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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Dynamic Undervolting to Improve Energy Efficiency on Multicore X86 CPUs

Chip manufacturers introduce redundancy at various levels of CPU design to guarantee correct operation, even for worst-case combinations of non-idealities in process variation and system operating conditions. This redundancy is implemented partly in the form of voltage margins. However, for a wide range of real-world execution scenarios these margins are excessive and merely translate to increased power consumption, hindering the effort towards higher-energy efficiency in both HPC and general purpose computing. Our study on the x86-64 Haswell and Skylake multicore microarchitectures reveals-wide voltage margins, which vary across different microarchitectures, different chip parts of the same microarchitecture, and across different workloads. We find that it is necessary to quantify-voltage margins using multi-threaded and multi-instance workloads, as characterization with single-threaded and single-instance workloads that do not stress the CPU to its full capacity typically identifies overly optimistic margins that lead to errors when applied in realistic program execution scenarios. In addition, we introduce, deploy and evaluate a run-time governor that dynamically reduces the supply voltage of modern multicore x86-64 CPUs. Our governor employs a model that takes as input a set of performance metrics which are directly measurable via performance monitoring counters and have high predictive value for the minimum tolerable supply voltage (V min ), to predict and apply the appropriate reduction for the workload at hand. Compared with the conventional DVFS governor, our approach in this study achieves up to 42 percent energy savings for the Skylake family and 34 percent for the Haswell family for complex, real-world applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Encapsulant Void Breakdown Voltage Modeling & Experiments

For high voltage electrical devices, prevention of high voltage breakdown is critical for device function. Use of polymeric encapsulation such as epoxies is common, but these may include air bubbles or other voids of varying size. The present work aimed to model and experimentally determine the size dependence of breakdown voltage for voids in an epoxy matrix, as a step toward establishing size criteria for void screening. Effects were investigated experimentally for both one-dimensional metal/epoxy/air/epoxy/metal gap sizes from 50 μm to 10 mm, as well as spherical voids of 250 μm, 500 μm, 1 mm and 2 mm sizes. These experimental results were compared to modified Paschen curve and particle-in-cell discharge models; minimum breakdown voltages of 6 - 8.5 kV appeared to be predicted by 1D models and experiments, with minimum breakdown voltage for void sizes of 0.2 - 1 mm. In a limited set of 3D experiments on 250 μm, 500 μm, 1 mm and 2 mm voids within epoxy, the minimum breakdown voltages observed were 18.5 - 20 kV, for 500 μm void sizes. These experiments and models are aimed at providing initial size and voltage criteria for tolerable void sizes and expected discharge voltages to support design of encapsulated high voltage components.

42 ENGINEERING↗
DOE OSTI2021

EMOS (Energy Management Optimization System) [SWR-21-46]

EMOS software performs a real-time hierarchal optimal control for energy systems like multi-port electric vehicles charging site with distributed energy resources (DERs) and energy storage systems (ESSs). It gathers information in real-time from electric vehicles, power grid, and DERs, solves a multi-objective energy management optimization problem, and output setpoint for chargers, ESS converters, DERs converters, and grid converters. The software incorporates a novel integration of two control tasks: a) An Energy Management Optimization (EMO), which is the brain of EMOS controller that gathers information in real-time from EVs [e.g., battery size, state-of-charge (SOC), desired SOC, and charge acceptance curve], grid (e.g., electricity price, allowed feeder capacity, ramp rate limit, and reactive power), and DERs (e.g., prediction for solar generation for PV systems). It solves a control optimization problem in real-time to find optimal setpoint for ESSs power dispatch, EVs charging rate, and grid inverters. The objectives are to (1) minimize the charging cost considering grid energy, demand charges, and battery energy, (2) minimize charging time to meet fast charging criterion, (3) keep high energy level on ESSs at the end of an operating period, while satisfying constraints related to grid, EVs, and power converters. b) Real-Time Energy Management System (RT-EMS) is a rule-based algorithm that has faster response than EMO. It receives optimal setpoint from EMO and actual measurements from the system and modify the setpoint to compensate for any fast disturbance in the system, until a new optimum solution is received. Fast disturbances may include vehicle connect/disconnect, unpredicted variation in DERs profiles, variation in grid voltage, errors in PV generation prediction, and others. In addition, RT-EMS regulates voltage at point of common coupling (PCC) by managing reactive power of grid converters.

