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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Tutorial: Electrified Aircraft Propulsion Approaches for Modeling and Electrical Hardware-in-the-Loop Testing

This tutorial session outlines capabilities made available by the National Aeronautics and Space Administration for testing electrified aircraft propulsion (EAP) hardware and software prior to using turbomachinery. Removing these components from the experimentation process until necessary significantly reduces the development and testing costs and safety risks. Three facilities, the NASA Electric Aircraft Testbed (NEAT) and the Hybrid Propulsion Emulation Rig (HyPER) are unique facilities that provide the following capabilities: (i) the verification of megawatt-scale electrical and electromechanical system components at altitude, (ii) the verification of EAP control systems on sub-scale representative electromechanical architectures. The importance, operation, and specifications of each facility is described with detail. Provided examples of past testing showcase the abilities of each facility. Simple and complex methods for replicating the steady state and dynamical mechanical loading on the electrical power system are discussed

Electrified Aircraft Propulsion↗
DOE OSTI2023

Compact Multicell Superconducting Crab Cavity for ILC

We propose a novel design of a crab cavity (CC) for the ILC project with low parasitic High Order Modes (HOM) losses and preserving the beam emittance, which is critical for operation with high beam current intensity. Multiple electrodes immersed in the hollow waveguide form a trapped-mode resonator. The transverse components of the electromagnetic field of the trapped dipole mode induce a transverse kick and efficiently deflect charged particles passing through the cavity. We present a scalable design of a superconducting Quasi-waveguide Multicell Resonator (QMiR) seamlessly connected with a beam vacuum chamber. The cavity is completely open at both ends, which significantly reduces the maximum loaded quality factor of the higher order modes (HOM), avoids complex HOM couplers, and thus simplifies the mechanical design of the cavity. The same port is used to feed RF power to the operating mode and to extract the same order modes (SOM). Finally, we estimate the expected cryogenic losses, HOM impedance limits, RF input power required, and frequency tuning for a QMiR cavity designed to operate at 2.6 GHz.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1990

Elimination of Gibbs' phenomena from error analysis of finite element results

This paper is one of a series on error analysis and correction of finite element solutions for plates and shells. The error analysis in the earlier papers used half-range double Fourier sine series for numerical harmonic analysis. The half-range formulas are simple to apply, but they can be inaccurate near the ends of the ranges of the independent variables. The Gibbs' phenomenon exhibited by half-range sine series in one independent variable has a two-dimensional analog; a classic example is the Navier solution in a double half-range sine series for the simply supported plate under a uniform load. A simple change of variables is introduced in the paper to improve the accuracy of the double Fourier sine series without adding complexity to the numerical analysis. The change of variables is applied to the problem of approximating a transverse load that is tabulated on a rectangular grid. A solution based on the change of variables is compared with results from the Navier solution for the simply supported plate problem and finite element results for the same problem.

Thurston, Gaylen A.↗
DOE OSTI2021

Simulation of hardened cement degradation and estimation of uncertainty in predicted failure times with peridynamics

Modeling the degradation of cement-based infrastructure due to aqueous environmental conditions continues to be a challenge. In order to develop a capability to predict concrete infrastructure failure due to chemical degradation, here we created a chemomechanical model of the effects of long-term water exposure on cement paste. The model couples the mechanical static equilibrium balance with reactive–diffusive transport and incorporates fracture and failure via peridynamics (a meshless simulation method). The model includes fundamental aspects of degradation of ordinary Portland cement (OPC) paste, including the observed softening, reduced toughness, and shrinkage of the cement paste, and increased reactivity and transport with water induced degradation. This version of the model focuses on the first stage of cement paste decalcification, the dissolution of portlandite. Given unknowns in the cement paste degradation process and the cost of uncertainty quantification (UQ), we adopt a minimally complex model in two dimensions (2D) in order to perform sensitivity analysis and UQ. We calibrate the model to existing experimental data using simulations of common tests such as flexure, compression and diffusion. Then we calculate the global sensitivity and uncertainty of predicted failure times based on variation of eleven unique and fundamental material properties. We observed particularly strong sensitivities to the diffusion coefficient, the reaction rate, and the shrinkage with degradation. Also, the predicted time of first fracture is highly correlated with the time to total failure in compression, which implies fracture can indicate impending degradation induced failure; however, the distributions of the two events overlap so the lead time may be minimal. Extension of the model to include the multiple reactions that describe complete degradation, viscous relaxation, post-peak load mechanisms, and to three dimensions to explore the interactions of complex fracture patterns evoked by more realistic geometry is straightforward and ongoing.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Exploring the parameter space of MagLIF implosions using similarity scaling. I. Theoretical framework

