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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

CSB-RNN: A Faster-Than-Realtime RNN Acceleration Framework with Compressed Structured Blocks

Recurrent Neural Networks (RNN) is widely applied to temporal sequence analysis, where real-time performance is usually in demand. However, RNN suffers a heavy computational workload as the model comes with a large weight matrix. To alleviate the pain, model compression (pruning) schemes have been proposed for RNN that pruning the redundant (near-zero) weight-values. On the one hand, the non-structured pruning methods achieve a considerable pruning rate while bringing the computational irregularity, which is un-friendly to parallel-hardware. On the other hand, the existing structured pruning methods consider the hardware parallelism; However, they suffer a poor pruning rate due to the restrict constraints on pruning structure. This paper presents CSB-RNN, an optimized full-stack RNN framework with the novel compressed structured block (CSB) technique. The CSB-pruned RNN model comes with both fine-granularity that benefits the pruning rate and regular structure that facilitates the hardware-parallelism. Further, we propose a novel hardware architecture for inferencing the CSB-pruned model. Different from conventional parallel hardware, this architecture solves the block-workload imbalance issue and achieves an over 95% hardware utilization. With the experiments on 10 RNN models in 5 application domains, the CSB-RNN realizes 7×-20× lossless compression and up to 50× acceptable lossy-compression, which is 2×-7× to the prior art. With the addition of the novel hardware, the compressed-RNN inference reaches a super real-time latency of 10-400µs with FPGA implementation.

Shi, Runbin↗
DOE OSTI2021

BETO 2021 Peer Review - Inverse Bioproduct Design Through Machine Learning and Molecular Simulation

This work aims to identify performance advantaged bioproducts (PABPs) through property prediction, which will guide experimental synthesis. The impact of this work will be faster market adoption of bioproducts with greater performance relative to incumbent products. We have identified >106 bioproduct candidates, but only some will have superior performance to create a market pull. High-throughput property prediction, enabled by machine learning, and elucidation of structure-function relationships, enabled by molecular simulation, provide a hypothesis driven approach for down selection of candidate biomolecules to pursue experimentally. To enable machine learning and molecular simulation for bioproduct discovery, automated structure generation and embedding must capture relevant features for prediction, databases must cover domains applicable to biobased products, and best practices for simulation of polymer systems must be developed. To address these challenges, we have established bioproduct relevant datasets, developed high-throughput polymer structure generation, and built end-to-end neural networks that have predicted 8 properties for >1.4 x 106 biopolymers. A molecular simulation pipeline for building, running, and analyzing polymers and polymer additives is being used to predict performance and develop design principles of biobased products. In collaboration with the PABP synthesis project, these computational tools are guiding synthesis and informing design of PABPs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices

Accessible machine learning algorithms, software, and diagnostic tools for energy-efficient devices and systems are extremely valuable across a broad range of application domains. In scientific domains, real-time near-sensor processing can drastically improve experimental design and accelerate scientific discoveries. To support domain scientists, we have developed hls4ml, an open-source software-hardware codesign workflow to interpret and translate machine learning algorithms for implementation with both FPGA and ASIC technologies. We expand on previous hls4ml work by extending capabilities and techniques towards low-power implementations and increased usability: new Python APIs, quantization-aware pruning, end-to-end FPGA workflows, long pipeline kernels for low power, and new device backends include an ASIC workflow. Taken together, these and continued efforts in hls4ml will arm a new generation of domain scientists with accessible, efficient, and powerful tools for machine-learning-accelerated discovery.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Synthetic Data Generation Using Machine Learning

Robust machine learning techniques for image analysis require a substantial amount of data to yield confident results. In the nuclear domain, data scarcity is a substantial challenge because there are so few facilities worldwide. This research focuses on being able alleviate the data scarcity problem by generating synthetic data to bridge the gap between large and small datasets. This work achieves that goal using a Generative Adversarial Network (GAN) architectural approach, by training a model on real-world data and expands that small dataset through synthetic data amendments. Model performance is impacted by the size of the real-world dataset and the number of training epochs utilized. This means that 1) It is important to develop your GAN to be optimized with the specific data type, and 2) approaches taken when training the GAN should be specialized to encompass important aspects of the dataset that it is generating. By taking a step to improve dataset sizes in this way, the gap between models trained by parties with significant amount of data and those without access to large data, closes, allowing for robust analyses of satellite imagery for nuclear domain applications.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

