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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

On the Ambiguity of Using Isc for Analyzing Suns-Voc Data on Modules

In standard silicon solar modules, cells are strung in series with bypass diodes incorporated to reduce the effects of shading. When all of the strings are partially shaded the measured short circuit current is affected and the Isc of the module will be equal to that of the lowest current cell in the least shadowed string. In bifacial modules the ability to assess losses due to shading is especially important. This paper advocates an alternative approach to converting the raw suns-voc data to pseudo IV data in modules where shading may be affecting the suns-voc curve results.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2025

Q-Cluster: Quantum Error Mitigation Through Noise-Aware Unsupervised Learning

Quantum error mitigation (QEM) is critical in reducing the impact of noise in the pre-fault-tolerant era, and is expected to complement error correction in fault-tolerant quantum computing (FTQC). In this work, we propose a novel QEM approach, Q-Cluster, that uses unsupervised learning (clustering) to reshape the measured bit-string distribution. Our approach starts with a simplified bit-flip noise model. It first performs clustering on noisy measurement results, i.e., bit-strings, based on the Hamming distance. The centroid of each cluster is calculated using a qubit-wise majority vote. Next, the noisy distribution is adjusted with the clustering outcomes and the bitflip error rates using Bayesian inference. Our simulation results show that Q-Cluster can mitigate high noise rates (up to 40% per qubit) with the simple bit-flip noise model. However, real quantum computers do not fit such a simple noise model. To address the problem, we (a) apply Pauli twirling to tailor the complex noise channels to Pauli errors, and (b) employ a machine learning model, ExtraTrees regressor, to estimate an effective bit-flip error rate using a feature vector consisting of machine calibration data (gate & measurement error rates), circuit features (number of qubits, numbers of different types of gates, etc.) and the shape of the noisy distribution (entropy). Our experimental results show that our proposed Q-Cluster scheme improves the fidelity by a factor of 1.46x, on average, compared to the unmitigated output distribution, for a set of low-entropy benchmarks on five different IBM quantum machines. Our approach outperforms the state-of-art QEM approaches RZNE [28], M3 [24], Hammer [35], and QBEEP [33] by 1.26x,1.29x,1.47x, and 2.65 x, respectively.

42 ENGINEERING↗
DOE OSTI2023

Design Study of a Superconducting Quadrupole Magnet System Sustainable Under Dark Current Heating in ILC Main Linac

The International Linear Collider (ILC) main linac comprises a series of 12 m-long cryomodules. The cryomodule contains eight 9-cell superconducting (SC) RF (SRF) cavities and an SC quadrupole magnet combined with dipole correctors to focus and steer electron and positron beams. The magnets are installed between the SRF cavity string and at the longitudinal center of a common cryomodule/cryostat. These magnets are conductively cooled by pure aluminum channels thermally connected to a 2 K two-phase helium pipe for cooling the SRF cavities. A recent study shows that field-emitted electrons, so-called “dark current” initiated in the SRF cavities, are transmitted through the SRF cavity string and reach the SC magnet. The energy is inevitably absorbed in the SC coil due to the magnetic field, resulting in risks of a quench caused by the coil heating. We are investigating alternate magnet designs by using Nb $_{3}$ Sn or $\text{MgB}_{2}$ SC and by adding the dark current absorber surrounding the beam-pipe to realize sustainable magnet operation under the dark current heating. We report the design study of the magnet system and interfaces to the cryomodule accommodating it with the SRF cavities.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Photovoltaic Inverter Failure Mechanism Estimation Using Unsupervised Machine Learning and Reliability Assessment

This article introduces a data-driven approach to assessing failure mechanisms and reliability degradation in outdoor photovoltaic (PV) string inverters. The manufacturer's stated PV inverter lifetime can vary due to the impact of operating site conditions. To address limitations in degradation estimation through accelerated testing, condition monitoring, or degradation modeling, we propose a machine learning (ML) oriented approach. Utilizing data from a 1.4 MW PV power plant operational since 2016, with 46 string PV inverters tied to the grid, we employ the unsupervised one-class support vector machine ML technique to analyze inverter and sensor data, capable of classifying humidity cycling and temperature fluctuations as dominant failure mechanisms. Utilizing the anomaly alert relationship and alert details specific to the inverter, the level of PV inverter output is considered as its availability or available reliability. Subsequently, a continuous Markov model is applied to six-month alert data, revealing an average stated reliability of 20% after 20 years of continuous operation. These results support recommendations for time-bound preventive measures to enhance PV inverter reliability under diverse outdoor conditions. Furthermore, the approach provides a nondestructive, top–down, and generalized method for analyzing any commercial PV inverter exposed to outdoor conditions, contingent on the availability of relevant data.

