Influence of bandwidth restriction on the signal-to-noise performance of a modulated PCM/NRZ signal, part 2 Final report
Influence of bandwidth restriction on signal to noise performance of modulated PCM/NRZ signal
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Influence of bandwidth restriction on signal to noise performance of modulated PCM/NRZ signal
A first step in the development of solar power from space is the flight demonstration of critical technologies. These fundamental technologies include efficient solar power collection and generation, power management and distribution, and thermal management. In addition, the integration and utilization of these technologies into a viable satellite bus could provide an energy-rich platform for a portfolio of payload experiments such as wireless power transmission (WPT). This paper presents the preliminary design of a concept for a 100 kW-class free-flying platform suitable for flight demonstration of technology experiments. Recent space solar power (SSP) studies by NASA have taken a stepping stones approach that lead to the gigawatt systems necessary to cost-effectively deliver power from space. These steps start with a 100 kW-class satellite, leading to a 500 kW and then a 1 MW-class platform. Later steps develop a 100 MW bus that could eventually lead to a 1-2 GW pilot plant for SSP. Our studies have shown that a modular approach is cost effective. Modular designs include individual laser-power-beaming satellites that fly in constellations or that are autonomously assembled into larger structures at geosynchronous orbit (GEO). Microwave power-beamed approaches are also modularized into large numbers of identical units of solar arrays, power converters, or supporting structures for arrays and microwave transmitting antennas. A cost-effective approach to launching these modular units is to use existing Earth-to-orbit (ETO) launch systems, in which the modules are dropped into low Earth orbit (LEO) and then the modules perform their own orbit transfer to GEO using expendable solar arrays to power solar electric thrusters. At GEO, the modules either rendezvous and are assembled robotically into larger platforms, or are deployed into constellations of identical laser power-beaming satellites. Since solar electric propulsion by the modules is cost-effective for both self-transport of the modules from LEO to GEO, and for on-orbit stationkeeping and repositioning capability during the satellite’s lifetime, this technology is also critical in technology development for SSP. The 100 kW-class technology demonstrator will utilize advanced solar power collection and generation technologies, power management and distribution, advanced thermal management, and solar electric propulsion. State-of-the-art solar concentrators, highly efficient multi-junction solar cells, integrated thermal management on the arrays, and innovative deployable structure design and packaging make the 100 kW satellite feasible for launch on one existing launch vehicle. Early SSP studies showed that a major percentage of the on-orbit mass for power-beaming satellites was from massive power converters at the solar arrays, at the bus, at the power transmitter, or at combinations of these locations. Higher voltage mays and power management and distribution (PMAD) systems reduce or eliminate the need for many of these massive power converters, and could enable direct-drive of high-voltage solar electric thrusters. Lightweight, highly efficient thermal management systems are a critical technology that must be developed and flown for SSP feasibility. Large amounts of power on satellites imply that large amounts of waste heat will need to be managed. In addition, several of the more innovative lightweight configurations proposed for SSP satellites take advantage of solar concentrators that are intractable without advanced thermal management technologies for the solar arrays. These thermal management systems include efficient interfaces with the WPT systems or other high-power technology experiments, lightweight deployable radiators that can be easily integrated into satellite buses, and efficient reliable thermal distribution systems that can pipe heat from the technology experiments to the radiators. In addition to demonstrating the integration and use of these mission-critical technologies, the 100 kw-class satellite will provide a large experiment deck for a portfolio of technology experiments. Current plans for this technology demonstrator allow 2000 kg of payload capability and up to 100 kW of power. The technology experiments could include one or more wireless power transmission demonstrations, either to the Earth’s surface or to a suitable space-based receiver. Technology experiments to quantify the on-orbit performance of critical technologies for SSP or space exploration are welcomed. In addition, the technology experiments provide an opportunity for international cooperation, to advance technology readiness levels of SSP technologies that require flight demonstration. This paper will present the preliminary design for a 100 kW solar-powered satellite and a variety of technology experiments that may be suitable for flight demonstration. In addition, a space-to-Earth-surface WPT experiment will be discussed.
