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At least 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Avoiding Selection Bias in Generating Examples of Plans in the Presence of Heuristic Error

It is generally understood that heuristic error hurts the performance of search algorithms, measured in terms of search effort. Hence there is an interest in understanding how to reduce heuristic error. One way to do this is to learn a heuristic from a set of examples of plans generated offline, e.g. bootstrapping methods. In this paper, we consider how some methods for generating examples of plans may skew the training set in the presence of heuristic errors. Initial theoretical results show that duplicate detection is one source of selection bias in the canonical A* algorithm. We introduce a duplicate selection scheme for A* that avoids selection bias in generating cost-optimal examples, without compromising memory efficiency, and develop ideas in the satisficing setting. We evaluate our approach on n x m grids with multiple cost-optimal solutions and synthetic heuristic error. Finally, we attempt to extend these ideas to the problem of generating extreme examples of plans.

Alison S Paredes↗
NASA NTRSDate not supplied

Retrieval-Augmented Generation and LLM Agents for Biomimicry Design Solutions

We present BIDARA, a Bio-Inspired Design And Research Assistant, to address the complexity of biomimicry – the practice of designing modern-day engineering solutions inspired by biological phenomena. Large Language Models (LLMs) have been shown to act as sufficient general purpose task solvers, but they often hallucinate and fail in regimes that require domain-specific and up-to-date knowledge. We integrate Retrieval-Augmented Generation (RAG) and Reasoning-and-Action agents to aid LLMs in avoiding hallucination and utilizing updated knowledge during generation of biomimetic design solutions. We find that incorporating RAG increases the feasibility of the design solutions in both prompting and agent settings, and we use these findings to guide our ongoing work. To the extent of our knowledge, this is the first work that integrates and evaluates Retrieval-Augmented Generation within LLM-generated biomimetic design solutions.

biomimicry↗
NASA NTRSDate not supplied

Retrieval-Augmented Generation and LLM Agents for Biomimicry Design Solutions

We present BIDARA, a Bio-Inspired Design And Research Assistant, to address the complexity of biomimicry – the practice of designing modern-day engineering solutions inspired by biological phenomena. Large Language Models (LLMs) have been shown to act as sufficient general purpose task solvers, but they often hallucinate and fail in regimes that require domain-specific and up-to-date knowledge. We integrate Retrieval-Augmented Generation (RAG) and Reasoning-and-Action agents to aid LLMs in avoiding hallucination and utilizing updated knowledge during generation of biomimetic design solutions. We find that incorporating RAG increases the feasibility of the design solutions in both prompting and agent settings, and we use these findings to guide our ongoing work. To the extent of our knowledge, this is the first work that integrates and evaluates Retrieval-Augmented Generation within LLM-generated biomimetic design solutions.

biomimicry↗
NASA NTRSDate not supplied

Implementation of a Hybrid Edge Node-Centroid Node Approach for the Generation of Reduced Thermal Models

Reduced thermal models are often required for delivery to organizations that manage models at the highest levels of assembly. The effort to generate/verify these models against their detailed counterparts is a challenge not yet conclusively solved. Higher level organizations often place a limit on the node count for delivered models, assuming smaller models result in faster computation. The burden of producing and verifying the accuracy of reduced models is primarily on the lower-level organizations, which consumes resources to produce these models. Limiting the number of allowable nodes may prevent users from taking full advantage of software capabilities that allow for faster generation of models, such as finite elements, which require more nodes than centroid based models. A methodology using Thermal Desktop was described in 2010 using: (1) finite elements and edge nodes for the conduction matrix, (2) centroid nodes for capacitance and radiative computations, and (3) the super network feature to produce a conduction matrix based only on the centroid nodes. While the methodology was clear, the implementation would have had to be done manually; with the inclusion of the OpenTD Application Programming Interface, this methodology can now be implemented programmatically and be a viable approach for the generation of reduced models. The approach was implemented at NASA-Goddard for the Capture-Containment-and-Return-System (CCRS) payload, resulting in the TCYEE tool. CCRS requires delivery with node limitations through the European Space Agency to its contractors. TCYEE generated the reduced model for delivery and the predictions compared favorably to the detailed model. Furthermore, TCYEE is being explored for potential use on the Roman Space Telescope (RST) for the generation of reduced models for delivery to the launch provider, which also has node limit requirements. This paper describes the methodology, implementation, and the performance of reduced models compared to their detailed counterparts.

