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.

SourceResearch connectionFrom yearThrough year

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

At least 685 records · Page 38

All sources NASA NTRS DOE OSTI

NASA NTRS2006

Modeling the Atmospheric Phase Effects of a Digital Antenna Array Communications System

In an antenna array system such as that used in the Deep Space Network (DSN) for satellite communication, it is often necessary to account for the effects due to the atmosphere. Typically, the atmosphere induces amplitude and phase fluctuations on the transmitted downlink signal that invalidate the assumed stationarity of the signal model. The degree to which these perturbations affect the stationarity of the model depends both on parameters of the atmosphere, including wind speed and turbulence strength, and on parameters of the communication system, such as the sampling rate used. In this article, we focus on modeling the atmospheric phase fluctuations in a digital antenna array communications system. Based on a continuous-time statistical model for the atmospheric phase effects, we show how to obtain a related discrete-time model based on sampling the continuous-time process. The effects of the nonstationarity of the resulting signal model are investigated using the sample matrix inversion (SMI) algorithm for minimum mean-squared error (MMSE) equalization of the received signal

Tkacenko, A.↗

NASA NTRSDate not supplied

Post-flight Analysis of Atmospheric Properties from Mars 2020 Entry, Descent, and Landing

The Mars 2020 spacecraft landed the Perseverance rover and Ingenuity helicopter successfully in Jezero crater on Feb. 18, 2021. The entry, descent, and landing (EDL) sequence of the spacecraft largely leveraged the previous Mars Science Laboratory (MSL) mission. The atmospheric modeling approach for Mars 2020 was also borrowed from MSL, and consisted of utilizing two mesoscale atmospheric models of the landing site during the Martian season of landing, and using that data to create a statistical model of the pressure, density, temperature, and winds that Mars 2020 could have expected to encounter. Additionally, Mars 2020 contained an optical sensor - Landing Vision System (LVS) - that relied on taking pictures of the terrain, and was sensitive to the dust opacity of the atmosphere. This paper will briefly describe the pre-flight atmospheric models used for Mars 2020, but will focus on post-flight assessment of these models by comparing them to near-landing day orbiter sounder data and onboard atmospheric measurements. Suggestions for potential model changes will be also discussed.

Soumyo Dutta↗

NASA NTRSDate not supplied

Post-flight Analysis of Atmospheric Properties from Mars 2020 Entry, Descent, and Landing

Soumyo Dutta↗

DOE OSTI2015

Vertical structure and physical processes of the Madden-Julian oscillation: Linking hindcast fidelity to simulated diabatic heating and moistening

Here, many theories for the Madden-Julian oscillation (MJO) focus on diabatic processes, particularly the evolution of vertical heating and moistening. Poor MJO performance in weather and climate models is often blamed on biases in these processes and their interactions with the large-scale circulation. We introduce one of the three components of a model evaluation project, which aims to connect MJO fidelity in models to their representations of several physical processes, focusing on diabatic heating and moistening. This component consists of 20 day hindcasts, initialized daily during two MJO events in winter 2009–2010. The 13 models exhibit a range of skill: several have accurate forecasts to 20 days lead, while others perform similarly to statistical models (8–11 days). Models that maintain the observed MJO amplitude accurately predict propagation, but not vice versa. We find no link between hindcast fidelity and the precipitation-moisture relationship, in contrast to other recent studies. There is also no relationship between models' performance and the evolution of their diabatic heating profiles with rain rate. A more robust association emerges between models' fidelity and net moistening: the highest-skill models show a clear transition from low-level moistening for light rainfall to midlevel moistening at moderate rainfall and upper level moistening for heavy rainfall. The midlevel moistening, arising from both dynamics and physics, may be most important. Accurately representing many processes may be necessary but not sufficient for capturing the MJO, which suggests that models fail to predict the MJO for a broad range of reasons and limits the possibility of finding a panacea.

