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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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At least 1,495 records · Page 83
All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

A comparison of optimization-based approaches for solving the aerodynamic design problem

Three optimization-based methods for solving aerodynamic design problems are compared. The Euler equations for one-dimensional duct flow was used as a model problem, and the three methods are compared for efficiency, robustness, and implementation difficulty. The smoothness of the design problem with respect to different shock-capturing finite difference schemes, and in the presence of grid refinement, is investigated.

Frank, Paul D.↗
NASA NTRS1998

UV Generation of 25 mJ/pulse at 289 nm for Ozone Lidar

Our paper describes a technique for generating tunable UV laser radiation between 250-300 nm capable of energies up to 30-5O mJ/pulse. The tunability of this source is attractive for selecting ozone absorption cross sections which are optimal for ozone DIAL detection throughout the troposphere. A Nd:YAG laser is used to pump a pulsed titanium sapphire laser which is then frequency tripled into the UV. Titanium sapphire (TiS) lases robustly between 750-900 nm. In initial experiments we have converted 110 mJ of 867 nm from a TiS laser into 28 mJ at 289 nm. The energy conversion efficiency was 62% for doubling into 433 nm and 25% into 289 nm.

Storm, Mark E.↗
NASA NTRS2006

An Overview of the Role of Systems Analysis in NASA's Hypersonics Project

NASA's Aeronautics Research Mission Directorate recently restructured its Vehicle Systems Program, refocusing it towards understanding the fundamental physics that govern flight in all speed regimes. Now called the Fundamental Aeronautics Program, it is comprised of four new projects, Subsonic Fixed Wing, Subsonic Rotary Wing, Supersonics, and Hypersonics. The Aeronautics Research Mission Directorate has charged the Hypersonics Project with having a basic understanding of all systems that travel at hypersonic speeds within the Earth's and other planets atmospheres. This includes both powered and unpowered systems, such as re-entry vehicles and vehicles powered by rocket or airbreathing propulsion that cruise in and accelerate through the atmosphere. The primary objective of the Hypersonics Project is to develop physics-based predictive tools that enable the design, analysis and optimization of such systems. The Hypersonics Project charges the systems analysis discipline team with providing it the decision-making information it needs to properly guide research and technology development. Credible, rapid, and robust multi-disciplinary system analysis processes and design tools are required in order to generate this information. To this end, the principal challenges for the systems analysis team are the introduction of high fidelity physics into the analysis process and integration into a design environment, quantification of design uncertainty through the use of probabilistic methods, reduction in design cycle time, and the development and implementation of robust processes and tools enabling a wide design space and associated technology assessment capability. This paper will discuss the roles and responsibilities of the systems analysis discipline team within the Hypersonics Project as well as the tools, methods, processes, and approach that the team will undertake in order to perform its project designated functions.

Robinson, Jeffrey S.↗
NASA NTRS2007

A Spitzer Infrared Radius for the Transiting Extrasolar Planet HD 209458 b

We have measured the infrared transit of the extrasolar planet HD 209458 b using the Spitzer Space Telescope. We observed two primary eclipse events (one partial and one complete transit) using the 24 micrometer array of the Multiband Imaging Photometer for Spitzer (MIPS). We analyzed a total of 2392 individual images (10-second integrations) of the planetary system, recorded before, during, and after transit. We perform optimal photometry on the images and use the local zodiacal light as a short-term flux reference. At this long wavelength, the transit curve has a simple box-like shape, allowing robust solutions for the stellar and planetary radii independent of stellar limb darkening, which is negligible at 24 micrometers. We derive a stellar radius of R(sub *) = 1.06 plus or minus 0.07 solar radius, a planetary radius of R(sub p) = 1.26 plus or minus 0.08 R(sub J), and a stellar mass of 1.17 solar mass. Within the errors, our results agree with the measurements at visible wavelengths. The 24 micrometer radius of the planet therefore does not differ significantly compared to the visible result. We point out the potential for deriving extrasolar transiting planet radii to high accuracy using transit photometry at slightly shorter IR wavelengths where greater photometric precision is possible.

