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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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NASA NTRS1989

An approach to knowledge engineering to support knowledge-based simulation of payload ground processing at the Kennedy Space Center

Planning for processing payloads was always difficult and time-consuming. With the advent of Space Station Freedom and its capability to support a myriad of complex payloads, the planning to support this ground processing maze involves thousands of man-hours of often tedious data manipulation. To provide the capability to analyze various processing schedules, an object oriented knowledge-based simulation environment called the Advanced Generic Accomodations Planning Environment (AGAPE) is being developed. Having nearly completed the baseline system, the emphasis in this paper is directed toward rule definition and its relation to model development and simulation. The focus is specifically on the methodologies implemented during knowledge acquisition, analysis, and representation within the AGAPE rule structure. A model is provided to illustrate the concepts presented. The approach demonstrates a framework for AGAPE rule development to assist expert system development.

Mcmanus, Shawn↗

NASA NTRS1991

Design concepts for integrating the IMKA technology with CLIPS

The author shares his experiences in evaluating the technical alternatives for integrating the Initiative for Managing Knowledge Assets (IMKA) frame-based knowledge acquisition system with the C language Integrated Production System (CLIPS). Integrating the IMKA technology with CLIPS allows application knowledge to be encoded naturally, using both frames and rules, and allows the knowledge stored in frames to be reasoned about using rules. An overview is given in viewgraph form of the features of the IMKA technology. The challenges of integrating the IMKA technology with CLIPS, three integration models, and the circumstances under which each is appropriate are discussed.

Scarola, David↗

NASA NTRS1992

Distributed traffic signal control using fuzzy logic

We present a distributed approach to traffic signal control, where the signal timing parameters at a given intersection are adjusted as functions of the local traffic condition and of the signal timing parameters at adjacent intersections. Thus, the signal timing parameters evolve dynamically using only local information to improve traffic flow. This distributed approach provides for a fault-tolerant, highly responsive traffic management system. The signal timing at an intersection is defined by three parameters: cycle time, phase split, and offset. We use fuzzy decision rules to adjust these three parameters based only on local information. The amount of change in the timing parameters during each cycle is limited to a small fraction of the current parameters to ensure smooth transition. We show the effectiveness of this method through simulation of the traffic flow in a network of controlled intersections.

Chiu, Stephen↗

NASA NTRS1993

Integration of domain and resource-based reasoning for real-time control in dynamic environments

A real-time software controller that successfully integrates domain-based and resource-based control reasoning to perform task execution in a dynamically changing environment is described. The design of the controller is based on the concept of partitioning the process to be controlled into a set of tasks, each of which achieves some process goal. It is assumed that, in general, there are multiple ways (tasks) to achieve a goal. The controller dynamically determines current goals and their current criticality, choosing and scheduling tasks to achieve those goals in the time available. It incorporates rule-based goal reasoning, a TMS-based criticality propagation mechanism, and a real-time scheduler. The controller has been used to build a knowledge-based situation assessment system that formed a major component of a real-time, distributed, cooperative problem solving system built under DARPA contract. It is also being employed in other applications now in progress.

Morgan, Keith↗

NASA NTRS1980

Cogeneration Technology Alternatives Study (CTAS). Volume 4: Energy conversion systems

Industrial processes from the largest energy consuming sectors were used as a basis for matching a similar number of energy conversion systems that are considered as candidate which can be made available by the 1985 to 2000 time period. The sectors considered included food, textiles, lumber, paper, chemicals, petroleum, glass, and primary metals. The energy conversion systems included steam and gas turbines, diesels, thermionics, stirling, closed-cycle and steam injected gas turbines, and fuel cells. Fuels considered were coal, both coal and petroleum-based residual and distillate liquid fuels, and low Btu gas obtained through the on-site gasification of coal. An attempt was made to use consistent assumptions and a consistent set of ground rules specified by NASA for determining performance and cost. The advanced and commercially available cogeneration energy conversion systems studied in CTAS are fined together with their performance, capital costs, and the research and developments required to bring them to this level of performance.

