Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Inverter”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,585 records · Page 88
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Thermal Management System of an Outer-Rotor-Motor-Based Traction Drive With Integrated Power Electronics in its Central Cavity

Increasing power densities of electric vehicle traction drive systems necessitates combining the electric motor and the power electronics into one unit. A compact, integrated traction drive unit with fewer components also drives production costs down, enabling wider adoption of electric vehicles. However, the integration of power electronics in the electric machine is associated with challenges of designing an effective thermal management solution for the combined traction drive system. This paper focuses on the thermal management approach selected for Oak Ridge National Laboratory’s outer-rotor-motor-based integrated traction drive and evaluates its potential performance. The outer-rotor-motor configuration provided an opportunity for integration of the six-phase inverter in the available space in the central cavity of the internal stator. A cylindrical inverter enclosure with integrated coolant (water-ethylene glycol) channels in its walls was designed to enable heat removal from the power electronics. Numerical thermal-fluid modeling and initial channel/fin optimization results for the cylindrical heat sink are presented here. As permanent magnets are integrated in a high-speed (20 000 RPM) outer rotor, forced air convection provides cooling for the magnets. The magnets were segmented axially to mitigate the eddy current losses. Heat generated in the stator windings and laminations is removed by water-ethylene glycol coolant circulating in interpolar T-shape ceramic heat exchangers inserted between windings. General design concepts and numerical thermal-fluid simulations illustrating the electric motor thermal management solution are also presented.

Kekelia, Bidzina↗
DOE OSTI2025

On the Source of Conductivity in Alkaline Zn Anodes: Zn Percolation and ZnO Bridging

Alkaline Zn anodes are fundamental to commercial Zn-MnO 2 batteries as well as emerging rechargeable designs. In these electrodes, Zn particles are both the active material and source of electronic conduction. However, there are known cases in which electronic connectivity between Zn particles or the current collecting pin is lost even though the battery continues to function. Here we use X-ray computed tomography (CT) of AA batteries to demonstrate several examples of Zn particle-to-particle connectivity breakdown, which is observed even in cases at relatively high discharge rate. This indicates maintenance of the electronic network through the less-conductive ZnO discharge product. We introduce a new equation for electronic conductivity maintained through bridges of ZnO formed between percolating Zn particles. This necessitates discarding the Bruggeman correlation and instead redefining effective electronic conductivity using percolation theory. We demonstrate that such a model for conduction enables prediction of an inverted reaction zone, which is an experimentally observed case in which significant Zn dissolution and ZnO formation occurs heavily near the current collecting pin. Current computational Zn-MnO 2 models never predict an inverted reaction zone, and thus the updated conductivity enables models to accurately explain a wider range of experimental conditions.

Guida, Dominick P. [Northeastern Univ., Boston, MA↗
DOE OSTI2021

The Power and Energy Storage Systems Toolbox -- PSTess v.1.0

The Power and Energy Storage Systems Toolbox (PSTess) is a MATLAB-based computing package for dynamic simulation and analysis of utility-scale battery storage systems. This codebase is a fork of the Power Systems Toolbox Version 3.0, developed at Rensselaer Polytechnic Institute (RPI) and Cherry Tree Scientific Software. While PSTess shares a common lineage with PST, it is a substantially different application. As the name implies, the main distinguishing characteristic of PSTess is its ability to model inverter-based energy storage systems (ESS). The model that enables this is called ess.m, and it serves the dual role of representing ESS operational constraints and the generator/converter interface. With PSTess, the generator/converter interface is modeled as a controllable current source with the ability to modulate both active and reactive current. The model ess.m permits four-quadrant modulation, which allows it to represent a wide variety of inverter-based resources beyond energy storage when paired with an appropriate supplemental control model. Examples include utility-scale photovoltaic (PV) power plants, type 4 wind plants, and static synchronous compensators (STATCOM). This capability is especially useful for modeling hybrid plants that combine energy storage with renewable resources. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. SAND2021-8322 O

Elliott, RyanT.↗
DOE OSTI2022

INN Airfoil Tools

This software package contains code to perform airfoil inverse shape design using a sparsified invertible neural network model. This is a specialized deep learning architecture that enables both network weights to be shared to evaluate forward or inverse passes through the network. As a design tool, this network allows practitioners to specify desired aerodynamic and structural properties and generate shapes that satisfy those properties. The invertible neural network is accompanied with tuned parameters as well as software tools to train new models as needed.