High-performance computing leadership to enable advances in artificial intelligence and a thriving compute ecosystem

The past three decades have witnessed the widespread adoption of high-performance computing (HPC) as an essential tool in the advancement of science. From accelerating critical research in the wake of a global pandemic to climate modelling and national security, HPC has become integral to the most cutting-edge scientific research around the world and across application domains. The global competition to debut the next fastest supercomputer keeps pushing the field forward. Meanwhile, increasing capabilities are bringing hallmarks of science fiction, such as artificial intelligence (AI), into daily life. However, the tremendous power of new computing systems comes with an increased concern about equitable access to these resources and their impact on supporting a thriving workforce.

Tourassi, Georgia↗
DOE OSTI2023

Language-Theoretic Data Analysis to Support ICS Protocol Baselining

Critical infrastructure stakeholders need to baseline their systems to understand expected protocol communications. Baseline behaviors may vary based on operational context. Expected operations during a maintenance window, for example, may be different from normal operations. Furthermore, constructing system baselines for Industrial Control Systems (ICS) is difficult and time-consuming. ICS processes generate artifacts expressed across heterogeneous data sources such as network traffic and device logs. This paper explores the hypothesis that such ICS artifacts form a language in the language-theoretic sense. From a theoretical perspective, the variety of implementations of ICS protocols and constrained environment of OT networks provide a rich application domain for language-theoretic approaches. We present several use cases related to the practical construction of system baselines: grammars for data fusion, language dialects for device fingerprinting, and security automata for system baselining

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Constraining Cosmology with Simulation-based inference and Optical Galaxy Cluster Abundance

We test the robustness of simulation-based inference (SBI) in the context of cosmological parameter estimation from galaxy cluster counts and masses in simulated optical datasets. We construct ``simulations'' using analytical models for the galaxy cluster halo mass function (HMF) and for the observed richness (number of observed member galaxies) to train and test the SBI method. We compare the SBI parameter posterior samples to those from an MCMC analysis that uses the same analytical models to construct predictions of the observed data vector. The two methods exhibit comparable performance, with reliable constraints derived for the primary cosmological parameters, ($\Omega_m$ and $\sigma_8$), and richness-mass relation parameters. We also perform out-of-domain tests with observables constructed from galaxy cluster-sized halos in the Quijote simulations. Again, the SBI and MCMC results have comparable posteriors, with similar uncertainties and biases. Unsurprisingly, upon evaluating the SBI method on thousands of simulated data vectors that span the parameter space, SBI exhibits worsened posterior calibration metrics in the out-of-domain application. We note that such calibration tests with MCMC is less computationally feasible and highlight the potential use of SBI to stress-test limitations of analytical models, such as in the use for constructing models for inference with MCMC.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Retaining Systems Engineering Model Meaning Through Transformation: Demo 2

