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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Engineering Perovskite Precursor Inks for Scalable Production of High‐Efficiency Perovskite Photovoltaic Modules

Abstract Blade coating of perovskite solar cells (PSCs) and modules has progressed considerably toward the industrial production of perovskite photovoltaics. Developing stable perovskite precursors is critical for achieving uniform coating over large areas. Here, the engineering of a perovskite precursor solution consisting of 2‐methoxyethanol (2‐Me) and 1,3‐dimethyl‐imidazolidinone (DMI) with superior intermediate phase stability that enables scalable production of efficient perovskite solar modules is reported. With this perovskite precursor solution, uniform and pinhole‐less perovskite film is deposited over a large area of > 100 cm 2 and higher‐efficiency PSCs and modules are obtained. The best‐performing unit cell and module with n‐i‐p configuration reach power conversion efficiencies of 23.4% and 20.1%, respectively. Additionally, a series of non‐destructive metrology methods, such as spectroscopic ellipsometry, hyperspectral photoluminescence, electroluminescence, and laser beam‐induced current mapping, are employed to assess and guide the development the blade‐coated perovskite modules. This results show that rational engineering of precursor inks for blade coating is promising for the scalable production of efficient perovskite solar modules.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Positron emission tomography harmonization in the Alzheimer's Disease Neuroimaging Initiative: A scalable and rigorous approach to multisite amyloid and tau quantification

Abstract INTRODUCTION A key goal of the Alzheimer's Disease NeuroImaging Initiative (ADNI) positron emission tomography (PET) Core is to harmonize quantification of β‐amyloid (Aβ) and tau PET image data across multiple scanners and tracers. METHODS We developed an analysis pipeline (Berkeley PET Imaging Pipeline, B‐PIP) for ADNI Aβ and tau PET images and applied it to PET data from other multisite studies. Steps include image pre‐processing, refacing, magnetic resonance imaging (MRI)/PET co‐registration, visual quality control (QC), quantification of tracer uptake, and standardization of Aβ and tau standardized uptake value ratios (SUVrs) across tracers. RESULTS Measurements from 10,105 cross‐sectional and longitudinal Aβ and tau PET scans acquired in several studies between 2010 and 2024 can be processed, harmonized, and directly merged across tracers and cohorts. DISCUSSION The B‐PIP developed in ADNI is a scalable image harmonization approach used in several observational studies and clinical trials that facilitates rigorous Aβ and tau PET quantification and data sharing. Highlights Quantitative results from ADNI Aβ and tau PET data are generated using a rigorous, scalable image processing pipeline This pipeline has been applied to PET data from several other large, multisite studies and trials Quantitative outcomes are harmonizable across studies and are shared with the scientific community

Neurosciences & Neurology↗
DOE OSTI2021

Life cycle energy demand and carbon emissions of scalable single-junction and tandem perovskite PV

Perovskite photovoltaics reached record efficiencies in the laboratory and, if sustainably commercialized, they would accelerate a green energy transition. Here, this article presents the development of life-cycle inventory material and energy databases of most promising four single-junction and three tandem scalable perovskite systems with assumptions regarding scalable production validated by industry experts. We conducted comprehensive “ex ante” Life Cycle Analysis (LCA) and Net Energy Analysis, analyzing their cumulative energy demand, global warming potential profiles, energy payback times and energy return on investment (EROI). LCA contribution analysis elucidates the most impactful material and process choices. It shows that solution-based perovskite manufacturing would have lower environmental impact than vapor-based methods, and that roll-to-roll (RtR) printing offers the lowest impact. Among material choices, MoOx/Al has lower impact than Ag, and fluorine-tin-oxide lower than indium-tin-oxide. Furthermore, we compare perovskites with commercial crystalline-silicon and thin-film PV, accounting for the most recent developments in crystalline-Si wafer production and differences in life expectancies and efficiencies. It is shown that perovskite systems produced with RtR manufacturing could reach in only 12 years of life the same EROI as that of single-crystalline-Si PV lasting 30-years. This work lays a foundation for sustainability investigations of perovskite large-scale deployment.