Richardson, L. Jeremy↗
NASA NTRS2008

High-Temperature Strain Sensing for Aerospace Applications

Thermal protection systems (TPS) and hot structures are utilizing advanced materials that operate at temperatures that exceed abilities to measure structural performance. Robust strain sensors that operate accurately and reliably beyond 1800 F are needed but do not exist. These shortcomings hinder the ability to validate analysis and modeling techniques and hinders the ability to optimize structural designs. This presentation examines high-temperature strain sensing for aerospace applications and, more specifically, seeks to provide strain data for validating finite element models and thermal-structural analyses. Efforts have been made to develop sensor attachment techniques for relevant structural materials at the small test specimen level and to perform laboratory tests to characterize sensor and generate corrections to apply to indicated strains. Areas highlighted in this presentation include sensors, sensor attachment techniques, laboratory evaluation/characterization of strain measurement, and sensor use in large-scale structures.

Piazza, Anthony↗
NASA NTRS2015

High Temperature Joining and Characterization of Joint Properties in Silicon Carbide-Based Composite Materials

Advanced silicon carbide-based ceramics and composites are being developed for a wide variety of high temperature extreme environment applications. Robust high temperature joining and integration technologies are enabling for the fabrication and manufacturing of large and complex shaped components. The development of a new joining approach called SET (Single-step Elevated Temperature) joining will be described along with the overview of previously developed joining approaches including high temperature brazing, ARCJoinT (Affordable, Robust Ceramic Joining Technology), diffusion bonding, and REABOND (Refractory Eutectic Assisted Bonding). Unlike other approaches, SET joining does not have any lower temperature phases and will therefore have a use temperature above 1315C. Optimization of the composition for full conversion to silicon carbide will be discussed. The goal is to find a composition with no remaining carbon or free silicon. Green tape interlayers were developed for joining. Microstructural analysis and preliminary mechanical tests of the joints will be presented.

Joining↗
NASA NTRS2015

3D Photocatalytic Air Processor for Dramatic Reduction of Life Support Mass and Complexity

To dramatically reduce the cost and risk of CO2 management systems in future extended missions, we have conducted preliminary studies on the materials and device development for advanced "artificial photosynthesis" reaction systems termed the High Tortuosity PhotoElectroChemical (HTPEC) system. Our Phase I studies have demonstrated that HTPEC operates in much the same way a tree would function, namely directly contacting the cabin air with a photocatalyst in the presence of light and water (as humidity) to immediately conduct the process of CO2 reduction to O2 and useful, "tunable" carbon products. This eliminates many of the inefficiencies associated with current ISS CO2 management systems. We have laid the solid foundation for Phase II work to employ novel and efficient reactor geometries, lighting approaches, 3D manufacturing methods and in-house grown novel catalytic materials.The primary objective of the proposed work is to demonstrate the scientific and engineering foundation for light-activated, compact devices capable of converting CO2 to O2 and mission-relevant carbon compounds. The proposed HTPEC CO2 management system will demonstrate a novel pathway with high efficiency and reliability in a compact, lightweight reactor architecture. The proposed HTPEC air processing concept can be developed in multiple architectures, such as centralized processing as well as "artificial leaves" distributed throughout the cabin that utilize pre-existing cabin ventilation (wind). Additionally, HTPEC can be deployed with spectrally tunable collection channels for selectable product generation. HTPEC employs light as its only energy source to remove and convert waste CO2 using a non-toxic composite catalyst.We have demonstrated in the Phase I studies the production, tunability and robustness of the novel composite catalysts following the preliminary work in the Chen laboratory. Additionally, we have designed, fabricated and tested all components of HTPEC device with active materials, including flow modeling to optimize flow mixing and pressure drop as well as the production of ethylene and other larger hydrocarbons. To best determine how this technology could be implemented, we also performed system integration optimization and trade studies. This includes parameters such as mass, volume, power in relation to selected mission configurations, CO2 delivery methods and light source/delivery approaches.

Artificial Photosynthesis↗
NASA NTRS2020

Launch Ready! High-Resolution Site Characterization and the Expedited Remediation of a Multi-Acre Chlorinated Volatile Organic Compound Plume at NASA’s Launch Complex 39B

