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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 NTRS1992

COINS: A composites information database system

An automated data abstraction form (ADAF) was developed to collect information on advanced fabrication processes and their related costs. The information will be collected for all components being fabricated as part of the Advanced Composite Technology (ACT) program and included in a COmposites INformation System (COINS) data base. The aim of the COINS development effort is to provide future airframe preliminary design and fabrication teams with a tool through which production cost can become a deterministic variable in the design optimization process. The effort was initiated by NASA Langley to implement the recommendations of a working group comprised of representatives from the commercial airframe companies. The principal working group recommendation was to reinstitute collection of composite part fabrication data in a format similar to the DOD/NASA Structural Composites Fabrication Guide. The fabrication information collection form was automated with current user friendly computer technology. The new automated form and features that make the form easy to use by an aircraft structural design manufacturing team are described.

Siddiqi, Shahid↗

NASA NTRSDate not supplied

Automation of the Uncertainty Quantification Process Based on Probability Boxes with DAKOTA

To date, while the use of CFD is prevalent, very few efforts have been undertaken that truly attempt to document all (or even most) of the sources of uncertainty in the simulations. Instead, the current state-of-the-art relies heavily on the experience of the CFD practitioner to estimate the uncertainty associated with their simulations through simple sensitivity studies or subject matter expertise. This practice will have to be replaced with a formal uncertainty quantification (UQ) process if CFD is to play an expanded role in the design research and engineering community, test and evaluation community, and ultimately certification for flight. This is especially true for hypersonic air-breathing propulsion systems due to the environment, scale, and duration limitations of ground test facilities. Accounting for uncertainties in a formal manner is a tedious process. Moreover, the typical CFD practitioner is not likely to be familiar with formal UQ methods. Hence, a major obstacle that has prevented the adoption of UQ methods for engineering design and development work is the lack of a tool set to automate most (if not all) of the UQ workflow. Towards this end, the SANDIA package DAKOTA (which has been developed to drive both UQ and optimization processes) will be tightly wrapped around the VULCAN-CFD code to automate the uncertainty quantification process. The automated process will be applied to an isolator turbulence model validation exercise that has previously been documented using a manual approach to the UQ process. Hence, the focus of this paper will be documenting the level to which automation can hide the UQ process details from the CFD practitioner rather than the UQ method itself.

CFD↗

DOE OSTI2024

Efficient sensitivity analysis of the thermal profile in powder bed fusion of metals using hypercomplex automatic differentiation finite element method

Rapid cyclic temperature fluctuation occurring in powder bed fusion of metals using a laser beam (PBF-LB/M) influences the formation of flaws in printed parts. Consequently, there is a pressing need to enhance the quality of printed parts by developing innovative methodologies that can predict thermal histories and help uncover the intricate relationships between process parameters and thermal profiles. Sensitivity Analysis (SA) emerges as an essential tool for this, offering the potential for process optimization and enhanced quality control. Nonetheless, conventional SA methodologies often incur in excessive computational costs and potential numerical approximation errors. Here, to address this technical challenge, we present a novel method for SA that integrates the HYPercomplex-based Automatic Differentiation (HYPAD) technique with transient thermal simulations conducted via the finite element method (FEM). Leveraging this methodology, we efficiently and accurately perform SA for PBF-LB/M processes in a post-processing step. Compared to traditional methods like Finite Differences (FD), HYPAD-FEM required 96 % less computational time for obtaining sensitivities for 22 process parameters, under a comparative study conducted within the context of the 2018–02 AM benchmark of the National Institute of Standards and Technology. In summary, HYPAD-FEM offers superior efficiency and accuracy in SA over conventional methods, delivering the best sensitivity of a model without the need for step-size selection and problem or parameter-based implementations.

