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With the growth of electric vehicle (EV) popularity, different charging options to increase user convenience and reduce charging times are being considered and researched. Among these, inductive wireless power transfer (WPT) systems for EVs are being designed to meet specifications such as stray field, power level, efficiency, misalignment tolerance, and ground clearance, which are all heavily influenced by the coil geometry. The proposed Fourier Analysis Method (FAM) is an analytical method to directly design coil geometries to meet stray field and power level requirements through an optimization of Fourier basis function coefficients. The outputs of the optimization are complex, planar coil geometries that meet the power level and stray field constraints with minimized loss factors. Contours of these potentials determine the coil conductor paths and loss models predict the system efficiency and performance over misalignment. The Fourier representation of the geometry is used to conveniently calculate the coupling over misalignment, external proximity effect loss, and ferrite loss. A 6.6 kW prototype WPT system with low stray field and high efficiency is built from the optimization results to validate the models and showcase the usefulness of the FAM design approach.
The high density fluctuation poloidal wavenumber, k θ (k θ > 8 cm –1 , k θ ρ s > 5, ρs is the ion gyro radius using the ion sound velocity), measurement capability of a new Doppler backscattering (DBS) system at the DIII-D tokamak has been experimentally evaluated. In DBS, wavenumber (k) matching becomes more important at higher wavenumbers, owing to the exponential dependence of the measured signal loss factor on wave vector mismatch. Wave vector matching allows for the Bragg scattering condition to be satisfied, which minimizes the signal loss at higher k’s. In the previous DBS system, without toroidal wave vector matching, the measured DBS signal-to-noise ratio at higher k θ (>8 cm –1 ) is substantially reduced, making it difficult to measure higher k θ turbulence. The new DBS system has been optimized to access higher wavenumber, k θ ≤ 20 cm –1 , density turbulence measurement. The optimization hardware addresses fluctuation wave vector matching using toroidal steering of the launch mirror to produce a backscattered signal with improved intensity. The probe’s sensitivity to high-k density fluctuations has been increased by approximately an order of magnitude compared to the old system that has been in use at DIII-D. Note that typical measurement locations are above or below the tokamak midplane on the low field side with normalized radial ranges of 0.5–1.0. As a result, the new DBS probe system with the toroidal matching of fluctuation wave vectors is thought to be critical to understanding high-k turbulent transport in fusion-relevant research at DIII-D.
A 2-terminal, dual-junction, epitaxially integrated, GaAsP/Si tandem solar cell with an 3rd party certified efficiency of 23.4 % was fabricated via MOCVD growth on an ex-situ produced Si sub cell. The drastic efficiency improvement over the authors previous peer-reviewed demonstration of such a device architecture is examined. Critical advancements in top cell design to maximize short wavelength response were critical in enabling improved top cell response. An in-depth analysis of this champion tandem cell has identified key loss mechanisms which elucidate the pathway for further efficiency gains. First, voltage dependent collection efficiency in the GaAsP top cell is the primary cause of fill factor losses currently limiting efficiency. Analysis of spectrally resolved I–V measurements and analytical device modeling and indicate poor diffusion length due to elevated dislocation densities as the likely cause for the voltage dependent collection efficiency. Second, modeling for the GaAs 0.75 P 0.25 top cell, using experimental data at multiple dislocation densities, provides quantitative understanding of the current and voltage losses associated with threading dislocations providing a clear efficiency pathway with reduction in dislocation density. Finally, Si subcell modeling identifies the pathway for further Si subcell advances over the present, simplistic design, which has yet to employ the known benefits of rear surface texture or dielectric passivation.
Partial shading of a solar module can induce a set of cells within the module to operate under reverse bias. Studies have shown that metal halide perovskite solar cells with a wide variety of compositions and contacts exhibit interesting behavior in reverse bias that includes both reversible performance loss and non-reversible degradation. In this paper, an advanced drift-diffusion approach incorporating an electrochemical term to explain the short-circuit, open circuit and fill factor losses that are experimentally measured after prolonged reverse bias is used. It is shown that holes can tunnel into the perovskite due to sharp band bending near the contact, accumulate within the bulk of the perovskite absorber, and trigger the oxidation of halides to form neutral halogens. The density of neutral halogens is much higher in reverse bias because there are hardly any electrons available to reduce the iodine. The resulting halogens act as bulk recombination centers. While the interstitial halogen density does decay when the cell is operated in forward bias, permanent degradation can occur if the iodine diffuses out of the perovskite layer. Finally, the ways in which changing parameters such as the mobile ion density or the series resistance at the contact can influence device performance and stability are discussed.
