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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Uncertainty in Experimental Data Analysis [Slides]

The talk was presented virtually to the Institute of Fundamental Technological Research, Polish Academy of Sciences in Warsaw, Poland, December 21, 2020. Astronomical observations, unusual medical cases, physics experiments too costly to repeat, natural events like earthquakes, hurricanes – all of them are impossible to repeat yet produce important scientific information not achievable in other way. This data should not be treated as qualitative, anecdotal evidence only. It should be analyzed in a mathematically rigorous way to produce quantitative experimental data. Analysis method for one-of-a-kind event data differs from analysis of a repeated experiment data. For a repeated experiments the experimental error includes a range of true values generated by repetitions of the experiment, and measurement uncertainty caused by detectors. They are independent. Repetitions of any experiment, as similar as achievable, always have built-in differences resulting in a range of the true values rather than in a single true experimental value. Measurement uncertainty depends on the measurement system only. Modern digital measurements have very small uncertainty, frequently smaller than the range of true experimental values resulting from built-in differences in the experiment repetitions. When data from one-of-a-kind experiment are analyzed, only the measurement uncertainty can be reported.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Renewable Energy Data, Analysis, and Decisions: A Guide for Practitioners

High-quality renewable energy resource data and other geographic information system (GIS) data are essential for the transition to a clean energy economy that prioritizes local resources, improves resiliency, creates jobs, and promotes energy independence. These data are crucial for making informed decisions—ranging from policy and investment decisions to reliable power sector planning. Decisions that are data-driven reflect appropriate ambition, maximize cost-effectiveness, and enable successful implementation of renewable energy investments. This guide is intended to support policymakers and planners, as well as technical experts, consultants, and academics in incorporating improved data and analysis into renewable energy decision-making.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2016

The implementation and data analysis of an interferometer for intense short pulse laser experiments

We present an interferometry setup and the detailed fringe analysis method for intense short pulse (SP) laser experiments. The interferometry scheme was refined through multiple campaigns to investigate the effects of pre-plasmas on energetic electrons at the Jupiter Laser Facility at Lawrence Livermore National Laboratory. The interferometer used a frequency doubled ( ${\it\lambda}=0.527~{\rm\mu}\text{m}$ ) 0.5 ps long optical probe beam to measure the pre-plasma density, an invaluable parameter to better understand how varying pre-plasma conditions affect the characteristics of the energetic electrons. The hardware of the diagnostic, data analysis and example data are presented. Here, the diagnostic setup and the analysis procedure can be employed for any other SP laser experiments and interferograms, respectively.

42 ENGINEERING↗
DOE OSTI2017

Geographic Information Systems as an Opportunity for Public Engagement in Environmental Data Analysis for Nuclear Waste Management - 17505

For half a century, scientists across government agencies, industry, academia and other research organizations have been using geographic information system (GIS) tools to display data collected for a wide variety of programs. Recent technology advances have made it possible for the public to also use GIS tools to display data. Meanwhile, around the world, volunteers are increasingly participating in citizen science activities that involve collecting environmental observations and measurements. Considering themes of broad interest to which citizen science and GIS tools could be applied, integrated nuclear waste management represents a potential opportunity for public engagement in environmental data analysis. In the United States, the Department of Energy's (DOE's) goal is to develop solutions for the long-term, sustainable management of the nation's spent nuclear fuel (SNF) and high-level radioactive waste (HLW) from commercial electricity generation, as well as national defense activities. The DOE has been exploring options for an integrated waste management system to transport, store, and dispose of SNF and HLW. To support its efforts, DOE has been exploring ways to seek broad input from the public. Literature was reviewed to assess recent advances in GIS technology and citizen science that could contribute to public engagement in waste management planning. Results identified a variety of promising tools and applications that are readily accessible online, including QGIS and the U.S. Environmental Protection Agency's (EPA's) Real Time Geo-spatial Data Viewer (RETIGO). For example, interested members of the public could illustrate air quality, water quality, and other environmental data in GIS plots to visually represent local conditions over time. This paper highlights potential opportunities for those interested in citizen science and GIS tools as part of exploring options for public engagement in planning for an integrated waste management system. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Final Report on DOE Grant for Large Experiment Data Analysis Collaboration