This report consists of four main sections. Part I: LiNbO3 microdisk resonant optical modulator. Brief review of microdisk optical resonator and RF ring resonator. Microwave and photonic design challenges for achieving simultaneous RF-optical resonance are addressed followed by our solutions. Part II: Experimental demonstration of LiNbO3 microdisk modulator performance in wired and wireless RF-optical links. Part III: Microphotonic RF receiver architecture that exploits the nonlinear modulation in the LiNbO3 microdisk modulator to achieve direct photonic down-conversion from RF carrier without using any high-speed electronic elements. Part IV: Ultimate sensitivity of the microdisk photonic receiver and the future road map toward a practical device.
This paper presents a description of the general construction of three high bandwidth liquid fuel modulators along with data corresponding to their respective modulation performance. These devices are a critical element of the National Aeronautics and Space Administration Glenn Research Center's (NASA GRC) Active Combustion Control (ACC) task. These devices are not commercially-off-the-shelf available, primarily due to a one kHz bandwidth requirement. Given their special nature, NASA GRC developed specifications for the modulation devices and then, through Small Business Innovative Research (SBIR) contracts, had vendors with expertise in valve design manufacture them. The modulators described in this paper are the Active Signal Modulator (ASM), the Jansen's Aircraft System Controls (JASC) modulator, and the WASK Engineering (WASK) modulator. These modulators utilize magnetostrictive, electric motor, or piezoelectric actuation, respectively. The specifications for these devices evolved over time to meet the needs of changing objectives in the ACC research task. This is primarily true with respect to flow number as their designs accommodate a range from one to eight (lbm./hr-psi0.5). These designs also exhibit relatively small volume, weight, and power consumption with an ability to modulate approximately +/- 30% from the mean on a pressure basis. The modulators have been characterized and have undergone laboratory performance tests by the vendors. Their data indicated suitability for continued use in ACC research testing.
Due to the High Luminosity-Large Hadron Collider (HL-LHC) upgrade, several detectors of the Compact Muon Solenoid (CMS) will need to be replaced, specifically the new Outer Tracker (OT) that will be assembled with 13,200 silicon modules. The OT is composed of two types of modules; the PS (pixel-strip) and the 2S (strip-strip) modules. With a large influx of production of modules, extensive testing is required to ensure quality of the modules. This research introduces POTATO (Phase-II Outer Tracker Analyzer of Test Outputs), a specialized software developed in C++ to analyze, grade, and store results in a centralized database. Module data will be collected from various international production facilities. The implementation of POTATO will facilitate with selecting the best performing modules for the Outer Tracker upgrade. This research is focused on the implementation of the analysis of 2S Module results within POTATO.
Gas-separation and reverse-osmosis membrane models are being developed in conjunction with membrane testing at NASA JSC. The completed gas-separation membrane model extracts effective component permeabilities from multicomponent test data, and predicts the effects of flow configuration, operating conditions, and membrane dimensions on module performance. Variable feed- and permeate-side pressures are considered. The model has been applied to test data for hollow-fiber membrane modules with simulated cabin-air feeds. Results are presented for a membrane designed for air drying applications. Extracted permeabilities are used to predict the effect of operating conditions on water enrichment in the permeate. A first-order reverse-osmosis model has been applied to test data for spiral wound membrane modules with a simulated hygiene water feed. The model estimates an effective local component rejection coefficient under pseudosteady-state conditions. Results are used to define requirements for a detailed reverse-osmosis model.
The improvements and the modifications of the NASA Aircraft Noise Prediction Program (ANOPP) and the Propeller Analysis System (PAS) are described. Comparisons of the predictions and the test data are included in the case studies for the flat plate model in the Boundary Layer Module, for the effects of applying compressibility corrections to the lift and pressure coefficients, for the use of different weight factors in the Propeller Performance Module, for the use of the improved retarded time equation solution, and for the effect of the number grids in the Transonic Propeller Noise Module. The DNW tunnel test data of a propeller at different angles of attack and the Dowty Rotol data are compared with ANOPP predictions. The effect of the number of grids on the Transonic Propeller Noise Module predictions and the comparison of ANOPP TPN and DFP-ATP codes are studied. In addition to the above impact studies, the transonic propeller noise predictions for the SR-7, the UDF front rotor, and the support of the enroute noise test program are included.
