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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Chemical signature characterization with hyperspectral imagery: novel deep learning model architectures and physically-motivated data augmentation techniques

The high spectral resolution afforded by Hyperspectral Imaging (HSI) sensors is poised to bring unprecedented advancements to signature characterization applications. Thus far, much of the research in the machine learning field devoted to HSI applications has focused on a few specific tasks like land-use land-cover classification. In land classification tasks, spatial information is very important, and model architectures are often designed to leverage spatial contexts. However, it is unclear how well these spatially-tuned models will translate to tasks where spectral information is critical, like the detection and characterization of chemicals. In this work, we compare spectral models (inputs are 1D spectra) and spatial-spectral models (inputs are 3D cubes) in the context of predicting chemical concentration maps. We find that spatial-spectral models perform the best, though we find a wide range in performance across the different architectures tested. Additionally, we find that model performance is impacted by the availability of training data, particularly in scenarios where the training data doesn't fully capture the true variance of real-world conditions. We find that data augmentation can help mitigate sparse coverage of observed parameter space (e.g., seasonal or geographic variability in ground cover), and present augmentation strategies that are tailored to hyperspectral data.

• Artificial intelligence (AI) / machine learning ↗
DOE OSTI2022

Molecular architecture of the Chikungunya virus replication complex

To better understand how positive-strand (+) RNA viruses assemble membrane-associated replication complexes (RCs) to synthesize, process, and transport viral RNA in virus-infected cells, we determined both the high-resolution structure of the core RNA replicase of chikungunya virus and the native RC architecture in its cellular context at subnanometer resolution, using in vitro reconstitution and in situ electron cryotomography, respectively. Within the core RNA replicase, the viral polymerase nsP4, which is in complex with nsP2 helicase-protease, sits in the central pore of the membrane-anchored nsP1 RNA-capping ring. The addition of a large cytoplasmic ring next to the C terminus of nsP1 forms the holo-RNA-RC as observed at the neck of spherules formed in virus-infected cells. These results represent a major conceptual advance in elucidating the molecular mechanisms of RNA virus replication and the principles underlying the molecular architecture of RCs, likely to be shared with many pathogenic (+) RNA viruses.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Dynamic molecular architecture of the synaptonemal complex

During meiosis, pairing between homologous chromosomes is stabilized by the assembly of the synaptonemal complex (SC). The SC ensures the formation of crossovers between homologous chromosomes and regulates their distribution. However, how the SC regulates crossover formation remains elusive. We isolated an unusual mutation in Caenorhabditis elegans that disrupts crossover interference but not SC assembly. This mutation alters the unique C terminal domain of an essential SC protein, SYP-4, a likely ortholog of the vertebrate SC protein SIX6OS1. We use three-dimensional stochastic optical reconstruction microscopy (3D-STORM) to interrogate the molecular architecture of the SC from wild-type and mutant C. elegans animals. Using a probabilistic mapping approach to analyze super-resolution image data, we detect changes in the organization of the synaptonemal complex in wild-type animals that coincide with crossover designation. We also found that our syp-4 mutant perturbs SC architecture. Our findings add to growing evidence that the SC is an active material whose molecular organization contributes to chromosome-wide crossover regulation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Diffusion power spectra as a window into dynamic materials architecture

Chemical recycling of commodity and specialty polymers presents a multifaceted challenge for industrial societies. On one hand, macromolecular architectures must be engineered to yield durable products that, on the other hand, rapidly deconstruct to recyclable monomers under pre-determined conditions. Polymer deconstruction is a chemical process that requires deep understanding of molecular reactivity in heterogeneous media, where porous material architectures evolve in both space and time. To build this understanding, we develop herein experimental and analytical methods describing sets of diffusive eigenmodes that exist within time-varying, non-Euclidean boundary conditions, a situation commonly encountered in the reactive deconstruction of polymers where chain fragments splay, alter their local dynamics, and evolve in their confinement of reacting media. Diffusion power spectra, discerned experimentally by NMR, yield polymer and solvent frequency-domain velocity autocorrelation functions that are analyzed in the context of physical models for chemical reactions parameterized with fractal mathematics. The results connect local motion in polymers to chemical reactivity during acidolysis of circular elastomers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

3D fault architecture controls the dynamism of earthquake swarms

The vibrant evolutionary patterns made by earthquake swarms are incompatible with standard, effectively two-dimensional (2D) models for general fault architecture. Here we leverage advances in earthquake monitoring with a deep-learning algorithm to image a fault zone hosting a 4-year-long swarm in southern California. We infer that fluids are naturally injected into the fault zone from below and diffuse through strike-parallel channels while triggering earthquakes. A permeability barrier initially limits up-dip swarm migration but ultimately is circumvented. This enables fluid migration within a shallower section of the fault with fundamentally different mechanical properties. Our observations provide high-resolution constraints on the processes by which swarms initiate, grow, and arrest. These findings illustrate how swarm evolution is strongly controlled by 3D variations in fault architecture.

