Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Knowledge bases”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Towards a decision support system for space flight operations

The Mission Operations Directorate (MOD) at the Johnson Space Center (JSC) has put in place a Model Based Systems Engineering (MBSE) technological framework for the development and execution of the Flight Production Process (FPP). This framework has provided much added value and return on investment to date. This paper describes a vision for a model based Decision Support System (DSS) for the development and execution of the FPP and its design and development process. The envisioned system extends the existing MBSE methodology and technological framework which is currently in use. The MBSE technological framework currently in place enables the systematic collection and integration of data required for building an FPP model for a diverse set of missions. This framework includes the technology, people and processes required for rapid development of architectural artifacts. It is used to build a feasible FPP model for the first flight of spacecraft and for recurrent flights throughout the life of the program. This model greatly enhances our ability to effectively engage with a new customer. It provides a preliminary work breakdown structure, data flow information and a master schedule based on its existing knowledge base. These artifacts are then refined and iterated upon with the customer for the development of a robust end-to-end, high-level integrated master schedule and its associated dependencies. The vision is to enhance this framework to enable its application for uncertainty management, decision support and optimization of the design and execution of the FPP by the program. Furthermore, this enhanced framework will enable the agile response and redesign of the FPP based on observed system behavior. The differences between the anticipated system behavior and the observed behavior may be due to the processing of tasks internally, or due to external factors such as changes in program requirements or conditions associated with other organizations that are outside of MOD. The paper provides a roadmap for the four increments of this vision. These increments include (1) the existing capabilities (2) hardware and software system components and interfaces with the NASA ground system, (3) uncertainty management and (4) re-planning and automated execution. Each of these increments provides value independently; but some may also enable building of a subsequent increment.

Ruszkowski, James↗
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 NTRS1993

Operational characterisation of requirements and early validation environment for high demanding space systems

The definition of some modern high demanding space systems requires a different approach to system definition and design from that adopted for traditional missions. System functionality is strongly coupled to the operational analysis, aimed at characterizing the dynamic interactions of the flight element with its surrounding environment and its ground control segment. Unambiguous functional, operational and performance requirements are to be defined for the system, thus improving also the successive development stages. This paper proposes a Petri Nets based methodology and two related prototype applications (to ARISTOTELES orbit control and to Hermes telemetry generation) for the operational analysis of space systems through the dynamic modeling of their functions and a related computer aided environment (ISIDE) able to make the dynamic model work, thus enabling an early validation of the system functional representation, and to provide a structured system requirements data base, which is the shared knowledge base interconnecting static and dynamic applications, fully traceable with the models and interfaceable with the external world.

Barro, E.↗
DOE OSTI2025

Intelligent Manufacturing Support: Specialized LLMs for Composite Material Processing and Equipment Operation

Engineering educational curriculum and standards cover many material and manufacturing options. However, engineers and designers are often unfamiliar with certain composite materials or manufacturing techniques. Large language models (LLMs) could potentially bridge the gap. Their capacity to store and retrieve data from large databases provides them with a breadth of knowledge across disciplines. However, their generalized knowledge base can lack targeted, industry-specific knowledge. To this end, we present two LLM-based applications based on the GPT-4 architecture: (1) The Composites Guide: a system that provides expert knowledge on composites material and connects users with research and industry professionals who can provide additional support and (2) The Equipment Assistant: a system that provides guidance for manufacturing tool operation and material characterization. By combining the knowledge of general AI models with industry-specific knowledge, both applications are intended to provide more meaningful information for engineers. In this paper, we discuss the development of the applications and evaluate it through a benchmark and two informal user studies. The benchmark analysis uses the Rouge and Bertscore metrics to evaluate our models’ performance against GPT-4o. The results show that GPT-4o and the proposed models perform similarly or better on the ROUGE and BERTScore metrics. The two user studies supplement this quantitative evaluation by asking experts to provide qualitative and open-ended feedback about our model’s performance on a set of domain-specific questions. The results of both studies highlight a potential for more detailed and specific responses with the Composites Guide and the Equipment Assistant.

