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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.

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At least 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A 20 Gbps Data Transmitting ASIC with PAM4 for Particle Physics Experiments

We present the design principle and test results of a data transmitting ASIC, GBS20, for particle physics experiments. The goal of GBS20 will be an ASIC that employs two serializers each from the 10.24 Gbps lpGBT SerDes, sharing the PLL also from lpGBT. A PAM4 encoder plus a VCSEL driver will be implemented in the same die to use the same clock system, eliminating the need of CDRs in the PAM4 encoder. This way the transmitter module, GBT20, developed using the GBS20 ASIC, will have the exact lpGBT data interface and transmission protocol, with an output up to 20.48 Gbps over one fiber. With PAM4 embedded FPGAs at the receiving end, GBT20 will halve the fibers needed in a system and better use the input bandwidth of the FPGA. A prototype, GBS20v0 is fabricated using a commercial 65 nm CMOS technology. This prototype has two serializers and a PAM4 encoder sharing the lpGBT PLL, but no user data input. An internal PRBS generator provides data to the serializers. GBS20v0 is tested barely up to 20.48 Gbps. With lessons learned from this prototype, we are designing the second prototype, GBS20v1, that will have 16 user data input channels each at 1.28 Gbps. We present the design concept of the GBS20 ASIC and the GBT20 module, the preliminary test results, and lessons learned from GBS20v0 and the design of GBS20v1 which will be not only a test chip but also a user chip with 16 input data channels.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

A Tool Kit for Generating Simulated Radiation Measurements for Advanced Reactor Safeguards and Security

A tool kit was developed to simulate and analyze passive radiation measurements of molten salt reactor (MSR) operations to support development of nuclear safeguards approaches for this emerging reactor technology. A Transient Simulation Framework of Reconfigurable Modules (TRANSFORM) multiphysics simulation of an MSR produces time-dependent isotopic inventories at user-selected locations within the model. The tool kit implements the Gamma Detector Response and Analysis Software (GADRAS) application programming interface to inject the TRANSFORM isotopic inventories extracted/processed by a Python pipeline into GADRAS models of user-defined geometries. The TRANSFORM inventories are the source terms used to obtain synthetic measurements from GADRAS-defined detectors. The speed of TRANSFORM and GADRAS simulations enables surveying the large design space of MSRs (e.g., fuel type, fuel salt composition, number of loops) and the plethora of measurements (e.g., location, detector type, and collimation) within the reactor. This has enabled timely assessment of the various measurement locations and detectors to identify the most effective and efficient safeguards approach for a specific MSR design. Lastly, the tool kit also simulates extracted samples that can be aged to a desired dose, enabling stakeholders to optimize a measurement plan to use sample analysis as an element within a broader material accountancy plan.

Westphal, Greg↗
DOE OSTI2023

Low-loss interconnects for modular superconducting quantum processors

Low-loss superconducting aluminium cables and on-chip impedance transformers can be used to link qubit modules and create superconducting quantum computing networks with high-fidelity intermodule state transfer. Scaling is now a key challenge in superconducting quantum computing. One solution is to build modular systems in which smaller-scale quantum modules are individually constructed and calibrated and then assembled into a larger architecture. This, however, requires the development of suitable interconnects. Here we report low-loss interconnects based on pure aluminium coaxial cables and on-chip impedance transformers featuring quality factors of up to 8.1 x 10 5 , which is comparable with the performance of our transmon qubits fabricated on a single-crystal sapphire substrate. We use these interconnects to link five quantum modules with intermodule quantum state transfer and Bell state fidelities of up to 99%. To benchmark the overall performance of the processor, we create maximally entangled, multiqubit Greenberger-Horne-Zeilinger states. The generated intermodule four-qubit Greenberger-Horne-Zeilinger state exhibits 92.0% fidelity. We also entangle up to 12 qubits in a Greenberger-Horne-Zeilinger state with 55.8 ± 1.8% fidelity, which is above the genuine multipartite entanglement threshold of 1/2.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Machine learning enhanced predictions of ICRF heating: Overcoming numerical limitations via data curation