Top-performing single-junction and two-terminal tandem devices that include at least one polycrystalline cell are compared with each other and their ideal limits. The parameters of open-circuit voltage, short-circuit current, and fill-factor are individually compared to the Shockley–Queisser limit to investigate where different technologies have room to improve. Technologies, such as silicon and cadmium telluride have the most room for improvement in open-circuit voltage currently utilizing 87% and 81% of their maxima, respectively. Detailed diode and fill-factor loss analysis is presented for single-junction devices to give further insight on how they compare and where efficiency is lost. Single-crystal technologies demonstrate a fill-factor closer to the Shockley–Queisser limit than polycrystalline devices. The high diode quality factor of polycrystalline devices is the leading cause of the decreased fill-factor. Similar analysis on tandem cells with at least one thin-film cell shows that although their efficiency exceeds that of the single-junction cells, the fraction of their ideal efficiency is smaller. Here, by comparing parameters to the Shockley–Queisser limit, it becomes clearer where certain technologies have the potential for improvement.
Abstract Fault detection and classification in photovoltaic (PV) systems through real‐time monitoring is a fundamental task that ensures quality of operation and significantly improves the performance and reliability of operating systems. Different statistical and comparative approaches have already been proposed in the literature for fault detection; however, accurate classification of fault and loss incidents based on PV performance time series remains a key challenge. Failure diagnosis and trend‐based performance loss routines were developed in this work for detecting PV underperformance and accurately identifying the different fault types and loss mechanisms. The proposed routines focus mainly on the differentiation of failures (e.g., inverter faults) from irreversible (e.g., degradation) and reversible (e.g., snow and soiling) performance loss factors based on statistical analysis. The proposed routines were benchmarked using historical inverter data obtained from a 1.8 MWp PV power plant. The results demonstrated the effectiveness of the routines for detecting failures and loss mechanisms and the capability of the pipeline for distinguishing underperformance issues using anomaly detection and change‐point (CP) models. Finally, a CP model was used to extract significant changes in time series data, to detect soiling and cleaning events and to estimate both the performance loss and degradation rates of fielded PV systems.
Improved analysis and reporting of photovoltaic (PV) field performance increases the certainty of owners and financiers that systems will perform as expected. Advanced module technologies (e.g., PERC, HJT, and bifacial) introduce new degradation mechanisms and performance characteristics. The FY19-21 Reducing Uncertainty project leveraged data from the ever-increasing PV fleet to develop models and understanding of the field performance of existing and new technologies. Specifically, we accomplished: report on field performance and degradation rates for high-efficiency silicon (HJT, PERC, IBC) and more conventional technologies; developed automated analysis techniques to quantify system performance (performance ratio, energy yield) and production shortfalls (soiling, degradation, availability); refined the RdTools software toolkit to bring standard, validated analysis techniques to bear on third-party data; analyzed and reported on large datasets including Treasury data and Lawrence Berkeley National Laboratory's Utility-Scale dataset to expand the high-quality degradation-rate histogram published previously; worked with industry partners and the DuraMAT data hub to enable private parties to share and aggregate PV production data anonymously, leveraging cloud-based data analysis infrastructure and publishing on US fleet-scale performance comprising over 7GW of operating systems. (https://www.nrel.gov/pv/fleet-performance-data-initiative.html). Through our industry collaborations we have engaged in NDA-covered data transfer with twelve PV fleet owners as of January 2022, with more agreements in negotiation. Our scalable cloud-based time series database contains over 30 billion rows (20TB) of PV time series data, representing over 1700 commercial and utility-scale systems, and over 7.2 GW of DC capacity (Fig 1). Initial field performance results have been distributed in several public reports. Because our fleet composition and data quality methods are continually improving, annual updates to these results are published to our PV Fleet webpage [ https://www.nrel.gov/pv/fleet-performance-data-initiative.html ] and DuraMAT data hub [DOI: 10.21948/1842958]. Another existing dissemination channel used for observed soiling losses is a map we maintain for soiling losses. Additional products developed include a report detailing fleet-wide performance index, availability, startup loss and snow loss factors, a detailed report on the 1603 grant dataset comprising over 100,000 PV systems with failure and performance details and a utility-scale report coauthored with LBNL on 31 GW of system performance.