Digital engineering strategies typically assume that digital engineering models interoperate seamlessly across the multiple different engineering modeling software applications involved, such as model- based systems engineering (MBSE), mechanical computer-aided design (MCAD), electrical computer-aided design (ECAD), and other engineering modeling applications. The presumption is that the data schema in these modeling software applications are structured in the familiar flat- tabular schema like any other software application. Engineering domain-specific applications (e.g., systems, mechanical, electrical, simulation) are typically designed to solve domain-specific problems, necessarily excluding explicit representations of non-domain information to help the engineer focus on the domain problems (system definition, design, simulation). Such exclusions become problematic in inter-domain information exchange. The obvious assumptions of one domain might not be so obvious to experts in another domain. Ambiguity in domain-specific language can erode the ability to enable different domain modeling applications to interoperate, unless the underlying language is understood and used as the basis for translation from one application to another. The engineering modeling software application industry has struggled for decades to enable these applications to interoperate. Industry standards have been developed, but they have not unified the industry. Why is this? The authors assert that the industry has relied on traditional database integration methods. The basic issue prohibiting successful application integration then is that traditional database-driven integration does not consider the distinct languages of each domain. An engineering models meaning is expressed through the underlying language of that engineering domain. In essence, traditional integration methods do not retain the semantic context (meaning) of the model. The basis of this research stems from the widely held assumption that systems engineering models are (or can be) structured according to the underlying semantic ontology of the model. This assumption can be imagined from two thoughts. 1) Digital systems engineering models are often represented using graph theory (the graph of a complex systems model can contain millions of nodes and edges). When examining the nodes one at a time and following the outbound edges of each node one by one, one can end up with rudimentary statements about the model (i.e., node A relates to node B), as in a semantic graph. 2) Likewise, from the study of natural languages, a sentence can be structured into unambiguous triples of subject-predicate-object within formal and highly expressive semantic ontologies. The rudimentary statements about a systems model discerned with graph theory closely mimic the triples used in the ontologies that try to structure natural languages. In other words, a systems models semantic graph can be (or is) structured into an ontology. Additionally, it is well established in industry that through natural language processing (NLP), which provides the means to create language structures, that computers can interpret ontological graphs. Therefore, the authors hypothesized that if the integrity of the underlying semantic structure of a systems model is retained, the contextual meaning of the model is retained. By structuring system models into the triples of the underlying ontology during the transformation from one MBSE application to another, the authors have provided a proof of the concept that the meaning of a system model can be retained during transformation. The authors assert that this is the missing ingredient in effective systems model-to-model interoperability. ACKNOWLEDGEMENTS The authors would like to thank the FY19 Model Interoperability team members who provided a solid foundation for the FY20 team to leverage: John McCloud, for the work he did to guide us toward the right use of technology that will appropriately discover and manipulate ontologies. Carlos Tafoya, for the work he did to develop an application programming interface (API)/Adapter that would export ontology-based data from GENESYS. Peter Chandler, for the work he did to architect our overall integration solution, with an eye toward the future that would influence a large-scale federated production-level systems engineering digital model ecosystem.

42 ENGINEERING↗
DOE OSTI2024

Hyperspectral acquisition with ScanImage at the single pixel level: application to time domain coherent Raman imaging

We present a comprehensive strategy and its practical implementation using the commercial ScanImage software platform to perform hyperspectral point scanning microscopy when a fast time-dependent signal varies at each pixel level. In the proposed acquisition scheme, the scan along the X-axis is slowed down while the data acquisition is maintained at a high pace to enable the rapid acquisition of the time-dependent signal at each pixel level. The ScanImage generated raw 2D images have a very asymmetric aspect ratio between X and Y, the X axis encoding both for space and time acquisition. The results are X-axis macro-pixel where the associated time-dependent signal is sampled to provide hyperspectral information. We exemplified the proposed hyperspectral scheme in the context of time-domain coherent Raman imaging, where a pump pulse impulsively excites molecular vibrations that are subsequently probed by a time-delayed probe pulse. In this case, the time-dependent signal is a fast acousto-optics delay line that can scan a delay of 4.5ps in 25 μ s at each pixel level. With this acquisition scheme, we demonstrate ultra-fast hyperspectral vibrational imaging in the low frequency range [10 cm −1 , 150 cm −1 ] over a 500 μm field of view (64 x 64 pixels) in 130ms (∼ 7.5 frames/s). The proposed acquisition scheme can be readily extended to other applications requiring the acquisition of a fast-evolving signal at each pixel level.

Metais, Samuel↗
DOE OSTI2020

Curved Three-Dimensional Cobalt Nanohelices for Use in Domain Wall Device Applications

Three-dimensional fabrication of nanostructures opens new possibilities to not only understand the fundamental effects of shape and of the interactions between the nanostructures, but also to develop novel technological applications. Of course, this is of particular importance for magnetic nanostructures, where curvilinear geometry can lead to the emergence of novel topological effects and spin excitations. In this work, we have used the focused electron beam ion deposition (FEBID) method to fabricate three-dimensional magnetic cobalt nanohelices with controlled geometry such as chirality, and curvature. Additionally, using a combination of off-axis electron holography and electron tomography, we are able to determine the way in which the quantitative nanoscale magnetization distribution is related to the 3D morphology and curvature of the nanohelices. These results pave a way forward for development of future 3D magnetic nanostructures to explore novel physics as well as for applications such as magnetic field sensors, spin-wave filters, and magneto-optical devices.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Exploring Causal Physical Mechanisms via Non-Gaussian Linear Models and Deep Kernel Learning: Applications for Ferroelectric Domain Structures

