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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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Modular Power Electronics Approach for High Power Dynamic Wireless Charging System

Dynamic wireless power transfer (DWPT) can provide energy to EVs in motion and extend the drive range. Upscaling the charging power to 200 kW (High Power DWPT) reduces the percentage of electrified roadway required, and the solution becomes cost-effective. To smooth the power at the battery and grid, a secondary regulation stage must be added. The DWPT system therefore relies on power electronics to interface with the coils and regulate the power flow. Designing this high power system using wide bandgap devices makes ensuring high efficiency, small size, and reliable operation very challenging, and significant engineering effort is required to build such complicated systems for large-scale installation and deployment. Here, this paper describes a modular design approach for the power electronics to achieve the 200 kW wireless power transfer. As described, the SiC power electronics building block is designed, simulated, and characterized. The approach is validated in the DWPT system to build the inverter, the rectifier, and the DC/DC converter, which demonstrated high performance and reliable operation with 188 kW power.

42 ENGINEERING↗
DOE OSTI2021

Modular Design of Receiver Side Power Electronics for 200 kW High Power Dynamic Wireless Charging System

Dynamic wireless power transfer (DWPT) can provide energy to EVs in motion and extend the drive range. By upscaling the charging power to 200 kW (High Power DWPT), the percentage of electrified roadway reduces and the solution becomes cost-effective. However, coil coupling-coefficient variation during vehicle movement fluctuates the transferred power which is unfavorable for vehicle battery. Secondary regulation design can smooth the power but the converter design becomes very challenging due to requirement in high power, high efficiency, fast control, as well as high power density since the unit will be onboard. This paper provides the modular design approach of a 200 kW secondary side unit to achieve high performance and scalability. The DC/DC converter using SiC devices demonstrated 98.3% efficiency.

Xue, Lincoln↗
NASA NTRS1990

Flight Dynamics Analysis System

Flight Dynamics Analysis System (FDAS) collection of computer programs provides environment for configuration and study of Ada software. Designed to support flight-dynamics research and analysis activities concerning software models, algorithms, and techniques used within flight Dynamics Division at Goddard Space Flight Center. Assists analysts and programmers in building, testing, and evaluating applications software by providing integrated support system for modification and reconfiguration of software. Includes capability of assembling reusable software components into applications programs, and reconfiguring assembled program after partially or fully completed. Developed in Ada for use on DEC VAX computer operating under VMS 4.3 or higher and version 1.3 or higher of VAX Ada Compilation System (ACS).

Tasaki, Keiji↗
NASA NTRS1985

Dynamics insulation systems

Advanced dynamic insulation systems were analyzed from a thermodynamic point of view. A particular performance measure is proposed in order to characterize various insulations in a unique manner. This measure is related to a base quantity, the refrigeration power ratio. The latter is the minimum refrigeration power, for a particular dynamic insulation limit, to the actual reliquefaction power associated with cryoliquid boiloff. This ratio serves as reference quantity which is approximately constant for a specific ductless insulation at a chosen normal boiling point. Each real container with support structure, vent tube, and other transverse components requires a larger refrigeration power. The ratio of the actual experimental power to the theoretical value of the support-less system is a suitable measure of the entire insulation performance as far as parasitic heat leakage is concerned. The present characterization is illustrated using simple thermodynamic system examples including experiments with liquid nitrogen. Numerical values are presented and a comparison with liquid helium is given.