58 GEOSCIENCES↗
DOE OSTI2023

Top-down design of protein architectures with reinforcement learning

As a result of evolutionary selection, the subunits of naturally occurring protein assemblies often fit together with substantial shape complementarity to generate architectures optimal for function in a manner not achievable by current design approaches. We describe a “top-down” reinforcement learning–based design approach that solves this problem using Monte Carlo tree search to sample protein conformers in the context of an overall architecture and specified functional constraints. Cryo–electron microscopy structures of the designed disk-shaped nanopores and ultracompact icosahedra are very close to the computational models. The icosohedra enable very-high-density display of immunogens and signaling molecules, which potentiates vaccine response and angiogenesis induction. Our approach enables the top-down design of complex protein nanomaterials with desired system properties and demonstrates the power of reinforcement learning in protein design.

Science & Technology - Other Topics↗
DOE OSTI2023

Soft syndrome iterative decoding of quantum LDPC codes and hardware architectures

In practical quantum error correction implementations, the measurement of syndrome information is an unreliable step—typically modeled as a binary measurement outcome flipped with some probability. However, the measured syndrome is in fact a discretized value of the continuous voltage or current values obtained in the physical implementation of the syndrome extraction. In this paper, we use this “soft” or analog information to benefit iterative decoders for decoding quantum low-density parity-check (QLDPC) codes. Syndrome-based iterative belief propagation decoders are modified to utilize the soft syndrome to correct both data and syndrome errors simultaneously. We demonstrate the advantages of the proposed scheme not only in terms of comparison of thresholds and logical error rates for quasi-cyclic lifted-product QLDPC code families but also with faster convergence of iterative decoders. Additionally, we derive hardware (FPGA) architectures of these soft syndrome decoders and obtain similar performance in terms of error correction to the ideal models even with reduced precision in the soft information. The total latency of the hardware architectures is about 600 ns (for the QLDPC codes considered) in a 20 nm CMOS process FPGA device, and the area overhead is almost constant—less than 50% compared to min-sum decoders with noisy syndromes.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Spatially resolved characterization of optical and recombination losses for different industrial silicon solar cell architectures

In this work, spatially resolved characterization methods are used to identify loss mechanisms for common p -type silicon solar cell architectures, including multicrystalline aluminum back surface field (Al-BSF), monocrystalline Al-BSF, monocrystalline passivated emitter and rear cells (PERC), and bifacial monocrystalline PERC. The characterization methods used in this work include suns-V OC , photoluminescence imaging, and spatially resolved external quantum efficiency and reflectance measurements. The optical and recombination losses are driven by the material properties, cell processing conditions, and device architecture. These losses are quantified and categorized in terms of underlying mechanisms (e.g., front reflectance, escape reflectance, front recombination, and parasitic optical absorption and recombination in the bulk and rear). The ability to create images of these loss parameters can be used to gain more insight into the materials and manufacturing processes used to produce solar cells, and examples are given in this work to illustrate how these images can help reveal the origin of defects.

14 SOLAR ENERGY↗
DOE OSTI2024

Accelerated Constrained Sparse Tensor Factorization on Massively Parallel Architectures

This study presents the first constrained sparse tensor factorization (cSTF) framework that optimizes and fully offloads computation to massively parallel GPU architectures, and the first performance characterization of cSTF on GPU architectures. In contrast to prior work on tensor factorization, where the matricized tensor times Khatri-Rao product (MTTKRP) is the primary performance bottleneck, our systematic analysis of the cSTF algorithm on GPUs reveals that adding constraints creates an additional bottleneck in the update operation for many real-world sparse tensors. While executing the update operation on the GPU brings significant speedup over its CPU counterpart, it remains a significant bottleneck. To further accelerate the update operation, we propose cuADMM, a new update algorithm that leverages algorithmic and code optimization strategies to minimize both computation and data movement on GPUs. As a result, our framework delivers significantly improved performance compared to prior state-of-the-art. On 10 real-world sparse tensors, our framework achieves geometric mean speedup of 5.1 × (max 41.59 ×) and 7.01 × (max 58.05 ×) on the NIVIDA A100 and H100 GPUs, respectively, over the state-of-the-art SPLATT library running on a 26-core Intel Ice Lake Xeon CPU.

