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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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All sourcesNASA NTRSDOE OSTI
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 OSTI2023

Generalized method for the optimization of pulse shape discrimination parameters

Organic scintillators exhibit fast timing, high detection efficiency for fast neutrons and pulse shape discrimination (PSD) capability. PSD is essential in mixed radiation fields, where different types of radiation need to be detected and discriminated. In neutron measurements for nuclear security and non proliferation effective PSD is crucial, because a weak neutron signature needs to be detected in the presence of a strong gamma-ray background. Here, the most commonly used deterministic PSD technique is charge integration (CI). This method requires the optimization of specific parameters to obtain the best gamma-neutron separation. These parameters depend on the scintillating material and light readout device and typically require a lengthy optimization process and a calibration reference measurement with a mixed source. In this paper, we propose a new method based on the scintillation fluorescence physics that enables to find the optimum PSD integration gates using only a gamma-ray emitter. We demonstrate our method using three organic scintillation detectors: deuterated trans-stilbene, small-molecule organic glass, and EJ-309. In all the investigated cases, our method allowed finding the optimum PSD CI parameters without the need of iterative optimization.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
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.↗
DOE OSTI2022

The Energy in Modular (EMOD) Buildings Method: A Guide to Energy-Efficient Design for Industrialized Construction of Modular Buildings

Industrialized construction has immense potential to address the growing need globally to build and upgrade the building stock to be affordable, energy-efficient, and resilient. It can also help achieve the United States' goal of a 50% reduction in U.S. greenhouse gas (GHG) emissions by 2030. Despite this potential, and the ever-increasing push for electrification and decarbonization of households in the United States, industrialized construction has not yet been leveraged specifically to help address these challenges and accelerate the pathway to meet these goals. The National Renewable Energy Laboratory (NREL) aims to claim this missed opportunity by focusing on delivering affordable, grid-efficient net-zero energy (NZE) modular buildings for underserved communities to ensure an equitable transition to the future of clean energy, accelerate decarbonization of the built environment, and support the development of a high-productivity construction and energy efficiency workforce. The Energy in Modular (EMOD) method is our approach to designing, producing, and delivering affordable, net-zero energy, low-carbon, and healthier buildings at scale. The following energy efficiency strategies are part of the scope of this guide: envelope thermal control, envelope infiltration control, mechanical, electrical, and plumbing systems, smart controls, and solar plus storage. We draw synergies between design for manufacturing and assembly, process optimization, retrofit technologies, and digitization. Our goal is to influence the improvement and production of buildings to increase performance, enhance energy efficiency, and reduce GHG emissions. This guide documents the research and development efforts initiated by a set of design objectives to "modularize" a set of energy efficiency and low-carbon strategies into a housing unit while preserving and enhancing energy efficiency benefits and decarbonization pathways. This guide is intended to serve as a framework for housing developers, housing agencies, architects, energy experts, and process engineers or factory operator personnel who are critical to today's modular builder teams. This guide focuses on specific energy efficiency strategies, decarbonization pathways, and associated processes as part of NREL's research efforts. Stakeholders may substitute other means, methods, and technologies for the ones evaluated in this study.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

A Boundary Topology Optimization Approach for Lightweighting Electric Machines Inspired by Additive Manufacturing

Minimizing the mass in electric machines while maintaining superior performance has become a new requirement for the advancement of drivetrains used in wind energy and electric mobility. Topology optimization for light-weighting electric machines using traditional approaches typically explore a restricted design space allowed by standard parametrizable geometry and manufacturing, while advanced methods like 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 TO 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 IEA-15MW radial flux direct-drive wind turbine generator. The free-from boundary TO resulted in smooth and concise shapes that can be easily additively manufactured with upto 20-ton reduction in electrical steel mass.

additive manufacturing↗
DOE OSTI2022

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

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 TO 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 free-form boundary TO 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↗
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

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.

Multifunctional↗
NASA NTRS1983

Aeroelastic tailoring of rotor blades for vibration reduction in forward flight

Modern structural optimization techniques are applied to vibration reduction of helicopter rotor blades in forward flight. The objective function minimized consists of the oscillatory vertical hub shears or the hub rolling moments at one particular advance ratio. The behavior constraints are the frequency placements of the blade and the requirement that aeroelastic stability margins, in hover, remain unaffected by the optimization process. The aeroelastic stability and response analysis is based on a fully coupled flap-lag-torsional analysis of the blade. The vertical hub shears and rolling moments used as the objective function are obtained by appropriate integration of the loads acting along the span of the blade combined with a transformation to a hub fixed coordinate system, and a summation over the total number of blades. Numerical results for both a stiff-in-plane and a soft-in-plane configuration are presented, indicating that structural optimization yields the highest benefits when applied to soft-in-plane blade configuration. The results indicate substantial (15-40 percent) reduction in vibration levels, as well as a blade which is 20 percent lighter than the initial design.

