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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Development of New Magnetron Sputter Deposition Processes for Laser Target Fabrication

Magnetron sputter deposition is an enabling technology for laser target fabrication. Solutions are readily available for the deposition of most sub-micron-thick elemental films on planar substrates. However, major challenges still remain for the development of robust deposition processes in regimes of ultrathick (over ~10 μm) coatings and nonplanar substrates. These challenging deposition regimes are directly relevant to laser target applications, including both sphero-cylindrical hohlraums and spherical ablators for inertial confinement fusion (ICF) targets. Understanding underlying physical mechanisms for a specific material system is crucial for process development, given the overall complexity of the deposition process, its nonlinear dependence on deposition parameters, and a very large process space, often precluding conventional process optimization approaches. Here, we describe our approach to developing new deposition processes and give practical advice with examples of new results from our ongoing studies of glassy boron carbide ceramics for next-generation ICF ablators and nonequilibrium gold-tantalum alloys for hohlraums for magnetized ICF schemes. Emphasis is given to two major challenges of ultrathick coatings related to achieving process stability and reducing residual stress.

36 MATERIALS SCIENCE↗
NASA NTRS1989

Integrated aerodynamic/dynamic optimization of helicopter rotor blades

An integrated aerodynamic/dynamic optimization procedure is used to minimize blade weight and 4 per rev vertical hub shear for a rotor blade in forward flight. The coupling of aerodynamics and dynamics is accomplished through the inclusion of airloads which vary with the design variables during the optimization process. Both single and multiple objective functions are used in the optimization formulation. The Global Criteria Approach is used to formulate the multiple objective optimization and results are compared with those obtained by using single objective function formulations. Constraints are imposed on natural frequencies, autorotational inertia, and centrifugal stress. The program CAMRAD is used for the blade aerodynamic and dynamic analyses, and the program CONMIN is used for the optimization. Since the spanwise and the azimuthal variations of loading are responsible for most rotor vibration and noise, the vertical airload distributions on the blade, before and after optimization, are compared. The total power required by the rotor to produce the same amount of thrust for a given area is also calculated before and after optimization. Results indicate that integrated optimization can significantly reduce the blade weight, the hub shear and the amplitude of the vertical airload distributions on the blade and the total power required by the rotor.

Chattopadhyay, Aditi↗
NASA NTRS2007

Topology Synthesis of Structures Using Parameter Relaxation and Geometric Refinement

Typically, structural topology optimization problems undergo relaxation of certain design parameters to allow the existence of intermediate variable optimum topologies. Relaxation permits the use of a variety of gradient-based search techniques and has been shown to guarantee the existence of optimal solutions and eliminate mesh dependencies. This Technical Publication (TP) will demonstrate the application of relaxation to a control point discretization of the design workspace for the structural topology optimization process. The control point parameterization with subdivision has been offered as an alternative to the traditional method of discretized finite element design domain. The principle of relaxation demonstrates the increased utility of the control point parameterization. One of the significant results of the relaxation process offered in this TP is that direct manufacturability of the optimized design will be maintained without the need for designer intervention or translation. In addition, it will be shown that relaxation of certain parameters may extend the range of problems that can be addressed; e.g., in permitting limited out-of-plane motion to be included in a path generation problem.

Hull, P. V.↗
DOE OSTI2022

Mechanical Characterization of Loblolly Pine

Biorefineries are faced with various material flow challenges during feeding and handling, due to inherent variability and inhomogeneity of biomass feedstocks. This impacts the efficiency of refinery processes and unit operations. Proper characterization of bulk properties allows for optimal process design and reduction of overall costs. This work is aimed at evaluating the physical and mechanical properties that impact bulk flow of loblolly pine. Cyclic axial compression tests are performed to elucidate consolidation behavior under different stress conditions. The results are then used to develop correlations between material parameters (particle size, tissue type, moisture content) and Elastic Modulus of bulk samples.