Soh, Yongseok↗
DOE OSTI2025

Architecture-Aware Models of AI Engines for High-Performance Matrix Matrix Multiplication

The AI Engine (AIE) architecture, available in systems from mobile SoCs to server-class FPGAs, aims to efficiently execute AI/ML tasks through a two-dimensional array of compute tiles. Previous work on AIEs has explored different approaches to mapping computation across spatial arrays, but the compute kernel running on each tile has not been the focus. Additionally, the AIE-ML architecture introduces memory tiles and omits programmable logic, requiring new approaches to staging and moving data throughout the array. In this work we update analytical models developed for CPUs to produce the design of high performance kernels while introducing new model considerations such as memory structure, throughput, and latency as required by the AIE hardware. We evaluate our models by developing AIE-ML kernels for matrix multiplication in low-precision data types showing performance up to 95% of compute peak for the kernel when data resides in local memory and above 90% of compute peak when data resides in main memory.

Binder, Elliott D. [Carnegie Mellon University, Pi↗
DOE OSTI2020

Architecture-Based Control of Temperature Gradient-Driven Water Transport in Polymer Electrolyte Fuel Cells

Temperature gradients can move water within the porous media of a polymer electrolyte fuel cell through phase-change-induced (PCI) flow. It is critical to understand PCI flow so that control and distribution of water within a fuel cell can be accomplished. This work investigates the role of architecture, specifically anode land width, on overall cell water content and distribution in the through-plane direction. A specially-designed 4.8 cm 2 fuel cell with precise thermal boundary conditions was imaged with neutrons using two different anode flow field configurations. A new, non-dimensional thermal transport number was developed which quantifies the relative influence of PCI flow on cell water transport. It was found that anode lands larger than the cathode lands cause large thermal gradients that instigate net water flux from the cathode to the anode. An asymmetric configuration with larger anode lands was found to have large changes in water content that were strongly sensitive to cell operating conditions. The thermal transport number developed here enables deduction of the net flux condition based on operating conditions and architecture. This approach enables design of high-performance systems with balanced water management.

25 ENERGY STORAGE↗
DOE OSTI2024

MAGMA: Enabling exascale performance with accelerated BLAS and LAPACK for diverse GPU architectures

MAGMA (Matrix Algebra for GPU and Multicore Architectures) is a pivotal open-source library in the landscape of GPU-enabled dense and sparse linear algebra computations. With a repertoire of approximately 750 numerical routines across four precisions, MAGMA is deeply ingrained in the DOE software stack, playing a crucial role in high-performance computing. Notable projects such as ExaConstit, HiOP, MARBL, and STRUMPACK, among others, directly harness the capabilities of MAGMA. In addition, the MAGMA development team has been acknowledged multiple times for contributing to the vendors’ numerical software stacks. Looking back over the time of the Exascale Computing Project (ECP), we highlight how MAGMA has adapted to recent changes in modern HPC systems, especially the growing gap between CPU and GPU compute capabilities, as well as the introduction of low precision arithmetic in modern GPUs. We also describe MAGMA’s direct impact on several ECP projects. Maintaining portable performance across NVIDIA and AMD GPUs, and with current efforts toward supporting Intel GPUs, MAGMA ensures its adaptability and relevance in the ever-evolving landscape of GPU architectures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Experimental study of airpath electrification in an opposed-piston two stroke (OP2S) engine architecture

The opposed-piston two stroke (OP2S) engine shows potential as an alternative engine architecture to the conventional four stroke engine due to its high-power density, thermal efficiency, and versatile airpath management system. Since the pistons of a two-stroke engine do not pump the air into and out of the cylinder like in a four-stroke engine, the selection of the air induction devices and airpath actuators becomes critical to optimize engine performance. Both the pumping losses and the in-cylinder combustion process can be affected by the scavenging process in a two-stroke engine. Therefore, this study compares two different airpath configurations for the same family of OP2S engines and investigates performance metrics like scavenging control, pumping work, net indicated and brake efficiencies, and engine-out emissions associated with each airpath. Data was collected on a 3.2 L, two-cylinder OP2S engine with an electrically assisted turbocharger (EAT) and a 4.9 L displacement, three-cylinder engine with a variable geometry turbocharger (VGT) and a supercharger. The experiments consisted of speed and load sweeps for both engines at the same operating conditions to compare scavenge control in both architectures. For the three-cylinder layout, the SE sweep range was much higher, and the intake pressure could be independently varied with air flowrate, thus providing more flexibility for scavenging control. The supercharger and the VGT usage was optimized based on its efficiency map and thus, this layout had lower pumping losses compared to the EAT. The two-cylinder engine had a higher overall SE as compared to the three-cylinder engine, but the intake pressure and air flowrate could not be decoupled, leading to over scavenging and increased short circuiting of fresh charge into the exhaust.