Friedmann, P. P.↗
NASA NTRS2015

A Four-Pole Power-Combiner Design for Far-Infrared and Submillimeter Spectroscopy

The far-infrared and submillimeter portions of the electromagnetic spectrum provide a unique view of the astrophysical processes present in the early universe. Micro-Spec (μ-Spec), a high-efficiency direct-detection spectrometer concept working in the 450–1000-micron wavelength range, will enable a wide range of spaceflight missions that would otherwise be challenging due to the large size of current instruments and the required spectral resolution and sensitivity. This paper focuses on the μ-Spec two-dimensional multimode region, where the light of different wavelengths diffracts and converges onto a set of detectors. A two-step optimization process is used to generate geometrical configurations given specific requirements on spectrometer size, operating spectral range, and performance. The canonically employed focal-plane constraints for the power combiner were removed to probe the design space in its entirety. A new four-stigmatic-point optical design solution is identified and explored for use in far-infrared and submillimeter spectroscopy.

electromagnetic spectrum↗
NASA NTRS1995

Comparison of Two Multidisciplinary Optimization Strategies for Launch-Vehicle Design

The investigation focuses on development of a rapid multidisciplinary analysis and optimization capability for launch-vehicle design. Two multidisciplinary optimization strategies in which the analyses are integrated in different manners are implemented and evaluated for solution of a single-stage-to-orbit launch-vehicle design problem. Weights and sizing, propulsion, and trajectory issues are directly addressed in each optimization process. Additionally, the need to maintain a consistent vehicle model across the disciplines is discussed. Both solution strategies were shown to obtain similar solutions from two different starting points. These solutions suggests that a dual-fuel, single-stage-to-orbit vehicle with a dry weight of approximately 1.927 x 10(exp 5)lb, gross liftoff weight of 2.165 x 10(exp 6)lb, and length of 181 ft is attainable. A comparison of the two approaches demonstrates that treatment or disciplinary coupling has a direct effect on optimization convergence and the required computational effort. In comparison with the first solution strategy, which is of the general form typically used within the launch vehicle design community at present, the second optimization approach is shown to he 3-4 times more computationally efficient.

Braun, R. D.↗
DOE OSTI2025

Molecular Layer Deposited Aluminum‐Based Hybrid Resist for High‐Resolution Nanolithography and Direct Ultra‐High Aspect Ratio Pattern Transfer

Inorganic-containing hybrid photoresists are critical for next-generation extreme ultraviolet (EUV) lithography and angstrom-era semiconductor miniaturization. However, associated conventional solution processing struggles to achieve ultrathin, uniform films with high conformality and compositional control, limiting overall patterning performance. Here, this study reports the systematic lithographic patterning characterization of an Al-based hybrid resist synthesized via vapor-phase molecular layer deposition (MLD), using the trimethylaluminum (TMA) metal precursor and the hydroquinone (HQ) aromatic organic linker. The resist supports both sub-20 nm high-resolution nanolithography and virtually infinite silicon plasma etch selectivity. Lithographic performance studied using electron beam lithography (EBL) as a proxy for EUV shows that, under an optimized process adopting post-exposure bake, the resist achieves the best resolution of 15.4 nm linewidth under stringent 1:1 line-space high-density patterning—limited only by the minimum beam size of the EBL system. The resist also shows wide dose latitude and balanced performance in resolution, roughness, and sensitivity. The infinite silicon etch selectivity, stemming from spontaneous aluminum oxyfluoride passivation layer formation, enables fabrication of micrometer-tall, 40 nm-wide silicon nanofin structures using only a ≈30 nm-thick resist layer, without no hard mask. These results highlight the potential of MLD-based hybrid resists for developing next-generation resist materials for micro/nanoelectronics manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Accelerating high-order mesh optimization using finite element partial assembly on GPUs

In this paper we present a new GPU-oriented mesh optimization method based on high order finite elements. Our approach relies on node movement with fixed topology, through the Target-Matrix Optimization Paradigm (TMOP) and uses a global nonlinear solve over the whole computational mesh, i.e., all mesh nodes are moved together. A key property of the method is that the mesh optimization process is recast in terms of finite element operations, which allows us to utilize recent advances in the field of GPU-accelerated high order finite element algorithms. For example, we reduce data motion by using tensor factorization and matrix-free methods, which have superior performance characteristics compared to traditional full finite element matrix assembly and offer advantages for GPU based HPC hardware. Furthermore, we describe the major mathematical components of the method along with their efficient GPU-oriented implementation. In addition, we propose an easily reproducible mesh optimization test that can serve as a performance benchmark for the mesh optimization community.

97 MATHEMATICS AND COMPUTING↗