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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 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Version 8 SBUV Ozone Profile Trends Compared with Trends from a Zonally Averaged Chemical Model

Linear regression trends for the years 1979-2003 were computed using the new Version 8 merged Solar Backscatter Ultraviolet (SBUV) data set of ozone profiles. These trends were compared to trends computed using ozone profiles from the Goddard Space Flight Center (GSFC) zonally averaged coupled model. Observed and modeled annual trends between 50 N and 50 S were a maximum in the higher latitudes of the upper stratosphere, with southern hemisphere (SH) trends greater than northern hemisphere (NH) trends. The observed upper stratospheric maximum annual trend is -5.5 +/- 0.9 % per decade (1 sigma) at 47.5 S and -3.8 +/- 0.5 % per decade at 47.5 N, to be compared with the modeled trends of -4.5 +/- 0.3 % per decade in the SH and -4.0 +/- 0.2% per decade in the NH. Both observed and modeled trends are most negative in winter and least negative in summer, although the modeled seasonal difference is less than observed. Model trends are shown to be greatest in winter due to a repartitioning of chlorine species and the increasing abundance of chlorine with time. The model results show that trend differences can occur depending on whether ozone profiles are in mixing ratio or number density coordinates, and on whether they are recorded on pressure or altitude levels.

Rosenfield, Joan E.↗
NASA NTRSDate not supplied

The Impact of Atmospheric Dynamics and Anthropogenic Very Short-Lived Chlorine Species on the Recovery of Extra-Polar Ozone

The successful implementation of the Montreal Protocol has led to a decrease in the atmospheric abundance of ozone-depleting substances and a slowing in the destruction of the ozone layer. Previous studies have suggested that atmospheric dynamics, or a rise in compounds not regulated by the Montreal Protocol, such as very short-lived chlorine species (VSL Cl), may lead to a slower than expected recovery of the ozone layer. In this presentation, we examine the expected recovery of total column ozone (TCO) and stratospheric column ozone (SCO) to values observed in 1980 using a novel multiple linear regression (MLR) model that involves a month-by-month regression. The MLR model is trained to TCO anomalies from six data records (SBUV v8.7 MOD, SBUV v8.6 COH, WOUDC, GSG, GTO-ECV, MSR-2) over 1979 to 2021 for the Northern Hemisphere (35N – 60N), the Southern Hemisphere (60S – 35S) and the Tropics (20S – 20N), and SCO anomalies from ML-TOMCAT for these same zonal bands. The MLR includes the effect of halogens (equivalent effective stratospheric chlorine (EESC)), total solar irradiance, stratospheric aerosol optical depth, quasi-biennial oscillation, and the El Niño Southern Oscillation as regressors. We also include the effect of atmospheric dynamics such as the Brewer-Dobson Circulation, Arctic Oscillation, and the Antarctic Oscillation, and VSL Cl species in the formulation of EESC. We use a novel approach to the MLR framework, by separating the observed and regressor time series into the respective months (separating all of the Januarys, Februarys, etc.). Then we conduct a regression for each month from 1979 to 2021 for the three zonal bands denoted above. The model results for each month are combined together to achieve a full monthly time series from 1979 to 2021. We use this novel approach to ascertain the effect of atmospheric dynamics on TCO and SCO, since the dynamical proxies affect ozone in a distinctly different manner for various months. In this presentation, we will quantify the role of both the inclusion of VSL Cl species in the formulation of EESC as well as atmospheric dynamics in explaining the slower than expected recovery of extra-polar TCO and SCO over the past decade.

Laura A. McBride↗
DOE OSTI2024

Evaluating county-level lung cancer incidence from environmental radiation exposure, PM 2.5 , and other exposures with regression and machine learning models