Carbon fiber composite's high specific strength makes it incredibly useful for structural applications. However, their low strain-to-failure can be problematic in structural applications that can potentially see high strain conditions or high fatigue cycles resulting in sudden, catastrophic failure. This is further complicated by damage manifesting within the composite thus not showing damage indicators on the surface. Therefore, monitoring the structural integrity and strain history of the composite in application by itself—while maintaining positive composite performance—is vital to ensure safe operation. In this study, a homogeneous dispersion of TiO 2 nanoparticles on carbon fiber is demonstrated to generate a piezoresistive carbon fiber polymer matrix composite with enhanced self-sensing capabilities. The nanoparticle-embedded composites also exhibited superior strength and damping potential compared to the nanoparticle-free composites. The apparent interlaminar shear strength increased by up to 15% and the damping loss factor increased by an average of 150% while the piezoresistive sensitivity increased by up to 180% with the addition of a small weight fraction of nanoparticles to the fiber surface. These results demonstrate an approach to simultaneously improve the sensing capabilities, damping behavior and mechanical strength of carbon fiber composites by simply adding nanoparticles in the fiber sizing using a commercially scalable deposition method.
Diverse bacteria lead a life as pathogens or predators of other bacteria in many environments. However, their impact on emerging ecological processes in natural settings remains to be assessed. Here we describe a novel type of obligate, intracellular predatory bacterium of widespread distribution that preys on soil cyanobacteria in biocrusts. The predator, Candidatus Cyanoraptor togatus, causes localized, cm-sized epidemics that are visible to the naked eye, obliterates cyanobacterial net primary productivity, and severely impacts crucial biocrust properties like nitrogen cycling, dust trapping and moisture retention. The combined effects of high localized morbidity and areal incidence result in decreases approaching 10% of biocrust productivity at the ecosystem scale. Our findings show that bacterial predation can be an important loss factor shaping not only the structure but also the function of microbial communities.
Pumped Thermal Electricity Storage (PTES) is an energy storage device that uses grid electricity to drive a heat pump that generates hot and cold storage reservoirs. This thermal potential is later used to power a heat engine and return electricity to the grid. In this article, a PTES variant that uses supercritical carbon dioxide (sCO2) as the working fluid is introduced. sCO2-PTES cycles have higher work ratios and power densities than the systems based on ideal gases that have been investigated to date. Furthermore, sCO2- PTES cycles may achieve higher round-trip efficiencies for a given hot storage temperature (up to 78% at 560°C). The sensitivity of PTES cycles to loss factors such as isentropic efficiencies and temperature differences between the power cycle and storage fluid is investigated. A second concept whereby an sCO2- PTES cycle is integrated with concentrating solar power (CSP) is introduced. This concept ‘time-shifts’ the recompression of an sCO2 recompression cycle to a period of lower electricity prices and stores the heat. When solar heat is dispatched, the recompressor may be avoided as the required heat is obtained from storage, thereby leading to increased heat engine efficiencies. The net work output of this integrated system is 10-18% greater than the conventional recompression cycle. Combining PTES with a CSP power cycle is therefore shown to improve the dispatch of solar heat as well as providing electricity storage services.
Cellular metamaterial structures with sub-micron features have shown the ability to become excellent energy absorbing materials for impact mitigation due to the enhanced mechanical properties of materials at the nanoscale. However, in order to optimize the design of these energy absorbing metamaterial structures we need to be able to measure the dynamic properties of the sub-micron features such as storage and loss moduli and the loss factor. Therefore, at scale testing is required to capture the scale, temperature, and strain rate dependent material properties of these nanoscale materials. This study presents the design, fabrication, and calibration of a MEMS-based dynamic mechanical analyzer (DMA) that can be directly integrated with the two photon lithography (TPL) process commonly used to fabricate metamaterial structures with nanoscale features. The MEMS-based DMA consists of a chevron style thermal actuator used to generate a tensile load on the structure and two differential capacitive sensors on each side of the structure used to measure load and displacement. This design demonstrated 1.5 ± 0.75 nm displacement resolution and 104 ± 52 nN load resolution, respectively. Dynamic mechanical analysis was successfully conducted on a single nanowire feature printed between the load and displacement stages of the MEMS device with testing frequencies ranging between 0.01–10 Hz and testing temperatures ranging between 22 °C–47 °C. These initial tests on an exemplar TPL part demonstrate that the printed nanowire behaves as a viscoelastic material wherein the transition from glassy to viscous behavior has already set in at the room temperature.