Rapid emergence of multimodal imaging in scanning probe, electron, and optical microscopies has brought forth the challenge of understanding the information contained in these complex data sets, targeting the intrinsic correlations between different channels, and further exploring the underpinning causal physical mechanisms. Here, we develop such an analysis framework for Piezoresponse Force Microscopy. We argue that under certain conditions, we can bootstrap experimental observations with the prior knowledge of materials structure to get information on certain nonobserved properties, and demonstrate linear causal analysis for PFM observables. We further demonstrate that the strength of individual causal links between complex descriptors can be ascertained using the deep kernel learning (DKL) model. In this DKL analysis, we use the prior information on domain structure within the image to predict the physical properties. This analysis demonstrates the correlative relationships between morphology, piezoresponse, elastic property, etc., at nanoscale. The prediction of morphology and other physical parameters illustrates a mutual interaction between surface condition and physical properties in ferroelectric materials. Overall, this analysis is universal and can be extended to explore the correlative relationships of other multichannel data sets, and allow for high-fidelity reconstruction of underpinning functionalities and physical mechanisms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

A quasi-meshfree method for constructing boundary-aware reproducing bases on geometrically complex domains using manifold geodesics

In many applications, physical domains are geometrically complex making it challenging to perform coarse-scale approximation. A defeaturing process is often used to simplify the domain in preparation for approximation and analysis at the coarse scale. In this paper, a methodology is presented for constructing a coarse-scale reproducing basis on geometrically complex domains given an initial fine-scale mesh of the fully featured domain. The initial fine-scale mesh can be of poor quality and extremely refined. The construction of the basis functions begins with a coarse-scale covering of the domain and generation of weighting functions with local support. Manifold geodesics are used to define distances within the local support for general applicability to non-convex domains. Conventional moving least squares is used to construct the coarse-scale reproducing basis. Applications in quasi-interpolation and linear elasticity are presented.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Data for Application of Time-Domain 1H NMR for Investigating Dynamics of Vegetative Lipids in Bioenergy Crops at Different Developmental Stages

Sweet sorghum is typically cultivated for the food and fodder market. Recently, sweet sorghum varieties are being metabolically transitioned to enhance energy density by accumulating oil droplets in their vegetative tissues for bioenergy applications. Owing to the high biomass yield of sorghum, the transgenic lines can compete with oil-seed crops for biodiesel yield per unit area. In the initial phase of transgenic development, a high-throughput phenotyping method can bridge the gap between the production pipeline and analysis to improve the efficiency of the process. To meet the requirement, the present study extends the application of time-domain 1H-NMR spectroscopy for rapid quantification and characterization of the total in-situ lipids of sweet sorghum ‘ramada’ to lay the groundwork for analyzing the upcoming large quantity of transgenic samples. NMR technology has been successfully established for analyzing lipid contents of vegetative tissues of non-transgenic variety. The multiexponential analysis of spin-lattice (T1) relaxation spectra obtained from TD-NMR aided the investigation of the dynamics of the free and bound lipid fraction with plant development. The total lipid concentration of bagasse and leaves of non-transgenic sweet sorghum remained unchanged throughout the plant development. Leaves displayed a higher percentage of bound lipids as compared to bagasse. A significant variation in the lipid concentration of juice was observed at the different growth stages with a maximum lipid accumulation of 1.21 ± 0.04% w/w at the boot stage that decreased with further maturity of the plant.

Biomass Analytics↗
DOE OSTI2023

Application of Time-Domain 1 H NMR for Investigating Dynamics of Vegetative Lipids in Bioenergy Crops at Different Developmental Stages

Sweet sorghum is typically cultivated for the food and fodder market. Recently, sweet sorghum varieties are being metabolically transitioned to enhance energy density by accumulating oil droplets in their vegetative tissues for bioenergy applications. Owing to the high biomass yield of sorghum, the transgenic lines can compete with oil-seed crops for biodiesel yield per unit area. In the initial phase of transgenic development, a high-throughput phenotyping method can bridge the gap between the production pipeline and analysis to improve the efficiency of the process. To meet the requirement, the present study extends the application of time-domain 1 H-NMR spectroscopy for rapid quantification and characterization of the total in-situ lipids of sweet sorghum ‘ramada’ to lay the groundwork for analyzing the upcoming large quantity of transgenic samples. NMR technology has been successfully established for analyzing lipid contents of vegetative tissues of non-transgenic variety. The multiexponential analysis of spin-lattice (T 1 ) relaxation spectra obtained from TD-NMR aided the investigation of the dynamics of the free and bound lipid fraction with plant development. The total lipid concentration of bagasse and leaves of non-transgenic sweet sorghum remained unchanged throughout the plant development. Leaves displayed a higher percentage of bound lipids as compared to bagasse. Here, a significant variation in the lipid concentration of juice was observed at the different growth stages with a maximum lipid accumulation of 1.21 ± 0.04% w/w at the boot stage that decreased with further maturity of the plant.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Bio-inspired redox mediated electrolyte for high performance flexible supercapacitor applications over broad temperature domain

