A Comprehensive Assessment of the Marginal Abatement Costs of CO 2 of Co-Optima Multi-Mode Vehicles
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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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Atmospheric River Detection Under Changing Seasonality and Mean-State Climate: ARTMIP Tier 2 Paleoclimate Experiments
Atmospheric rivers (ARs) are filamentary structures within the atmosphere that account for a substantial portion of poleward moisture transport and play an important role in Earth's hydroclimate. However, there is no one quantitative definition for what constitutes an atmospheric river, leading to uncertainty in quantifying how these systems respond to global change. This study seeks to better understand how different AR detection tools (ARDTs) respond to changes in climate states utilizing single-forcing climate model experiments under the aegis of the Atmospheric River Tracking Method Intercomparison Project (ARTMIP). We compare a simulation with an early Holocene orbital configuration and another with CO2 levels of the Last Glacial Maximum to a preindustrial control simulation to test how the ARDTs respond to changes in seasonality and mean climate state, respectively. We find good agreement among the algorithms in the AR response to the changing orbital configuration, with a poleward shift in AR frequency that tracks seasonal poleward shifts in atmospheric water vapor and zonal winds. In the low CO2 simulation, the algorithms generally agree on the sign of AR changes, but there is substantial spread in their magnitude, indicating that mean-state changes lead to larger uncertainty. This disagreement likely arises primarily from differences between algorithms in their thresholds for water vapor and its transport used for identifying ARs. These findings warrant caution in ARDT selection for paleoclimate and climate change studies in which there is a change to the mean climate state, as ARDT selection contributes substantial uncertainty in such cases.
Atmospheric river, paleoclimate↗
City Climate Action Plans Through the Lens of The Food-Energy-Water Nexus
Interdependent resource management concepts originated in the mid-20th century, and the recent use of the term “nexus” highlights interconnected relationships among resources like food, energy, and water. United States and European science foundations have shown a growing interest in the food-energy-water (FEW) nexus, leading to increased research on their joint management. However, one major criticism of the FEW nexus is the limited translation of the research into practical policies and implementation, such as climate action plans. To assess the incorporation of FEW nexus principles into climate planning, we systematically evaluate 100 climate action plans from large U.S. cities. Our findings show that the most discussed FEW links involve energy, particularly the water-for-energy and food-for-energy connections, found in over half of the documents (56%). Our analysis promotes the integration of the FEW nexus into climate action plans while addressing the barriers to its effective implementation.
Mounir, Adil [Air Force Institute of Technology]↗
Dirac's equation and its implications for density functional theory based calculations of materials containing heavy elements
Electronic structure calculations based on density functional theory (DFT) give quantitatively accurate predictions of properties of most materials containing light elements. For heavy materials, and in particular for f -electron systems, DFT based methods can fail both qualitatively and quantitatively for two distinct reasons: their failure to describe confinement effects arising from localized f -electron behavior and their incomplete or approximate treatment of relativity. In addition, different methods for incorporating relativistic effects, which give identical results in most light materials, can give different predictions in heavy elements. In order to develop a quantitative capability for calculating the properties of these materials, it is essential to separate the predictions of the underlying equations from the uncertainty introduced in approximations used in computation. Working toward that goal, here we have developed a code, called dirac-fp , which is based directly on solving the Dirac-Kohn-Sham equations and uses the full-potential linear muffin-tin orbital (FP-LMTO) approach to electronic structure. In order to assess the performance of dirac-fp , we perform calculations on three different face-centered cubic materials using different approximate treatments of relativity: the scalar relativistic (SR) approach commonly used in most solid-state DFT codes, the scalar relativistic plus spin-orbit coupling corrections (SR+SO) approach which includes spin-orbit coupling self-consistently using the SR states inside the muffin tins, and the Dirac-Kohn-Sham (Dirac) approach implemented in dirac-fp . Performing calculations on thorium, in which relativistic effects should be strong, aluminum, in which relativistic effects should be negligible, and gold, in which relativistic effects play an intermediate role, we find that the Dirac approach is able to provide theoretically consistent results in the electronic structure and ground-state properties across all three materials.