Geosyntec teamed with the National Aeronautics and Space Administration (NASA) to restore groundwater resources and protect sensitive aquatic habitats at Launch Complex 39B (LC39B), a National Historic Site located within the John F. Kennedy Space Center (KSC) on the east coast of Florida (Figure 1). The goal of this project was to remediate chlorinated volatile organic compound (CVOC) impacts in groundwater that had the potential to discharge to the sensitive habitat surrounding LC39B and to complete the remediation before NASA’s Artemis Program’s Space Launch System missions began. The Geosyntec NASA team met this goal by developing a robust Conceptual Site Model (CSM) using High Resolution Site Characterization (HRSC), using the CSM to design a multifaceted and highly specialized air sparge (AS) system to remediate the groundwater, and installing, operating, and optimizing the AS system to achieve the Corrective Action Objective (CAOs). The CAOs were achieved after only 2 years of AS system operation, which has significantly reduced the CVOC concentrations and mass present in the groundwater and mitigated the potential discharge of impacted groundwater to the surrounding sensitive aquatic habitat, all before the Artemis Program’s launches at LC39B.

Rebecca C. Deprato↗
NASA NTRS2025

Mission-Maps For Outbound Cislunar Transfer Trajectories

This study quantifies the robustness and sensitivity of an outbound cislunar trajectory for a lunar lander in the form of mission-maps, or topological maps that allows either a computer program or mission designer to intuitively optimize the placement of critical outbound correction burns from the derived sensitivity data. The non-linear multi-body dynamics are applied to generate an outbound cislunar reference profile used by a linear covariance analysis (LinCov) tool to compute the expected Δv and trajectory dispersions due to the initial state uncertainty, sensor errors, maneuver execution errors, and disturbance accelerations along the outbound cislunar profile. The rapid performance analysis capabilities of LinCov are complimented with parallel processing techniques to evaluate hundreds and thousands of different translational burn locations, placements, and targeting constraints to identify the combination that minimizes the total Δv usage (nominal plus 3σ Δv) and trajectory dispersions at lunar orbit insertion. This study utilizes a generalized reference targeting algorithm to quickly assess the integrated closed-loop GN&C system performance due to different targeting configurations and constraints. The resulting mission maps provide an intuitive insight to ascertain each trajectory correction maneuver’s (TCM) sensitivity to different burn times along an outbound cislunar trajectory and quickly identify desirable engineering tradeoffs when performing analysis on the number and placement of these burns that nominally zero. Multiple mission maps are generated for a variety of different performance parameters that allow engineers to visually identify optimal solutions for trajectory correction maneuver placements, the number of correction burns, and the targeting constraints for each burn.

GN&C↗
NASA NTRSDate not supplied

A Nonlinear Schur Complement Solver for CFD-Based Multidisciplinary Models

CFD-based multidisciplinary models are the fundamental building blocks of multidis- ciplinary design optimization frameworks. Linear and nonlinear solutions of these coupled models are difficult, especially when the Jacobian matrices represent a saddle point problem, where a block-diagonal corresponding to a discipline is non-invertible. These scenarios necessitate the use of a coupled solver algorithm such as the Newton’s method instead of the popular block Gauss– Seidel-based methods because of this non-invertible block. To address this challenge, we introduce a nonlinear Schur complement solver suitable for CFD-based multidisciplinary models. The solver leverages the specialized linear and nonlinear solvers of the CFD code, and therefore, does not require the solution of a large coupled linear system as the coupled Newton’s method. Further- more, because the solver primarily uses the specialized linear and nonlinear solvers of the CFD code, it does not suffer from the same robustness limitations as the coupled Newton’s method. In this work, we will implement this solver in NASA’s OpenMDAO framework and demonstrate its effectiveness using a CFD-based aeropropulsive model. The solver will contribute to the develop- ment of aeropropulsive design optimization and CFD-based design optimization methods with the ultimate goal of accelerating the design and integration of advanced propulsion systems.