Brown, D. H.↗

DOE OSTI2021

Formation and surface melting of nanoparticle superlattices in a solution

The wisdom in the saying of “There are no two snowflakes alike” lies in the importance of history or kinetic pathways in the phase transitions of solids. Likewise, “artificial solids,” namely superlattices consisting of functional nanoparticles, have lattice size, surface morphology, crystallinity, symmetry, and structural reconfiguration (for example, transition into a disordered state) highly dependent on the kinetic pathways as the nanoparticles interact with each other in solution [1]. Great progresses have been made in understanding the formation pathways of superlattices using liquid-phase transmission electron microscopy (TEM) [2-4]. For example, by tracking single nanoparticle’s trajectories, especially aided by U-net neural network-based machine learning, previous studies mapped the fundamental nanoparticle interactions at nanometer resolution [5]. Nonclassical, two-step nucleation pathway has also been elucidated in the system of nanoprisms, by optimizing protocols such as loading nanoparticle suspensions over the supersaturation threshold and minimizing particle‒substrate interaction [2]. Surface morphologies or exposed facets of superlattices have been shown to follow the principles of Wulff construction rule, where the facet-dependent surface energy can be measured based on the capillary wave theory [4]. However, the reverse process of crystallization of superlattices, the conversion from crystalline to disordered state, has been much less explored. On one hand, the melting of nanoparticle superlattices can provide a preferred pathway to induce structural reorganization or shuffling of building blocks for them to transform into different types of crystal structures. On the other hand, understanding nanoscale superlattice melting and comparing such behaviors with the prevailing surface melting theories developed for atomic/molecular solids can provide a potent way to engineer phase transitions of supra- and hierarchical structures constructed from nanoscale entities (e.g., DNA-coated nanoparticles, proteins), for their applications in reprogrammable and switchable materials with multifunctional properties [6, 7]. The experimental challenges to observe melting of superlattices are twofold. Practically it is difficult to load the initial superlattice form, in an intact manner, into the highly confined liquid-phase TEM chamber for in-situ observation. Here, the triggering of melting also needs meticulous manipulation of nanoparticle concentration, interparticle interaction, and solution environment.

Kim, Ahyoung↗

NASA NTRS1989

Methods and decision making on a Mars rover for identification of fossils

A system for automated fusion and interpretation of image data from multiple sensors, including multispectral data from an imaging spectrometer is being developed. Classical artificial intelligence techniques and artificial neural networks are employed to make real time decision based on current input and known scientific goals. Emphasis is placed on identifying minerals which could indicate past life activity or an environment supportive of life. Multispectral data can be used for geological analysis because different minerals have characteristic spectral reflectance in the visible and near infrared range. Classification of each spectrum into a broad class, based on overall spectral shape and locations of absorption bands is possible in real time using artificial neural networks. The goal of the system is twofold: multisensor and multispectral data must be interpreted in real time so that potentially interesting sites can be flagged and investigated in more detail while the rover is near those sites; and the sensed data must be reduced to the most compact form possible without loss of crucial information. Autonomous decision making will allow a rover to achieve maximum scientific benefit from a mission. Both a classical rule based approach and a decision neural network for making real time choices are being considered. Neural nets may work well for adaptive decision making. A neural net can be trained to work in two steps. First, the actual input state is mapped to the closest of a number of memorized states. After weighing the importance of various input parameters, the net produces an output decision based on the matched memory state. Real time, autonomous image data analysis and decision making capabilities are required for achieving maximum scientific benefit from a rover mission. The system under development will enhance the chances of identifying fossils or environments capable of supporting life on Mars

Eberlein, Susan↗

NASA NTRS1994

AI and workflow automation: The prototype electronic purchase request system

Automating 'paper' workflow processes with electronic forms and email can dramatically improve the efficiency of those processes. However, applications that involve complex forms that are used for a variety of purposes or that require numerous and varied approvals often require additional software tools to ensure that (1) the electronic form is correctly and completely filled out, and (2) the form is routed to the proper individuals and organizations for approval. The prototype electronic purchase request (PEPR) system, which has been in pilot use at NASA Ames Research Center since December 1993, seamlessly links a commercial electronics forms package and a CLIPS-based knowledge system that first ensures that electronic forms are correct and complete, and then generates an 'electronic routing slip' that is used to route the form to the people who must sign it. The PEPR validation module is context-sensitive, and can apply different validation rules at each step in the approval process. The PEPR system is form-independent, and has been applied to several different types of forms. The system employs a version of CLIPS that has been extended to support AppleScript, a recently-released scripting language for the Macintosh. This 'scriptability' provides both a transparent, flexible interface between the two programs and a means by which a single copy of the knowledge base can be utilized by numerous remote users.

