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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

Thermal barrier coating life prediction model development, phase 2

The objective of this program was to generate a life prediction model for electron-beam-physical vapor deposited (EB-PVD) zirconia thermal barrier coating (TBC) on gas turbine engine components. Specific activities involved in development of the EB-PVD life prediction model included measurement of EB-PVD ceramic physical and mechanical properties and adherence strength, measurement of the thermally grown oxide (TGO) growth kinetics, generation of quantitative cyclic thermal spallation life data, and development of a spallation life prediction model. Life data useful for model development was obtained by exposing instrumented, EB-PVD ceramic coated cylindrical specimens in a jet fueled burner rig. Monotonic compression and tensile mechanical tests and physical property tests were conducted to obtain the EB-PVD ceramic behavior required for burner rig specimen analysis. As part of that effort, a nonlinear constitutive model was developed for the EB-PVD ceramic. Spallation failure of the EB-PVD TBC system consistently occurred at the TGO-metal interface. Calculated out-of-plane stresses were a small fraction of that required to statically fail the TGO. Thus, EB-PVD spallation was attributed to the interfacial cracking caused by in-plane TGO strains. Since TGO mechanical properties were not measured in this program, calculation of the burner rig specimen TGO in-plane strains was performed by using alumina properties. A life model based on maximum in-plane TGO tensile mechanical strain and TGO thickness correlated the burner rig specimen EB-PVD ceramic spallation lives within a factor of about plus or minus 2X.

Meier, Susan Manning↗
NASA NTRS1975

Verification by Remote Sensing of an Oil Slick Movement Prediction Model

The author has identified the following significant results. LANDSAT, aircraft, ships, and air-dropped current drogues were deployed to determine current circulation and to track oil slick movement on four different dates in Delaware Bay. Results were used to verify a predictive model for oil slick movement and dispersion. The model predicts the behavior of oil slicks given their size, location, tidal stage (current), weather (wind), and nature of crude. Both LANDSAT satellites provided valuable data on gross circulation patterns and convergent coastal fronts which by capturing oil slicks significantly influence their movement and dispersion.

Klemas, V.↗
NASA NTRS1981

Freeze Prediction Model

Measurements of wind speed, net irradiation, and of air, soil, and dew point temperatures in an orchard at the Rock Springs Agricultural Research Center, as well as topographical and climatological data and a description of the major apple growing regions of Pennsylvania were supplied to the University of Florida for use in running the P-model, freeze prediction program. Results show that the P-model appears to have considerable applicability to conditions in Pennsylvania. Even though modifications may have to be made for use in the fruit growing regions, there are advantages for fruit growers with the model in its present form.

Morrow, C. T.↗
NASA NTRS1982

Empirical testing of an analytical model predicting electrical isolation of photovoltaic models

A major design requirement for photovoltaic modules is that the encapsulation system be capable of withstanding large DC potentials without electrical breakdown. Presented is a simple analytical model which can be used to estimate material thickness to meet this requirement for a candidate encapsulation system or to predict the breakdown voltage of an existing module design. A series of electrical tests to verify the model are described in detail. The results of these verification tests confirmed the utility of the analytical model for preliminary design of photovoltaic modules.

Garcia, A., III↗
DOE OSTI2025

A neural-network-enhanced parameter-varying framework for multi-objective model predictive control applied to buildings

Management of the electrical grid is becoming more complex due to the increased penetration of alternative energy generation technologies and a broadening diversity of electric loads. This complexity creates challenges in balancing demand and generation that can increase the potential for grid instabilities. One effective way to address this issue is to leverage previously unexploited demand flexibility through advanced control strategies. In this work, we propose an advanced control method, called adaptive neural parameter-varying model predictive control (ANPV-MPC), to control the temperature and energy consumption of a building via its Heating, Ventilation, and Air Conditioning system. ANPV-MPC combines key ideas in parameter-varying control, adaptive control, and online learning strategies to bridge the gap between computationally efficient linear model predictive control and more accurate nonlinear model predictive control. The novelty in ANPV-MPC is the use of a physics-inspired Bayesian neural network to estimate the coefficients of the parameter-varying linear control model. The Bayesian neural network additionally provides uncertainty estimates, triggering online training to capture evolving building system conditions. We show that ANPV-MPC can approximate the building system dynamics with a 28.39% higher accuracy than traditional linear model predictive control, resulting in 36.23% better control performance without increasing complexity of the optimal control problem. ANPV-MPC also adapts in real time to previously unseen conditions using online learning, further improving its performance.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Subseasonal Forecasting and MJO Teleconnections in Machine Learning Weather Prediction Models

