Exploring the limits of the Data-Model-Theory synergy: “Hot” MW transitions for rovibrational IR studies
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Xinchuan Huang (黄新川)↗
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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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Exploring Memory Hierarchy to Improve Scientific Data Read Performance
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Liu, Qing Gary↗
Ascribe XR v0.1.0
Ascribe XR is an immersive visualization software designed for scientists and engineers working with 3D data sets. Its key features include interactive exploration, multi-user collaboration, and flexible data import capabilities, supporting various formats such as meshes, volumes, and terrain maps. The software utilizes Godot, OpenXR and PC-VR technology to provide an immersive experience. Ascribe XR is used for data analysis, visualization, and collaboration in various fields, enabling users to gain deeper insights into complex data sets. Its advantages over similar technologies include its flexibility, customizability, and ease of use. Ascribe XR's interactive and immersive environment facilitates collaboration and accelerates the discovery process. Compared to traditional 2D visualization tools, Ascribe XR offers a more engaging and intuitive experience, allowing users to explore complex data sets in a more natural and interactive way. Its ability to support multi-user collaboration and flexible data import capabilities make it a versatile tool for various applications. Overall, Ascribe XR provides a unique combination of features, usability, and performance, making it an attractive solution for scientists and engineers working with 3D data sets.
Pandolfi, Ronald [Lawrence Berkeley National Labor↗
Monitoring X-Ray Emission from X-Ray Bursters
The scientific goal of this project was to monitor a selected sample of x-ray bursters using data from the All-Sky Monitor (ASM) on the Rossi X-Ray Timing Explorer together with data from the Burst and Transient Source Experiment (BATSE) on the Compton Gamma-Ray Observatory to study the long-term temporal evolution of these sources in the x-ray and hard x-ray bands. The project was closely related to "Long-Term Hard X-Ray Monitoring of X-Ray Bursters", NASA project NAG5-3891, and and "Hard x-ray emission of x-ray bursters", NASA project NAG5-4633, and shares publications in common with both of these. The project involved preparation of software for use in monitoring and then the actual monitoring itself. These efforts have lead to results directly from the ASM data and also from Target of Opportunity Observations (TOO) made with the Rossi X-Ray Timing Explorer based on detection of transient hard x-ray outbursts with the ASM and BATSE.
Halpern, Jules P.↗
Trade Study: Storing NASA HDF5/netCDF-4 Data in the Amazon Cloud and Retrieving Data Via Hyrax Server Data Server
This study explored three candidate architectures with different types of objects and access paths for serving NASA Earth Science HDF5 data via Hyrax running on Amazon Web Services (AWS). We studied the cost and performance for each architecture using several representative Use-Cases. The objectives of the study were: Conduct a trade study to identify one or more high performance integrated solutions for storing and retrieving NASA HDF5 and netCDF4 data in a cloud (web object store) environment. The target environment is Amazon Web Services (AWS) Simple Storage Service (S3). Conduct needed level of software development to properly evaluate solutions in the trade study and to obtain required benchmarking metrics for input into government decision of potential follow-on prototyping. Develop a cloud cost model for the preferred data storage solution (or solutions) that accounts for different granulation and aggregation schemes as well as cost and performance trades.We will describe the three architectures and the use cases along with performance results and recommendations for further work.
AWS cost↗
Hybrid Exploration Agent Platform and Sensor Web System
A sensor web to collect the scientific data needed to further exploration is a major and efficient asset to any exploration effort. This is true not only for lunar and planetary environments, but also for interplanetary and liquid environments. Such a system would also have myriad direct commercial spin-off applications. The Hybrid Exploration Agent Platform and Sensor Web or HEAP-SW like the ANTS concept is a Sensor Web concept. The HEAP-SW is conceptually and practically a very different system. HEAP-SW is applicable to any environment and a huge range of exploration tasks. It is a very robust, low cost, high return, solution to a complex problem. All of the technology for initial development and implementation is currently available. The HEAP Sensor Web or HEAP-SW consists of three major parts, The Hybrid Exploration Agent Platforms or HEAP, the Sensor Web or SW and the immobile Data collection and Uplink units or DU. The HEAP-SW as a whole will refer to any group of mobile agents or robots where each robot is a mobile data collection unit that spends most of its time acting in concert with all other robots, DUs in the web, and the HEAP-SWs overall Command and Control (CC) system. Each DU and robot is, however, capable of acting independently. The three parts of the HEAP-SW system are discussed in this paper. The Goals of the HEAP-SW system are: 1) To maximize the amount of exploration enhancing science data collected; 2) To minimize data loss due to system malfunctions; 3) To minimize or, possibly, eliminate the risk of total system failure; 4) To minimize the size, weight, and power requirements of each HEAP robot; 5) To minimize HEAP-SW system costs. The rest of this paper discusses how these goals are attained.
