Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Strings”

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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Conductive Adhesives for Metallization of Interdigitated Back Contact Solar Cells

The metallization and interconnection of Si solar cells to form a module is usually a multi-step process: cells are metallized individually, soldered into strings, and subsequently assembled into a module. Furthermore, the metallization process for individual cells must be compatible with the doped surfaces being contacted, which creates additional challenges when moving from diffused junctions to heterojunctions and/or passivated contact structures. To mitigate these problems and streamline module manufacturing for the case of interdigitated back contact (IBC) cell modules, we developed an approach wherein unmetallized IBC cells are bonded directly to a circuitized backsheet using a conductive adhesive (CA) that only conducts out-of-plane. The metal/CA/silicon stack is bonded by hot-pressing at 100-200 degrees C.For a CA consisting of ethylene vinyl acetate (EVA) filled with Ag-coated spheres or indium powder we find that contact resistivities in the range of 1-10 Wcm 2 are obtained, regardless of which conductive filler and which hot pressing parameters are used. XPS revealed that about 1-2 nm EVA residue bonds to the Si in these samples, suggesting the formation of an insulating barrier that is responsible for the high contact resistance. Using indium powder alone, improved contact resistivities <0.6 Wcm 2 , high shunt resistances, and an implied open-circuit voltage loss below 10 mV were obtained. Furthermore, a cost estimate is presented, indicating that substantial cost savings of up to $10/m 2 , or about $0.05/W DC , are possible through CA-based IBC module manufacturing.

14 SOLAR ENERGY↗
DOE OSTI2021

Distribution of the Spectral Response of Cells in Silicon Modules - Mechanisms and Implications

Measurements of the spectral response of cells in a PV module can provide important insight into the physics of the device and are also needed for the accurate calibration of the performance of these devices using solar simulators. While techniques for measuring the spectral response of PV cells are well established, measurement of the spectral response of all cells in a module is not straightforward since in this case one has electrical access to a string of cells and not to cells individually. Here we present a new approach for testing the spectral response of individual cells in silicon modules that allows for the fast mapping of the response at different locations of the module. We show that in some cases the spectral response of different cells in a module can vary considerably and propose an underlying mechanism for this variation. We also discuss the implications of this observation for the uncertainty of the spectral correction and of the I SC calibration using a solar simulator.

modules↗
DOE OSTI2021

Performance Index Assessment for the PV Fleet Performance Data Initiative

We report on 250 PV systems throughout the United States, comprising 157 MWdc of system capacity and more than 10,000 monthly performance index (PI) values. Loss factors were isolated including first-year start-up issues, snowfall, soiling and inverter downtime. Inverter availability was found to contribute significant system energy loss during the first six months of operation, with an average of 8% loss occurring during this period, and 2.3% on average thereafter. Other start-up issues beyond inverter downtime, such as partial string outage, contributed additional underperformance in the first year of operation across the fleet. Winter performance was also found to be below summer performance on average, likely due to snowfall. A relationship was found between monthly snowfall accumulation in centimeters and monthly under-performance, indicating a 6%-40% loss in months with measured snowfall, depending on climate. After correcting for availability, snow and startup loss, over 90% of systems were performing within 10% of monthly expectation based on satellite resource data and PVWatts production estimates.

loss factors↗
DOE OSTI2022

Field Experience Detecting PV Underperformance in Real Time Using Existing Instrumentation

Maintenance at large-scale photovoltaic plants employs a mix of preventative and corrective maintenance practices. Large outages, such as an inverter tripping offline, are often easy to detect. More subtle sub-inverter faults and failures can accumulate and go unnoticed for months or years. A software-based fault detection method has been developed to analyze commonly measured data from large-scale PV plants for more timely detection of subtle underperformance. The method has been demonstrated on eight datasets from large-scale plants with high accuracy of detection. Results are validated using aerial infrared scanning. String outages are detected with a true positive rate of 73 percent and tracker issues are detected with a true positive rate of 88 percent. The developed method can be uniformly applied to photovoltaic plants across a range of scales and configurations to assess performance, quickly detect underperformance, and determine the source and location of failures. The results inform and improve operations and maintenance at PV plants, ultimately aiding in improved affordability, reliability, availability, and resiliency of solar electricity.

