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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 NTRS1980

Supersonic flow over three-dimensional ablated nosetips using an unsteady implicit numerical procedure

The three-dimensional supersonic flow over passive, that is, nonablating, indented nosetips of reentry vehicles is determined using an unsteady implicit numerical algorithm which solves either the inviscid Euler equations or the 'thin-layer' Navier-Stokes equations. A nonorthogonal independent variable transformation is used to map the distorted physical domain, containing multiple zones of embedded subsonic flow and separated flow regions into a rectangular computational volume at whose boundaries the required permeable or impermeable boundary conditions are simulated. Use of the implicit algorithm results in faster convergence to the steady state because of a larger allowable time step over conventional explicit schemes. The numerical results obtained compare favorably with existing numerical solutions and experimental data for simple spheres which validates the program. Results are also presented for analytically defined indented bodies for both laminar and turbulent flow conditions that demonstrate the program's capability for computing such flows.

Kutler, P.↗

NASA NTRS2011

Characterizing the Joint Effect of Diverse Test-Statistic Correlation Structures and Effect Size on False Discovery Rates in a Multiple-Comparison Study of Many Outcome Measures

In their 2009 Annals of Statistics paper, Gavrilov, Benjamini, and Sarkar report the results of a simulation assessing the robustness of their adaptive step-down procedure (GBS) for controlling the false discovery rate (FDR) when normally distributed test statistics are serially correlated. In this study we extend the investigation to the case of multiple comparisons involving correlated non-central t-statistics, in particular when several treatments or time periods are being compared to a control in a repeated-measures design with many dependent outcome measures. In addition, we consider several dependence structures other than serial correlation and illustrate how the FDR depends on the interaction between effect size and the type of correlation structure as indexed by Foerstner s distance metric from an identity. The relationship between the correlation matrix R of the original dependent variables and R~, the correlation matrix of associated t-statistics is also studied. In general R~ depends not only on R, but also on sample size and the signed effect sizes for the multiple comparisons.

Feiveson, Alan H.↗

NASA NTRS2015

Conclusion: Major Findings and Future Activities.

The Agricultural Model Intercomparison and Improvement Project (AgMIP) incorporates a number of major advances in the way that climate impacts, vulnerability, and adaptation are being simulated. At its core is a protocol approach that results in impact assessments being more scientifically credible and thus ultimately having greater value to the wide range of agricultural stakeholders. Moreover, the use of the protocol approach enables closer scrutiny and intercomparison of models and methods so that they can be improved over time. By creating a truly trans-disciplinary, systems-based approach, AgMIP impact assessments and evaluation of adaptations become useful to agricultural decision-makers at multiple scales. The chapters in this two-part set demonstrate the use of this approach and represent early steps towards the full realization of these new methods and their application.

damage assessment↗

NASA NTRSDate not supplied

Harnessing the Digital Transformation for Development of Electrified Aircraft Propulsion Control Systems

Hybrid electric aircraft propulsion is an emerging technology that presents a variety of potential benefits along with technical integration challenges. Developing these new propulsion architectures with their complex control systems, and ultimately proving their benefit, is a multistep process that goes from concept, to analysis, to dynamic simulation, to hardware in the loop testing, to full scale testing and beyond. This effort is being revolutionized and indeed enabled by new digital tools that support the increasing technology readiness level throughout the maturation process. NASA has developed and made available a suite of digital tools that ease the path from concept to implementation. The three packages are the Toolbox for the Modeling and Analysis of Thermodynamic Systems (T-MATS), the Electrical Modeling and Thermal Analysis Toolbox (EMTAT), and the Thermal Systems Analysis Toolbox (TSAT). These tools are interactive, complementary, and compatible with each other. T-MATS is a modular thermodynamic modeling framework designed for creating custom component level models of jet engines. EMTAT is a modeling framework used to simulate a variety of power electronic devices, using both physics-based and power flow calculations. TSAT is a framework for modeling and analysis of dynamic heat transfer. These packages all consist of graphical, drag-and-drop, parameterizable building blocks representing various components of the system to be modeled, e.g., compressors, turbines, motors, energy storage devices, etc. They are designed to enable the user to model and simulate the end-to-end dynamic operation of a hybrid powertrain at the timescale of the turbomachinery, capturing mechanical, electrical, and thermal interactions. This paper will describe through multiple examples how these tools have been used successfully in several of the early stages of hybrid electric propulsion system development, from the initial system modeling to real-time interactive pilot-in-the-loop simulation to physical hardware-in-the-loop testing, each step bringing the technology closer to fruition.