3-dimensional systems↗
Exploring the Landscape of Distributed Graph Clustering on Leadership Supercomputers
The rapid growth of large-scale datasets in fields like biology and social networks has driven the need for advanced graph analytics techniques. Community detection, a fundamental task in graph analytics, identifies closely connected groups of nodes within a network, providing valuable insights across various disciplines. This study focuses on two classic community detection methods, the Louvain algorithm and Markov Clustering (MCL), and evaluates the performance of two prominent distributed community detection algorithms: HiPDPL-GPU, our prior implementation, and HipMCL. We conduct experiments on GPU-accelerated heterogeneous HPC systems, Summit and Frontier, to assess their performance under varying conditions. Our objective is to identify the strengths and weaknesses of these algorithms in terms of scalability, and quality of solutions. We evaluate these algorithms on a diverse set of 70+ networks spanning 13 domains, with sizes ranging up to 4.2 billion edges. Our results demonstrate that HiPDPL-GPU consistently outperforms HipMCL, especially for large-scale networks. HiPDPL-GPU achieves significantly faster runtimes (47x to 1439x), higher modularity scores, and improved scalability. These findings highlight HiPDPL-GPU as a promising solution for efficient and effective large-scale graph analytics in diverse application domains, and provide insights into the feasibility of using MCL-based approaches for certain application domains.
Community detection, graph algorithms↗
How Much Reserve Fuel: Quantifying the Maximal Energy Cost of System Disturbances
Motivated by the design question of additional fuel needed to complete a task in an uncertain environment, this paper introduces metrics to quantify the maximal additional energy used by a control system in the presence of bounded disturbances, compared to a nominal, disturbance-free system. In particular, we consider the task of finite-time stabilization for a linear, time-invariant system. We compare the nominal energy required to achieve this task in the disturbance-free system to the worst-case energy over all feasible disturbances. Solving for the worst-case energy over all disturbances first leads to an optimal control problem with a least-squares solution, and then an infinite-dimensional optimization problem where we derive an upper bound on the solution. The comparison of energies is accomplished using additive and multiplicative metrics, for which we derive bounds. Simulation examples on an ADMIRE fighter jet model demonstrate the practicability of these metrics, and their variation with the distance of the initial condition from the origin and the task completion time.
koopman operator, resilience↗
Optimization-Based Model Reduction Scheme for Renewable Energy Power Plants Using Standardized Testing Scenarios
This paper presents an optimization-based model reduction scheme for renewable energy (RE) power plants consisting of inverter-based resources (IBRs) operating in grid-following (GFL) or grid-forming (GFM) modes. More importantly, the datasets feeding the optimization-based model reduction scheme are generated and re-used through the standardized grid-interactive testing scenarios. Particularly, the proposed scheme makes use of the power plant point of common coupling (PCC) measurements of various quantities specified by standardized tests (e.g., voltage and frequency ride through) as per IEEE 2800, to estimate the parameters of the reduced-order model such that its dynamic performance aligns with the original detailed power plant model. The proposed model reduction approach does not require the parameters of individual IBRs and using standardized test data as input to the formulated optimization problem simplifies the reduced-order modelling scheme. Extensive case studies following standardized test scenarios verified the remarkable accuracy of the proposed approach.
Yallamilli, Ram S. [Purdue University]↗
New Roadmap for Microelectronics: Charting the Semiconductor Industry's Path Over the Next 5, 10, and 20 Years
In the past, the role of strategic planning for semiconductor industry was met by the International Technology Roadmap for Semiconductors (ITRS), serving as a guiding light that provided manufacturers, designers, equipment suppliers, and researchers with direction years in advance. By providing a common framework for coordination across semiconductor industry stakeholders, technology development efforts were efficient and aligned. However, the dissolution of the ITRS in 2015 left a void, leading to years of disjointed efforts. Recognizing the need for unified guidance, the industry rallied for the creation of a new strategic plan, the Microelectronics and Advanced Packaging Technologies (MAPT) Roadmap (2023) was developed, through the efforts of hundreds of industry, academic and government experts, to provide detailed strategies for achieving these goals. This comprehensive plan outlines ambitious goals for the industry's future.
HPC, Roadmap, AI, Liquid Cooling, Power, Silicon, ↗
AutoCheck: Automatically Identifying Variables for Checkpointing by Data Dependency Analysis
Checkpoint/Restart (C/R) has been widely deployed in numerous HPC systems, Clouds, and industrial data centers, which are typically operated by system engineers. Nevertheless, there is no existing approach that helps system engineers without domain expertise and domain scientists without system fault tolerance knowledge identify those critical variables accounted for correct application execution restoration in a failure for C/R. To address this problem, we propose an analytical model and a tool (AutoCheck) that can automatically identify critical variables to checkpoint for C/R. AutoCheck relies on first, analytically tracking and optimizing data dependency between variables and other application execution state, and second, a set of heuristics that identify critical variables for checkpointing from the refined data dependency graph (DDG). AutoCheck allows programmers to pinpoint critical variables to checkpoint quickly within a few minutes. We evaluate AutoCheck on 13 representative HPC benchmarks, demonstrating that AutoCheck can efficiently identify correct critical variables to checkpoint.
