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Traction Inverter Integrated On-Board DC Fast Charging through Partial Power Processing
This paper introduces an innovative on-board integrated DC charging approach through partial power processing (PPP) in a traction inverter system. The proposed system, incorporating a series-connected partial power dual-active-bridge (DAB) converter, efficiently regulates the DC link voltage to achieve an optimal bus voltage for traction operation across a wide speed and torque range. Leveraging partial and bidirectional power processing, the battery current during charging is precisely regulated without the need for external DC-DC charging equipment, ensuring seamless integration to a DC hub with different voltage levels. This advanced integration also endows the system with bidirectional power exchange capability to/from the external DC hub, i.e., vehicle-to-DC (V2DC).
Multiport Control with Partial Power Processing in Solid-State Transformer for PV, Storage, and Fast-Charging Electric Vehicle Integration
This article proposes a multiport control method to enable partial power processing (PPP) in a medium-voltage (MV) multiport solid-state transformer (SST). MV multiport SSTs are promising in integrating low-voltage DC sources or loads such as solar photovoltaic, energy storage, and electric vehicles into smart grids without bulky line-frequency transformers. Compared to voltage-source SST, current-source (CS) SST features single-stage isolated bidirectional AC/AC, AC/DC, or DC/DC conversion using an inductive DC link. For a multiport CS SST, it is revealed in this article that the PPP capability can be enabled through the proposed control without extra hardware, different from the case of voltage-source converters where special hardware architecture is required for the PPP. With the PPP, most power exchange between LV ports is processed by only a fraction of the entire conversion stage, leading to reduced DC-link current, volume, loss, and improved efficiency. The proposed multiport PPP control scheme is analyzed to verify the advantages across a wide voltage and power range against conventional full power processing (FPP) multiport control, using the soft-switching solid-state transformer (S4T) with reduced conduction loss as an example. Comparative experimental results based on a SiC three-port S4T prototype verify the effectiveness of the proposed PPP scheme against the FPP scheme under both steady state and dynamic conditions. Here, the DC-link current reduction is measured to be more than 36%. Significantly, the proposed multiport PPP control scheme is generic and applicable to any hard-switching or soft-switching CS SSTs without extra hardware.
PV-BESS DC-Series Integration for Regulated DC Systems
DC-series integration introduces a novel approach to seamlessly integrate a solar photovoltaic (PV) array and a battery energy storage (BES) in series. This system, referred to as the PV-integrated battery energy storage system—dc series (PVBESS-DCS), simplifies integration and enhances power density by leveraging the inherent voltage-source characteristics of batteries and adopting the concept of partial power processing. However, addressing voltage variations of a PV array and a BES under different operational conditions remains a challenge to apply the PVBESS-DCS to a regulated dc system, where the dc-bus voltage is fixed. This article proposes a PVBESS-DCS solution tailored for regulated dc systems. Within this framework, we present a controllable series voltage source compensating for the voltage differences between the PV and BES sources and the dc bus. Thus, the system can perform PV maximum power point tracking and curtailment control while charging and discharging BES and connected to a regulated dc bus. Additionally, we present a single-stage multiport partial power processing dc–dc converter designed for the proposed PVBESS-DCS. It utilizes a triple active bridge dc–dc converter topology. Finally, this article presents operating principles, control strategies, simulation results, and experimental findings of the proposed PVBESS-DCS for regulated dc systems.
The Series Connected Buck Boost Regulator Concept for High Efficiency Light Weight DC Voltage Regulation
Improvements in the efficiency and size of DC-DC converters have resulted from advances in components, primarily semiconductors, and improved topologies. One topology, which has shown very high potential in limited applications, is the Series Connected Boost Unit (SCBU), wherein a small DC-DC converter output is connected in series with the input bus to provide an output voltage equal to or greater than the input voltage. Since the DC-DC converter switches only a fraction of the power throughput, the overall system efficiency is very high. But this technique is limited to applications where the output is always greater than the input. The Series Connected Buck Boost Regulator (SCBBR) concept extends partial power processing technique used in the SCBU to operation when the desired output voltage is higher or lower than the input voltage, and the implementation described can even operate as a conventional buck converter to operate at very low output to input voltage ratios. This paper describes the operation and performance of an SCBBR configured as a bus voltage regulator providing 50 percent voltage regulation range, bus switching, and overload limiting, operating above 98 percent efficiency. The technique does not provide input-output isolation.
