Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Statistical modeling”

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 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Theoretical study of various nonlinear phenomena in plasma systems and scaling of magneto-inertial-fusion targets

Plasma physics is an exciting field of study with a wide variety of nonlinear processes that come into play. Examples of such processes include the interaction of small-scale turbulence with large-scale plasma structures and the nonlinear saturation of plasma instabilities, for example those of magneto-hydrodynamical nature. During this Truman LDRD project, I studied a collection of nonlinear problems that are of interest to the field of plasma physics. This LDRD report summarizes four main research accomplishments. First, a new statistical model for describing inhomogeneous drift-wave turbulence inter- acting with zonal flows was developed. This new model includes the effects of nonlinear wave-wave collisions, which are expected to change the spectrum of the underlying DW turbulence and therefore the generation of zonal flows. Second, a new mathematical formalism was proposed to systematically apply the non- linear WKB approximation to general field theories, including those often used in fluid dynamics. This formalism represents an interesting tool for studying physical systems that show an explicit scale separation. Third, a weakly nonlinear model was developed to describe the magneto-Rayleigh-Taylor instability. This instability is of paramount importance to understand as it can reduce the performance of magnetic-inertial-fusion (MIF) platforms. The developed models captures the effects of harmonic generation and saturation of the linear growth of the instability. Finally, a framework was proposed for scaling magneto-inertial fusion (MIF) targets to larger pulsed-power drivers. From this framework, a set of scaling rules were derived that conserve the physical regimes of MIF systems when scaling up in peak current. By doing so, deleterious nonlinear processes that affect MIF performance may be kept at bay.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Feature Detection

Focal Area(s): This proposal aims to develop and evaluate statistical models and machine learning algorithms for detecting and tracking features in spatiotemporal remotely sensed data with uncertainty quantification. We focus a particular application on the detection of sea ice leads and ridges in the Arctic and use these key sea ice features for model calibration and to gain insight into the physics of sea ice thermodynamics and deformation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Evaluating Offshore Infrastructure Integrity

Drilling in the offshore environment involves a complex network of infrastructure including pipelines, platforms, rigs, subsea installations, ports, and terminals. Government and industry partners have developed this network over many decades and it remains a critical part of the United States (U.S.) energy portfolio. Many of the major components of this system have been designed with a 20- to 30-year lifespan, yet consistent and growing energy demands support the need to extend the design life of existing infrastructure or repurpose it for secondary needs (i.e. enhanced oil recovery, carbon storage, and new wells). As a result, a growing portion of the offshore infrastructure in the U.S. is approaching or has exceeded its original design life. A critical step in ensuring the continued safe and effective operation of offshore infrastructure is developing a comprehensive understanding of the state of offshore infrastructure and the factors that effect it. The purpose of this project is to assess the current state of existing infrastructure and identify the factors involved in infrastructure degradation through the development and application of big data analytics, machine learning, and advanced spatio-temporal analysis. The project leverages existing data at NETL and combines it with new information on offshore oil and gas structures and the ambient offshore environment in an effort to identify patterns associated with infrastructure integrity. Building on the identified trends and patterns, this project incorporates exploratory analytics and spatial analysis tools in conjunction with machine learning and statistical models to characterize the condition of existing platforms in the offshore environment and predict their risk of failure.

02 PETROLEUM↗
DOE OSTI2021

Statistical Distributions for Mesh Independent Solutions in ALEGRA

The representation of material heterogeneity (also referred to as "spatial variation") plays a key role in the material failure simulation method used in ALEGRA. ALEGRA is an arbitrary Lagrangian-Eulerian shock and multiphysics code developed at Sandia National Laboratories and contains several methods for incorporating spatial variation into simulations. A desirable property of a spatial variation method is that it should produce consistent stochastic behavior regardless of the mesh used (a property referred to as "mesh independence"). However, mesh dependence has been reported using the Weibull distribution with ALEGRA's spatial variation method. This report describes efforts towards providing additional insight into both the theory and numerical experiments investigating such mesh dependence. In particular, we have implemented a discrete minimum order statistic model with properties that are theoretically mesh independent.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Stochastic Modeling in a Multimaterial Continuum Mixture Shock Physics Code