09 BIOMASS FUELS↗
DOE OSTI2025

Optimization of direct air capture processes using reactive transport models of adsorption-desorption cycles

In this study, we develop and implement a reactive transport model in COMSOL Multiphysics® to address the challenges of direct air carbon capture. The model is validated against experimental data and used to simulate the cyclic steady state of the adsorption-desorption process. The optimization of this model is achieved through advanced trust-region methods integrated with Gaussian Processes. Key decision variables, including adsorption and desorption times, desorption temperature and pressure, input velocity, bed porosity, column length, and radius were optimized to minimize the capture cost. After optimization, a sensitivity analysis revealed the complex interplay between the decision variables and their effect on the specific energy and cost of removing the CO 2 . We optimized the capture cost while taking into account the trade-off between energy consumption and productivity. The resulting minimum capture cost was determined to be 265.2 $/t-CO 2 , which aligns with expected values reported in the literature. Numerical results suggest the effectiveness of the optimization strategies applied, and underscore the importance of simultaneous decision variable selection in improving the performance in direct air capture processes. We also extend the modeling approach to a 2D axisymmetric model to better visualize CO₂ uptake and temperature profiles, revealing significant radial gradients during the regeneration step. As a main drawback, this enhanced model comes with a computational cost approximately 40 times higher than that of the 1D model.

Adsorption-desorption process↗
DOE OSTI2023

Invertible neural networks for real-time control of extrusion additive manufacturing

Material extrusion additive manufacturing (AM) has enabled an elegant fabrication pathway for a vast material library. Nonetheless, each material requires optimization of printing parameters generally determined through significant trial-and-error testing. To eliminate arduous, iteration-based optimization approaches, many researchers have used machine learning (ML) algorithms which provide opportunities for automated process optimization. Here, in this work, we demonstrate the use of an ML-driven approach for real-time material extrusion print-parameter optimization through in-situ monitoring of printed line geometry. To do this, we use deep invertible neural networks (INNs) which can solve both forward and inverse, or optimization, problems using a single network. By combining in-situ computer vision and deep INNs, the printing parameters can be autonomously optimized to print a target line width in 1.2 s. Furthermore, defects that occur during printing can be rapidly identified and corrected autonomously. The methods developed and presented in this work eliminate user-intensive, time-consuming, and iterative parameter discovery approaches that currently limit accelerated implementation of extrusion-based AM processes. Furthermore, the presented approach can be generalized to provide real-time monitoring and optimization pathways for increasingly complex AM environments.

36 MATERIALS SCIENCE↗
NASA NTRS1992

An optimal GPS data processing technique

A formula is derived to optimally combine dual-frequency GPS (Global Positioning System) pseudorange and carrier phase data streams into a single equivalent data stream, reducing the data volume and computing time in the filtering process for parameter estimation by a factor of four. The resulting single data stream is that of carrier phase measurements with both data noise and bias uncertainty strictly defined. With this analytical formula the single stream of equivalent GPS measurements can be efficiently formed by simple numerical calculations without any degradation in data strength. The formulation for the optimally combined GPS data and their covariances are given in closed form. Carrier phase ambiguity resolution, when feasible, is improved due to the preservation of the full data strength with the optimal data combining process.

Wu, S. C.↗
DOE OSTI2025

Modeling diffusion and depletion in high-aspect-ratio atomic layer deposition processes: Process parameters and manufacturing impacts