Magneto-inertial fusion concepts, such as the magnetized liner inertial fusion (MagLIF) platform, constitute an alternative path for achieving ignition and significant fusion yields in the laboratory. The space of experimental input parameters defining a MagLIF load is highly multi-dimensional, and the implosion itself is a complex event involving many physical processes. In the first paper of this series, we develop a simplified analytical model that identifies the main physical processes at play during a MagLIF implosion. Using non-dimensional analysis, we determine the most important dimensionless parameters characterizing MagLIF implosions and provide estimates of such parameters using typical fielded or experimentally observed quantities for MagLIF. Here, we then show that MagLIF loads can be “incompletely” similarity scaled, meaning that the experimental input parameters of MagLIF can be varied such that many (but not all) of the dimensionless quantities are conserved. Based on similarity-scaling arguments, we can explore the parameter space of MagLIF loads and estimate the performance of the scaled loads. Then, in the follow-up papers of this series, we test the similarity-scaling theory for MagLIF loads against simulations for two different scaling “vectors,” which include current scaling and rise-time scaling.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Physics-Informed Machine Learning Model for Ceramic Matrix Composite Creep

A physics-informed recurrent neural network (RNN) based surrogate model is developed to emulate the nonlinear, time-dependent constitutive behavior of ceramic matrix composites (CMCs) driven by matrix damage and constituent creep at the microscale. Physics-informed constraints are introduced into the surrogate model through regularization to ground the prediction in physics and improve its predictive capabilities. Training data is generated using the high-fidelity generalized method of cells (HFGMC) approach which calls appropriate creep and damage models for each of the constituents. This coupling permits simulating the nonlinear behavior of CMCs based on constituent response at the microscale along with microstructural features such as fiber and porosity volume fraction and fiber radius. The microscale repeating unit cell is loaded under creep fatigue conditions to replicate the material loading experienced in a turbine engine. Therefore, the RNN-based surrogate model is tasked with predicting, as a function of variable input stress sequence, temperature, and microstructural features, the resulting strain history response while satisfying physical constraints related to creep rate, isochoric inelastic deformation, and strain energy density. The trained surrogate model is shown to effectively match the strain history over quantified distributions of microstructural features and relevant loading regimes and temperatures. Neural network based surrogate models can offer efficient alternatives to running computationally intensive multiscale material models to simulate the nonlinear response of large structural models. Therefore, the presented work provides evidence towards the feasibility of developing, training, and running such models for CMCs with complex microstructures, nonlinear time-dependent material response, and under non-monotonic loading conditions.

ceramic matrix composites↗
DOE OSTI2025

Limitations of Fault-Tolerant Quantum Linear System Solvers for Quantum Power Flow

Quantum computers hold promise for solving problems intractable for classical computers, especially those with high time or space complexity. Practical quantum advantage can be said to exist for such problems when the end-to-end time for solving such a problem using a classical algorithm exceeds that required by a quantum algorithm. Reducing the power flow (PF) problem into a linear system of equations allows for the formulation of quantum PF (QPF) algorithms, which are based on solving methods for quantum linear systems such as the Harrow-Hassidim-Lloyd (HHL) algorithm. Speedup from using QPF algorithms is often claimed to be exponential when compared to classical PF solved by state-of-the-art algorithms. Here, we investigate the potential for practical quantum advantage in solving QPF compared to classical methods on gate-based quantum computers. Notably, this paper does not present a new QPF solving algorithm but scrutinizes the end-to-end complexity of the QPF approach, providing a nuanced evaluation of the purported quantum speedup in this problem. Our analysis establishes a best-case bound for the HHL-based quantum power flow complexity, conclusively demonstrating that the HHL-based method has higher runtime complexity compared to the classical algorithm for solving the direct current power flow (DCPF) and fast decoupled load flow (FDLF) problem. Notably, our analysis and conclusions can be extended to any quantum linear system solver with rigorous performance guarantees, based on the known complexity lower bounds for this problem. Additionally, we establish that for potential practical quantum advantage (PQA) to exist it is necessary to consider DCPF-type problems with a very narrow range of condition number values and readout requirements.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Synthesizing, Compounding, and Characterizing a Heat Labile Polyurethane Foam