14 SOLAR ENERGY↗
DOE OSTI2021

A Modular Mechanism for Downhole Weight-on-Bit and Torque Reaction in Small Diameter Boreholes

Drilling systems that use downhole rotation must react torque either through the drill-string or near the motor to achieve effective drilling performance. Problems with drill-string loading such as buckling, friction, and twist become more severe as hole diameter decreases. Therefore, for small holes, reacting torque downhole without interfering with the application of weight-on-bit, is preferred. In this paper, we present a novel mechanism that enables effective and controllable downhole weight on bit transmission and torque reaction. This scalable design achieves its unique performance through four key features: (1) mechanical advantage based on geometry, (2) direction dependent behavior using rolling and sliding contact, (3) modular scalability by combining modules in series, and (4) torque reaction and weight on bit that are proportional to applied axial force. As a result, simple mechanical devices can be used to react large torques while allowing controlled force to be transmitted to the drill bit. We outline our design, provide theoretical predictions of performance, and validate the results using full-scale testing. The experimental results include laboratory studies as well as limited field testing using a percussive hammer. These results demonstrate effective torque reaction, axial force transmission, favorable scaling with multiple modules, and predictable performance that is proportional to applied force.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

A Variational Autoencoder Model Toward Molecular Structure Representation Learning of Fuels

Here, in this work, a Variational Autoencoder (VAE)-based data-driven modeling framework is developed with the overarching goal of enabling fuel design. The VAE model is trained on a large dataset with several chemical species to learn a compressed latent space molecular representation. Chemical structure in the form of Simplified Molecular Input Line Entry System (SMILES) string is fed as input, encoded into the VAE latent space, and decoded back to the SMILES string using Long Short-Term Memory (LSTM) networks. Complexities of the VAE training loss function are thoroughly examined by varying the weightage (beta (𝜷) parameter) of the latent space regularization term, thereby assessing the balance between reconstruction accuracy and validity, and focusing on both accurate molecular structure reconstruction and latent space consistency. Two different strategies for 𝜷 variation are evaluated: linear annealing and cyclic annealing. In addition, the impact of total correlation adjustment and hierarchical priors is also studied with regard to the balance between reconstruction fidelity and latent space regularization, and potential issues such as posterior collapse, over-regularization, and poor disentanglement of latent variables. Overall, the best performance of the model is achieved with hierarchical priors and incrementally increasing 𝜷 from 0 to a threshold value of 0.25 over 75 epochs. The generative VAE model can be readily coupled with Quantitative Structure–Property Relationship (QSPR) analysis to develop an integrated end-to-end framework for fuel-property prediction and molecular design of novel promising fuels.

fuel design↗
DOE OSTI2023

Channel capacity: limitation of entropy rate during cell signal transduction

Cellular signal transduction is mediated through chain activation reactions involving signalling molecules. Information thermodynamics may be a promising approach for quantifying signal transduction. A cell signal is transduced through the signal molecule activation as a step of a signal transduction cascade. In each step, two types of entropy can be defined. First, the signal transduction was defined using thermodynamic entropy production during the chemical activation of signal molecules. Second, the chronology was described as a code string, and the logarithm of the number of signal code strings corresponded to the information entropy. Finally, the total entropy during signal transduction was determined by summing the two entropies. When maximising the total entropy, the total entropy rate per signalling molecule concentration was found to be consistent independently of each transduction step (i.e., signal molecule types). Consequently, the speed limit of the entropy rate can be calculated, providing the capacity, which is an essential property of the signal transduction cascade. The theory introduced here is expected to provide a basic framework for understanding new methods of discovering signal cascades and intracellular signal transduction.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

UnitTesterSG

UnitTesterSG is a unit testing framework that is designed for nested and/or scientific/engineering data structures. It is designed primarily for testing the outputs if a single function or simulation run by storing the expected results file such that comparisons to the stored output can be made with unit tests after the function or software has been edited. This module is designed to be able to unit test arrays, strings, as well as nested and deeply nested objects (including arrays with tuples inside, arrays with strings inside, etc.). The module is compatable with nested objects, and thus has a dependency on the nestedObjectsFunctions module within. The module takes *calculated* results and then compares them to *expected* results. If no expected results are available (or they do not match the calculated results) the module then offers to store the calculated results as expected results for next time.