Abstract In recent years, machine‐learning (ML) models trained on reanalysis data have rivaled physics‐based forecast models in terms of performance skill for global weather forecasting. With increased rollout stability, the question of how these models perform for subseasonal to seasonal (S2S, week 3–8) forecasting has emerged. In this study we run a large set of subseasonal hindcasts over 2004–2023 to evaluate two ML weather forecast models at the S2S time scale, SFNO‐HENS (Nvidia, fully ML) and NeuralGCM (Google Research, hybrid). Corresponding hindcasts from the European Centre for Medium‐Range Weather Forecasts (ECMWF) are used as a baseline for comparison to a physics‐based model. Because our focus is on predicting moisture transport over the Western United States between October and March, we evaluate the models' prediction skill for the Madden‐Julian Oscillation (MJO) and its associated teleconnections in the North Pacific. We find that both ML models are competitive with the ECWMF model, with comparable skill in predicting the North Pacific large‐scale circulation and the MJO at week 3 and beyond. Even though overall the mid‐latitude subseasonal prediction skill remains low, the ML models exhibit interesting behavior such as a realistic propagation of the MJO across the Maritime Continent and realistic teleconnections. A SFNO‐HENS sensitivity experiment with altered initial conditions in the tropics demonstrates the stability of the model, and it illustrates the capability of ML models to represent important physical processes of the atmosphere at the S2S time scale. Plain Language Summary Predicting weather patterns and precipitation a few weeks in advance (subseasonal time scale) is of great interest for stakeholders such as water managers in the Southwest United States (US), where arid conditions prevail. Subseasonal forecasts from traditional weather forecast models exhibit low skill in the region, limiting their applicability. Here we examine whether the recent breakthrough in weather forecasting made with machine learning/artificial intelligence models can translate to improved subseasonal forecasts. Recently‐developed machine learning models exhibit comparable skill to a state‐of‐the‐art physics‐based model for predicting weather patterns in the North Pacific/North America region, and associated moisture transport. The same applies to their skill in predicting the tropical pattern, the Madden‐Julian Oscillation, and its important remote perturbations over the midlatitude East Pacific and Southwest US. Additionally, a perturbation experiment carried out with one of the machine learning models illustrates their ability to not only predict the evolution of atmospheric fields, but also to learn and represent physical processes such as tropics‐extratropics Rossby wave propagation. Key Points Two machine learning weather forecast models exhibit state‐of‐the‐art prediction skill at the subseasonal time scale in the Pacific sector The models equal ECWMF in terms of Madden‐Julian oscillation (MJO) prediction skill, and they accurately predict the MJO propagation and associated teleconnections The two machine‐learning models represent key physical processes for subseasonal prediction, despite being trained for weather forecasting

Peings, Yannick↗
NASA NTRS1996

Predictive Modeling of Cardiac Ischemia

The goal of the Contextual Alarms Management System (CALMS) project is to develop sophisticated models to predict the onset of clinical cardiac ischemia before it occurs. The system will continuously monitor cardiac patients and set off an alarm when they appear about to suffer an ischemic episode. The models take as inputs information from patient history and combine it with continuously updated information extracted from blood pressure, oxygen saturation and ECG lines. Expert system, statistical, neural network and rough set methodologies are then used to forecast the onset of clinical ischemia before it transpires, thus allowing early intervention aimed at preventing morbid complications from occurring. The models will differ from previous attempts by including combinations of continuous and discrete inputs. A commercial medical instrumentation and software company has invested funds in the project with a goal of commercialization of the technology. The end product will be a system that analyzes physiologic parameters and produces an alarm when myocardial ischemia is present. If proven feasible, a CALMS-based system will be added to existing heart monitoring hardware.

Anderson, Gary T.↗
NASA NTRS1989

Short-range forecasting and nowcasting, using a simple, isentropic prediction model

A numerical advection model which can be run on a local computer in a real-time forecast environment is described. This isentropic forecast model provides the local forecast office with easy access to temporally and spatially detailed estimates of atmospheric temperature, moisture, and wind field changes between 12-h rawinsonde observations. Case studies are presented based on the use of the model to predict the preconvective environment in spring and summer situations. Short-term forecasts of midtropospheric static stability patterns and stability index changes are traced for several severe storm events with and without the inclusion of surface data observed during the day. Forecast images of VAS low- and midlevel moisture fields and vertical moisture gradients are compared with the observations to determine the utility of the combined model/VAS imagery as a nowcasting guide.