Characterizing the interplay between exposures shaping the human exposome is vital for uncovering the etiology of complex diseases. For example, cancer risk is modified by a range of multifactorial external environmental exposures. Environmental, socioeconomic, and lifestyle factors all shape lung cancer risk. However, epidemiological studies of radon aimed at identifying populations at high risk for lung cancer often fail to consider multiple exposures simultaneously. For example, moderating factors, such as PM 2.5 , may affect the transport of radon progeny to lung tissue. This ecological analysis leveraged a population-level dataset from the National Cancer Institute’s Surveillance, Epidemiology, and End-Results data (2013–17) to simultaneously investigate the effect of multiple sources of low-dose radiation (gross γ activity and indoor radon) and PM 2.5 on lung cancer incidence rates in the USA. County-level factors (environmental, sociodemographic, lifestyle) were controlled for, and Poisson regression and random forest models were used to assess the association between radon exposure and lung and bronchus cancer incidence rates. Tree-based machine learning (ML) method perform better than traditional regression: Poisson regression: 6.29/7.13 (mean absolute percentage error, MAPE), 12.70/12.77 (root mean square error, RMSE); Poisson random forest regression: 1.22/1.16 (MAPE), 8.01/8.15 (RMSE). The effect of PM 2.5 increased with the concentration of environmental radon, thereby confirming findings from previous studies that investigated the possible synergistic effect of radon and PM 2.5 on health outcomes. In summary, the results demonstrated (1) a need to consider multiple environmental exposures when assessing radon exposure’s association with lung cancer risk, thereby highlighting (1) the importance of an exposomics framework and (2) that employing ML models may capture the complex interplay between environmental exposures and health, as in the case of indoor radon exposure and lung cancer incidence.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2024

Leveraging design of experiments to build chemometric models for the quantification of uranium (VI) and HNO3 by Raman spectroscopy

Partial least squares regression (PLSR) and support vector regression (SVR) models were optimized for the quantification of U(VI) (10–320 g L −1 ) and HNO 3 (0.6–6 M) by Raman spectroscopy with optimized calibration sets chosen by optimal design of experiments. The designed approach effectively minimized the number of samples in the calibration set for PLSR and SVR by selecting sample concentrations with a quadratic process model, despite complex confounding and covarying spectral features in the spectra. The top PLS2 model resulted in percent root mean square errors of prediction for U(VI), HNO 3 , and NO 3 − of 3.7%, 3.6%, and 2.9%, respectively. PLS1 models performed similarly despite modeling an analyte with a majority linear response (i.e., uranyl symmetric stretch) and another with more covarying vibrational modes (i.e., HNO 3 ). Partial least squares (PLS) model loadings and regression coefficients were evaluated to better understand the relationship between weaker Raman bands and covarying spectral features. Support vector machine models outperformed PLS1 models, resulting in percent root mean square error of prediction values for U(VI) and HNO 3 of 1.5% and 3.1%, respectively. The optimal nonlinear SVR model was trained using a similar number of samples (11) compared with the PLSR model, even though PLS is a linear modeling approach. The generic D-optimal design presented in this work provides a robust statistical framework for selecting training set samples in disparate two-factor systems. This approach reinforces Raman spectroscopy for the quantification of species relevant to the nuclear fuel cycle and provides a robust chemometric modeling approach to bolster online monitoring in challenging process environments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Deep energy-pressure regression for a thermodynamically consistent EOS model

Abstract In this paper, we aim to explore novel machine learning (ML) techniques to facilitate and accelerate the construction of universal equation-Of-State (EOS) models with a high accuracy while ensuring important thermodynamic consistency. When applying ML to fit a universal EOS model, there are two key requirements: (1) a high prediction accuracy to ensure precise estimation of relevant physics properties and (2) physical interpretability to support important physics-related downstream applications. We first identify a set of fundamental challenges from the accuracy perspective, including an extremely wide range of input/output space and highly sparse training data. We demonstrate that while a neural network (NN) model may fit the EOS data well, the black-box nature makes it difficult to provide physically interpretable results, leading to weak accountability of prediction results outside the training range and lack of guarantee to meet important thermodynamic consistency constraints. To this end, we propose a principled deep regression model that can be trained following a meta-learning style to predict the desired quantities with a high accuracy using scarce training data. We further introduce a uniquely designed kernel-based regularizer for accurate uncertainty quantification. An ensemble technique is leveraged to battle model overfitting with improved prediction stability. Auto-differentiation is conducted to verify that necessary thermodynamic consistency conditions are maintained. Our evaluation results show an excellent fit of the EOS table and the predicted values are ready to use for important physics-related tasks.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1975

Comparison of some biased estimation methods (including ordinary subset regression) in the linear model

Ridge, Marquardt's generalized inverse, shrunken, and principal components estimators are discussed in terms of the objectives of point estimation of parameters, estimation of the predictive regression function, and hypothesis testing. It is found that as the normal equations approach singularity, more consideration must be given to estimable functions of the parameters as opposed to estimation of the full parameter vector; that biased estimators all introduce constraints on the parameter space; that adoption of mean squared error as a criterion of goodness should be independent of the degree of singularity; and that ordinary least-squares subset regression is the best overall method.