In this work, we present the development of robust surrogate models for Ion Cyclotron Range of Frequencies (ICRF) and High-Harmonic Fast Wave (HHFW) heating predictions in fusion plasmas. Building upon our previous efforts to achieve real-time capable models, we identify the cause of the outliers found using TORIC in certain HHFW heating scenarios. The outliers are observed to be spurious ion Bernstein wave (IBW)-like modes caused by a wavelength control algorithm designed to address challenging scenarios with high perpendicular wavenumbers. The effect arises from the modulation in the perpendicular susceptibility, which can induce sign reversal and IBW-like propagation for scenarios featuring normalized ion Larmor radius λ i ≫ 1. We use TORIC with this algorithm disabled to generate a novel HHFW-NSTX database that is free of outliers. Surrogate models trained on this database, including Random Forest Regressor (RFR), Multi-Layer Perceptrons, and Gaussian Process Regressors (GPR), demonstrate the ability to accurately predict HHFW heating profiles, with regression scores of R 2 ∈[0.93−0.99]. Additionally we demonstrate that it is possible to generalize predictions beyond training data by the use of both RFR and GPR models, enabling the prediction of scenarios previously limited to the original model. GPR models also provide uncertainty quantification, offering insights into model confidence. This work introduces a comprehensive Verification, Validation, and Uncertainty Quantification methodology for surrogate modeling, applicable not only to ICRF heating but also to other RF heating challenges and fusion physics problems. Beyond accelerated inference, these models show effective extrapolation capabilities, providing an alternative for addressing numerical challenges.

Artificial neural networks↗
DOE OSTI2021

Quantum Engineering With Hybrid Magnonic Systems and Materials (Invited Paper)

Quantum technology has made tremendous strides over the past two decades with remarkable advances in materials engineering, circuit design and dynamic operation. In particular, the integration of different quantum modules has benefited from hybrid quantum systems, which provide an important pathway for harnessing the different natural advantages of complementary quantum systems and for engineering new functionalities. This review focuses on the current frontiers with respect to utilizing magnetic excitations or magnons for novel quantum functionality. Magnons are the fundamental excitations of magnetically ordered solid-state materials and provide great tunability and exibility for interacting with various quantum modules for integration in diverse quantum systems. The concomitant rich variety of physics and material selections enable exploration of novel quantum phenomena in materials science and engineering. In addition, the relative ease of generating strong coupling and forming hybrid dynamic systems with other excitations makes hybrid magnonics a unique platform for quantum engineering. We start our discussion with circuit-based hybrid magnonic systems, which are coupled with microwave photons and acoustic phonons. Subsequently, we are focusing on the recent progress of magnon-magnon coupling within confined magnetic systems. Next we highlight new opportunities for understanding the interactions between magnons and nitrogen-vacancy centers for quantum sensing and implementing quantum interconnects. Lastly, we focus on the spin excitations and magnon spectra of novel quantum materials investigated with advanced optical characterization.

42 ENGINEERING↗
DOE OSTI2024

Moment Tensor Inversion Toolkit

The MTINV toolkit (2002-present) is a collection of computer codes and applications written to invert for the moment tensor of a seismic source given the three components of ground motion recorded at regional seismic stations (e.g., Ichinose et al., 2003). The computer codes and workflow are organized to generate moment tensor solutions for a range of source depths and origin times because of the trade-off between these two quantities. The metric used is the variance reduction and variance reduction modulated by the percent double-couple to determine the best-fit moment-tensor solution. We can solve for a deviatoric moment tensor with a constraint added for no isotropic component although this constraint can be lifted for estimating the full moment tensor like mining collapses or explosion sources.

Ichinose, GeneA↗
DOE OSTI2020

Advanced Modular Sub-Atmospheric Hybrid Heat Engine (Final Report)

The Phase 1 Final Technical Report describes the results of the work completed during the “Advanced Modular Sub-Atmospheric Hybrid Heat Engine” project. A key part of the Phase 1 work was the completion of a thermodynamic cycle analysis for the MHHE at the selected module size. The hybrid heat engine has been developed as a modular unit (sized in the range of 500kW – 60MW) that can be used with modular coal or biomass gasifiers, with distributed power generation systems, with large power plants comprised of multiple generating units, and with natural gas compression stations. The MHHE will provide cleaner, more efficient, and lower cost generation with better load following capabilities than existing competing technologies with a singular generating source such as solar farm, gas turbine, or combustion engine. The drivers of the MHHE technology are: benefits of modular power generation (reduced equipment cost, construction cost and implementation time, connection ready on delivery, flexible scalability, serviceability), fuel flexibility, lowest cost power generation and reduced emissions.A logical progression of work and a clear path forward toward meeting the FOA goals and objectives have been established. Namely, a preliminary market analysis and primary fuel identification was completed first and then the modularity of the system was defined. The benefits of the proposed hybrid and modular heat engine were described when applied to modular coal gasifiers, distributed power generators, and larger power plants. Based on the market analysis, modularity, and chosen primary fuel, a conceptual design and layout of the hybrid heat engine was developed, analyzed, and characterized. The technology gaps already identified have been reviewed and expanded upon, and a test plan to address these gaps through bench scale testing in Phase 2 has been developed. Cost estimate methodology and considerations in support of a potential Phase 2 project have been described.

