SEARCH · Engineering Papers

Results for “statistical model”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,171 records · Page 65

All sources NASA NTRS DOE OSTI

NASA NTRS2012

Compositional Effects on Nickel-Base Superalloy Single Crystal Microstructures

Fourteen nickel-base superalloy single crystals containing 0 to 5 wt% chromium (Cr), 0 to 11 wt% cobalt (Co), 6 to 12 wt% molybdenum (Mo), 0 to 4 wt% rhenium (Re), and fixed amounts of aluminum (Al) and tantalum (Ta) were examined to determine the effect of bulk composition on basic microstructural parameters, including gamma' solvus, gamma' volume fraction, volume fraction of topologically close-packed (TCP) phases, phase chemistries, and gamma - gamma'. lattice mismatch. Regression models were developed to describe the influence of bulk alloy composition on the microstructural parameters and were compared to predictions by a commercially available software tool that used computational thermodynamics. Co produced the largest change in gamma' solvus over the wide compositional range used in this study, and Mo produced the largest effect on the gamma lattice parameter and the gamma - gamma' lattice mismatch over its compositional range, although Re had a very potent influence on all microstructural parameters investigated. Changing the Cr, Co, Mo, and Re contents in the bulk alloy had a significant impact on their concentrations in the gamma matrix and, to a smaller extent, in the gamma' phase. The gamma phase chemistries exhibited strong temperature dependencies that were influenced by the gamma and gamma' volume fractions. A computational thermodynamic modeling tool significantly underpredicted gamma' solvus temperatures and grossly overpredicted the amount of TCP phase at 982 C. Furthermore, the predictions by the software tool for the gamma - gamma' lattice mismatch were typically of the wrong sign and magnitude, but predictions could be improved if TCP formation was suspended within the software program. However, the statistical regression models provided excellent estimations of the microstructural parameters based on bulk alloy composition, thereby demonstrating their usefulness.

MacKay, Rebecca A.↗

DOE OSTI2021

Artificial spin ice: Paths forward

Artificial spin ice systems are lithographically prepared arrays of interacting nanoscale magnetic moments with collective behavior resulting from the chosen array geometry. These many-body systems are unusual in that their simple constituent elements can be configured to design their interactions with exquisite control. They can then be probed experimentally over a remarkably large range of time scales and length scales, including imaging of the individual moments. The study of artificial spin ice has broadened well beyond the original focus on systems with connection to spin ice and ice models, and now the term is used in reference to many other structures that exhibit a much wider range of physical phenomena. Here in this perspective, we review progress in the field of artificial spin ice since its inception more than a decade ago. We then discuss prospects for future directions – continuing the exploration of nanomagnetism, statistical spin models, and the potential for technological devices.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2021

A hierarchical Bayesian model for background variation in radiation source localization

In this paper, we apply a new model to account for varying background radiation in radiological source localization. We present a hierarchical Bayesian model that simultaneously infers background and source location parameters without requiring separate estimation of the background radiation at each detector location. We employ a simplified photon transport model to reduce the computational expense of Bayesian model calibration. Furthermore, we demonstrate the model accuracy by localizing a cesium-137 source in a simulated city block, and we analyze experimental field measurements with varying background. In both cases, the model provides sufficient fidelity that we can locate the source while simultaneously estimating background radiation.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS1975

Revised Perturbation Statistics for the Global Scale Atmospheric Model

Magnitudes and scales of atmospheric perturbations about the monthly mean for the thermodynamic variables and wind components are presented by month at various latitudes. These perturbation statistics are a revision of the random perturbation data required for the global scale atmospheric model program and are from meteorological rocket network statistical summaries in the 22 to 65 km height range and NASA grenade and pitot tube data summaries in the region up to 90 km. The observed perturbations in the thermodynamic variables were adjusted to make them consistent with constraints required by the perfect gas law and the hydrostatic equation. Vertical scales were evaluated by Buell's depth of pressure system equation and from vertical structure function analysis. Tables of magnitudes and vertical scales are presented for each month at latitude 10, 30, 50, 70, and 90 degrees.

