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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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Introduce mobility challenge, how air mobility can address the mobility challenge, then how an Air Mobility Data & Reasoning Fabric can enable the envisioned future air mobility. Poses the question of what is an effective role for NASA in enabling an Air Mobility Data & Reasoning Fabric.
As envisioned, Advanced Air Mobility (AAM) and Urban Air Mobility (UAM) will introduce new vehicles and operations within the national airspace, moving people and cargo safely and efficiently at a much larger scale than today. Driven by transformative technology and revolutionary aircraft, this movement must still manage technical, regulatory, operational, and policy challenges. NASA’s work in support of AAM and UAM includes, but is not limited to tools, technologies, and architectures for distributed sensing of aircraft, data & reasoning services exchange, Human-Autonomy Teaming (HAT), contingency management, and vehicle health management. This paper builds upon these concepts and evaluates the use of distributed sensing and infrastructure assistance towards health management and mission assurance of UAM vehicles in specific operational scenarios. Through analysis of these example missions, aided by the data produced by the conceptual distributed sensing and reasoning infrastructure, we define opportunities for state estimation, diagnosis, and key decision points affecting the health state of the vehicle and the airspace volume. As a result, we define a number of measurable health state parameters providing relevant information to drive decision-making in contingency situations or feed automation tools in support of operators and managers.
Scientific hypothesis generation represents a fundamental challenge in contemporary research due to exponentially expanding literature volumes and increasing disciplinary specialization. Large language models (LLMs) have emerged as transformative tools for automated scientific discovery, moving beyond traditional rule-based and literature-mining approaches. Four paradigmatic approaches define current LLM-driven hypothesis generation: direct prompting and fine-tuning methods, knowledge-enhanced frameworks integrating retrieval-augmented generation (RAG), multi-agent collaborative systems simulating research teams, and reasoning-focused approaches implementing cognitive architectures. Domain-specific applications demonstrate statistical equivalence to human expert performance in social psychology, experimental validation in biomedical research, and near-expert quality in astronomy. Evaluation methodologies encompass human expert assessment, LLM-as-judge frameworks, and comprehensive benchmarking systems. Technical challenges include hallucination management, knowledge integration limitations, and balancing novelty with feasibility. Future directions emphasize hybrid neural-symbolic architectures and sophisticated human-AI collaboration models for responsible scientific discovery acceleration.
This paper describes rover safety issues and presents an approximate reasoning approach to maintaining vehicle safety in a navigational context.
This paper discusses the system engineering techniques used to date, especially the application of heuristic reasoning, in the design, development, and test of the Deep Space One spacecraft.
The Data & Reasoning Fabric activity, under the Convergent Aeronautics Solutions (CAS) Project has developed a video, suitable for public release.
The Data & Reasoning Fabric activity, under the Convergent Aeronautics Solutions (CAS) Project has developed images and website content suitable for public release.
The Data & Reasoning Fabric activity, under the Convergent Aeronautics Solutions (CAS) Project has developed images and content suitable for public outreach. Content and images are expected to be presented to National League of Cities, US Ignite, Association for Unmanned Vehicle Systems International (AUVSI), Federal Aviation Administration (FAA), cities, and local governments.
The Data & Reasoning Fabric activity, under the Convergent Aeronautics Solutions (CAS) Project has developed images and content for an e-brochure suitable for public outreach.
The Data & Reasoning Fabric activity, under the Convergent Aeronautics Solutions (CAS) Project has developed a presentation suitable for public outreach. Content and images are expected to be presented publicly, including at EAA Airventure Oshkosh 2022.
The DRF team in collaboration with the California Civil Air Patrol (CAP), executed a field test that identified a preliminary set of data and reasoning service areas that can be enabled and enhanced by DRF to potentially bring forth improved accuracy and reduced latency to CAP’s mission critical decision making, in wildfire detection.
There are multiple opportunities to utilize Data & Reasoning Fabric technologies in wildfire response: (1) Supporting autonomous agents to discover data sources and transact with systems, (2) Fusing data in real-time, and (3) Supporting complex operations in a dynamic real-time environment .
The Data & Reasoning Fabric activity is submitting a response to a request for information answering, "Topic 2: Critical elements of AAM requiring global interoperability and harmonization". If selected this paper will be expected to be presented publicly.
With the rise of Large Language Model (LLM) generated code, including in domains like scientific computing, ensuring not only syntactical, but also mathematical correctness, has become a critical task. Traditional formal methods approaches often struggle with the ambiguity of floating-point code, and full symbolic execution is extremely costly and limited. We propose a chain-of-reasoning approach that iteratively lifts basic semantics from code into the SPIRAL system and then establishes numerical equivalency to the desired mathematical operation. Here, we leverage the ample mathematical knowledge already formalized in SPIRAL to enable the system to recognize not just different implementations of the same algorithm but fully separate approaches to solving the given problem. The chain establishes tight error bounds on the output of given code with respect to the true continuous solution it approximates, quantifying all sources of error. We demonstrate this approach by establishing the correctness of a pseudospectral solver for a simple 1-dimensional Poisson problem.
ChemReasoner - Catalyst Discovery via Large Language Model-driven Reasoning
Reasons for yearly variation of atmospheric electric field potential gradient
When heuristic problem-solving programs are faced with large data bases that contain numbers of facts far in excess of those needed to solve any particular problem, their performance rapidly deteriorates. In this paper, the correspondence between a new unsolved problem and a previously solved analogous problem is computed and invoked to tailor large data bases to manageable sizes. This paper outlines the design of an algorithm for generating and exploiting analogies between theorems posed to a resolution-logic system. These algorithms are believed to be the first computationally feasible development of reasoning by analogy to be applied to heuristic theorem proving.
The reasons for the low aerodynamic performance of a 13.5 cm tip diameter aircraft engine starter turbine were investigated. Both the stator and the stage were evaluated. Approximately 10 percent improvement in turbine efficiency was obtained when the honeycomb shroud over the rotor blade tips was filled to obtain a solid shroud surface. Efficiency improvements were obtained for three rotor configurations when the shroud was filled. It is suggested that the large loss associated with the open honeycomb shroud is due primarily to energy loss associated with gas transportation as a result of the blade to blade pressure differential at the tip section.