Chen, W. E. W.↗
DOE OSTI2023

Deep-Learning-Based Koopman Modeling for Online Control Synthesis of Nonlinear Power System Transient Dynamics

Power system stability and control have become more challenging due to the increasing uncertainty associated with renewable generation. Here, the performance of conventional control is highly driven by the physics-based offline-developed dynamic models that can deviate from the actual system characteristics under different operating conditions and/or configurations. Data-driven approaches based on online measurements can be a better solution to addressing these issues by capturing real-time operation conditions. This article describes a novel fully data-driven probabilistic framework to derive a linear representation of postcontingency grid dynamics and online prescribe control based on the derived model to enhance transient stability. The complex nonlinear power system dynamics is approximated by a linear model by using multiple neural network modules that infer distributions of the observations and introducing a Koopman layer to sample possible Koopman linear models from the inferred distributions. The trained model features linearity that can be easily incorporated into the existing linear control design paradigm and ease the controller design process. The effectiveness of Koopman-based control designs is validated through comparative case studies, which demonstrate increased prediction accuracy and control performance when applied to a power system with heterogeneous generator dynamics.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1994

MME-based attitude dynamics identification and estimation for SAMPEX

A method is described for obtaining optimal attitude estimation algorithms for spacecraft lacking attitude rate measurement devices (rate gyros), and then demonstrated using actual flight data from the Solar, Anomalous, and Magnetospheric Particle Explorer (SAMPEX) spacecraft. SAMPEX does not have on-board rate sensing, and relies on sun sensors and a three-axis magnetometer for attitude determination. Problems arise since typical attitude estimation is accomplished by filtering measurements of both attitude and attitude rates. Rates are nearly always sampled much more densely than are attitudes. Thus, the absence/loss of rate data normally reduces both the total amount of data available and the sampling density (in time) by a substantial fraction. As a result, the sensitivity of the estimates to model uncertainty and to measurement noise increases. In order to maintain accuracy in the attitude estimates, there is increased need for accurate models of the rotational dynamics. The proposed approach is based on the minimum model error (MME) optimal estimation strategy, which has been successfully applied to estimation of poorly modeled dynamic systems which are relatively sparsely and/or noisily measured. The MME estimates may be used to construct accurate models of the system dynamics (i.e. perform system model identification). Thus, an MME-based approach directly addresses the problems created by absence of attitude rate measurements.

Depena, Juan↗
DOE OSTI2023

Bayesian High-Rank Hankel Matrix Completion for Nonlinear Synchrophasor Data Recovery

Phasor measurement units (PMUs) provide high temporal-resolution synchrophasor measurements for power system monitoring and control. The frequent data quality issues, such as missing and bad data, prevent the incorporation of synchrophasor data in real-time operations. Most existing data-driven data recovery methods assume the power system dynamics can be approximated by a linear dynamical system, and the recovery performance degrades significantly when the power system is experiencing nonlinear dynamics during significant events. Here, this paper proposes a data-driven Bayesian nonlinear synchrophasor data recovery method (Ba-NSDR) that can recover a consecutive time period of simultaneous data losses or errors across all channels, even when the underlying system is highly nonlinear. The idea is to lift the Hankel matrix of the spatial-temporal synchrophasor data to a higher dimension such that the lifted Hankel matrix is low-rank in that space and can be processed with the kernel trick. Our proposed Bayesian method then infers the probabilistic distributions of synchrophasor from the partial observations. Some distinctive features of Ba-NSDR include an uncertainty index to measure the accuracy of the recovery result and the robustness to parameter selections. Our method is verified on both synthetic and recorded event datasets.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1990

On dynamic stability boundaries for binary systems

Dynamic stability boundaries are developed for linear two-degree-of-freedom systems with damping and elastic couplings. Special emphasis is placed on the influence of natural frequency proximity and those instabilities which stem from skew-symmetric stiffness properties. These arise in aeroelasticity and flight dynamics systems. Insight is provided into the destabilizing effects of the 'dreaded modal resonance' which results when the two natural frequencies in the modal natural frequency ratio match or nearly match.