Justus, C. G.↗

NASA NTRSDate not supplied

Optimization of Airport Runway Configuration with Forecast-Augmented Offline Reinforcement Learning

Runway configuration Management (RCM) governs the optimal utilization of runways based on variables such as traffic and meteorological conditions, making it a daunting task in air traffic management due to its dependency on volatile operational and environmental factors. This paper improves upon our previous work [1] on using offline model-free reinforcement learning for creating a Runway Configuration Assistance (RCA) decision-support tool. A novel integration of forecast data from LAMP (Localized Aviation Model Output Statistics Program) and TAF (Terminal Area Forecast) is introduced, enhancing the tool’s accuracy and also its adaptability to quick wind changes. The performance is evaluated using two major US airports, Charlotte Douglas International Airport (CLT) and Denver International Airport (DEN). To counter scalability issues presented by the addition of discrete forecast variables, we transitioned to a continuous state space model, ensuring scalability and inclusion of longer forecast data. The results of our experiments reflect significant improvements in the RCA tool’s prediction accuracy.

Sumanth Nethi↗

DOE OSTI2024

Experimental and statistical study on the effect of process parameters on the quality of continuous fiber composites made via additive manufacturing

Ongoing research in additive manufacturing towards structural and industrial application has led to the use of commingled roving as a manufacturing feedstock for printing high fiber volume fraction composites. The prospects of using this technology for high performance applications necessitates the need for a comprehensive experimental investigation into the effects of processing parameters on the quality of an additively manufactured composite printed from commingled roving feedstock. Here, in this work, transverse flexure and void fraction matrix pyrolysis testing are both performed to evaluate composite quality. The transverse flexure test is a testing approach that evaluates the quality of the interfacial fiber-matrix bond while the void fraction test estimates the void content in the printed composite. A full observational study consisting of 27 different test combinations is done to investigate the effects of three different process parameters namely, temperature, pressure, and print speed across three different levels. Composite samples were made from commingled roving of E-glass and amorphous PET using an in-house built continuous fiber composite digital manufacturing system. Least squares regression analysis is performed to study the main, interaction and quadratic effects of process parameters. A statistical regression model having an R2 adjusted value of 80.1% is generated from the transverse flexure study, which is used to explain main and interaction effects and also predict performance. Response surface plots are also generated and are used to optimize process parameters which can subsequently be of help in scaling up composite manufacturing. Results show that all three process parameters are highly statistically significant at the 0.01 level of significance. Pressure * Temperature and Pressure * Printspeed are significant interaction terms. Pressure plays a weightier role when print speed is increased or temperature is decreased as it closes more voids that would ordinarily have been introduced because of drop in polymer melt viscosity. Micrographic analysis is also performed.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Development and assessment of a reactor system prognosis model with physics-guided machine learning

Autonomous control systems provide recommendations to help operators in decision-making during plant operations ranging from normal operation to accident management. An important step of autonomous control is prognosis. In nuclear engineering domain, prognosis is the process of predicting future conditions of a system or equipment based on present signs and symptoms of a fault. The prognosis model allows predicting future reactor states for possible candidate control strategies so that the outcomes can be evaluated to determine the best control strategy. The prognosis model requires representing direct relationships between the symptoms and the predictions. In nuclear engineering, computational simulations are approximate representations of the operation of the real system. However, prognosis with computational simulations requires high computation power and time due to possible large number of scenarios. Necessary computation resources can be reduced with machine learning (ML) approach for fast predictions by building a surrogate function using the simulation data. A critical issue is, ML models are ignorant of physical knowledge, and these models approximate statistical relationships between the system variables. This ignorance can produce results that are inconsistent with physical laws, even if an optimal result is achieved from a mathematical point of view. Physics-guided machine learning (PGML) is an approach to tackle this issue. Here, this work formulates and illustrates a framework to guide development and assessment of the ML-based prognosis model for autonomous control systems. The development of the prognosis model considers the training of a ML model which consists of optimizing many aspects of the ML approach. The assessment of the prognosis model considers training data limitations and uncertainties of the ML approach. Prognosis models with standalone ML and PGML are developed and assessed on the loss-of-flow scenario of Experimental Breeder Reactor II. The results indicate that PGML based prognosis model has the best performance compared to other prognosis models.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2022

