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

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Statistical Techniques For Real-time Anomaly Detection Using Spark Over Multi-source VMware Performance Data

Anomaly detection refers to the identi cation of an irregular or unusual pat- tern which deviates from what is standard, normal, or expected. Such deviated patterns typically correspond to samples of interest and are assigned different labels in different domains, such as outliers, anomalies, exceptions, or malware. Detecting anomalies in fast, voluminous streams of data is a formidable chal- lenge. This paper presents a novel, generic, real-time distributed anomaly detection framework for heterogeneous streaming data where anomalies appear as a group. We have developed a distributed statistical approach to build a model and later use it to detect anomaly. As a case study, we investigate group anomaly de- tection for a VMware-based cloud data center, which maintains a large number of virtual machines (VMs). We have built our framework using Apache Spark to get higher throughput and lower data processing time on streaming data. We have developed a window-based statistical anomaly detection technique to detect anomalies that appear sporadically. We then relaxed this constraint with higher accuracy by implementing a cluster-based technique to detect sporadic and continuous anomalies. We conclude that our cluster-based technique out- performs other statistical techniques with higher accuracy and lower processing time.

Apache Spark↗
DOE OSTI2024

Characterizing the Uncertainty of Measurement of Traceable Isotope Ratios with Bayesian Statistical Techniques

Analytical techniques such as multicollector—inductively coupled plasma—mass spectrometry (MC-ICP-MS) are routinely employed at SRNL, other National Laboratories, and in academia to determine the precise isotopic composition of diverse natural and anthropogenic samples (e.g., rocks and nuclear materials). Quantifying and reporting uncertainty in such analyses, while regularly performed, have a rigorous statistical foundation. The Guide to the Expression of Uncertainty in Measurement 4 (GUM) outlines conventional techniques used to assess such uncertainty. As the accessibility and speed of statistical computing increase, there is a need to modernize conventional techniques. For example, Supplement 1 to the 3rd to the GUM suggests the use of approximation methods as an updated approach to the GUM.

McLarty, Ellis C.↗
DOE OSTI2023

Understanding capacity fade in organic redox-flow batteries by combining spectroscopy with statistical inference techniques

Organic redox-active molecules are attractive as redox-flow battery (RFB) reactants because of their low anticipated costs and widely tunable properties. Unfortunately, many lab-scale flow cells experience rapid material degradation (from chemical and electrochemical decay mechanisms) and capacity fade during cycling (>0.1%/day) hindering their commercial deployment. In this work, we combine ultraviolet-visible spectrophotometry and statistical inference techniques to elucidate the Michael attack decay mechanism for 4,5-dihydroxy-1,3-benzenedisulfonic acid (BQDS), a once-promising positive electrolyte reactant for aqueous organic redox-flow batteries. We use Bayesian inference and multivariate curve resolution on the spectroscopic data to derive uncertainty-quantified reaction orders and rates for Michael attack, estimate the spectra of intermediate species and establish a quantitative connection between molecular decay and capacity fade. Our work illustrates the promise of using statistical inference to elucidate chemical and electrochemical mechanisms of capacity fade in organic redox-flow battery together with uncertainty quantification, in flow cell-based electrochemical systems.

25 ENERGY STORAGE↗
DOE OSTI2017

Confidence Intervals from Realizations of Simulated Nuclear Data

Various statistical techniques are discussed that can be used to assign a level of confidence in the prediction of models that depend on input data with known uncertainties and correlations. The particular techniques reviewed in this paper are: 1) random realizations of the input data using Monte-Carlo methods, 2) the construction of confidence intervals to assess the reliability of model predictions, and 3) resampling techniques to impose statistical constraints on the input data based on additional information. These techniques are illustrated with a calculation of the keff value, based on the 235 U(n, f) and 239 Pu (n, f) cross sections.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