14 SOLAR ENERGY↗
DOE OSTI2022

Evaluating the Durability of Balance of Systems Components Using Combined-Accelerated Stress Testing

The degradation of photovoltaic (PV) balance of systems (BoS) components is not well-studied, but the consequences include offline modules, strings, and inverters; system shutdown; arc faults; and fires. A utility provider experienced a ~30% failure rate in their power transfer chain, originally attributed to branch connectors. Field-failed specimen assemblies were therefore examined, consisting of cable connector, branch connector, and discrete fuse components. In this study, unused field-vintage specimens are examined using combined-accelerated stress testing (C-AST) to clarify the most influential environmental stressors as well as the effect of external mechanical perturbation. A benchtop prototype fixture was used to develop the perturbation capability for the C-AST chamber. The benchtop experiments were also used to develop the in-situ data acquisition of specimen current, voltage, and temperature. A significant increase in operating temperature (~100 degrees C from ~40 degrees C) and a different failure mode were observed promptly once periodic mechanical perturbation was applied. The current at failure was decreased from 35 A (with failure in the fuses) to 15 A (failure at the male/female metal pin connection). After initial examination using X-ray computed tomography, the external plastic was machined away from failed specimens to allow for failure analysis, including the extraction of the internal convolute springs for morphological examination (optical and electron microscopy).

balance of systems↗
DOE OSTI2024

Rapid Characterization and Statistical Analysis of High-Volume Field-Harvested Photovoltaic Connectors

Photovoltaic (PV) installations heavily depend on connectors for efficient module and string interconnections without requiring skilled labor. Yet this seemingly innocuous component of PV systems is a leading cause of module failures, multiple high-profile fires, and lawsuits in the PV industry. This work aims to answer critical questions regarding why connectors fail and the contributing factors to their failure. The study involves collecting and analyzing more than 17,000 field-harvested connectors from various solar installations across the United States. The vast dataset, which includes connector metadata, visual inspections, and resistance measurements, provides unprecedented insight into the state of health of PV connectors across the US, including the geographic locations, connector types, and installation practices most prone to failures. The work presented here describes a novel rapid characterization method for processing large numbers of connectors and is supported by parallel forensic analysis to discern the root causes of failures as well as a levelized cost of lifetime model to determine the economic ramifications of connector failure. Ultimately, the findings may inform PV developers about the best practices to extend connector longevity and lead to more resilient and reliable PV systems.

connectors↗
DOE OSTI2023

Evaluating Robust Entanglement on a Trapped Ion Platform

We present proof of principle results for evaluating an entanglement volumetric benchmark [1] on trapped ion platforms. The benchmark quantifies the robustness of multipartite and bipartite entanglement using stabilizer measurements and witness functions. Each n-qubit graph state is prepared and used to evaluate n state-specific stabilizer strings. These stabilizer measurements are used to evaluate entanglement witness functions. The entanglement benchmark defines families of graph states associated with an initial sub-graph of the hardware qubit connectivity - with all-to-all connectivity, trapped ion systems provide a flexibility in the choice of this initial graph, and this will affect the associated family. In this work, we present results targeting several classes of entangled states: 1) 1-D cluster states, 2) n-qubit GHZ states, and 3) cycle graph states. These states have been found in the literature as standard hardware benchmarks, and have connections to many near-term applications.