Kapoor, Gunnika [Oak Ridge National Laboratory (OR↗
DOE OSTI2026

Quantum Computing for AI-based Design and Optimization of Electric Motors

Knowledge-based artificial intelligence and hierarchical fuzzy logic offer an interpretable framework for electricvehicle motor preliminary design, but their computational burden grows with linguistic granularity and coupled design-space size. This paper presents a reduced quantum reformulation of the hierarchical fuzzy inference of air-gap flux density, a representative level-one motor-design parameter. Starting from the published electric-vehicle motor-design framework, a three-term fuzzy prototype is constructed from the original inference structure. The reduced model is then reformulated as a modular quantum register-oracle system, in which each hierarchical subrelation is encoded as a block oracle and evaluated through superpositionbased candidate-label testing. The proposed modular quantum formulation reproduces the reduced classical prototype after block fusion. A resource analysis shows that the reduced modular system requires seven qubits per block and twenty-two qubits in a straightforward four-block implementation. Finally, a crossovercomplexity model is derived to identify the regime in which quantum candidate search may become favorable relative to hierarchical fuzzy inference. The results show that no quantum advantage should be claimed for the present one-output reduced benchmark, but that a plausible crossover emerges for larger joint candidate spaces and higher linguistic granularity. The work therefore establishes a technically consistent starting point for future quantum-assisted electric-vehicle motor-design optimization.

Kumar, Praveen [ORNL] (ORCID:0000000291877857)↗
NASA NTRS2010

The Era of International Space Station Utilization Begins: Research Strategy, International Collaboration, and Realized Potential

With the assembly of the International Space Station (ISS) nearing completion and the support of a full-time crew of six, a new era of utilization for research is beginning. For more than 15 years, the ISS international partnership has weathered financial, technical and political challenges proving that nations can work together to complete assembly of the largest space vehicle in history. And while the ISS partners can be proud of having completed one of the most ambitious engineering projects ever conceived, the challenge of successfully using the platform remains. During the ISS assembly phase, the potential benefits of space-based research and development were demonstrated; including the advancement of scientific knowledge based on experiments conducted in space, development and testing of new technologies, and derivation of Earth applications from new understanding. The configurability and human-tended capabilities of the ISS provide a unique platform. The international utilization strategy is based on research ranging from physical sciences, biology, medicine, psychology, to Earth observation, human exploration preparation and technology demonstration. The ability to complete follow-on investigations in a period of months allows researchers to make rapid advances based on new knowledge gained from ISS activities. During the utilization phase, the ISS partners are working together to track the objectives, accomplishments, and the applications of the new knowledge gained. This presentation will summarize the consolidated international results of these tracking activities and approaches. Areas of current research on ISS with strong international cooperation will be highlighted including cardiovascular studies, cell and plant biology studies, radiation, physics of matter, and advanced alloys. Scientific knowledge and new technologies derived from research on the ISS will be realized through improving quality of life on Earth and future spaceflight endeavours. Extension of the ISS through 2020 and beyond will insure that the benefits of research will be achievable for the International Partnership.

Thumm, Tracy↗
NASA NTRS1988

A generic expert system for materials processing in space

A generic expert system is described for inspecting materials processed in space (MPS). The system may be applied, with the appropriate knowledge base, to any of the nondestructive testing methods (NDT) which are appropriate to MPS. Regardless of the method being used, the inspection process consists of three tasks: (1) signal or image processing of the NDT output and feature extraction, (2) interpretation of features in terms of MPS discontinuities, and (3) evaluation of the quality of the MPS based upon industry standards. In contrast to rule based systems, this system represents its knowledge as multidimensional vectors and appropriate functions on them. Currently, the expert system accepts manual input of observed features. Once the expert system has been tested and compared to human expert inspectors, a vision front-end will be developed to complete automation of the expert MPS inspection system, based on visual discontuities. Then the data base will be extended to include a variety of other NDT methods. In addition to functional performance, ease of use was established through menu window driven input as well as flexibility in building, using and modifying data bases for different applications.

Andersen, Kristinn↗
DOE OSTI2023

KEBLM: Knowledge-Enhanced Biomedical Language Models

Pretrained language models (PLMs) have demonstrated strong performance on many natural language processing (NLP) tasks. Despite their great success, these PLMs are typically pretrained only on unstructured free texts without leveraging existing structured knowledge bases that are readily available for many domains, especially scientific domains. As a result, these PLMs may not achieve satisfactory performance on knowledge-intensive tasks such as biomedical NLP. Comprehending a complex biomedical document without domain-specific knowledge is challenging, even for humans. Inspired by this observation, we propose a general framework for incorporating various types of domain knowledge from multiple sources into biomedical PLMs. We encode domain knowledge using lightweight adapter modules, bottleneck feed-forward networks that are inserted into different locations of a backbone PLM. For each knowledge source of interest, we pretrain an adapter module to capture the knowledge in a self-supervised way. We design a wide range of self-supervised objectives to accommodate diverse types of knowledge, ranging from entity relations to description sentences. Once a set of pretrained adapters is available, we employ fusion layers to combine the knowledge encoded within these adapters for downstream tasks. Each fusion layer is a parameterized mixer of the available trained adapters that can identify and activate the most useful adapters for a given input. Our method diverges from prior work by including a knowledge consolidation phase, during which we teach the fusion layers to effectively combine knowledge from both the original PLM and newly-acquired external knowledge using a large collection of unannotated texts. After the consolidation phase, the complete knowledge-enhanced model can be fine-tuned for any downstream task of interest to achieve optimal performance. Extensive experiments on many biomedical NLP datasets show that our proposed framework consistently improves the performance of the underlying PLMs on various downstream tasks such as natural language inference, question answering, and entity linking. These results demonstrate the benefits of using multiple sources of external knowledge to enhance PLMs and the effectiveness of the framework for incorporating knowledge into PLMs. Finally, while primarily focused on the biomedical domain in this work, our framework is highly adaptable and can be easily applied to other domains, such as the bioenergy sector.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2023