This is the final report for DoE grant DE-FG02-92ER54139 entitled "Large Experiment Data Analysis Collaboration." This research project began with collaborations on analyzing TFTR experimental data from 1992 through 1998. After TFTR was shut down, the research migrated to collaborations on analyzing DIII-D experimental data. This research project's objectives, strategy and most important results obtained through these collaborative studies over the past quarter century are summarized here.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2018

Real-time data analysis for medical diagnosis using FPGA-accelerated neural networks

Real-time analysis of patient data during medical procedures can provide vital diagnostic feedback that significantly improves chances of success. With sensors becoming increasingly fast, frameworks such as Deep Neural Networks are required to perform calculations within the strict timing constraints for real-time operation. However, traditional computing platforms responsible for running these algorithms incur a large overhead due to communication protocols, memory accesses, and static (often generic) architectures. In this work, we implement a low-latency Multi-Layer Perceptron (MLP) processor using Field Programmable Gate Arrays (FPGAs). Unlike CPUs and Graphics Processing Units (GPUs), our FPGA-based design can directly interface sensors, storage devices, display devices and even actuators, thus reducing the delays of data movement between ports and compute pipelines. Moreover, the compute pipelines themselves are tailored specifically to the application, improving resource utilization and reducing idle cycles. We demonstrate the effectiveness of our approach using mass-spectrometry data sets for real-time cancer detection. We demonstrate that correct parameter sizing, based on the application, can reduce latency by 20% on average. Furthermore, we show that in an application with tightly coupled data-path and latency constraints, having a large amount of computing resources can actually reduce performance. Using mass-spectrometry benchmarks, we show that our proposed FPGA design outperforms both CPU and GPU implementations, with an average speedup of 144x and 21x, respectively. In our work, we demonstrate the importance of application-specific optimizations in order to minimize latency and maximize resource utilization for MLP inference. By directly interfacing and processing sensor data with ultra-low latency, FPGAs can perform real-time analysis during procedures and provide diagnostic feedback that can be critical to achieving higher percentages of successful patient outcomes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Final Report: Sublinear Algorithms for In-situ and In-transit Data Analysis at Exascale.

Post-Moore's law scaling is creating a disruptive shift in simulation workflows, as saving the entirety of raw data to persistent storage becomes expensive. We are moving away from a post-process centric data analysis paradigm towards a concurrent analysis framework, in which raw simulation data is processed as it is computed. Algorithms must adapt to machines with extreme concurrency, low communication bandwidth, and high memory latency, while operating within the time constraints prescribed by the simulation. Furthermore, in- put parameters are often data dependent and cannot always be prescribed. The study of sublinear algorithms is a recent development in theoretical computer science and discrete mathematics that has significant potential to provide solutions for these challenges. The approaches of sublinear algorithms address the fundamental mathematical problem of understanding global features of a data set using limited resources. These theoretical ideas align with practical challenges of in-situ and in-transit computation where vast amounts of data must be processed under severe communication and memory constraints. This report details key advancements made in applying sublinear algorithms in-situ to identify features of interest and to enable adaptive workflows over the course of a three year LDRD. Prior to this LDRD, there was no precedent in applying sublinear techniques to large-scale, physics based simulations. This project has definitively demonstrated their efficacy at mitigating high performance computing challenges and highlighted the rich potential for follow-on re- search opportunities in this space.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

GMT: A deep learning approach to generalized multivariate translation for scientific data analysis and visualization

In scientific visualization, despite the significant advances of deep learning for data generation, researchers have not thoroughly investigated the issue of data translation. We present a new deep learning approach called generalized multivariate translation (GMT) for multivariate time-varying data analysis and visualization. Like V2V, GMT assumes a preprocessing step that selects suitable variables for translation. However, unlike V2V, which only handles one-to-one variable translation during training and inference, GMT enables one-to-many and many-to-many variable translation in the same framework. We leverage the recent StarGAN design from multi-domain image-to-image translation to achieve this generalization capability. We experiment with different loss functions and injection strategies to explore the best choices and leverage pre-training for performance improvement. We compare GMT with other state-of-the-art methods (i.e., Pix2Pix, V2V, StarGAN). Furthermore, the results demonstrate the overall advantage of GMT in translation quality and generalization ability.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Local coexistence of VO 2 phases revealed by deep data analysis