Petersen, Ralph A.↗
DOE OSTI2025

Closing the loop: model-predictive control for a closed-circuit reverse osmosis system

This article presents a model-predictive controller (MPC) for the maximization of the energy efficiency of a closed-circuit desalination reverse osmosis (CCRO) system. CCRO is a process for producing drinking water that is based on a cyclic operation with the following two phases: (a) filtration and (b) drain. In this article, we test model predictive control for optimal control of this process. The most important features of our approach are as follows: (a) the selection of a model structure that enables reliable forecasts of the filtration phase (up to 3 h), (b) an on-line model calibration strategy that ensures model forecast reliability, and (c) the satisfaction of equipment safety and operational constraints on the selected setpoints. We challenge this through deliberate introduction of changes in the unmeasured feed concentration and the applied constraints. Our results indicate that frequent model parameter updates are critical to maintain model reliability for MPC purposes. In addition, we illustrate that parameter identifiability is not guaranteed and that deliberate variation in flow rates is necessary even though the process never operates in steady state. Finally, MPC can compute flow rate setpoints that maximize the energy efficiency of the CCRO process while satisfying the applicable equipment and safety constraints.

closed-circuit reverse osmosis↗
NASA NTRS1976

Atmospheric analysis and prediction model development, volume 1

A set of hemispheric atmospheric analysis and prediction models was designed and tested. All programs were executed on either a 63 x 63 or 187 x 187 polar stereographic grid of the Northern Hemisphere. Parameters for objective analysis included sea surface temperature, sea level pressure, and twelve levels (from 1,000 to 100 millibars) of temperatures, heights, and winds. Stratospheric extensions (up to 10 millibars) were also provided. Four versions of a complex atmospheric prediction model, based on primitive equations, were programmed and tested. These models were executed on either the 63 x 63 or 187 x 187 grid, using either five or ten computational layers. The coarse-mesh (63 x 63) models were tested using real data for the period 21-23 April 1976. The fine-mesh (187 x 187) models were debugged, but insufficient computer resources precluded production tests. Preliminary test results for the 63 x 63 models are provided. Problem areas and proposed solutions are discussed.

Kesel, P. G.↗
DOE OSTI2025

Experimental test of model predictive control in a variable air volume system

Model predictive control (MPC) has been widely studied as a promising approach for improving energy efficiency and operational flexibility in buildings, yet its real-world performance for commercial variable air volume (VAV) systems remains insufficiently characterized. In particular, the impacts of model mismatch on control robustness, real-time computational burden, and device-level operation are rarely evaluated using long-term field data. Here, this study presents a comprehensive experimental evaluation of MPC applied to a full-scale VAV system in Oak Ridge National Laboratory’s Flexible Research Platform-2 building with constant cooling/heating temperature setpoints and no occupancy. The study offers three key advantages over existing work: (1) it uses a representative building in a full-scale experimental test, capturing realistic system dynamics and complexity; (2) it evaluates a relatively sophisticated MPC formulation using two different optimization solvers (Gurobi and PSO), fully accounting for computational complexity and methodological diversity; and (3) it systematically assesses potential negative impacts on various building devices, benchmark against a well-established baseline, ASHRAE Guideline 36 (G36). To isolate zone- and air-handling-unit–level supervisory control effects, the supply fan was operated with a fixed static pressure setpoint under all strategies, and the trim-and-response static pressure reset in G36 was not enabled. Results show that MPC maintained thermal comfort while improving energy efficiency. Abrupt solar radiation variations degraded performance. Computation times ranged from ∼1 s (Gurobi) to ∼ 70 s (PSO). Compared with G36, MPC achieves 33% energy savings and reduces median reheat coil output by approximately a factor of 5–10 for a representative cooling day under matched weather conditions. However, it increases the maximum discomfort deviation from 0.5 to 1°C and results in a 32% increase in staging frequency. In addition, PSO-based MPC introduced damper oscillations, also affecting actuator longevity.