Stochastic modelling approaches are presented to capture random effects at multiple time and length scales. Random processes that occur at the microscale produce nondeterministic effects at the macroscale. Here we present three stochastic modeling approaches that describe random processes at microscopic length scales and map these processes to the macroscopic length scale. The first stochastic modeling approach is based upon a particle based numerical technique to solve a Stochastic Differential Equation (SDE) using an arbitrary diffusion process to capture random processes at the microstructural level. The second approach prescribes a Probability Density Function (PDF) for the drift and diffusion of the random variable derived using the forward and backward Kolmogorov equations. This method requires mean and drift evolution PDF transport equations. The third approach is the coupling of multiple random variables which are dependent on each other. The relationship of the PDFs and a coupling function, known as a copula, produces a Joint Probability Density Function (JPDF). These stochastic modeling approaches are implemented into a Multiple Component (MC) shock physics computational code and used to model statistical fracture and reactive flow applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

A Structural engineering Graduate Degree Program with specialization in structural health monitoring & non-destructive evaluation [Slides]

The promotional presentation discusses the graduate degree program at the University of California, San Diego, Jacobs School of Engineering, related to structural engineering. Structural Health Monitoring (SHM) encompasses the integration of a broad range of technologies from sensing/actuating, data interrogation, and statistical modeling to enable system-level or component-level health assessment and predictive modeling. The course is available via distance learning to Los Alamos National Laboratory engineers.

42 ENGINEERING↗
DOE OSTI2022

Uncertainty in Annual Energy Resulting from Uncertain Irradiance Measurements

We report an analysis quantifying the contribution to uncertainty in annual energy projections from uncertainty in ground-measured irradiance. Uncertainty in measured irradiance is quantified for eight instruments by the difference from a well maintained, secondary standard pyranometer which is regarded as truthful. We construct a statistical model of irradiance uncertainty and apply the model to generate a sample of 100 annual time series of irradiance for each instrument. The sample is propagated through a common performance model for a reference photovoltaic system to quantify variation in annual energy. Although the measured irradiance varies from the reference by a few percent (standard deviation of 1-2%) the uncertainty in annual energy is on the order of a fraction of one percent. We propose a model for a factor that represents uncertainty in modeled annual energy that arises from uncertainty in ground-measured irradiance.

14 SOLAR ENERGY↗
DOE OSTI2024

Microscopic Evidence for Scission Neutrons

We discuss the neck rupture stage of fission and the emission of particles afterwards, two extremely rapid and highly non-equilibrium processes. Currently, the neck rupture cannot be directly probed by experiment, highlighting the importance of reliable theoretical predictions for this stage of fission. Time-dependent density functional theory (TDDFT) is used to simulate the spontaneous fission of 252 Cf. In conjunction with statistical models, inputs from microscopic calculations can be used to make predictions for prompt neutron and gamma emission spectra, quantities which can be experimentally measured. Here we characterize the scission mechanism within TDDFT and estimate the number of scission neutrons and their kinetic energies.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Improving Self-Driving Labs: Quantifying System-Level Experiment Repeatability and Broadening Instrument-Level Compatibility

Modular Autonomous Research System (MARS) is a self-driving laboratory (SDL) which performs wet-lab science with peptide-lanthanide combinations in an automated and, ultimately, an autonomous manner to aid in soil analysis for domestic lithium mining. Autonomous experimentation involves automated experimentation, experiment planning, and active learning. MARS consists of a 6-axis robotic arm (UR5e) on a linear rail, pipette robots (Opentrons 2), and microplate readers. These components transport, operate on, and collect data with chemical solutions in standard labware. For effective autonomy, MARS must perform system-level labware operations repeatably, plan experiments autonomously, and be portable between research-domains. Repeatability is evaluated by labware placement precision, such that future operations can properly locate labware, as well as the elapsed time, so that low variance mean estimates of experiment duration can inform high-level researcher decision making. Autonomous experiment planning is the next step to decouple experimentation from human management; however, there is a conflict between the ideal system-level experiment goals and the constraints imposed by instruments’ limitations. Sub-domain portability is a long-term goal to extend MARS’ research beyond the chemistry of peptide-lanthanide binding to other sub-domains without having to invest significant overhead to system retrofitting. To address these goals, we manually trained the robotic arm labware placement and modelled statistical failurerate and uncertainty Additionally, we benchmarked the duration and variance of each experiment sub-operation as a heuristic for research decision making. Next, we use a parameterized geometric program (PGP) approach to design experiments that optimize system-level objectives and satisfy instrument-level constraints. Lastly, we proposed a Python framework to maximize MARS’ extensibility to other scientific sub-domains through a JSON-based experiment specification.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A Framework for the Optimization of Water Treatment Processes Under Uncertainty Assessed through Process Operability