Machine learning guided optimal composition selection of niobium alloys for high temperature applications

Nickel- and cobalt-based superalloys are commonly used as turbine materials for high-temperature applications. However, their maximum operating temperature is limited to about 1100 °C. Therefore, to improve turbine efficiency, current research is focused on designing materials that can withstand higher temperatures. Niobium-based alloys can be considered as promising candidates because of their exceptional properties at elevated temperatures. The conventional approach to alloy design relies on phase diagrams and structure–property data of limited alloys and extrapolates this information into unexplored compositional space. In this work, we harness machine learning and provide an efficient design strategy for finding promising niobium-based alloy compositions with high yield and ultimate tensile strength. Unlike standard composition-based features, we use domain knowledge-based custom features and achieve higher prediction accuracy. We apply Bayesian optimization to screen out novel Nb-based quaternary and quinary alloy compositions and find these compositions have superior predicted strength over a range of temperatures. We develop a detailed design flow and include Python programming code, which could be helpful for accelerating alloy design in a limited alloy data regime.

Mohanty, Trupti (ORCID:0000000342701430)↗
DOE OSTI2025

Atomic layer deposition of nanofilms on porous polymer substrates: Strategies for success

Atomic layer deposition (ALD) is a versatile technique for engineering the surfaces of porous polymers, imbuing the flexible, high-surface-area substrates with inorganic and hybrid material properties. Previously reported enhancements include fouling resistance, electrical conductance, thermal stability, photocatalytic activity, hydrophilicity, and oleophilicity. However, there are many poorly understood phenomena that introduce challenges in applying ALD to porous polymers. In this paper, we address five common challenges and ways to overcome them: (1) entrapped precursor, (2) embrittlement, (3) film fracture, (4) deformation, and (5) pore collapse. These challenges are often interrelated and can exacerbate one another. To investigate these phenomena, we applied various ALD chemistries to porous polymers including polyethersulfone, polysulfone, polyvinylidene fluoride, and polycarbonate track-etched membranes. Reaction-diffusion modeling revealed why certain precursors and processing conditions result in embrittling subsurface material growth, entrapment of unreacted precursors, and nongrowth. We quantify the limits of ALD processing temperatures that are dictated by thermal expansion mismatch and can lead to fractured ALD films. The results herein allow us to make recommendations to avoid, mitigate, or overcome the difficulties encountered when performing ALD and plasma-enhanced ALD on porous polymers. We intend this article to serve as a “lessons learned” guide informed by previous experience to provide a better understanding of the difficulties and limitations of ALD on porous polymers and knowledge-based guidelines for successful depositions. This knowledge can accelerate future research and help experimentalists navigate and troubleshoot as they expose porous polymers to reactive precursor vapors.

36 MATERIALS SCIENCE↗
NASA NTRS1985

An investigation of the 'von Restorff' phenomenon in post-test workload ratings

The von Restorff effect in post-task ratings of task difficulty is examined. Nine subjects performed a hovercraft simulation task which combined elements of skill-based tracking and rule- and knowledge-based process control for five days of one hour sessions. The effects of isolated increases in workload on rating of task performance, and on the number of command errors and river band hits are analyzed. It is observed that the position of the workload increase affects the number of bank hits and command errors. The data reveal that factors not directly related to the task performance influence subjective rating, and post-task ratings of workload are biased.

Thornton, D. C.↗
NASA NTRS1990

A development framework for artificial intelligence based distributed operations support systems

Advanced automation is required to reduce costly human operations support requirements for complex space-based and ground control systems. Existing knowledge based technologies have been used successfully to automate individual operations tasks. Considerably less progress has been made in integrating and coordinating multiple operations applications for unified intelligent support systems. To fill this gap, SOCIAL, a tool set for developing Distributed Artificial Intelligence (DAI) systems is being constructed. SOCIAL consists of three primary language based components defining: models of interprocess communication across heterogeneous platforms; models for interprocess coordination, concurrency control, and fault management; and for accessing heterogeneous information resources. DAI applications subsystems, either new or existing, will access these distributed services non-intrusively, via high-level message-based protocols. SOCIAL will reduce the complexity of distributed communications, control, and integration, enabling developers to concentrate on the design and functionality of the target DAI system itself.

