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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

MOM3D method of moments code theory manual

MOM3D is a FORTRAN algorithm that solves Maxwell's equations as expressed via the electric field integral equation for the electromagnetic response of open or closed three dimensional surfaces modeled with triangle patches. Two joined triangles (couples) form the vector current unknowns for the surface. Boundary conditions are for perfectly conducting or resistive surfaces. The impedance matrix represents the fundamental electromagnetic interaction of the body with itself. A variety of electromagnetic analysis options are possible once the impedance matrix is computed including backscatter radar cross section (RCS), bistatic RCS, antenna pattern prediction for user specified body voltage excitation ports, RCS image projection showing RCS scattering center locations, surface currents excited on the body as induced by specified plane wave excitation, and near field computation for the electric field on or near the body.

Shaeffer, John F.↗
NASA NTRSDate not supplied

Probing Sensitivity of Discharge Characteristics to Model Selection using Uncertainty Quantification in an aprotic Li-Oxygen Battery

Currently, there are several models in the literature, such as kinetic models, microstructural models, and mass transport models that describe a Li-air battery's discharge behavior. Many of these models are calibrated and tested at low current densities and cannot be easily transferred to high current densities. Even at low current densities, there is no quantitative method for a researcher to choose a reaction kinetic model such as classical Butler-Volmer and its derivatives, and modified Marcus-Hush-Chidsey, a resistance model for lithium peroxide such as electron transport via tunneling or linear resistivity, a surface coverage model (lithium peroxide growth) such as partial coverage or full coverage, and mass transport model (discussed in Ref. [1]). Also, it is time-consuming to test different models at high current density (1C) due to a lack of well-tested models and well-calibrated model parameters. For this presentation, we will develop an analytical model, which acts as a surrogate model for a sophisticated finite element model to predict discharge time and discharge voltage. Next, we use an uncertainty quantifying technique called reduced-order stochastic optimization [2, 3] to determine the uncertainty in model parameters for rate kinetics, lithium peroxide resistivity, and parasitic resistance. Finally, a finite element simulation is performed to determine the error introduced by the surrogate model and its influence on the uncertainty in the model parameters.

M Mehta↗
NASA NTRS1993

A battery power model for the EUVE spacecraft

This paper describes a battery power model that has been developed to simulate and predict the behavior of the 50 ampere-hour nickel-cadmium battery that supports the Extreme Ultraviolet Explorer (EUVE) spacecraft in its low Earth orbit. First, for given orbit, attitude, solar array panel and spacecraft load data, the model calculates minute-by-minute values for the net power available for charging the battery for a user-specified time period (usually about two weeks). Next, the model is used to calculate minute-by-minute values for the battery voltage, current and state-of-charge for the time period. The model's calculations are explained for its three phases: sunrise charging phase, constant voltage phase, and discharge phase. A comparison of predicted model values for voltage, current and state-of-charge with telemetry data for a complete charge-discharge cycle shows good correlation. This C-based computer model will be used by the EUVE Flight Operations Team for various 'what-if' scheduling analyses.

Yen, Wen L.↗
NASA NTRS1997

NiH2 Battery Reconditioning for LEO Applications

This paper summarizes reasons for and benefits of reconditioning nickel-hydrogen (NiH2) batteries used for Low Earth Orbit (LEO) applications. NiH2 battery cells do not have the classic discharge voltage problems more commonly associated with nickel-cadmium (NiCd) cells. This is due, in part, to use of hydrogen electrodes in place of cadmium electrodes. The nickel electrode, however, does have a similar discharge voltage signature for both cell designs. This can have an impact on LEO applications where peak loads at higher relative depths of discharge can impact operations. Periodic reconditioning provides information which can be used for analyzing long term performance trends to predict usable capacity to a specified voltage level. The reconditioning process described herein involves discharging NiH2 batteries at C/20 rates or less, to an average cell voltage of 1.0 volts or less. Recharge is performed at nominal C/5 rates to specified voltage/temperature (V/T) charge levels selected to restore required capacity with minimal overcharge. Reconditioning is a process of restoring reserve capacity lost on cycling, which is commonly called the memory effect in NiCd cells. This effect is characterized by decreases in the discharge voltage curve with operational life and cycling. The end effect of reconditioning NiH2 cells may be hidden in the versatility, of that design over the NiCd cell design and its associated negative electrode fading problem. The process of deep discharge at lower rates by way of reconditioning tends to redistribute electrolyte and water in the NiH2 cell electrode stack, while improving utilization and charge efficiency. NiH2 battery reconditioning effects on life are considered beneficial and may, in fact. extend life based on NiCd experience. In any case, usable capacity data obtained from reconditioning is required for performance evaluation and trend analysis. Characterization and life tests have provided the historical data base used to determine the need for reconditioning in most battery applications. The following sections briefly describe the background of NiH2 battery reconditioning and testing at Lockheed Martin Missiles & Space (LMMS) and other aerospace companies.