Glaws, Andrew↗
DOE OSTI2021

Reinforcement Learning for Distribution Grid Optimization (PyCIGAR) v0.1

PyCIGAR is a python software package that merges off-the-shelf reinforcement learning libraries (RLLib and Ray) with electric power distribution system simulation tools (OpenDSS and a custom power flow solver built by LBL). PyCIGAR enables the training of neural networks to optimize the behavior of different components in the electric distribution grid, such as control systems in photovoltaic rooftop solar inverters and electric battery storage systems. The software package has been used to train neural networks to update settings in photovoltaic rooftop solar inverter control systems to mitigate cyber attacks on other solar photovoltaic rooftop devices.

Arnold, Daniel↗
DOE OSTI2020

Optimal Estimation Retrievals and Their Uncertainties: What Every Atmospheric Scientist Should Know

Remote sensing instruments are heavily used to provide observations for both the operational and research communities. These sensors do not provide direct observations of the desired atmospheric variables, but instead, retrieval algorithms are necessary to convert the indirect observations into the variable of interest. It is critical to be aware of the underlying assumptions made by many retrieval algorithms, including that the retrieval problem is often ill posed and that there are various sources of uncertainty that need to be treated properly. In short, the retrieval challenge is to invert a set of noisy observations to obtain estimates of atmospheric quantities. The problem is often complicated by imperfect forward models, by imperfect prior knowledge, and by the existence of nonunique solutions. Optimal estimation (OE) is a widely used physical retrieval method that combines measurements, prior information, and the corresponding uncertainties based on Bayes’s theorem to find an optimal solution for the atmospheric state. Furthermore, OE also allows the relative contributions of the different sources of error to the uncertainty in the final retrieved atmospheric state to be understood. Here, we provide a novel Python library to illustrate the use of OE for inverse problems in the atmospheric sciences. We introduce two example problems: how to retrieve drop size distribution parameters from radar observations and how to retrieve the temperature profile from ground-based microwave sensors. Using these examples, we discuss common pitfalls, how the various error sources impact the retrieval, and how the quality of the retrieval results can be quantified.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Double band inversion in the topological phase transition of Ge 1-x Sn x alloys

Abstract We use first-principles simulation and alchemical mixing approximation to reveal the unique double band inversion and topological phase transition in Ge Sn alloys. Wave function parity, spatial charge distribution and surface state spectrum analyses suggest that the band inversion in Ge Sn is relayed by its first valence band. As the system evolves from Ge to -Sn, its conduction band moves down, and inverts with the first and the second valence bands consecutively. The first band inversion makes the system nontrivial, while the second one does not change the topological invariant of the system. Both of the band inversions yield surface modes spanning the individual inverted gaps, but only the surface mode in the upper gap associates with the nontrivial nature of tensile-strained -Sn.

Physics↗
DOE OSTI2026

UPS XML Format and Data

This is the XML data and format for UPS supercomponent including subcomponents: Grid following inverter (GFL), Grid forming inverter (GFM), and dc-dc converter connected to the energy storage and the AI load.

Marthi, Phani Ratna Vanamali [ORNL] (ORCID:0000000↗
DOE OSTI2023

Precision engineering of biological function with large-scale measurements and machine learning

