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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Efficient Organic Solar Cell with 16.88% Efficiency Enabled by Refined Acceptor Crystallization and Morphology with Improved Charge Transfer and Transport Properties

Abstract Single‐layered organic solar cells (OSCs) using nonfullerene acceptors have reached 16% efficiency. Such a breakthrough has inspired new sparks for the development of the next generation of OSC materials. In addition to the optimization of electronic structure, it is important to investigate the essential solid‐state structure that guides the high efficiency of bulk heterojunction blends, which provides insight in understanding how to pair an efficient donor–acceptor mixture and refine film morphology. In this study, a thorough analysis is executed to reveal morphology details, and the results demonstrate that Y6 can form a unique 2D packing with a polymer‐like conjugated backbone oriented normal to the substrate, controlled by the processing solvent and thermal annealing conditions. Such morphology provides improved carrier transport and ultrafast hole and electron transfer, leading to improved device performance, and the best optimized device shows a power conversion efficiency of 16.88% (16.4% certified). This work reveals the importance of film morphology and the mechanism by which it affects device performance. A full set of analytical methods and processing conditions are executed to achieve high efficiency solar cells from materials design to device optimization, which will be useful in future OSC technology development.

Zhu, Lei↗
DOE OSTI2025

Mechanistic Study of Functional Electrolyte Solvents for High-Voltage Lithium Batteries

The pervasive use of Ni-rich cathode active materials, e.g., LiNi 0.8 Mn 0.1 Co 0.1 O 2 (NMC811), for high-energy-density Li-ion batteries (LIBs) has been hindered by rapid battery capacity decay when cycled with high charge cutoff voltages due to electrolyte decomposition in the conventional carbonate solvent-based electrolytes, oxidative parasitic side reactions at the electrolyte/cathode interface, and irreversible phase changes in the cathode active materials leading to dissolution of transition metals into the electrolytes. Various functional electrolyte solvents have been studied to tackle the above technical challenges, yet the roles of individual solvents in the performance of LIBs remain poorly understood. Here, in this study, we systematically investigate electrochemical performance mechanisms of fluorinated and organosilicon single solvents and cosolvents, for the first time, in high-voltage Li/NMC811 batteries, using electrochemical and analytical characterizations and density functional theory modeling. We observe that some unique combinations of the functional solvents can lead to exceptionally stable high-voltage cycle performance in the Ni-rich cathode-based LIBs. Our mechanistic study reveals that the synergistic effect of solvents plays a vital role in enabling electrochemical stability at both the Ni-rich cathode and the Li metal anode. Understanding the electrochemical performance mechanisms of functional solvents can greatly help in designing and formulating advanced electrolytes that enable the development of high-voltage, high-energy-density, long-cycle-life lithium batteries.

density functional theory modeling↗
DOE OSTI2021

A High Performance Sparse Tensor Algebra Compiler in MLIR

Sparse tensor algebra is widely used in many applications, including scientific computing, machine learning, and data analytics. The performance of sparse tensor algebra kernels strongly depends on the intrinsic characteristics of the input tensors, hence many storage formats are designed for tensors to achieve optimal performance for particular applications/architectures, which makes it challenging to implement and optimize every tensor operation of interest on a given architecture. We propose a tensor algebra domain-specific language (DSL) and compiler framework to automatically generate kernels for mixed sparse-dense tensor algebra operations. The proposed DSL provides high-level programming abstractions that resemble the familiar Einstein notation to represent tensor algebra operations. The compiler introduces a new Sparse Tensor Algebra dialect built on top of LLVM's extensible MLIR compiler infrastructure for efficient code generation while covering a wide range of tensor storage formats. Our compiler also leverages input-dependent code optimization to enhance data locality for better performance. Our results show that the performance of automatically generated kernels outperforms the state-of-the-art sparse tensor algebra compiler, with up to 20.92x, 6.39x, and 13.9x performance improvement over state-of-the-art tensor algebra compilers, for parallel SpMV, SpMM, and TTM, respectively.