Stoffel, A. William↗
Visualization of Real-Time Data
In this project we explored various approaches to presenting real-time data from the numerous systems monitored on the space shuttle to computer users. We examined the approach that several projects at the Kennedy Space Center (KSC) used to accomplish this. We undertook to build a prototype system to demonstrate that the Internet and the Java programming language could be used to present the real-time data conveniently. Several Java programs were developed that presented real-time data in different forms including one form that emulated the display screens of the PC GOAL system which is familiar to many at KSC. Also, we developed several communications programs to supply the data continuously. Furthermore, a framework was created using the World Wide Web (WWW) to organize the collection and presentation of the real-time data. We believe our demonstration project shows the great flexibility of the approach. We had no particular use of the data in mind, instead we wanted the most general and the least complex framework possible. People who wish to view data need only know how to use a WWW browser and the address (the URL). People wanting to build WWW documents containing real-time data need only know the values of a few parameters, they do not need to program in Java or any other language. These are stunning advantages over more monolithic systems.
Stansifer, Ryan↗
Selection and Ranking of Experiments from the Halden Database in support of Multiscale Model Validation
Validating fuel performance codes, such as BISON, requires an extensive amount of experiments covering a wide range of operating conditions and fuel types. Within the light-water reactor (LWR) space, there have been several international experimental programs that have contributed to the wealth of available experimental data available for use. One of those international programs, the Halden Reactor Project (HRP), began in 1958 and utilized the Halden Boiling Water Reactor to conduct many highly instrumented experiments until the reactor closed in 2018. Idaho National Laboratory, through the U.S. Department of Energy, has utilized several Halden experiments to perform the initial validation of the BISON code based upon their inclusion in international modeling and simulation benchmarks. Recently, the HRP has provided member organizations a complete copy of all available data, reports, and presentations since the HRP began. This report provides an initial exploration of the data available in the database for use in validating the multiscale models under development in the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program for LWR applications. Ranking tables that identify potential validation cases are provided for the high priority models of interest. It was found that some of the recommended high priority experiments correspond to additional rods in existing assemblies already available in the BISON validation suite. It is expected that several of these cases will be incorporated into future NEAMS milestones in the fuels technical area for increased validation of BISON.
11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Graph Mining Meets the Semantic Web
The Resource Description Framework (RDF) and SPARQL Protocol and RDF Query Language (SPARQL) were introduced about a decade ago to enable flexible schema-free data interchange on the Semantic Web. Today, data scientists use the framework as a scalable graph representation for integrating, querying, exploring and analyzing data sets hosted at different sources. With increasing adoption, the need for graph mining capabilities for the Semantic Web has emerged. We address that need through implementation of three popular iterative Graph Mining algorithms (Triangle count, Connected component analysis, and PageRank). We implement these algorithms as SPARQL queries, wrapped within Python scripts. We evaluate the performance of our implementation on 6 real world data sets and show graph mining algorithms (that have a linear-algebra formulation) can indeed be unleashed on data represented as RDF graphs using the SPARQL query interface.
Lee, Sangkeun↗
Adapting Grid Criticality for Data Centers
This presentation explores the evolving definition of “critical load” in the electric grid, emphasizing the growing importance of digital infrastructure—particularly data centers—in grid resilience, restoration, and modernization. As utilities increasingly rely on AI-driven analytics and software-defined control systems, data centers have shifted from passive electricity consumers to essential computational hubs that enable National Critical Functions (NCFs) and support real-time grid operations. The deck examines the scale and impact of digital loads, the need for grid modernization to manage rapid load growth, and the diverse computing paradigms required for AI deployment. It introduces a tiered taxonomy for classifying critical loads, highlights operational dependencies between the grid and digital infrastructure, and discusses policy implications for integrating data centers into emergency planning and restoration protocols. Through case studies and practical frameworks, the presentation provides actionable insights for utilities, regulators, and planners navigating the digital transformation of the power sector.
29 - ENERGY PLANNING, POLICY AND ECONOMY↗
Efficient data acquisition and training of collisional-radiative model artificial neural network surrogates through adaptive parameter space sampling
Abstract Effective plasma transport modeling of magnetically confined fusion devices relies on having an accurate understanding of the ion composition and radiative power losses of the plasma. Generally, these quantities can be obtained from solutions of a collisional-radiative (CR) model at each time step within a plasma transport simulation. However, even compact, approximate CR models can be computationally onerous to evaluate, and in-situ evaluation of these models within a larger plasma transport code can lead to a rigid bottleneck. As a way to bypass this bottleneck, we propose deploying artificial neural network (ANN) surrogates to allow rapid evaluation of the necessary plasma quantities. However, one issue with training an accurate ANN surrogate is the reliance on a sufficiently large and representative training and validation data set, which can be time-consuming to generate. In this work we explore a data-driven active learning and training routine to allow autonomous adaptive sampling of the problem parameter space to ensure a sufficiently large and meaningful set of training data is assembled for the network training. As a result, we can demonstrate approximately order-of-magnitude savings in required training data samples to produce an accurate surrogate.