58 GEOSCIENCES↗

DOE OSTI2016

Using geochemical indicators to distinguish high biogeochemical activity in floodplain soils and sediments

A better understanding of how microbial communities interact with their surroundings in physically and chemically heterogeneous subsurface environments will lead to improved quantification of biogeochemical reactions and associated nutrient cycling. This paper develops a methodology to predict potential elevated rates of biogeochemical activity (microbial “hotspots”) in subsurface environments by correlating microbial DNA and aspects of the community structure with the spatial distribution of geochemical indicators in subsurface sediments. Multiple linear regression models of simulated precipitation leachate, HCl and hydroxylamine extractable iron and manganese, total organic carbon (TOC), and microbial community structure were used to identify sample characteristics indicative of biogeochemical hotspots within fluvially-derived aquifer sediments and overlying soils. The method has been applied to (a) alluvial materials collected at a former uranium mill site near Rifle, Colorado and (b) relatively undisturbed floodplain deposits (soils and sediments) collected along the East River near Crested Butte, Colorado. At Rifle, 16 alluvial samples were taken from 8 sediment cores, and at the East River, 46 soil/sediment samples were collected across and perpendicular to 3 active meanders and an oxbow meander. Regression models using TOC and TOC combined with extractable iron and manganese results were determined to be the best fitting statistical models of microbial DNA (via 16S rRNA gene analysis). Finally, fitting these models to observations in both contaminated and natural floodplain deposits, and their associated alluvial aquifers, demonstrates the broad applicability of the geochemical indicator based approach.

58 GEOSCIENCES↗

NASA NTRS1989

Predictions Of Fatigue Damage From Strain Histories

Semiempirical mathematical model of fatigue damage in stressed objects uses experimental histories of strains in those objects to predict fatigue lives. Accounts for initiation and propagation of fatigue cracks on cycle-by-cycle basis. Measured strain history first digitized, then converted to history of turning-point strains for purposes of analysis. Data between turning points not used. When model calibrated against proper test data for each type of object characterized, its predictions of fatigue lives superior to statistical models as one based on root-mean-square strain.

Sire, Robert A.↗

NASA NTRS1990

Progress and challenges in modeling turbulent aerodynamic flows

Progress in modeling external aerodynamic flows achieved by using computations and experiments designed to guide turbulence modeling is presented. The computational procedures emphasize utilization of the Reynolds-averaged Navier-Stokes equations and various statistical modeling approaches. Developments for including the influence of compressibility are provided; they point up some of the complexities involved in modeling high-speed flows. Examples of complementary studies that provide the status, limitations, and future challenges of modeling for transonic, supersonic, and hypersonic flows are given.

Marvin, Joseph G.↗

DOE OSTI2024

Assessing correlated truncation errors in modern nucleon-nucleon potentials

We test the BUQEYE model of correlated effective field theory (EFT) truncation errors on Reinert, Krebs, and Epelbaum's semilocal momentum-space implementation of the chiral EFT (𝜒⁢EFT ) expansion of the nucleon-nucleon (NN) potential. This Bayesian model hypothesizes that dimensionless coefficient functions extracted from the order-by-order corrections to NN observables can be treated as draws from a Gaussian process (GP). We combine a variety of graphical and statistical diagnostics to assess when predicted observables have a 𝜒⁢EFT convergence pattern consistent with the hypothesized GP statistical model. Our conclusions are that, first, the BUQEYE model is generally applicable to the potential investigated here, which enables statistically principled estimates of the impact of higher EFT orders on observables. Second, parameters defining the extracted coefficients such as the expansion parameter 𝑄 must be well chosen for the coefficients to exhibit a regular convergence pattern—a property we exploit to obtain posterior distributions for such quantities. Third, the assumption of GP stationarity across lab energy and scattering angle is not generally met; this necessitates adjustments in future work. We provide a workflow and interpretive guide for our analysis framework, and show what can be inferred about probability distributions for 𝑄, the EFT breakdown scale Λ 𝑏 , the scale associated with soft physics in the 𝜒⁢EFT potential 𝑚 eff , and the GP hyperparameters. All our results can be reproduced using a publicly available Jupyter notebook, which can be straightforwardly modified to analyze other 𝜒⁢EFT NN potentials.