Mohamed, Ahmed↗
DOE OSTI2020

Phase Measurements of a 140-GHz Confocal Gyro-Amplifier

The phase stability of a 140-GHz, 1-kW pulsed gyro-amplifier system and the phase dependence on the cathode voltage were experimentally measured. To optimize the measurement precision, the amplifier was operated at 47 kV and 1 A, where the output power was ~ 30 W. The phase was determined to be stable both pulse-to-pulse and during each pulse, so far as the cathode voltage and electron beam current are constant. The phase variation with voltage was measured and found to be 130 ± 30° /kV, in excellent agreement with simulations. The electron gun used in this device is non-adiabatic, resulting in a steep slope of the beam pitch factor with respect to cathode voltage. This was discovered to be the dominant factor in the phase dependence on voltage. The use of an adiabatic electron gun is predicted to yield a significantly smaller phase sensitivity to voltage, and thus a more phase-stable performance. To our knowledge, these are the first phase measurements reported for a gyro-amplifier operating at a frequency above W-band.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Phase Measurements of a 140-GHz Confocal Gyro-Amplifier

The phase stability of a 140-GHz, 1-kW pulsed gyro-amplifier system and the phase dependence on the cathode voltage were experimentally measured. To optimize the measurement precision, the amplifier was operated at 47 kV and 1 A, where the output power was ~30 W. The phase was determined to be stable both pulse-to-pulse and during each pulse, so far as the cathode voltage and electron beam current are constant. The phase variation with voltage was measured and found to be 130 ± 30 deg/kV, in excellent agreement with simulations. The electron gun used in this device is non-adiabatic, resulting in a steep slope of the beam pitch factor with respect to cathode voltage. This was discovered to be the dominant factor in the phase dependence on voltage. The use of an adiabatic electron gun is predicted to yield a significantly smaller phase sensitivity to voltage, and thus a more phase-stable performance. To our knowledge, these are the first phase measurements reported for a gyro-amplifier operating at a frequency above W-band.

Rosenzweig, Guy↗
DOE OSTI2025

Radiation Response of Ga 2 O 3 MOSFETs Probed via Focused Particle Beams

Gallium Oxide (Ga 2 O 3 ), particularly in its β-phase, is attracting lots of interest for high-power and high-voltage electronics due to its wide bandgap, high breakdown field, and thermal stability. This study investigates the radiation response of Ga 2 O 3 Metal-Oxide-Semiconductor Field-Effect Transistors (MOSFETs) under Total Ionizing Dose (TID) and Displacement Damage (DD) conditions, which are critical for applications in radiation environments. Utilizing a dual-beam Focused Ion Beam and Scanning Electron Microscope setup, microscale analysis of radiation effects on individual devices is performed. The findings reveal distinct behaviors under TID and DD + TID conditions, with TID leading to threshold voltage shifts due to trapped charges, while DD results in decreased drive current attributed to increased carrier scattering from lattice defects. Notably, it is demonstrated that the TID effect can be mitigated through dynamic threshold voltage adjustments and that the predicted TID from ions calculated by Monte Carlo simulations overestimates actual TID due to unaccounted charge yield effects. In conclusion, this research enhances the understanding of Ga2O3 MOSFETs' performance in harsh radiation environments, providing insights for the design of robust electronic devices for space and nuclear applications.

MOSFET↗
DOE OSTI2023

Learning the right channel in multimodal imaging: automated experiment in piezoresponse force microscopy

We report the development and experimental implementation of the automated experiment workflows for the identification of the best predictive channel for a phenomenon of interest in spectroscopic measurements. The approach is based on the combination of ensembled deep kernel learning for probabilistic predictions and a basic reinforcement learning policy for channel selection. It allows the identification of which of the available observational channels, sampled sequentially, are most predictive of selected behaviors, and hence have the strongest correlations. We implement this approach for multimodal imaging in piezoresponse force microscopy (PFM), with the behaviors of interest manifesting in piezoresponse spectroscopy. We illustrate the best predictive channel for polarization-voltage hysteresis loop and frequency-voltage hysteresis loop areas is amplitude in the model samples. The same workflow and code are applicable for any multimodal imaging and local characterization methods.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Piezosuperconductivity: Novel effects in noncentrosymmetric superconductors