ABSTRACT A need exists for a packaging foam material that can be converted from solid to gaseous degradation products at reasonably low energy levels or temperatures, such as 100O C. This paper will primarily discuss the approaches currently being used to synthesize and characterize such a material. These approaches include the incorporation of novel polyols such as azo containing diols, polycarbonate diols, and polypropylene carbonate polyols into polyurethane foams. Characterization methods include NMR, FTIR, TGA, finite element analysis, impact strength, and others. This project will be funded for a duration of three years. Year 1 focused on developing the proposed test methods and producing an initial rigid polyurethane foam. Year 2 focuses on refining the materials and test methods. If appropriate, design of experiment (doe) techniques will be used to optimize components, component levels, density and other variables to attain required final material properties (TGA weight loss, impact strength, etc.). Year 3 focuses on scaling up to larger engineering quantities. The application for this material is in load securement for transportation of low level radioactive waste materials within the US Department of Energy (DOE) complex. Foam in place process equipment and operators will be shielded using this novel method over current practice. Current practice can involve time consuming methods of load securement in low level radiation environments. This new technique would eliminate exposure time securing the load and greatly improve the As Low As Reasonably Attainable (ALARA) conditions. The objective is to progress to higher Technical Readiness Levels (TRL) and larger pilot scale quantities. This paper discusses methodologies and presents current results to date.

Kranjc, Mark D.↗
NASA NTRS2010

Computational Model of the Chilldown and Propellant Loading of the Space Shuttle External Tank

This paper describes a computational model of the chilldown and propellant loading of the Space Shuttle External Tank liquid oxygen and hydrogen tanks at Launch Complex 39B at Kennedy Space Center. The purpose of the computational model is to predict the time required to chilldown the entire assembly consisting of the ground system transfer line and propellant tanks in order to compare with observed loading times, to evaluate the feasibility of similar models developed for the Ares I Upper Stage. The model also predicts the history of inflow and outflow from the tank, pressure and temperature inside the tank, and heat leak through the walls. The Generalized Fluid System Simulation Program (GFSSP), a general purpose network flow analysis code, has been used to develop this computational model. The paper describes the simulation of the loading process for both tanks and compares the resulting predictions to measurements

LeClair, Andre C.↗
DOE OSTI2022

Effect of Micro-Structural Dispersity of SiMo Ductile Iron on High Temperature Performance during Static Oxidation

High silicon and molybdenum (SiMo) ductile iron is commonly used for car exhaust systems, and its micro-structural dispersity depends on intrinsic parameters, which include alloy composition and inoculation efficiency, as well as extrinsic factors, such as casting wall thickness and molding material, which define cooling rate during solidification. Micro-structural dispersity is referred to as the degree of heterogeneity of sizes of structural constituencies within the microstructure. A variation in the micro-structural dispersity could impact the high temperature performance of SiMo ductile iron during static oxidation and transient thermo-mechanical loading conditions. In this study, static high temperature tests were performed on SiMo ductile iron solidified in a casting with varying wall thicknesses from 5 mm to 100 mm. The faster solidified specimens (taken from near chilled casting surfaces) had extremely high micro-structural dispersity as compared to the thicker section samples. After thermal exposure, each of the samples were characterized using 2D sections and 3D µCT images, and the results indicated an order of magnitude difference in graphite phase dispersity. The surface degradation was quantified after static oxidation experiments were implemented at temperature intervals between 650 °C and 800 °C. Non-destructive µCT 3D analysis and SEM/EDS were performed on cross sections and used to quantify the scale topology and structure. Carbon analysis was used to decouple the scale formation and decarburization phenomena that occurred within the samples. These methods enabled the quantification of the oxidation of the SiMo cast iron with different micro-structural dispersity levels after being exposed to high temperature static oxidation. Additionally, the complex material behavior during oxidation-assisted transient thermo-mechanical loading will be presented in a separate article.

36 MATERIALS SCIENCE↗
NASA NTRS2011

Comprehensive Forced Response Analysis of J2X Turbine Bladed-Discs with 36- Degree Variation in CFD Loading

At NASA/MSFC, Structural Dynamics personnel continue to perform advanced analysis for the turbomachinery in the J2X Rocket Engine, which is under consideration for the new Space Launch System. One of the most challenging analyses in the program is predicting turbine blade structural capability. Resonance was predicted by modal analysis, so comprehensive forced response analyses using high fidelity cyclic symmetric finite element models were initiated as required. Analysis methodologies up to this point have assumed the flow field could be fully described by a sector, so the loading on every blade would be identical as it travelled through it. However, in the J2X the CFD flow field varied over the 360 deg of a revolution because of the flow speeds and tortuous axial path. MSFC therefore developed a complex procedure using Nastran Dmap's and Matlab scripts to apply this circumferentially varying loading onto the cyclically symmetric structural models to produce accurate dynamic stresses for every blade on the disk. This procedure is coupled with static, spin, and thermal loading to produce high cycle fatigue safety factors resulting in much more accurate analytical assessments of the blades.