We report a synergistic approach of micro-Raman spectroscopic mapping and deep data analysis to study the distribution of crystallographic phases and ferroelastic domains in a defected Al-doped VO 2 microcrystal. Bayesian linear unmixing revealed an uneven distribution of the T phase, which is stabilized by the surface defects and uneven local doping that went undetectable by other classical analysis techniques such as PCA and SIMPLISMA. This work demonstrates the impact of information recovery via statistical analysis and full mapping in spectroscopic studies of vanadium dioxide systems, which is commonly substituted by averaging or single point-probing approaches, both of which suffer from information misinterpretation due to low resolving power.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Bloodhound 0.8: A Python package for infrasound data analysis

This report provides details of the algorithms in the Bloodhound package for infrasound data analysis. The report provides a detailed description of the algorithms, general instructions on tuning Bloodhound for different signal types, and a complete listing of all input parameters and the complete output schema. Several Jupyter notebooks are provided with the distribution for illustrating how to use Bloodhound for different workflows.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

NREL Solar Data, Analysis, & Tools: FY19 Research Dissemination Statistics

This report summarizes the key Fiscal Year 2019 (FY19) outreach metrics for the NREL Solar Data, Analysis, and Tools (DAT) portfolio. All metrics herein reflect the reporting period of Oct. 1, 2018, through Sept. 30, 2019. These projects are all supported by the U.S. Department of Energy Solar Energy Technologies Office Soft Costs program.

14 SOLAR ENERGY↗
DOE OSTI2024

Topological Data Analysis for Particulate Gels

Soft gels, formed via the self-assembly of particulate materials, exhibit intricate multiscale structures that provide them with flexibility and resilience when subjected to external stresses. Here, this work combines particle simulations and topological data analysis (TDA) to characterize the complex multiscale structure of soft gels. Our TDA analysis focuses on the use of the Euler characteristic, which is an interpretable and computationally scalable topological descriptor that is combined with filtration operations to obtain information on the geometric (local) and topological (global) structure of soft gels. We reduce the topological information obtained with TDA using principal component analysis (PCA) and show that this provides an informative low-dimensional representation of the gel structure. We use the proposed computational framework to investigate the influence of gel preparation (e.g., quench rate, volume fraction) on soft gel structure and to explore dynamic deformations that emerge under oscillatory shear in various response regimes (linear, nonlinear, and flow). Our analysis provides evidence of the existence of hierarchical structures in soft gels, which are not easily identifiable otherwise. Moreover, our analysis reveals direct correlations between topological changes of the gel structure under deformation and mechanical phenomena distinctive of gel materials, such as stiffening and yielding. In summary, we show that TDA facilitates the mathematical representation, quantification, and analysis of soft gel structures, extending traditional network analysis methods to capture both local and global organization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Functional Data Analysis Applied to Modeling of Severe Acute Mucositis and Dysphagia Resulting From Head and Neck Radiation Therapy

Purpose: Current normal tissue complication probability modeling using logistic regression suffers from bias and high uncertainty in the presence of highly correlated radiation therapy (RT) dose data. This hinders robust estimates of dose-response associations and, hence, optimal normal tissue–sparing strategies from being elucidated. Using functional data analysis (FDA) to reduce the dimensionality of the dose data could overcome this limitation. Methods and Materials: FDA was applied to modeling of severe acute mucositis and dysphagia resulting from head and neck RT. Functional partial least squares regression (FPLS) and functional principal component analysis were used for dimensionality reduction of the dose-volume histogram data. The reduced dose data were input into functional logistic regression models (functional partial least squares–logistic regression [FPLS-LR] and functional principal component–logistic regression [FPC-LR]) along with clinical data. This approach was compared with penalized logistic regression (PLR) in terms of predictive performance and the significance of treatment covariate–response associations, assessed using bootstrapping. Results: The area under the receiver operating characteristic curve for the PLR, FPC-LR, and FPLS-LR models was 0.65, 0.69, and 0.67, respectively, for mucositis (internal validation) and 0.81, 0.83, and 0.83, respectively, for dysphagia (external validation). The calibration slopes/intercepts for the PLR, FPC-LR, and FPLS-LR models were 1.6/−0.67, 0.45/0.47, and 0.40/0.49, respectively, for mucositis (internal validation) and 2.5/−0.96, 0.79/−0.04, and 0.79/0.00, respectively, for dysphagia (external validation). The bootstrapped odds ratios indicated significant associations between RT dose and severe toxicity in the mucositis and dysphagia FDA models. Cisplatin was significantly associated with severe dysphagia in the FDA models. None of the covariates was significantly associated with severe toxicity in the PLR models. Dose levels greater than approximately 1.0 Gy/fraction were most strongly associated with severe acute mucositis and dysphagia in the FDA models. Conclusions: FPLS and functional principal component analysis marginally improved predictive performance compared with PLR and provided robust dose-response associations. FDA is recommended for use in normal tissue complication probability modeling.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2017

