To apply this methodology to flight control systems, we introduce a simple yet expressive performance model modified from a reference linear design model. The proposed framework is applied to guidance of a simulated Advanced Air Mobility class concept aircraft experiencing control effector failures. We demonstrate that, by optimizing performance model parameters in real time, guidance commands can be intelligently adjusted to recover system stability and the performance of a full-order vehicle model, even in the event of effector failures. Furthermore, the reduced-order performance model requires a fraction of the computational cost of the full-order model, facilitating real-time use of adaptive, optimal guidance.

Differential dynamic programming↗

NASA NTRSDate not supplied

Trajectory Planning and Online Performance Model Estimation for Advanced Air Mobility

Differential dynamic programming↗

NASA NTRSDate not supplied

Combinatorial Reasoning: Selecting Reasons in Generative AI Pipelines via Combinatorial Optimization

Recent Large Language Models (LLMs) have demonstrated impressive capabilities at tasks that require human intelligence and are a significant step towards human-like artificial intelligence (AI). Yet the performance of LLMs at reasoning tasks have been subpar and the reasoning capability of LLMs is a matter of significant debate. While it has been shown that the choice of the prompting technique to the LLM can alter its performance on a multitude of tasks, including reasoning, the best performing techniques require human-made prompts with the knowledge of the tasks at hand. We introduce a framework for what we call Combinatorial Reasoning (CR), a fully-automated prompting method, where reasons are sampled from an LLM pipeline and mapped into a Quadratic Unconstrained Binary Optimization (QUBO) problem. The framework investigates whether QUBO solutions can be profitably used to select a useful subset of the reasons to construct a Chain-of-Thought style prompt. We explore the acceleration of CR with specialized solvers. We also investigate the performance of simpler zero-shot strategies such as linear majority rule or random selection of reasons. Our preliminary study indicates that coupling a combinatorial solver to generative AI pipelines is an interesting avenue for AI reasoning and elucidates design principles for future CR methods.

combinatorial reasoning↗

NASA NTRS2002

Turbopump Performance Improved by Evolutionary Algorithms

The development of design optimization technology for turbomachinery has been initiated using the multiobjective evolutionary algorithm under NASA's Intelligent Synthesis Environment and Revolutionary Aeropropulsion Concepts programs. As an alternative to the traditional gradient-based methods, evolutionary algorithms (EA's) are emergent design-optimization algorithms modeled after the mechanisms found in natural evolution. EA's search from multiple points, instead of moving from a single point. In addition, they require no derivatives or gradients of the objective function, leading to robustness and simplicity in coupling any evaluation codes. Parallel efficiency also becomes very high by using a simple master-slave concept for function evaluations, since such evaluations often consume the most CPU time, such as computational fluid dynamics. Application of EA's to multiobjective design problems is also straightforward because EA's maintain a population of design candidates in parallel. Because of these advantages, EA's are a unique and attractive approach to real-world design optimization problems.

Oyama, Akira↗

NASA NTRS1992

Multidisciplinary design optimization using response surface analysis

Aerospace conceptual vehicle design is a complex process which involves multidisciplinary studies of configuration and technology options considering many parameters at many values. NASA Langley's Vehicle Analysis Branch (VAB) has detailed computerized analysis capabilities in most of the key disciplines required by advanced vehicle design. Given a configuration, the capability exists to quickly determine its performance and lifecycle cost. The next step in vehicle design is to determine the best settings of design parameters that optimize the performance characteristics. Typical approach to design optimization is experience based, trial and error variation of many parameters one at a time where possible combinations usually number in the thousands. However, this approach can either lead to a very long and expensive design process or to a premature termination of the design process due to budget and/or schedule pressures. Furthermore, one variable at a time approach can not account for the interactions that occur among parts of systems and among disciplines. As a result, vehicle design may be far from optimal. Advanced multidisciplinary design optimization (MDO) methods are needed to direct the search in an efficient and intelligent manner in order to drastically reduce the number of candidate designs to be evaluated. The payoffs in terms of enhanced performance and reduced cost are significant. A literature review yields two such advanced MDO methods used in aerospace design optimization; Taguchi methods and response surface methods. Taguchi methods provide a systematic and efficient method for design optimization for performance and cost. However, response surface method (RSM) leads to a better, more accurate exploration of the parameter space and to estimated optimum conditions with a small expenditure on experimental data. These two methods are described.

Unal, Resit↗

NASA NTRS1989

KL techniques for optimal processing of time sequential imagery

Time-sequential imagery is difficult to analyze because of its high dimensionality. This paper advances a new algorithm that screens input data in an intelligent way, discards data with negligible information, and uses the remaining images to represent the sequence in an optimal compact form. Data are presented to illustrate how this algorithm can be used to do novelty filtering, novelty detection, segmentation, background independent modeling, and classification.

Vermeulen, Pieter↗

NASA NTRS1987

EUREX D: An expert system for failure diagnosis and recovery in the TCS of the European retrievable carrier EURECA

An expert system for diagnosis and recovery of failures in the Freon cooling loop of the European retrievable experiment carrier EURECA is described. The system demonstrates the feasibility of a functional scope of expert diagnostic systems which appears to be essential for practical applications of such systems in space technology. The scope includes early warning and treatment of incomplete information, fault tolerance, identification of failure superpositions, intelligent reaction to unforeseen events, and detailed status display for optimal recovery action.