Exploring Functional Materials by Understanding Beam‐Sample Interactions

Abstract Ultra‐low‐dose electron diffraction is performed with a double metal cyanide catalyst (DMC) to understand how electron irradiation stimulates structural alterations in functional materials. The commonly fading diffraction patterns with dose accumulation depend on the irradiated area and the beam current even when below 50 femto Amperes. Heat generation is observed and modeled by statistical, inelastic scattering events to describe how phonon excitations modulate radiation hardness. Specifically, the characteristic 1/e‐decay of Bragg intensities from DMC is delayed from 6 to 30 eÅ −2 at room temperature, which is comparable to the effect of embedding radiation soft matter in ice. DMC's radiation hardness is enhanced by a latency dose that forms during a phase transformation. This unifying model predicts that a critical dose rate exists for any material that varies between 0.1 and 10 4 eÅ −2 s −1 because of a material dependent competition of heat generation and spread. It shows that Brillouin scattering causes time dependent perturbations in electron irradiated solids that trigger time‐temperature‐transformations on a time scale of nanoseconds to microseconds at room temperature, which is not included in traditional models describing the decay of Bragg intensities by radiolysis.

Kisielowski, Christian↗

NASA NTRS2018

Structural Certification of Human-Rated Inflatable Space Structures

This paper details the results of an initial study to develop a certification plan for human-rated inflatable space structures, including guidelines for qualification testing. Habitable softgoods inflatables are multi-layered shell structures that use high-strength webbing, cordage and broadcloth fabric to carry the skin loads of a variety of volumetric shapes and structural architectures. The primary objectives of this study are to define the key parameters that affect these structures and propose a statistically robust approach to defining safety and knockdown factors based on test and analysis. Current NASA standards for habitable inflatable space structures use a factor of safety of 4, which was inherited from airship design criteria. An updated approach to defining a design factor, taking into account material strength variability, load variability in the article, number of test samples, and damage and degradation effects is specified. Accurate analytical modeling of these structures is hindered by the difficulty of obtaining accurate and consistent material data due to load-history- dependent, nonlinear load versus strain behavior. A building block approach to certification is detailed that uses stochastic modeling and statistical test design and analysis to address the unique challenges these high-strength softgoods structures present. Human-rated inflatable modules are a transformative capability for launching much larger habitable volumes into space than is possible with rigid shell structures. This research aims to provide the framework for certifying these structures for future human space exploration missions.

Jones, Thomas C.↗

NASA NTRS2017

The Integrated Medical Model: Outcomes from Independent Review

In 2016, the Integrated Medical Model (IMM) v4.0 underwent an extensive external review in preparation for transition to an operational status. In order to insure impartiality of the review process, the Exploration Medical Capabilities Element of NASA's Human Research Program convened the review through the Systems Review Office at NASA Goddard Space Flight Center (GSFC). The review board convened by GSFC consisted of persons from both NASA and academia with expertise in the fields of statistics, epidemiology, modeling, software development, aerospace medicine, and project management (see Figure 1). The board reviewed software and code standards, as well as evidence pedigree associated with both the input and outcomes information. The board also assesses the models verification, validation, sensitivity to parameters and ability to answer operational questions. This talk will discuss the processes for designing the review, how the review progressed and the findings from the board, as well as summarize the IMM project responses to those findings. Overall, the board found that the IMM is scientifically sound, represents a necessary, comprehensive approach to identifying medical and environmental risks facing astronauts in long duration missions and is an excellent tool for communication between engineers and physicians. The board also found IMM and its customer(s) should convene an additional review of the IMM data sources and to develop a sustainable approach to augment, peer review, and maintain the information utilized in the IMM. The board found this is critically important because medical knowledge continues to evolve. Delivery of IMM v4.0 to the Crew Health and Safety (CHS) Program will occur in the 2017. Once delivered for operational decision support, IMM v4.0 will provide CHS with additional quantitative capability in to assess astronaut medical risks and required medical capabilities to help drive down overall mission risks.

medical equipment↗

DOE OSTI2022

Bayesian optimized collection strategies for fatigue strength testing

Abstract A statistical framework is presented enabling optimal sampling and analysis of constant life fatigue data. Protocols using Bayesian maximum entropy sampling are built based on conventional staircase and stress step methods, reducing the requirement of prior knowledge for data collection. The Bayesian Staircase method shows improved parameter estimation efficiency, and the Bayesian Stress Step method shows equal accuracy to the standard method at larger step size allowing experimentalists to lessen concerns of loading history. Statistical methods for determining model suitability are shown, highlighting the influence of protocol. Experimental validation is performed, showing the applicability of the methods in laboratory testing.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Understanding protein-complex assembly through grand canonical maximum entropy modeling

