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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

23.4% monolithic epitaxial GaAsP/Si tandem solar cells and quantification of losses from threading dislocations

A 2-terminal, dual-junction, epitaxially integrated, GaAsP/Si tandem solar cell with an 3rd party certified efficiency of 23.4 % was fabricated via MOCVD growth on an ex-situ produced Si sub cell. The drastic efficiency improvement over the authors previous peer-reviewed demonstration of such a device architecture is examined. Critical advancements in top cell design to maximize short wavelength response were critical in enabling improved top cell response. An in-depth analysis of this champion tandem cell has identified key loss mechanisms which elucidate the pathway for further efficiency gains. First, voltage dependent collection efficiency in the GaAsP top cell is the primary cause of fill factor losses currently limiting efficiency. Analysis of spectrally resolved I–V measurements and analytical device modeling and indicate poor diffusion length due to elevated dislocation densities as the likely cause for the voltage dependent collection efficiency. Second, modeling for the GaAs 0.75 P 0.25 top cell, using experimental data at multiple dislocation densities, provides quantitative understanding of the current and voltage losses associated with threading dislocations providing a clear efficiency pathway with reduction in dislocation density. Finally, Si subcell modeling identifies the pathway for further Si subcell advances over the present, simplistic design, which has yet to employ the known benefits of rear surface texture or dielectric passivation.

14 SOLAR ENERGY↗
DOE OSTI2025

Three-dimensional photoluminescence imaging of threading dislocations in GaN by sub-band optical excitation

GaN is rapidly gaining attention for implementation in power electronics but is still impacted by its high density of threading dislocations (TDs), which have been shown to facilitate current leakage through devices limiting their performance and reliability. Here, we discuss a novel implementation of photoluminescence (PL) imaging to study TDs in regions within vertically structured p-i-n GaN (PIN) diodes consisting of metalorganic chemical vapor deposition (MOCVD) epitaxial layers grown on ammonothermal GaN (am-GaN) substrates. PL imaging with a sub-bandgap excitation energy (3.1 eV) reveals TDs with excellent clarity in three dimensions within the am-GaN substrate. Galvanometric-driven PL imaging allows the microstructure of hundreds of devices to be characterized in a single session, enhancing the screening process through the addition of device specific TD location tracking and density mapping. The visibility, structural characteristics, luminescent nature and evolution of TDs through the GaN growth process are described, potentially providing the ability to define TD structures associated with leakage current.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Challenges of relaxed n -type GaP on Si and strategies to enable low threading dislocation density

We directly show that doping type strongly affects the threading dislocation density (TDD) of relaxed GaP on Si, with n-type GaP having a TDD of ~3.1 × 10 7 cm -2 , nearly 30× higher than both p-type and unintentionally doped GaP at ~1.1 × 10 6 cm -2 . Such a high TDD is undesirable since n-GaP on Si serves as the starting point for the growth of epitaxial III-V/Si multi-junction solar cells. After highlighting additional challenges for highly n-doped GaP on Si including increased surface roughness, anisotropic strain relaxation, and inhomogeneous TDD distributions from blocking of the dislocation glide, we go on to show that the TDD of n-GaP on Si rises by 10× as the doping concentration increases from ~5 × 10 16 to ~2 × 10 18 cm -3 . Next, we investigate the effects of additional dopant choices on the TDD, determining that electronic effects dominate over solute effects on the dislocation velocity at these concentrations. Finally, we demonstrate the respective roles of compressively strained superlattices, low-temperature initiation, and lowered n-type doping concentration on reducing the TDD for n-GaP on Si. By combining all three, we attain relaxed n-GaP on Si with a TDD of 1.54(±0.20) × 10 6 cm -2 , approaching parity with p-GaP on Si. Such high-quality n-GaP on Si will play an important role in boosting the efficiency of epitaxial III-V/Si multi-junction solar cells.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Impact of threading dislocations on the V-defect assisted lateral carrier injection and recombination in InGaN quantum well LEDs