Sidik, S. M.↗
NASA NTRS2011

Lateral-Directional Parameter Estimation on the X-48B Aircraft Using an Abstracted, Multi-Objective Effector Model

The problem of parameter estimation on hybrid-wing-body aircraft is complicated by the fact that many design candidates for such aircraft involve a large number of aerodynamic control effectors that act in coplanar motion. This adds to the complexity already present in the parameter estimation problem for any aircraft with a closed-loop control system. Decorrelation of flight and simulation data must be performed in order to ascertain individual surface derivatives with any sort of mathematical confidence. Non-standard control surface configurations, such as clamshell surfaces and drag-rudder modes, further complicate the modeling task. In this paper, time-decorrelation techniques are applied to a model structure selected through stepwise regression for simulated and flight-generated lateral-directional parameter estimation data. A virtual effector model that uses mathematical abstractions to describe the multi-axis effects of clamshell surfaces is developed and applied. Comparisons are made between time history reconstructions and observed data in order to assess the accuracy of the regression model. The Cram r-Rao lower bounds of the estimated parameters are used to assess the uncertainty of the regression model relative to alternative models. Stepwise regression was found to be a useful technique for lateral-directional model design for hybrid-wing-body aircraft, as suggested by available flight data. Based on the results of this study, linear regression parameter estimation methods using abstracted effectors are expected to perform well for hybrid-wing-body aircraft properly equipped for the task.

Ratnayake, Nalin A.↗
NASA NTRS1997

Assessment of a remote sensing-based model for predicting malaria transmission risk in villages of Chiapas, Mexico

A blind test of two remote sensing-based models for predicting adult populations of Anopheles albimanus in villages, an indicator of malaria transmission risk, was conducted in southern Chiapas, Mexico. One model was developed using a discriminant analysis approach, while the other was based on regression analysis. The models were developed in 1992 for an area around Tapachula, Chiapas, using Landsat Thematic Mapper (TM) satellite data and geographic information system functions. Using two remotely sensed landscape elements, the discriminant model was able to successfully distinguish between villages with high and low An. albimanus abundance with an overall accuracy of 90%. To test the predictive capability of the models, multitemporal TM data were used to generate a landscape map of the Huixtla area, northwest of Tapachula, where the models were used to predict risk for 40 villages. The resulting predictions were not disclosed until the end of the test. Independently, An. albimanus abundance data were collected in the 40 randomly selected villages for which the predictions had been made. These data were subsequently used to assess the models' accuracies. The discriminant model accurately predicted 79% of the high-abundance villages and 50% of the low-abundance villages, for an overall accuracy of 70%. The regression model correctly identified seven of the 10 villages with the highest mosquito abundance. This test demonstrated that remote sensing-based models generated for one area can be used successfully in another, comparable area.

Insect Vectors/growth & development↗
DOE OSTI2021

Comparison of Probabilistic Forecasts for Predictive Voltage Control

This paper explores predictive cooperative voltage control in distribution systems with highly variable sources such as photovoltaics (PV). The goal is to maintain the voltage profile within the limits despite the fluctuations due to sudden changes in solar power generation. The predictive voltage control method relies on probabilistic solar power and load forecasts to select the optimum Voltage Regulator (VR) taps appropriately. VR taps are selected to minimize the risk of voltage violation. A modified version of the IEEE 123 system is used as the case study. A 100% penetration of solar power is assumed for the distribution system with profiles for solar generation and loads added to the system. Three different probabilistic forecast models (Quantile Regression (QR), Gaussian distribution and volatility forecasting using Generalized Autoregressive Conditional Heteroskedasticity (GARCH)) are explored in this study. The results for the VR taps and Voltage Deviation Index (VDI) are compared to find the most effective forecast model.

