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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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46 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Simulation of Satellite, Airborne and Terrestrial LiDAR with DART (I):Waveform Simulation with Quasi-Monte Carlo Ray Tracing

Light Detection And Ranging (LiDAR) provides unique data on the 3-D structure of atmosphere constituents and the Earth's surface. Simulating LiDAR returns for different laser technologies and Earth scenes is fundamental for evaluating and interpreting signal and noise in LiDAR data. Different types of models are capable of simulating LiDAR waveforms of Earth surfaces. Semi-empirical and geometric models can be imprecise because they rely on simplified simulations of Earth surfaces and light interaction mechanisms. On the other hand, Monte Carlo ray tracing (MCRT) models are potentially accurate but require long computational time. Here, we present a new LiDAR waveform simulation tool that is based on the introduction of a quasi-Monte Carlo ray tracing approach in the Discrete Anisotropic Radiative Transfer (DART) model. Two new approaches, the so-called "box method" and "Ray Carlo method", are implemented to provide robust and accurate simulations of LiDAR waveforms for any landscape, atmosphere and LiDAR sensor configuration (view direction, footprint size, pulse characteristics, etc.). The box method accelerates the selection of the scattering direction of a photon in the presence of scatterers with non-invertible phase function. The Ray Carlo method brings traditional ray-tracking into MCRT simulation, which makes computational time independent of LiDAR field of view (FOV) and reception solid angle. Both methods are fast enough for simulating multi-pulse acquisition. Sensitivity studies with various landscapes and atmosphere constituents are presented, and the simulated LiDAR signals compare favorably with their associated reflectance images and Laser Vegetation Imaging Sensor (LVIS) waveforms. The LiDAR module is fully integrated into DART, enabling more detailed simulations of LiDAR sensitivity to specific scene elements (e.g., atmospheric aerosols, leaf area, branches, or topography) and sensor configuration for airborne or satellite LiDAR sensors.

Gastellu-Etchegorry, Jean-Philippe↗
NASA NTRS2019

A Simple Control to Reduce the Voltage Stress of Non-Conducting Switches in Three-Level ANPC Converter

With the development of wide band-gap (WBG) technology, the switching speed of power semiconductor devices is increased, which makes circuits more sensitive to parasitics. For three-level active neutral point clamped (3L-ANPC) converters, the over-voltage of non-conducting switches can be an issue. This paper analyzes the multiple commutation loops in 3L-ANPC converter and summarizes the impact factors of the over-voltage for the non-conducting switch. It is found that the nonlinearity of the output capacitance of the device can significantly influence the over-voltage. A simple control without introducing any additional hardware circuit is proposed to attenuate the impact of the nonlinearity. With the proposed control, the peak over-voltage of the non-conducting switch can be reduced significantly. Multi-pulse test is conducted for a 3L- ANPC converter built with silicon carbide (SiC) MOSFETs. The testing results show that the peak over-voltage decreases from 892 V to 624 V with the proposed control. More detailed analysis and experimental results will be provided in the final paper.

SiC MOSFET↗
NASA NTRS2020

Small Business Launches Down-to-Earth Products via NASA SBIR Program

Through the SBIR program, DSSP developed a multi-pulse solid rocket motor for NASA that will enable stabilized interplanetary flight for small satellites. Another of DSSP’s core innovations with roots in the SBIR program is already having an even greater impact. Originally proposed to fuel a microsatellite thruster for Earth-observation missions, DSSP’s HAN-based green electric monopropellants (GEM and AF-M315E) technology is powering larger satellites, enhancing downhole oil and gas recovery, and much more. Highly insensitive to ignition by spark, flame, or shock, GEM-based innovations are stable, non-toxic, and more controllable than conventional propellants.