The hybrid mesons quest: the MesonEx experiment at Jefferson Laboratory

The meson spectroscopy plays nowadays a central role in the investigation of hadron structure thanks to the possible existence of exotic hybrid mesons, quark-antiquark-gluon bound states. Their explicit gluonic degrees of freedom which should clearly emerge from a Partial Wave Analysis (PWA) of the corresponding Dalitz plot of the exotic particle decay, may result in final JPC configurations not allowed in the constituent quark model. Besides this clear signature, hybrid mesons are also expected to have a large particle multiplicity decays, requiring for their search an experimental apparatus with high performances in terms of rate capability, resolution and almost a full acceptance to apply PWA methods. New-generation experiments are planned at Thomas Jefferson National Laboratory (VA, USA) for which an unprecedented statistics of large multiplicity decay events with fully reconstructed kinematics will be available. In particular for the MesonEx (CLAS12) experiment in Hall B, a wide scientific program that will start in 2016 has been deployed to study the meson spectrum at energies up to 11 GeV. A key role in such program is played by the Forward Tagger apparatus of the experiment, which will allow to extend the study of meson electro-production to very low Q2 values, in a quasi-real photo production kinematical region, where the production of hybrid mesons is expected to be favorite. Currently a new analysis framework for the search of the hybrid mesons is being set up by the HASPECT network, an international structure which gather people involved into theoretical and experimental hadronic physics all over the world. The goals of the network is to develop new analysis models and statistical techniques to unfold the signal and background distributions in high-statistics datasets. In this work are briefly presented the first preliminary results from the application of a statistical technique, namely the sPlot, to the data already acquired by the CLAS experiment for the decay f 1 (1285) --> (eta)pi+pi-.

Rizzo, Alessandro↗
DOE OSTI2015

Statistical optimisation techniques in fatigue signal editing problem

Success in fatigue signal editing is determined by the level of length reduction without compromising statistical constraints. A great reduction rate can be achieved by removing small amplitude cycles from the recorded signal. The long recorded signal sometimes renders the cycle-to-cycle editing process daunting. This has encouraged researchers to focus on the segment-based approach. This paper discusses joint application of the Running Damage Extraction (RDE) technique and single constrained Genetic Algorithm (GA) in fatigue signal editing optimisation.. In the first section, the RDE technique is used to restructure and summarise the fatigue strain. This technique combines the overlapping window and fatigue strain-life models. It is designed to identify and isolate the fatigue events that exist in the variable amplitude strain data into different segments whereby the retention of statistical parameters and the vibration energy are considered. In the second section, the fatigue data editing problem is formulated as a constrained single optimisation problem that can be solved using GA method. The GA produces the shortest edited fatigue signal by selecting appropriate segments from a pool of labelling segments. Challenges arise due to constraints on the segment selection by deviation level over three signal properties, namely cumulative fatigue damage, root mean square and kurtosis values. Experimental results over several case studies show that the idea of solving fatigue signal editing within a framework of optimisation is effective and automatic, and that the GA is robust for constrained segment selection.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Scalability Testing Approach for Internet of Things for Manufacturing SQL and NoSQL Database Latency and Throughput

The proliferation of low-cost sensors and industrial data solutions has continued to push the frontier of manufacturing technology. Machine learning and other advanced statistical techniques stand to provide tremendous advantages in production capabilities, optimization, monitoring, and efficiency. The tremendous volume of data gathered continues to grow, and the methods for storing the data are critical underpinnings for advancing manufacturing technology. This work aims to investigate the ramifications and design tradeoffs within a decoupled architecture of two prominent database management systems (DBMS): sql and NoSQL. A representative comparison is carried out with Amazon Web Services (AWS) DynamoDB and AWS Aurora MySQL. The technologies and accompanying design constraints are investigated, and a side-by-side comparison is carried out through high-fidelity industrial data simulated load tests using metrics from a major US manufacturer. The results support the use of simulated client load testing for comparing the latency of database management systems as a system scales up from the prototype stage into production. As a result of complex query support, MySQL is favored for higher-order insights, while NoSQL can reduce system latency for known access patterns at the expense of integrated query flexibility. Here, by reviewing this work, a manufacturer can observe that the use of high-fidelity load testing can reveal tradeoffs in IoTfM write/ingestion performance in terms of latency that are not observable through prototype-scale testing of commercially available cloud DB solutions.