Hybrid electric propulsion↗

NASA NTRSDate not supplied

Harnessing the Digital Transformation for Development of Hybrid Electric Aircraft Propulsion Control Systems

Electrified Propulsion↗

DOE OSTI2021

Fully Recyclable Polycarbonates from Simple, Bio-Derived Building Blocks

Plastic buildup and accumulation in the environment are an increasingly large problem facing civilization. Petroleum-based plastics can exist for hundreds to thousands of years in the environment, destroying habitats and polluting water. Environmentally conscious replacements for plastics are urgently needed. In this publication, we present a biobased alternative to petroleum-based polycarbonates. Using a diol monomer derived from glycerol and glycerol products, we have synthesized aliphatic polycarbonates with comparable physical properties to petroleum-based incumbents. The polymer can be quantitatively depolymerized using warm methanol to recover the monomer which can be repolymerized multiple times, or alternatively, the monomer, which is inherently nontoxic, can slowly break apart to the original components. This provides two end-of-life options for this material recycle or decomposition under environmental conditions to benign building blocks, thus providing a potential pathway to avoid environmental and bioaccumulation of plastics. We also demonstrate the ability to selectively recover the monomer from a simulated mixed-plastic waste environment; the monomer recovered this way functions identically to the virgin monomer after purification. This work represents an important step in the progress toward environmentally conscious polymer design with multiple end-of-life options.

36 MATERIALS SCIENCE↗

NASA NTRS2017

Low-Thrust Trajectory Optimization with Simplified SQP Algorithm

The problem of low-thrust trajectory optimization in highly perturbed dynamics is a stressing case for many optimization tools. Highly nonlinear dynamics and continuous thrust are each, separately, non-trivial problems in the field of optimal control, and when combined, the problem is even more difficult. This paper de-scribes a fast, robust method to design a trajectory in the CRTBP (circular restricted three body problem), beginning with no or very little knowledge of the system. The approach is inspired by the SQP (sequential quadratic programming) algorithm, in which a general nonlinear programming problem is solved via a sequence of quadratic problems. A few key simplifications make the algorithm presented fast and robust to initial guess: a quadratic cost function, neglecting the line search step when the solution is known to be far away, judicious use of end-point constraints, and mesh refinement on multiple shooting with fixed-step integration.In comparison to the traditional approach of plugging the problem into a “black-box” NLP solver, the methods shown converge even when given no knowledge of the solution at all. It was found that the only piece of information that the user needs to provide is a rough guess for the time of flight, as the transfer time guess will dictate which set of local solutions the algorithm could converge on. This robustness to initial guess is a compelling feature, as three-body orbit transfers are challenging to design with intuition alone. Of course, if a high-quality initial guess is available, the methods shown are still valid.We have shown that endpoints can be efficiently constrained to lie on 3-body repeating orbits, and that time of flight can be optimized as well. When optimizing the endpoints, we must make a trade between converging quickly on sub-optimal endpoints or converging more slowly on end-points that are arbitrarily close to optimal. It is easy for the mission design engineer to adjust this trade based on the problem at hand.The biggest limitation to the algorithm at this point is that multi-revolution transfers (greater than 2 revolutions) do not work nearly as well. This restriction comes in because the relationship between node 1 and node N becomes increasingly nonlinear as the angular distance grows. Trans-fers with more than about 1.5 complete revolutions generally require the line search to improve convergence. Future work includes: Comparison of this algorithm with other established tools; improvements to how multiple-revolution transfers are handled; parallelization of the Jacobian computation; in-creased efficiency for the line search; and optimization of many more trajectories between a variety of 3-body orbits.

Parrish, Nathan L.↗

DOE OSTI2024

Experimental analysis of convective drying of paper and board

Conventional multi-cylinder drying of paper and board involves a mixture of conductive drying from steam-heated dryer cylinders and convective drying by the flow of heated air over the surface of the paper web in the pockets. Pocket ventilation is a critical component in assisting heat and mass transfer during the drying process but is the primary contributor towards removing evaporated water from the web. Air temperature, velocity, and humidity are critical parameters involved in the convective drying process. This paper covers an experimental study involving the design and development of a small lab-scale setup for convective drying of various grades of paper and board, monitoring multiple parameters like paper temperature, moisture content, air humidity, temperature, and velocity measured in situ as the drying proceeds with continuous and accurate sampling capabilities for all parameters in the sample and the system. Instantaneous drying rates, heat, and mass transfer coefficients were also deduced for every time step till the paper completely dried. Furthermore, the coefficients obtained were also reported in the form of dimensionless correlations, and the results were compared against traditional correlations used in the modelling of paper drying. Furthermore, this data will be useful in process development, modelling, design, and the paper drying process simulation.