Conference presentation conveying work conducted on developing a framework for the optimization of water treatment processes after applying robust optimization and process operability tools. The objective of this framework is to optimize treatment processes under the uncertainty of source water conditions. This work contributes to robust optimization and process operability methodologies, allowing for the extension of probability from statistical models to operability calculations.

Barber, Hunter↗
DOE OSTI2025

Many-body Nuclear Dynamics

The principal goal of this work was to better understand the fusion of neutron-rich nuclei a topic relevant to the fields of both nuclear physics and nuclear astrophysics. The work provides insight into the structure and reactions of neutron-rich nuclei namely the extent of their neutron density distribution and its polarizability. By comparing the fusion excitation functions for a chain of isotopes with a common target nucleus changes in the attractive nuclear potential are assessed. This change in the attractive potential is related to changes in the neutron density distribution with increasing number of neutrons or changes in fusion dynamics with increasing neutron number. The impact of the pairing of valence neutrons and protons on the fusion cross-section is also examined. Measurement of an isotopic chain is a powerful tool to address this topic. The experimental program made use of several different accelerator facilities. The core of the experimental work involved experiments at the ReA3 accelerator and the Facility for Rare Isotope Beams (FRIB) situated at Michigan State University, at GANIL, the French national nuclear physics laboratory, and the University of Notre Dame. At ReA3 the degree to which α-clusters associated with fusion of 28,30,32 Si + 28 Si result from the collision dynamics or reflect an initial α-cluster structure was explored. Alpha clusters observed in fusion reactions exceed the predictions of the standard statistical model. This experiment provides a measure of how clusterization is impacted by the increasing neutron-richess of the system. In the experiment at GANIL we investigated fusion in 19 O + 12 C and 20 O + 12 C. This experiment utilized the recently developed active-target detector MuSIC@Indiana. Using this proven, highly efficient, active-target detector we measured the fusion excitation function for these neutron-rich systems. Grounded by these experimental measurements, theoretical models were used to examine the role of unpaired valence neutrons on the fusion cross-section at energies just above the fusion barrier.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

A Multi-Sensor Approach for Measuring Bird and Bat Collisions with Offshore Wind Turbines (Final Technical Report)