08 HYDROGEN↗
DOE OSTI2023

Deployment of Dynamic Neural Network Optimization to Minimize Heat Rate During Ramping for Coal Power Plants (Final Technical Report)

Much success was achieved throughout the course of this project. A successful implementation of Dynamic Neural Network Optimization (D-NNO) was coupled with Adaptive Predictive Controls (APC) and a novel hardware installation comprised of an advanced sensor network (ASN) measuring mass-weighted averages of flue gas constituents above the horizontal superheater of a coal-fired utility boiler. From 2019 through 2023 (including an extension due to COVID delays), the team was able to prototype, evaluate, deploy, iterate, and ultimately finalize an advanced closed-loop control D-NNO system which demonstrated the ability to: •improve unit efficiency ~2.0% relative to unoptimized operation (represented as total fuel fired per MWh generated) •improve unit NOx emission rates 10%+ beyond static optimization baselines •improve unit temperature stability as much as 58% and on average 12% •improve operating load stability as much as 35% The culmination of this project has generated an advanced methodology of deploying specially designed recurrent neural networks (long short-term memory, gated recurrent unit, encoder-decoder networks, transformers, etc.), customized trajectory planning and closed-loop optimization modules capable of adapting to live electric grid responses and demands, self-tuning and adaptive expert controls constantly adjusting prediction parameters to real-time unit behavior, and a hardware/software package able to reliably calculate net unit heat rate (NUHR) in real-time using flue gas constituents, machine learning, and known combustion relationships. Through this real-time NUHR value, immediate feedback on system adjustments relative to operating efficiency was available, allowing for rapid improvements to system performance. In addition to development and deployment of the advanced D-NNO system, the approach methodology has been readily commercialized through the project platform Griffin Open Systems, LLC, the D-NNO software platform host. Similar methodologies to those developed by this project have already been deployed at 5 other units across the United States, with another 6 implementations scheduled, and more expected. Over the course of the project, multiple academic papers were submitted and accepted for publication within esteemed academic journals, and PhD students were trained and graduated, as well as undergraduate students becoming involved and participating to project objectives.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2022

Modeling and Analysis of a Thermophotovoltaic Integrated Self-Powered Furnace

This work investigates the energy efficiency and carbon reduction potential of self-powered residential building heating equipment using a thermodynamic modeling approach. An integrated thermophotovoltaic power module and residential scale furnace system (40,000 Btu/h) were modeled and studied in detail to assess the influence of different design configurations on primary energy efficiency. Operational characteristics such as total power generation, electrical efficiency, and heat recovery were examined in a self-powered system configuration. A sensitivity analysis was conducted to determine the influence of the electric grid’s carbon dioxide footprint (carbon intensity) and the cost of electricity on the environmental, as well as the economic, benefit associated with the self-powered configuration. Compared with a traditional furnace powered by an electric grid at a carbon intensity of 0.5 kg CO 2eq /kWh EL , the self-powered furnace was shown to decrease the annual carbon dioxide emissions by approximately 550 kg (~75% reduction), while also saving more than USD 200 in utility expenses, annually. Additionally, the carbon emission reduction potential of blending different concentrations of hydrogen in natural gas fuel was also studied.

14 SOLAR ENERGY↗
DOE OSTI2023

HIGH-LOW FIDELITY THERMAL HYDRAULIC COUPLING USING AI/MACHINE LEARNING ALGORITHMS

The primary goal of the US Department of Energy (DOE) office of Nuclear Energy Integrated Energy Systems (IES) program is to develop the tools and framework for coupling multi-scale and multi-physical thermal and electrical energy usage and storage systems. High- and low-fidelity (high–low) coupling is a key feature of multi-scale, multi-component systems and has been an important focus of research in the nuclear energy community for the past two decades. An essential feature of demonstrating the capability to couple high-fidelity and low-fidelity systems for real-time applications are surrogate/reduced order models (ROM). For the purposes of this study, surrogate models are essentially Blackbox models, typically developed using supervised Machine learning (ML) algorithms. The surrogate models can be used to mimic the response of high-fidelity models to represent large historical datasets and coupled with more general low-fidelity system models distributed as Functional Mock-up Interface (FMI) or Functional Mock-up Units (FMU) modules. The example is demonstrated with Spallation Neutron Source (SNS) First Target Station flow loop data. The flow loop is a liquid mercury loop with a pump, piping, heat exchange, and internal heat generation in the target window. This work elucidates some of the potential benefits and future needs of developing tools for high–low system coupling of energy systems.

