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

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At least 1,513 records · Page 84
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Addressing bias in bagging and boosting regression models

As artificial intelligence (AI) becomes widespread, there is increasing attention on investigating bias in machine learning (ML) models. Previous research concentrated on classification problems, with little emphasis on regression models. This paper presents an easy-to-apply and effective methodology for mitigating bias in bagging and boosting regression models, that is also applicable to any model trained through minimizing a differentiable loss function. Our methodology measures bias rigorously and extends the ML model's loss function with a regularization term to penalize high correlations between model errors and protected attributes. We applied our approach to three popular tree-based ensemble models: a random forest model (RF), a gradient-boosted model (GBT), and an extreme gradient boosting model (XGBoost). We implemented our methodology on a case study for predicting road-level traffic volume, where RF, GBT, and XGBoost models were shown to have high accuracy. Despite high accuracy, the ML models were shown to perform poorly on roads in minority-populated areas. Our bias mitigation approach reduced minority-related bias by over 50%.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Parameterization of cloud transmittance for expeditious assessment and forecasting of all-sky DNI

Radiative transfer models require vast computing resources to solve cloud transmittance and reflectance from the radiative transfer equation. As a result, models offering precise simulation in operations often acquire individual cloud transmittance or reflectance from a lookup table precomputed for practicable scenarios. To further expedite the computation of global horizontal irradiance and to reduce the storage requirements, the Fast All-sky Radiation Model for Solar applications (FARMS) parameterized the lookup table using elementary functions with specified coefficients. This study extends FARMS direct normal irradiance (DNI) computation by utilizing hyperbolic tangent functions and various polynomial functions to parameterize the cloud transmittance for scattered solar radiation in the circumsolar region. The parameterization is implemented in FARMS with DNI (FARMS-DNI) and accounts for the circumsolar radiation when assessing or forecasting DNI. The evaluation, with long-term observations at the National Renewable Energy Laboratory's, Solar Radiation Research Laboratory, and the Atmospheric Radiation Measurement, Southern Great Plains, Central Facility, shows that the parameterized DNIs are virtually identical with those computed by coupling FARMS-DNI to a lookup table of cloud transmittance. This parameterization has diverse applications in radiative transfer models and numerical weather prediction models used to assess or forecast direct solar radiation.

14 SOLAR ENERGY↗
DOE OSTI2025

Chlorophyll- a dynamics in the lower Amazon River: insights from in situ and hyperspectral remote sensing using OCI-PACE

Chlorophyll-a concentration (Chla) is a key indicator of phytoplankton biomass and aquatic trophic status. However, satellite-derived Chla in sediment-rich waters, such as those found in the Lower Amazon River, remains challenging. The present study characterizes in situ Chla levels and their relationships with geographic, physical, and biogeochemical parameters in the Lower Amazon. Data collected between 2014 and 2017 across four hydrological seasons included measurements of Chla, remote sensing reflectance, and water quality parameters such as total suspended sediment, conductivity, water surface temperature, dissolved oxygen, pH, dissolved organic carbon and coloured dissolved organic matter. An empirical model was developed to estimate Chla using simulated hyperspectral bands from NASA’s PACE mission, achieving high performance (R 2 = 0.76; RMSE = 0.11 μg·L −1 ). Red bands proved particularly effective for Chla retrieval, while the addition of ultraviolet bands further enhanced model accuracy. The application of the developed model to satellite imagery yielded results consistent with in situ observations for the same hydrologic season. Seasonal variation and geographic location were major factors influencing Chla dynamics. This study provides a novel contribution to Chla estimation in optically complex, highly turbid waters and highlights the potential of the PACE mission to enhance global aquatic ecosystem monitoring. In conclusion, by offering freely available hyperspectral data with high radiometric resolution, PACE represents a significant advancement in the realm of remote sensing of aquatic environments.

Amazon River↗
DOE OSTI2025

An Advanced Microscopic Energy Consumption Model for Automated Vehicle:Development, Calibration, Verification

The automated vehicle (AV) equipped with the Adaptive Cruise Control (ACC) system is expected to reduce the fuel consumption for the intelligent transportation system. This paper presents the Advanced ACC-Micro (AA-Micro) model, a new energy consumption model based on micro trajectory data, calibrated and verified by empirical data. Utilizing a commercial AV equipped with the ACC system as the test platform, experiments were conducted at the Columbus 151 Speedway, capturing data from multiple ACC and Human-Driven (HV) test runs. The calibrated AA-Micro model integrates features from traditional energy consumption models and demonstrates superior goodness of fit, achieving an impressive 90% accuracy in predicting ACC system energy consumption without overfitting. A comprehensive statistical evaluation of the AA-Micro model's applicability and adaptability in predicting energy consumption and vehicle trajectories indicated strong model consistency and reliability for ACC vehicles, evidenced by minimal variance in RMSE values and uniform RSS distributions. Conversely, significant discrepancies were observed when applying the model to HV data, underscoring the necessity for specialized models to accurately predict energy consumption for HV and ACC systems, potentially due to their distinct energy consumption characteristics.