Hamilton, Kathleen↗
DOE OSTI2022

Tuning of Multicell Superconducting Accelerating Cavities Using Pressurized Balloons

Plastic tuning of multicell superconducting accelerating cavities is crucial in the development cycle of cavities for particle accelerators. Cavities must meet stringent requirements regarding the operating mode frequency, field flatness, and eccentricity before lining them up in a cryomodule string. After dressing bare cavities with helium vessels, the welded vessel prevents access to individual cavity cells disallowing any further localized tuning. Currently, there is no straightforward way to tune dressed cavities other than cutting the vessel and then tuning the bare cavity and dressing it back, which would significantly impact cost and schedule. In this work, we present a novel tuning technique for already jacketed cavities that is non-invasive and cost-effective. The proposed scheme employs pressurized balloons to be temporarily deployed inside the cavity as a means to localize mechanical deformation in specific cells. The proposed tuning technique was successfully utilized to recover a 9-cell 1.3 GHz tesla-style cavity.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Bi-Filar Coil Winding for Fast Quench Protection

As superconducting magnet technology is pushed towards higher performance, energy density and total stored energy follow exponentially. Protecting magnets becomes substantially more challenging with traditional methods being stretched to their limits. New technologies such as CLIQ (Coupling Loss Induced Quench) promise to provide a robust method to protect advanced magnets, however they become inductance limited in large magnet strings or at low field, leading to more complex configurations. A technique to substantially reduce this limitation and improve response time is presented, by winding coils in a bifilar fashion and connecting them in series for typical operation, while providing an anti-parallel connection for quasi-zero-inductance in a protection case. This allows for extremely high di/dt. The concept is then demonstrated on a small REBCO coil.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Automating Genetic Algorithm Mutations for Molecules Using a Masked Language Model

Inspired by the evolution of biological systems, genetic algorithms have been applied to generate solutions for optimization problems in a variety of scientific and engineering disciplines. For a given problem, a suitable genome representation must be defined along with a mutation operator to generate subsequent generations. Unlike natural systems which display a variety of complex rearrangements (e.g. mobile genetic elements), mutation for genetic algorithms commonly utilizes only random point-wise changes. Furthermore, generalizing beyond point-wise mutations poses a key difficulty as useful genome rearrangements depend on the representation and problem domain. To move beyond the limitations of manually defined point-wise changes, here we propose the use of techniques from masked language models to automatically generate mutations. As a first step, common subsequences within a given population are used to generate a vocabulary. The vocabulary is then used to tokenize each genome. A masked language model is trained on the tokenized data in order to generate possible rearrangements (i.e. mutations). In order to illustrate the proposed strategy, we use string representations of molecules and use a genetic algorithm to optimize for drug-likeness and synthesizability. Finally, our results show that moving beyond random point-wise mutations accelerates genetic algorithm optimization.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Measurement of DC Arc-Flash Incident Energy in Large-Scale Photovoltaic Plants: A Basis for Standardization

The deployment of high-power dc equipment is increasing in solar photovoltaic (PV) plants, but very few studies have quantified dc arc-flash risks. Currently, PV plant owners and operators rely on theoretical, simplified models, such as those in NFPA-70E and other publications, for the assessment of risk associated with dc arc-flash. Here, this article presents an overview of arc-flash risks in a PV system based on a series of field experiments based on IEEE-1584 in two large-scale ground-mounted PV plants. The experiments include various high-power dc equipment of a PV plant, such as central inverters, combiner boxes, recombiner boxes, string inverters, and multiple configurations of electrodes in a 20-in calibration cube. The study reveals that none of the available dc arc-flash models are applicable for a PV plant. This work is an important first step toward developing an improved model that more accurately assesses dc arc-flash risk in a PV plant.