Bhatt, Ankur [Clemson University, Clemson, SC, USA↗
DOE OSTI2020

ADEPT: a domain independent sequence alignment strategy for gpu architectures

Bioinformatic workflows frequently make use of automated genome assembly and protein clustering tools. At the core of most of these tools, a significant portion of execution time is spent in determining optimal local alignment between two sequences. This task is performed with the Smith-Waterman algorithm, which is a dynamic programming based method. With the advent of modern sequencing technologies and increasing size of both genome and protein databases, a need for faster Smith-Waterman implementations has emerged. Multiple SIMD strategies for the Smith-Waterman algorithm are available for CPUs. However, with the move of HPC facilities towards accelerator based architectures, a need for an efficient GPU accelerated strategy has emerged. Existing GPU based strategies have either been optimized for a specific type of characters (Nucleotides or Amino Acids) or for only a handful of application use-cases. In this paper, we present ADEPT, a new sequence alignment strategy for GPU architectures that is domain independent, supporting alignment of sequences from both genomes and proteins. Our proposed strategy uses GPU specific optimizations that do not rely on the nature of sequence. We demonstrate the feasibility of this strategy by implementing the Smith-Waterman algorithm and comparing it to similar CPU strategies as well as the fastest known GPU methods for each domain. ADEPT’s driver enables it to scale across multiple GPUs and allows easy integration into software pipelines which utilize large scale computational systems. We have shown that the ADEPT based Smith-Waterman algorithm demonstrates a peak performance of 360 GCUPS and 497 GCUPs for protein based and DNA based datasets respectively on a single GPU node (8 GPUs) of the Cori Supercomputer. Overall ADEPT shows 10x faster performance in a node-to-node comparison against a corresponding SIMD CPU implementation. ADEPT demonstrates a performance that is either comparable or better than existing GPU strategies. We demonstrated the efficacy of ADEPT in supporting existing bionformatics software pipelines by integrating ADEPT in MetaHipMer a high-performance denovo metagenome assembler and PASTIS a high-performance protein similarity graph construction pipeline. Our results show 10% and 30% boost of performance in MetaHipMer and PASTIS respectively.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A Cost-Effective Electro-Hydraulic Actuator Solution with Open Circuit Architecture

With the recent electrification trend in the fluid power area, more research has been incentivized to propose cost-effective and energy-efficient solutions for hydraulic systems. Hence, electro-hydraulic actuator (EHA) architectures receive increasing attention. The paper proposes a novel open-circuit EHA architecture, with the goal to obtain a cost-effective solution for mobile applications while maximizing the overall system efficiency. The proposed EHA is capable of meeting or exceeding traditional off-road machine performance, therefore enabling further electrification of off-road machines. Four-quadrant functionality, covering the full speed range, is achieved by a combination of a variable electro-hydraulic drive and valves with different functions. Focusing on the steady-state performance, the functionality is validated by numerical as well as experimental methods. A simulation model based on the Amesim environment and a dedicated test setup was developed to verify the performance. Furthermore, the good match between simulation and experimental results confirms the potential of the formulation approach of the proposed EHA for applications with different duty cycles and power levels.

4-quadrant functionality↗
DOE OSTI2024

Flexible silicon photonic architecture for accelerating distributed deep learning

The increasing size and complexity of deep learning (DL) models have led to the wide adoption of distributed training methods in datacenters (DCs) and high-performance computing (HPC) systems. However, communication among distributed computing units (CUs) has emerged as a major bottleneck in the training process. In this study, we propose Flex-SiPAC, a flexible silicon photonic accelerated compute cluster designed to accelerate multi-tenant distributed DL training workloads. Flex-SiPAC takes a co-design approach that combines a silicon photonic hardware platform with a tailored collective algorithm, optimized to leverage the unique physical properties of the architecture. The hardware platform integrates a novel wavelength-reconfigurable transceiver design and a micro-resonator-based wavelength-reconfigurable switch, enabling the system to achieve flexible bandwidth steering in the wavelength domain. The collective algorithm is designed to support reconfigurable topologies, enabling efficient all-reduce communications that are commonly used in DL training. The feasibility of the Flex-SiPAC architecture is demonstrated through two testbed experiments. First, an optical testbed experiment demonstrates the flexible routing of wavelengths by shuffling an array of input wavelengths using a custom-designed spatial-wavelength selective switch. Second, a four-GPU testbed running two DL workloads shows a 23% improvement in job completion time compared to a similarly sized leaf-spine topology. We further evaluate Flex-SiPAC using large-scale simulations, which show that Flex-SiPAC is able to reduce the communication time by 26% to 29% compared to state-of-the-art compute clusters under representative collective operations.

