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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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NASA NTRS2005

Reference Models for Structural Technology Assessment and Weight Estimation

Previously the Exploration Concepts Branch of NASA Langley Research Center has developed techniques for automating the preliminary design level of launch vehicle airframe structural analysis for purposes of enhancing historical regression based mass estimating relationships. This past work was useful and greatly reduced design time, however its application area was very narrow in terms of being able to handle a large variety in structural and vehicle general arrangement alternatives. Implementation of the analysis approach presented herein also incorporates some newly developed computer programs. Loft is a program developed to create analysis meshes and simultaneously define structural element design regions. A simple component defining ASCII file is read by Loft to begin the design process. HSLoad is a Visual Basic implementation of the HyperSizer Application Programming Interface, which automates the structural element design process. Details of these two programs and their use are explained in this paper. A feature which falls naturally out of the above analysis paradigm is the concept of "reference models". The flexibility of the FEA based JAVA processing procedures and associated process control classes coupled with the general utility of Loft and HSLoad make it possible to create generic program template files for analysis of components ranging from something as simple as a stiffened flat panel, to curved panels, fuselage and cryogenic tank components, flight control surfaces, wings, through full air and space vehicle general arrangements.

Cerro, Jeff↗

NASA NTRS2002

Neural Network and Regression Soft Model Extended for PAX-300 Aircraft Engine

In fiscal year 2001, the neural network and regression capabilities of NASA Glenn Research Center's COMETBOARDS design optimization testbed were extended to generate approximate models for the PAX-300 aircraft engine. The analytical model of the engine is defined through nine variables: the fan efficiency factor, the low pressure of the compressor, the high pressure of the compressor, the high pressure of the turbine, the low pressure of the turbine, the operating pressure, and three critical temperatures (T(sub 4), T(sub vane), and T(sub metal)). Numerical Propulsion System Simulation (NPSS) calculations of the specific fuel consumption (TSFC), as a function of the variables can become time consuming, and numerical instabilities can occur during these design calculations. "Soft" models can alleviate both deficiencies. These approximate models are generated from a set of high-fidelity input-output pairs obtained from the NPSS code and a design of the experiment strategy. A neural network and a regression model with 45 weight factors were trained for the input/output pairs. Then, the trained models were validated through a comparison with the original NPSS code. Comparisons of TSFC versus the operating pressure and of TSFC versus the three temperatures (T(sub 4), T(sub vane), and T(sub metal)) are depicted in the figures. The overall performance was satisfactory for both the regression and the neural network model. The regression model required fewer calculations than the neural network model, and it produced marginally superior results. Training the approximate methods is time consuming. Once trained, the approximate methods generated the solution with only a trivial computational effort, reducing the solution time from hours to less than a minute.

Patnaik, Surya N.↗

NASA NTRS2020

Airborne Science Mission Capabilities of the NASA DC-8 and Possible Alternative Aircraft

Age-related concerns associated with operating the 50-year-old NASA Airborne Science Program DC-8 Flying Laboratory aircraft are raising concerns about the prospects of someday retiring the NASA DC-8. Given that eventuality, this study examines how current and anticipated science requirements can be incorporated with aeronautical performance and cost metrics for assessing, comparing, and down-selecting candidate replacement aircraft. A literature review of NASA DC-8 missions and interviews with current DC-8 science users provides context for the initial phase of this study and offers a pathway for collecting aircraft specifications. Performance models are developed and calibrated against published range-payload data for various mission fuel and payload weight combinations. The models are extended to three airborne science missions, with payload weights ranging from 30,000-52,000 lb and mission ranges up to 5,050 nmi. Results point towards the 767-200ER aircraft as a top contender offering a bit more experimenter floor-space, payload capacity, and range than the NASA DC-8, and to the U.S. Navy P-8 aircraft as a viable option offering perhaps not too much less of those items, with the relative benefit of using less fuel per mission.