Compton, Michael M.↗

DOE OSTI2026

A Decomposition-Based Learn-To-Optimize Approach with Feasibility Layer Assistance for Sub-Hourly Unit Commitment

Sub-hourly unit commitment (UC) with 15-min intervals is gaining significant attention as a way to respond rapidly to the fluctuations in electricity supply and demand introduced by renewable resources. However, the increased temporal resolution and complex inter-temporal dependencies pose substantial computational challenges for traditional optimization methods. To this end, this paper explores a decomposition-based learn-to-optimize approach. Building on recent advances in machine learning, our method revisits the long- overlooked Lagrangian relaxation framework, which is a classical decomposition technique that enables tractable subproblem solving. These smaller subproblems are inherently well-suited for machine learning, as their reduced dimensionality and structural regularity allow predictive models to efficiently learn and generalize solution patterns. We thus propose a generic predictive model, which embeds Gated Recurrent Units (GRUs) and Attention in the encoder-decoder structure, and integrate a rule-based feasibility layer to capture temporal dependencies, reduce training effort, and improve feasibility w.r.t. unit-level constraints. Our method has been validated on the IEEE 118-bus system, demonstrating promising performance in solving sub-hourly UC problems efficiently and feasibly.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1985

Issues in rule identification and logical induction

The relationship between language and empirical fitting of data is discussed. The production system is presented as an appropriate description of human behavior in Man-Machine systems. Issues arising in the identification of rules from data are examined. Rules identified through logical generalization are shown to be equivocal. Difficulties arising from the use of logic-based procedures with human performance data containing errors are explored. Problems relating to rule sets which are not disjoint are discussed and a solution presented. Significant testing issues are raised for rule identification and a procedure based on controlling contrivedness is presented. A synthesis of data and knowledge-based approaches is suggested as a remedy to many of the difficulties discussed.

Lewis, C. M.↗

NASA NTRS1988

An interactive testbed for development of expert tools for pattern recognition

The initial implementation of an interactive testbed for development of expert system applications in image processing, i.e., a toolbox of procedures designed to facilitate the capture of expert knowledge for region grouping and analysis is described. The user can elect to interactively enter commands (via a command interpreter) for region manipulation to, in effect, simulate the actions of a hypothetical expert system. The user can then incorporate any rules and procedures as derived from interactive experimentation into customized region processing procedures using the library of utility functions. An iterative technique based on image pyramids is used to compute the initial region segmentation without the use of process parameters. These regions can then be interactively examined and manipulated using the command interpreter.

Wharton, Stephen W.↗

NASA NTRS1990

Building engineering expert systems in CLIPS

This paper is intended for CLIPS developers with a working knowledge of expert systems and the CLIPS syntax. It discusses Rete pattern matching and rule-fact interaction, explains several development and debug techniques, and gives advice on compiling CLIPS and knowledge bases. The techniques apply to CLIPS versions 4.2 and 4.3, especially in the PC/DOS environment. Two examples developed by the author are analyzed and compared.

Porter, Ken↗

NASA NTRS1998

Two-Dimensional High-Lift Aerodynamic Optimization Using Neural Networks

The high-lift performance of a multi-element airfoil was optimized by using neural-net predictions that were trained using a computational data set. The numerical data was generated using a two-dimensional, incompressible, Navier-Stokes algorithm with the Spalart-Allmaras turbulence model. Because it is difficult to predict maximum lift for high-lift systems, an empirically-based maximum lift criteria was used in this study to determine both the maximum lift and the angle at which it occurs. The 'pressure difference rule,' which states that the maximum lift condition corresponds to a certain pressure difference between the peak suction pressure and the pressure at the trailing edge of the element, was applied and verified with experimental observations for this configuration. Multiple input, single output networks were trained using the NASA Ames variation of the Levenberg-Marquardt algorithm for each of the aerodynamic coefficients (lift, drag and moment). The artificial neural networks were integrated with a gradient-based optimizer. Using independent numerical simulations and experimental data for this high-lift configuration, it was shown that this design process successfully optimized flap deflection, gap, overlap, and angle of attack to maximize lift. Once the neural nets were trained and integrated with the optimizer, minimal additional computer resources were required to perform optimization runs with different initial conditions and parameters. Applying the neural networks within the high-lift rigging optimization process reduced the amount of computational time and resources by 44% compared with traditional gradient-based optimization procedures for multiple optimization runs.

Greenman, Roxana M.↗

NASA NTRS2004

InvestigationOrganizer: The Development and Testing of a Web-based Tool to Support Mishap Investigations

InvestigationOrganizer (IO) is a collaborative web-based system designed to support the conduct of mishap investigations. IO provides a common repository for a wide range of mishap related information, and allows investigators to make explicit, shared, and meaningful links between evidence, causal models, findings and recommendations. It integrates the functionality of a database, a common document repository, a semantic knowledge network, a rule-based inference engine, and causal modeling and visualization. Thus far, IO has been used to support four mishap investigations within NASA, ranging from a small property damage case to the loss of the Space Shuttle Columbia. This paper describes how the functionality of IO supports mishap investigations and the lessons learned from the experience of supporting two of the NASA mishap investigations: the Columbia Accident Investigation and the CONTOUR Loss Investigation.