Nonlinear Solvers↗
DOE OSTI2021

Artificial Intelligence for Autonomous Molecular Design: A Perspective

Domain-aware artificial intelligence has been increasingly adopted in recent years to expedite molecular design in various applications, including drug design and discovery. Recent advances in areas such as physics-informed machine learning and reasoning, software engineering, high-end hardware development, and computing infrastructures are providing opportunities to build scalable and explainable AI molecular discovery systems. This could improve a design hypothesis through feedback analysis, data integration that can provide a basis for the introduction of end-to-end automation for compound discovery and optimization, and enable more intelligent searches of chemical space. Several state-of-the-art ML architectures are predominantly and independently used for predicting the properties of small molecules, their high throughput synthesis, and screening, iteratively identifying and optimizing lead therapeutic candidates. However, such deep learning and ML approaches also raise considerable conceptual, technical, scalability, and end-to-end error quantification challenges, as well as skepticism about the current AI hype to build automated tools. To this end, synergistically and intelligently using these individual components along with robust quantum physics-based molecular representation and data generation tools in a closed-loop holds enormous promise for accelerated therapeutic design to critically analyze the opportunities and challenges for their more widespread application. This article aims to identify the most recent technology and breakthrough achieved by each of the components and discusses how such autonomous AI and ML workflows can be integrated to radically accelerate the protein target or disease model-based probe design that can be iteratively validated experimentally. Taken together, this could significantly reduce the timeline for end-to-end therapeutic discovery and optimization upon the arrival of any novel zoonotic transmission event. Our article serves as a guide for medicinal, computational chemistry and biology, analytical chemistry, and the ML community to practice autonomous molecular design in precision medicine and drug discovery.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRSDate not supplied

Optimizing Thermal Management Systems in Electric Aircraft

The development of electric aircraft poses significant gains for the environment; however, the integration of high-power systems increases the number of heat sources that need to be managed in-flight. This 30-minute session discusses the importance of robust thermal management systems and reviews new design concepts to improve overall aircraft efficiency.

Rodger Dyson↗
DOE OSTI2024

AI-assisted detector design for the EIC (AID(2)E)

Artificial Intelligence is poised to transform the design of complex, large-scale detectors like ePIC at the future Electron Ion Collider. Featuring a central detector with additional detecting systems in the far forward and far backward regions, the ePIC experiment incorporates numerous design parameters and objectives, including performance, physics reach, and cost, constrained by mechanical and geometric limits. This project aims to develop a scalable, distributed AI-assisted detector design for the EIC (AID(2)E), employing state-of-the-art multiobjective optimization to tackle complex designs. Supported by the ePIC software stack and using G EANT 4 simulations, our approach benefits from transparent parameterization and advanced AI features. The workflow leverages the PanDA and iDDS systems, used in major experiments such as ATLAS at CERN LHC, the Rubin Observatory, and sPHENIX at RHIC, to manage the compute intensive demands of ePIC detector simulations. Tailored enhancements to the PanDA system focus on usability, scalability, automation, and monitoring. Ultimately, this project aims to establish a robust design capability, apply a distributed AI-assisted workflow to the ePIC detector, and extend its applications to the design of the second detector (Detector-2) in the EIC, as well as to calibration and alignment tasks. Additionally, we are developing advanced data science tools to efficiently navigate the complex, multidimensional trade-offs identified through this optimization process.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Utilizing ensemble learning for performance and power modeling and improvement of parallel cancer deep learning CANDLE benchmarks

Abstract Machine learning (ML) continues to grow in importance across nearly all domains in modeling to learn from data. Often a tradeoff exists between a model's ability to minimize bias and variance. In this article, we utilize ensemble learning to combine linear, nonlinear, and tree‐/rule‐based ML methods to cope with the bias‐variance tradeoff and result in more accurate models. We use the datasets collected for two parallel cancer deep learning CANDLE benchmarks, NT3 and P1B2, to build performance and power models based on hardware performance counters using single‐object and multiple‐objects ensemble learning to identify the most important counters for improvement on the Cray XC40 Theta at Argonne National Laboratory. Based on the insights from these models, we improve the performance and energy of P1B2 and NT3 by optimizing the deep learning environments TensorFlow, Keras, Horovod, and Python under the huge page size of 8 MB. Experimental results show that ensemble learning not only produces more accurate models but also provides more robust performance counter ranking. We achieve up to 61.15% performance improvement and up to 62.58% energy saving for P1B2 and up to 55.81% performance improvement and up to 52.60% energy saving for NT3 on up to 24,576 cores.