Low levelized cost of electricity (LCOE) has been identified as critical for widespread adoption of photovoltaics (PV) without subsidies. Maintaining decades-long high-energy production is often an under-recognized opportunity in meeting cost goals because component lifetimes are not fully quantified at the time of manufacture. Whereas certain standardized tests minimize risk of early failure, there is little guidance to quantitatively predict degradation (or lack thereof) over decades, based on accelerated tests. In this article, we move toward bridging the understanding between indoor accelerated tests and outdoor performance data, with the goal of predicting energy yield with enough accuracy to inform financial decisions. Light- and elevated temperature-induced degradation (LETID) in p-type Si modules is analyzed in terms of impact on long-term module performance and thus LCOE. A method to predict the progression of LETID, using fixed kinetic constants and a numerical solution to the basic reaction rate equations, is detailed. Predictions are compared against both published data and that new to this study. These data include both indoor accelerated tests and fielded modules. We use the results in financial models to derive LCOE of modules in different climates with varying amounts of LETID, including uncertainty. Cost models based on the predictions indicate that LETID has a significant and climate-dependent impact on LCOE. Finally, we show that - even given the uncertainties identified in the study - these financial calculations can provide useful guidance to quantify risk based on accelerated test results. The analysis serves as an example of developing a predictive approach to PV reliability using physics of failure.
Potential induced degradation (PID) is a significant reliability issue for photovoltaic modules in the field. Conventionally, to detect the PID-affected modules, all the modules in a string are individually disconnected and measured using I-V (current–voltage) tracers. It is extremely time-consuming, expensive and unsafe (if the module connectors are field aged) approach. In our approach, we aim detecting the PID-affected modules quickly, inexpensively and safely without disconnecting individual modules in the string. In this study, contactless electrostatic voltmeter’s (ESV) strength was explored to detect the PID-affected cadmium telluride (CdTe) modules in a string. These contactless measurements on high-voltage PV strings could make this technique unique and preferable to detect PID-affected modules in a string compared to the I-V curve method. In this work, a CdTe module was stressed for PID using the Aluminum (Al) foil method in a walk-in environmental chamber, which resulted in a reduction of module's peak current (I max ), peak voltage (Vmax), and fill factor (FF), and hence power (Pmax). Outdoor light I-V, EL, and ESV measurements were performed pre- and post-PID to determine the effectiveness of the non-contact ESV technique. Further, the ESV technique successfully detected the change in V max , and hence Pmax, compared to the pre-and post-PID conditions. A difference of less than 2% was observed in the results of the non-contact and non-interruptive method compared to the conventionally used interruptive voltmeter or I-V tracer method. This technique and the test results demonstrate a significant promise to identify poor-performing modules in PV string without disconnection of individual modules in the string.
The post-test evaluation of a single heat exchanger sodium hydroxide thermal energy storage module for use in solar electric generation is reported. Chemical analyses of the storage medium used in the experimental model are presented. The experimental verification of the module performance using an alternate heat transfer fluid, Caloria HT-43, is described. Based on these results, a design analysis of a dual heat exchanger concept within the storage module is presented. A computer model and a reference design for the dual system (storage working fluid/power cycle working fluid) were completed. The dual system is estimated to have a capital cost of approximately one half that of the single heat exchanger concept.
We have developed two modules for use in computer-aided design (CAD) of crewstation environments that enhance the designer's appreciation of factors influencing the pilot's vision and visual processing capacity. The Binocular Optics Module (BOM) is an interactive tool for visualizing geometric aspects of (1) how retinal imagery of the environment changes on the pilot's retinas under conditions of eye and object motion, and (2) how visual capabilities that can be modeled as regions or contours on the retinas, affect spatial perception of the environment. The Visual Performance Module (VPM) contains a signal processing model of human visual discrimination that quantitatively predicts visual discrimination performance. The outputs of the VPM are retinal contours that represent performance probabilities. These contours may be used as inputs to the BOM for visualizing those volumes of space within the crewstation that bound different levels of the pilot's of visual discrimination capability. Used together, the BOM and VPM provide the designer with the opportunity to interactively explore relationships between environmental retinal imagery and visual function, and the ability to factor the pilot's visual capabilities into the earliest phases of crewstation CAD.
The recent development of a shingle-type solar-cell module makes it possible to incorporate easily photovoltaic power generation into the sloping roofs of residential or commercial buildings. These modules, which use a closely packed array of nineteen 53-mm-diameter circular solar cells, are capable of producing 101 watts/sq m of module area under standard operating conditions. This module performance is achievable by the use of solar cells with an average efficiency of 13.3 percent at 1 kW/sq m air-mass-1.5 insolation and at a cell temperature of 28 C. When these modules are mounted on a sloping south-facing roof which is insulated on the rear surface, the annual energy generated at the maximum power operating point will vary from 255.6 to 137.3 kWh/sq m of module area depending on the site location, with Albuquerque, NM, and Seattle, WA, representing the highest and lowest values of the thirteen sites considered.