42 ENGINEERING↗

DOE OSTI2020

Parallel-in-Time Multigrid Methods for Hyperbolic Problems, with a Focus on the Shallow Water Equations

The coming massive parallelism of exascale computing presents a pressing challenge for the many DOE simulations of time-dependent partial differential equations, which typically use traditional sequential time stepping methods. Since this traditional approach is inherently serial, it presents a sequential bottleneck when moving to exascale computing, because future performance gains will come through greater concurrency, not faster clock speeds. Thus, the goal of this work is to research parallelism in time, i.e., methods that compute multiple time values simultaneously, not sequentially. The focus will be on hyperbolic problems of programmatic interest to DOE, with the goal of enabling scalable simulations of time-dependent hyperbolic problems on future architectures. The difficulty lies in the fact that hyperbolic problems are well-known to be difficult for parallel-in-time methods, with the most common parallel-in-time method, Parareal, diverging in many cases. Here, the chosen methodology for scalably solving these hyperbolic space-time equations is multigrid-reductionin-time (MGRIT), because multigrid (when it works) is a powerful, optimal, and scalable solver for discretized PDEs. To further research in this area, this project will explore a model hyperbolic problem (the shallow water equations) in the context of recent advances (e.g., by Wingate and Haut) in constructing improved coarse time-grid time-propagators by using the slow asymptotic structure of the equations. In particular, we will investigate if such coarse time-propagators offer significant advantages in an MGRIT setting, and explore any broader insights gained into parallel-in-time for hyperbolic problems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Parallel Multigrid in Time and Space for Extreme-Scale Computational Science

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Parallel Multigrid in Time and Space for Extreme-Scale Computational Science: Chaotic and Hyperbolic Problems

The coming massive parallelism of exascale computing presents a pressing challenge for the many DOE simulations of time-dependent partial differential equations (PDEs), which typically use traditional sequential time stepping methods. Since this traditional approach is inherently serial, it presents a sequential bottleneck when moving to exascale computing, because future performance gains will come through greater concurrency, not faster clock speeds. Thus, the goal of this work is to research parallelism in time, i.e., methods that compute multiple time values simultaneously, not sequentially. The focus will be on hyperbolic and chaotic problems of interest to DOE, with the goal of enabling scalable simulations of time-dependent hyperbolic and chaotic problems on future architectures. The chosen methodology for solving these problems parallel-in-time is multigrid, because multigrid (when it works) is a powerful, optimal, and scalable solver for discretized PDEs. Multigrid is already commonly used in many DOE simulations for scalably and optimally solving space-only PDE problems. The areas of hyperbolic and chaotic problems are chosen because of their relevance to problems of programmatic interest to DOE. However, these problems are also well-known to be difficult for parallelin-time methods, with the most common method, parareal, diverging in many cases. The current stateof-the-art for parallel-in-time at LLNL is the multigrid reduction in time (MGRIT) XBraid package, which also struggles for such problems, while still showing some improvement over parareal. In summary, new methods are needed for an efficient parallel-in-time scheme for hyperbolic and chaotic problems, and this work shall research promising new multigrid methods in this area. In particular, we take inspiration from the Least Squares Shadowing (LSS by Wang) approach for solving chaotic problems. Here, an optimization approach is able to find “well-conditioned” shadow trajectories/solutions to the original “ill-conditioned” chaotic problem. Thus, the new multigrid methods researched here also arise in an optimization context.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Multigrid Reduction in Time for Chaotic and Hyperbolic Problems (Final Report)

The coming massive parallelism of exascale computing presents a pressing challenge for the many DOE simulations of time-dependent partial differential equations (PDEs), which typically use traditional sequential time stepping methods. Since this traditional approach is inherently serial, it presents a sequential bottleneck when moving to exascale computing, because future performance gains will come through greater concurrency, not faster clock speeds. Thus, the goal of this work is to research parallelism in time, i.e., methods that compute multiple time values simultaneously, not sequentially. The focus will be on hyperbolic and chaotic problems of interest to DOE, with the goal of enabling scalable simulations of time-dependent hyperbolic and chaotic problems on future architectures. The chosen methodology for solving these problems parallel-in-time is multigrid, because multigrid (when it works) is a powerful, optimal, and scalable solver for discretized PDEs. Multigrid is already commonly used in many DOE simulations for scalably and optimally solving space-only PDE problems. The areas of hyperbolic and chaotic problems are chosen because of their relevance to problems of programmatic interest to DOE. However, these problems are also well-known to be difficult for parallelin-time methods, with the most common method, parareal, diverging in many cases. The current state-of-the-art for parallel-in-time at LLNL is the multigrid reduction in time (MGRIT) XBraid package, which also struggles for such problems, while still showing some improvement over parareal. In summary, new methods are needed for an efficient parallel-in-time scheme for hyperbolic and chaotic problems, and this work shall research promising new multigrid methods in this area. In particular, this work shall continue researching the directions from the current collaboration with Dr. Falgout, which are laid out in the work Toward Parallel in Time for Chaotic Dynamical Systems and showed the first known results of a parallel-in-time speedup for a chaotic problem. This work outlines two key improvements to XBraid for chaotic problems, the so-called “theta” and “delta-correction” methods. Here, these two improvements will be implemented in a high-performance but general way in XBraid and explored for more complicated problems. We will additionally research, as time allows, improvements to these techniques, as well as multigrid relaxation techniques based on Least Squares Shadowing (LSS by Wang) and a nonintrusive block tridiagonal solver based on MGRIT, called TriMGRIT.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Multigrid Reduction in Time for Chaotic and Hyperbolic Problems (Final Report)