Inside a cell, heterotypic proteins assemble in inhomogeneous, crowded systems where the abundance of these proteins vary with cell types. While some protein complexes form putative structures that can be visualized with imaging, there are far more protein complexes that are yet to be solved because of their dynamic associations with one another. Nevertheless, it is possible to infer these protein complexes through a physical model. However, it is often not clear to physicists what kind of data from biology is necessary for such a modeling endeavor. Here, we aim to model these clusters of coarse-grained protein assemblies from multiple subunits through the constraints of interactions among the subunits and the chemical potential of each subunit. We obtained the constraints on the interactions among subunits from the known protein structures. We inferred the chemical potential that dictates the particle number distribution of each protein subunit from the knowledge of protein abundance from experimental data. Guided by the maximum entropy principle, we formulated an inverse statistical mechanical method to infer the distribution of particle numbers from the data of protein abundance as chemical potentials for a grand canonical multicomponent mixture. Using grand canonical Monte Carlo simulations, we captured a distribution of high-order clusters in a protein complex of succinate dehydrogenase with four known subunits. The complexity of hierarchical clusters varies with the relative protein abundance of each subunit in distinctive cell types such as lung, heart, and brain. When the crowding content increases, we observed that crowding stabilizes emergent clusters that do not exist in dilute conditions. We, therefore, proposed a testable hypothesis that the hierarchical complexity of protein clusters on a molecular scale is a plausible biomarker of predicting the phenotypes of a cell.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

What controls the interannual variation of Hadley cell extent in the Northern Hemisphere: physical mechanism and empirical model for edge variation

Abstract The Hadley circulation is the most prominent atmospheric meridional circulation, reducing the radiatively driven equator-to-pole temperature gradient. While the Hadley cell extent varies by several degrees from year to year, the detailed dynamical mechanisms behind such variations have not been well elucidated. During the expanded phase of the Hadley cell, many regions on the periphery of the subtropics experience unfavorable climatic conditions. In this study, using ERA5 reanalysis data, we examine the physical chain of events responsible for the interannual variation of the Hadley cell edge (HCE) latitude in the Northern Hemisphere. This variation is mainly caused by changing eddy activity and wave breaking from both stationary and transient waves. In particular, we show that transient waves cause the HCE to shift poleward by increasing the eddy momentum flux divergence (EMFD) and reducing the baroclinicity over 20°–40°N, shifting the region of peak baroclinicity poleward. El Niño/La Niña and the Arctic Oscillation (AO) account for a significant portion (60%) of the interannual fluctuation of the HCE latitude. Through the poleward displacement of eddy activity, La Niña and a positive AO state are associated with the poleward shift of the HCE. The analysis of 28 CMIP5 models reveals statistical relationships between EMFD, vertical shear, and HCE latitude similar to those observed.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1983

A stochastic-dynamic model for the spatial structure of forecast error statistics

The present investigation is concerned with the presentation of a simplified model of the spatial structure of forecast error statistics, a comparison of the model with actual numerical weather prediction results, and the extent to which simplifying assumptions made in the model are justified. A stochastic-dynamic model is derived for the spatial structure of the global atmospheric mass-field forecast error. The model states that the relative potential vorticity of the forecast error is random. The covariance function of the model's solutions is found to be governed by a simple deterministic equation. The agreement between the stochastic model and actual mass-field forecast errors fields for 12-36 h periods validates the assumptions on which the model is derived. Within this period, the difference between the potential voriticity fields of the atmosphere and of the numerical forecasts used in the comparison is well represented by white noise.