Bayesian methods↗

NASA NTRS1972

A Management Information System Model for Program Management

The development of a model to simulate the information system of a program management type of organization is reported. The model statistically determines the following parameters: type of messages, destinations, delivery durations, type processing, processing durations, communication channels, outgoing messages, and priorites. The total management information system of the program management organization is considered, including formal and informal information flows and both facilities and equipment. The model is written in General Purpose System Simulation 2 computer programming language for use on the Univac 1108, Executive 8 computer. The model is simulated on a daily basis and collects queue and resource utilization statistics for each decision point. The statistics are then used by management to evaluate proposed resource allocations, to evaluate proposed changes to the system, and to identify potential problem areas. The model employs both empirical and theoretical distributions which are adjusted to simulate the information flow being studied.

Shipman, D. L.↗

DOE OSTI2024

Discovering the Unknowns: A First Step

This article aims at discovering the unknown variables in the system through data analysis. The main idea is to use the time of data collection as a surrogate variable and try to identify the unknown variables by modeling gradual and sudden changes in the data. We use Gaussian process modeling and a sparse representation of the sudden changes to efficiently estimate the large number of parameters in the proposed statistical model. The method is tested on a realistic dataset generated using a one-dimensional implementation of a Magnetized Liner Inertial Fusion (MagLIF) simulation model, and encouraging results are obtained.

42 ENGINEERING↗

DOE OSTI2021

Experimentally Inferred Fusion Yield Dependencies of OMEGA Inertial Confinement Fusion Implosions

Statistical modeling of experimental and simulation databases has enabled the development of an accurate predictive capability for deuterium-tritium layered cryogenic implosions at the OMEGA laser. Here, a physics-based statistical mapping framework is described and used to uncover the dependencies of the fusion yield. This model is used to identify and quantify the degradation mechanisms of the fusion yield in direct-drive implosions on OMEGA. The yield is found to be reduced by the ratio of laser beam to target radius, the asymmetry in inferred ion temperatures from the $l$ = 1 mode, the time span over which tritium fuel has decayed, and parameters related to the implosion hydrodynamic stability. When adjusted for tritium decay and $l$ = 1 mode, the highest yield in OMEGA cryogenic implosions is predicted to exceed 2 × 10 14 fusion reactions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Identifying microbial drivers in biological phenotypes with a Bayesian network regression model

Abstract In Bayesian Network Regression models, networks are considered the predictors of continuous responses. These models have been successfully used in brain research to identify regions in the brain that are associated with specific human traits, yet their potential to elucidate microbial drivers in biological phenotypes for microbiome research remains unknown. In particular, microbial networks are challenging due to their high dimension and high sparsity compared to brain networks. Furthermore, unlike in brain connectome research, in microbiome research, it is usually expected that the presence of microbes has an effect on the response (main effects), not just the interactions. Here, we develop the first thorough investigation of whether Bayesian Network Regression models are suitable for microbial datasets on a variety of synthetic and real data under diverse biological scenarios. We test whether the Bayesian Network Regression model that accounts only for interaction effects (edges in the network) is able to identify key drivers (microbes) in phenotypic variability. We show that this model is indeed able to identify influential nodes and edges in the microbial networks that drive changes in the phenotype for most biological settings, but we also identify scenarios where this method performs poorly which allows us to provide practical advice for domain scientists aiming to apply these tools to their datasets. BNR models provide a framework for microbiome researchers to identify connections between microbes and measured phenotypes. We allow the use of this statistical model by providing an easy‐to‐use implementation which is publicly available Julia package at https://github.com/solislemuslab/BayesianNetworkRegression.jl .

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Adaptation Strategies Strongly Reduce the Future Impacts of Climate Change on Simulated Crop Yields

Abstract Simulations of crop yield due to climate change vary widely between models, locations, species, management strategies, and Representative Concentration Pathways (RCPs). To understand how climate and adaptation affects yield change, we developed a meta‐model based on 8703 site‐level process‐model simulations of yield with different future adaptation strategies and climate scenarios for maize, rice, wheat and soybean. We tested 10 statistical models, including some machine learning models, to predict the percentage change in projected future yield relative to the baseline period (2000–2010) as a function of explanatory variables related to adaptation strategy and climate change. We used the best model to produce global maps of yield change for the RCP4.5 scenario and identify the most influential variables affecting yield change using Shapley additive explanations. For most locations, adaptation was the most influential factor determining the projected yield change for maize, rice and wheat. Without adaptation under RCP4.5, all crops are expected to experience average global yield losses of 6%–21%. Adaptation alleviates this average projected loss by 1–13 percentage points. Maize was most responsive to adaptive practices with a projected mean yield loss of −21% [range across locations: −63%, +3.7%] without adaptation and −7.5% [range: −46%, +13%] with adaptation. For maize and rice, irrigation method and cultivar choice were the adaptation types predicted to most prevent large yield losses, respectively. When adaptation practices are applied, some areas are predicted to experience yield gains, especially at northern high latitudes. These results reveal the critical importance of implementing adequate adaptation strategies to mitigate the impact of climate change on crop yields.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