HPC↗
Scrutinizing Variables for Checkpoint Using Automatic Differentiation
Checkpoint/Restart (C/R) saves the running state of the programs periodically, which consumes considerable time and system resources. We observe that not every piece of data is involved in the computation in typical HPC applications; such unused data should be excluded from checkpointing for better storage and compute efficiency. We propose a systematic approach that leverages automatic differentiation (AD) to scrutinize every element within variables (e.g., arrays) necessary for checkpointing. This allows us to identify critical and uncritical elements and eliminate uncritical elements from checkpointing. Specifically, we inspect every single element within a variable necessary for checkpointing with an AD tool to determine whether the element has an impact on the application output or not. We validate our approach with all benchmarks from the NPB suite. We visualize the distribution of critical and uncritical elements within a variable with respect to its binary impact (yes or no) on the application output.
Huang, Xin [Kobe University]↗
The promising role of proteomes and metabolomes in defining the single-cell landscapes of plants
The plant community has a strong track-record of RNA sequencing technology deployment, which combined with the recent advent of spatial platforms (e.g., 10x genomics), has resulted in an explosion of outstanding single cell and nuclei datasets that can be put in an in situ context within tissues (e.g., a cell atlas)1. In the genomics era, application of proteomics technologies in the plant sciences has always trailed behind that of RNA sequencing technologies, largely due to accessibility, ease-of-use and access to expertise along with depth of analysis benefits. On the other hand, the use of early analytical tools for characterizing small molecules (metabolites) from plant systems predates nucleic acid sequencing and proteomics analysis2, as the search for plant-based natural products has played a significant role in improving human health throughout history. However, the employment of proteomics and metabolomics assays for characterizing plant cell processes now remains significantly behind transcriptional approaches, even though both provide a direct functional readout of cell states and phenotypes.
Anderton, Christopher R. [BATTELLE (PACIFIC NW LAB↗
Why the CHI Community Should be Involved in Standards: Stories from Three CHI Participants
In this article, the three authors tell their stories about involvement in standards that are relevant to the computer human interaction community(CHI). Their reasoning for being involved is given and readers are also given information about different organizations that develop standards relevant to CHI.
99 GENERAL AND MISCELLANEOUS↗
Unlocking Scholarly Insights: Leveraging Machine Learning Approaches for Citation Analysis and Intent Classification
Publicly funded organizations, notably institutions like the Los Alamos National Laboratory (LANL), are deeply vested in acquiring robust productivity metrics to gauge the entirety of their research output. Motivated by the imperative to enhance institutional productivity assessment, this study investigates the utilization of Large Language Models (LLM) such as BERT-based models, as well as local LLaMa-30b-instruct and Mixtral-8x7b-instruct architectures for classifying type of URL referenced resources in academic papers such as software, dataset, as well as authorship intent. Challenges in discerning resource types from context are highlighted, along with the potential of BERT and LLMs to address these challenges. Through comprehensive analysis, this research unveils a notable surge in documents featuring URL citations, indicative of the escalating importance of digital resources in scholarly publications. Moreover, citations to datasets and software demonstrate consistent growth over time, underscoring their increasing significance. Our findings also reveal that LANL authors contribute substantially to accessible science, comprising about 10% of dataset and software mentions in LANL
Large Language Models, BERT, citation classificati↗
Accelerating GNNs on GPU Sparse Tensor Cores through N:M Sparsity-Oriented Graph Reordering
Recent advancements in GPU hardware support have introduced the capability to leverage N:M sparse patterns for substantial performance gains. Graphs in Graph Neural Networks (GNNs) are typically sparse, but the sparsity is often irregular, not conforming to such sparse patterns. In this paper, we propose a novel graph reordering algorithm, the first of its kind, to reshape irregular graph data into the N:M structured sparse pattern at the tile level, allowing linear-algebra-based graph operations in GNNs to benefit from the N:M sparse hardware. The optimization is lossless, maintaining the accuracy of GNN. It can remove 98-100\% violations of the N:M sparse patterns at the vector level, and increase the proportion of conforming graphs in SuiteSparse collection from 5-9\% to 88.7-93.5\%. On A100 GPUs, the optimization accelerates Sparse Matrix Matrix (SpMM) by up to 43X (2.3X -- 7.5X on average) and speeds up the key graph operations in GNNs on real graphs by as much as 8.6X (3.5X on average).
artificial intelligence, graph neural networks↗
Graph neural network tutorial for digital twin application
Tutorial on graph neural network (GNN) for the digital twin (DT) team at ORNL.