Kellner, A.↗

NASA NTRS1994

From neural-based object recognition toward microelectronic eyes

Engineering neural network systems are best known for their abilities to adapt to the changing characteristics of the surrounding environment by adjusting system parameter values during the learning process. Rapid advances in analog current-mode design techniques have made possible the implementation of major neural network functions in custom VLSI chips. An electrically programmable analog synapse cell with large dynamic range can be realized in a compact silicon area. New designs of the synapse cells, neurons, and analog processor are presented. A synapse cell based on Gilbert multiplier structure can perform the linear multiplication for back-propagation networks. A double differential-pair synapse cell can perform the Gaussian function for radial-basis network. The synapse cells can be biased in the strong inversion region for high-speed operation or biased in the subthreshold region for low-power operation. The voltage gain of the sigmoid-function neurons is externally adjustable which greatly facilitates the search of optimal solutions in certain networks. Various building blocks can be intelligently connected to form useful industrial applications. Efficient data communication is a key system-level design issue for large-scale networks. We also present analog neural processors based on perceptron architecture and Hopfield network for communication applications. Biologically inspired neural networks have played an important role towards the creation of powerful intelligent machines. Accuracy, limitations, and prospects of analog current-mode design of the biologically inspired vision processing chips and cellular neural network chips are key design issues.

Sheu, Bing J.↗

NASA NTRS2010

Integration of Advanced Concepts and Vehicles Into the Next Generation Air Transportation System: Introduction, Key Messages, and Vehicle Attributes - Volume 1

Raytheon, in partnership with NASA, is leading the way in ensuring that the future air transportation continues to be a key driver of economic growth and stability and that this system provides an environmentally friendly, safe, and effective means of moving people and goods. A Raytheon-led team of industry and academic experts, under NASA contract NNA08BA47C, looked at the potential issues and impact of introducing four new classes of advanced aircraft into the next generation air transportation system -- known as NextGen. The study will help determine where NASA should further invest in research to support the safe introduction of these new air vehicles. Small uncrewed or unmanned aerial systems (SUAS), super heavy transports (SHT) including hybrid wing body versions (HWB), very light jets (VLJ), and supersonic business jets (SSBJ) are the four classes of aircraft that we studied. Understanding each vehicle's business purpose and strategy is critical to assessing the feasibility of new aircraft operations and their impact on NextGen's architecture. The Raytheon team used scenarios created by aviation experts that depict vehicles in year 2025 operations along with scripts or use cases to understand the issues presented by these new types of vehicles. The information was then mapped into the Joint Planning and Development Office's (JPDO s) Enterprise Architecture to show how the vehicles will fit into NextGen's Concept of Operations. The team also identified significant changes to the JPDO's Integrated Work Plan (IWP) to optimize the NextGen vision for these vehicles. Using a proven enterprise architecture approach and the JPDO s Joint Planning Environment (JPE) web site helped make the leap from architecture to planning efficient, manageable and achievable. Very Light Jets flying into busy hub airports -- Supersonic Business Jets needing to climb and descend rapidly to achieve the necessary altitude Super-heavy cargo planes requiring the shortest common flight path -- are just a few of the potential new operations in the future National Airspace System. To assess the impact of these new scenarios on overall national airspace operations, the Raytheon team used the capabilities of a suite of tools such as NASA's Airspace Concepts Evaluation System (ACES), the Flight Optimization System (FLOPS), FAA's Aviation Environmental Design Tool (AEDT), Intelligent Automations Kinematic Trajectory Generator (KTG) and the Aviation Safety Risk Model (ASRM). Detailed metroplex modeling, surface delay models for super heavy transports, prioritized routing and corridors for supersonics business jets, and VLJ demand models are some of the models developed by the Raytheon team to study the effect of operating these new vehicles in the future NAS. Using this suite of models, several trade studies were conducted to evaluate these effects in terms of delays, equity in access, safety, and the environment. Looking at the impact of each vehicle, a number of critical issues were identified. The Raytheon team concluded that strict compliance to NextGen's 4-dimensional trajectory (4DT) management will be required to accommodate these vehicles unique operations and increased number of flights in the future air space system. The next section provides a discussion of this and the other key findings from our study.

Zellweger, Andres↗

NASA NTRS1993

A neural based intelligent flight control system for the NASA F-15 flight research aircraft

A flight control concept that can identify aircraft stability properties and continually optimize the aircraft flying qualities has been developed by McDonnell Aircraft Company under a contract with the NASA-Dryden Flight Research Facility. This flight concept, termed the Intelligent Flight Control System, utilizes Neural Network technology to identify the host aircraft stability and control properties during flight, and use this information to design on-line the control system feedback gains to provide continuous optimum flight response. This self-repairing capability can provide high performance flight maneuvering response throughout large flight envelopes, such as needed for the National Aerospace Plane. Moreover, achieving this response early in the vehicle's development schedule will save cost.

Urnes, James M.↗

NASA NTRS1984

An adaptive interpolator algorithm for area-array fine guiance sensors

The present paper provides a description of recent and continuing work related to the development of tracking systems possessing artificial intelligence. It is pointed out that artificial intelligence is being used to provide trackers with the abilities to sense environmental changes which can affect tracker performance, to optimize tracker performance in a changing environment, and to report on the accuracy and stability of the pointing data. The implementation of artificial intelligence in tracking systems is based on the use of adaptive algorithms which correct for tracking errors by applying polynomial corrections to raw tracking output data. The basic operation of a centroid tracking system is discussed, taking into account the limitations on accuracy and stability which exist in the absence of intelligence. Attention is given to the correction method and the implementation of artificial intelligence to improve tracking.

Glavich, T. A.↗