Space-filling designs for computer experiments: A review

Improving the quality of a product/process using a computer simulator is a much less expensive option than the real physical testing. However, simulation using computationally intensive computer models can be time consuming and therefore, directly doing the optimization on the computer simulator can be infeasible. Experimental design and statistical modeling techniques can be used for overcoming this problem. This article reviews experimental designs known as space-filling designs that are suitable for computer simulations. In the review, a special emphasis is given for a recently developed space-filling design called maximum projection design. Furthermore, its advantages are illustrated using a simulation conducted for optimizing a milling process.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2013

Modeled Neutron Induced Nuclear Reaction Cross Sections for Radiochemsitry in the region of Thulium, Lutetium, and Tantalum I. Results of Built in Spherical Symmetry in a Deformed Region

We have developed a set of modeled nuclear reaction cross sections for use in radiochemical diagnostics. Systematics for the input parameters required by the Hauser-Feshbach statistical model were developed and used to calculate neutron induced nuclear reaction cross sections for targets ranging from Terbium (Z = 65) to Rhenium (Z = 75). Of particular interest are the cross sections on Tm, Lu, and Ta including reactions on isomeric targets.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2018

Unsupervised Parsing of Gaze Data with a Beta-process Vector Auto-regressive Hidden Markov Model

The first stage of analyzing eye-tracking data is commonly to code the data into sequences of fixations and saccades. This process is usually automated using simple, predetermined rules for classifying ranges of the time series into events, such as “if the dispersion of gaze samples is lower than the threshold, then code as a fixation; otherwise code as a saccade.” More recent approaches incorporate additional eye-movement categories in automated parsing algorithms, particularly glissades, by using time-varying, data-driven thresholds. We describe an alternative approach using the beta-process auto-regressive hidden Markov model (BP-AR-HMM). The BP-AR-HMM offers two main advantages over existing frameworks. First, it provides a statistical model for eye movement classification rather than a single estimate. Second, the BP-AR-HMM uses a latent process to model the number and nature of the types of eye-movements and hence is not constrained to predetermined categories. We applied the BP-AR-HMM both to high-sampling rate gaze data from Andersson, Larsson, Holmqvist, Stridh, and Nyström (2016) and to low-sampling rate data from the DIEM project (Mital, Smith, Hill, & Henderson, 2011). Driven by the data properties, the BP-AR-HMM identified clusters of samples resembling saccades, fixations and smooth pursuit, and a category that approximately corresponds to glissades (as well as blinks, errors, etc.). The BP-AR-HMM serves as an effective algorithm for data-driven event parsing alone or as an initial step in exploring the characteristics of gaze data sets.

eye tracking, fixation, fixation, saccade, smooth ↗

NASA NTRS1967

Lunar surface roughness, shadowing and thermal emission

Statistical model of lunar surface roughness, shadow, and thermal emission

SURFACE ROUGHNESS↗

NASA NTRS1985

Spectral estimation of green leaf area index of oats

Green leaf area index (LAI) is a measure of vegetative growth and development and is frequently used as an input parameter in yield estimation and evapotranspiration models. Extensive destructive sampling is usually required to achieve accurate estimates of green LAI in natural situations. In this investigation, a statistical modeling approach was used to predict the green LAI of oats from bidirectional reflectance data collected with multiband radiometers. Stepwise multiple regression models based on two sets of spectral reflectance factors accounted for 73 percent and 65 percent of the variance in green LAI of oats. Exponential models of spectral data transformations of greenness, normalized difference, and near-infrared/red ratio accounted for more of the variance in green LAI than the multiple regression models.

Best, R. G.↗