36 MATERIALS SCIENCE↗

NASA NTRS2017

Sintered Cathodes for All-Solid-State Structural Lithium-Ion Batteries

All-solid-state structural lithium ion batteries serve as both structural load-bearing components and as electrical energy storage devices to achieve system level weight savings in aerospace and other transportation applications. This multifunctional design goal is critical for the realization of next generation hybrid or all-electric propulsion systems. Additionally, transitioning to solid state technology improves upon battery safety from previous volatile architectures. This research established baseline solid state processing conditions and performance benchmarks for intercalation-type layered oxide materials for multifunctional application. Under consideration were lithium cobalt oxide and lithium nickel manganese cobalt oxide. Pertinent characteristics such as electrical conductivity, strength, chemical stability, and microstructure were characterized for future application in all-solid-state structural battery cathodes. The study includes characterization by XRD, ICP, SEM, ring-on-ring mechanical testing, and electrical impedance spectroscopy to elucidate optimal processing parameters, material characteristics, and multifunctional performance benchmarks. These findings provide initial conditions for implementing existing cathode materials in load bearing applications.

Ceramics↗

DOE OSTI2026

Using Flory–Huggins-informed human-in-the-loop Bayesian optimization to map the phase diagram of polymer blends

Mapping the phase diagram of polymer blends is an essential step in controlling the structure–property relationship of polymer-based materials. However, traditional grid-based approaches are inefficient and rely on subjective judgements for terminating the experimental campaign. Artificial intelligence-guided experimentation offers a compelling alternative, especially when data-driven decision-making is interfaced with established polymer thermodynamics to improve efficiency and interpretability. Here, we introduce a physics-informed Bayesian optimization approach to guide the mapping of the phase diagram of a model blend containing poly(methyl methacrylate) and poly(styrene-ran-acrylonitrile). Physical information is derived from a Flory–Huggins representation of the spinodal curve, which is integrated into the Bayesian optimization process as a structured prior mean that acts as a soft constraint. Implemented as a human-in-the-loop workflow, the approach leverages optical imaging of film cloudiness with iterative Gaussian process surrogate modeling and a parameter selection decision policy to identify the composition-temperature conditions for sequential iterations. Convergence of kernel and Flory–Huggins-based hyperparameters provided a stopping criterion, ensuring an objective and interpretable termination of the experimental campaign. The framework recovered the known lower critical solution temperature (∼160 °C), while increasing material efficiency through targeted sampling. This work establishes a proof-of-concept for the application of Bayesian optimization workflows to study polymer blend miscibility.

36 MATERIALS SCIENCE↗

NASA NTRS2007

Mars Radiation Risk Assessment and Shielding Design for Long-term Exposure to Ionizing Space Radiation

NASA is now focused on the agency's vision for space exploration encompassing a broad range of human and robotic missions including missions to Moon, Mars and beyond. As a result, there is a focus on long duration space missions. NASA is committed to the safety of the missions and the crew, and there is an overwhelming emphasis on the reliability issues for space missions and the habitat. The cost-effective design of the spacecraft demands a very stringent requirement on the optimization process. Exposure from the hazards of severe space radiation in deep space and/or long duration missions is a critical design constraint and a potential 'show stopper'. Thus, protection from the hazards of severe space radiation is of paramount importance to the agency's vision. It is envisioned to have long duration human presence on the Moon for deep space exploration. The exposures from ionizing radiation - galactic cosmic radiation and solar particle events - and optimized shield design for a swing-by and a long duration Mars mission have been investigated. It is found that the technology of today is inadequate for safe human missions to Mars, and revolutionary technologies need to be developed for long duration and/or deep space missions. The study will provide a guideline for radiation exposure and protection for long duration missions and career astronauts and their safety.

Tripathi, Ram K.↗

NASA NTRS2012

Design and Performance Optimizations of Advanced Erosion-Resistant Low Conductivity Thermal Barrier Coatings for Rotorcraft Engines

Thermal barrier coatings will be more aggressively designed to protect gas turbine engine hot-section components in order to meet future rotorcraft engine higher fuel efficiency and lower emission goals. For thermal barrier coatings designed for rotorcraft turbine airfoil applications, further improved erosion and impact resistance are crucial for engine performance and durability, because the rotorcraft are often operated in the most severe sand erosive environments. Advanced low thermal conductivity and erosion-resistant thermal barrier coatings are being developed, with the current emphasis being placed on thermal barrier coating toughness improvements using multicomponent alloying and processing optimization approaches. The performance of the advanced thermal barrier coatings has been evaluated in a high temperature erosion burner rig and a laser heat-flux rig to simulate engine erosion and thermal gradient environments. The results have shown that the coating composition and architecture optimizations can effectively improve the erosion and impact resistance of the coating systems, while maintaining low thermal conductivity and cyclic oxidation durability