The coming massive parallelism of exascale computing presents a pressing challenge for the many DOE simulations of time-dependent partial differential equations (PDEs), which typically use traditional sequential time stepping methods. Since this traditional approach is inherently serial, it presents a sequential bottleneck when moving to exascale computing, because future performance gains will come through greater concurrency, not faster clock speeds. Thus, the goal of this work is to research parallelism in time, i.e., methods that compute multiple time values simultaneously, not sequentially. The focus will be on hyperbolic and chaotic problems of interest to DOE, with the goal of enabling scalable simulations of time-dependent hyperbolic and chaotic problems on future architectures. The chosen methodology for solving these problems parallel-in-time is multigrid, because multigrid (when it works) is a powerful, optimal, and scalable solver for discretized PDEs. Multigrid is already commonly used in many DOE simulations for scalably and optimally solving space-only PDE problems. The areas of hyperbolic and chaotic problems are chosen because of their relevance to problems of programmatic interest to DOE. However, these problems are also well-known to be difficult for parallel-in-time methods, with the most common method, parareal, diverging in many cases. The current state of-the-art for parallel-in-time at LLNL is the multigrid reduction in time (MGRIT) XBraid package, which also struggles for such problems, while still showing some improvement over parareal. In summary, new methods are needed for an efficient parallel-in-time scheme for hyperbolic and chaotic problems, and this work shall research promising new multigrid methods in this area. In particular, this work shall continue researching the directions from the current collaboration with Dr. Falgout, which are laid out in the work Toward Parallel in Time for Chaotic Dynamical Systems and showed the first known results of a parallel-in-time speedup for a chaotic problem. This work outlines two key improvements to XBraid for chaotic problems, the so-called “theta” and “delta-correction” methods. Here, these two improvements will be further researched and improved (including with a new relaxation method inspired by on Least Squares Shadowing (LSS)) and explored for more complicated problems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Seeking Repeating Anthropogenic Seismic Sources: Implications for Seismic Velocity Monitoring at Fault Zones

Abstract Seismic velocities in rocks are highly sensitive to changes in permanent deformation and fluid content. The temporal variation of seismic velocity during the preparation phase of earthquakes has been well documented in laboratories but rarely observed in nature. It has been recently found that some anthropogenic, high‐frequency (>1 Hz) seismic sources are powerful enough to generate body waves that travel down to a few kilometers and can be used to monitor fault zones at seismogenic depth. Anthropogenic seismic sources typically have fixed spatial distribution and provide new perspectives for velocity monitoring. In this work, we propose a systematic workflow to seek such powerful seismic sources in a rapid and straightforward manner. We tackle the problem from a statistical point of view, considering that persistent, powerful seismic sources yield highly coherent correlation functions (CFs) between pairs of seismic sensors. The algorithm is tested in California and Japan. Multiple sites close to fault zones show high‐frequency CFs stable for an extended period of time. These findings have great potential for monitoring fault zones, including the San Jacinto Fault and the Ridgecrest area in Southern California, Napa in Northern California, and faults in central Japan. However, extra steps, such as beamforming or polarization analysis, are required to determine the dominant seismic sources and study the source characteristics, which are crucial to interpreting the velocity monitoring results. Train tremors identified by the present approach have been successfully used for seismic velocity monitoring of the San Jacinto Fault in previous studies.