A High Efficiency DC Bus Regulator/RPC for Spacecraft Applications
DC bus voltage regulation may be required in future high powered spacecraft due to the length of the busses or because they are not generated at precise voltage levels. In these cases the regulation range is often only a few percent increase or decrease, but conventional DC voltage regulators switch all the power passing through them, and this level of power switched determines the size and losses in the regulator. A recently developed concept used a low power DC-DC converter in series with the bs to raise or lower the bus voltage over a small range. This partial power processing technique combines the small size and power losses of the low power converter with the ability to regulate, (over a small range) a high power bus. The Series Connected Buck Boost Regulator (SCBBR) described herein provides bus regulation with an efficiency of 98%. The circuit also provides bus switching and overcurrent limiting functions of a Remote Power Controller (RPC). This paper described the circuit design and performance of a breadboard SCBBR configured as a bus voltage regulator providing plus of minus 40% voltage regulation range, bus switching, and overload limiting.
Power spectra of quasi-periodic oscillations in luminous X-ray stars
A variety of partial random processes are considered in order to study the effects on the power-density spectrum of several physical phenomena that may occur in the beat-frequency modulated accretion (BFMA) model of QPOs. The relevant physical properties of BFMA model are summarized, and the class of random processes used to represent the X-ray intensity time series predicted by the model are introduced. The dependence of the power-density spectrum on pulse shape is discussed. Pulse-lifetime and orbital-frequency distributions are introduced, illustrating how these affect the power-density spectrum. The effect of nonuniform initial-pulse phase distributions and of nonuniform pulse-time distributions are considered. The effects of a particular type of pulse-to-pulse correlation that may be natural in the context of the BFMA model and that greatly increases the power of the oscillations relative to that in the red noise is analyzed.
HyPerPy (Hydrogen Extraction and Parabolic Trough Plant Performance Models using Python) [SWR-21-53]
The HyPerPy package consists of three scripts: 1) Model for Hydrogen Tracking in Parabolic Trough Power Plants, 2) Model for Receiver Performance, and 3) Model for Hydrogen Extraction Process. The power plant model (1) tracks hydrogen generation and transport within the circulating heat transfer fluid (HTF) of the power plant. This script is a transient, initial value simulation, in which the hydrogen concentration in the circulating HTF is 0 moles per cubic meter everywhere at time 0 seconds. Hydrogen concentration is calculated at discrete locations within the circulating HTF with 4-second time resolution. During each time step, the change in hydrogen concentration due to hydrogen generation and permeation is calculated for each location according to the local HTF temperature, vessel or piping properties, hydrogen concentration and partial pressure. This model predicts hourly hydrogen concentrations for typical operating days in the spring, summer, fall, and winter seasons. Data is used to create hourly mappings of hydrogen concentrations for a typical operating year. The receiver model (2) uses a 1-hour time step to estimate getter loading and annulus pressure. For each time step, the model uses HTF and ambient temperature data to estimate absorber tube, bellows, and getter temperatures for the time step. In addition, the model uses hourly HTF hydrogen concentrations that are generated by the power plant model (1), and annulus hydrogen pressure from the previous time step. With these data, the model calculates the moles of hydrogen permeating across the absorber tube and bellows during the time step. The net change in moles hydrogen is added or subtracted from the getter loading for the previous time step, and the hydrogen pressure is re-calculated based on getter loading and temperature. The model uses this algorithm to simulate hydrogen permeation and loading 24 hours per day, 365 days per year using seasonal temperature data. The model repeats these calculations for 25 years to create a mapping of receiver getter loading and annulus hydrogen pressure for four seasons of each year. The model for hydrogen extraction (3) estimates hydrogen extraction rates for a specific separation module configuration. The rate depends primarily on membrane area, vacuum pump performance, headspace gas flowrate to the membrane, and headspace gas hydrogen partial pressure. This model has two versions. The steady-state version predicts hydrogen extraction rates when the module is operating in separation mode. The dynamic version predicts hydrogen transfer through the membrane when the module is operating in sensor mode. The steady-state version is used with the plant model (1) to predict hydrogen partial pressures in the power plant when the extraction process is operating.