Computer Programming and Software↗
NASA NTRSDate not supplied

Evaluating the Efficacy of Conditional Variational Autoencoders in Generating Synthetic Single Nuclei RNA-Seq Data for Space Biology Research

Astronauts are subject to unique stressors during spaceflight, leading to changes in their cellular function. However, neither astronauts nor model organisms respond the same to spaceflight, and research implicates a contribution of omics components in differential responses. Understanding how gene expression affects astronaut health is critical for the success of long-term space missions, prompting interest in developing personalized predictive models leveraging artificial intelligence (AI) and machine learning (ML) techniques. Developing such models requires extensive data, which is challenging to obtain and share. This study explores the use of conditional variational autoencoders (CVAEs) to synthetically generate single-nuclei RNA-seq (snRNA-seq) data. CVAEs build on standard variational autoencoders (VAEs) by conditioning data generation on covariates like sample identity and mission parameters, enhancing the relevance of generated data for specific contexts. For our work, we built two CVAEs with varying degrees of sparsity to optimize both interpretability and generative power. We train and validate models on existing snRNA-seq data collected from the brain tissue of mice subjected to spaceflight conditions and their ground control counterparts. We evaluate model performance using statistical tests and visualizations to compare synthetic data to real data. We aim to demonstrate that these prototype CVAE architectures could be used in future space biology work and that this is a method worth further exploring.

Sarah Golts↗
NASA NTRSDate not supplied

Numerical Investigation of Heat Transfer and Fluid Flow within Electrochemical Hydrogen Peroxide Generation Unit

Long-term manned space missions require the onboard production of disinfectants essential for maintaining crew health and supporting life systems. Currently, disinfection aboard the International Space Station (ISS) relies on disposable wetted wipes, which are regularly resupplied from Earth. This approach imposes a significant burden on resupply logistics, storage, and waste management. To address these challenges and support future missions, efforts are underway to develop an in-situ solution that electrochemically generates hydrogen peroxide disinfectant using onboard resources. In collaboration with NASA, Faraday Technology, Inc. has advanced this concept through a series of Small Business Innovation Research (SBIR) projects, resulting in the development of a Peroxide Generation Unit (PGU). The PGU can produce up to 3 wt.% hydrogen peroxide on-demand at a rate of 1 liter per day, providing a sustainable alternative to Earth-dependent supplies. The resulting aqueous hydrogen peroxide (H₂O₂) is an effective disinfectant, safe for crew use, compatible with spacecraft systems, and free from volatiles, off-gassing, or residues. This innovation offers a reliable, efficient solution for onboard disinfection, reducing dependence on Earth-based resupply while ensuring the health and safety of space crews. Generating hydrogen peroxide at the required rate needs high voltages and currents, exceeding 20V and 2A respectively, which leads to significant heat generation from Joule heating. This temperature rise poses a risk to sensitive system components, especially critical and expensive membranes that can degrade under thermal stress. To mitigate this risk, the thermal, fluid, and electrical flows within the system are modeled computationally using the commercial software COMSOL. The numerical simulations are validated against experimental data from both sub-scale and alpha-scale systems. Once verified, the model is employed to identify thermal hotspots, investigate their underlying causes, and explore solutions to prevent them.