Using Machine Learning and Data Analysis to Improve Customer Acquisition and Marketing in Residential Solar

High customer acquisition costs remain a persistent challenge in the U.S. residential solar industry. Effective customer acquisition in the residential solar market is increasingly achieved with the help of data analysis and machine learning, whether that means more targeted advertising, understanding customer motivations, or responding to competitors. New research by the National Renewable Energy Laboratory, Sandia National Laboratories, Vanderbilt University, University of Pennsylvania, and the California Center for Sustainable Energy and funded through the U.S. Department of Energy's Solar Energy Evolution and Diffusion (SEEDS) program demonstrates novel computational methods that can help drive down costs in the residential solar industry.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

A spark‐based big data analysis framework for real‐time sentiment prediction on streaming data

Summary There are many data sources that produce large volumes of data. The Big Data nature requires new distributed processing approaches to extract the valuable information. Real‐time sentiment analysis is one of the most demanding research areas that requires powerful Big Data analytics tools such as Spark. Prior literature survey work has shown that, though there are many conventional sentiment analysis researches, there are only few works realizing sentiment analysis in real time. One major point that affects the quality of real‐time sentiment analysis is the confidence of the generated data. In more clear terms, it is a valuable research question to determine whether the owner that generates sentiment is genuine or not. Since data generated by fake personalities may decrease accuracy of the outcome, a smart/intelligent service that can identify the source of data is one of the key points in the analysis. In this context, we include a fake account detection service to the proposed framework. Both sentiment analysis and fake account detection systems are trained and tested using Naïve Bayes model from Apache Spark's machine learning library. The developed system consists of four integrated software components, ie, (i) machine learning and streaming service for sentiment prediction, (ii) a Twitter streaming service to retrieve tweets, (iii) a Twitter fake account detection service to assess the owner of the retrieved tweet, and (iv) a real‐time reporting and dashboard component to visualize the results of sentiment analysis. The sentiment classification performances of the system for offline and real‐time modes are 86.77% and 80.93%, respectively.

Kılınç, Deniz↗
DOE OSTI2024

ATLAS Data Analysis using a Parallel Workflow on Distributed Cloud-based Services with GPUs

A new type of parallel workflow is developed for the ATLAS experiment at the Large Hadron Collider, that makes use of distributed computing combined with a cloud-based infrastructure. This has been developed for a specific type of analysis using ATLAS data, one popularly referred to as Simulation-Based Inference (SBI). The JAX library is used for the parts of the workflow to compute gradients as well as accelerate program execution using just-in-time compilation, which becomes essential in a full SBI analysis and can also offer significant speed-ups in more traditional types of analysis.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Incorporating Beam Attenuation Calculations into an Integrated Data Analysis Model for Ion Effective Charge

Here, we have created a forward model for charge-exchange impurity density measurements that incorporates neutral beam attenuation measurements self-consistently for the purpose of determining the ion-effective charge Z eff . The model is constructed within an integrated data analysis framework to include a self-consistent calculation of neutral beam attenuation due to multiple impurity species into the measurement of a single impurity density. The model includes measurements of the beam Doppler-shift spectrum and shine-through particle flux to determine the neutral beam particle density which is attenuated by ion collisions. Synthetic data are generated from the diagnostic forward model using statistical and calibration uncertainties. These “noisy” data are used in the analysis to evaluate how accurately Z eff is determined. Methods of experimental design are employed to calculate the information gained from different diagnostic combinations. The analysis shows that while attenuation measurements alone do not provide a unique impurity density measurement in the case of a multispecies inhomogeneous plasma, they do provide an effective measurement of the Z eff profile and place constraints on the impurity density profiles.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