Young, M. I.↗
DOE OSTI2020

System and method for modeling and forecasting cyclical demand systems with dynamic controls and dynamic incentives

Systems and methods for modeling and forecasting cyclical demand systems in the presence of dynamic control or dynamic incentives. A method for modeling a cyclical demand system comprises obtaining historical data on one or more demand measurements over a plurality of demand cycles, obtaining historical data on incentive signals over the plurality of demand cycles, constructing a model using the obtained historical data on the one or more demand measurements and the incentive signals, wherein constructing the model comprises specifying a state-space model, specifying variance parameters in the model, and estimating unknown variance parameters.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Sensitivity Analysis of Compensation Topologies for Dynamic WPT System

The dynamic wireless power transfer (DWPT) or in-motion charging of electric vehicles (EVs) may alleviate the challenges encountered by the plug-in charging systems and assist in the penetration of the EVs at a larger scale. Similar to its stationary charging counterpart, DPWT also makes use of compensation circuits to reduce the inductive reactance for enabling higher current on the coupler winding and improve the power transfer. However, the behavior of compensation circuits has not been comprehensively analyzed in a DWPT system. This paper analyzes in detail the performance of three compensation topologies applied to the DWPT system. A combination of finite element analysis (FEA) and circuit analysis is employed to study the compensation topologies when the receiving coil moves over the transmitting coil. Moreover, the variations in operating frequency have also been considered in the analysis. Furthermore, the theoretical calculations for the dynamic WPT system are verified using PLECS simulation model.

Haruna, Josiah↗
DOE OSTI2017

Stochastic Dynamic Analysis for Power Systems Under Uncertain Variability

This paper proposes a novel method to analyze impacts of uncertain variability on power system dynamics. There is considerable interest in integrating intermittent renewable energy and plug-in electric vehicles into power systems. Therefore, power systems are in an environment with increasing uncertain variability. In this paper, the uncertain variability is described as a continuous-time stochastic process, and thus, the power system under uncertain variability is modeled by stochastic differential equations. To quantify impacts of uncertain variability on power system dynamics, an intra-region probability index is presented. Based on the stochastic averaging method, an analytical method is proposed to calculate the intra-region probability. Compared to Monte Carlo simulation, the proposed method is many orders of magnitude faster without sacrificing the result accuracy. As a result, several insights are given to improve the power system dynamics under uncertain variability.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Improving logistic efficiency of WEEE collection through dynamic scheduling using simulation modeling

Highlights: • Latest EU regulation on WEEE require more flexible logistics models. • The implementation of dynamic scheduling is evaluated for WEEE collection. • A hybrid simulation model compares dynamic (pure and “mixed”) and static collection schemes. • Results confirm the higher flexibility and efficiency of dynamic systems. • Dynamic scheduling “mixed” solutions result more efficient than pure ones. - Abstract: The complexity of collection systems for Waste from Electric and Electronic Equipment (WEEE) in the EU is increasing, due to the latest directive that sets new collection targets and modes. The high variability and the uncertainty of reverse flows require innovative logistic approaches. One recent option for increasing efficiency and responsiveness in waste collection services, boosted by new technological solutions for waste level monitoring, is to adopt a dynamic collection scheme, where the collection frequency is not established a priori (based on a fixed plan), but it is based on the actual filling levels of waste bins. This option can allow the service provider to plan the collection service following the actual demand, resulting in a more responsive service, while improving the logistic efficiency. This paper evaluates the implementation of dynamic scheduling schemes for the collection of WEEE. A hybrid simulation model has been developed in order to support researchers and practitioners in assessing quantitative impacts of adopting dynamic scheduling in WEEE collection. Three logistic alternatives (a fixed collection schedule scheme, a pure dynamic scheme and a mixed one) have been compared in a test case based on data of an Italian municipality; collection services for different types of WEEE (i.e. large appliances and small items) have been analyzed. Results show a promising performance of dynamic schedules compared to the fixed one, revealing, for the specific test case, how a mixed solution can combine the advantages of dynamic and fixed scheduling, gaining flexibility towards customer demand while improving truck resource utilization.

