Search NASA⌕ Search

SEARCH · Search NASA

Results for “extensible architecture”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

A Collaborative Extensible User Environment for Simulation and Knowledge Management

In scientific simulation, scientists use measured data to create numerical models, execute simulations and analyze results from advanced simulators executing on high performance computing platforms. This process usually requires a team of scientists collaborating on data collection, model creation and analysis, and on authorship of publications and data. This paper shows that scientific teams can benefit from a user environment called Akuna that permits subsurface scientists in disparate locations to collaborate on numerical modeling and analysis projects. The Akuna user environment is built on the Velo framework that provides both a rich client environment for conducting and analyzing simulations and a Web environment for data sharing and annotation. Akuna is an extensible toolset that integrates with Velo, and is designed to support any type of simulator. This is achieved through data-driven user interface generation, use of a customizable knowledge management platform, and an extensible framework for simulation execution, monitoring and analysis. This paper describes how the customized Velo content management system and the Akuna toolset are used to integrate and enhance an effective collaborative research and application environment. The extensible architecture of Akuna is also described and demonstrates its usage for creation and execution of a 3D subsurface simulation.

collaborative science tools↗
DOE OSTI2021

The SvFUL2 transcription factor is required for inflorescence determinacy and timely flowering in Setaria viridis

Inflorescence architecture in cereal crops directly impacts yield potential through regulation of seed number and harvesting ability. Extensive architectural diversity found in inflorescences of grass species is due to spatial and temporal activity and determinacy of meristems, which control the number and arrangement of branches and flowers, and underlie plasticity. Timing of the floral transition is also intimately associated with inflorescence development and architecture, yet little is known about the intersecting pathways and how they are rewired during development. Here, we show that a single mutation in a gene encoding an AP1/FUL-like MADS-box transcription factor significantly delays flowering time and disrupts multiple levels of meristem determinacy in panicles of the C 4 model panicoid grass, Setaria viridis. Previous reports of AP1/FUL-like genes in cereals have revealed extensive functional redundancy, and in panicoid grasses, no associated inflorescence phenotypes have been described. In S. viridis, perturbation of SvFul2, both through chemical mutagenesis and gene editing, converted a normally determinate inflorescence habit to an indeterminate one, and also repressed determinacy in axillary branch and floral meristems. Our analysis of gene networks connected to disruption of SvFul2 identified regulatory hubs at the intersection of floral transition and inflorescence determinacy, providing insights into the optimization of cereal crop architecture.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

MedNAS: Multiscale Training-Free Neural Architecture Search for Medical Image Analysis

Deep neural networks have demonstrated impressive results in medical image analysis, but designing suitable architectures for each specific task is expertise dependent and time consuming. Neural architecture search (NAS) offers an effective means of discovering architectures. It has been highly successful in numerous applications, particularly in natural image classification. Yet, medical images possess unique characteristics, such as small regions and a wide variety of lesion sizes, that differentiate them from natural images. Furthermore, most current NAS methods struggle with high computational costs, especially when dealing with high-resolution image datasets. In this article, we present a novel evolutionary NAS method called multiscale training-free neural architecture search (MSTF-NAS) to address these challenges. Specifically, to accommodate the broad range of lesion region sizes in disease diagnosis, we develop a new reduction cell search space that enables the search algorithm to explicitly identify the optimal scale combination for multiscale feature extraction. Further, to overcome the issue of high computational costs, we utilize training-free indicators as performance measures for candidate architectures, which allows us to search for the optimal architecture more efficiently. More specifically, by considering the capability and simplicity of various networks, we formulate a multiobjective optimization problem that involves two training-free indicators and model complexity for candidate architectures. Extensive experiments on a large medical image benchmark and a publicly available breast cancer detection dataset are conducted. The empirical results demonstrate that our MSTF-NAS outperforms both human-designed architectures and current state-of-the-art NAS algorithms on both datasets, indicating the effectiveness of our proposed method.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Wolf

The Workflow Orchestration Language Framework (WOLF) is an agentic framework grounded in natural language with an architecture inspired by reinforcement learning (RL)—designed to orchestrate, scale, and accelerate complex workflows. The concept of WOLF was born out of the very successful ASC Tri-lab Multi-Agent Design Assistant (MADA) project, but extends beyond its domain-specific design agents to provide a more general and extensible architecture. WOLF capitalizes on the lessons learned from MADA and is fully aligned with Sutton’s The Bitter Lesson—that the most enduring progress in AI comes from general-purpose methods that scale with computation, rather than narrow techniques built on domain-specific human knowledge. In this spirit, WOLF enables agents to autonomously learn workflows, capture strategies as reusable playbooks, and build a growing corpus of interpretable, auditable “wisdom artifacts.” These artifacts, expressed in natural language, bridge human and machine understanding while preserving adaptability and scalability as computational power continues to expand.