As synthetic biology expands and accelerates into real-world applications, methods for quantitatively and precisely engineering biological function become increasingly relevant. This is particularly true for applications that require programmed sensing to dynamically regulate gene expression in response to stimuli. However, few methods have been described that can engineer biological sensing with any level of quantitative precision. Here, we present two complementary methods for precision engineering of genetic sensors: in silico selection and machine-learning-enabled forward engineering. Both methods use a large-scale genotype-phenotype dataset to identify DNA sequences that encode sensors with quantitatively specified dose response. First, we show that in silico selection can be used to engineer sensors with a wide range of dose-response curves. To demonstrate in silico selection for precise, multi-objective engineering, we simultaneously tune a genetic sensor’s sensitivity (EC 50 ) and saturating output to meet quantitative specifications. In addition, we engineer sensors with inverted dose-response and specified EC 50 . Second, we demonstrate a machine-learning-enabled approach to predictively engineer genetic sensors with mutation combinations that are not present in the large-scale dataset. We show that the interpretable machine learning results can be combined with a biophysical model to engineer sensors with improved inverted dose-response curves.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Data and scripts associated with the manuscript "A Hydrogeophysical Framework to Assess Infiltration During a Simulated Ecosystem-Scale Flooding Experiment”

This package contains the data and scripts used in "A hydrogeophysical framework to assess infiltration during a simulated ecosystem-scale flooding experiment" (Adebayo et al., 2023). The Geophysics folder contains two subfolders containing Ground Penetrating Radar (GPR) and Electrical Resistivity Imaging (ERI) datasets acquired between August 23 and August 26, 2021. The GPR subfolder contains raw data (Tempest_Project.GPZ) and processed results using the Sensors and Software EKKO Project software. The ERI subfolder contains two additional subfolders named Static and Timelapse. The Static subfolder contains background resistivity datasets acquired before infiltration, both raw and inverted results using the ResIpy code. The Timelapse subfolder contains repeated resistivity datasets acquired during infiltration on two profiles, along with raw and inverted results using the ResIpy code. The Flow model folder contains ATS model simulation files. The Geophysics-informed subfolder contains simulation datasets for two profiles generated using information from the geophysics data. The National_database-informed subfolder contains simulation datasets for two profiles generated using information from a national soil database. Each subfolder contains initial simulation results, multiple ensembles, and final simulation files. The Sensitivity_analysis folder contains the observation files exported from the flow model, notebooks used in running the code, and sensitivity analysis results showing the sensitivity of different parameters over a 48-hour period. We used mutual information-based global sensitivity analysis for the sensitivity analysis, following Jiang et al., 2022. This study shows a novel approach that integrates geophysical data with hydrological modeling to understand subsurface flow during coastal flooding.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Multi-Event Explosive Seismic Source for the 2022 M w 6.3 Hunga Tonga Submarine Volcanic Eruption

The eruption of the Hunga Tonga–Hunga Ha’apai submarine volcano on 15 January 2022 produced a variety of geophysical responses, including a significant seismic signal. We study the seismic source process of this event by inverting for moment tensors (MTs) using regional surface waves (Rayleigh, Love). By comparing inversion results for the eruption with eight nearby earthquakes, we show that it is possible to discriminate MT source types. Our inversion yields a shallow explosive source for the eruption and reveals the importance of trade-offs among depth, magnitude, and source type. We illustrate these trade-offs by representing the misfit variations over the eigenvalue lune. Finally, we invert for the source-time function of the sequence of explosions that occurred in the first minutes of the eruption. The multi-event source-time function comprises four subevents spanning ~270 s, with a total magnitude estimate of M w 6.34 ± 0.10.

58 GEOSCIENCES↗
DOE OSTI2021

Redesign of the Jefferson Lab -300 kV DC Photo-Gun for High Bunch Charge Operations

Production of high bunch charge beams for the Electron-Ion Collider (EIC) is a challenging task. High bunch charge (a few nC) electron beam studies at Jefferson Lab using an inverted insulator DC high voltage photo-gun showed evidence of space charge limitations starting at 0.3 nC, limiting the maximum delivered bunch charge to 0.7 nC for beam at -225 kV, 75 ps (FWHM) pulse width, and 1.64 mm (rms) laser spot size. The low extracted charge is due to the modest longitudinal electric field (Ez) at the photocathode leading to beam loss at the anode and downstream beam pipe. To reach the few nC high bunch charge goal, and to correct the beam deflection exerted by the non-symmetric nature of the inverted insulator photo-gun the existing photo-gun was modified. This contribution discusses the electrostatic design of the modified photo-gun obtained using CST Studio Suite’s electromagnetic field solver. Beam dynamics simulations performed using General Particle Tracer (GPT) with the resulting electrostatic field map obtained from the modified electrodes confirmed the validity of the new design.