Williams, Wesley↗
DOE OSTI2021

Ultrathin and Ultrasensitive Direct X‐ray Detector Based on Heterojunction Phototransistors

Abstract Most contemporary X‐ray detectors adopt device structures with non/low‐gain energy conversion, such that a fairly thick X‐ray photoconductor or scintillator is required to generate sufficient X‐ray‐induced charges, and thus numerous merits for thin devices, such as mechanical flexibility and high spatial resolution, have to be compromised. This dilemma is overcome by adopting a new high‐gain device concept of a heterojunction X‐ray phototransistor. In contrast to conventional detectors, X‐ray phototransistors allow both electrical gating and photodoping for effective carrier‐density modulation, leading to high photoconductive gain and low noise. As a result, ultrahigh sensitivities of over 10 5 μC Gy air −1 cm −2 with low detection limit are achieved by just using an ≈50 nm thin photoconductor. The employment of ultrathin photoconductors also endows the detectors with superior flexibility and high imaging resolution. This concept offers great promise in realizing well‐balanced detection performance, mechanical flexibility, integration, and cost for next‐generation X‐ray detectors.

Gao, Yuanhong↗
DOE OSTI2024

Five amino acid mismatches in the zinc-finger domains of Cellulose Synthase 5 and Cellulose Synthase 6 cooperatively modulate their functional properties by controlling homodimerization in Arabidopsis

Cellulose synthase 5 (CESA5) and CESA6 are known to share substantial functional overlap. In the zinc-finger domain (ZN) of CESA5, there are five amino acid (AA) mismatches when compared to CESA6. These mismatches in CESA5 were replaced with their CESA6 counterparts one by one until all were replaced, generating nine engineered CESA5s. Each N-terminal enhanced yellow fluorescent protein-tagged engineered CESA5 was introduced to prc1-1, a cesa6 null mutant, and resulting mutants were subjected to phenotypic analyses. For this work, we found that five single AA-replaced CESA5 proteins partially rescue the prc1-1 mutant phenotypes to different extents. Multi-AA replaced CESA5s further rescued the mutant phenotypes in an additive manner, culminating in full recovery by CESA5 G43R + S49T+S54P+S80A+Y88F . Investigations in cellulose content, cellulose synthase complex (CSC) motility, and cellulose microfibril organization in the same mutants support the results of the phenotypic analyses. Bimolecular fluorescence complementation assays demonstrated that the level of homodimerization in every engineered CESA5 is substantially higher than CESA5. The mean fluorescence intensity of CSCs carrying each engineered CESA5 fluctuates with the degree to which the prc1-1 mutant phenotypes are rescued by introducing a corresponding engineered CESA5. Taken together, these five AA mismatches in the ZNs of CESA5 and CESA6 cooperatively modulate the functional properties of these CESAs by controlling their homodimerization capacity, which in turn imposes proportional changes on the incorporation of these CESAs into CSCs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Agent-Based Modeling for the Circular Economy: Lessons Learned From Three Case Studies