58 GEOSCIENCES↗

DOE OSTI2023

A Deep Learning Pipeline for Optimizing Large-scale Phase Field Simulations

Phase field (PF) simulations are computationally expensive but remain a key analysis tool to understand the complex mechanisms of additive manufacturing (AM) processes. Each PF simulation-aided analysis requires thousands of node hours on leadership-class supercomputers. One of the main goals of these analyses is the study of microstructure evolution during the build process which begins with the onset of nucleation. Nucleation occurs under certain thermomechanical conditions which are not known a priori and many PF simulations are required to identify ranges of input thermo-mechanical parameters that can result in the onset of nucleation. Since many of the simulations do not result in nucleation, an analysis campaign often ends up wasting tremendous amounts of precious computing resources executing nucleation-absent simulations. The goal of this work is to design and train deep learning models to inform a PF simulation about the likelihood of the occurrence of nucleation in a future simulation time-step based on the state summary over a finite number of past time-steps of a running simulation. If the prediction determines that the running simulation is unlikely to reach nucleation in the allotted time, then its execution is stopped immediately ultimately resulting in vast reduction in wasted computations when accrued over all the PF simulations typically performed in a single or multiple analysis campaign(s). The paper presents the performance of a machine learning pipeline that uses a convolutional neural network (CNN) model to learn an embedding which is then used with a self-attention network to build a multi-task deep learning model to predict the likelihood of nucleation. The model also predicts the input parameters used in a simulation. Performance is compared with a baseline pipeline that uses an off-the-shelf LeNet-5 model to learn the initial embedding. Despite their smaller size, performance results indicate significant improvement in accuracy of the proposed models compared to the larger baseline models.

Kannan, Ramakrishnan {ramki}↗

NASA NTRS2010

Human Operations Beyond LEO by the End of the Decade: An Affordable Near-Term "Stepping Stone"

For more than a decade, several teams have assessed designs for a long-duration free-space human habitat beyond low-Earth orbit (LEO), building upon years of hard-won experience with the International Space Station (ISS). These systems would enable multiple achievements for science and human space flight. Most were intended to be deployed using available or near-future capabilities within about a decade after funding begins and serve as the first major human "stepping stone" beyond LEO. Last year, Thronson and Talay summarized work up to that time on expandable or inflatable concepts for deployment at an Earth-Moon (E-M) L1 or L2 location. Here we summarize our team's more recent work both on a long-duration human habitat that could be deployed beyond LEO within a decade and on the priority goals that such a habitat might accomplish. Particulars of this and other concepts for human operations in cis-lunar space are posted on the web and will be presented at professional conferences, and detailed in future publications by our group.

Thronson, Harley↗

DOE OSTI2020

Large Single Crystals of V 1– x Mo x O 2 from a Two-Step Chemical Vapor Transport Synthesis

This article details the synthesis of single crystals with the chemical formulas V 1-x Mo x O 2 ( 0 ≤ x ≤ 0.6) using the chemical vapor transport method at high temperatures (850 to 950 °C). These compounds are of interest due to the special relationship between their structure and electronic properties. Understanding this relationship will help guide future efforts in designing energy-saving devices. While some of these compounds have been synthesized before, this is the first time their synthesis has been reported for such a wide range of compounds in the form of bulk single crystals. The single crystal form is required for detailed characterization of the compounds. The materials were characterized chemically and structurally in order to validate the precise chemical compositions that were reported. Characterization methods included x-ray diffraction, inductively-coupled plasma atomic emission spectroscopy, and electron dispersive spectroscopy. The synthesis method is tunable, and the introduction of multiple steps both overcome the previous chemical challenges precluding synthesis of high molybdenum content crystals and allow for the creation of larger single crystals that were otherwise possible. The crystals synthesized this way are suitable for specialized measurements for determining the precise physical properties and atomic structure.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2026

First light from beam neutrinos on an LAPPD in ANNIE

The Accelerator Neutrino Neutron Interaction Experiment (ANNIE) is both a physics experiment and a technology testbed for next-generation light-based neutrino detection. In this paper, we report the first demonstration of a fully integrated Large Area Picosecond Photodetector (LAPPD) operating in a running neutrino beam experiment. Particular focus is given to the design, commissioning, and successful deployment of the Packaged ANNIE LAPPD (PAL), a waterproof, self-triggering module incorporating fast waveform digitization and precision timing synchronized to the ANNIE detector subsystems. We identify beam-correlated LAPPD data frames consistent with charged-current neutrino interactions observed in multiple detector subsystems, establishing the first detection of neutrino-induced Cherenkov light with an LAPPD. These results validate the system-level performance of LAPPDs under realistic experimental conditions — including long-term stability, timing synchronization, and event matching with conventional PMT and muon detector systems — marking a critical step toward their deployment in future large-scale neutrino and particle detectors.

Cherenkov detectors↗