The nonuniform hole distribution between InGaN quantum wells (QWs) of light emitting diodes (LEDs) has a negative impact on LED efficiency. The uniformity can be increased by using lateral hole injection through sidewalls of V-defects, which form at threading dislocations. However, the inherent coupling between the V-defects and dislocations might affect efficiency of the hole injection and nonradiative recombination. In this work, we have tested the possible impact of the dislocations on the injection and recombination by means of scanning near-field electroluminescence and photoluminescence spectroscopy on single green-emitting InGaN QW LEDs containing large (∼0.5 μm) V-defects. The measurements have not provided any evidence of a lower hole injection efficiency or enhanced nonradiative recombination at the dislocations located at the V-defect facets or their apexes. This shows that large V-defects are excellent volumetric injectors for long wavelength InGaN LEDs. Furthermore, it was established that V-defects are preferential hole injectors even in single quantum well devices. Compared to vertical injection, the V-defect injection allows lowering the operating voltage, which should contribute to an enhanced wall plug efficiency.

42 ENGINEERING↗
DOE OSTI2024

Distributed Order Recording Techniques for Efficient Record-and-Replay of Multi-threaded Programs

After all these years and all these other shared memory programming frameworks, OpenMP is still the most popular one. However, its greater levels of non-deterministic execution makes debugging and testing more challenging. The ability to record and deterministically replay the program execution is key to address this challenge. However, scalably replaying OpenMP programs is still an unresolved problem. In this paper, we propose two novel techniques that use Distributed Clock (DC) and Distributed Epoch (DE) recording schemes to eliminate excessive thread synchronization for OpenMP record and replay. Our evaluation on representative HPC applications with ReOMP, which we used to realize DC and DE recording, shows that our approach is 2-5x more efficient than traditional approaches that synchronize on every shared-memory access. Furthermore, we demonstrate that our approach can be easily combined with MPI-level replay tools to replay non-trivial MPI+OpenMP applications. We achieve this by integrating ReOMP into ReMPI, an existing scalable MPI record-and-replay tool, with only a small MPI-scale-independent runtime overhead.

Fu, Xiang↗
DOE OSTI2026

Accelerating Bilevel Optimization With Hierarchical Many-Threaded Parallel Differential Evolution

Bilevel optimization is encountered in many relevant real-world applications. The main feature of this type of problem is that an upper-level optimization problem is constrained by a nested lower-level optimization problem. Because of this nested structure, bilevel problems (BLPs) are usually computationally expensive to solve. Differential evolution (DE) has demonstrated promising results in solving BLPs of relatively small scales. As the problem scale increases, the decision space becomes intrinsically larger, requiring a growing number of function evaluations for the method to work properly. In this context, heavy parallelization and high-performance computing techniques are indispensable to enable the resolution of more complex and challenging optimization problems. Hence, we propose a hierarchical many-threaded parallel DE approach for BLPs, where both levels are parallelized. The computational experiments demonstrate that the parallel implementation achieved runtime speeds ranging from 44 to 2559 times faster than the sequential version on a well-known scalable SMD benchmark test problem when executed on an NVIDIA A100 GPU. The findings indicate that the algorithm’s convergence is strongly influenced by the number of both upper- and lower-level generations. Moreover, the success of experiments with large-scale problems is closely linked to the choice of small population sizes.

Dufek, Amanda S↗
DOE OSTI2024

SPARTA: High-Level Synthesis of Parallel Multi-Threaded Accelerators

This article presents a methodology for the Synthesis of PARallel multi-Threaded Accelerators (SPARTA) from OpenMP annotated C/C++ specifications. SPARTA extends an open-source HLS tool, enabling the generation of accelerators that provide latency tolerance for irregular memory accesses through multithreading, support fine-grained memory-level parallelism through a hot-potato deflection-based network-on-chip (NoC), support synchronization constructs, and can instantiate memory-side caches. Our approach is based on a custom runtime OpenMP library, providing flexibility and extensibility. Experimental results show high scalability when synthesizing irregular graph kernels. The accelerators generated with our approach are, on average, 2.29x faster than state-of-the-art HLS methodologies.