Wu, Zhenguo (ORCID:0000000322847985)↗
DOE OSTI2021

A pathway to desired functionalities in vertically aligned nanocomposites and related architectures

Epitaxial vertically aligned nanocomposites (VANs) and their related architectures have shown many intriguing features that are not available from conventional two-dimensional planar multilayers and heterostructures. The ability to control constituent, interface, microstructure, strain, and defects based on VANs has enabled the multiple degrees of freedom to manipulate the optical, magnetic, electrochemical, electronic, ionic, and superconducting properties for specific applications. This field has rapidly expanded from the interest in oxide:oxide to oxide:metal, metal:nitride and nitride:nitride systems. To achieve unparalleled properties of the materials, three-dimensional super-nanocomposites based on a hybrid of VAN and multilayer architectures have been recently explored as well. The challenges and opportunities of VAN films are also discussed in this article.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Revolutionizing Turbine Cooling with Micro-Architectures Enabled by Direct Metal Laser Sintering

The objective of this research effort was to explore innovative cooling architectures enabled by additive manufacturing techniques for improved turbine cooling performance. The ability to create complex internal geometries was leveraged to better distribute coolant as well as to integrate inherently unstable flow devices to enhance internal and external heat transfer. This was accomplished with a multi-faceted approach including analytical, experimental, and computational components. This final report documents progress during the total 4.25 year effort that was extended (at no additional cost) from the original 3-year cooperative agreement. Prior to starting the project, significant effort was invested in investigating innovative cooling designs from the open literature. When the project started, we quickly down-selected to 4 configurations: impingement jet fluidic oscillators (for internal leading edge cooling), reverse film cooling (for pressure surface), sweeping film cooling jets (for suction surface), and trailing edge slot cooling with microchannels. During the 2nd year, the reverse film cooling design was shown to be sub-optimal and work on that topic was halted. The three remaining technologies were integrated into a large scale nozzle guide vane and installed into a low-speed linear cascade facility for further interrogation. Initial results showed the sweeping film cooling jets (on the suction surface) to be the most promising technology compared to a baseline diffusion shaped film hole (777 design). The benefit was particularly evident at high blowing ratios (>1.5) when the 777 coolant film separated from the downstream surface. Benefits were also evident at elevated freestream turbulence levels. The leading edge cooling with unsteady jets was less effective in terms of peak or average cooling – however it was superior to round hole impingement cooling in terms of spatial uniformity. Finally, the trailing edge cooling design with micro-channels was scrapped in favor of a pinned arrangement with centerbody. During the 3rd year, the same three technologies were integrated into a transonic linear cascade for an assessment of compressibility effects. Again, the sweeping film cooling jets proved superior at high blowing ratios while the unsteady leading edge impingement and trailing edge pin-fins with centerbody designs yielded mixed results. Finally, during the 4th and final year, these 3 technologies were integrated into a direct metal laser sintered (DMLS) nozzle guide vane for testing in a high temperature, transonic annular vane cascade. The facility matches the flow temperature, Mach number, and coolant pressure ratios of an actual gas turbine. Before doing this, an additional series of tests were completed to validate a redesigned trailing edge cooling architecture. This final design included a centerbody with triangular pins between the centerbody and the vane external skin. The majority of these triangular pins are fabricated with a 30% gap to the centerbody – reducing their pressure drop considerably while still providing excellent heat transfer augmentation. This additional test campaign required an additional 3-month extension request to complete testing in the high temperature NGV test facility (Turbine Reacting Flow Rig – TuRFR). Testing of the DMLS vane in TuRFR pitted the 3 innovative cooling technologies against more traditional technologies (shaped 777 film hole, round impingement jet, and full pin arrangement in the trailing edge. The sweeping film cooling jets (on the suction surface) showed approximately 15% improvement in overall effectiveness compared to the 777 film hole while the trailing edge showed up to 20% improvement. The leading edge impingement was disappointing with a substantially lower effectiveness than traditional direct impingement. In summary, the study was successful in demonstrating that DMLS-enabled cooling technologies can yield significant gains in cooling performance.

03 NATURAL GAS↗