Panamtash, Hossein↗
DOE OSTI2025

Parameter uncertainties for imperfect surrogate models in the low-noise regime

Abstract Bayesian regression determines model parameters by minimizing the expected loss, an upper bound to the true generalization error. However, this loss ignores model form error, or misspecification, meaning parameter uncertainties are significantly underestimated and vanish in the large data limit. As misspecification is the main source of uncertainty for surrogate models of low-noise calculations, such as those arising in atomistic simulation, predictive uncertainties are systematically underestimated. We analyze the true generalization error of misspecified, near-deterministic surrogate models, a regime of broad relevance in science and engineering. We show that posterior parameter distributions must cover every training point to avoid a divergence in the generalization error and design a compatible ansatz which incurs minimal overhead for linear models. The approach is demonstrated on model problems before application to thousand-dimensional datasets in atomistic machine learning. Our efficient misspecification-aware scheme gives accurate prediction and bounding of test errors in terms of parameter uncertainties, allowing this important source of uncertainty to be incorporated in multi-scale computational workflows.

Swinburne, Thomas D. (ORCID:0000000232554257)↗
DOE OSTI2020

Probabilistic Power Consumption Modeling for Commercial Buildings Using Logistic Regression Markov Chain

The total energy consumed by buildings takes up to 40% of U.S. energy use, in which a large portion is contributed by commercial buildings. Building performance optimization is desirable but requires accurate building models with uncertainties taken into account. This paper proposes a novel probabilistic modeling method using Logistic Regression Markov Chain (LRMC). The LRMC model enhances the performance of traditional Markov Chain (MC) models by adopting time-variant transition matrices calibrated using logistic regression with exogenous inputs. Compared with existing building models, the proposed model produces accurate multi-step modeling results with full probability distribution. The proposed probabilistic building model is tested using actual commercial building measurements and modeling performance is evaluated with two probabilisitc metrics. The results show that the LRMC model has higher accuracy than traditional MC model and Logistic Regression (LR) model in that it yields lower error scores under both evaluation metrics.

Building modeling↗
DOE OSTI2023

MLMOD: Machine Learning Methods for Data-Driven Modeling in LAMMPS

MLMOD is a software package for incorporating machine learning approaches and models into simulations of microscale mechanics and molecular dynamics in LAMMPS. Recent machine learning approaches provide promising data-driven approaches for learning representations for system behaviors from experimental data and high fidelity simulations. The package facilitates learning and using data-driven models for (i) dynamics of the system at larger spatial-temporal scales (ii) interactions between system components, (iii) features yielding coarser degrees of freedom, and (iv) features for new quantities of interest characterizing system behaviors. MLMOD provides hooks in LAMMPS for (i) modeling dynamics and time-step integration, (ii) modeling interactions, and (iii) computing quantities of interest characterizing system states. The package allows for use of machine learning methods with general model classes including Neural Networks, Gaussian Process Regression, Kernel Models, and other approaches. Here we discuss our prototype C++/Python package, aims, and example usage. For related papers, examples, updates, and additional information see https://github.com/atzberg/mlmod and http://atzberger.org/.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2001

Time-dependent oral absorption models

The plasma concentration-time profiles following oral administration of drugs are often irregular and cannot be interpreted easily with conventional models based on first- or zero-order absorption kinetics and lag time. Six new models were developed using a time-dependent absorption rate coefficient, ka(t), wherein the time dependency was varied to account for the dynamic processes such as changes in fluid absorption or secretion, in absorption surface area, and in motility with time, in the gastrointestinal tract. In the present study, the plasma concentration profiles of propranolol obtained in human subjects following oral dosing were analyzed using the newly derived models based on mass balance and compared with the conventional models. Nonlinear regression analysis indicated that the conventional compartment model including lag time (CLAG model) could not predict the rapid initial increase in plasma concentration after dosing and the predicted Cmax values were much lower than that observed. On the other hand, all models with the time-dependent absorption rate coefficient, ka(t), were superior to the CLAG model in predicting plasma concentration profiles. Based on Akaike's Information Criterion (AIC), the fluid absorption model without lag time (FA model) exhibited the best overall fit to the data. The two-phase model including lag time, TPLAG model was also found to be a good model judging from the values of sum of squares. This model also described the irregular profiles of plasma concentration with time and frequently predicted Cmax values satisfactorily. A comparison of the absorption rate profiles also suggested that the TPLAG model is better at prediction of irregular absorption kinetics than the FA model. In conclusion, the incorporation of a time-dependent absorption rate coefficient ka(t) allows the prediction of nonlinear absorption characteristics in a more reliable manner.