Design automation↗
DOE OSTI2023

Foreign material exclusion plug for structural threads with breakaway tab

A foreign material exclusion plug is disclosed. The foreign material plug includes a cylindrical body, a slot, and a breakaway tab. The cylindrical body includes an externally-threaded cylindrical structure. The slot is formed in the cylindrical body extending into the cylindrical body from a top surface of the cylindrical body. The breakaway tab is connected to the cylindrical body and extends outwards from the top surface.

Cole, Robert N.↗
DOE OSTI2024

Embedded symmetric positive semi-definite machine-learned elements for reduced-order modeling in finite-element simulations with application to threaded fasteners

Here, we present a machine-learning strategy for finite element analysis of solid mechanics wherein we replace complex portions of a computational domain with a data-driven surrogate. In the proposed strategy, we decompose a computational domain into an “outer” coarse-scale domain that we resolve using a finite element method (FEM) and an “inner” fine-scale domain. We then develop a machine-learned (ML) model for the impact of the inner domain on the outer domain. In essence, for solid mechanics, our machine-learned surrogate performs static condensation of the inner domain degrees of freedom. This is achieved by learning the map from displacements on the inner-outer domain interface boundary to forces contributed by the inner domain to the outer domain on the same interface boundary. We consider two such mappings, one that directly maps from displacements to forces without constraints, and one that maps from displacements to forces by virtue of learning a symmetric positive semi-definite (SPSD) stiffness matrix. We demonstrate, in a simplified setting, that learning an SPSD stiffness matrix results in a coarse-scale problem that is well-posed with a unique solution. We present numerical experiments on several exemplars, ranging from finite deformations of a cube to finite deformations with contact of a fastener-bushing geometry. We demonstrate that enforcing an SPSD stiffness matrix drastically improves the robustness and accuracy of FEM–ML coupled simulations, and that the resulting methods can accurately characterize out-of-sample loading configurations with significant speedups over the standard FEM simulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

The use of digital thread for reconstruction of local fiber orientation in a compression molded pin bracket via deep learning

A deep convolutional neural network (DCNN) was used for microstructure reconstruction using artificial intelligence (MR-AI) by predicting local average fiber orientation distributions (FOD) in a 3D prepreg platelet molded composite (PPMC) pin bracket. To train the MR-AI model, surface strain fields from residual stresses simulated in PPMC plates were used as the input to the DCNN. A training dataset included PPMC plates with various degrees of global fiber alignment, based on the information obtained from high-fidelity flow simulation of a pin bracket. Further, the MR-AI model was then deployed to analyze FOD in the 3D pin bracket by conducting thermo-elastic residual stress analysis. Initially, the MR-AI model was established entirely on the synthetic simulation data. Then, a μCT scan of a physically molded pin bracket was used to create a finite element model that provided data for additional validation of the DCNN model. For the μCT scan finite element pin bracket the MR-AI model predicted the distribution of fiber orientation tensor components with MAE of 0.10 indicating a global prediction error of 10%. For the flow simulated pin bracket, the MR-AI model predicted the distribution of fiber orientation tensor components with a global prediction error of 11%. The MR-AI model showed the ability to predict regions of varying alignment in the base and flange of the pin bracket. The proposed MR-AI methodology allows for rapid prediction of FOD in geometrically complex parts and offers a promising path to detecting unique fiber orientation states in molded components.

42 ENGINEERING↗
DOE OSTI2022

Untangling the threads of cellulose mercerization

Naturally occurring plant cellulose, our most abundant renewable resource, consists of fibers of long polymer chains that are tightly packed in parallel arrays in either of two crystal phases collectively referred to as cellulose I. During mercerization, a process that involves treatment with sodium hydroxide, cellulose goes through a conversion to another crystal form called cellulose II, within which every other chain has remarkably changed direction. We designed a neutron diffraction experiment with deuterium labelling in order to understand how this change of cellulose chain direction is possible. Here we show that during mercerization of bacterial cellulose, chains fold back on themselves in a zigzag pattern to form crystalline anti-parallel domains. This result provides a molecular level understanding of one of the most widely used industrial processes for improving cellulosic materials.

59 BASIC BIOLOGICAL SCIENCES↗