Zhu, Dongming↗

NASA NTRS2004

Implementation of ALARA radiation protection on the ISS through polyethylene shielding augmentation of the Service Module Crew Quarters

With 5-7 month long duration missions at 51.6 degrees inclination in Low Earth Orbit, the ionizing radiation levels to which International Space Station (ISS) crewmembers are exposed will be the highest planned occupational exposures in the world. Even with the expectation that regulatory dose limits will not be exceeded during a single tour of duty aboard the ISS, the "as low as reasonably achievable" (ALARA) precept requires that radiological risks be minimized when possible through a dose optimization process. Judicious placement of efficient shielding materials in locations where crewmembers sleep, rest, or work is an important means for implementing ALARA for spaceflight. Polyethylene (CnHn) is a relatively inexpensive, stable, and, with a low atomic number, an effective shielding material that has been certified for use aboard the ISS. Several designs for placement of slabs or walls of polyethylene have been evaluated for radiation exposure reduction in the Crew Quarters (CQ) of the Zvezda (Star) Service Module. Optimization of shield designs relies on accurate characterization of the expected primary and secondary particle environment and modeling of the predicted radiobiological responses of critical organs and tissues. Results of the studies shown herein indicate that 20% or more reduction in equivalent dose to the CQ occupant is achievable. These results suggest that shielding design and risk analysis are necessary measures for reducing long-term radiological risks to ISS inhabitants and for meeting legal ALARA requirements. Verification of shield concepts requires results from specific designs to be compared with onboard dosimetry. c2004 COSPAR. Published by Elsevier Ltd. All rights reserved.

NASA Center JSC↗

NASA NTRS2002

Artificial Neural Networks Applications: from Aircraft Design Optimization to Orbiting Spacecraft On-board Environment Monitoring

This paper reviews some of the recent applications of artificial neural networks taken from various works performed by the authors over the last four years at the NASA Glenn Research Center. This paper focuses mainly on two areas. First, artificial neural networks application in design and optimization of aircraft/engine propulsion systems to shorten the overall design cycle. Out of that specific application, a generic design tool was developed, which can be used for most design optimization process. Second, artificial neural networks application in monitoring the microgravity quality onboard the International Space Station, using on-board accelerometers for data acquisition. These two different applications are reviewed in this paper to show the broad applicability of artificial intelligence in various disciplines. The intent of this paper is not to give in-depth details of these two applications, but to show the need to combine different artificial intelligence techniques or algorithms in order to design an optimized or versatile system.

Jules, Kenol↗

NASA NTRS2004

Optimal Micro-Vane Flow Control for Compact Air Vehicle Inlets

The purpose of this study on micro-vane secondary flow control is to demonstrate the viability and economy of Response Surface Methodology (RSM) to optimally design micro-vane secondary flow control arrays, and to establish that the aeromechanical effects of engine face distortion can also be included in the design and optimization process. These statistical design concepts were used to investigate the design characteristics of "low unit strength" micro-effector arrays. "Low unit strength" micro-effectors are micro-vanes set at very low angles-of-incidence with very long chord lengths. They were designed to influence the near wall inlet flow over an extended streamwise distance, and their advantage lies in low total pressure loss and high effectiveness in managing engine face distortion. Therefore, this report examines optimal micro-vane secondary flow control array designs for compact inlets through a Response Surface Methodology.

Anderson, Bernhard H.↗

DOE OSTI2024

Direct microstability optimization of stellarator devices

Turbulent transport is regarded as one of the key issues in magnetic confinement nuclear fusion, both for tokamaks and stellarators. Here, in this work, we show that a significant decrease in a microstability-based proxy, as opposed to a geometric one, for the turbulent heat flux, namely the quasilinear heat flux, can be obtained in an efficient manner by coupling stellarator optimization with linear gyrokinetic simulations. This is accomplished by computing the quasilinear heat flux at each step of the optimization process, as well as the deviation from quasisymmetry, and minimizing their sum, leading to a balance between neoclassical and the turbulent transport proxy.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Improved loss functions for machine-learned atomic potentials