Life Support Systems (LSS)↗
NASA NTRSDate not supplied

Americium-Fuelled Radioisotope Stirling Generator Elegant Breadboard

High power solutions for spacecraft are becoming increasingly sought after in the new era of space exploration, with energy intensive in-situ lunar surface exploration becoming a key part of the roadmap for major space agencies, as well as commercial companies. Nuclear power provides the most feasible solution to achieve continued operation through the long lunar night, and in extended periods in Permanently Shadowed Regions (PSRs). Americium-241 (241Am) is used as the fuel of choice in European Radioisotope Power Systems (RPS). The University of Leicester have developed a number of systems using this fuel type, including a 200 Wth heat source. Building on existing international partnerships, the University of Leicester is continuing to work with NASA Glenn Research Center (GRC) to maximise the electrical output that can be generated from the heat source by progressing the radioisotope-Stirling generator concept to elegant breadboard level, building on the long heritage of dynamic power conversion developments at NASA Glenn. Presented in the following conference paper is the next iteration of the Americium-fuelled Radioisotope Stirling Generator (Am-RSG) Elegant Breadboard, following on from a successful technology demonstration design conducted at NASA Glenn in early 2025. The design laid-out in this paper is a more flight-like version of the technology demonstrator, and is intended to pair with the next generation of Stirling convertor technology, the SunPower Robust Stirling Converter (SRSC). The SRSC is able to operate more efficiently at lower thermal power levels than the previous Advanced Stirling Convertor (ASC), making it more suitable for the 241Am system.

Lunar↗
NASA NTRS2016

Maturing Technologies for Stirling Space Power Generation

Stirling Radioisotope Power Systems (RPS) are being developed as an option to provide power on future space science missions where robotic spacecraft will orbit, flyby, land or rove. A Stirling Radioisotope Generator (SRG) could offer space missions a more efficient power system that uses one fourth of the nuclear fuel and decreases the thermal footprint of the current state of the art. The RPS Program Office, working in collaboration with the U.S. Department of Energy (DOE), manages projects to develop thermoelectric and dynamic power systems, including Stirling Radioisotope Generators (SRGs). The Stirling Cycle Technology Development (SCTD) Project, located at Glenn Research Center (GRC), is developing Stirling-based subsystems, including convertors and controllers. The SCTD Project also performs research that focuses on a wide variety of objectives, including increasing convertor temperature capability to enable new environments, improving system reliability or fault tolerance, reducing mass or size, and developing advanced concepts that are mission enabling. Research activity includes maturing subsystems, assemblies, and components to prepare them for infusion into future convertor and generator designs. The status of several technology development efforts are described here. As part of the maturation process, technologies are assessed for readiness in higher-level subsystems. To assess the readiness level of the Dual Convertor Controller (DCC), a Technology Readiness Assessment (TRA) was performed and the process and results are shown. Stirling technology research is being performed by the SCTD Project for NASA's RPS Program Office, where tasks focus on maturation of Stirling-based systems and subsystems for future space science missions.

Advanced Stirling Radioisotope Generator↗
NASA NTRS2016

Maturing Technologies for Stirling Space Power Generation

Stirling Radioisotope Power Systems (RPS) are being developed as an option to provide power on future space science missions where robotic spacecraft will orbit, flyby, land or rove. A Stirling Radioisotope Generator (SRG) could offer space missions a more efficient power system that uses one fourth of the nuclear fuel and decreases the thermal footprint of the current state of the art. The RPS Program Office, working in collaboration with the U.S. Department of Energy (DOE), manages projects to develop thermoelectric and dynamic power systems, including Stirling Radioisotope Generators (SRGs). The Stirling Cycle Technology Development (SCTD) Project, located at Glenn Research Center (GRC), is developing Stirling-based subsystems, including convertors and controllers. The SCTD Project also performs research that focuses on a wide variety of objectives, including increasing convertor temperature capability to enable new environments, improving system reliability or fault tolerance, reducing mass or size, and developing advanced concepts that are mission enabling. Research activity includes maturing subsystems, assemblies, and components to prepare them for infusion into future convertor and generator designs. The status of several technology development efforts are described here. As part of the maturation process, technologies are assessed for readiness in higher-level subsystems. To assess the readiness level of the Dual Convertor Controller (DCC), a Technology Readiness Assessment (TRA) was performed and the process and results are shown. Stirling technology research is being performed by the SCTD Project for NASA's RPS Program Office, where tasks focus on maturation of Stirling-based systems and subsystems for future space science missions.