ASHRAE guideline 36↗
NASA NTRS1986

Thermal barrier coating life-prediction model development

The program focuses on predicting the lives of two types of strain-tolerant and oxidation-resistant thermal barrier coating (TBC) systems that are produced by commercial coating suppliers to the gas turbine industry. The plasma-sprayed TBC system, composed of a low-pressure plasma-spray (LPPS) or an argon shrouded plasma-spray (ASPS) applied oxidation resistant NiCrAlY or (CoNiCrAlY) bond coating and an air-plasma-sprayed yttria partially stabilized zirconia insulative layer, is applied by both Chromalloy, Klock, and Union Carbide. The second type of TBS is applied by the electron beam-physical vapor deposition (EB-PVD) process by Temescal. The second year of the program was focused on specimen procurement, TMC system characterization, nondestructive evaluation methods, life prediction model development, and TFE731 engine testing of thermal barrier coated blades. Materials testing is approaching completion. Thermomechanical characterization of the TBC systems, with toughness, and spalling strain tests, was completed. Thermochemical testing is approximately two-thirds complete. Preliminary materials life models for the bond coating oxidation and zirconia sintering failure modes were developed. Integration of these life models with airfoil component analysis methods is in progress. Testing of high pressure turbine blades coated with the program TBS systems is in progress in a TFE731 turbofan engine. Eddy current technology feasibility was established with respect to nondestructively measuring zirconia layer thickness of a TBC system.

Strangman, T. E.↗
NASA NTRS1993

A thermal NO(x) prediction model - Scalar computation module for CFD codes with fluid and kinetic effects

A thermal NO(x) prediction model is developed to interface with a CFD, k-epsilon based code. A converged solution from the CFD code is the input to the postprocessing model for prediction of thermal NO(x). The model uses a decoupled analysis to estimate the equilibrium level of (NO(x))e which is the constant rate limit. This value is used to estimate the flame (NO(x)) and in turn predict the rate of formation at each node using a two-step Zeldovich mechanism. The rate is fixed on the NO(x) production rate plot by estimating the time to reach equilibrium by a differential analysis based on the reaction: O + N2 = NO + N. The rate is integrated in the nonequilibrium time space based on the residence time at each node in the computational domain. The sum of all nodal predictions yields the total NO(x) level.

Mcbeath, Giorgio↗
NASA NTRS1981

A split explicit reformulation of the regional numerical weather prediction model of the Japan Meteorological Agency

The split explicit integration scheme for numerical weather prediction models is employed in a version of the regional numerical weather prediction model of the Japan Meteorological Agency. The finite-difference scheme of the model is designed in the manner proposed by Okamura (1975). The horizontal advection terms in the governing equations are integrated with a time step limited by the wind speed while the terms which describe inertial-gravity oscillations are integrated in a succession of shorter time steps. The physical processes included within the model are precipitation, small-scale convection, surface exchanges of sensible and latent heat, and radiative heating and cooling. An example of a surface pressure forecast over Europe is shown for initial data observed at 0000 GMT 29 December 1979. Quantitative precipitation forecasts over Europe and North America for the 24 h period beginning at 0000 GMT 30 December 1979 are also shown. It is concluded that the model is capable of realistically depicting the evolution of synoptic-scale systems.

Duffy, D. G.↗
NASA NTRS1986

Thermal barrier coating life prediction model

The objectives of this program are to determine the predominant modes of degradation of a plasma sprayed thermal barrier coating system, and then to develop and verify life prediction models accounting for these degradation modes. The program is divided into two phases, each consisting of several tasks. The work in Phase 1 is aimed at identifying the relative importance of the various failure modes, and developing and verifying life prediction model(s) for the predominant model for a thermal barrier coating system. Two possible predominant failure mechanisms being evaluated are bond coat oxidation and bond coat creep. The work in Phase 2 will develop design-capable, causal, life prediction models for thermomechanical and thermochemical failure modes, and for the exceptional conditions of foreign object damage and erosion.

Pilsner, B. H.↗
NASA NTRS1985

Thermal barrier coating life-prediction model development

Life predictions are made for two types of strain-tolerant and oxidation-resistant Thermal Barrier Coating (TBC) systems produced by commercial coating suppliers to the gas turbine industry. The plasma-sprayed TBC system, composed of a low-pressure plasma spray (LPPS) applied oxidation-resistant NiCrAlY bond coating and an air-plasma-sprayed yttria (8 percent) partially stabilized zirconia insulative layer, is applied by both Chromalloy and Klock. The second type of TBC is applied by the electron-beam/physical vapor deposition process by Temescal. Thermomechanical and thermochemical testing of the program TBCs is in progress. A number of the former tests has been completed. Fracture mechanics data for the Chromalloy plasma-sprayed TBC system indicate that the cohesive toughness of the zirconia layer is increased by thermal cycling and reduced by high temperature exposure at 1150 C. Eddy current technology feasibility has been established with respect to nondestructively measuring zirconia layer thickness of a TBC system. High pressure turbine blades have been coated with program TBC systems for a piggyback test in a TFE731-5 turbofan factory engine test. Data from this test will be used to validate the TBC life models.

Strangman, T. E.↗