For practical supercapacitors, recent advances in electrolytes have diminished long-standing problems such chemical stability, low ionic conductivity and limited electrochemical performance. Herewith, we communicate a facile method to produce anhydrous redox-active Bio-inspired electrolytes for flexible supercapacitors with application over a broad temperature range. The glycerol (Gly) gel electrolyte consists of various fractions of potassium hydroxide (KOH) and redox active unit, i.e., ammonium molybdate, (NH 4 ) 2 MoO 4 (abbreviated as Mo), in the anhydrous state. The doped glycerol matrix acts as a framework that provides a stable gel electrolyte, forming hierarchical pathway for ion diffusion. The developed gel network with Gly-KOH-MoX (X = 3, 5, 10, and 15%, w/w) provides an effective route to carry the ions onto the pores of the electrodes. The supercapacitor with Gly-KOH5-Mo10 gel provides a specific capacitance of 328 F g –1 , and the same device achieved an energy density of 45.6 Wh kg –1 at a power density of 497 W kg –1 . In addition, the supercapacitor maintained extraordinary cyclic robustness and wide temperature tolerance. Thus, the low cost and environmentally benign, cyclic durability of Gly-KOH-Mo makes these gel electrolytes a potential candidate for use in various energy storage devices.

25 ENERGY STORAGE↗
DOE OSTI2023

Quality appraisal of clinical practice guidelines for the management of Dysphagia after acute stroke

Objectives Dysphagia is a common complication in stroke patients, widely affecting recovery and quality of life after stroke. The objective of this systematic review is to identify the gaps that between evidence and practice by critically assessing the quality of clinical practice guidelines (CPGs) for management of dysphagia in stroke. Methods We systematically searched academic databases and guideline repositories between January 1, 2014, and August 1, 2023. The Appraisal of Guidelines for Research and Evaluation (AGREE II) instrument was used by two authors to independently assess CPG quality. Results In a total of 14 CPGs included, we identified that three CPGs obtained a final evaluation of “high quality,” nine CPGs achieved “moderate quality” and two CPGs received “low quality.” The domain of “scope and purpose” achieved the highest mean score (91.1%) and the highest median (IQR) of 91.7% (86.1, 94.4%), while the domain of “applicability” received the lowest mean score (55.8%) and the lowest median (IQR) of 55.4% (43.2, 75.5%). Conclusion The CPG development group should pay more attention to improving the methodological quality according to the AGREE II instrument, especially in the domain of “applicability” and “stakeholder involvement;” and each item should be refined as much as possible.

Gao, Shi-Lin↗
DOE OSTI2023

Domain Adaptation for Measurements of Strong Gravitational Lenses

Upcoming surveys are predicted to discover galaxy-scale strong lenses on the order of 10\textsuperscript{5}, making deep learning methods necessary in lensing data analysis. Currently, there is insufficient real lensing data to train deep learning algorithms, but the alternative of training only on simulated data results in poor performance on real data. Domain Adaptation may be able to bridge the gap between simulated and real datasets. We utilize domain adaptation for the estimation of Einstein radius ($\Theta_E$) in simulated galaxy-scale gravitational lensing images with different levels of observational realism. We evaluate two domain adaptation techniques - Domain Adversarial Neural Networks (DANN) and Maximum Mean Discrepancy (MMD). We train on a source domain of simulated lenses and apply it to a target domain of lenses simulated to emulate noise conditions in the Dark Energy Survey (DES). We show that both domain adaptation techniques can significantly improve the model performance on the more complex target domain dataset. This work is the first application of domain adaptation for a regression task in strong lensing imaging analysis. Our results show the potential of using domain adaptation to perform analysis of future survey data with a deep neural network trained on simulated data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