Reinforcement learning for adaptive maintenance policy optimization under imperfect knowledge of the system degradation model and partial observability of system states
Maintenance policy optimization usually is faced with challenges that arise from an imperfect knowledge of system degradation models and from the partial observability of system degradation states. Here, this paper proposes a reinforcement learning method to address these two challenges for a class of maintenance problems with Markov degradation processes. The reinforcement learning approach consists of a learning component and a planning component. Using sequentially collected observations, at each step of decision-making the learning component improves the knowledge of system degradation in terms of the probability distributions of the transition rates based on sequential Bayesian inference. Using the updated transition rates, at each step of decision-making the maintenance policy optimization problem is then formulated as a partially observable Markov decision problem, and the planning component computes the optimal maintenance policy that maximizes the expected cumulative reward. The proposed method is illustrated using a numerical example with repair and inspection maintenance actions. The result shows that as more observations are collected, the learning component progressively learns the true system degradation process, and the planning component adjusts the optimal maintenance policy accordingly as well, which leads to increased reward.
Modeling and simulation of spacecraft power systems
EASY5 modeling of a complete spacecraft power processing system is presented. Component models are developed, and several system models including a solar array switching system, a partially-shunted solar array system and COBE system are simulated. The power system's modes of operation, such as shunt mode, battery-charge mode, and battery-discharge mode, are simulated for a complete orbit cycle.
NMPC for Mode-Switching Operation of Reversible Solid Oxide Cell Systems
Solid oxide cells (SOCs) are a promising dual-mode technology that generates hydrogen through high-temperature water electrolysis and generates power through a fuel cell reaction that consumes hydrogen. Reversible operation of SOCs requires a transition between these two modes for hydrogen production setpoints as the demand and price of electricity fluctuate. Moreover, a well-functioning control system is important to avoid cell degradation during mode-switching operation. In this work, we apply nonlinear model predictive control (NMPC) to an SOC module and supporting equipment and compare NMPC performance to classical proportional integral (PI) control strategies, while ramping between the modes of hydrogen and power production. While both control methods provide similar performance in many metrics, NMPC significantly reduces cell thermal gradients and curvatures (mixed spatial-temporal partial derivatives) during mode switching. A dynamic process flowsheet of the reversible SOC system was developed in the open-source, equation-based IDAES modeling framework. Our IDAES dynamic simulation results show that NMPC can ramp the SOC system between hydrogen and power production targets within short mode-switching times. Moreover, NMPC can comply with operating limits in the SOC system more effectively than PI, and only NMPC can directly enforce user-specified limits for mixed spatial-temporal partial derivatives of temperature. This allows for management of the trade-off be-tween operating efficiency and cell degradation, which is dependent on these temperature curvatures.
Insoluble Coatings for Stirling Engine Heat Pipe Condenser Surfaces
The principal objective of this Phase 2 SBIR program was to develop and demonstrate a practically insoluble coating for nickel-based superalloys for Stirling engine heat pipe applications. Specific technical objectives of the program were: (1) Determine the solubility corrosion rates for Nickel 200, Inconel 718, and Udimet 72OLI in a simulated Stirling engine heat pipe environment, (2) Develop coating processes and techniques for capillary groove and screen wick structures, (3) Evaluate the durability and solubility corrosion rates for capillary groove and screen wick structures coated with an insoluble coating in cylindrical heat pipes operating under Stirling engine conditions, and (4) Design and fabricate a coated full-scale, partial segment of the current Stirling engine heat pipe for the Stirling Space Power Convertor program. The work effort successfully demonstrated a two-step nickel aluminide coating process for groove wick structures and interior wall surfaces in contact with liquid metals; demonstrated a one-step nickel aluminide coating process for nickel screen wick structures; and developed and demonstrated a two-step aluminum-to-nickel aluminide coating process for nickel screen wick structures. In addition, the full-scale, partial segment was fabricated and the interior surfaces and wick structures were coated. The heat pipe was charged with sodium, processed, and scheduled to be life tested for up to ten years as a Phase 3 effort.