Atomic layer deposition (ALD) is a powerful technique for modifying the surface chemistry and properties of substrates with complex and nonplanar topologies. However, achieving uniform and conformal deposition on ultrahigh-aspect-ratio substrates remains challenging, typically requiring large quantities of precursors and long exposure times. Furthermore, process optimization is often performed empirically and involves substantial trial and error. In this work, we perform a combined experimental and computational study of ALD Al 2 O 3 infiltration into silica aerogel monoliths (aspect ratio >10 5 ). A reaction-diffusion model is used to explore the effects of key processing parameters, namely, exposure time per dose, precursor source temperature, number of aerogels in the reactor, and reactor volume. The model is based on quasi-static mode ALD, where the dosed precursor is held in the chamber for a fixed period of time before purging. We analyze the trade-offs between process throughput and precursor utilization for each of these parameters. Furthermore, we investigate the co-optimization and interactions between multiple process parameters, demonstrating the potential for further improvements. Furthermore, this physics-based model can be used to identify a set of process parameters for high-aspect-ratio ALD that meet specific manufacturing objective functions, including throughput, cost, and sustainability.

Aerogel↗
DOE OSTI2020

Simulation-driven optimization of high-order meshes in ALE hydrodynamics

Here we propose tools for high-order mesh optimization and demonstrate their benefits in the context of multi-material Arbitrary Lagrangian-Eulerian (ALE) compressible shock hydrodynamic applications. The mesh optimization process is driven by information provided by the simulation which uses the optimized mesh, such as shock positions, material regions, known error estimates, etc. These simulation features are usually represented discretely, for instance, as finite element functions on the Lagrangian mesh. The discrete nature of the input is critical for the practical applicability of the algorithms we propose and distinguishes this work from approaches that strictly require analytical information. Our methods are based on node movement through a high-order extension of the Target-Matrix Optimization Paradigm (TMOP). The proposed formulation is fully algebraic and relies only on local Jacobian matrices, so it is applicable to all types of mesh elements, in 2D and 3D, and any order of the mesh. We discuss the notions of constructing adaptive target matrices and obtaining their derivatives, reconstructing discrete data in intermediate meshes, node limiting that enables improvement of global mesh quality while preserving space-dependent local mesh features, and appropriate normalization of the objective function. The adaptivity methods are combined with automatic ALE triggers that can provide robustness of the mesh evolution and avoid excessive remap procedures. The benefits of the new high-order TMOP technology are illustrated on several simulations performed in the high-order ALE application BLAST.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

A novel electro-hydraulic unit design based on a shaftless integration of an internal gear machine and a permanent magnet electric machine

In recent years, increasingly stringent emission regulations have spurred an electrification trend in off-highway vehicle technology. To address challenges such as the high cost of power electronics components, limited battery capacity, and the substantial modifications required for the vehicles, there is a pressing need to develop high-speed, cost-effective, compact, and efficient electro-hydraulic units capable of powering vehicle functions. In response to these demands, this paper introduces an innovative morphology for an electro-hydraulic unit and outlines the integration method for a crescent-type internal gear machine with a permanent magnet synchronous electric machine. The proposed morphology aims to minimize component count through a shaftless solution while incorporating a cooling system that utilizes the same working fluid as the hydraulic machine. The design approach utilizes a genetic algorithm optimization process to maximize overall energy efficiency and compactness. Insights gained from the optimization results shed light on the relationship between key design parameters and unit performance, enhancing the understanding of this electro-hydraulic unit. A prototype of the unit was manufactured and tested, demonstrating a volumetric efficiency ranging from 81 % to 97 % at a maximum rotational velocity of the pinion of 6000 rpm. Finally, these results validate both the morphology and the design approach, indicating the feasibility of designing compact electro-hydraulic units that leverage hydraulic machines with higher maximum rotational velocities than commercially available counterparts as a mean to enhance efficiency and compactness.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2024