The circular economy (CE) aims at decoupling human activities from resource use, creating wealth in the process. Recently, many scholars have questioned the link between increased circularity and sustainability, resulting in many methodological approaches being developed for that purpose. This presentation summarizes the insights gained from the application of agent-based modeling (ABM) to study the techno-economic and social conditions promoting circularity and sustainability of three technologies: photovoltaic (PV) modules, hard disk drives (HDDs), and wind blades. Four main categories of agents are defined in the ABM: asset owners, service providers (e.g., refurbishers), recyclers, and manufacturers. Two main CE strategies are represented: lifetime extension (through repair or reuse) and recycling. The developed models start by projecting installed capacities and end-of-life (EOL) quantities. Then the theory of planned behavior - a social psychology model explaining behavior adoption based on attitude, peer influence, and costs - is used to model the asset owners' EOL decision (i.e., landfill, recycle or extend the lifetime of the asset). Then, the quantities of assets flowing to the recycler and service provider agents and quantities of materials flowing to manufacturers are computed. Recyclers' economies of scale are dynamically modeled, and the value generated by the CE strategies for the recyclers, service providers, and manufacturers is computed within the model. When data are available, avoided greenhouse gas emissions resulting from the CE strategies adoption are calculated exogenously from the ABM simulations. Results show that with improved used PV modules warranties, the reuse CE strategy adoption increases from 1% to 23% between 2020 and 2050. Similarly, improved standards could enhance HDDs end-users trust in data-wiping - a prerequisite to reuse - leading to a 3-fold increase in the reuse rate and avoid about 5 million tons of CO2 eq by 2050. Regarding wind blades, 5-15 years lifetime extension could reduce EOL blade quantities by 13%. High costs and logistic issues prevent blades from being recycled in greater quantities. One insight from the case studies is the necessity to have mature secondary markets for reuse to be a viable option. Interestingly, PV reuse is limited by the willingness of PV owners to purchase used modules (on the demand side), while HDDs reuse is constrained by the lack of trust toward data-wiping (limiting the supply of used HDDs). The six limits of the CE concepts presented by Korhonen et al. (2018) are finally used to interpret the results. The HDD case study is an exemplary lock-in, where the first accepted practice (shredding) retains most of the market. The PV results illustrate the technical limitation to reuse, as the growing demand cannot be supplied entirely with used PV modules. The wind case study shows the relevance of clearly defining physical flows - what type of waste should wind blades be considered, how should they be transported and landfilled? - a crucial consideration that also applies to PV modules. Finally, the three case studies highlight the relevance of studying a technology's technical, economic, and market material efficiency potentials altogether and the potential benefit of coupling ABM to life cycle assessment.

agent-based modeling↗
DOE OSTI2026

Evidence for Photoinduced Polaron Generation in a High Persistence Length Low Bandgap Conjugated Polymer in Solution

Using ultrafast time-resolved infrared (TRIR) spectroscopy, we studied the solution-phase excited-state structural evolution of an indacenodithiophene-co-benzothiadiazole polymer (C8-IDTBT). Following band gap excitation, the TRIR spectra reveal vibrational features that develop within 10 ps and decay over 4 ns. Using pulse radiolysis measurements, charge-modulation spectroscopy, and quantum-chemical calculations, the IR features are assigned to polaron pairs. Interestingly, these features appear on an evolving broad mid-IR electronic absorption background, with kinetics correlating with the formation and decay of the cationradical vibrational bands. A three-state kinetic model successfully reproduces the spectral evolution, revealing that the polaron and exciton populations exist in dynamic equilibrium on picosecond time scales, with time constants for exciton dissociation in the range of 3− 5 ps and polaron-to-exciton reformation between 20 and 100 ps, while both species decay to the ground state on much slower nanosecond time scales (∼1 ns), yielding a remarkably high polaron-generation efficiency, higher than 50%. These findings provide fundamental insights into intramolecular charge photogeneration mechanisms in conjugated polymers, demonstrating efficient bound-polaron formation in isolated polymer chains.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Improved noise performance from the next-generation buried-channel p-MOSFET SiSeROs

The Single electron Sensitive Read Out (SiSeRO) is a novel on-chip charge detector output stage for charge-coupled device (CCD) image sensors. Developed at MIT Lincoln Laboratory, this technology uses a p-MOSFET transistor with a depleted internal gate beneath the transistor channel. The transistor source-drain current is modulated by the transfer of charge into the internal gate. At Stanford, we have developed a readout module based on the drain current of the on-chip transistor to characterize the device. In our earlier work, we characterized a number of first prototype SiSeROs with the MOSFET transistor channels at the surface layer. An equivalent noise charge (ENC) of around 15 electrons root mean square (RMS) was obtained. In this work, we examine the first buried-channel SiSeRO. Here we have achieved substantially improved noise performance of around 4.5 electrons root mean square (RMS) and a full width half maximum (FWHM) energy resolution of 132 eV at 5.9 keV, for a readout speed of 625 kpixel/s. We also discuss how digital filtering techniques can be used to further improve the SiSeRO noise performance. Additional measurements and device simulations will be essential to further mature the SiSeRO technology. This new device class presents an exciting new technology for the next-generation astronomical X-ray telescopes requiring fast, low-noise, radiation-hard megapixel imagers with moderate spectroscopic resolution.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Co-simulation Framework for Community-scale Building-grid Integration [SWR-21-75]