Abstract SOLPS-ITER simulations performed for Q DT = 10, P SOL = 100 MW burning plasmas on ITER extend the existing database to high values of separatrix averaged neon impurity concentration (⟨ c Ne ⟩ ≈ 6%) and divertor neutral pressure (⟨ p div ⟩ > 25 Pa) in order to determine the heat flux mitigation capability of these scenarios and whether strongly detached states are accessible. In the existing database of ITER simulations, the level of detachment was limited to cases where the integral ion flux to the outer target was greater than 80% of the value at rollover, with the impurity radiation localized near the target. With the possibility of narrow heat flux channels and increased deposited power due to tile shaping, it is important to explore operation at a higher degree of detachment. Two series of simulations were explored to extend the database of SOLPS simulations. By increasing the deuterium and neon puff rates proportionally, the peak divertor energy flux ( q ⊥,max ) is decreased from 5 to 3 MW m −2 while ⟨ p div ⟩ increased from 11 to 27 Pa. By increasing only the neon puff, q ⊥, max can be reduced to <1MW m −2 while ⟨ p div ⟩ is maintained at ∼ 11 Pa. As the neon puff level is increased, the position of the impurity radiation peak is shifted towards the X-point. At the highest neon puff levels with steady-state solutions, the electron temperature is reduced below 1 eV across 50 cm of each divertor target. The new cases extend previously observed tight relationships in power and momentum loss factors to low electron temperature improving their utility for highly detached regimes.
We describe and demonstrate an open-source algorithm for simultaneously quantifying degradation and soiling of photovoltaic (PV) systems from energy-production time series data. The new analysis is based on year-on-year degradation rate analysis combined with stochastic rate and recovery soiling analysis. The algorithm is designed to fit into the workflow provided by RdTools, a Python module maintained by NREL and collaboratively developed with the community, which provides a framework and functions for degradation and loss-factor analysis of PV field data. We demonstrate the method on numerically simulated PV data sets and show that it reduces the root-mean-square error of the P50 degradation rate estimate when soiling is present.
We consider the time and space required for quantum computers to solve a wide variety of problems involving matrices, many of which have only been analyzed classically in prior work. Our main results show that for a range of linear algebra problems—including matrix-vector product, matrix inversion, matrix multiplication and powering—existing classical time-space tradeoffs, several of which are tight for every space bound, also apply to quantum algorithms with at most a constant factor loss. For example, for almost all fixed matrices 𝐴, including the discrete Fourier transform matrix, we prove that quantum circuits with at most 𝑇 input queries and 𝑆 qubits of memory require 𝑇 = Ω(𝑛 2 /𝑆) to compute matrix-vector product 𝐴𝑥 for 𝑥 ∈{0,1 𝑛 . We similarly prove that matrix multiplication for 𝑛 ×𝑛 binary matrices requires 𝑇 = Ω(𝑛 3 /$\sqrt{𝑆}$). Because many of our lower bounds are matched by deterministic algorithms with the same time and space complexity, our results show that quantum computers cannot provide any asymptotic advantage for these problems with any space bound. We obtain matching lower bounds for the stronger notion of quantum cumulative memory complexity—the sum of the space per layer of a circuit. We also consider Boolean (i.e., AND-OR) matrix multiplication and matrix-vector products, improving the previous quantum time-space tradeoff lower bounds for 𝑛 × 𝑛 Boolean matrix multiplication to 𝑇 = Ω(𝑛 2.5 /𝑆 1/4 ) from 𝑇 = Ω(𝑛 2.5 /𝑆 1/2 ). Our improved lower bound for Boolean matrix multiplication is based on a new coloring argument that extracts more from the strong direct product theorem that was the basis for prior work. To obtain our tight lower bounds for linear algebra problems, we require much stronger bounds than strong direct product theorems. We obtain these bounds by adding a new bucketing method to the quantum recording-query technique of Zhandry that lets us apply classical arguments to upper bound the success probability of quantum circuits.