Active interlocking metasurfaces enabled by shape memory alloys

Interlocking metasurfaces (ILMs) are a newly developed joining technology that relies on arrays of interlocking features that transmit force and constrain motion between adjoining bodies in one or more directions. This study explores harnessing the shape memory effect (SME) in Nickel-Titanium shape memory alloys (NiTi SMAs) in structures fabricated using additive manufacturing (AM) to advance the development of active ILMs by creating unit cells that open or close at specific temperatures. The study encompasses designing and fabricating two distinct interlocking array configurations using near-equiatomic NiTi powder and the laser powder bed fusion (L-PBF) AM technique, following a previously developed AM process optimization framework to manufacture defect-free parts. To guide the design process, finite element analysis (FEA) was employed to predict strain values during engage-disengage cycles. The martensitic transformation characteristics of the ILMs were characterized. Thermomechanical testing revealed that the ILMs demonstrate high locking force once engaged, coupled with complete shape recovery and good cyclic stability. Digital image correlation (DIC) was also employed to validate the FEA predictions during the engage-disengage cycles. The results indicate that NiTi SMA-based ILMs can be designed and fabricated into complex shapes using L-PBF. By leveraging the SME, the functionality of an ILM can be improved upon. The combination of computational modeling, additive manufacturing, and thermomechanical and physical property characterization provides a framework for designing future ILMs out of active materials.

Additive manufacturing↗
DOE OSTI2022

A New Shape Optimization Approach for Lightweighting Electric Machines Inspired by Additive Manufacturing

Minimizing the mass in electric machines while maintaining superior performance is a new requirement for the advancement of drivetrains used in wind energy and electric mobility. Topology optimization (TO) for lightweighting electric machines using traditional approaches typically explores a restricted design space allowed by standard parametrizable geometry and manufacturing, while advanced methods, such as cell-based density approaches, suffer from a lack of robust manufacturability constraints during the optimization process. To overcome these drawbacks, we explore a grid-independent, boundary optimization where the outer shape of the magnet is parameterized using Bezier curves. We conduct a design of experiments (DOE) to study the effect of different magnet shapes on machine performance by varying the control points on the Bezier curves. A machine-learning-based surrogate model is constructed using the data from the DOE to quantify the relationship between the control points, air-gap torque, and mass. The control points are then optimized to maximize the torque density. The approach is used for minimizing electrical steel mass in the International Energy Agency (IEA) 15-MW radial flux direct-drive wind turbine generator. The new approach to shape optimization resulted in smooth and concise shapes that can be easily additively manufactured with up to a 20-ton reduction in electrical steel mass.

additive manufacturing↗
DOE OSTI2020

Thermal Image Processing for Feature Extraction from Encapsulated Phase Change Materials

Encapsulated inorganic particles with high melting points (>300 °C) are desired as high-temperature Phase Change Materials (PCMs) for next-generation Latent Heat Thermal Energy Storage (LHTES) systems. One of the many challenges during the development of PCMs is to achieve a high throughput that in turn depends on accurately modeling the relation between process parameters and geometric & thermal properties of the PCMs particle. During the production of the PCMs, a high-speed infrared camera is used to acquire images of the encapsulated material under controlled illumination conditions. This research article focuses on the development of image processing techniques for both geometric and thermal feature extraction during the development of the PCMs. A user-friendly GUI has been designed in MATLAB and preliminary experimental results have demonstrated that the method is fast, accurate and reliable for a high throughput production. The extracted features will be used to develop Machine Learning (ML) models to predict the geometric and thermal properties of the PCM based on the process parameter settings. The ML model will accelerate the search for the optimized process settings to boost the throughput of the production.

25 ENERGY STORAGE↗
DOE OSTI2020

Multiobjective and categorical global optimization of photonic structures based on ResNet generative neural networks

Abstract We show that deep generative neural networks, based on global optimization networks (GLOnets), can be configured to perform the multiobjective and categorical global optimization of photonic devices. A residual network scheme enables GLOnets to evolve from a deep architecture, which is required to properly search the full design space early in the optimization process, to a shallow network that generates a narrow distribution of globally optimal devices. As a proof-of-concept demonstration, we adapt our method to design thin-film stacks consisting of multiple material types. Benchmarks with known globally optimized antireflection structures indicate that GLOnets can find the global optimum with orders of magnitude faster speeds compared to conventional algorithms. We also demonstrate the utility of our method in complex design tasks with its application to incandescent light filters. These results indicate that advanced concepts in deep learning can push the capabilities of inverse design algorithms for photonics.