Carvalho, Robert F.↗

NASA NTRS2019

Spitzer Microlensing Parallax for OGLE-2016-BLG-1067: A Sub-Jupiter Orbiting an M Dwarf in the Disk

We report the discovery of a sub-Jupiter-mass planet orbiting beyond the snow line of an M dwarf most likely in the Galactic disk as part of the joint Spitzer and ground-based monitoring of planetary microlensing anomalies toward the Galactic bulge. Most of the microlensing parameters are strongly constrained by the light-curve modeling, and in particular there is a Spitzer-based measurement of the microlens parallax, π(E). However, there are no caustic crossings, so the angular Einstein radius has only an upper limit based on the light-curve modeling alone. Additionally, the analysis leads us to identify eight degenerate configurations: the fourfold microlensing parallax degeneracy being doubled by a degeneracy in the caustic structure present at the level of the ground-based solutions. To calculate the physical parameters, and at the same time to break the parallax degeneracy, we make use of a series of arguments: the χ(exp 2) hierarchy, the Rich argument (stating that the small-parallax solution is more likely), and a prior Galactic model. The preferred configuration, favored by a likelihood ratio of at least 4000, is for a host at D(L)={3.73}(-0.67+0.66)kpc with mass M(L)=0.30(-0.12+0.15) M(☉) , orbited by a Saturn-like planet with M(planet) =0.43(-0.17+0.21) M(Jup) at projected separation a(⊥) =1.70(-0.39+0.38) au, about 2.1 times beyond the system snow line. Therefore, it adds to the growing population of sub-Jupiter planets orbiting beyond the snow line of M dwarfs discovered by microlensing. Based on the rules of the real-time protocol for the selection of events to be followed up with Spitzer, this planet will not enter the sample for measuring the Galactic distribution of planets.

S. Calchi Novati↗

NASA NTRS1997

Towards High-Assurance High-Performance Program Synthesis

Domain-specific automatic program synthesis tools, also called application generators, are playing an ever-increasing role in software development. However, high-performance application generators require difficult manual construction, and are very difficult to verify correct. This paper describes research and an implemented system that transforms program synthesis tools based on deductive synthesis into high-performance application generators. Deductive synthesis uses theorem-proving to construct solutions when given problem specifications. The verification condition for a deductive synthesis tool is essentially the soundness of the implemented inference rules. Theory Operationalization for Program Synthesis (TOPS) synergistically combines reformulation, automated mathematical classification, and compilation through partial deduction to decision procedures. It transforms general-purpose deductive synthesis, with exponential performance, into efficient special-purpose deductive synthesis, with near-linear performance. This paper describes our experience with and empirical results of PD(TH) theory-based partial deduction - in which partial deduction of a set of first-order formulae is performed within the context of a background theory. The implemented TOPS system currently performs a special variant of PD(TH) in which the compilation process results in the transformation of a set of first order formulae into the theory of an instantiated library decision procedure augmented by a compiled unit theory.

Lowry, Michael↗

NASA NTRS1992

A composite self tuning strategy for fuzzy control of dynamic systems

The feature of self learning makes fuzzy logic controllers attractive in control applications. This paper proposes a strategy to tune the fuzzy logic controller on-line by tuning the data base as well as the rule base. The structure of the controller is outlined and preliminary results are presented using simulation studies.

Shieh, C.-Y.↗

NASA NTRS2006

Deductive Glue Code Synthesis for Embedded Software Systems Based on Code Patterns

Automated code synthesis is a constructive process that can be used to generate programs from specifications. It can, thus, greatly reduce the software development cost and time. The use of formal code synthesis approach for software generation further increases the dependability of the system. Though code synthesis has many potential benefits, the synthesis techniques are still limited. Meanwhile, components are widely used in embedded system development. Applying code synthesis to component based software development (CBSD) process can greatly enhance the capability of code synthesis while reducing the component composition efforts. In this paper, we discuss the issues and techniques for applying deductive code synthesis techniques to CBSD. For deductive synthesis in CBSD, a rule base is the key for inferring appropriate component composition. We use the code patterns to guide the development of rules. Code patterns have been proposed to capture the typical usages of the components. Several general composition operations have been identified to facilitate systematic composition. We present the technique for rule development and automated generation of new patterns from existing code patterns. A case study of using this method in building a real-time control system is also presented.

automated code synthesis↗