Boureima, Ismaeal↗
DOE OSTI2021

Graphitic Aza-Fused π-Conjugated Networks: Construction, Engineering, and Task-Specific Applications

2D π-conjugated networks linked by aza-fused units represent a pivotal category of graphitic materials with stacked nanosheet architectures. Extensive efforts have been directed at their fabrication and application since the discovery of covalent triazine frameworks (CTFs). Besides the triazine cores, tricycloquinazoline and hexaazatriphenylene linkages are further introduced to tailor the structures and properties. Diverse related materials have been developed rapidly, and a thorough outlook is necessitated to unveil the structure–property–application relationships across multiple subcategories, which is pivotal to guide the design and fabrication toward enhanced task-specific performance. Herein, the structure types and development of related materials including CTFs, covalent quinazoline networks, and hexaazatriphenylene networks, are introduced. Advanced synthetic strategies coupled with characterization techniques provide powerful tools to engineer the properties and tune the associated behaviors in corresponding applications. Case studies in the areas of gas adsorption, membrane-based separation, thermo-/electro-/photocatalysis, and energy storage are then addressed, focusing on the correlation between structure/property engineering and optimization of the corresponding performance, particularly the preferred features and strategies in each specific field. In the last section, the underlying challenges and opportunities in construction and application of this emerging and promising material category are discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Laue-DIALS: Open-source software for polychromatic x-ray diffraction data

Most x-ray sources are inherently polychromatic. Polychromatic (“pink”) x-rays provide an efficient way to conduct diffraction experiments as many more photons can be used and large regions of reciprocal space can be probed without sample rotation during exposure—ideal conditions for time-resolved applications. Analysis of such data is complicated, however, causing most x-ray facilities to discard >99% of x-ray photons to obtain monochromatic data. Key challenges in analyzing polychromatic diffraction data include lattice searching, indexing and wavelength assignment, correction of measured intensities for wavelength-dependent effects, and deconvolution of harmonics. We recently described an algorithm, Careless, that can perform harmonic deconvolution and correct measured intensities for variation in wavelength when presented with integrated diffraction intensities and assigned wavelengths. Here, we present Laue-DIALS, an open-source software pipeline that indexes and integrates polychromatic diffraction data. Laue-DIALS is based on the dxtbx toolbox, which supports the DIALS software commonly used to process monochromatic data. As such, Laue-DIALS provides many of the same advantages: an open-source, modular, and extensible architecture, providing a robust basis for future development. We present benchmark results showing that Laue-DIALS, together with Careless, provides a suitable approach to the analysis of polychromatic diffraction data, including for time-resolved applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Dynamics of poly[n]catenane melts

Inspired by advances in the chemical synthesis of interlocking polymer architectures, extensive molecular dynamics simulations have been conducted to study the dynamical properties of poly[n]catenanes-polymers composed entirely of interlocking rings-in the melt state. Both the degree of polymerization (number of links) and the number of beads per ring are systematically varied, and the results are compared to linear and ring polymers. Herein, a simple Rouse-like model is presented, and its analytical solution suggests a decomposition of the dynamics into "ring-like" and "linear-like" regimes at short and long times, respectively. In agreement with this picture, multiple sub-diffusive regimes are observed in the monomer mean-squared-displacements even though interchain entanglement is not prevalent in the system. However, the Rouse-type model does not account for the topological effects of the mechanical bonds, which significantly alter the dynamics at intermediate length scales both within the rings and at the chain segment scales. The stress relaxation in the system is extremely rapid and may be conveniently separated into ring-like and linear-like contributions, again in agreement with the Rouse picture. However, the viscosity has a non-monotonic dependence on the ring size for long chains, which disagrees strongly with theoretical predictions. This unexpected observation cannot be explained in terms of chain disentanglement and is inconsistent with other measures of polymer relaxation. Possible mechanisms for this behavior are proposed and implications for materials design are discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Coding the Computing Continuum: Fluid Function Execution in Heterogeneous Computing Environments