Ma, Ke↗
NASA NTRS1975

The evolution of climate

A quantitative evaluation is made of the climatic effects of polar wandering plus continental drift in order to determine wether this mechanism alone could explain the deterioration of climate that occurred from the warmth of Mesozoic time to the ice age conditions of the late Cenozoic. By way of procedure, to investigate the effect of the changing geography of the past on climate Adem's thermodynamic model was selected. The application of the model is discussed and preliminary results are given.

Donn, W. L.↗
NASA NTRS1976

Formulation of detailed consumables management models for the development (preoperational) period of advanced space transportation system. Volume 1: Detailed requirements for the mission planning processor

Detailed requirements for the mission planning processor are documented. The mission planning processor is a user oriented tool for consumables management and is part of the total consumables subsystem management concept. A quasi top-down approach was applied to the design of the mission planning processor. Interface requirements, input/output, and data base concepts were considered before computational processing. Existing analytical models were investigated for applicability before new models were developed. The mission planning processor was designed for an interactive system using demand terminals for input/outputdisplay and interfacing with an updateable mission data bank. The control and support routines provide the user interface, peripheral data handling, program control, and support functions required by the mission planning processor for execution on an interactive system. A description, interface requirements, definition of internal variables, listing of input data, processing flow diagram, and listing of output data are presented for each routine.

Connelly, L. C.↗
NASA NTRS1977

Reliability based fatigue design and maintenance procedures

A stochastic model has been developed to describe a probability for fatigue process by assuming a varying hazard rate. This stochastic model can be used to obtain the desired probability of a crack of certain length at a given location after a certain number of cycles or time. Quantitative estimation of the developed model was also discussed. Application of the model to develop a procedure for reliability-based cost-effective fail-safe structural design is presented. This design procedure includes the reliability improvement due to inspection and repair. Methods of obtaining optimum inspection and maintenance schemes are treated.

Hanagud, S.↗
NASA NTRS1980

Spatial aspects of low- and medium-energy electron degradation in N2

Spatial (radial and longitudinal) yield spectra for electron energy degradation in molecular nitrogen gas for 25-eV to 10-keV incident electrons have been generated by using a Monte Carlo technique. These spatial yield spectra associated with the electron degradation process can be employed to calculate a 'yield' for any inelastic state at any position in the medium. These have been analytically represented in terms of a model containing three simple 'microplumes'. Five-dimensional yield spectra which contain the information about the polar angle of the electron have also been analytically represented within the framework of the microplume model. Aeronomical and radiological applications of our model are discussed.

Singhal, R. F.↗
NASA NTRS1984

Considerations for damage analysis of gas turbine hot section components

The hot flowpath of a gas turbine engine contains static and rotating components operating in a very hostile environment. Since the reliable operation of these components is critical to the safe and efficient performance of the engine, structural life analysis of these members is carried out with great care. However, the complex nature of the strain-temperature-time cycle affecting the engine makes a general analysis procedure difficult and usually leads to separating the damage into regimes where one damage mode dominates. In particular, cycle dependent, time dependent, and thermomechanical fatigue regimes have been identified and some general considerations of each region are discussed. This discussion includes both the damage models themselves and the application of the models. Specific examples of several models are given and important factors affecting each are presented.

Cook, T. S.↗
NASA NTRS1985

A demonstrative model of a lunar base simulation on a personal computer

The initial demonstration model of a lunar base simulation is described. This initial model was developed on the personal computer level to demonstrate feasibility and technique before proceeding to a larger computer-based model. Lotus Symphony Version 1.1 software was used to base the demonstration model on an personal computer with an MS-DOS operating system. The personal computer-based model determined the applicability of lunar base modeling techniques developed at an LSPI/NASA workshop. In addition, the personnal computer-based demonstration model defined a modeling structure that could be employed on a larger, more comprehensive VAX-based lunar base simulation. Refinement of this personal computer model and the development of a VAX-based model is planned in the near future.