14 SOLAR ENERGY↗
DOE OSTI2023

Seed‐Assisted Growth for Scalable and Efficient Perovskite Solar Modules

Perovskite solar modules (PSMs) have shown remarkable photovoltaic potentials, but they still suffer from large power conversion efficiency (PCE) loss on scale‐up and instability due to inferior uniformity and crystallization over large areas. Herein, the scalable production of efficient and stable PSMs using a suite of all‐scalable fabrication methods featuring a two‐step blade/dip‐coating approach to deposit the perovskite absorber layer is demonstrated. Rubidium chloride is introduced to embed (PbI 2 ) 2 RbCl complex seeds in the first‐deposited PbI 2 precursor, which assists in uniform crystallization of the perovskite layer with high crystallinity and reduced defect density over large areas. Following the optimization of RbCl additives, a champion PSM with 17.9% PCE on a 7.6 × 7.6 cm 2 substrate with a 37 cm 2 aperture area is achieved. Moreover, the RbCl‐incorporated PSMs demonstrate excellent reproducibility and stability under continuous 1 sun illumination. This work shows that the two‐step blade/dip coating is a promising method for producing high‐efficiency and stable PSMs on an industrially relevant scale.

Energy & Fuels↗
DOE OSTI2020

Scalable Data-Intensive Geocomputation: A Design for Real-Time Continental Flood Inundation Mapping

The convergence of data-intensive and extreme-scale computing enables an integrated software and data ecosystem for scientific discovery. Developments in this realm will fuel transformative research in data-driven interdisciplinary domains. Geocomputation provides computing paradigms in Geographic Information Systems (GIS) for interactive computing of geographic data, processes, models, and maps. Because GIS is data-driven, the computational scalability of a geocomputation workflow is directly related to the scale of the GIS data layers, their resolution and extent, as well as the velocity of the geo-located data streams to be processed. Unique in high user interactivity and low end-to-end latency requirements, geocomputation applications will dramatically benefit from the convergence of high-end data analytics (HDA) and high-performance computing (HPC). The application level challenge, however, is to identify and eliminate computational bottlenecks that arise along a geocomputation workflow. Indeed, poor scalability at any of the workflow components is detrimental to the entire end-to-end pipeline. Here, we study a large geocomputation use case in flood inundation mapping that handles multiple national-scale geospatial datasets and targets low end-to-end latency. We discuss benefits and challenges for harnessing both HDA and HPC for data-intensive geospatial data processing and intensive numerical modeling of geographic processes. We propose an HDA+HPC geocomputation architecture design that couples HDA (e.g., Spark)-based spatial data handling and HPC-based parallel data modeling. Key techniques for coupling HDA and HPC to bridge the two different software stacks are reviewed and discussed.

Liu, Yan↗
DOE OSTI2021

Phoenix: A Scalable Streaming Hypergraph Analysis Framework

We present Phoenix, a scalable hypergraph analytics framework for data analytics and knowledge discovery that was implemented on the leadership class computing platforms at Oak Ridge National Laboratory (ORNL). Our software framework comprises a distributed implementation of a streaming server architecture which acts as a gateway for various hypergraph generators/external sources to connect. Phoenix has the capability to utilize diverse hypergraph generators, including HyGen, a very large-scale hypergraph generator developed by ORNL. Phoenix incorporates specific algorithms for efficient data representation by exploiting hidden structures of the hypergraphs. Our experimental results demonstrate Phoenix’s scalable and stable performance on massively parallel computing platforms. Phoenix’s superior performance is due to the merging of high-performance computing with data analytic.

Kurte, Kuldeep↗
DOE OSTI2025

Lessons Learned and Scalability Achieved When Porting Uintah to DOE Exascale Systems

A key challenge faced when preparing codes for Department of Energy (DOE) exascale systems was designing scalable applications for systems featuring hardware and software not yet available at leadership-class scale. With such systems now available, it is important to evaluate scalability of the resulting software solutions on these target systems. One such code designed with the exascale DOE Aurora and DOE Frontier systems in mind is the Uintah Computational Framework, an open-source asynchronous many-task (AMT) runtime system. To prepare for exascale, Uintah adopted a portable MPI+X hybrid parallelism approach using the Kokkos performance portability library (i.e., MPI+Kokkos). This paper complements recent work with additional details and an evaluation of the resulting approach on Aurora and Frontier. Results are shown for a challenging benchmark demonstrating interoperability of 3 portable codes essential to Uintah-related combustion research. These results demonstrate single-source portability across Aurora and Frontier with scaling characteristics shown to 3,072 Aurora nodes and 9,216 Frontier nodes. In addition to showing results run to new scales on new systems, this paper also discusses lessons learned through efforts preparing Uintah for exascale systems.