Adler, Richard M.↗
NASA NTRS1990

The real-time learning mechanism of the Scientific Research Associates Advanced Robotic System (SRAARS)

Scientific research associates advanced robotic system (SRAARS) is an intelligent robotic system which has autonomous learning capability in geometric reasoning. The system is equipped with one global intelligence center (GIC) and eight local intelligence centers (LICs). It controls mainly sixteen links with fourteen active joints, which constitute two articulated arms, an extensible lower body, a vision system with two CCD cameras and a mobile base. The on-board knowledge-based system supports the learning controller with model representations of both the robot and the working environment. By consecutive verifying and planning procedures, hypothesis-and-test routines and learning-by-analogy paradigm, the system would autonomously build up its own understanding of the relationship between itself (i.e., the robot) and the focused environment for the purposes of collision avoidance, motion analysis and object manipulation. The intelligence of SRAARS presents a valuable technical advantage to implement robotic systems for space exploration and space station operations.

Chen, Alexander Y.↗
NASA NTRS1993

An intelligent planning and scheduling system for the HST servicing missions

A new, intelligent planning and scheduling system has been delivered to NASA-Goddard Space Flight Center (GSFC) to provide support for the up-coming Hubble Space Telescope (HST) Servicing Missions. This new system is the Servicing Mission Planning and Replanning Tool (SM/PART). SM/PART is written in C and runs on a UNlX-based workstation (IBM RS/6000) under Motif. SM/PART effectively automates the complex task of building or rebuilding integrated timelines and command plans which are required by HST Servicing Mission personnel at their consoles during the missions. SM/PART is able to quickly build or rebuild timelines based on information stored in a Knowledge Base (KB) by using an Artificial Intelligence (AI) tool called the Planning And Resource Reasoning (PARR) shell. After a timeline has been built in the batch mode, it can be displayed and edited in an interactive mode with help from the PARR shell. Finally a detailed command plan is generated. The capability to quickly build or rebuild timelines and command plans provides an additional safety factor for the HST, Shuttle and Crew.

Johnson, Jay↗
NASA NTRS1990

An application of design knowledge captured from multiple sources

The Hubble Space Telescope Operational Readiness Expert Safemode Investigation System (HSTORESIS) is a reusable knowledge base shell used to demonstrate the integration and application of design knowledge captured from multiple technical domains. The design of HSTORESIS is based on a partitioning of knowledge to maximize the potential for reuse of certain types of knowledge.

Cox, Preston A.↗
NASA NTRS1994

AMPHION: Specification-based programming for scientific subroutine libraries

AMPHION is a knowledge-based software engineering (KBSE) system that guides a user in developing a diagram representing a formal problem specification. It then automatically implements a solution to this specification as a program consisting of calls to subroutines from a library. The diagram provides an intuitive domain oriented notation for creating a specification that also facilitates reuse and modification. AMPHION'S architecture is domain independent. AMPHION is specialized to an application domain by developing a declarative domain theory. Creating a domain theory is an iterative process that currently requires the joint expertise of domain experts and experts in automated formal methods for software development.

Lowry, Michael↗
NASA NTRS2022

Increasing the Uptake of Ecological Model Results in Policy Decisions to Improve Biodiversity Outcomes

Models help decision-makers anticipate the consequences of policies for ecosystems and people; for instance, improving our ability to represent interactions between human activities and ecological systems is essential to identify pathways to meet the 2030 Sustainable Development Goals. However, use of modeling outputs in decision-making remains uncommon. We share insights from a multidisciplinary National Socio-Environmental Synthesis Center working group on technical, communication, and process-related factors that facilitate or hamper uptake of model results. We emphasize that it is not simply technical model improvements, but active and iterative stakeholder involvement that can lead to more impactful outcomes. In particular, trust- and relationship-building with decision-makers are key for knowledge-based decision making. In this respect, nurturing knowledge exchange on the interpersonal (e.g., through participatory processes), and institutional level (e.g., through science-policy interfaces across scales), represent promising approaches. To this end, we offer a generalized approach for linking modeling and decision-making.

Biodiversity-ecosystem function relationships↗
NASA NTRS1994

Automated Consultation for the Diagnosis for Interplanetary Telecommunications

SHARP (Spacecraft Health Automated Reasoning Program) is a knowledge-based consultant for diagnosing problems in NASA'a Deep Space Network (DSN) telecommunications link in near real-time...This paper describes the most recent version, which is an operational system used by spacecraft mission contollers; previous versions are described elsewhere...

DSN deep space network↗