Savara, Aditya [Oak Ridge National Lab. (ORNL), Oa↗
DOE OSTI2021

Codon2Vec v1.0

Background: Codon2Vec is an embedding neural network that predicts 'high' or 'low' gene expression directly from the protein-coding sequences. Embedding neural networks are commonly used for natural language processing (NLP) applications. Analogous to how an English sentence is a string of words, a gene can be thought of as a string of codons. Similar to how NLP neural networks model English sentences as a non-random sequence of words, we considered a coding sequence as a non-random non-overlapping array of codons (k-mers of length = 3). Value Proposition: - Codon2Vec achieved a high median AUC-ROC score of 83.8% when trained and applied to transcriptomic data from 300 fungal species - Unlike Codo2Vec, conventional methods predicting for expression based on codon usage rely on a priori knowledge of optimal codons or a set of reference genes. - Unlike Codon2vec, these methods do not account for the effect of codon order on gene expression. - Codon2Vec neural network bypasses the need for artisanal feature selection step that is necessary for traditional machine learning models.

Wint, Rhondene↗
DOE OSTI2023

EI_MS_ML

The unambiguous identification of compounds from their electron ionization mass (EI-MS) spectra remains a significant unsolved problem in the field of metabolomics and analytical chemistry as a whole. Typically EI-MS spectra are compared using various mathematical operations that convert the spectral similarity or differences into a distance-like metric that roughly approximates the similarity of any two spectra. A commonly used metric for this is the cosine similarity metric which has values close to one for very similar spectra and a value of zero for very dissimilar spectra; however, no metric is perfect. Due to the prevalence of structurally-similar compounds such as isomers and the prevalence of certain fragmentation patterns across structurally-dissimilar compounds, the unambiguous assignment of EI-MS spectra compounds remains difficult. Frequently, querying an observed EI-MS spectrum against a large database such as the NIST17 library yields multiple possible assignments requiring the end user to distinguish between multiple high scoring hits, or multiple low scoring hits while keeping in mind that the correct hit may not be in the database at all. Although techniques such as orthogonal information from techniques such as chromatography can greatly aid in unambiguous assignment, this also requires more complicated experimental designs and access to more complicated analytical instrumentation. Substructures can be trivially detected and represented as strings using a previously published technique called node coloring from a known chemical structure. However, for experimentally-derived EI-MS spectra this information must be derived from the spectra itself (i.e., because we do not know what compound it represents). To achieve this, the software uses techniques from the field of machine learning and a large training dataset of EI-MS spectra corresponding to known structures annotated with substructure strings, to build models that can predict the presence of a given chemical substructure from an EI-MS spectrum directly.If these predictions are of high-quality (i.e., are unlikely to be false positives), the presence of one or more predicted substructures can be used to constrain the number of possible hits for a query spectrum. Mathematically, this restriction could be expressed in many forms, but the most straight-forward implementation is to weight the cosine similarity of a query spectrum and a plausible database match with a Tanimoto-like coefficient based on the ratio of the number of substructures predicted to the number of substructures present in the potential database hit. Determining which combination of models best reduces assignment ambiguity will be achieved using a combination of manual curation and optimization techniques such as genetic algorithms. This software will perform all the steps necessary to construct said models from a training dataset and evaluate them using a holdout dataset. Various statistical analyses can be performed to determine if this approach does decrease assignment ambiguity. For example, if this approach works, on average, the rank-order of the correct assignment for the holdout set of EI-MS spectra should decrease and the weighted cosine similarities for most of the possible matches in the database should be better than the unweighted cosine similarities. Furthermore, this same pipeline can be used on real experimental data to generate less ambiguous assignments.