Bruce R Cogan↗
NASA NTRS2024

Flickering Gamma Flashes, the Missing Link Between Gamma Glows and TGFs

Two different hard radiation phenomena are known to originate from thunderclouds: Terrestrial Gamma-ray Flashes (TGFs) and gamma-ray glows. Both involve avalanche of electrons accelerated to relativistic energies but are different phenomena. Glows are known to last for one-to-hundreds of seconds, have moderate intensities and originate from quasi-stationary thundercloud fields. TGFs exhibit high intensities and have characteristic durations of tens-to-hundreds of microseconds. TGFs often show close association with emission of strong radio signals and optical pulses indicating involvement of lightning leaders in their generation. Here we report unique observations of a different phenomenon, which we call Flickering Gamma-ray Flashes (FGFs). FGFs resemble usual multi-pulse TGFs, but with larger number of pulses and each pulse has a longer duration than ordinary TGFs. FGF durations span from 20 to 250 milliseconds, which reaches the lower boundary of the gamma-ray glow duration. FGFs are radio and optically silent, which makes them distinct from normal TGFs. An FGF starts as an ordinary gamma-ray glow, then suddenly increases exponentially in intensity, and turns into an unstable, “flickering” mode with a sequence of pulses. FGFs could be the missing link between the gamma-ray glows and conventional TGFs, whose absence has been puzzling atmospheric electricity community for two decades.

N Østgaard↗
DOE OSTI2021

Experiment Based Superposition Thermal Modeling of Laser Powder Bed Fusion

This paper evaluates experiment-based superposition thermal modeling for Laser Powder Bed Fusion (LPBF) with a pulsed laser. An analytical pulsed laser thermal model establishes the modeling procedure. The framework inverts a powder bed’s single pulse temperature response from experimental spatiotemporal Short-Wave Infrared (SWIR) camera data. Superimposing this response along a scan path simulates multi-pulse LPBF. Results show the experimentally informed superposition model rapidly and accurately predicts a layer’s temperature history. The model has applications in correction of thermally driven LPBF errors and in-situ part qualification.

Lough, Cody↗
DOE OSTI2023

Examination of a Methane/Diesel RCCI Engine Using Pele: Preprint

Multi-fuel, advanced injection strategies have become increasingly promising as a strategy to mitigate the emissions generated from internal combustion engines. By carefully controlling the combustion phasing in-cylinder, these new multi-pulse, multi-fuel injection strategies are able to burn in the low-temperature combustion regime where both NOx and soot are not readily produced, reducing the need for extensive exhaust gas recirculation systems. In this study, we examine a reactivity-controlled compression ignition (RCCI) strategy that uses an early pre-filled methane-air mixture with low turbulence background as the low-reactivity fuel and a direct injection of four discrete dodecane jets as a surrogate for the high-reactivity diesel fuel. We use the Pele software suite, a highly optimized, exascale-ready, adaptive mesh refinement codebase to perform high-resolution numerical simulations of a scaled down, single cylinder from the RCCI engine. Here, we resolve the ignition kernels down to micrometer scales and present several statistical quantities evaluating the development of the flow and detailing the onset of ignition and subsequent flame development. Particular attention is paid to the conditions surrounding the onset of the first ignition kernels and discussing what led to the development of those conditions.

CFD↗
DOE OSTI2023

Deep reinforcement learning to discover multi-fuel injection strategies for compression ignition engines

Over the past several decades, regulation of compression ignition engine emissions has become increasingly stringent as concern about the environmental and health implications of these emissions has grown. These changing constraints have led to a series of new, alternative fuel injection strategies that aim to maintain power output while reducing in-cylinder generated emissions by operating in the low-temperature combustion (LTC) regime. These advanced injection strategies are created and retuned for individual combinations of engine geometry, fuel, and emissions constraints. Deep reinforcement learning has been shown to be an effective alternative to traditional optimization approaches for highly combinatorial control problems, such as discovering the optimal injection schedules for compression ignition engines. In this study, we deploy a previously presented deep reinforcement learning framework to iteratively optimize a series of engine geometries over a range of increasingly strict NO x emissions regulations. We then examine the resulting injection schedules. We discuss the potential for using this deep reinforcement learning framework for fuel selection screening and for discovering unique injection strategies for different engine geometries and future emissions standards.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Thin-Foil Heating with Intense Relativistic Electron Beams

Thin-metal foils are used in intense relativistic beam experiments for various purposes. In planning these experiments and analyzing the results it is often desirable to have an estimate of the temperature reached during the beam pulse. This can be deduced from an estimate of the energy deposited by the beam, and the specific heat capacity of the material, which depends on the temperature. The methodology for doing this is explained herein, and a metric for foil survivability under heating by an intense relativistic electron beam is suggested.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