Wu, Xingfu↗
DOE OSTI2025

Enriched immersed finite element and isogeometric analysis: algorithms and data structures

Immersed finite element methods provide a convenient analysis framework for problems involving geometrically complex domains, such as those found in topology optimization and microstructures for engineered materials. However, their implementation remains a major challenge due to, among other things, the need to apply nontrivial stabilization schemes and generate custom quadrature rules. This article introduces the robust and computationally efficient algorithms and data structures comprising an immersed finite element preprocessing framework. The input to the preprocessor consists of a background mesh and one or more geometries defined on its domain. The output is structured into groups of elements with custom quadrature rules formatted such that common finite element assembly routines may be used without or with only minimal modifications. The key to the preprocessing framework is the construction of material topology information, concurrently with the generation of a quadrature rule, which is then used to perform enrichment and generate stabilization rules. While the algorithmic framework applies to a wide range of immersed finite element methods using different types of meshes, integration, and stabilization schemes, the preprocessor is presented within the context of the extended isogeometric analysis. This method utilizes a structured B-spline mesh, a generalized Heaviside enrichment strategy considering the material layout within individual basis functions’ supports, and face-oriented ghost stabilization. Using a set of examples, the effectiveness of the enrichment and stabilization strategies is demonstrated alongside the preprocessor’s robustness in geometric edge cases. Additionally, the performance and parallel scalability of the implementation are evaluated.

Computer implementation↗
DOE OSTI2025

Pavement condition and climatic data in southeast Texas: A dataset for evaluating flood impacts on pavement performance

Effective pavement maintenance is essential for economic stability, optimal network performance, and roadway safety. Achieving this requires thorough evaluation of pavement conditions, including structural integrity, surface roughness, and distress characteristics. Pavement performance indicators play a critical role in influencing vehicle safety and ride quality. Recent advances have emphasized the use of data-driven modeling to anticipate pavement behavior, with the goal of optimizing resource allocation and refining Maintenance and Rehabilitation (M&R) strategies through accurate condition assessment. A foundational requirement for these modeling efforts is the availability of standardized, high-quality datasets that can support robust and reproducible infrastructure analysis. This data article presents a comprehensive dataset assembled to facilitate pavement performance prediction, with a geographic focus on Southeast Texas, particularly the flood-vulnerable area of Beaumont. The dataset encompasses pavement and traffic attributes, meteorological records, flood simulation outputs, ground deformation measurements, and topographic indices, enabling detailed examination of both load-associated and non-load-associated degradation mechanisms. Data preprocessing was performed using ArcGIS Pro, Microsoft Excel, and Python to ensure consistency and usability in data-driven modeling applications, including machine learning workflows. Key contributions of this dataset include its utility in analyzing the climatic and environmental factors affecting pavement conditions, identifying critical predictive features, and enabling in-depth correlation analysis across diverse variables. By filling existing gaps in input variable selection resources, this dataset supports the development of predictive tools for estimating future maintenance demand and enhancing the resilience of pavement networks in flood-impacted areas. The resource highlights the importance of standardized datasets for advancing pavement management practices and provides a robust foundation for ongoing infrastructure performance modeling.

42 ENGINEERING↗
DOE OSTI2026

Distributed optimization and control for autonomous distributed energy resource power dispatch and frequency regulation considering communication failures

Distributed energy resources (DERs) can significantly enhance grid flexibility by supplying power and secondary frequency regulation (SFR) services. Yet, their large number and dispersed nature limit the effectiveness of traditional centralized power dispatch and frequency control schemes, and expose the system to single-point communication failures. This paper presents a unified distributed optimization and control framework for DER power dispatch and SFR provision. A transmission-loss-aware distributed direct current optimal power flow is first formulated to optimize DER power dispatch and reserve scheduling. On top of this, a fully distributed control scheme for DER autonomous SFR provision is developed to allocate automatic generation control signals cost-effectively while ensuring robustness to communication link disruptions. The framework is validated on IEEE 14-bus and 39-bus transmission systems with large-scale distribution feeders using transmission-distribution dynamic co-simulation, demonstrating reliable and autonomous DER power dispatch and SFR provision under diverse operating conditions and communication contingencies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Quantum Chemistry-Informed Active Learning to Accelerate the Design and Discovery of Sustainable Energy Storage Materials

Here we employed Density Functional Theory (DFT) to compute oxidation potentials of 1,400 homobenzylic ether molecules to search for the ideal sustainable redoxmer design. The generated data were used to construct an active learning model based on Bayesian optimization (BO) that targets candidates with desired oxidation potentials utilizing only a minimal number of DFT calculations. The active learning model demonstrated not only significant efficiency improvement over the random selection approach but also robust capability in identifying desired candidates in an untested set of 112,000 homobenzylic ether molecules. Our findings highlight the efficacy of quantum chemistry-informed active learning to accelerate the discovery of materials with desired properties from a vast chemical space.

25 ENERGY STORAGE↗