Balgovind, R.↗

DOE OSTI2021

Aerosol emissions from water-lean solvents for post-combustion CO 2 capture

Advanced water-lean solvents (WLS) for post-combustion CO 2 capture have been gaining interest due to their ability to reduce the parasitic penalty from energy needed for solvent regeneration. Commercial implementation of these novel CO 2 capture technologies hinges on successful control of amine emissions. RTI conducted a parametric study of fundamental and operational variables influence on overall amine aerosol and vapor emissions from our water-lean solvent eCO 2 Sol™ using our 6-kW equivalent bench-scale gas absorption system. The parametric testing used a simulated flue gas with 15 % CO 2 , 2.3–4.2 % H 2 O, and 0–6 ppm sulfite (SO 3 ) to examine the impact of the presence of aerosols to the capture performance and amine emissions from the system. The SO 3 reacts with water in the flue gas to create H 2 SO 4 , which forms liquid aerosol droplets and provide nucleation sites for growth of aerosols. Scanning Mobility Particle Sizer and Aerodynamic Particle Sizer instruments monitored the aerosol particle size distribution. Parametric testing results suggested that the presence of the aerosols in the flue gas could increase the overall amine emissions by 10X compared to the baseline emissions from WLS’s vapor pressure. Principal component analysis (PCA) and projection to latent squares (PLS) developed models to predict the aerosol-based amine emissions from process data. The predictive PLS model had a correlation coefficient (Q 2 ) of 0.92 and could predict the aerosol-based emissions from the NAS process with ±15 % accuracy (average absolute deviation, AAD). The PLS regression model also identified key variables affecting aerosol-based emissions from WLS.

42 ENGINEERING↗

DOE OSTI2020

Probabilistic Solar Power Forecasting Using Bayesian Model Averaging

There is rising interest in probabilistic forecasting to mitigate risks from solar power uncertainty, but the numerical weather prediction (NWP) ensembles readily available to system operators are often biased and underdispersed. We propose a Bayesian model averaging (BMA) post-processing method suitable for forecasting power from utility-scale photovoltaic (PV) plants at multiple time horizons up to at least the day-ahead timescale. BMA is a kernel dressing technique for NWP ensembles in which the forecast is a weighted sum of member-specific probability density functions. We tailor BMA for utility-scale PV forecasting by modeling power clipping at the AC inverter rating and advance the theory of BMA with a new beta kernel parameterization that accommodates theoretical constraints not previously addressed. BMA is demonstrated for a case study of 11 utility-scale PV plants in Texas, forecasting at hourly resolution for the complete year 2018. BMA's mixture-model approach mitigates underdispersion of the raw ensemble to significantly improve forecast calibration, while consistently outperforming an ensemble model output statistics (EMOS) parametric approach from the literature. At 4-hour lead time, the BMA post-processing achieves continuous ranked probability skill scores of 2--36% over the raw ensemble, with consistent performance at multiple lead times suitable for power system operations.

14 SOLAR ENERGY↗

NASA NTRS1994

Statistical mechanics in biology: how ubiquitous are long-range correlations?

The purpose of this opening talk is to describe examples of recent progress in applying statistical mechanics to biological systems. We first briefly review several biological systems, and then focus on the fractal features characterized by the long-range correlations found recently in DNA sequences containing non-coding material. We discuss the evidence supporting the finding that for sequences containing only coding regions, there are no long-range correlations. We also discuss the recent finding that the exponent alpha characterizing the long-range correlations increases with evolution, and we discuss two related models, the insertion model and the insertion-deletion model, that may account for the presence of long-range correlations. Finally, we summarize the analysis of long-term data on human heartbeats (up to 10(4) heart beats) that supports the possibility that the successive increments in the cardiac beat-to-beat intervals of healthy subjects display scale-invariant, long-range "anti-correlations" (a tendency to beat faster is balanced by a tendency to beat slower later on). In contrast, for a group of subjects with severe heart disease, long-range correlations vanish. This finding suggests that the classical theory of homeostasis, according to which stable physiological processes seek to maintain "constancy," should be extended to account for this type of dynamical, far from equilibrium, behavior.

Non-NASA Center↗

DOE OSTI2024

Optimizing Prediction Error for Time-dependent Solar Radiation Modeling

Numerical weather forecasting models and statistical methods have found wide use to help power companies estimate renewable output, but better methods are needed, particularly for extended forecasts. Machine learning approaches have been used here as well, but so far a major limitation is the ability to also predict the corresponding uncertainty in a forecast. Here we show that both can be done and demonstrate this using a long-term-short memory neural network where the difference between predicted and ground truth data are used to train a model for the corresponding forecast uncertainties.

97 MATHEMATICS AND COMPUTING↗