Photovoltaic module degradation from a high system voltage is a prevalent degradation mode in the field, where the enabling degradation mechanisms are inherently dependent on the voltage bias polarity of the installed system. In this study, the effects of positive bias on module performance are confirmed and the underlying chemical degradation processes are more thoroughly investigated to reveal different degradation pathways from those previously reported in negative bias studies. When cells are under +1000 V stress, crystalline silicon mini-modules with poly(ethylene-co-vinyl acetate) (EVA) encapsulant demonstrated a significant photocurrent loss due to EVA discoloration and delamination from increased chemical reactivity at the front-side EVA/cell metallization interface. Brown discoloration of the EVA encapsulant near the cell gridlines is linked to an electrochemical reaction at the Ag gridlines under hot and humid conditions (85 °C, 85% relative humidity). Chemical compositional analysis using X-ray photoelectron spectroscopy (XPS) confirmed that the discoloration is attributed to the formation of silver sulfide (Ag 2 S) and/or silver oxide (Ag 2 O) species at the EVA/Ag gridline interface. The subsequent migration of Ag ions from the cell gridlines into the bulk of the EVA was evident from XPS depth profiling and optical microscopy. However, the Ag signal was not detected at the EVA/glass interface, inferring limited ionic transport through the nominally 0.45 mm thick encapsulant. For the samples studied herein, the sulfur is believed by the process of elimination to come from the ambient air, diffusing into the module through the permeable polymer backsheet.
Emerging PV technologies (e.g. Perovskite, and Quantum Dot) are commonly known to possess challenges for accurate performance measurement under the existing IEC 60904 series of standards, which were developed for conventional Si solar cells. Potential performance artifacts depending on scan rates and directions and light bias exposure history are often seen in those emerging solar cells. To avoid these artifacts and provide an unbiased and reliable efficiency measurement, NREL's Cell and Module Performance (CMP) Group has developed a steady-state performance calibration protocol - the asymptotic P MAX method. In this paper, we applied this procedure to four PV cell technologies, Si, CIGS, perovskite, and Quantum Dot (QD), and compared their performance variations between the transient and the steady-state conditions. By comparison, we found that the performance parameters ( i.e. V OC , I SC , FF, ..eta..) measured between fast I-V scans (and the asymptotic method (steady-state) change significantly for perovskite and QD cells. These changes do not happen for Si and CIGS cells. Furthermore, the statistical performance analysis on nearly 100 emerging cells received globally (including OPV, Perovskite, and QD) shows that over 70 % of the fast I-V scans have a relative performance deviation larger than 1% compared to those determined using the asymptotic P MAX scan. Given the complex dynamic behavior observed in emerging PV devices, the CMP group at NREL thus only certifies their steady steady-state performance using the Asymptotic P MAX method. We highly recommend similar steady-state performance calibration protocol for all researchers in emerging PV because accuracy in reported efficiencies is critical to the long-term success of those promising new PV technologies.
This final technical report describes work conducted by Membrane Technology and Research, Inc. (MTR) for the Department of Energy, National Energy Technology Laboratory (DOE NETL) on development of the second generation (Gen-2) Proteus™ membrane modules and a pre-combustion membrane process for carbon dioxide (CO 2 ) capture from an Integrated Gasification Combined Cycle (IGCC) plant for power generation (award number DE-FE0031632). The work was conducted from October 1, 2018 through March 30, 2022. The overall goal of this project was to bring a Gen-2 version of the H2-selective Proteus membrane to bench-scale module (component) testing with real syngas. MTR was assisted in this project by Susteon, a technology development company with extensive experience in gasification processes, and the University of North Dakota Energy & Environmental Research Center (EERC), who provided the host site for the slipstream field testing. This report details the work conducted to optimize the Gen-2 Proteus membrane and develop modules capable of operation at 200°C; demonstrate membrane module performance processing coal-derived syngas during a field test at EERC; and optimize integration of a dual-membrane process into an IGCC with carbon capture. Work for this project included membrane optimization and scale-up, module component screening and fabrication of high-temperature lab- and bench-scale modules, design and fabrication of a bench-scale field test membrane skid, operation of the field test skid processing coal-derived syngas at EERC, and a detailed techno-economic analysis (TEA) of the MTR dual-membrane process for IGCC power plant pre-combustion CO 2 capture. This project validated recent membrane technology advancements, mitigates risk in future scale-up activities, and moved the membrane pre-combustion capture technology from TRL-4 to TRL-5. Key results for each major task are discussed in the report.