Machine learning (ML) has become an invaluable tool across a wide array of domains in science as researchers find new ways to leverage its predictive power. This is especially true in chemistry, where ML is used to fit chemical properties or desirable attributes to the local structure of molecules and materials. In the pursuit of greater accuracy, it is relatively simple to increase the size or complexity of such models, although this often requires simultaneously seeking larger datasets in order to both fit and interpret the larger number of parameters. However, it is equally important to assess the quality and relative importance of the data and how these factors impact the training process. We, therefore, investigate the impact of using different loss functions for training neural network potentials (NNPs), as the loss function defines the error and parameter gradients used to train the NNP. In particular, we test the mean-squared error and Huber loss functions and, using insight from these functions, derive a new loss function based on the Asinh function, which yields significant improvement in the accuracy and generality of NNPs. We show that by discounting/minimizing errors and anomalies in the optimization process, both the Huber and Asinh loss functions improve the training of NNPs, leading to a final potential with a greater effective dimensionality.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2004

Strategy Developed for Selecting Optimal Sensors for Monitoring Engine Health

Sensor indications during rocket engine operation are the primary means of assessing engine performance and health. Effective selection and location of sensors in the operating engine environment enables accurate real-time condition monitoring and rapid engine controller response to mitigate critical fault conditions. These capabilities are crucial to ensure crew safety and mission success. Effective sensor selection also facilitates postflight condition assessment, which contributes to efficient engine maintenance and reduced operating costs. Under the Next Generation Launch Technology program, the NASA Glenn Research Center, in partnership with Rocketdyne Propulsion and Power, has developed a model-based procedure for systematically selecting an optimal sensor suite for assessing rocket engine system health. This optimization process is termed the systematic sensor selection strategy. Engine health management (EHM) systems generally employ multiple diagnostic procedures including data validation, anomaly detection, fault-isolation, and information fusion. The effectiveness of each diagnostic component is affected by the quality, availability, and compatibility of sensor data. Therefore systematic sensor selection is an enabling technology for EHM. Information in three categories is required by the systematic sensor selection strategy. The first category consists of targeted engine fault information; including the description and estimated risk-reduction factor for each identified fault. Risk-reduction factors are used to define and rank the potential merit of timely fault diagnoses. The second category is composed of candidate sensor information; including type, location, and estimated variance in normal operation. The final category includes the definition of fault scenarios characteristic of each targeted engine fault. These scenarios are defined in terms of engine model hardware parameters. Values of these parameters define engine simulations that generate expected sensor values for targeted fault scenarios. Taken together, this information provides an efficient condensation of the engineering experience and engine flow physics needed for sensor selection. The systematic sensor selection strategy is composed of three primary algorithms. The core of the selection process is a genetic algorithm that iteratively improves a defined quality measure of selected sensor suites. A merit algorithm is employed to compute the quality measure for each test sensor suite presented by the selection process. The quality measure is based on the fidelity of fault detection and the level of fault source discrimination provided by the test sensor suite. An inverse engine model, whose function is to derive hardware performance parameters from sensor data, is an integral part of the merit algorithm. The final component is a statistical evaluation algorithm that characterizes the impact of interference effects, such as control-induced sensor variation and sensor noise, on the probability of fault detection and isolation for optimal and near-optimal sensor suites.

Source record↗

NASA NTRS1990

Stabilization of high T(sub c) phase in bismuth cuprate superconductor by lead doping

It has widely been ascertained that doping of lead in Bi:Sr:Ca:Cu:O systems promotes the growth of high T(sub c) (110 K) phase, improves critical current density, and lowers processing temperature. A systematic investigation is undertaken to determine optimum lead content and processing conditions to achieve these. A large number of samples with cationic compositions of Bi(2-x)Pb(x)Sr2Ca2Cu3 (x = 0.2 to 2.0) were prepared by conventional solid state reaction technique. Samples of all compositions were annealed together at a temperature and characterized through resistance-temperature (R-T) measurements and x ray diffraction (XRD) to determine the zero resistance temperature, T(sub c)(0) and to identify presence of phases, respectively. The annealing temperature was varied between 790 C to optimize processing parameters. Results are given. In brief, an optimum process is reported along with composition of leaded bismuth cuprate superconductor which yields nearly a high T(sub c) single phase with highly stable superconducting properties.