Armantrout, J. D.↗
NASA NTRS2019

Inclusion of Radiation Environment Variability for Reliability Estimates for SiC Power MOSFETs

Variability of the solar energetic particle environment is investigated for single-event-burnout reliability of silicon-carbide power metal-oxide-semiconductor field effect transistors. A probabilistic assessment of failure evaluates the benefits of de-rating voltage, shielding, and mission length. The Prediction of Solar particle Yields for Characterizing Integrating Circuits code is used to calculate a cumulative density function for the fluence of the environment. The lethal ion method is then used to determine what proportion of the environment will cause single-event-burnout. The operating voltage determines the lowest linear-energy-transfer particle that will cause single-event-burnout and that should be included in the environment distribution. The shielding and mission length also determine the final environment distribution of the mission fluence. Through calculating the reliability for different operating voltages, shielding, and mission length for a specific device, it is shown that shielding thickness and operating voltage have a large effect on reliability and can be traded off during the design.

Heavy ion↗
NASA NTRS2014

Online Prediction of Battery Discharge and Estimation of Parasitic Loads for an Electric Aircraft

Predicting whether or not vehicle batteries contain sufficient charge to support operations over the remainder of a given flight plan is critical for electric aircraft. This paper describes an approach for identifying upper and lower uncertainty bounds on predictions that aircraft batteries will continue to meet output power and voltage requirements over the remainder of a flight plan. Battery discharge prediction is considered here in terms of the following components; (i) online battery state of charge estimation; (ii) prediction of future battery power demand as a function of an aircraft flight plan; (iii) online estimation of additional parasitic battery loads; and finally, (iv) estimation of flight plan safety. Substantial uncertainty is considered to be an irremovable part of the battery discharge prediction problem. However, high-confidence estimates of flight plan safety or lack of safety are shown to be generated from even highly uncertain prognostic predictions.

Battery Discharge Prediction↗
DOE OSTI2021

Supply voltage decoupling circuits for voltage droop mitigation

Circuits and methods are provided for utilizing decoupling capacitors to mitigate voltage droop on power supply lines of a power distribution network. A power supply line is capacitively decoupled using a first decoupling capacitor connected to the power supply line and charged to a first voltage level of the power supply line. A second decoupling capacitor is pre-charged to a second voltage level greater than the first voltage level and held in standby. A control circuit determines or predicts an occurrence of a droop event in which the first voltage decreases to a level which is at or below a droop threshold voltage level, and selectively connects the pre-charged second decoupling capacitor to the power supply line to source additional boosting current through discharging of the second decoupling capacitor and thereby capacitively decouple the power supply line using the higher second voltage and additional boosting current.

Zhang, Xin↗
NASA NTRS1979

Analytical predictions of RTG power degradation

The DEGRA computer code that is based on a mathematical model which predicts performance and time-temperature dependent degradation of a radioisotope thermoelectric generator is discussed. The computer code has been used to predict performance and generator degradation for the selenide Ground Demonstration Unit (GDS-1) and the generator used in the Galileo Project. Results of parametric studies of load voltage vs generator output are examined as well as the I-V curve and the resulting predicted power vs voltage. The paper also discusses the increased capability features contained in DEGRA2 and future plans for expanding the computer code performance.

Noon, E. L.↗
DOE OSTI2020

AMPPED LDRD: Advanced Models of the Physics & Phenomena of Electrical Discharge.

This Laboratory Directed Research and Development (LDRD) project aimed at addressing an unmet need for physics-based models that enable prediction of electrical breakdown in the presence of high voltage gradients and real-world environments. This project focused on understanding coupled plasma-surface feedback phenomena critical to electrical discharge processes and design of breakdown voltages, breakdown times, and breakdown paths. New secondary electron emission and coupled plasma-surface measurement methods and models were developed. Highly reproducible discharge experiments were developed to provide current-voltage data as a function of time, and methods were developed to condition electrodes for predictable discharge. To assess plasma-surface feedback mechanisms, new surface physics capabilities, including measurement systems for local and global photoemission yield and ion-induced secondary electron emission yield were developed. Discharge models were developed to incorporate these yields for predicting discharge behavior to understand influence of surface physics on discharge current and time evolution, and methods to accelerate these simulations were demonstrated. A key finding was that the initiation process of a non-vacuum discharge appears highly dependent on photoemission at the cathode. Advanced diagnostics were developed, including spatial electrical field imaging, spectroscopy of plasmas, and high-speed imaging of discharge event s in metal and dielectric environments. These diagnostics and improved electrical models are a step toward enabling quantitative design margins, assurance of safety architectures, and implementation of novel materials and technologies for wide ranging energy and electronics applications.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