97 MATHEMATICS AND COMPUTING↗
NuSTAR AND MULTIFREQUENCY STUDY OF THE TWO HIGH-REDSHIFT BLAZARS S5 0836+710 AND PKS 2149–306
Powerful blazars are flat-spectrum radio quasars whose emission is dominated by a Compton component peaking between a few hundred keV and a few hundred MeV. We observed two bright blazars, PKS 2149–306 at redshift z = 2.345 and S5 0836+710 at z = 2.172, in the hard X-ray band with the Nuclear Spectroscopic Telescope Array satellite. Simultaneous soft-X-rays and UV–optical observations were performed with the Swift satellite, while near-infrared (near-IR) data were obtained with the Rapid Eye Mount telescope. To study their variability, we repeated these observations for both sources on a timescale of a few months. While no fast variability was detected during a single observation, both sources were variable in the X-ray band, up to 50%, between the two observations, with larger variability at higher energies. No variability was detected in the optical/NIR band. These data, together with Fermi-Large Area Telescope, Wide-field Infrared Survey Explorer, and other literature data, are then used to study the overall spectral energy distributions (SEDs) of these blazars. Although the jet nonthermal emission dominates the SED, it leaves the UV band unhidden, allowing us to detect the thermal emission of the disk and to estimate the black hole mass. The nonthermal emission is well reproduced by a one-zone leptonic model by the synchrotron, self-Compton, and external Compton processes. Our data are better reproduced if we assume that the location of the dissipation region of the jet, R{sub diss}, is in between the torus and the broad-line region. The observed variability is explained by changing a minimum number of model parameters by a very small amount.
79 ASTRONOMY AND ASTROPHYSICS↗
Exploiting Commonly-Reported Age-Hardening Data and Discovering Systematics Across Metallic Alloys and Alloy Systems to Identify Corrosion’s Most Influential Factors
This work explored ways to data mine legacy literature and predict solid-solid precipitation in support of simpler assessment of corrosion propensity in metallic alloys. Of interest was locating the peak age watershed (maximum hardness or strength), beyond which lies the regime termed “overaging.” A diligent search of literature and reference books, discussions with SMEs, and application of various algorithms showed that none of the premises going in held up to scrutiny.
36 MATERIALS SCIENCE↗
Temporal Cyber Attack Detection.
Rigorous characterization of the performance and generalization ability of cyber defense systems is extremely difficult, making it hard to gauge uncertainty, and thus, confidence. This difficulty largely stems from a lack of labeled attack data that fully explores the potential adversarial space. Currently, performance of cyber defense systems is typically evaluated in a qualitative manner by manually inspecting the results of the system on live data and adjusting as needed. Additionally, machine learning has shown promise in deriving models that automatically learn indicators of compromise that are more robust than analyst-derived detectors. However, to generate these models, most algorithms require large amounts of labeled data (i.e., examples of attacks). Algorithms that do not require annotated data to derive models are similarly at a disadvantage, because labeled data is still necessary when evaluating performance. In this work, we explore the use of temporal generative models to learn cyber attack graph representations and automatically generate data for experimentation and evaluation. Training and evaluating cyber systems and machine learning models requires significant, annotated data, which is typically collected and labeled by hand for one-off experiments. Automatically generating such data helps derive/evaluate detection models and ensures reproducibility of results. Experimentally, we demonstrate the efficacy of generative sequence analysis techniques on learning the structure of attack graphs, based on a realistic example. These derived models can then be used to generate more data. Additionally, we provide a roadmap for future research efforts in this area.
97 MATHEMATICS AND COMPUTING↗
K-Means Cluster Study for Radiofrequency Propagation Characterization
The objective of this study is to design a simple method for mining radio frequency (RF) propagation data. The study explored the characteristics of a large dataset of propagation experiments conducted over the span of years and using several ground stations around the world. Furthermore, this study developed simple predictive models that can be used for link characterization and overall propagation behavior description, without the need for physical measurements on-site. It is understood that such statistical learning has several drawbacks in terms of accuracy and precision. K-means clustering was used to characterize the data set in a way never explored before in an attempt to create useful tools that reduce cost, time and risk. K-means clustering was used to characterize the data set. Cosine distance was used as a method to determine the optimal number for clustering each feature. Dependence and independence analysis was performed to explore intra and inter-sensitivity between the presented features, with respect to each other and time. Several predicative models were generated and evaluated with respect to a test set to assess a measure of prediction accuracy and precision. A simple method for data analysis was developed and tested as the basis for further studies and future refinement to produce optimal performing models.