Jiang, Jiaqi↗
NASA NTRS1991

Integration of dynamic, aerodynamic, and structural optimization of helicopter rotor blades

Summarized here is the first six years of research into the integration of structural, dynamic, and aerodynamic considerations in the design-optimization process for rotor blades. Specifically discussed here is the application of design optimization techniques for helicopter rotor blades. The reduction of vibratory shears and moments at the blade root, aeroelastic stability of the rotor, optimum airframe design, and an efficient procedure for calculating system sensitivities with respect to the design variables used are discussed.

Peters, David A.↗
NASA NTRS1994

JPL-ANTOPT antenna structure optimization program

New antenna path-length error and pointing-error structure optimization codes were recently added to the MSC/NASTRAN structural analysis computer program. Path-length and pointing errors are important measured of structure-related antenna performance. The path-length and pointing errors are treated as scalar displacements for statics loading cases. These scalar displacements can be subject to constraint during the optimization process. Path-length and pointing-error calculations supplement the other optimization and sensitivity capabilities of NASTRAN. The analysis and design functions were implemented as 'DMAP ALTERs' to the Design Optimization (SOL 200) Solution Sequence of MSC-NASTRAN, Version 67.5.

Strain, D. M.↗
NASA NTRS2017

A Requirements-Driven Optimization Method for Acoustic Liners Using Analytic Derivatives

More than ever, there is flexibility and freedom in acoustic liner design. Subject to practical considerations, liner design variables may be manipulated to achieve a target attenuation spectrum. But characteristics of the ideal attenuation spectrum can be difficult to know. Many multidisciplinary system effects govern how engine noise sources contribute to community noise. Given a hardwall fan noise source to be suppressed, and using an analytical certification noise model to compute a community noise measure of merit, the optimal attenuation spectrum can be derived using multidisciplinary systems analysis methods. In a previous paper on this subject, a method deriving the ideal target attenuation spectrum that minimizes noise perceived by observers on the ground was described. A simple code-wrapping approach was used to evaluate a community noise objective function for an external optimizer. Gradients were evaluated using a finite difference formula. The subject of this paper is an application of analytic derivatives that supply precise gradients to an optimization process. Analytic derivatives improve the efficiency and accuracy of gradient-based optimization methods and allow consideration of more design variables. In addition, the benefit of variable impedance liners is explored using a multi-objective optimization.

Aircraft Noise↗
DOE OSTI2024

A comparative analysis of residual stresses from friction stir processing of aluminum cast 380 and wrought 7075 alloy sheets: experimental characterization and modeling

Residual stresses are often overlooked in friction stir processing (FSP), but their significant impact on fatigue performance necessitates their consideration in optimizing processing parameters. The first step in this effort is understanding how process conditions influence residual stress distributions, especially across different alloys. This study focuses on determining and explaining the through-thickness residual stress variations and the effect of process temperature on the residual stress magnitude in wrought AA7075 and cast AA380.0 alloys. Additionally, for AA380.0, the impact of a second FSP pass was investigated. To achieve this, hole-drilling electronic speckle pattern interferometry (ESPI) and the thermal pseudo-mechanical (TPM) model within finite element analysis were employed to study the 3D distributions of in-plane residual stresses in processed samples under various conditions. A key finding was the varying impact of process temperatures on residual stress magnitudes. Higher process temperatures reduced stresses in AA380.0 but increased them in AA7075. Additionally, the through-thickness stress distributions differed between the two alloys. Further analysis revealed that yield stresses are crucial in explaining these phenomena and the effects of additional FSP passes. Further, this fundamental understanding will be vital in guiding the efforts to mitigate residual stresses and assess their impact on the performance of FSP aluminum alloys.

36 MATERIALS SCIENCE↗