AWS↗
DOE OSTI2016

Robust verification analysis

We introduce a new methodology for inferring the accuracy of computational simulations through the practice of solution verification. We demonstrate this methodology on examples from computational heat transfer, fluid dynamics and radiation transport. Our methodology is suited to both well- and ill-behaved sequences of simulations. Our approach to the analysis of these sequences of simulations incorporates expert judgment into the process directly via a flexible optimization framework, and the application of robust statistics. The expert judgment is systematically applied as constraints to the analysis, and together with the robust statistics guards against over-emphasis on anomalous analysis results. We have named our methodology Robust Verification. Our methodology is based on utilizing multiple constrained optimization problems to solve the verification model in a manner that varies the analysis' underlying assumptions. Constraints applied in the analysis can include expert judgment regarding convergence rates (bounds and expectations) as well as bounding values for physical quantities (e.g., positivity of energy or density). This approach then produces a number of error models, which are then analyzed through robust statistical techniques (median instead of mean statistics). This provides self-contained, data and expert informed error estimation including uncertainties for both the solution itself and order of convergence. Our method produces high quality results for the well-behaved cases relatively consistent with existing practice. The methodology can also produce reliable results for ill-behaved circumstances predicated on appropriate expert judgment. We demonstrate the method and compare the results with standard approaches used for both code and solution verification on well-behaved and ill-behaved simulations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Muon Antineutrino Disappearance in the MINOS Experiment

Antineutrinos from the set of 7.09$\times 10^{20}$ Protons-on-target (POT) of $\nu_\mu\textrm{-dominant}$ and 3.40$\times 10^{20}$ POT of $\bar\nu_\mu\textrm{-enhanced}$ MINOS beam data is analysed to extract the antineutrino atmospheric-scale oscillation parameters $\left|\Delta\bar m^2\right|$ and $\sin^2\left(2\bar\theta\right)$. Using the Feldman-Cousins statistical technique to account for systematic and statistical errors, $\left|\Delta\bar m^2\right|=2.58^{+0.26}_{-0.17} \times 10^{-3}~\textrm{eV}^2$ and $\sin^2\left(2\bar\theta\right)=0.96^{+0.04}_{-0.10}$ were measured. This measurement of $\left|\Delta\bar m^2\right|$ improves on the previous world limits from Super-K by a factor of five. Any observed difference between the neutrino and antineutrino oscillation parameters could be an indication of CPT violation, or otherwise physics beyond the standard model. This measurement is consistent with the global limits on $\left|\Delta m^2\right|$ and $\sin^2\left(2\theta\right)$ for neutrinos.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

Percutaneous Renal Tumor Ablation: Radiation Exposure During Cryoablation and Radiofrequency Ablation

IntroductionOnce reserved solely for non-surgical cases, percutaneous ablation is becoming an increasingly popular treatment option for a wider array of patients with small renal masses and the radiation risk needs to be better defined as this transition continues.Materials and MethodsRetrospective review of our renal tumor ablation database revealed 425 patients who underwent percutaneous ablation for treatment of 455 renal tumors over a 5-year time period. Imparted radiation dose information was reviewed for each procedure and converted to effective patient dose and skin dose using established techniques. Statistical analysis was performed with each ablative technique.ResultsFor the 331 cryoablation procedures, the mean DLP was 6987 mGycm (SD = 2861) resulting in a mean effective dose of 104.7 mSv (SD = 43.5) and the mean CTDI{sub vol} was 558 mGy (SD = 439) resulting in a mean skin dose of 563.2 mGy (SD = 344.1). For the 124 RFA procedures, the mean DLP was 3485 mGycm (SD = 1630) resulting in a mean effective dose of 50.3 mSv (SD = 24.0) and the mean CTDI{sub vol} was 232 mGy (SD = 149) resulting in a mean skin dose of 233.2 mGy (SD = 117.4). The difference in patient radiation exposure between the two renal ablation techniques was statistically significant (p < 0.001).ConclusionBoth cryoablation and RFA imparted an average skin dose that was well below the 2 Gy deterministic threshold for appreciable sequela. Renal tumor cryoablation resulted in a mean skin and effective radiation dose more than twice that for RFA. The radiation exposure for both renal tumor ablation techniques was at the high end of the medical imaging radiation dose spectrum.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Design and implementation of the ABRACADABRA-10 cm axion dark matter search

In the past, we have seen a renewed interest in the search for light particle dark matter. ABRACADABRA is a new experimental program to search for axion dark matter over a broad range of masses, 10 - 12 ≲ m a ≲ 10 - 6 eV . ABRACADABRA-10 cm is a small-scale prototype for a future detector that could be sensitive to QCD axion couplings. In this paper, we present the details of the design, construction, and data analysis for the first axion dark matter search with the ABRACADABRA-10 cm detector. Furthermore, we include a detailed discussion of the statistical techniques used to extract the limit from the first result with an emphasis on creating a robust statistical footing for interpreting those limits.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