Cell voltage at high current densities (HCD) of an operating proton-exchange membrane fuel cell (PEMFC) suffers from losses due to the local-O 2 and bulk-H + transport resistances in the cathode catalyst layer (CCL). Particularly, the interaction of perfluorosulfonic acid (PFSA) ionomer with the carbon supported platinum catalyst plays a critical role in controlling reactant transport to the active site. In this study, we perform a systematic analysis of the side chain length and equivalent weight (EW) of PFSA ionomers on the CCL transport resistances. Ex situ measurements were carried out to quantify the ionomer characteristics such as the molecular weight, proton conductivity and water uptake. Nanomorphology of ionomers cast as 60–120 nm thin-films is characterized using grazing-incidence X-ray scattering. In situ fuel cell electrochemical diagnostic measurements were carried out to quantify the reactant (H + /O 2 ) transport properties of the CCL. Ionomer EW was found to play a major role with decreasing EW yielding higher proton conductivity and water uptake that led to lower bulk-H + and local-O 2 transport resistances in the CCL. Finally, a 1D-semi-empirical performance model has been developed to quantify the impact of ionomer EW on cell voltage loss factors.
Reliable and fast wakefield calculations are important for beam dynamics and THz generation in accelerators. We compare approaches and limitations with different available simulation tools and compare results. As an example, we analyse a cylindrical corrugated waveguide with narrower and wider radii of 5 and 6 mm, and aperture width and periodicity of 1 and 2 mm using ABCI by KEK and ANSYS HFSS simulation software. First, ABCI was used to determine the resonant frequency of the cylindri-cal corrugated waveguide with different lengths. The corresponding results were taken as a reference to exam-ine the simulation method in the ANSYS HFSS environ-ment. The eigenmode solver of ANSYS HFSS was used to determine different resonant frequencies. This was fol-lowed by examining a cylindrical corrugated waveguide that was implemented having a length of 17 mm for dif-ferent conductor settings. The corresponding waveguide was fed by a plane wave having a resonant frequency that satisfies the intersection of the light line and dispersion curve of the corresponding mode. The analysis showed that the maximum loss factor was achieved at 39.20 and 40.07 GHz using ANSYS HFSS simulation data for differ-ent conductors, whereas ABCI resulted in 38.50 GHz. The talk will present the results gathered by different simula-tion setups implemented in ANSYS HFSS.
We develop new approximate compilation schemes that significantly reduce the expense of compiling the Quantum Approximate Optimization Algorithm (QAOA) for solving the Max-Cut problem. Our main focus is on compilation with trapped-ion simulators using Pauli-X operations and all-to-all Ising Hamiltonian HIsing evolution generated by Molmer-Sorensen or optical dipole force interactions, though some of our results also apply to standard gate-based compilations. Our results are based on principles of graph sparsification and decomposition; the former reduces the number of edges in a graph while maintaining its cut structure, while the latter breaks a weighted graph into a small number of unweighted graphs. Though these techniques have been used as heuristics in various hybrid quantum algorithms, there have been no guarantees on their performance, to the best of our knowledge. This work provides the first provable guarantees using sparsification and decomposition to improve quantum noise resilience and reduce quantum circuit complexity. For quantum hardware that uses edge-by-edge QAOA compilations, sparsification leads to a direct reduction in circuit complexity. For trapped-ion quantum simulators implementing all-to-all HIsing pulses, we show that for a (1−ϵ) factor loss in the Max-Cut approximation (ϵ>0), our compilations improve the (worst-case) number of HIsing pulses from O(n2) to O(nlog(n/ϵ)) and the (worst-case) number of Pauli-X bit flips from O(n2) to O(nlog(n/ϵ)ϵ2) for n-node graphs. This is an asymptotic improvement for any constant ϵ>0. We demonstrate that significant improvements to the approximation ratio are obtained using decomposition in simulated trapped-ion experiments with dephasing noise. We further present a generic argument showing that sparsification results in an exponentially improved circuit fidelity lower bound in digital computing schemes based on one- and two-qubit gates, which are relevant to a wide variety of hardwares such as superconducting qubits and certain neutral atom or trapped ion setups, and more sophisticated noise models. We anticipate these approximate compilation techniques will be useful tools in a variety of future quantum computing experiments.