Thomas A Ozoroski↗

DOE OSTI2024

Prediction of laser beam spatial profiles in a high-energy laser facility by use of deep learning

We adapt the significant advances achieved recently in the field of generative artificial intelligence/machine-learning to laser performance modeling in multipass, high-energy laser systems with application to high-shot-rate facilities relevant to inertial fusion energy. Advantages of neural-network architectures include rapid prediction capability, data-driven processing, and the possibility to implement such architectures within future low-latency, low-power consumption photonic networks. Four models were investigated that differed in their generator loss functions and utilized the U-Net encoder/decoder architecture with either a reconstruction loss alone or combined with an adversarial network loss. We achieved inference times of 1.3 ms for a 256 × 256 pixel near-field beam with errors in predicted energy of the order of 1% over most of the energy range. It is shown that prediction errors are significantly reduced by ensemble averaging the models with different weight initializations. These results suggest that including the temporal dimension in such models may provide accurate, real-time spatiotemporal predictions of laser performance in high-shot-rate laser systems.

47 OTHER INSTRUMENTATION↗

NASA NTRS1975

Intumescent coating modeling

A thermal response mathematical model for an intumescent coating system is developed. The model predicts indepth temperature profiles of the solid as a function of the fundamental kinetic, mass, and heat-transfer properties of the original coating system. In the model, swelling is based on an empirical function of the paint's weight loss. The model is used to describe the effectiveness of the coating for thermal protection. Using the thermal response time at the backface of the material as a criterion, a sensitivity analysis is made with respect to variation in individual properties, such as total expansion, to provide a guide for designing improved intumescing agents. The sensitivity analysis shows that total expansion and rate of expansion, along with other material properties, are important parameters for providing thermal protection. Finally, backface temperature histories are predicted and compared to experimental data obtained from a representative fire environment. Predicted temperatures agree within a maximum error of plus or minus 20%.

Cagliostro, D. E.↗

NASA NTRS1994

Determination of ocean tides from the first year of TOPEX/POSEIDON altimeter measurements

An improved geocentric global ocean tide model has been determined using 1 year of TOPEX/POSEIDON altimeter measurements to provide corrections to the Cartwright and Ray (1991) model (CR91). The corrections were determined on a 3 deg x 3 deg grid using both the harmonic analysis method and the response method. The two approaches produce similar solutions. The effect on the tide solution of simultaneously adjusting radial orbit correction parameters using altimeter measurements was examined. Four semidiurnal (N(sub 2), M(sub 2), S(sub 2) and K(sub 2)), four diurnal (Q(sdub 1), O(sub 1), P(sub 1), and K(sub 1)), and three long-period (S(sub sa), M(sub m), and M(sub f)) constituents, along with the variations at the annual frequency, were included in the harmomnic analysis solution. The observed annual variations represents the first global measurement describing accurate seasonal changes of the ocean during an El Nino year. The corrections to the M(sub 2) constituent have an root mean square (RMS) of 3.6 cm and display a clear banding pattern with regional highs and lows reaching 8 cm. The improved tide model reduces the weighted altimeter crossover residual from 9.8 cm RMS, when the CR91 tide model is used, to 8.2 cm on RMS. Comparison of the improved model to pelagic tidal constants determined from 80 tide gauges gives RMS differences of 2.7 cm for M(sub 2) and 1.7 cm for K(sub 1). Comparable values when the CR91 model is used are 3.9 cm and 2.0 cm, respectively. Examination of TOPEX/POSEIDON sea level anomaly variations using the new tide model further confirms that the tide model has been improved.