Accurate estimation of photovoltaic (PV) system performance is crucial for determining its feasibility as a power generation technology and financial asset. PV-based energy solutions offer a viable alternative to traditional energy resources due to their superior Levelized Cost of Energy (LCOE). A significant challenge in assessing the LCOE of PV systems lies in understanding the Performance Loss Rate (PLR) for large fleets of PV systems. Estimating the PLR of PV systems becomes increasingly important in the rapidly growing PV industry. Precise PLR estimation benefits PV users by providing real-time monitoring of PV module performance, while explainable PLR estimation assists PV manufacturers in studying and enhancing the performance of their products. However, traditional PLR estimation methods based on statistical models have notable drawbacks. Firstly, they require user knowledge and decision-making. Secondly, they fail to leverage spatial coherence for fleet-level analysis. Additionally, these methods inherently assume the linearity of degradation, which is not representative of real world degradation. To overcome these challenges, we propose a novel graph deep learning-based decomposition method called the Spatio-Temporal Graph Neural Network for fleet-level PLR estimation (PV-stGNN-PLR). PV-stGNN-PLR decomposes the power timeseries data into aging and fluctuation components, utilizing the aging component to estimate PLR. PV-stGNN-PLR exploits spatial and temporal coherence to derive PLR estimation for all systems in a fleet and imposes flatness and smoothness regularization in loss function to ensure the successful disentanglement between aging and fluctuation. We have evaluated PV-stGNN-PLR on three simulated PV datasets consisting of 100 inverters from 5 sites. Experimental results show that PV-stGNN-PLR obtains a reduction of 33.9% and 35.1% on average in Mean Absolute Percent Error (MAPE) and Euclidean Distance (ED) in PLR degradation pattern estimation compared to the state-of-the-art PLR estimation methods.
The purpose of this report is to document improvements to the Propeller Analysis System of the Aircraft Noise Prediction Program (PAS-ANOPP) and to serve as a users guide. An overview of the functional modules and modifications made to the Propeller ANOPP system are described. Propeller noise predictions are made by executing a sequence of functional modules through the use of ANOPP control statements. The most commonly used ANOPP control statements are discussed with detailed examples demonstrating the use of each control statement. Originally, the Propeller Analysis System included the angle-of-attack only in the performance module. Recently, modifications have been made to also include angle-of-attack in the noise prediction module. A brief description of PAS prediction capabilities is presented which illustrate the input requirements necessary to run the code by way of ten templates. The purpose of the templates are to provide PAS users with complete examples which can be modified to serve their particular purposes. The examples include the use of different approximations in the computation of the noise and the effects of synchrophasing. Since modifications have been made to the original PAS-ANOPP, comparisons of the modified ANOPP and wind tunnel data are also included. Two appendices are attached at the end of this report which provide useful reference material. One appendix summarizes the PAS functional modules while the second provides a detailed discussion of the TABLE control statement.
Due to software fragmentation, PV system modeling teams can be limited to language specific packages, preventing cross-sectional analysis of different modeling techniques and workflows. To this end, PVplr, a popular PV performance modeling R software package, has been ported to the Python programming language. To verify and test the robustness of the port, NSRDB data has been used to simulated PV installations at native resolution (~2 million Sites), with a variety of degradation rates, degradation patterns, and modules. Performance Ratios were calculated using the ported functions from pvplr-python and compared against Rdtools YoY values. Due to the complicated nature of degradation, a new metric has been proposed to quantify the performance loss of a system. The cumulative production loss, is the total amount of energy lost due to the degrading performance of the system. Cumulative production loss alleviates the problems with fitting linear functions to non-linear degradation. Cumulative Production loss was shown to better estimate the total lost revenue for non-linear degradation patterns. $XbX + UTC$ was found to most accurately predict the total lost revenue in simulated systems.