Mitchell, Joshua↗
DOE OSTI2024

GenConfig

SAND2025-04091O GenConfig converts a build name into a set of configuration flags or CMake fragment files for use with CMake. This is accomplished using ConfigKeywordParser and two configuration files. GenConfig is the main tool in a set of software libraries used for generating and configuring an environment and configuration flags. The tool uses other modules within the GenConfig family to ultimately parse and enable an environment that is ready for development from a given build name string. The unique algorithms used in GenConfig mainly pertain to validating the format and checking the existence of the given build string in the expected configuration .ini files. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Gates, Jason↗
DOE OSTI2023

Bloom filters for molecules

Abstract Ultra-large chemical libraries are reaching 10s to 100s of billions of molecules. A challenge for these libraries is to efficiently check if a proposed molecule is present. Here we propose and study Bloom filters for testing if a molecule is present in a set using either string or fingerprint representations. Bloom filters are small enough to hold billions of molecules in just a few GB of memory and check membership in sub milliseconds. We found string representations can have a false positive rate below 1% and require significantly less storage than using fingerprints. Canonical SMILES with Bloom filters with the simple FNV (Fowler-Noll-Voll) hashing function provide fast and accurate membership tests with small memory requirements. We provide a general implementation and specific filters for detecting if a molecule is purchasable, patented, or a natural product according to existing databases at https://github.com/whitead/molbloom .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

ORNL_AISD_NiNb

This dataset describes the nickel-niobium solid solution binary alloy, where the two constituent elements nickel (Ni) and niobium (Nb) are randomly placed on an underlying crystal lattice. This dataset for nickel-niobium (Ni-Nb) alloys available includes the formation energy and bulk modulus for each crystal structure. Each atomic sample has a disordered phase which is obtained starting from an initial regular crystal structure of type body-centered cubic (BCC), face-centered cubic (FCC), or hexagonal compact packed (HCP). The geometry optimization ensures that all the alloy samples reached the equilibrium with negative formation energy. We perform geometry optimizations using the LAMMPS simulation package [1], a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales. We utilized the embedded atom model (EAM) potential for Ni and Nb developed in a previous study [2]. The potential could describe behaviors of the liquid and solid phases of Ni-Nb alloy. The structural factors and angular distributions of three atoms are well-matched with X-ray and ab initio-based molecular dynamics data. We prepared the three different crystals with different initial lattice parameters (3.52 Ã… for FCC, 3.32 Ã… for BCC, and 3.5 Ã… for HCP). We performed energy minimization in two steps. Firstly, we minimized the structures with an isotropic unit cell to minimize the side effects from our arbitrary lattice parameters for all other compositions. Then, we applied geometry optimization with a triclinic (non-orthogonal) unit cell to fully minimize the stress components to calculate the elastic constants. In this procedure, we chose 10,000 as the maximum number of allowable steps aimed at obtaining fully relaxed atomic geometries. The dataset consists of three sets of crystal structures. The first set contains 46,086 irregular crystal structures, each of them with 54 atoms, obtained through optimization starting from a regular BCC crystal structure. The second set contains 24,543 irregular crystal structures, each of them with 32 atoms, obtained through optimization starting from a regular FCC crystal structure. The third set contains 39,303 irregular crystal structures, each of them with 48 atoms, obtained through optimization starting from a regular HCP crystal structure. The atomic configurations within each set span the possible compositional range. The three sets have been unified in a global dataset, which is extremely heterogeneous in terms of crystal structures, lattice volumes, and atomic configurations. Organization of files inside the dataset: the dataset contains three subdirectories called • BCC_opt • FCC_opt • HCP_opt based on the type of initial regular structure used to start the geometry optimization. Inside each of these folders, every atomic structure is identified by a string “A_B_Câ€, where A denotes the number of Nb in the system, B denotes index of structure with a given Nb number, and C denotes the total number of structures generated with a given Nb number. For each optimized crystal structure identified by the unique string of characters “A_B_Câ€, three files are provided: • A_B_C_opt.xyz: The optimized geometries in xyz format • A_B_C_opt.cfg: The optimized geometries in cfg format. It includes cell information and atomic energy, and forces calculated from LAMMPS. • A_B_C.elastic: Raw data of 21 elastic constants from LAMMPS output. • A_B_C.bulk: Calculated upper and lower bounds of bulk modulus and averaged one based on Voigt-Reuss-Hill approach from *.elastic. References: [1] A. P. Thompson, H. M. Aktulga, R. Berger, D. S. Bolintineanu, W. M. Brown, P. S. Crozier, P. J. in 't Veld, A. Kohlmeyer, S. G. Moore, T. D. Nguyen, R. Shan, M. J. Stevens, J. Tranchida, C. Trott, and S. J. Plimpton. LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales. Comp. Phys. Comm., 271:108171, 2022. [2] Y Zhang, R Ashcraft, MI Mendelev, CZ Wang, and KF Kelton. Experimental and molecular dynamics simulation study of structure of liquid and amorphous ni62nb38 alloy. The Journal of chemical physics, 145(20):204505, 2016.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Profile Images and Annotations for Vehicle Re-identification Algorithms (PRIMAVERA)