Cognitive↗
Removal of Congo Red from Aqueous Solution by Hydroxyapatite Nanoparticles Loaded on Zein as an Efficient and Green Adsorbent: Response Surface Methodology and Artificial Neural Network-Genetic Algorithm
This study is based on the application of hydroxyapatite nanoparticles loaded on Zein (Zein/nHAp) as an efficient adsorbent for the removal of Congo red from aqueous solutions. The properties of the adsorbent were characterized using various techniques including FT-IR, XRD, FE-SEM, and BET. The influence of five parameters such as pH, temperature, contact time, initial dye concentration, and adsorbent dosage on the removal percentage of the dye was investigated. The optimum conditions of 5.83, 34.32 °C, 5.20 min, 392.10 ppm, and 0.007 g were achieved for pH, temperature, contact time, initial dye concentration, and adsorbent dosage, respectively. The maximum removal percentage of 99.48% was obtained under the optimum condition that exhibited high adsorption potential of the used adsorbent. Central composite design (CCD) under response surface methodology and artificial neural network-genetic algorithm (ANN-GA) were utilized for optimization of parameters. Comparison of the results of the two models in terms of coefficient of determination (R{sup 2}) and mean absolute percentage error confirmed the prediction potential of CCD and ANN-GA. Higher ability and accuracy of ANN-GA in prediction was found based on given results. The experimental equilibrium data were studied by Langmuir, Freundlich, Temkin and Dubinin-Radushkevic isotherm models and explored that the data well presented by Langmuir model with maximum adsorption capacity of 416.66 mg/g. The adsorption kinetic was well-fitted by the pseudo-second-order model. The thermodynamics of the adsorption displayed spontaneous and endothermic nature of the process. Regeneration investigation showed that Zein/nHAp can impressively be reused, indicating that the adsorbent was a promising one for the removal of Congo red from aqueous solution. Graphical Abstract: .
77 NANOSCIENCE AND NANOTECHNOLOGY↗
Clustering and statistical analyses of air-conditioning intensity and use patterns in residential buildings
Energy conservation in residential buildings has gained increased attention due to its large portion of global energy use and potential of energy savings. Occupant behavior has been recognized as a key factor influencing the energy use and load diversity in buildings, therefore more realistic and accurate air-conditioning (AC) operating schedules are imperative for load estimation in equipment design and operation optimization. With the development of sensor technology, it became easier to access an increasing amount of heating/cooling data from thermal energy metering systems in residential buildings, which provides another possible way to understand building energy usage and occupant behaviors. However, except for cooling energy consumption benchmarking, there currently lacks effective and easy approaches to analyze AC usage and provide actionable insights for occupants. To fill this gap, this study proposes clustering analysis to identify AC use patterns of residential buildings, and develops new key performance indicators (KPIs) and data analytics to explore the AC operation characteristics using the long-term metered cooling energy use data, which is of great importance for inhabitants to understand their thermal energy use and save energy cost through adjustment of their AC use behavior. We demonstrate the proposed approaches in a residential district comprising 300 apartments, located in Zhengzhou, China. Main outcomes include: Representative AC use patterns are developed for three room types of residential buildings in the cold climate zone of China, which can be used as more realistic AC schedules to improve accuracy of energy simulation; Distributions of KPIs on household cooling energy usage are established, which can be used for household AC use intensity benchmarking and performance diagnoses.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
MINE: a new way to design genetics experiments for discovery
Abstract The Maximally Informative Next Experiment or MINE is a new experimental design approach for experiments, such as those in omics, in which the number of effects or parameters p greatly exceeds the number of samples n (p > n). Classical experimental design presumes n > p for inference about parameters and its application to p > n can lead to over-fitting. To overcome p > n, MINE is an ensemble method, which makes predictions about future experiments from an existing ensemble of models consistent with available data in order to select the most informative next experiment. Its advantages are in exploration of the data for new relationships with n < p and being able to integrate smaller and more tractable experiments to replace adaptively one large classic experiment as discoveries are made. Thus, using MINE is model-guided and adaptive over time in a large omics study. Here, MINE is illustrated in two distinct multiyear experiments, one involving genetic networks in Neurospora crassa and a second one involving a genome-wide association study in Sorghum bicolor as a comparison to classic experimental design in an agricultural setting.
Biochemistry & Molecular Biology↗