Collision of birds and bats with wind turbines is a conservation concern for both land-based and offshore wind projects. The fatality rates of birds and bats at land-based turbines are well documented. The measurement strategies on land focus on finding carcasses following collision, estimating the number of carcasses missed through searcher efficiency, carcass persistence trials and carcass fall distributions, and modeling statistically robust fatality rates. Few technologies have been developed to monitor offshore bird and bat collisions, and many that have been developed focused on detecting collisions with large birds. The few studies that have attempted to document collisions at offshore turbines do not account for smaller bodied animals or for collisions that might be missed, which prevents the calculation of statistically robust fatality rates. The overall goal of this report, A Multi-Sensor Approach for Measuring Bird and Bat Collisions with Offshore Wind Turbines (Project), was to develop an effective multi-sensor system for quantifying bird and bat collision rates, specifically for offshore wind facilities. The Project goal and resulting automated collision detection system was achieved through two major technological advancements: 1) refining The Netherlands Organisation for Applied Scientific Research’s (TNO’s) existing WT-Bird® vibration sensing system, that had successfully detected large bird collisions during daytime, to allow for improved detection of smaller birds and bats during both daytime and nighttime hours and 2) improving image processing systems and developing and integrating machine learning algorithms to automatically detect and classify small and large bird and bat collisions with offshore turbines. This final technical report (FTR) summarizes Methods , Results , Conclusions , and Lessons Learned during each of the five Tasks identified for this research and development effort. This FTR includes summaries of the following: Task 1. Initial Engineering Tests to Improve WT-Bird® Task 2. Installation of WT‐Bird® on a Utility-scale Turbine at the National Wind Technology Center – National Renewable Energy Laboratory Task 3. Field Tests and Refinement of the Object Detection System Task 4. Validation of WT-Bird® on a Land-based Turbine Task 5. Preparation for the Implementation of WT-Bird® on an Offshore Turbine. This research and development effort documented successful improvement of the WT Bird® collision detection system to detect small birds and bats, and WT-Bird® is the first collision detection system to validate results compared to land-based post-construction monitoring. The collision trials provide estimates of missed targets that can be used to estimate fatality rates, a significant improvement relative to other offshore collision monitoring systems. Advances were made in developing an edge-processing solution to reduce data storage requirements, which is important if the system is deployed for long periods of time at offshore turbines. The improved WT-Bird® system also provides an important option for wind operators on land or offshore who need to document specific details about when collisions occur, particularly efforts to further research on bat impact minimization, or when standard fatality searches are impractical (e.g. offshore) or inadequate (e.g. challenging locations on land).

17 WIND ENERGY↗
DOE OSTI2026

Criteria for Retention of 3013 S1 Containers Based on Relative Risk and Expert Judgment

An evaluation was performed to assess the suitability of thirty-three 3013 containers proposed for retention. These containers have moisture levels greater than 0.08 wt.% – the S1 population. The remainder of the S1 population stored at SRS will be down blended and disposed of by the end of 2028. Based on field surveillance and shelf-life data available to date as well as informed technical judgment, no container is currently expected to fail in its 50-year storage period. However, corrosion risk varies across the S1 population. Relative risks were evaluated using predicted Consensus Scores and their 95% Upper Prediction Limits (UPLs). The predicted values are based on a statistical model of Consensus Score as a function of moisture, chloride, and whether the packaged material was electrorefining scrap packaged at Hanford. Consensus Score has been shown to be a useful indicator of corrosion potential, and the UPL captures uncertainty in the model predictions, providing a conservative indicator of corrosion potential. Using UPLs to determine relative risks, together with expert review, three containers were identified as not suitable for retention, and the remainder were determined to be suitable.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Data to Accompany: Expanding the access of wearable silicone wristbands in community-engaged research through best practices in data analysis and integration

Wearable silicone wristbands are a rapidly growing exposure assessment technology that offer researchers the ability to study previously inaccessible cohorts and have the potential to provide a more comprehensive picture of chemical exposure within diverse communities. However, there are no established best practices for analyzing the data within a study or across multiple studies, thereby limiting impact and access of these data for larger meta-analyses. We utilize data from three studies, from over 600 wristbands worn by participants in New York City and Eugene, Oregon, to present a first-of-its-kind manuscript detailing wristband data properties. We further discuss and provide concrete examples of key areas and considerations in common statistical modeling methods where best practices must be established to enable meta-analyses and integration of data from multiple studies. Finally, we detail important and challenging aspects of machine learning, meta-analysis, and data integration that researchers will face in order to extend beyond the limited scope of individual studies focused on specific populations.

Bramer, Lisa M↗
DOE OSTI2024

Evaluating the Impacts of Autonomous Electric Vehicles Adoption on Vehicle Miles Traveled and CO2 Emissions