Wijethunga, S.A.K.↗
DOE OSTI2025

Normalizing flows for high-dimensional detector simulations

Whenever invertible generative networks are needed for LHC physics, normalizing flows show excellent performance. In this work, we investigate their performance for fast calorimeter shower simulations with increasing phase space dimension. We use fast and expressive coupling spline transformations applied to the CaloChallenge datasets. In addition to the base flow architecture we also employ a VAE to compress the dimensionality and train a generative network in the latent space. We evaluate our networks on several metrics, including high-level features, classifiers, and generation timing. Our findings demonstrate that invertible neural networks have competitive performance when compared to autoregressive flows, while being substantially faster during generation.

Ernst, Florian↗
DOE OSTI2021

Evaluating Utility-Scale PV-Battery Hybrids in an Operational Model for the Bulk Power System

Systems that combine solar photovoltaic and battery energy storage technologies (PV-BES) are increasingly being proposed and deployed on the bulk power system. The operations and value of PV-BES systems have been extensively studied from the project developer's perspective through analyses that maximize plant-level revenue. However, PV-BES hybrids' operational characteristics are seldom studied from the perspective of bulk power system operators, who seek to optimize the performance of a suite of generation and storage assets that are connected via the transmission network. This work presents modeling approaches for representing and evaluating PV-BES hybrids in a model that optimizes operations across the bulk power system. Its novel contributions include demonstrating a technique to modify a unit commitment and dispatch model to represent the operational synergies of PV-BES hybrids. In particular, we describe the challenges and an approach for representing so-called DC-coupled PV-BES - which utilize a single bi-directional inverter - as a dispatchable resource in a commercial, production cost model (PCM), PLEXOS. We demonstrate this technique in a PCM study of the Los Angeles Department of Water and Power (LADWP) test system, by replacing existing PV and battery generators on the test system with our PV-BES hybrids. We then pursue scenario analysis that is designed to isolate the various drivers of operational strategies for DC-coupled PV-BES hybrids, including the nature of coupling, PV penetration on the system, and varying inverter loading ratios (or degrees of over-sizing of the PV field). Results from the analysis include utilization profiles for the PV DC energy across available pathways, dispatch profiles for the battery component, and the hybrid technologies' impacts on system-wide production costs. The approach presented in this paper can be used in any PCM that is looking to study PV-BES hybrids as a resource in different power system configurations and services.

14 SOLAR ENERGY↗
DOE OSTI2021

Representing DC-Coupled PV+Battery Hybrids in a Capacity Expansion Model

Recent technology cost and performance improvements and the federal investment tax credit (ITC) have driven growing interest in coupling solar photovoltaic (PV) and battery systems. Combining these technologies into co-located or hybridized PV+battery systems has the potential to lower costs and increase energy output relative to multiple independent systems. In this work, we provide an overview of PV+battery systems and demonstrate methods for incorporating them into NREL’s Regional Energy Deployment System (ReEDS) capacity expansion model. Although the methods are applied to a specific model, we anticipate that the approaches used here can be useful for informing PV+battery method development for other capacity expansion models. The implemented method relies heavily on the native representations of PV and battery technologies; therefore, the focus of this work is on capturing and parameterizing the interactions between them for a configuration in which the PV and battery technologies share a single bi-directional inverter. This work also demonstrates the impacts of including PV+battery systems in the ReEDS optimization for the conterminous United States through 2050. In particular, we perform parametric sensitivities for input assumptions that are uncertain and expected to influence PV+battery deployment levels, including (a) the cost of PV+battery systems relative to independent PV and battery systems, (b) the battery component’s qualification for the ITC, and (c) future cost trajectories for PV and battery systems. We find that PV+battery deployment could occur throughout the conterminous United States if there are cost savings associated with DC coupling PV and battery technologies. If even modest (5%) cost savings can be achieved (through a shared inverter and balance-of-system costs), then approximately one-third of utility-scale PV deployment through 2050 adopts the DC-coupled hybrid configuration, resulting in total PV+battery deployment that exceeds the magnitude of PV+battery projects in U.S. interconnection queues in 2020. If greater cost savings can be achieved through DC coupling (e.g., due to a growing amount of shared balance-of-system costs, reduced financial risk, or modularity) or more rapid cost and performance improvements are realized for PV and battery technologies, then total PV+battery deployment and the share of PV and battery deployment that adopts the hybrid configuration grows (to >50%). In all cases, growing PV+battery deployment primarily displaces independent PV and battery technologies, indicating the strong competition between the hybrid and independent configurations comprising technologies with similar performance characteristics.