The power of neural nets
Implementation of the Hopfield net which is used in the image processing type of applications where only partial information about the image may be available is discussed. The image classification type of algorithm of Hopfield and other learning algorithms, such as the Boltzmann machine and the back-propagation training algorithm, have many vital applications in space.
Measuring Nd(III) Solution Concentration in the Presence of Interfering Er(III) and Cu(II) Ions: A Partial Least Squares Analysis of Ultraviolet–Visible Spectra
Optical spectroscopy is a powerful characterization tool with applications ranging from fundamental studies to real-time process monitoring. However, it can be difficult to apply to complex samples that contain interfering analytes which are common in processing streams. Multivariate (chemometric) analysis has been examined for providing selectivity and accuracy to the analysis of optical spectra and expanding its potential applications. Here we will discuss chemometric modeling with an in-depth comparison to more simplistic analysis approaches and outline how chemometric modeling works while exploring the limits on modeling accuracy. Understanding the limitations of the chemometric model can provide better analytical assessment regarding the accuracy and precision of the analytical result. This will be explored in the context of UV–Vis absorbance of neodymium (Nd 3+ ) in the presence of interferents, erbium (Er 3+ ) and copper (Cu 2+ ) under conditions simulating the liquid–liquid extraction approach used to recycle plutonium (Pu) and uranium (U) in used nuclear fuel worldwide. Finally, the selected chemometric model, partial least squares regression, accurately quantifies Nd 3+ with a low percentage error in the presence of interfering analytes and even under conditions that the training set does not describe.
Hydrogen Mitigation Process Testing at Nevada Solar One
The National Renewable Energy Laboratory (NREL) and Acciona Solar Power (ASP) developed and installed a process that addresses the issue of hydrogen buildup in Acciona's Nevada Solar One power plant. Our method selectively removes hydrogen from the expansion tanks of the power plant to control hydrogen levels in the circulating heat-transfer fluid (HTF). During previous work, we developed a sensor that measures hydrogen partial pressure in the expansion-tank headspace gas. We demonstrated that our sensor measures hydrogen levels over a wide range of partial pressure from 10 mbar down to 0.003 mbar. More recently, we conceived and developed an integrated process module that performs both hydrogen sensing and separating functions. The sensor/separator measures hydrogen partial pressure in the headspace gas in the same way as our original sensor design. Additionally, the integrated module separates hydrogen from the headspace gas to reduce hydrogen to the level needed to maintain the performance of receivers in the collector field. Laboratory testing at NREL showed that the sensor function had an accuracy of +/-7%, and the hydrogen extraction rate for separator function was consistent with our modeling predictions. The primary benefit of this module is its simple design, both in terms of function and incorporation into the HTF subsystem of the power plant. Most recently, NREL and ASP completed installation and initial testing of a mitigation process at ASP's Nevada Solar One power plant in Boulder City, Nevada. In this paper, we report on the completed installation, initial testing, and plans to bring the process to full automation, so that it can be operated unattended on a daily schedule.