Autonomous electric vehicles (AEVs) can potentially revolutionize the transportation landscape, offering a safer, contact-free, easily accessible, and more eco-friendly mode of travel. Prior to the market uptake of AEVs, it is critical to understand the consumer segments that are most likely to adopt these vehicles. Beyond market adoption, it is also important to quantify the impact of AEVs on broader transportation systems and the environment, such as impacts on the annual vehicle miles traveled (VMT) and greenhouse gas (GHG) emissions. In this pilot study, using survey data, a statistical model correlating AEV adoption intention and socioeconomic and built environment attributes was estimated, and a sensitivity analysis was conducted to understand the importance of factors impacting AEV adoption. We found that the market segments range from early adopters who are wealthy, technologically savvy, and relatively young to non-adopters who are more cautious to new technologies. This is followed by a synthetic population microsimulation of market penetration for the San Francisco Bay Area. With five household vehicle replacement scenarios, we assessed the annual VMT and tailpipe carbon dioxide (CO2) emissions change associated with vehicle replacement. It is found that adopting AEVs can potentially reduce more than 5 megatons of CO2 yearly, which is approximately 30% of the total CO2 emitted by internal combustion engine (ICE) cars in the region.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Detecting the Presence of Intrusive Drilling in Secure Transport Containers Using Non-Contact Millimeter-Wave Radar

We employ a 77–81 GHz frequency-modulated continuous-wave (FMCW) millimeter-wave radar to sense anomalous vibrations during vehicle transport at highway speeds for the first time. Secure metallic containers can be breached during transport by means of drilling into their sidewalls but detecting a drilling signature is difficult because the large vibrations of transport drown out the small vibrations of drilling. For the first time, we demonstrate that it is possible to use a non-contact millimeter-wave radar sensor to detect this micron-scale intrusive drilling while highway-speed vehicle movement shakes the container. With the millimeter-wave radar monitoring the microdoppler signature of the container’s vibrating walls, we create a novel signal-processing pipeline consisting of range–angle tracking, time–frequency analysis, horizontal stripe image convolution, and principal component analysis to create a robust and powerful detection statistic to alarm if drilling is present. To support this pipeline, we develop a statistical model combining the vibrating container and the random vibrations induced by vehicle movement to explore the robustness of the sensor’s detection capabilities. The presented results strongly support the inclusion of a millimeter-wave radar vibration sensor into a transport security system.

Wagner, Samuel↗
DOE OSTI2022

Dependence of the Ratio of Total to Visible Mass on Observable Properties of Sloan Digital Sky Survey MaNGA Galaxies

Abstract Using spectroscopic observations from the Sloan Digital Sky Survey Mapping Nearby Galaxies at Apache Point Observatory Data Release 15, we study the relationships between the ratio of total to visible mass and various parameters characterizing the evolution and environment of the galaxies in this survey. Measuring the rotation curve with the relative velocities of the H α emission line across a galaxy’s surface, we estimate each galaxy’s total mass. We develop a statistical model to describe the observed distribution in the ratio of total to visible mass, from which we extract the most probable value of this mass ratio for a given sample of galaxies. We present the relationships between the ratio of total to visible mass and several characteristics describing galactic evolution, such as luminosity, gas-phase metallicity, distance to the nearest neighbor, and position on the color–magnitude diagram. We find that faint galaxies with low metallicities, typically in the blue cloud, have the highest ratios of total to visible mass. This mass ratio is significantly reduced when we include the H i mass in the total visible mass, implying that feedback mechanisms are not as strong in low-mass galaxies as previously thought. Those galaxies that exhibit the second-highest ratios of total to visible mass are the brightest with high metallicities, typically members of the red sequence or green valley. Active galactic nucleus activity is likely both the quenching mechanism and the feedback that drives the mass ratio higher in these massive galaxies. Finally, we introduce a parameterization that predicts a galaxy’s ratio of total to visible mass based only on its photometry and luminosity.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

A Computer Design Tool for Ceramic Receivers

Gen3 solar receivers and other components will experience a combination of high temperatures and high stress caused by thermal stress and internal pressure. Under these conditions metallic components, even those manufactured from nickel-based superalloys, have poor reliability. Engineering ceramics could be a solution, as these materials have excellent high temperature strength. However, accurately assessing the reliability of a ceramic component operating in these conditions requires an entirely different approach compared to metallic materials. This paper describes the implementation of statistical models for evaluating the reliability of high temperature ceramic components in srlife – an open-source software package for estimating the life of high temperature concentrating solar power equipment. This new capability allows users to make fair comparisons between competing metallic and ceramic component designs and to accurately assess the plant efficiency and economics of ceramic solar receivers and other components.

Messner, Mark (ORCID:0000000200404385)↗