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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

ForSE: A GAN-based Algorithm for Extending CMB Foreground Models to Subdegree Angular Scales

We present ForSE (Foreground Scale Extender), a novel Python package that aims to overcome the current limitations in the simulation of diffuse Galactic radiation, in the context of cosmic microwave background (CMB) experiments. ForSE exploits the ability of generative adversarial neural networks (GANs) to learn and reproduce complex features present in a set of images, with the goal of simulating realistic and non-Gaussian foreground radiation at subdegree angular scales. This is of great importance in order to estimate the foreground contamination to lensing reconstruction, delensing, and primordial B-modes for future CMB experiments. We applied this algorithm to Galactic thermal dust emission in both total intensity and polarization. Our results show how ForSE is able to generate small-scale features (at 12') having as input the large-scale ones (80'). The injected structures have statistical properties, evaluated by means of the Minkowski functionals, in good agreement with those of the real sky and which show the correct amplitude scaling as a function of the angular dimension. Furthermore, the obtained thermal dust Stokes Q and U full-sky maps as well as the ForSE package are publicly available for download.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

In-Package Common-Mode Filter for GaN Power Module with Improved Radiated EMI Performance

This paper discusses the impact of parasitic inductances on the electromagnetic interference (EMI) performance at radiated frequency and provides a new concept for high-frequency wide bandgap (WBG) power module package design with integrated π-type common mode filter (π-CMF). The connection parasitic-inductances of a π-type CMF model are analyzed, and the parasitic inductances from the CMF to the CM noise source and to the heatsink are minimized to improve the CMF's EMI performance in the radiated frequency range. Therefore, placing the π-CMF closer to the power module (i.e. in-package CMF) provides a larger noise attenuation compared to placing it outside the module (i.e. external CMF). To verify the theoretical analysis, a half-bridge GaN power module with an in-package π-CMF is designed, and experiments are conducted by comparing the attenuated noise spectrums of a 70-V/1.75-A hard-switching buck converter built by the designed module with an external CMF and the power-module integrated CMF. According to the experiment results, up to 10 dBμV more attenuation is achieved by the in-package CMF than the external CMF, validating the analytical conclusion.

Xue, Lincoln↗
DOE OSTI2025

Packaging a 650V/400A GaN Half-bridge Power Module with Ultra-low Parasitics for Electric Vehicle Drive Applications

This paper proposes a compact and efficient half-bridge power module with three 650 V / 150 A GaN dies in parallel. The power module incorporates a main power printed circuit board (PCB), an interface PCB, and a flex PCB to achieve low parasitics in both power loop and gate-side connection, resolving the issue of high parasitics typically encountered with wire bonding in high-current applications. Additionally, the interface PCB decouples the design constraints between the power loop and the gate loops. The proposed design is optimized with a vertical loop configuration to reduce power loop inductance through magnetic flux cancellation. Finite element analysis indicates that the power loop inductance is 0.58 nH at 100 MHz, while the maximum die junction temperature reaches 131 °C under an ambient temperature of 65 °C and a load current of 385 A. The proposed multi-piece PCB structure reduces the inductance of the drive circuit to minimize EMI and to mitigate false triggering. At the same time, it reduces impedance mismatches across different driver circuits, thereby achieving dynamic current sharing in multi-chip parallel configurations. Under simulation conditions of 400 V / 385 A, the current imbalance among chips was limited to 5 A. A 400 V / 385 A double-pulse test was conducted to experimentally validate the performance of the proposed power module.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2023

Investigation of AlGaN-Delta–GaN-Based UV Photodiodes in a Metal–Semiconductor–Metal Configuration for Efficient and Fast Solar Blind UV Sensing

Metal–semiconductor–metal (MSM) configured UV photodiodes (PD’s) were designed and fabricated on an AlGaN/GaN–based substrate for efficient and ultrafast UV detection. The purpose was to investigate the feasibility of obtaining efficient and ultrafast temporal response from these devices in the UV given the challenges associated with the formation of Schottky contacts on laterally oriented AlGaN/GaN thin films. Two sets of devices were implemented using Pt and Au as metal contacts with 5-µm finger width, 5-µm finger spacing, and a 50-µm x 50-µm active area. Spectral and voltage bias studies were done to establish the spectral profile and the effect of bias voltage on the responsivity of the detectors at 265 nm. Here, the best vertical MSM PD’s produced 0.6-A/W responsivity under 10-V bias voltage at 265 nm. Peak spectral responsivities were recorded as 1.35 A/W and 1.25 A/W at 240 nm for Pt and Au PD’s, respectively.

42 ENGINEERING↗
DOE OSTI2020

Materials Data on GaN by Materials Project

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

36 MATERIALS SCIENCE↗
DOE OSTI2024

A Dataset of CFD Simulated Industrial Furnace Images for Conditional Automatic Generation with GANs

The steel industry is constantly looking for ways to automate processes and improve efficiency. A standard practice in industry is to simulate how complex systems will operate before they are actually used. Some complex systems, including steel industry processes such as blast furnaces, require complex physics-based simulations utilizing computational fluid dynamics (CFD). These CFD physics-based simulations are very accurate but can take significant time and computational resources to process, resulting in challenges for the implementation of the models in real-world operational environments. In recent years, deep learning (DL) has been considered as a substitute for these CFD models. DL models can be trained on validated CFD simulation data and then used for industrial process inference. Previous DL-based solutions have made great contributions for industrial automation but are currently missing the additional visualization component that CFD simulations also provide. In this paper, we propose a dataset for simple DL generative approaches that can help to address this issue. The dataset and methodology under development to approach this prediction are discussed in this work.

Calix, Ricardo↗