Elrod, David↗
NASA NTRS1991

Maximized gust loads for a nonlinear airplane using matched filter theory and constrained optimization

This paper describes and illustrates two matched-filter-theory based schemes for obtaining maximized and time-correlated gust-loads for a nonlinear airplane. The first scheme is computationally fast because it uses a simple one-dimensional search procedure to obtain its answers. The second scheme is computationally slow because it uses a more complex multidimensional search procedure to obtain its answers, but it consistently provides slightly higher maximum loads than the first scheme. Both schemes are illustrated with numerical examples involving a nonlinear control system.

Scott, Robert C.↗
NASA NTRS1976

Experimental investigation of a double-diffused MOS structure

Self-aligned polysilicon gate technology was applied to double-diffused MOS (DMOS) construction in a manner that retains processing simplicity and effectively eliminates parasitic overlap capacitance because of the self-aligning feature. Depletion mode load devices with the same dimensions as the DMOS transistors were integrated. The ratioless feature results in smaller dimension load devices, allowing for higher density integration with no increase in the processing complexity of standard MOS technology. A number of inverters connected as ring oscillators were used as a vehicle to test the performance and to verify the anticipated benefits. The propagation time-power dissipation product and process related parameters were measured and evaluated. This report includes (1) details of the process; (2) test data and design details for the DMOS transistor, the load device, the inverter, the ring oscillator, and a shift register with a novel tapered geometry for the output stages; and (3) an analytical treatment of the effect of the distributed silicon gate resistance and capacitance on the speed of DMOS transistors.

Lin, H. C.↗
DOE OSTI2025

Dynamic Boundary Microgrids Under Privatization Considerations

Microgrids have physical, electrical, and logical (data, network, and ownership) boundaries. To power unserved customer loads during an outage, microgrids can extend the traditional operational boundaries. This can become complex when considering microgrid-to-microgrid (M2M) interactions where sensitive information such as competitive microgrid operational data is not shared. This work proposes an optimization method coordinated between microgrid controllers and distribution management systems that limits data sharing. The method involves a competitive bidding strategy that maximizes unserved load coverage while minimizing resource utilization and sensitive operational data sharing among entities. The work is validated on a two-microgrid system with photovoltaic and energy storage systems and curves of load derived from real world residential buildings datasets. Results show that the proposed method, when applied for three distinct use cases of energy storage sufficiency to cover the predefined boundary and/or the expanded boundary, can successfully select and bid the available load coverage.

Starke, Michael [ORNL] (ORCID:0000000221211195)↗
DOE OSTI2021

A Multiport DC Transformer to Enable Flexible Scalable DC as a Service

The rapid adoption of new DC loads and sources, including photovoltaic arrays, DC fast charging of electric vehicle, battery energy storage and data centers, requires a large amount of DC power conversion. A majority of new deployments also necessitate the integration of multiple DC loads and sources at one site. The traditional approach to serve these new applications relies on multiple standard power converters, each dedicated to a source or load, and results in highly customized systems, with challenging control coordination, complex protection strategies, and poor scalability. Instead, this paper proposes the concept of a multiport DC transformer (MDCT) as a modular building block for realizing a flexible, scalable DC as a Service system and address this rapidly growing need. The MDCT uses the S4T to achieve very tight control of cycle-by-cycle energy exchange between multiple ports, with high efficiency. A single multiport converter replaces 4-6 distinct converters, integrates all energy flows and manages protection. As a result, a new layered control architecture is introduced to ensure stability and scalability of the MDCT, with multiple S4T power converter building blocks connected in parallel to realize a fully modular system and reach the target power levels.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2020

Deep Reinforcement Learning for Residential HVAC Control with Consideration of Human Occupancy

The Artificial Intelligence (AI) development described herein uses model-free Deep Reinforcement Learning (DRL) to minimize energy cost during residential heating, ventilation, and air conditioning (HVAC) operation. Building cooling loads and HVAC operation are difficult to accurately model due to complexity, lack of measurements and data, and model specific performance, so online machine learning is used to allow for real-time readjustment in performance. Energy costs for the multi-zone cooling unit shown in this work are minimized by scheduling on/off commands around dynamic prices. By taking advantage of precooling events that take place when the price is low, the agent is able to reduce operational cost without violating user comfort. The DRL controller was tested in simulation where the learner achieved a 43.89% cost reduction when compared to traditional, fixed-setpoint operation. The system is now ready for the next phase of testing in a live, real-time home environment.

Mckee, Evan↗
DOE OSTI2022

Predicting Performance of Macroalgae Farms with Hydrodynamic and Biological Modeling

As part of the ARPA-E MARINER program a set of simulation tools and findings were developed for the hydrodynamic, biological, and economic modeling of large scale offshore macroalgae farms. Results suggest the utility of the tools in understanding the complex interplay of design choices and environmental conditions on the structural loading and farm performance which drive the costs for macroalgae production.

09 BIOMASS FUELS↗