Advances in network technologies have greatly decreased barriers to accessing physically distributed computers. This newfound accessibility coincides with increasing hardware specialization, creating exciting new opportunities to dispatch workloads to the best resource for a specific purpose, rather than those that are closest or most easily accessible. We present Delta, a service designed to intelligently schedule function-based workloads across a distributed set of heterogeneous computing resources. Delta implements an extensible architecture in which different predictors and scheduling algorithms can be integrated to provide dynamically evolving estimates of function execution times on different resources-estimates that can be used to determine the most appropriate location for execution. We describe predictors for function runtime, data transfer time, and cold-start resource provisioning and configuration delay; dynamic learning methods that update predictor models over time; and scheduling strategies that take into account both function and endpoint information. We show that these methods can halve workload makespan when compared with a strategy that selects the fastest resource, and decrease makespan by a factor of five when compared to a round robin strategy, when deployed on a heterogeneous testbed with resources ranging from a Raspberry Pi to a GPU node in an academic cloud.

Computing continuum↗
DOE OSTI2025

Plant Reload Optimization (prlo)

The PRLO framework is built on a modular and extensible architecture that tightly couples advanced evolutionary optimization algorithms with nuclear fuel depletion solvers (i.e., nuclear physics neutronics code). It supports exploring complex, high-dimensional design spaces constrained by user-specified operational, safety, and economic constraints. Objectives such as minimizing fresh fuel enrichment, flattening radial and axial power distributions, and maximizing discharge burnup are evaluated. PRLO’s equilibrium cycle optimization capability enables the identification of core configurations that maintain fuel cycle sustainability over extended planning horizons. Its integration with the RAVEN platform facilitates optimization of loading patterns or fuel shuffling schemes across multiple cycles. The interface with SIMULATE, a licensed industry-standard nodal code developed by Studsvik, ensures accurate neutronic and thermal-hydraulic feedback for reactor core design. PRLO’s automated workflow engine supports iterative design refinement, enabling utilities to streamline core design processes and meet evolving performance and regulatory targets.

Kim, Junyung [Idaho National Laboratory] (00090005↗
DOE OSTI2024

UMap: An application-oriented user level memory mapping library

Exploiting the prominent role of complex memories in exascale node architecture, the UMap page fault handler offers new capabilities to access large memory-mapped data sets directly. UMap provides flexible configuration options to customize page handling to each application, including analysis of massive observational and simulation data sets. The high-performance design features I/O decoupling, dynamic load balancing, and application-level controls. Page faults triggered by application threads and processes accessing data mapped to a UMapp’ed region are handled via the Linux userfaultfd protocol, an asynchronous message-oriented kernel-user communication mechanism that avoids the context switch penalty of traditional signal fault handlers. UMap is fully open source. In this paper, we give an overview of the UMap library architecture, its extensible plugin architecture, and the use/performance of UMap in emerging heterogeneous memory hierarchies such as near-node Non-volatile Memory (NVM) and network attached memories. We highlight new capabilities in two pagefault management plugins, the NetworkStore and SparseStore. We demonstrate the integration between UMap and multiple ECP products including Caliper, Metall, ZFP, Mochi, and Ripples.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Evolutionary Architecture Search for Generative Adversarial Networks Based on Weight Sharing

Generative adversarial networks (GANs) are a powerful generative technique but frequently face challenges with training stability. Network architecture plays a significant role in determining the final output of GANs, but designing a fine architecture demands extensive domain expertise. This article aims to address this issue by searching for high-performance generator’s architectures through neural architecture search (NAS). The proposed approach, called evolutionary weight sharing GANs (EWSGAN), is based on weight sharing and comprises two steps. First, a supernet of the generator is trained using weight sharing. Second, a multiobjective evolutionary algorithm (MOEA) is employed to identify optimal subnets from the supernet. These subnets inherit weights directly from the supernet for fitness assessment. Two strategies are used to stabilize the training of the generator supernet: 1) a fair single-path sampling strategy and 2) a discarding strategy. Experimental results indicate that the architecture searched by our method achieved a new state-of-the-art among NAS–GAN methods with a Fréchet inception distance (FID) of 9.09 and an inception score (IS) of 8.99 on the CIFAR-10 dataset. Finally, it also demonstrates competitive performance on the STL-10 dataset, achieving FID of 21.89 and IS of 10.51.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Apache Calcite: A Foundational Framework for Optimized Query Processing Over Heterogeneous Data Sources

Apache Calcite is a foundational software framework that provides query processing, optimization, and query language support to many popular open-source data processing systems such as Apache Hive, Apache Storm, Apache Flink, Druid, and MapD. The goal of this paper is to formally introduce Calcite to the broader research community, brie y present its history, and describe its architecture, features, functionality, and patterns for adoption. Calcite's architecture consists of a modular and extensible query optimizer with hundreds of built-in optimization rules, a query processor capable of processing a variety of query languages, an adapter architecture designed for extensibility, and support for heterogeneous data models and stores (relational, semi-structured, streaming, and geospatial). This exible, embeddable, and extensible architecture is what makes Calcite an attractive choice for adoption in big-data frameworks. It is an active project that continues to introduce support for the new types of data sources, query languages, and approaches to query processing and optimization.