The Use of Machine Learning Models for Predicting the Dielectric Strength of Gases

Technological advancements in high voltage systems have pushed sulfur hexafluoride (SF6) to its operational limits. Furthermore, this gas has other drawbacks including a high liquefaction temperature and a high global warming potential. Therefore, there has been an urgent need to find alternative gases with high dielectric strength (DS). In this work, density functional theory (DFT) is used to calculate molecular descriptors that are fed into an artificial neural network (ANN) and a random forest (RF). These machine learning (ML) models are then used to predict the DS for hundreds of molecules. A finite element model (FEM) is also used to calculate the electric field profile of multiple simple electrode geometries as the applied voltage to the system is increased. Results indicate that the random forest model has better generalization to unseen data than the neural network. The highest DS value predicted by the RF was 2.16 relative to the experimental DS of SF6. The results also demonstrate how choosing a gas with a higher DS and a geometry with minimal edges and corners can significantly increase the operating voltage of an electrical system. Due to its superior generalization, the RF represents the most promising path toward an accurate DS predictor once sufficient experimental data are available.

Mileski, Matthew [AFIT]↗
DOE OSTI2021

A Poisson–Nernst–Planck Model of Ion Transport and Interface Segregation in Metal–Insulator–Semiconductor Structures and Solar Cells

A numerical model that describes the transport of mobile ionic species in metal–insulator–semiconductor (MIS) and photovoltaic (PV) devices subject to temperature and voltage stress is presented. The finite element method (FEM) is used to solve the Nernst–Planck equation while imposing Poisson's equation self-consistently as a restriction for the electrostatic potential. This allows the contribution of the ionic species to the potential to be taken into account. Using a variational formulation eases the implementation of diverse boundary conditions, including the incorporation of segregation kinetics at the device interfaces. Segregation across the dielectric–semiconductor interface is relevant to modeling the electronic device degradation in systems where contamination reaches the semiconductor. The model in closed systems with no-flux boundary conditions is validated first. In the limiting case of low contamination levels with respect to the gate bias, the FEM solution matches analytically derived approximations. Then, the implementation is broadened to include an open boundary at the dielectric–semiconductor interface to account for leakage of ions. The predicted time dependence of the flatband voltage in Na-contaminated MIS test structures agrees well with measurements. This model successfully captures the role of long-range ion transport at concentrations of relevance to electronic and PV device instability and neuromorphic computing.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1998

Analysis and Testing of Plates with Piezoelectric Sensors and Actuators

Piezoelectric material inherently possesses coupling between electrostatics and structural dynamics. Utilizing linear piezoelectric theory results in an intrinsically coupled pair of piezoelectric constitutive equations. One equation describes the direct piezoelectric effect where strains produce an electric field and the other describes the converse effect where an applied electrical field produces strain. The purpose of this study is to compare finite element analysis and experiments of a thin plate with bonded piezoelectric material. Since an isotropic plate in combination with a thin piezoelectric layer constitutes a special case of a laminated composite, the classical laminated plate theory is used in the formulation to accommodated generic laminated composite panels with multiple bonded and embedded piezoelectric layers. Additionally, the von Karman large deflection plate theory is incorporated. The formulation results in laminate constitutive equations that are amiable to the inclusion of the piezoelectric constitutive equations yielding in a fully electro-mechanically coupled composite laminate. Using the finite element formulation, the governing differential equations of motion of a composite laminate with embedded piezoelectric layers are derived. The finite element model not only considers structural degrees of freedom (d.o.f.) but an additional electrical d.o.f. for each piezoelectric layer. Comparison between experiment and numerical prediction is performed by first treating the piezoelectric as a sensor and then again treating it as an actuator. To assess the piezoelectric layer as a sensor, various uniformly distributed pressure loads were simulated in the analysis and the corresponding generated voltages were calculated using both linear and nonlinear finite element analyses. Experiments were carried out by applying the same uniformly distributed loads and measuring the resulting generated voltages and corresponding maximum plate deflections. It is found that a highly nonlinear relationship exists between maximum deflection and voltage versus pressure loading. In order to assess comparisons of predicted and measured piezoelectric actuation, sinusoidal excitation voltages are simulated/applied and maximum deflections are calculated/measured. The maximum deflection as a function of time was determined using the linear finite elements analysis. Good correlation between prediction and measurement was achieved in all cases.