Non-NASA Center↗
NASA NTRS2014

Peak Oxygen Uptake during and after Long-duration Space Flight

Aerobic capacity (VO2peak) previously has not been measured during or after long-duration spaceflight. PURPOSE: To measure VO2peak and submaximal exercise responses during and after International Space Station (ISS) missions. METHODS: Astronauts (9 M, 5 F: 49 +/- 5 yr, 175 +/- 7 cm, 77.2 +/- 15.1 kg, 40.6 +/- 6.4 mL/kg/min [mean +/-SD]) performed graded peak cycle tests ~90 days before spaceflight, 15 d (FD15) after launch and every ~30 d thereafter during flight, and 1 (R+1), 10 (R+10), and 30 d (R+30) after landing. Oxygen consumption (VO2) and heart rate (HR) were measured from rest to peak exercise, while cardiac output (Q), stroke volume (SV), and arterial-venous oxygen difference (a-vO2diff) were measured only during rest and submaximal exercise. Data were analyzed using mixed-model linear regression. Body mass contributed significantly to statistical models, and thus results are reported as modeled estimates for an average subject. RESULTS: Early inflight (FD15) VO2peak was 17% lower (95% CI = - 22%, -13%) than preflight. VO2peak increased during spaceflight (0.001 L/min/d, P = 0.02) but did not return to preflight levels. On R+1 VO2peak was 15% (95% CI = -19%, -10%) lower than preflight but recovered to within 2% of preflight by R+30 (95% CI = -6%, +3%). Peak HR was not significantly different from preflight at any time. Inflight submaximal VO2 and a-vO2diff were generally lower than preflight, but the Q vs. VO2 slope was unchanged. In contrast, the SV vs. VO2 slope was lower (P < 0.001), primarily due to elevated SV at rest, and the HR vs. VO2 slope was greater (P < 0.001), largely due to elevated HR during more intense exercise. On R+1 although the relationships between VO2 and Q, SV, and HR were not statistically different than preflight, resting and submaximal exercise SV was lower (P < 0.001), resting and submaximal exercise HR was higher (P < 0.002), and a-vO2diff was unchanged. HR and SV returned to preflight levels by R+30. CONCLUSION: In the average astronaut VO2peak was reduced during spaceflight and immediately after landing but factors contributing to lower VO2peak may be different during spaceflight and recovery. Maintaining Q while VO2 is reduced inflight may be suggestive of an elevated blood flow to vascular beds other than exercising muscles, but decreased SV after flight likely reduces Q at peak exertion.

Moore, Alan D., Jr.↗
DOE OSTI2020

Development of the Intelligent, Preventive Infrared (IR) Inspection System Housed in Hybrid Robotic Platforms