Begoli, Edmon↗
DOE OSTI2023

A Backend-agnostic, Quantum-classical Framework for Simulations of Chemistry in C ++

As quantum computing hardware systems continue to advance, the research and development of performant, scalable, and extensible software architectures, languages, models, and compilers is equally as important to bring this novel coprocessing capability to a diverse group of domain computational scientists. For the field of quantum chemistry, applications and frameworks exist for modeling and simulation tasks that scale on heterogeneous classical architectures, and we envision the need for similar frameworks on heterogeneous quantum-classical platforms. Furthermore, we present the XACC system-level quantum computing framework as a platform for prototyping, developing, and deploying quantum-classical software that specifically targets chemistry applications. We review the fundamental design features in XACC, with special attention to its extensibility and modularity for key quantum programming workflow interfaces and provide an overview of the interfaces most relevant to simulations of chemistry. A series of examples demonstrating some of the state-of-the-art chemistry algorithms currently implemented in XACC are presented, while also illustrating the various APIs that would enable the community to extend, modify, and devise new algorithms and applications in the realm of chemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

An Accuracy-Maximization Approach for Claims Classifiers in Document Content Analytics for Cybersecurity

This paper presents our research approach and findings towards maximizing the accuracy of our classifier of feature claims for cybersecurity literature analytics, and introduces the resulting model ClaimsBERT. Its architecture, after extensive evaluations of different approaches, introduces a feature map concatenated with a Bidirectional Encoder Representation from Transformers (BERT) model. We discuss deployment of this new concept and the research insights that resulted in the selection of Convolution Neural Networks for its feature mapping aspects. We also present our results showing ClaimsBERT to outperform all other evaluated approaches. This new claims classifier represents an essential processing stage within our vetting framework aiming to improve the cybersecurity of industrial control systems (ICS). Furthermore, in order to maximize the accuracy of our new ClaimsBERT classifier, we propose an approach for optimal architecture selection and determination of optimized hyperparameters, in particular the best learning rate, number of convolutions, filter sizes, activation function, the number of dense layers, as well as the number of neurons and the drop-out rate for each layer. Fine-tuning these hyperparameters within our model led to an increase in classification accuracy from 76% obtained with BertForSequenceClassification’s original model to a 97% accuracy obtained with ClaimsBERT.

Ameri, Kimia (ORCID:0000000328791871)↗
DOE OSTI2021

A Cobalt– and Manganese–Free High–Nickel Layered Oxide Cathode for Long–Life, Safer Lithium–Ion Batteries

High-nickel LiNi 1–x–y Mn x Co y O 2 and LiNi 1–x–y Co x Al y O 2 cathodes are receiving growing attention due to the burgeoning demands on high-energy-density lithium-ion batteries. The presence of both cobalt and manganese in them, however, triggers multiple issues, including high cost, high toxicity, rapid surface deterioration, and severe transition-metal dissolution. Herein, a Co- and Mn-free ultrahigh-nickel LiNi 0.93 Al 0.05 Ti 0.01 Mg 0.01 O 2 (NATM) cathode that exhibits 82% capacity retention over 800 deep cycles in full cells, outperforming two representative high-Ni cathodes LiNi 0.94 Co 0.06 O 2 (NC, 52%) and LiNi 0.90 Mn 0.05 Co 0.05 O 2 (NMC, 60%) is presented. It is demonstrated that a titanium-enriched surface along with aluminum and magnesium as the stabilizing ions in NATM not only ameliorates unwanted side reactions with the electrolyte and structural disintegrity, but also mitigates transition-metal dissolution and active lithium loss on the graphite anode. As a result, the graphite anode paired with NATM displays an ultrathin (≈8 nm), monolayer anode-electrolyte interphase architecture after extensive cycling. Furthermore, NATM displays considerably enhanced thermal stability with an elevated exothermic temperature (213 °C for NATM vs 180 and 190 °C for NC and NMC, respectively) and remarkably reduced heat release. This work sheds light on rational compositional design to adopt ultrahigh-Ni cathodes in lithium-based batteries with low cost, long service life, and improved thermal stability.