Bevan, Jeffrey S.↗
DOE OSTI2025

Evaluating switch lifetime in soft-switched single-stage differential-mode SST

The reliability of semiconductor switches in single-stage differential-mode solid-state transformers (DM-SSTs) has not been systematically evaluated under soft-switching operation and realistic grid conditions. This paper presents a switch-level reliability analysis for soft-switched and hard-switched DM-SST configurations by integrating converter-specific power loss modeling with empirical lifetime prediction. Analytical derivation of device current profiles specific to the DM-SST is used to characterize electrothermal stress, which is then mapped to lifetime using degradation models obtained from power cycling tests (PCTs). Applied to realistic SST load profiles and grid voltage variations, this approach provides a probabilistic prediction of switch lifetime for the DM-SST. Lifetime estimates for both SiC MOSFETs and Si IGBTs are presented, offering insight into device degradation under converter operating conditions. The results quantify the reliability benefits of soft switching in single-stage SSTs, highlighting how switching dynamics influence long-term switch degradation.

14 SOLAR ENERGY↗
DOE OSTI2023

Early Fault Detection in Particle Accelerator Power Electronics Using Ensemble Learning

Early fault detection and fault prognosis are crucial to ensure efficient and safe operations of complex engineering systems such as the Spallation Neutron Source (SNS) and its power electronics (high voltage converter modulators). Following an advanced experimental facility setup that mimics SNS operating conditions, the authors successfully conducted 21 early fault detection experiments, where fault precursors are introduced in the system to a degree enough to cause degradation in the waveform signals, but not enough to reach a real fault. Nine different machine learning techniques based on ensemble trees, convolutional neural networks, support vector machines, and hierarchical voting ensembles are proposed to detect the fault precursors. Although all 9 models have shown a perfect and identical performance during the training and testing phase, the performance of most models has decreased in the next test phase once they got exposed to realworld data from the 21 experiments. The hierarchical voting ensemble, which features multiple layers of diverse models, maintains a distinguished performance in early detection of the fault precursors with 95% success rate (20/21 tests), followed by adaboost and extremely randomized trees with 52% and 48% success rates, respectively. The support vector machine models were the worst with only 24% success rate (5/21 tests). The study concluded that a successful implementation of machine learning in the SNS or particle accelerator power systems would require a major upgrade in the controller and the data acquisition system to facilitate streaming and handling big data for the machine learning models. In addition, this study shows that the best performing models were diverse and based on the ensemble concept to reduce the bias and hyperparameter sensitivity of individual models.

43 PARTICLE ACCELERATORS↗
NASA NTRS1973

Evaluation of the electro-optic direction sensor

Evaluation of a no-moving-parts single-axis star tracker called an electro-optic direction sensor (EODS) concept is described and the results are given in detail. The work involved experimental evaluation of a breadboard sensor yielding results which would permit design of a prototype sensor for a specific application. The laboratory work included evaluation of the noise equivalent input angle of the sensor, demonstration of a technique for producing an acquisition signal, constraints on the useful field-of-view, and a qualitative evaluation of the effects of stray light. In addition, the potential of the silicon avalanche-type photodiode for this application was investigated. No benefit in noise figure was found, but the easily adjustable gain of the avalanche device was useful. The use of mechanical tuning of the modulating element to reduce voltage requirements was also explored. The predicted performance of EODS in both photomultiplier and solid state detector configurations was compared to an existing state-of-the-art star tracker.

Johnson, A. R.↗
NASA NTRS1976

Space shuttle engineering and operations support. Orbiter to spacelab electrical power interface. Avionics system engineering

The results are presented of an investigation of the factors which affect the determination of Spacelab (S/L) minimum interface main dc voltage and available power from the orbiter. The dedicated fuel cell mode of powering the S/L is examined along with the minimum S/L interface voltage and available power using the predicted fuel cell power plant performance curves. The values obtained are slightly lower than current estimates and represent a more marginal operating condition than previously estimated.

Emmons, T. E.↗
NASA NTRS1982

An improved switching converter model

The nonlinear modeling and analysis of dc-dc converters in the continuous mode and discontinuous mode was done by averaging and discrete sampling techniques. A model was developed by combining these two techniques. This model, the discrete average model, accurately predicts the envelope of the output voltage and is easy to implement in circuit and state variable forms. The proposed model is shown to be dependent on the type of duty cycle control. The proper selection of the power stage model, between average and discrete average, is largely a function of the error processor in the feedback loop. The accuracy of the measurement data taken by a conventional technique is affected by the conditions at which the data is collected.

Shortt, D. J.↗
NASA NTRS1989

Comprehensive Silicon-Solar-Cell Program

Comprehensive Silicon Solar Cell Computer Modeling (SICELL) program simulates silicon solar cell. Predicts device parameters as efficiency, voltage-vs.-current characteristic fill factor, and temperature coefficients of parameters. Technique used similar to numerical-integration methods, but commonality described by use of mesh-point field. Validation studies show accuracies of simulations range from 0.08 percent to 3.6 percent for 27 experimental data points over temperature range of 300K to 421K. Results obtained by use of 10 mesh points in n- and p-type regions and for two iterations. SICELL runs interactively on VAX computer under VMS and written in VAX/VMS FORTRAN 77.

Lamorte, Michael F.↗