Zhang, Chen [Oak Ridge National Laboratory (ORNL),↗
Flux REaction TArget Prioritization (Flux RETAP) v1
Metabolic engineering is evolving rapidly as a result of new advances in synthetic biology and automation, as well as the irruption of machine learning (ML). ML has been shown to provide the predictive power synthetic biology lacked and needed, and to be able to effectively guide the metabolic engineering process. However, current technical limitations prevent the independent application of ML approaches to metabolic engineering without the use of previous biological knowledge in the form of a prioritized list of desirable engineering targets. Here, we present FluxRETAP, a simple and computationally inexpensive method that leverages the prior mechanistic knowledge embedded in genome-scale metabolic models (GSMs) for suggesting targets for genetic overexpression, downregulation or deletion, with the final goal of increasing metabolite production. FluxRETAP captured 100% of reaction targets experimentally verified to improve Escherichia coli isoprenol production in the literature accessible to us, 50% of targets that experimentally improved taxadiene production in E. coli and ~60% of genetic targets from a verified minimal constrained cut-set in Pseudomonas putida while providing additional high priority targets that could be tested. Overall, FluxRETAP is an efficient algorithm for identifying a prioritized list of testable genetic and reaction targets which can also be utilized in ML pipelines.
Czajka, Jeffrey [Battelle Memorial Institute, Paci↗
Collaborative Visualization: Definition, Challenges, and Research Agenda
Collaborative visualization has emerged as a new research direction which offers the opportunity to reach new audiences and application areas for visualization tools and techniques. Technology now allows us to easily connect and collaborate with one another - in settings as diverse as over networked computers, across mobile devices, or using shared displays such as interactive walls and tabletop surfaces. Any of these collaborative settings carries a set of challenges and opportunities for visualization research. Digital information is already regularly accessed by multiple people together in order to share information, to view it together, to analyze it, or to form decisions. However, research on how to best support collaboration with and around visualizations is still in its infancy and has so far focused only on a small subset of possible application scenarios. The purpose of this article is (1) to provide a clear scope, definition, and overview of the evolving field of collaborative visualization, (2) to help pinpoint the unique focus of collaborative visualization with its specific aspects, challenges, and requirements within the intersection of general computer-supported collaborative work (CSCW) and visualization research, and (3) to draw attention to important future research questions to be addressed by the community. Thus, the goal of the paper is to discuss a research agenda for future work on collaborative visualization, including our vision for how to meet the grand challenge and to urge for a new generation of visualization tools that were designed with collaboration in mind from their very inception.
99 GENERAL AND MISCELLANEOUS↗
AmeriFlux FLUXNET-1F US-xNW NEON Niwot Ridge Mountain Research Station (NIWO)
This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-xNW NEON Niwot Ridge Mountain Research Station (NIWO). This is the FLUXNET version of the carbon flux data for the site US-xNW NEON Niwot Ridge Mountain Research Station (NIWO) produced by applying the standard ONEFlux (1F) software. Site Description - The Niwot Ridge sits approximately 27 km west of Boulder, Colorado, and 6 km east of the Continental Divide. Topography, climate, and biota of the site are representative of Rocky Mountain alpine ecosystems, including extensive alpine tundra (mostly herbs, some shrubs and scree) and subalpine coniferous forests (Abies lasciocarpa and Picea engelmanii at higher elevations), talus slopes, wetlands and a variety of glacial landforms. Characterized by cold and relatively long winters, Niwot Ridge has an average annual temperature of 1.5°C and average annual precipitation of 800 mm. Most precipitation falls as snow and summer precipitation falls primarily during afternoon thunderstorms. Located on the eastern side of the Continental Divide at 3,000-3,500 m elevation, the site best captures chemical inputs produced along the Front Range and is well situated to observe other east/west flows across the Southern Rockies in conjunction with other NEON sites.
Network), NEON (National Ecological Observatory [N↗