Holmen, John [ORNL] (ORCID:0000000259342641)↗
DOE OSTI2022

Development of Aluminum Scandium Nitride Molecular Dynamics Force Fields with Scalable Multi-Objective Bayesian Optimization

Scandium (Sc)-doped aluminum nitride (AlN) exhibits improved piezoelectric properties, which is favorable for sensor applications. Although many experimental studies exist to fine tune the material properties for design purposes, an atomistic level understanding of the structure-property (S-P) relationships is needed, which is the aim of this work. Molecular dynamics can be used to understand the S-P relationships. However, the limited availability of suitable force fields has been a major challenge for accurate property predictions. In this article, a robust force field calibration method using a scalable multi-objective Bayesian optimization approach is presented. Optimizations with three, six, and eight objectives are applied to calibrate aluminum scandium nitride force fields based on the piezoelectric characteristics, modulus of elasticity, and lattice parameters at different Sc-doped levels. Furthermore, the performances of the different force fields are compared, and the performance of the higher dimensional objective problems is discussed. The highly scalable molecular dynamics force field development method is successfully implemented, resulting in the creation of several aluminum scandium nitride molecular dynamics force fields for piezoelectric applications at varying Sc dope levels.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Scalable synthesis of nanoporous silicon microparticles for highly cyclable lithium-ion batteries

Nanoporous silicon is a promising anode material for high energy density batteries due to its high cycling stability and high tap density compared to other nanostructured anode materials. However, the high cost of synthesis and low yield of nanoporous silicon limit its practical application. Here, we develop a scalable, low-cost top-down process of controlled oxidation of Mg 2 Si in the air, followed by HCl removal of MgO to generate nanoporous silicon without the use of HF. By controlling the synthesis conditions, the oxygen content, grain size and yield of the porous silicon are simultaneously optimized from commercial standpoints. In situ environmental transmission electron microscopy reveals the reaction mechanism; the Mg 2 Si microparticle reacts with O 2 to form MgO and Si, while preventing SiO 2 formation. Owing to the low oxygen content and microscale secondary structure, the nanoporous silicon delivers a higher initial reversible capacity and initial Coulombic efficiency compared to commercial Si nanoparticles (3,033 mAh/g vs. 2,418 mAh/g, 84.3% vs. 73.1%). Synthesis is highly scalable, and a yield of 90.4% is achieved for the porous Si nanostructure with the capability to make an excess of 10 g per batch. Our synthetic nanoporous silicon is promising for practical applications in next generation lithium-ion batteries.

25 ENERGY STORAGE↗
DOE OSTI2023

Scalable DPG multigrid solver for Helmholtz problems: A study on convergence

This paper presents a scalable multigrid preconditioner targeting large-scale systems arising from discontinuous Petrov–Galerkin (DPG) discretizations of high-frequency wave operators. This work is built on previously developed multigrid preconditioning techniques of Petrides and Demkowicz (Comput. Math. Appl. 87 (2021) pp. 12–26) and extends the convergence results from $\mathscr{O}$(10 7 ) degrees of freedom (DOFs) to $\mathscr{O}$(10 9 ) DOFs using a new scalable parallel MPI/OpenMP implementation. Novel contributions of this paper include an alternative definition of coarse-grid systems based on restriction of fine-grid operators, yielding superior convergence results. In the uniform refinement setting, a detailed convergence study is provided, demonstrating h and p robust convergence and linear scaling with respect to the wave frequency. Finally, the paper concludes with numerical results on hp -adaptive simulations including a large-scale seismic modeling benchmark problem with high material contrast.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Scalable membrane-less microbial electrolysis cell with multiple compact electrode assemblies for high performance hydrogen production