Gupta, Ram. P.↗

DOE OSTI2024

Transformer Neural Networks with Spatiotemporal Attention for Predictive Control and Optimization of Industrial Processes

In the context of real-time optimization and model predictive control of industrial systems, machine learning, and neural networks represent cutting-edge tools that hold promise for enhancing dynamic modeling. This work presents a novel transformer neural network architecture for real-time optimization and model predictive control. This network design includes a modified attention mechanism inspired by positional embedding attention from vision transformers and task-specific modifications to the input-output structure of the transformer’s decoder stack. Experiments were conducted using data from a 450 MW coal-fired power plant to evaluate this approach's effectiveness. The transformer neural network was compared with conventional recurrent models, including GRU and LSTM. The transformer exhibited a 6% increase in the R-squared (R2) value of predictions and an 83% reduction in mean squared error (MSE). Computation time was also reduced by 84% compared to conventional recurrent models.

Gallup, Ethan R.↗

NASA NTRS1991

Stabilization of high Tc phase in bismuth cuprate superconductor by lead doping

It has been widely ascertained that doping of lead in Bi-Sr-Ca-Cu-O systems promotes the growth of high T sub c (110 K) phase, improves critical current density, and lowers processing temperature. A systematic study was undertaken to determine optimum lead content and processing conditions to achieve these properties. A large number of samples with cationic compositions of Bi(2-x)Pb(x)Sr2Ca2Cu3 (x = 0.2 to 2.0) were prepared by conventional solid state reaction technique. Samples of all compositions were annealed together at a temperature and characterized through resistance temperature (R-T) measurements and x ray diffraction to determine the zero resistance temperature, T sub c(0) and to identify presence of phases, respectively. The annealing temperature was varied between 790 and 880 C to optimize processing parameters. Results are given. In brief, an optimum process is reported along with composition of leaded bismuth cuprate superconductor which yields nearly a high T sub c single phase with highly stable superconducting properties.

Gupta, Ram. P.↗

NASA NTRS2018

Lightweight, Durable, and Multifunctional Electrical Insulation Material Systems for High Voltage Applications

Newly developed multilayer structures of well-known polymer insulation materials significantly improved dielectric breakdown voltage, VB, or dielectric strength, K, if well-bonded, when compared to those of single material insulations or the commercial SOA systems, such as Teflon-Kapton-Teflon (TKT), at the same overall thickness. To date, the greatest improvement of the new structures from a few candidate materials, including various types of Kapton PIs and PFA or PET as bond layer (BL), was about 61% higher than that of the Kapton PI alone films, 40.1 vs. 24.9 kV, which was translated to 86.3% decrease in insulation thickness, thus significant volume and weight reduction of the final system. However, it was of interest to note that most improvements of the multilayer structures occurred at thicker overall thicknesses, above ~ 0.15 mm. Extensive analyses also showed that K of the multilayer structures increased with (i) decreasing individual layer thickness regardless of material type, (ii) increasing total accumulated thickness of PI or overall PI/BL ratio, and (iii) increasing number of interface or total number of layers, but only above the aforementioned overall thickness limit. Increases in VB of the multilayer structures were directly correlated with damage evolution and failure mode. With further material-design-process optimizations of the multilayer structures, it was expected to achieve other multifunctionalities, such as high partial discharge (PD) resistance, improved durability, EMI shielding, and high thermal dissipation in addition to high dielectric strength. These new structures can be used in various high voltage and high temperature applications, such as future hybrid or all electric aircraft wiring and power transmission as well as many other non-aerospace high power cables, electronic parts and components, printed circuit board, and so forth. The multilayer insulation system can be easily processed and manufactured with various conductor types via calendaring, compression-molding, stamping, laminating, vacuum-bagging and autoclaving, or 3D printing, even for complex 3-D components. Based on their unique structural configurations and potential capabilities, the new insulation system was identified as micro-multilayer multifunctional electrical insulation (MMEI). Patent application of the MMEI concept and current design configurations was filed for a 1-year provisional application (OAI-58834, Serial No.: 62/659,234), pending conversion to a U.S. utility application. This paper presents details of the MMEI structures, their dielectric performance analyses, potential mechanisms, and commercial scaleup feasibility assessment.

Electric aircrafts↗

NASA NTRS2018

Lightweight, Durable, and Multifunctional Electrical Insulation Material Systems for High Voltage Applications

Multifunctional↗