High-Temperature Linear Alternator↗
NASA NTRS2002

Probabilistic #D data fusion for multiresolution surface generation

In this paper we present an algorithm for adaptive resolution integration of 3D data collected from multiple distributed sensors. The input to the algorithm is a set of 3D surface points and associated sensor models. Using a probabilistic rule, a surface probability function is generated that represents the probability that a particular volume of space contains the surface. The surface probability function is represented using an octree data structure; regions of space with samples of large conariance are stored at a coarser level than regions of space containing samples with smaller covariance. The algorithm outputs an adaptive resolution surface generated by connecting points that lie on the ridge of surface probability with triangles scaled to match the local discretization of space given by the algorithm, we present results from 3D data generated by scanning lidar and structure from motion.

3D data fusion multiresolution surface generation ↗
NASA NTRS2018

Automated Scenario Generation for Human-in-the-Loop Simulations

Automated Multi-Aircraft Control System scenario generation for Human-in-the-Loop evaluations of air traffic management concepts is described. Methods for analyzing and comparing the seed-scenario generated using the automated process and the Human-in-the-Loop-scenario designed to meet the experiment objectives are discussed. The main findings are: (1) many of the characteristics of the seed-scenario used for constructing the Human-in-the-Loop-scenario are preserved in the Human-in-the-Loop Scenario, (2) landing characteristics of the traffic generated by the Multi-Aircraft Control System using the input scenario compare reasonably well with that intended in the input scenario, and (3) many of desired characteristics of the Human-in-the-Loop-scenario can be achieved by further automation.

SMART-NAS Testbed↗
NASA NTRS2018

Automated Scenario Generation for Human-in-the-Loop Simulations

Automated Multi-Aircraft Control System scenario generation for Human-in-the-Loop (HITL) evaluations of air traffic management concepts is described. The objective is to replace the difficult manual process with the automated process for creating an initial (seed) scenario that serves as a starting point for manual adjustments for creating the Human-in-the-Loop scenario. Methods for analyzing and comparing the seed-scenario generated using the automated process and the Human-in-the-Loop-scenario derived from it to meet the experiment objectives are discussed. Results of comparison of input Human-in-the-Loop-scenario with the Multi-Aircraft Control System output are also presented. The main findings are: (1) many of the characteristics of the seed-scenario used for constructing the Human-in-the-Loop-scenario are preserved in the Human-in-the-Loop-scenario, (2) landing rate profile of the traffic generated by the Multi-Aircraft Control System (MACS) using the input scenario compares reasonably well with that intended in the input scenario, and (3) many of the desired characteristics of the Human-in-the-Loop-scenario can be achieved by further automation.

Automated Scenario Generation↗
NASA NTRS2015

Generation of Library Models for Verification of Android Applications

Android applications are difficult to verify and test since they have many external dependencies. To overcome this problem, environment generation can be used to create a model of the environment to simulate the behavior of these external dependencies. Creating this environment model manually is a tedious process and although there are many techniques available to generate models, the key lies in identifying how these techniques can be applied to a specific domain. In this paper we discuss two static analysis tools OCSEGen and Modgen and how they can be applied to the Android domain to generate models for specific parts of the environment.

Verification↗
NASA NTRSDate not supplied

Inlet Vortex Generator Design for the X-59 Low Boom Flight Demonstrator

An array of vortex generators was designed for the X-59 Low Boom Flight Demonstrator aircraft inlet. Prior to the a High Speed Inlet Distortion Test, concern regarding potentially high measurements of steady state or dynamic distortion prompted an effort to examine the use of passive flow control to reduce distortion levels. Design of experiments methodology was used to develop two matrices of vortex generators placed inside the inlet serpentine duct and upstream of the inlet on the compression surface, varying on several geometric parameters. Reynolds-Averaged Navier-Stokes simulations were performed using the FUN3D flow solver at Mach 1.45 to analyze the impact of each configuration on total pressure recovery, circumferential distortion, and radial distortion. Results from simulations of the baseline aircraft are compared with data acquired in the NASA Glenn Research Center’s 8- by 6-foot Supersonic Wind Tunnel. A vortex generator configuration with vanes placed upstream of the inlet terminal shock was shown to significantly increase total pressure recovery and decrease circumferential distortion with no penalty to radial distortion levels. Inlet mass flow sweeps at Mach 1.45, 0.6, and 0.3 indicated no detrimental effects from the vanes at off-design conditions.