This dataset contains 636,246 profile images of vehicles representing 13,963 unique vehicles. The data was collected by a set of roadside sensors over the course of three years. Each time a vehicle passed by one of the sensors, a series of images was collected. The images were processed to detect and localize each vehicle, and a license plate reader collocated with the sensor was used to provide a unique ID for the vehicle. Actual license plate numbers have been obfuscated by replacing with an arbitrary numerical ID for each vehicle. After localizing the vehicle in each image, the original RGB image was rotated, scaled, and shifted to produce a new RGB image of size 234x234 pixels such that the outermost two wheels are located at predetermined pixel locations in the image. In this way, all vehicle images are aligned to one another. This registration process occasionally results in a portion of certain vehicles being cutoff at the edges of the image. The dataset has been partitioned into two sets called training and validation. The two partitions no common vehicles, i.e., a vehicle present in one partition is guaranteed not to be present in the other. In this way, an algorithm can be validated against a set of new vehicles that were not seen during the training process. The training set contains 543,926 images from 64,440 vehicle passes representing 11,918 unique vehicles, while the validation set contains 92,320 images from 10,991 vehicle passes representing 2,045 unique vehicles. Vehicle images are organized by directories corresponding to unique vehicles. The file naming scheme is as follows: veh_{vehID}_tr_{passID}_{frameID}_{elevation}_{timeofday}.jpg where {vehID} is the vehicle ID (unique across the entire dataset), {passID} is an identifier for each tracked vehicle pass (unique across the entire dataset), {frameID} is the index of the frame within the given vehicle pass starting at 0, {elevation} is a two-letter string indicating whether the sensor was elevated (el) or at ground-level (gl), and {timeofday} is a two-letter string indicating whether the image was captured during daytime (dt) or nighttime (nt).

image↗
DOE OSTI2024

Underwater Target Detection Software Demonstration on the RivGen Turbine

This repository contains data and processing scripts necessary to train the object detection models utilized in the underwater target detection software demonstration on the RivGen turbine project and to produce performance metrics (precision, recall, mAP50, mAP50-95). - Contents - Data consist of "images" and "labels". Each image has an associated label, both share the same time string in its file name (e.g., 2024_05_25_09_01_57.98.jpg and 2024_05_25_09_01_57.98.txt). Time strings have the format %yyyy_%mm_%dd_%HH_%MM_%SS.%3f. Images and labels were curated from 2021 and 2024 smolt outmigration periods at the project site in Igiugig, AK. Images are monochrome 8-bit images of objects (smolt, debris, and other) passing through the field of view of the deployed cameras during various operational stages of the RivGen turbine. Labels are text files indicating the class and bounding polygon of each object in an image. The provided labels use the "YOLO" label format. - Requirements - Python3.8+ is required to install and run the train and validation script. The README.md provides instruction for installing the requirements from the requirements.py file. - Instructions - The "example_train.py" file ingests the provided data, trains a model, and produces model performance metrics at completion. NOTE: model performance metrics will vary from run to run as a consequence of the random selection of training and validation data.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

Describing hadronization via histories and observables for Monte-Carlo event reweighting

We introduce a novel method for extracting a fragmentation model directly from experimental data without requiring an explicit parametric form, called Histories and Observables for Monte-Carlo Event Reweighting (HOMER), consisting of three steps: the training of a classifier between simulation and data, the inference of single fragmentation weights, and the calculation of the weight for the full hadronization chain. We illustrate the use of HOMER on a simplified hadronization problem, a q\bar{q} q q ‾ string fragmenting into pions, and extract a modified Lund string fragmentation function f(z) f ( z ) . We then demonstrate the use of HOMER on three types of experimental data: (i) binned distributions of high-level observables, (ii) unbinned event-by-event distributions of these observables, and (iii) full particle cloud information. After demonstrating that f(z) f ( z ) can be extracted from data (the inverse of hadronization), we also show that, at least in this limited setup, the fidelity of the extracted f(z) f ( z ) suffers only limited loss when moving from (i) to (ii) to (iii). Public code is available at https://gitlab.com/uchep/mlhad.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Low Cost (CAPEX and variable): Tool design for cell and module fabrication with thin, free-standing silicon wafers