Space Shuttle Main Engine Joint Data List Applying Today's Desktop Technologies to Facilitate Engine Processing
Boeing-Rocketdyne's Space Shuttle Main Engine (SSME) is the world's first large reusable liquid rocket engine. The space shuttle propulsion system has three SSMEs, each weighing 7,400 lbs and providing 470,000 lbs of thrust at 100% rated power level. To ensure required safety and reliability levels are achieved with the reusable engines, each SSME is partially disassembled, inspected, reassembled, and retested at Kennedy Space Center between each flight. Maintenance processing must be performed very carefully to replace any suspect components, maintain proper engine configuration, and avoid introduction of contaminants that could affect performance and safety. The long service life, and number, complexity, and pedigree of SSME components makes logistics functions extremely critical. One SSME logistics challenge is documenting the assembly and disassembly of the complex joint configurations. This data (joint nomenclature, seal and fastener identification and orientation, assembly sequence, fastener torques, etc.) must be available to technicians and engineers during processing. Various assembly drawings and procedures contain this information, but in this format the required (practical) joint data can be hard to find, due to the continued use of archaic engineering drawings and microfilm for field site use. Additionally, the release system must traverse 2,500 miles between design center and field site, across three time zones, which adds communication challenges and time lags for critical engine configuration data. To aid in information accessibility, a Joint Data List (JDL) was developed that allows efficient access to practical joint data. The published JDL has been a very useful logistics product, providing illustrations and information on the latest SSME configuration. The JDL identifies over 3,350 unique parts across seven fluid systems, over 300 joints, times two distinct engine configurations. The JDL system was recently converted to a web-based, navigable electronic manual that contains all the required data and illustrations in expanded view format using standard PC products (Word, Excel, PDF, Photoshop). The logistics of accurately releasing this information to field personnel was greatly enhanced via the utilization of common office products to produce a more user-friendly format than was originally developed under contract to NASA. This was done without reinventing the system, which would be cost prohibitive on a program of this maturity. The brunt of the joint part tracking is done within the logistics organization and disseminated to all field sites, without duplicating effort at each site. The JDL is easily accessible across the country via the NASA intranet directly at the SSME workstand. The advent of this logistics data product has greatly enhanced the reliability of tracking dynamic changes to the SSME and greatly reduces engineering change turnaround time and potential for errors. Since the inception of the JDL system in 1997, no discrepant parts have propagated to engine assembly operations. This presentation focuses on the challenges overcome and the techniques used to apply today's desktop technologies to an existing logistics data source.
Status and Evaluation of Microwave Furnace Capabilities at NASA Glenn Research Center
The microwave (MW) furnace is a HY-Tech Microwave Systems, 2 kW 2.45 GHz Single Mode Microwave Applicator operating in continuous wave (CW) with variable power. It is located in Cleveland, Ohio at NASA Glenn Research Center. Until recently, the furnace capabilities had not been fully realized due to unknown failure that subsequently damaged critical furnace components. Although the causes of the problems were unknown, an assessment of the furnace itself indicated operational failure may have been partially caused by power quality. This report summarizes the status of the MW furnace and evaluates its capabilities in materials processing.
Assessment of Anthropogenic and Climatic Impacts on the Global Carbon Cycle Using a 3-D Model Constrained by Isotopic Carbon Measurements and Remote Sensing of Vegetation
Our original proposal called for improved modeling of the terrestrial biospheric carbon cycle, specifically using biome-specific process models to account for both the energy and water budgets of plant growth, to facilitate investigations into recent changes in global atmospheric CO2 abundance and regional distribution. The carbon fluxes predicted by these models were to be incorporated into a global model of CO2 transport to establish large-scale regional fluxes of CO2 to and from the terrestrial biosphere subject to constraints imposed by direct measurements of atmospheric CO2 and its 13C/12C isotopic ratio. Our work was coordinated with a NASA project (NASA NAGW-3151) at the University of Montana under the direction of Steven Running, and was partially funded by the Electric Power Research Institute. The primary objective of this project was to develop and test the Biome-BGC model, a global biological process model with a daily time step which simulates the water, energy and carbon budgets of plant growth. The primary product, the unique global gridded daily land temperature, and the precipitation data set which was used to drive the process model is described. The Biome-BGC model was tested by comparison with a simpler biological model driven by satellite-derived (NDVI) Normalized Difference Vegetation Index and (PAR) Photosynthetically Active Radiation data and by comparison with atmospheric CO2 observations. The simple NDVI model is also described. To facilitate the comparison with atmospheric CO2 observations, a three-dimensional atmospheric transport model was used to produce predictions of atmospheric CO2 variations given CO2 fluxes owing to (NPP) Net Primary Productivity and heterotrophic respiration that were produced by the Biome-BGC model and by the NDVI model. The transport model that we used in this project, and errors associated with transport simulations, were characterized by a comparison of 12 transport models.