14 SOLAR ENERGY↗
DOE OSTI2023

Leaf cell wall properties and stomatal density influence oxygen isotope enrichment of leaf water

Measurements of oxygen isotope enrichment of leaf water above source water (Δ 18 O LW ) can improve our understanding of the interaction between leaf anatomy and physiology on leaf water transport. Models have been developed to predict Δ 18 O LW such as the string-of-lakes model, which describes the mixing of leaf water pools, and the Péclet effect model, which incorporates transpiration rate and the mixing length between unenriched xylem and enriched mesophyll water in the mesophyll (L m ) or veins (L v ). Here, in this work, we compare measurements and models of Δ 18 O LW on two cell wall composition mutants grown under two light intensities and relative humidities to evaluate cell wall properties on leaf water transport. In maize (Zea mays), the compromised ultrastructure of the suberin lamellae in the bundle sheath of the ALIPHATIC SUBERIN FERULOYL TRANSFERASE mutant (Zmasft) reduced barriers to apoplastic water movement, resulting in higher E and, potentially, L v and, consequently, lower Δ 18 O LW . The difference in Δ 18 O LW in cellulose synthase-like F6 (CslF6) mutants and wild-type of rice (Oryza sativa) grown under two light intensities co-varied with stomatal density. These results show that cell wall composition and stomatal density influence Δ 18 O LW and that stable isotopes can facilitate the development of a physiologically and anatomically explicit water transport model.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Fast External Quantum Efficiency Measurements of Photovoltaic Modules

The external quantum efficiency (EQE) or associated spectral response is widely used to evaluate the performance of photovoltaics, light emitting diodes, photodetectors, photodiodes, semiconductor lasers, laser-induced refrigeration of solids etc. For photovoltaic (PV) devices, The EQE is used to determine the spectral mismatch correction that is used to convert a performance measurement from a non-ideal simulator spectrum to a standard spectrum in PV device calibrations. Moreover, EQE can provide important insight into the physics of a PV device. For instance, the EQE shape can be used to infer mechanisms limiting the performance of the device. However, EQE measurements of full-size PV modules remain a challenge in the PV community due to rare availability of reliable module QE measurement tools worldwide and the size and complexity of module architecture that comprises several cells connected in strings (series or/and parallel). Here, we use a customized nondestructive tool made of high-power light emitting diodes to perform high-throughput EQE measurements on PV modules. Circuit simulations show that the measurement system can safely be used to measure both crystalline silicon and thin film-based PV modules with series connected cells without the risk of damage through reverse bias. In addition, EQE mapping combined with electroluminescence show a strong correlation and provide a way to determine both spatial non-uniformity of the spectral response and defects/degradation effects across the module.

PV module↗
DOE OSTI2022

Exploring far-from-equilibrium ultrafast polarization control in ferroelectric oxides with excited-state neural network quantum molecular dynamics

Ferroelectric materials exhibit a rich range of complex polar topologies, but their study under far-from-equilibrium optical excitation has been largely unexplored because of the difficulty in modeling the multiple spatiotemporal scales involved quantum-mechanically. To study optical excitation at spatiotemporal scales where these topologies emerge, we have performed multiscale excited-state neural network quantum molecular dynamics simulations that integrate quantum-mechanical description of electronic excitation and billion-atom machine learning molecular dynamics to describe ultrafast polarization control in an archetypal ferroelectric oxide, lead titanate. Far-from-equilibrium quantum simulations reveal a marked photo-induced change in the electronic energy landscape and resulting cross-over from ferroelectric to octahedral tilting topological dynamics within picoseconds. The coupling and frustration of these dynamics, in turn, create topological defects in the form of polar strings. The demonstrated nexus of multiscale quantum simulation and machine learning will boost not only the emerging field of ferroelectric topotronics but also broader optoelectronic applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Attention to quantum complexity

The imminent era of error-corrected quantum computing demands robust methods to characterize quantum state complexity from limited, noisy measurements. We introduce the Quantum Attention Network (QuAN), a classical artificial intelligence (AI) framework leveraging attention mechanisms tailored for learning quantum complexity. Inspired by large language models, QuAN treats measurement snapshots as tokens while respecting permutation invariance. Combined with our parameter-efficient miniset self-attention block, this enables QuAN to access high-order moments of bit-string distributions and preferentially attend to less noisy snapshots. We test QuAN across three quantum simulation settings: driven hard-core Bose-Hubbard model, random quantum circuits, and toric code under coherent and incoherent noise. QuAN directly learns entanglement and state complexity growth from experimental computational basis measurements, including complexity growth in random circuits from noisy data. In regimes inaccessible to existing theory, QuAN unveils the complete phase diagram for noisy toric code data as a function of both noise types, highlighting AI’s transformative potential for assisting quantum hardware.