Source record↗
NASA NTRS2020

Composite Piezoelectric Resonator 1D Modeling with Loss

Abstract – Maximizing the benefit of applying piezoelectric materials in composite structures, which are a mix of piezoelectric and elastic elements, requires analytical modeling. In this paper we describe two main classes of resonators and how to model using network equivalent including material losses. These include extensional devices where the layer areas are parallel to the direction of wave travel as found in a variety of thickness or length mode transducers (BC. Stress T1 = T2 at boundary) and transverse mode resonators (BC. Strain S1 = S2 at boundary) where the major layer area is perpendicular to the wave direction as is found in radial mode or length thickness mode composite resonators. In order to illustrate these models we will use Mason’s equivalent circuits with elastic, dielectric and piezoelectric loss. It is noted that in order to maintain consistency with the linear equations of piezoelectricity and the wave equation care is required when applying complex loss coefficients to the models. Although these techniques are applicable to other models we use Mason’s network equivalent circuit because it is intuitive for composite modeling due to the frequency independent turns ratio and the independence of the equations describing the elastic layers from the adjoining layer properties which aids in understanding the boundary conditions to impose on the layer boundaries. We will present network models for a variety of applications with loss and show how to calculate their impedance spectra, interface stress, strain velocity and displacement spectra.

Sherrit, Stewart↗
NASA NTRSDate not supplied

TPS Certification by Analysis: Model-Driven Characterization of Properties and Failure in Woven Thermal Protection Systems

Woven, ablative thermal protection system (TPS) materials provide a robust option for aggressive (re)entries and thus have been baselined for the upcoming Mars Sample Return (MSR) mission's Earth Entry System (EES). The reliability requirements for MSR-EES necessitate understanding of material property variability, which could be significant given the complex structure and anisotropic nature of properties in TPS weaves, as well as the response to potential impact with micrometeoroids or orbital debris during the EES re-entry. The TPS Certification by Analysis effort within the Entry Systems Modeling project seeks to provide computational models and analyses that support the certification against such material-based risks. For the present talk, focus will be given to the characterization of baseline woven TPS material properties and mechanical failure limits, which entails (1) use of computational techniques (e.g., machine learning) to interpret computed tomography images of the weave to generate representative structural models and (2) application of multiscale material modeling approaches to characterize thermomechanical and failure properties.

thermal protection systems↗
NASA NTRSDate not supplied

TPS Certification by Analysis: Model-Driven Characterization of Properties and Failure in Woven Thermal Protection Systems

Woven, ablative thermal protection system (TPS) materials provide a robust option for aggressive (re)entries and thus have been baselined for the upcoming Mars Sample Return (MSR) mission’s Earth Entry System (EES). The reliability requirements for MSR-EES necessitate understanding of material property variability, which could be significant given the complex structure and anisotropic nature of properties in TPS weaves, as well as the response to potential impact with micrometeoroids or orbital debris during the EES re-entry. The TPS Certification by Analysis effort within the Entry Systems Modeling project seeks to provide computational models and analyses that support the certification against such material-based risks. For the present talk, focus will be given to the characterization of baseline woven TPS material properties and mechanical failure limits, which entails (1) use of computational techniques (e.g., machine learning) to interpret computed tomography images of the weave to generate representative structural models and (2) application of multiscale material modeling approaches to characterize thermomechanical and failure properties.

Justin B. Haskins↗
DOE OSTI2022

Physics-Informed Neural Network Modeling of Li-Ion Batteries: Preprint

Li-ion batteries (LIB) are a promising solution to enable storage of intermittent energy sources due to their high energy density. However, LIBs are known to significantly degrade after about 1000 charge-discharge cycles. LIBs degrade following different degradation modes and at a rate that depends on the operating conditions (external temperature, load). To plan the installation of batteries, appropriate understanding and prediction capabilities of their lifecycle is needed. High-fidelity numerical models of LIBs such as the pseudo-two-dimensional (P2D) model have been shown to accurately represent the charge-discharge-cycle of an LIB given ac- curate choice of the physical parameters. Given the large number of P2D parameters, adjusting them using forward runs is intractable. This work describes the development of a physics-informed neural network (PINN) as a surrogate substitute of the P2D model that captures parameter dependence. The PINN is advantageous as it needs little to no data, and can naturally encode the dependence of every model parameter. Here, a specific training procedure is adopted to efficiently cover parameter space, handle model stiffness and enforce boundary conditions. The trained PINN is validated against numerical solutions of the P2D model, and its applicability to battery degradation modeling is discussed.

battery degradation↗
DOE OSTI2020

Validation and application of a multiphase CFD model for hydrodynamics, temperature field and RTD simulation in a pilot-scale biomass pyrolysis vapor phase upgrading reactor

Accurate prediction of transport phenomena is critical for VPU reactor design, optimization, and scale-up. The current study focused on the validation and application of a multiphase CFD model within an open-source code MFiX for hydrodynamics, temperature field, and residence time distribution (RTD) simulation in a non-reacting circulating fluidized bed riser for biomass pyrolysis vapor phase upgrading (VPU). First, an Eulerian-Eulerian approach three-dimensional CFD model was employed to simulate the pilot-scale VPU riser on the supercomputer Joule. Excellent quantitative agreement between experimental and simulated results was achieved for pressure drops and temperature field in a range of operating conditions. Then the validated multiphase CFD model was applied to predict gas and solid residence time distributions (RTDs) since prediction and analysis of RTD is an important tool to study the complex multiphase flow behavior and mixing inside chemical reactors. The predictions show that solid mean residence time is 3.5 times the gas residence time; the solid RTD is more sensitive to the process gas flow rate than the solids circulation rate.