25 ENERGY STORAGE↗
DOE OSTI2019

Toward Long-Life, Ultrahigh-Nickel Layered Oxide Cathodes for Lithium-Ion Batteries: Optimizing the Interphase Chemistry with a Dual-Functional Polymer

Boosting the Ni content in LiMO 2 (M = Ni, Co, Mn, etc.) layered oxides is a promising way to establish high-energydensity, low-cost cathodes, but the poor cathode surface stability is a daunting challenge for their practical viability. Herein, by constructing a dual-functional binder framework with a conductive polymer - polyaniline (PANI), the ultrahigh-Ni layered oxide cathode (LiNi 0.94 Co 0.06 O 2 ) exhibits significantly improved cyclability, with a capacity retention greatly increased from 47% to 81% over 1,000 cycles in full cells. It is demonstrated that the acidic species (e.g. HF) in the electrolyte can be efficiently scavenged through a protonation process of PANI, hence the cathode surface reactivity is greatly suppressed and the rock-salt phase propagation into bulk structure is considerably alleviated. Furthermore, the PANI binder system effectively prevents both the cathode-electrolyte interphase (CEI) and anode-electrolyte interphase (AEI) from degrading to a thick “triple-layer” architecture upon extensive cycling, resulting in more robust, thinner CEI and AEI with regulated interphasial chemistry. Moreover, the delocalized π-conjugated electrons along the backbone of PANI facilitate fast electron transfer and promote rate capability even at low temperatures (-20 °C). Finally, this work sheds light on rational binder engineering for developing high-energy-density lithium-ion batteries with acceptable cycle life.

25 ENERGY STORAGE↗
DOE OSTI2017

Structural hierarchy controlling dimerization and target DNA recognition in the AHR transcriptional complex

The aryl hydrocarbon receptor (AHR) belongs to the PAS (PER-ARNT-SIM) family transcription factors and mediates broad responses to numerous environmental pollutants and cellular metabolites, modulating diverse biological processes from adaptive metabolism, acute toxicity, to normal physiology of vascular and immune systems. The AHR forms a transcriptionally active heterodimer with ARNT (AHR nuclear translocator), which recognizes the dioxin response element (DRE) in the promoter of downstream genes. We determined the crystal structure of the mammalian AHR–ARNT heterodimer in complex with the DRE, in which ARNT curls around AHR into a highly intertwined asymmetric architecture, with extensive heterodimerization interfaces and AHR interdomain interactions. Specific recognition of the DRE is determined locally by the DNA-binding residues, which discriminates it from the closely related hypoxia response element (HRE), and is globally affected by the dimerization interfaces and interdomain interactions. Changes at the interdomain interactions caused either AHR constitutive nuclear localization or failure to translocate to nucleus, underlying an allosteric structural pathway for mediating ligand-induced exposure of nuclear localization signal. Furthermore, these observations, together with the global higher flexibility of the AHR PAS-A and its loosely packed structural elements, suggest a dynamic structural hierarchy for complex scenarios of AHR activation induced by its diverse ligands.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Targeting 100! Advanced Energy Efficient Building Technologies for High Performance Hospitals: Executive Summary.

This research, Targeting 100!, provides a conceptual framework and decision-making structure at a schematic design level of precision for hospital owners, architects and engineers to radically reduce energy use in hospitals. Following the goals of Architecture 2030 and The 2030 Challenge, it offers access to design strategies and the cost implications of those strategies for new hospitals to utilize 60% less energy. The name, Targeting 100!, comes from the 2030 Challenge energy reduction goal for hospitals; a 60% energy use reduction from typical acute care hospital targets approximately 100 KBtu/SF Year, thus the name “Targeting 100!”. Targeting 100! was developed through funding partnerships with the US Department of Energy and the Northwest Energy Efficiency’s BetterBricks Initiative. The technical team was led by the University of Washington Integrated Design Lab supported by deep collaboration with Solarc Architecture and Engineering, TBD Cost Consultants, and NBBJ Architecture. Through extensive research and design development, Targeting 100! provides a framework for developing high performance healthcare projects today and into the future. An online tool houses a Targeting 100! knowlegebase and roadmap. It can be accessed at: www.idlseattle.com/t100. The webtool is structured from high-level overview materials to detailed library with modeling inputs and outputs, providing a comprehensive report of the background, data, and outcomes from the project.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