Robots and robotic systems that are designed for inspection, environmental study, and health and safety aid are becoming an increasing necessity. However, there is a number of challenges that accompany robots that are designed for these specific applications. These challenges include: navigating compact, enclosed spaces, travelling over multiple terrains and large obstacles, using the proper sensing and detection methods to assess an environment, and the use of lightweight and durable materials. The robotic platforms currently in development look at all of these challenges and attempt to overcome them. These designs specific use of hybrid robotic platforms, or platform that utilizes soft and rigid materials, allows for a more flexible platform and makes environments more navigable. To further improve the navigation of the platforms and environmental assessment, a novel infrared detection system is housed in the platforms to create a robot that can be used for the applications listed above and more. Objectives: Further develop two types of robotic platforms that utilize additive manufacturing, soft materials, and rigid materials. Continue the development of an intelligent and inhibitory infrared detection system based on an artificial intelligence (AI) algorithm. Continued study and fabrication of active soft materials designed for both sensing and actuation in hybrid robotic systems. Improve additive manufacturing fabrication to design rigid and semi-rigid components for hybrid robotic platforms. Transformable Wheel Robotic Platform: The chassis, wheels, tires and inspection system housing use different additive manufacturing techniques for fabrication. Continued work with additive manufacturing has lead to studies in metal-based printing and modular design and manufacturing. The new platform design with integrated electrical component printed. This will allow integration of the sensor housing onto the platform. Electrical components are being tested for battery life and performance. To improve this performance, such as integration of Lithium Polymer (LiPo) batteries. Snake Robotic Platform: The main focus of the development has centered around liquid-based soft actuators. That act on the principles of electrostatic and hydraulic actuation. A liquid dielectric sits between two compliant electrodes, contained by a flexible polymer shell. The electrodes and film gradually collapse toward each other from one corner of the electrode to the other. When the electrodes and film close together, a majority of the fluid is pushed into the area not covered by an electrode. A thin layer of the liquid dielectric remains between the electrode. The actuators will be stacked to cause large displacement, and move the linkages. The chassis of this platform uses purely additively manufactured linkages. Intelligent, Preventive IR Inspection System: Development of the AI for the system has lead to using a Scikit-Learn which assists in creating predictive models based on Regression, clustering, classification etc. To improve the infrared thermometry for low emissivity sources, work on the fabrication of a tandem photoconductive infrared thermometer was a main focus. Distance-Voltage-Temperature response data has been collected in the range of 7 cm - 100 cm and 200-400 deg. C. Modifications were made to the existing test bench to have a better control over the data. Automated data collection was realized using a Python code and an Arduino controlled stepper motor to increase the sample rate. A protective enclosure has been built around the setup to minimize the effect of the environment on the measurements. To better predict temperature, different AI models are being optimized. The regression model, LARS showed a high accuracy but had convergence issues and only works for the current test set-up. Results: The Transformable Wheel Robot has developed into a more flexible and modular platform. With the improvements to the current work, effort on the tire or soft gripper design been a large focus. The soft grippers will be interchange able to allow for increased performance in identified terrain types. The development of the liquid-based actuators allows the snake robotic platform to achieve the goals of being flexible and able to navigate confined spaces. However, there is room for improvement. Optimization work is currently being done in COMSOL Multiphysics. With the current IR system set-up the LARS model perfectly predicts the data; however, considering the mobility aspect of the project other models will allow for an optimized system. Testing of other model types is currently being done.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Tensor Basis Gaussian Process Models of Hyperelastic Materials

In this study, we develop Gaussian process regression (GPR) models of isotropic hyperelastic material behavior. First, we consider the direct approach of modeling the components of the Cauchy stress tensor as a function of the components of the Finger stretch tensor in a Gaussian process. We then consider an improvement on this approach that embeds rotational invariance of the stress-stretch constitutive relation in the GPR representation. This approach requires fewer training examples and achieves higher accuracy while maintaining invariance to rotations exactly. Finally, we consider an approach that recovers the strain-energy density function and derives the stress tensor from this potential. Although the error of this model for predicting the stress tensor is higher, the strain-energy density is recovered with high accuracy from limited training data. The approaches presented here are examples of physics-informed machine learning. They go beyond purely data-driven approaches by embedding the physical system constraints directly into the Gaussian process representation of materials models.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Generation of Data-Driven Expected Energy Models for Photovoltaic Systems

Although unique expected energy models can be generated for a given photovoltaic (PV) site, a standardized model is also needed to facilitate performance comparisons across fleets. Current standardized expected energy models for PV work well with sparse data, but they have demonstrated significant over-estimations, which impacts accurate diagnoses of field operations and maintenance issues. This research addresses this issue by using machine learning to develop a data-driven expected energy model that can more accurately generate inferences for energy production of PV systems. Irradiance and system capacity information was used from 172 sites across the United States to train a series of models using Lasso linear regression. The trained models generally perform better than the commonly used expected energy model from international standard (IEC 61724-1), with the two highest performing models ranging in model complexity from a third-order polynomial with 10 parameters (Radj2 = 0.994) to a simpler, second-order polynomial with 4 parameters (Radj2=0.993), the latter of which is subject to further evaluation. Subsequently, the trained models provide a more robust basis for identifying potential energy anomalies for operations and maintenance activities as well as informing planning-related financial assessments. We conclude with directions for future research, such as using splines to improve model continuity and better capture systems with low (≤1000 kW DC) capacity.

14 SOLAR ENERGY↗
NASA NTRS1990

Modeling Solar Lyman Alpha Irradiance

Solar Lyman alpha irradiance is estimated from various solar indices using linear regression analyses. Models developed with multiple linear regression analysis, including daily values and 81-day running means of solar indices, predict reasonably well both the short- and long-term variations observed in Lyman alpha. It is shown that the full disk equivalent width of the He line at 1083 nm offers the best proxy for Lyman alpha, and that the total irradiance corrected for sunspot effect also has a high correlation with Lyman alpha.

Pap, J.↗