This project aimed to develop technologies that can potentially enable free-standing thin (<80 μm) wafer in today’s manufacturing lines with high production yield, and thereby reduce capex barriers of silicon photovoltaics cells and modules. One of the major benefits is that thin wafer dramatically reduces the amount of polysilicon required. As a result, it can lead to reduction in the capital expenditures associated with polysilicon refining and wafer fabrication, which together are more than half of the total capital expenditure to manufacture Si PV module. We focused our efforts on developing the tools needed to enable high yield fabrication of wafer, cell, and module with thin silicon wafers. However, as the wafer thickness reduces, the major challenge is that wafer breakage increases significantly. Three technological areas were explored in this project to improve the production yield of the silicon wafer, namely detection of edge cracks via dark-field near-infrared (NIR) scattering; (2) wafer handling using controlled temperature profiles; (3) manufacturable low-stress cell interconnection for multiwire. First, the formation of wafer cracks in submillimeter length is one of the reasons that causes wafer breakages. Crack detection tools are needed to enable us to locate and track the wafer crack during manufacturing, so that we can improve the process to reduce initialization. The state-of-the-art crack detection technique cannot fulfill the need for measuring submillimeter edge cracks detrimental for thin wafers. The prototype developed in this project demonstrates the scanning of microcracks near wafer edges. With a semi-automatic laboratory setup, the submillimeter cracks were reliably detected near the edges in multi-Si wafers. The smallest detectable crack is 200 µm in length in slow scans; and submillimeter cracks are detected in high-throughput scans at the scan speed of >0.5 m/s, which is compatible with the inline detection of a manufacturing line at least 1 sec/wafer. This detection limit is a significant advancement in comparison to the benchmarked industrial tool. Second, wafer handling with the well-controlled temperature profile was thought to be the solution to reduce crack initiation and propagation during the manufacturing. However, without crack detection being widely adopted in production line, we did not find a strong industrial pull toward this technology. We did an initial literature survey and then diverted our efforts to the other tasks. Third, the innovation on low-stress multi-wire interconnection tackles a fundamental problem in the standard interconnection scheme. The standard over-under “zig-zag” interconnection induces a significant amount of stress into the soldering point on the cell whenever PV module is under stress, e.g., thermal cycling, transportation, and installation. Therefore, the interconnection process was re-designed in this project to allow for significant movement between adjacent solar cells, e.g., due to thermal expansion, without building up stresses on the solar cells or solder joints. The new interconnection method with the cross-connect wire also simplifies the tabbing and stringing process by replacing the standard over-under method with an off-cell interconnect from top to bottom. A manual tabbing and stringing tool for this new process was prototyped in the lab to demonstrate the fabrication of this new interconnection design. During the test with brass sheets as our “testing cells”, it was found that the mechanical cycling test only broke the interconnection after more than 50,000 cycles, which is equivalent to more than 130 years of the day-and-night thermal cycles. Lastly, throughout the project, we continuously analyzed the PV market with techno-economic analysis to identify the opportunity for thin Si adoption. Even though the drastic cost reduction has already happened in the past five years, our analysis results indicated that we can still save quite significantly in both manufacturing cost and factory capex, Particularly, in order to grow the PV manufacturing capacity to multi-terawatt level, reducing the thickness of silicon wafer is one of the most effective ways to quickly reduce factory capex, and sustain the high growth rate. The technologies developed in this project are readily available to provide some assistances in tackling the production yield problem.

14 SOLAR ENERGY↗
DOE OSTI2021

Results from Accelerated Environmental Testing of Barcoded Identifiers for UF 6 Cylinders

This report provides the qualitative and quantitative results from the accelerated environmental testing campaign. A total of 444 samples were subjected to 10 tests that included UV, salt fog, temperature, blowing dust, high pressure and temperature water, and impact exposure. Each sample included a two-dimensional barcode and an alphanumeric string representing the cylinder identification number. When evaluating the results, primary attention was placed on the decodability of the barcodes using a barcode verifier device, since machine scanning a barcode offers the greatest efficiency and accuracy gains when reading cylinder identification numbers during safeguards activities. Contrast was considered the critical test parameter because maintaining contrast under a wide range of environmental conditions is of fundamental importance to decoding the identification number of the barcodes. Additionally, manual readability of the alphanumeric string was considered an important factor in determining whether an inspector or operator could still read the identification number of the cylinder if the barcode proved to be undecodable.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