Kim, Hyejin [Cornell Univ., Ithaca, NY (United Sta↗
DOE OSTI2020

Multiplicity dependence of (multi-)strange hadron production in proton-proton collisions at $\sqrt{s}$s = 13 TeV

The production rates and the transverse momentum distribution of strange hadrons at mid-rapidity ( | y | < 0.5 ) are measured in proton-proton collisions at $\sqrt{s}$ = 13 TeV as a function of the charged particle multiplicity, using the ALICE detector at the LHC. The production rates of K$^0_S$, Λ, &#x039E;<!-- Ξ --> , and Ω ncrease with the multiplicity faster than what is reported for inclusive charged particles. The increase is found to be more pronounced for hadrons with a larger strangeness content. Possible auto-correlations between the charged particles and the strange hadrons are evaluated by measuring the event-activity with charged particle multiplicity estimators covering different pseudorapidity regions. When comparing to lower energy results, the yields of strange hadrons are found to depend only on the mid-rapidity charged particle multiplicity. Several features of the data are reproduced qualitatively by general purpose QCD Monte Carlo models that take into account the effect of densely-packed QCD strings in high multiplicity collisions. However, none of the tested models reproduce the data quantitatively. This work corroborates and extends the ALICE findings on strangeness production in proton-proton collisions at 7 TeV.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Measurements of multiplicity fluctuations of identified hadrons in inelastic proton–proton interactions at the CERN Super Proton Synchrotron

Measurements of multiplicity fluctuations of identified hadrons produced in inelastic p+p interactions at 31, 40, 80, and 158 $\text {Ge}\text {V}/c$ beam momentum are presented. Three different measures of multiplicity fluctuations are used: the scaled variance $\omega $ and strongly intensive measures $\Sigma $ and $\Delta $. These fluctuation measures involve second and first moments of joint multiplicity distributions. Data analysis is preformed using the Identity method which corrects for incomplete particle identification. Strongly intensive quantities are calculated in order to allow for a direct comparison to corresponding results on nucleus–nucleus collisions. The results for different hadron types are shown as a function of collision energy. A comparison with predictions of string-resonance Monte-Carlo models: Epos, Smash and Venus, is also presented.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Measurements of ${\Xi \left( 1530\right) ^{0}} $ and ${\overline{\Xi }\left( 1530\right) ^{0}} $ production in proton–proton interactions at $\sqrt{s_{NN}}$ = 17.3 $\text{ GeV }$ in the NA61/SHINE experiment

Double-differential yields of ${\Xi \left( 1530\right) ^{0}} $ and ${\overline{\Xi }\left( 1530\right) ^{0}} $ resonances produced in p+p interactions were measured at a laboratory beam momentum of 158 $\text{ GeV }\!/\!c$. This measurement is the first of its kind in p+p interactions below LHC energies. It was performed at the CERN SPS by the NA61/SHINE collaboration. Double-differential distributions in rapidity and transverse momentum were obtained from a sample of $26\times 10^6$ inelastic events. The spectra are extrapolated to full phase space resulting in mean multiplicity of ${\Xi \left( 1530\right) ^{0}} $ ($6.73 \pm 0.25\pm 0.67)\times 10^{-4}$ and ${\overline{\Xi }\left( 1530\right) ^{0}} $ ($2.71 \pm 0.18\pm 0.18)\times 10^{-4}$. The rapidity and transverse momentum spectra and mean multiplicities were compared to predictions of string-hadronic and statistical model calculations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