14 SOLAR ENERGY↗
DOE OSTI2021

Keystone Solar Future Project (KSFP)

The Keystone Solar Future Project (KSFP, also referred to as “Keystone Project” in the report) implements a cost-effective, secure, reliable, and safe technology platform that paves the way for future interconnected distributed energy resources (DER), creates a framework that enables high penetration of DER, and transforms the DER interconnection process. The project is important and necessary in a few aspects. First, the industry definition of Distributed Energy Resource Management System (DERMS) is not yet validated against field deployments and mostly implemented in small pilots relying on third party integration. Keystone project implements a direct plug and play communication to inverter based DER systems. Secondly, most interconnection application portal is not yet fully integrated with back-end system, thus lacks the capability to drive a complete and accurate modeling of DER assets into the planning and operational systems. Keystone project rolled out a fully integrated modeling process of DERs from interconnection to operational management. Lastly, customers were given an opportunity to learn about the advanced functions of the inverters and take full advantages of their benefits through participation in the pilot program. During the project, the technical effectiveness and economic feasibility of DER management were investigated and demonstrated in real life events and simulation studies. The project highlighted the importance of coordinated DER interconnection, installation and management with the rest of the distribution system.

14 SOLAR ENERGY↗
DOE OSTI2020

Scalable Open-Air Deposition of Compact ETL TiOx on Perovskite for Fullerene-Free Solar Cells

We report on a scalable, low-temperature TiOx electron transport layer (ETL) produced directly on top of perovskite in an inverted architecture device by an open-air chemical vapor deposition (OA-CVD) process. The OA-CVD pyrolysis of a titanium ethoxide precursor forms a fully inorganic, conformal, and compact TiOx film. The resultant film is mechanically robust and exhibits a fracture toughness 30-fold higher than commonly used fullerene-based ETL films. The TiOx is an effective barrier layer against environmental degradation—unencapsulated devices with the TiOx ETL retain 60% of their initial efficiency after 1000 hours in ambient aging conditions—compared to devices with a fullerene ETL which were inoperable after 144 hours. Inverted architecture devices with an optimized TiOx ETL thickness of 70nm exhibited high open-circuit voltages of 1.08V. We comment on the photocatalytic properties of TiOx and the influence of an amorphous film on maximum power point stability.

Chen, Justin↗
DOE OSTI2021

The Power and Energy Storage Systems Toolbox–PSTess (V1.0)

This document describes the Power and Energy Storage Systems Toolbox for MATLAB, abbreviated as PSTess. This computing package is a fork of the Power Systems Toolbox (PST). PST was originally developed at Rensselaer Polytechnic Institute (RPI) and later upgraded by Dr. Graham Rogers at Cherry Tree Scientific Software. While PSTess shares a common lineage with PST Version 3.0, it is a substantially different application. This document supplements the main PST manual by describing the features and models that are unique to PSTess. As the name implies, the main distinguishing characteristic of PSTess is its ability to model inverter-based energy storage systems (ESS). The model that enables this is called ess.m , and it serves the dual role of representing ESS operational constraints and the generator/converter interface. As in the WECC REGC_A model, the generator/converter interface is modeled as a controllable current source with the ability to modulate both real and reactive current. The model ess.m permits four-quadrant modulation, which allows it to represent a wide variety of inverter-based resources beyond energy storage when paired with an appropriate supplemental control model. Examples include utility-scale photovoltaic (PV) power plants, Type 4 wind plants, and static synchronous compensators (STATCOM). This capability is especially useful for modeling hybrid plants that combine energy storage with renewable resources or FACTS devices.

97 MATHEMATICS AND COMPUTING↗