09 BIOMASS FUELS↗
DOE OSTI2019

Advancing Turbulent Spray and Combustion Models for Compression Ignition Engine Simulations

This thesis seeks to investigate the turbulent mixing influence on spray atomization and combustion processes encountered in compression ignition diesel engines. Despite greater thermal efficiency of diesel engine than spark ignition engine, the nature of stratified air-fuel mixture and non-premixed flame gives rise to unacceptable levels of nitrogen oxides (NOx) and particulate matter (PM), thus the use of diesel engines has often been limited to heavy-duty vehicle and industrial power sources. However, recent advancement in diesel engine combustion strategies, e.g. low temperature com-bustion (LTC), has demonstrated promising pathways towards improvement in the engine-out pollutants. Therefore, particularly in the effort of computer-aided en-gine design tasks, such a new engine design concept requires more accurate modeling techniques applicable over a broader range of engine operating conditions than those of conventional engine strategies. In the notion of challenges in new engine operat-ing conditions, this thesis aims to present successful implementation of improvement in numerical modeling techniques in high-pressure spray atomization and resulting turbulent spray flame of interest. Three-dimensional Computational Fluid Dynamics (CFD) in in-cylinder turbu-lent combustion is considered an integral part of engine design progress, but rather a cost-prohibitive to apply over a broad range of engine relevant conditions. In spite of successful use of existing spray atomization modeling, prior researchers have pointed out some degree of failure in LTC targeted injection strategies. Furthermore, finite rate and strong nonlinearity of chemistry influenced by local turbulent mixing still re-main in challenges to account for in cost-efficient CFD analysis. In this context, a new attempt of hybrid spray primary breakup modeling is presented and demonstrated in successful application aimed at LTC technique. In addition, the Representative Interactive Flamelets (RIF) model with aid of multi-flamelets approach is extensively assessed in terms of predictive capability against classical combustion model. The combustion model employed in this study are fully examined in the general diesel combustion metric, e.g., ignition delay and flame lift-off length as well as newly sug-gested test metric, combustion recession. The combustion recession has been recently idenfied, but still remain largely unknown. Since the governing physics of this phe-nomenon is characterized by turbulent mixing coupled with finite rate chemistry, this can be considered as a relevant test metric for turbulent combustion models. In ad-dition, very recent experimental studies have introduced a new non-sooting diesel combustion technique by manipulating direct injection method. The ducted fuel in-jection (DFI) has thus been demonstrated with its potential of low soot emissions. Knowing that the duct equipped ahead of injector nozzle was identifed to enhance turbulent mixing, investigations of DFI combustion may prove the effectiveness of turbulece-chemistry interaction modeling. This thesis presents comprehensive under-standings of aformentioned diesel combustion techniques in terms of several important physics keywords, e.g., turbulent mixing and detailed chemistry

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Application of machine learning for modeling brønsted-guggenheim-scatchard specific ion interaction theory (SIT) coefficients

Machine learning methodologies can provide insight into Brønsted-Guggenheim-Scatchard specific ion interaction theory (SIT) parameter values where experimental data availability may be limited. This study develops and executes machine learning frameworks to model the SIT interaction coefficient, ε. Key findings include successful estimations of ε via artificial neural networks using clustering and value prediction approaches. Additionally, applicability to other chemical parameters is also assessed briefly. Models developed here provide support for a use-case of machine learning in geologic nuclear waste disposal research applications, namely in predictions of chemical behaviors of high ionic strength solutions (i.e., subsurface brines).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

$\mathrm{PPT}$-Multicore: performance prediction of Open$\mathrm{MP}$ applications using reuse profiles and analytical modeling

In this report we present PPT-Multicore, an analytical model embedded in the Performance Prediction Toolkit (PPT) to predict parallel applications’ performance running on a multicore processor. PPT-Multicore builds upon our previous work towards a multicore cache model. We extract LLVM basic block labeled memory trace using an architecture-independent LLVM-based instrumentation tool only once in an application’s lifetime. The model uses the memory trace and other parameters from an instrumented sequentially executed binary. We use probabilistic and computationally efficient reuse profiles to predict the cache hit rates and runtimes of OpenMP programs’ parallel sections. We model Intel’s Broadwell, Haswell, and AMD’s Zen2 architectures and validate our framework using different applications from PolyBench and PARSEC benchmark suites. The results show that PPT-Multicore can predict cache hit rates with an overall average error rate of 1.23% while predicting the runtime with an error rate of 9.08%.

97 MATHEMATICS AND COMPUTING↗