Distributed energy resources (DERs), including rooftop solar, energy storage, and flexible loads, are gaining popularity as costs decline and as building owners and utilities realize their benefits. DERs can improve distribution system efficiency, help prevent the need for expensive grid upgrades, and increase the resilience of local communities. However, they can also cause difficulties in grid operations and can require controls to achieve their benefits. To address this challenge, NREL researchers have developed a community-scale solution that assesses the impacts of DERs and their control strategies on a distribution system. The framework has been shown to reduce solar photovoltaic (PV) curtailment to 0%, mitigate the adverse impact of solar variability on the distribution voltage, and provide up to 5-day critical load support during emergency events. Utilizing 5 different modules representing the feeder, buildings, home energy management systems, an aggregator, and a utility controller, NREL expects this simulation technology to play a critical role in the continued integration of DERs. According to the Energy Information Administration (EIA), solar curtailments accounted for 94% of the total energy curtailed in the California Independent System Operator (CAISO) in 2020. By enabling Independent System Operators (ISOs) and utility operators to bring solar curtailments to 0%, the electrical grid can become less dependent on fossil-fueled power generation sources. NREL's co-simulation framework contains five major components: Distribution Feeder Model: describes the distribution feeder topology using OpenDSS, including the locations of all DERs. Residential Building Model: simulates a large number of buildings at a high resolution using OCHRETM. The model is equipped to control equipment based on signals from an external module. The model includes major household appliances such as HVAC and a water heater, non-dispatchable load models, a distributed PV system, and a home battery system. Home Energy Management System: optimizes the controls for the devices in a home using foreseeTM. The control can adjust based on the user preferences including cost, comfort, and convenience. In hierarchical control scenarios, where the houses follow signals from an aggregator, the home energy management system provides a flexibility band with a range of power and follows the dispatch signals received from aggregator. Community-Level Aggregator: solves for optimal energy dispatch based on the flexibility bands received from each home and the grid service signal received from the utility controller. Utility-Level Controller: provides grid signals for voltage control using Distributed Energy Resources (DERs), such as solar systems, in the community.

Balamurugan, Sivasathya Pradha↗
DOE OSTI2021

The Role of Radiative Interactions in Tropical Cyclone Development under Realistic Boundary Conditions

Abstract The impact of radiative interactions on tropical cyclone (TC) climatology is investigated using a global, TC-permitting general circulation model (GCM) with realistic boundary conditions. In this model, synoptic-scale radiative interactions are suppressed by overwriting the model-generated atmospheric radiative cooling rates with their monthly varying climatological values. When radiative interactions are suppressed, the global TC frequency is significantly reduced, indicating that radiative interactions are a critical component of TC development even in the presence of spatially varying boundary conditions. The reduced TC activity is primarily due to a decrease in the frequency of pre-TC synoptic disturbances (“seeds”), whereas the likelihood that the seeds undergo cyclogenesis is less affected. When radiative interactions are suppressed, TC genesis shifts toward coastal regions, whereas TC lysis locations stay almost unchanged; together the distance between genesis and lysis is shortened, reducing TC duration. In a warmer climate, the magnitude of TC reduction from suppressing radiative interactions is diminished due to the larger contribution from latent heat release with increased sea surface temperatures. These results highlight the importance of radiative interactions in modulating the frequency and duration of TCs.

Meteorology & Atmospheric Sciences↗
DOE OSTI2020

Failure analysis of field-failed bypass diodes

Defective bypass diodes are often found as the largest factor leading to power loss in solar modules. Here, we report on failure mechanisms by investigating shunted bypass diodes from a rooftop installation, using a combination of multiple characterizations including current–voltage analysis, thermal-runaway testing, X-ray computed tomography, lock-in thermography, focused ion-beam cross-section imaging, chemical decapsulation, optical microscopy, and scanning electron microscopy with energy-dispersive X-ray spectroscopy. Differing from static discharge typically associated with lightning strikes on modules, we found diode failure by the mechanism of thermal damage under continuous, long-term overstress in forward bias. Our conclusion is based on evidence of energy dissipated—the small to medium extent of melt-through on the Schottky diode face. The diode failure shows distortions or roughening of the Schottky diode metal-semiconductor interface with the die attach, and some failures are accompanied by die-attach solder melting. We propose that nonuniform irradiance on the modules caused diode shunting due to extended periods of heat dissipation in the modules, because modules in this string were placed in two different orientations. In contrast, a second parallel module string of the same module type on the same rooftop with a unique plane of array did not show any diode failures. The thermal damage failure of melt-through was caused by the long-term current generated by overstressing of diodes that may have had crystalline and impurity defects.

14 SOLAR ENERGY↗