Here we study effects in noncentrosymmetric superconductors arising from their unique coupling of Cooper-pair condensate and elasticity. We show that although the much discussed Lifshitz coupling is not observable in a uniform bulk state, it strikingly endows dislocations with a fractional magnetic flux. We also predict a generation of voltage-free strain by a DC current in a P- and T-breaking Josephson junction. Viewing superconductors through the lens of higher-form symmetries we identify the Lifshitz coupling as a chemical potential for the approximately conserved winding number, drawing an analogy with pyroelectric insulators.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Low Mach number fluctuating hydrodynamics model for ionic liquids

We present a new mesoscale model for ionic liquids based on a low Mach number fluctuating hydrodynamics formulation for multicomponent charged species. The low Mach number approach eliminates sound waves from the fully compressible equations leading to a computationally efficient incompressible formulation. The model uses a Gibbs free energy functional that includes enthalpy of mixing, interfacial energy, and electrostatic contributions. These lead to a new fourth-order term in the mass equations and a reversible stress in the momentum equations. We calibrate our model using parameters for [DMPI+][F6P-], an extensively-studied room temperature ionic liquid (RTIL), and numerically demonstrate the formation of mesoscopic structuring at equilibrium in two and three dimensions. In simulations with electrode boundaries the measured double layer capacitance decreases with voltage, in agreement with theoretical predictions and experimental measurements for RTILs. Finally, we present a shear electroosmosis example to demonstrate that the methodology can be used to model electrokinetic flows.

42 ENGINEERING↗
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↗
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↗
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↗
DOE OSTI2022

Modular medium voltage fast chargers

Certain embodiments involve a modular medium voltage fast charger. The modular medium voltage fast charger can include: (1) a predictive power factor correction (“PFC”) controller (or predictive controller) for series-interleaved multi-cell three-level boost (“SIMCB”) converters, (2) an active neutral point clamped (“NPC”) dual active bridge (“DAB”) modulation scheme to achieve soft switching, (3) an auxiliary capacitor to reduce NPC DAB turn-off voltages, (4) a comprehensive and scalable protection circuit, and (5) a high-isolation pulse transformer with a bobbin for reducing coupling capacitance of the pulse transformer.

Lukic, Srdjan↗
DOE OSTI2023

Experimental Aging and Lifetime Prediction in Grid Applications for Large-Format Commercial Li-Ion Batteries

Due to the growth of electric vehicle and stationary energy storage markets, the production and use of lithium-ion batteries has grown exponentially in recent years. For many of these applications, large-format lithium-ion batteries are being utilized, as large cells have less inactive material relative to their energy capacity and require fewer electrical connections to assemble into packs. And especially for stationary energy storage systems, where energy delivered is the only revenue source, the economics of these battery systems is highly dependent on cell lifetime. However, testing of large-format lithium-ion batteries is time consuming and requires high current channels and large testing chambers, making information on the performance of commercial, large-format lithium-ion batteries hard to come by. Here, accelerated aging test data from four commercial large-format lithium-ion batteries is reported. These batteries span both NMC-Gr and LFP-Gr cell chemistries, pouch and prismatic formats, and a range of cell designs with varying power capabilities. Accelerated aging test results are analyzed to examine both cell performance, in terms of efficiency and thermal response under load, as well as cell lifetime. Cell thermal response is characterized by measuring temperature during cycle aging, which is used to calculated a normalized thermal resistance value that may help estimate both cell cooling needs or to help extrapolate aging test results to different thermal environments. Cell lifetime is evaluated qualitatively, considering simply the average calendar and cycle life across a range of conditions, as well as quantitatively, using statistical modeling and machine-learning methods to identify predictive aging models from the accelerated aging data. These predictive aging models are then used to investigate cell sensitivities to stressors, such as cycling temperature, voltage window, and C-rate, as well as to predict cell lifetime in various stationary storage applications. Results from this work show that cell lifetime and sensitivity to aging conditions varies substantially across commercial cells, necessitating testing for specific cell formats to make quantitative lifetime predictions. That being said, all commercial cells tested here are predicted to reach at least 10-year lifetimes for stationary storage applications. Based on the aging test results and modeling, some cells are expected to be relatively insensitive to temperature and use-case, making them suited for simple use cases with little or no thermal management and simple controls, while the lifetime of other cells could be extended to 20+ years if operated with thermal management and degradation-aware controls.

battery↗