Ma, X. C.↗

NASA NTRS2015

Improving Climate Projections Using "Intelligent" Ensembles

Recent changes in the climate system have led to growing concern, especially in communities which are highly vulnerable to resource shortages and weather extremes. There is an urgent need for better climate information to develop solutions and strategies for adapting to a changing climate. Climate models provide excellent tools for studying the current state of climate and making future projections. However, these models are subject to biases created by structural uncertainties. Performance metrics-or the systematic determination of model biases-succinctly quantify aspects of climate model behavior. Efforts to standardize climate model experiments and collect simulation data-such as the Coupled Model Intercomparison Project (CMIP)-provide the means to directly compare and assess model performance. Performance metrics have been used to show that some models reproduce present-day climate better than others. Simulation data from multiple models are often used to add value to projections by creating a consensus projection from the model ensemble, in which each model is given an equal weight. It has been shown that the ensemble mean generally outperforms any single model. It is possible to use unequal weights to produce ensemble means, in which models are weighted based on performance (called "intelligent" ensembles). Can performance metrics be used to improve climate projections? Previous work introduced a framework for comparing the utility of model performance metrics, showing that the best metrics are related to the variance of top-of-atmosphere outgoing longwave radiation. These metrics improve present-day climate simulations of Earth's energy budget using the "intelligent" ensemble method. The current project identifies several approaches for testing whether performance metrics can be applied to future simulations to create "intelligent" ensemble-mean climate projections. It is shown that certain performance metrics test key climate processes in the models, and that these metrics can be used to evaluate model quality in both current and future climate states. This information will be used to produce new consensus projections and provide communities with improved climate projections for urgent decision-making.

Baker, Noel C.↗

NASA NTRS2015

Improving Climate Projections Using "Intelligent" Ensembles

Baker, Noel C.↗

NASA NTRS1993

Steady induction effects in geomagnetism. Part 1C: Geomagnetic estimation of steady surficial core motions: Application to the definitive geomagnetic reference field models

In the source-free mantle/frozen-flux core magnetic earth model, the non-linear inverse steady motional induction problem was solved using the method presented in Part 1B. How that method was applied to estimate steady, broad-scale fluid velocity fields near the top of Earth's core that induce the secular change indicated by the Definitive Geomagnetic Reference Field (DGRF) models from 1945 to 1980 are described. Special attention is given to the derivation of weight matrices for the DGRF models because the weights determine the apparent significance of the residual secular change. The derived weight matrices also enable estimation of the secular change signal-to-noise ratio characterizing the DGRF models. Two types of weights were derived in 1987-88: radial field weights for fitting the evolution of the broad-scale portion of the radial geomagnetic field component at Earth's surface implied by the DGRF's, and general weights for fitting the evolution of the broad-scale portion of the scalar potential specified by these models. The difference is non-trivial because not all the geomagnetic data represented by the DGRF's constrain the radial field component. For radial field weights (or general weights), a quantitatively acceptable explication of broad-scale secular change relative to the 1980 Magsat epoch must account for 99.94271 percent (or 99.98784 percent) of the total weighted variance accumulated therein. Tolerable normalized root-mean-square weighted residuals of 2.394 percent (or 1.103 percent) are less than the 7 percent errors expected in the source-free mantle/frozen-flux core approximation.

Voorhies, Coerte V.↗

DOE OSTI2026

A transfer learning approach to energy-efficient control of small and medium-sized commercial buildings

Model-free reinforcement learning (RL) provides a data-driven and adaptive approach to optimize building energy use while satisfying occupant comfort. This powerful tool does not need any prior knowledge about the environment and system it is optimizing and can adapt its policy based on the changes in captures. Like any other data-driven tool, it faces high training costs due to the extensive agent-environment interactions required to capture long-term building dynamics and user comfort. Transfer learning, particularly policy distillation, offers a promising way to accelerate training by leveraging pretrained RL agents in different building and system types. Here, this study investigates online student distillation, in which the student model updates its neural network weights using outputs from teacher models. The work introduces a student distillation strategy designed for efficient knowledge transfer, along with a teacher selection method that ensures high-quality guidance. The approach is validated using a highly calibrated whole building energy model for a small/medium commercial building test facility. Results show substantial reductions in training time and data requirements while surpassing the performance of ASHRAE Guideline 36, an advanced rule-based control strategy. The distilled RL model required 45% less data and achieved 20% higher cumulative rewards than a state-of-the-art RL model, with faster convergence and lower energy consumption. These outcomes demonstrate that effective transfer learning enables a scalable and data-efficient energy management solution for commercial buildings.