Bioelectrochemical hydrogen production via microbial electrolysis cells (MECs) is a promising method for sustainable energy production and decarbonization of energy systems. However, the application of MECs is limited by the electrochemical performance, scalability, and the cost associated with expensive materials. Here, in this study, a scalable MEC (500 mL) with novel compact electrode assemblies and high electrode surface area to volume ratio (160 m 2 /m 3 ) was designed and constructed. The use of membranes, precious metal catalyst, and current collectors with high costs was avoided. A high current density at the steady state of 49.5 ± 5.3 A/m 2 was achieved using acetate as the substrate with phosphate buffer under the applied voltage of 1.01 V. The corresponding volumetric current density was 3948 ± 422 A/m 3 . The compact electrode assembly design limited methane production rate to 3.9 ± 0.2 L/L/D, while achieving a hydrogen production rate of 33.7 ± 1.7 L/L/D. With the suppression of microbial hydrogen consumption, the hydrogen production rate was 39.8 ± 1.9 L/L/D, higher by almost one order of magnitude than those of MECs with scaling up attempts. The compact electrode configuration reduced internal resistance to 88.5 ± 4.4 Ω cm 2 . The energy efficiency based on input electricity was 146 ± 7 % to 189 ± 9 % within the applied voltage range of 0.71 to 1.05 V. The results in this study demonstrated successful scaling up of high performance small MECs and offered a new possible approach of scaling up MECs by stacking high-performance subunits, with no trade-offs on electrochemical performance.

08 HYDROGEN↗
DOE OSTI2026

A scalable framework for efficient coupling of thermal and microstructural simulations in additive manufacturing

Predicting microstructure evolution in metal additive manufacturing (AM) is important for process optimization, but spatiotemporal scale disparities between thermal transport and microstructure evolution create significant challenges for efficient data transfer between simulation codes. To address this, we present Stork, a scalable framework for coupling thermal and microstructural simulations. Stork uses a sparse data representation to identify and store active solidification sub-volumes, enabling highly parallel quad-linear interpolation from coarse thermal grids to fine microstructure grids without large intermediate storage. We demonstrate the framework by coupling the semi-analytic heat transfer code 3DThesis with the time-parallel cellular automata code Toucan. This approach achieves over two orders of magnitude reduction in data generation time and file size compared to prior workflows. Numerical studies show that quad-linear interpolation preserves grain morphology and crystallographic texture in laser powder bed fusion (LPBF) simulations for coarsening ratios up to 16. Overall, Stork provides a scalable pathway for high-throughput, component-scale AM simulations on modern high-performance computing systems.

36 MATERIALS SCIENCE↗
DOE OSTI2013

On Eliminating Synchronous Communication in Molecular Simulations to Improve Scalability

Molecular dynamics simulation, as a complementary tool to experimentation, has become an important methodology for the understanding and design of molecular systems as it provides access to properties that are difficult, impossible or prohibitively expensive to obtain experimentally. Many of the available software packages have been parallelized to take advantage of modern massively concurrent processing resources. The challenge in achieving parallel efficiency is commonly attributed to the fact that molecular dynamics algorithms are communication intensive. This paper illustrates how an appropriately chosen data distribution and asynchronous one-sided communication approach can be used to effectively deal with the data movement within the Global Arrays/ARMCI programming model framework. A new put_notify capability is presented here, allowing the implementation of the molecular dynamics algorithm without any explicit global or local synchronization or global data reduction operations. In addition, this push-data model is shown to very effectively allow hiding data communication behind computation. Rather than data movement or explicit global reductions, the implicit synchronization of the algorithm becomes the primary challenge for scalability. Without any explicit synchronous operations, the scalability of molecular simulations is shown to depend only on the ability to evenly balance computational load.

Straatsma, TP↗
DOE OSTI2017

A derivation and scalable implementation of the synchronous parallel kinetic Monte Carlo method for simulating long-time dynamics

Kinetic Monte Carlo (KMC) simulations are used to study long-time dynamics of a wide variety of systems. Unfortunately, the conventional KMC algorithm is not scalable to larger systems, since its time scale is inversely proportional to the simulated system size. A promising approach to resolving this issue is the synchronous parallel KMC (SPKMC) algorithm, which makes the time scale size-independent. This paper introduces a formal derivation of the SPKMC algorithm based on local transition-state and time-dependent Hartree approximations, as well as its scalable parallel implementation based on a dual linked-list cell method. The resulting algorithm has achieved a weak-scaling parallel efficiency of 0.935 on 1024 Intel Xeon processors for simulating biological electron transfer dynamics in a 4.2 billion-heme system, as well as decent strong-scaling parallel efficiency. The parallel code has been used to simulate a lattice of cytochrome complexes on a bacterial-membrane nanowire, and it is broadly applicable to other problems such as computational synthesis of new materials.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Ultrahigh-order Maxwell solver with extreme scalability for electromagnetic PIC simulations of plasmas