42 ENGINEERING↗
DOE OSTI2021

Vibration-Based Sensor Design: A Grey-Box Approach

Knowledge of the internal structure of an object or device under investigation proceeds from the basic idea of constructing its dynamic behavioral relations governed by a set of differential/algebraic equations that characterize its response. These equations can be partial differential equations leading to finite element or finite difference relations requiring a complex numerical solution on a super computer or ordinary differential equations requiring sophisticated numerical integration techniques to obtain the desired solution. Discrete dynamic systems evolving from digitized data acquisition are typically captured by sampled-data (continuous-to-discrete) representations characterized by a set of difference equations specifying the underlying system dynamics. In any case, with a mathematical description in hand, Grey-Box modeling techniques have evolved, concerned with the estimation of model parameters embedded in a prescribed set of equations (the system) governing its behavior, while capturing the underlying physical phenomenology of the problem at hand.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Offshore Wind Energy

In 1991, the Vindeby Offshore Wind Farm, the first offshore wind farm in the world, started feeding electricity to the grid off the coast of Lolland, Denmark. Since then, offshore wind energy has developed from this early experiment to a multibillion dollar market and an important pillar of worldwide renewable energy production. Unit sizes grew from 450 kW at Vindeby to the 7.5 MW-class offshore wind turbines (OWT ) that are currently (by October 2014) in the prototyping phase. This chapter gives an overview of the state of the art in offshore wind turbine (OWT) technology and introduces the principles of modeling and simulating an OWT. The OWT components -- including the rotor, nacelle, support structure, control system, and power electronics -- are introduced, and current technological challenges are presented. The OWT system dynamics and the environment (wind and ocean waves) are described from the perspective of OWT modelers and designers. Finally, an outlook on future technology is provided. The descriptions in this chapter are focused on a single OWT -- more precisely, a horizontal-axis wind turbine -- as a dynamic system. Offshore wind farms and wind farm effects are not described in detail in this chapter, but an introduction and further references are given.

17 WIND ENERGY↗
DOE OSTI2025

Generative learning of densities on manifolds

A generative modeling framework is proposed that combines diffusion models and manifold learning to efficiently sample data densities on manifolds. The approach utilizes Diffusion Maps to uncover possible low-dimensional underlying (latent) spaces in the high-dimensional data (ambient) space. Two approaches for sampling from the latent data density are described. The first is a score-based diffusion model, which is trained to map a standard normal distribution to the latent data distribution using a neural network. The second one involves solving an Itô stochastic differential equation in the latent space. Additional realizations of the data are generated by lifting the samples back to the ambient space using Double Diffusion Maps , a recently introduced technique typically employed in studying dynamical system reduction; here the focus lies in sampling densities rather than system dynamics. The proposed approaches enable sampling high dimensional data densities restricted to low-dimensional, a priori unknown manifolds. The efficacy of the proposed framework is demonstrated through a benchmark problem and a material with multiscale structure.

Double diffusion maps↗
DOE OSTI2021

Adaptive Machine Learning for Robust Diagnostics and Control of Time-Varying Particle Accelerator Components and Beams

Machine learning (ML) is growing in popularity for various particle accelerator applications including anomaly detection such as faulty beam position monitor or RF fault identification, for non-invasive diagnostics, and for creating surrogate models. ML methods such as neural networks (NN) are useful because they can learn input-output relationships in large complex systems based on large data sets. Once they are trained, methods such as NNs give instant predictions of complex phenomenon, which makes their use as surrogate models especially appealing for speeding up large parameter space searches which otherwise require computationally expensive simulations. However, quickly time varying systems are challenging for ML-based approaches because the actual system dynamics quickly drifts away from the description provided by any fixed data set, degrading the predictive power of any ML method, and limits their applicability for real time feedback control of quickly time-varying accelerator components and beams. In contrast to ML methods, adaptive model-independent feedback algorithms are by design robust to un-modeled changes and disturbances in dynamic systems, but are usually local in nature and susceptible to local extrema. In this work, we propose that the combination of adaptive feedback and machine learning, adaptive machine learning (AML), is a way to combine the global feature learning power of ML methods such as deep neural networks with the robustness of model-independent control. We present an overview of several ML and adaptive control methods, their strengths and limitations, and an overview of AML approaches.

97 MATHEMATICS AND COMPUTING↗