ASHRAE guideline 36↗

NASA NTRS1999

Real-time Adaptive Control Using Neural Generalized Predictive Control

The objective of this paper is to demonstrate the feasibility of a Nonlinear Generalized Predictive Control algorithm by showing real-time adaptive control on a plant with relatively fast time-constants. Generalized Predictive Control has classically been used in process control where linear control laws were formulated for plants with relatively slow time-constants. The plant of interest for this paper is a magnetic levitation device that is nonlinear and open-loop unstable. In this application, the reference model of the plant is a neural network that has an embedded nominal linear model in the network weights. The control based on the linear model provides initial stability at the beginning of network training. In using a neural network the control laws are nonlinear and online adaptation of the model is possible to capture unmodeled or time-varying dynamics. Newton-Raphson is the minimization algorithm. Newton-Raphson requires the calculation of the Hessian, but even with this computational expense the low iteration rate make this a viable algorithm for real-time control.

Haley, Pam↗

NASA NTRS2008

SEE Rate Estimation: Model Complexity and Data Requirements

Statistical Methods outlined in [Ladbury, TNS20071 can be generalized for Monte Carlo Rate Calculation Methods Two Monte Carlo Approaches: a) Rate based on vendor-supplied (or reverse-engineered) model SEE testing and statistical analysis performed to validate model; b) Rate calculated based on model fit to SEE data Statistical analysis very similar to case for CREME96. Information Theory allows simultaneous consideration of multiple models with different complexities: a) Model with lowest AIC usually has greatest predictive power; b) Model averaging using AIC weights may give better performance if several models have similar good performance; and c) Rates can be bounded for a given confidence level over multiple models, as well as over the parameter space of a model.

Ladbury, Ray↗

NASA NTRS2017

The Impacts of Rising Temperatures on Aircraft Takeoff Performance

Steadily rising mean and extreme temperatures as a result of climate change will likely impact the air transportation system over the coming decades. As air temperatures rise at constant pressure, air density declines, resulting in less lift generation by an aircraft wing at a given airspeed and potentially imposing a weight restriction on departing aircraft. This study presents a general model to project future weight restrictions across a fleet of aircraft with different takeoff weights operating at a variety of airports. We construct performance models for five common commercial aircraft and 19 major airports around the world and use projections of daily temperatures from the CMIP5 model suite under the RCP 4.5 and RCP 8.5 emissions scenarios to calculate required hourly weight restriction. We find that on average, 10 - 30% of annual flights departing at the time of daily maximum temperature may require some weight restriction below their maximum takeoff weights, with mean restrictions ranging from 0.5 to 4% of total aircraft payload and fuel capacity by mid- to late century. Both mid-sized and large aircraft are affected, and airports with short runways and high temperatures, or those at high elevations, will see the largest impacts. Our results suggest that weight restriction may impose a non-trivial cost on airlines and impact aviation operations around the world and that adaptation may be required in aircraft design, airline schedules, and/or runway lengths.

air transportation system↗

NASA NTRS1995

The use of a block diagram simulation language for rapid model prototyping

The research performed this summer focussed on the development of a predictive model for the loading of liquid oxygen (LO2) into the external tank (ET) of the shuttle prior to launch. A predictive model can greatly aid the operational personnel since instrumentation aboard the orbiter and ET is limited due to weight constraints. The model, which focuses primarily on the orbiter section of the system was developed using a block diagram based simulation language known as VisSim. Simulations were run on LO2 loading data for shuttle flights STS50 and STS55 and the model was demonstrated to accurately predict the sensor data recorded for these flights. As a consequence of the simulation results, it can be concluded that the software tool can be very useful for rapid prototyping of complex models.