The advent of massively parallel supercomputers, with their distributed-memory technology using many processing units, has favored the development of highly-scalable local low-order solvers at the expense of harder-to-scale global very high-order spectral methods. Indeed, FFT-based methods, which were very popular on shared memory computers, have been largely replaced by finite-difference (FD) methods for the solution of many problems, including plasmas simulations with electromagnetic Particle-In-Cell methods. For some problems, such as the modeling of so-called “plasma mirrors” for the generation of high-energy particles and ultra-short radiations, we have shown that the inaccuracies of standard FD-based PIC methods prevent the modeling on present supercomputers at sufficient accuracy. Here, we demonstrate here that a new method, based on the use of local FFTs, enables ultrahigh-order accuracy with unprecedented scalability, and thus for the first time the accurate modeling of plasma mirrors in 3D.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Materials Learning Algorithms (MALA): Scalable machine learning for electronic structure calculations in large-scale atomistic simulations

We present the Materials Learning Algorithms (MALA) package, a scalable machine learning framework designed to accelerate density functional theory (DFT) calculations suitable for large-scale atomistic simulations. Using local descriptors of the atomic environment, MALA models efficiently predict key electronic observables, including local density of states, electronic density, density of states, and total energy. The package integrates data sampling, model training and scalable inference into a unified library, while ensuring compatibility with standard DFT and molecular dynamics codes. We demonstrate MALA's capabilities with examples including boron clusters, aluminum across its solid-liquid phase boundary, and predicting the electronic structure of a stacking fault in a large beryllium slab. Scaling analyses reveal MALA's computational efficiency and identify bottlenecks for future optimization. With its ability to model electronic structures at scales far beyond standard DFT, MALA is well suited for modeling complex material systems, making it a versatile tool for advanced materials research.

Density functional theory↗
DOE OSTI2015

Scalable tuning of building models to hourly data

Energy models of existing buildings are unreliable unless calibrated so they correlate well with actual energy usage. Manual tuning requires a skilled professional, is prohibitively expensive for small projects, imperfect, non-repeatable, non-transferable, and not scalable to the dozens of sensor channels that smart meters, smart appliances, and cheap/ubiquitous sensors are beginning to make available today. A scalable, automated methodology is needed to quickly and intelligently calibrate building energy models to all available data, increase the usefulness of those models, and facilitate speed-and-scale penetration of simulation-based capabilities into the marketplace for actualized energy savings. The "Autotune'' project is a novel, model-agnostic methodology which leverages supercomputing, large simulation ensembles, and big data mining with multiple machine learning algorithms to allow automatic calibration of simulations that match measured experimental data in a way that is deployable on commodity hardware. This paper shares several methodologies employed to reduce the combinatorial complexity to a computationally tractable search problem for hundreds of input parameters. Furthermore, accuracy metrics are provided which quantify model error to measured data for either monthly or hourly electrical usage from a highly-instrumented, emulated-occupancy research home.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

A scalable 3D lithium metal anode

A scalable 3D electrode to serve as a lithium metal host is developed to enable stable lithium metal cycling. The electrode is simply fabricated by coating a slurry of well mixed LiNO 3 , carbon black, and PVDF on the Cu foil. The pores in the electrode serve as lithium metal hosts while the LiNO 3 particles act both as a structural skeleton and an electrolyte additive. Micron-level dense Li chunks are deposited into the 3D electrode at a current density of 2 mA cm -2 . Moreover, high coulombic efficiencies of 98.4% and 97.1% are achieved in carbonate-based electrolytes at current densities of 0.25 mA cm -2 and 2 mA cm -2 , respectively. The 3D electrode is tested in an anode free cell with LiFePO 4 , which exhibits a good capacity retention of 49.1% after 100 cycles and correspond to an average efficiency of 99.3% per cycle. This work provides a facile and scalable method to fabricate multi-functional 3D electrodes for dendrite-free and high coulombic efficiency Li metal anodes.

25 ENERGY STORAGE↗