Vortex generators↗
NASA NTRSDate not supplied

Inlet Vortex Generator Design for the X-59 Low Boom Flight Demonstrator

An array of vortex generators was designed for the X-59 Low Boom Flight Demonstrator aircraft inlet. Prior to the a High Speed Inlet DistortionTest, concern regarding potentially high measurements of steady state or dynamic distortion prompted an effort to examine the use of passive flow control to reduce distortion levels. Design of experiments methodology was used to develop two matrices of vortex generators placed inside the inlet serpentine duct and upstream of the inlet on the compression surface, varying on several geometric parameters. Reynolds- Averaged Navier-Stokes simulations were performed using the FUN3D flow solver at Mach 1.45 to analyze the impact of each configuration on total pressure recovery, circumferential distortion, and radial distortion. Results fromsimulations of the baseline aircraft are compared with data acquired in the NASA Glenn Research Center’s 8- by 6-foot SupersonicWind Tunnel. A vortex generator configuration with vanes placed upstream of the inlet terminal shock was shown to significantly increase total pressure recovery and decrease circumferential distortion with no penalty to radial distortion levels. Inlet mass flow sweeps at Mach 1.45, 0.6, and 0.3 indicated no detrimental effects from the vanes at off-design conditions.

Vortex generators↗
NASA NTRSDate not supplied

Aerodynamic loss generation in a low-pressure turbine stage with high lift blading

The present work aims to investigate the loss generation mechanisms in a highly loaded low-pressure turbine (LPT) stage. The turbine employs high-lift airfoils to reduce the number of blades and therefore the overall engine weight. Large Eddy Simulation (LES) is applied to study the detailed flow fields at two representative Reynolds numbers (150,000 and 40,000). The calculated flow fields are compared with available measured data. LES calculates the measured aerodynamic performance of the current turbine stage well. Both the measurements and the CFD simulations indicate that most of the aerodynamic loss is generated by the development of secondary flows near the rotor hub. An area of low momentum and high aerodynamic loss forms at roughly 25 % span above the hub downstream of the rotor. Formation and migration of a vortex system near the hub is the primary source of the loss generation. The hub wall vortex system in this highly loaded turbine stage is quite different from the horse-shoe vortex system in a typical stationary turbine cascade. Calculated instantaneous and time-averaged flow fields at two Reynolds numbers are examined in detail to study Reynolds number effects on the turbine stage performance. Steady and unsteady flow separation on the rotor suction side is calculated at both high and low Reynolds numbers. At the lower Reynolds number of 40,000, thicker blade boundary layers increase the area of flow separation on the rotor suction side, resulting in a 1.9 % increase in aerodynamic loss compared to the higher Reynolds number case.

low pressure turbine↗
NASA NTRS2023

Loft: An Automated Mesh Generator for Stiffened-Shell Aerospace Vehicles

Loft is an automated mesh generation code designed for aerospace vehicle structures. Based on user input, it can generate meshes for wings, noses, tanks, fuselage sections, thrust structures, etc. As the mesh is generated, each element is assigned properties that mark what part of the vehicle it is associated with. This property assignment is an extremely powerful feature making possible detailed analysis tasks such as load application and sizing. Loft can save its meshes in NASTRAN bulk data deck, EDS’ I-DEAS Universal File format, Abaqus input file format, VRML 2.0 (Virtual Reality Modeling Language) and STL (Stereo Lithography) files. The property assignment scheme was designed to make sizing in Collier Research’s HyperSizer easy. Support for other mesh storage formats can be added as needed.

Structural modeling↗
NASA NTRS2023

Utilizing Code Generation from Models for Electric Aircraft Motor Controller Flight Software

The processes followed to develop and test safety-critical flight software significantly increase development effort and overhead in comparison to both non-safety-critical and non-flight software. Generating code from models can be used to reduce development effort; however, the processes for using code generation to develop flight software are relatively immature. This work presents an approach for developing safety-critical flight software utilizing code generation from models to reduce development time and effort. The presented use-case is of electric aircraft motor control software developed for NASA's X-57 Maxwell aircraft.

flight software↗