Whitlow, Jonathan E.↗

NASA NTRS2023

Marin County Wildland Fires: Examining Fuel Load and Land Cover Change to Inform Fire Prevention and Suppression Decisions in Marin County, CA

Heightened occurrence of severe wildfires in the Western United States is increasing the need to better understand regions of high potential wildfire severity and develop methodologies for identifying the best locations for fuels reduction and active wildfire suppression, especially in populated regions such as Marin County, California. Marin County, located in the San Francisco Bay Area, has had significant development in the wildland-urban interface and periods of highly wildfire-prone conditions. The NASA DEVELOP team collaborated with Fire Foundry (a Marin-based fire service workforce development program) and the Marin County Fire Department to develop models to assist with fire management. Using data from Sentinel-2A, PlanetScope, ECOSTRESS, a county-wide LiDAR mapping effort, Landsat 7 Enhanced Thematic Mapper (ETM+), and Landsat 8 Operational Land Imager (OLI), our team developed a number of input data layers for three different models to evaluate wildfire severity. One model performed a suitability analysis with weights based on scientific literature; another model utilized a U-Net Convolutional Neural Network trained on previous fires in Marin and neighboring Sonoma County to predict the difference normalized burn severity; and the third inputted data layers into the FlamMap tool that outputs risk categories. We compared model outputs and performed a weighted overlay analysis to identify specific locations where a fireline could be constructed to interrupt the progress of an active fire. These tools will assist partners in preparing for and managing active wildfire situations.

Remote sensing↗

DOE OSTI2025

Neural refinement of sample weights

Monte Carlo simulations are an essential tool in particle physics data analysis. Events are typically generated alongside weights that redistribute the cross section of the simulated process across the phase space. These weights can be negative, and several post hoc methods have been developed to eliminate or mitigate the negative values. All of these methods share the common strategy of approximating the average weight as a function of phase space. We introduce an alternative approach, which, instead of reweighting to the average, refines the initial weights with a scaling transformation, utilizing a phase space-dependent factor. Since this new refinement method does not need to model the full weight distribution, it can be more accurate. High-dimensional and unbinned phase space is processed using neural networks for the refinement method. In addition to the refinement method, we introduce a new resampling protocol, which can be used in conjunction with any weight transformation to not only preserve the average weight but also the statistical uncertainties of the initial distribution. Using both realistic and synthetic examples, we show that the new neural refinement method is able to match or exceed the accuracy of similar weight transformations and that the new resampling protocol is simpler in implementation than previous methods while exhibiting equivalent statistical properties.

Artificial neural networks↗

NASA NTRS2014

Propulsion System Models for Rotorcraft Conceptual Design

The conceptual design code NDARC (NASA Design and Analysis of Rotorcraft) was initially implemented to model conventional rotorcraft propulsion systems, consisting of turboshaft engines burning jet fuel, connected to one or more rotors through a mechanical transmission. The NDARC propulsion system representation has been extended to cover additional propulsion concepts, including electric motors and generators, rotor reaction drive, turbojet and turbofan engines, fuel cells and solar cells, batteries, and fuel (energy) used without weight change. The paper describes these propulsion system components, the architecture of their implementation in NDARC, and the form of the models for performance and weight. Requirements are defined for improved performance and weight models of the new propulsion system components. With these new propulsion models, NDARC can be used to develop environmentally-friendly rotorcraft designs.

Propulsion System Models↗

NASA NTRS1999

Optimal System Realization in Frequency Domain

Several approaches are presented to identify an experimental system model directly from frequency response data. The formulation begins with a matrix-fraction description as the model structure. Frequency weighting such as exponential weighting is introduced to solve a weighted least-squares problem to obtain the coefficient matrices for the matrix-fraction description. A multi-variable state-space model can then be formed using the coefficient matrices of the matrix-fraction description. An approach is introduced to fine-tune the model using non-linear programming methods to minimize the desired cost function. The method deals with the model in the real Schur or modal form and reassigns a subset of system poles using a nonlinear optimizer. At every optimization step, the input and output influence matrices are refined through least-squares procedures. The proposed approaches are used to identify an analytical model for a NASA testbed from experimental data.

Juang, Jer-Nan↗