Tian, Ruiqin↗
DOE OSTI2022

Residual elastic strain evolution due to thermal cycling of a ceramic-metal composite (WC-Cu) via high energy X-ray diffraction and analytical modeling

Residual stress, when superimposed with in-service loading, can significantly reduce the lifetime and performance of a component. Ceramic-metal composites are susceptible to residual stresses due to the thermal expansion mismatch of the ceramic and metallic phases. The WC-Cu composite explored in the present study provides a promising combination of thermal conductivity and strength properties, while exhibiting counterintuitive improvements in strength and ductility after thermal cycling. Further, this work quantifies the evolution of the residual elastic strains as a result of processing and cyclic thermal loading in a co-continuous WC-Cu composite through experimental high energy X-ray diffraction and kinetics-based modeling. Both analyses indicate that processing-induced residual tensile stress in the copper phase is relieved upon subsequent thermal cycling, with kinetics modeling revealing the cyclic-dependent nature of the active power-law creep mechanisms. The results indicate that, through stress relaxation, this material system maintains structural stability during thermal cycling. The illustrated kinetics of relaxation can inform general material processors and designers of ceramic-metal composites to minimize detrimental residual stress and improve performance of these material systems.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Sandia Electromagnetic Environment Simulator Facilities

Sandia National Laboratories’ (Sandia’s) Electrical Sciences Group (1350) provides a spectrum of solutions for electromagnetic (EM) environment effects on electrical systems to advance physical understanding including experiments, analytical and numerical model development leveraging first-principles physics, and high-performance computational modeling and simulation (M&S). The six departments in the Electrical Sciences Group provide analysis, design guidance, and experiments in support of nuclear weapons qualification in normal, abnormal, and hostile environments, as well as research and development for advanced electrical systems that can operate through these environments. This document is intended to provide a quick summary of Sandia’s EM experimental facilities, most of which provide unique national capabilities.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Combined Creep and Fatigue Modeling

Simultaneous consideration of creep and fatigue is of paramount importance to accurately predict component life in many industrial applications. For example, growing penetration of renewable energy onto the grid is forcing many power plants to transition from base-load power generation to more complex operation modes, characterized by a combination of base-load generation and frequent start-up and shut-down cycles. The frequent start-stop of power plants is causing a significant toll in the component life due to fatigue. As such, creep alone is not adequate anymore to predict the component life. Most existing life prediction models consider creep or fatigue independently while neglect their interactions. In this presentation, we talk about some recent efforts at NETL to develop a unified modeling framework where both creep and fatigue can be considered. The creep-fatigue interaction mechanism is studied. High-throughput simulations are performed under various hold stress and hold time, and analytical expressions are fitted for the crack growth rates under creep and/or fatigue conditions demonstrating some applications of this newly developed modeling capability.

advanced alloy development↗
DOE OSTI2024

SOMA: Observability, monitoring, and in situ analytics for exascale applications

With the rise of exascale systems and large, data-centric workflows, the need to observe and analyze high performance computing (HPC) applications during their execution is becoming increasingly important. HPC applications are typically not designed with online monitoring in mind, therefore, the observability challenge lies in being able to access and analyze interesting events with low overhead while seamlessly integrating such capabilities into existing and new applications. We explore how our service-based observation, monitoring, and analytics (SOMA) approach to collecting and aggregating both application-specific diagnostic data and performance data addresses these needs. Furthermore, we present our SOMA framework and demonstrate its viability with LULESH, a hydrodynamics proxy application. Then we focus on Astaroth, a multi-GPU library for stencil computations, highlighting the integration of the TAU and APEX performance tools and SOMA for application and performance data monitoring.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Estimating Lossy Compressibility of Scientific Data Using Deep Neural Networks

Simulation based scientific applications generate increasingly large amounts of data on high-performance computing (HPC) systems. To allow data to be stored and analyzed efficiently, data compression is often utilized to reduce the volume and velocity of data. However, a question often raised by domain scientists is the level of compression that can be expected so that they can make more informed decisions, balancing between accuracy and performance. In this letter, we propose a deep neural network based approach for estimating the compressibility of scientific data. To train the neural network, we build both general features as well as compressor-specific features so that the characteristics of both data and lossy compressors are captured in training. Our approach is demonstrated to outperform a prior analytical model as well as a sampling based approach in the case of a biased estimation, i.e., for SZ. However, for the unbiased estimation (i.e., ZFP), the sampling based approach yields the best accuracy, despite the high overhead involved in sampling the target dataset.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Tailored Fiber Placement for Complex Preforms

Tailored Fiber Placement (TFP) offers a novel approach to optimize fiber architecture for the fabrication of complex, structural parts not traditionally suitable for advanced composites. This technology not only offers new routes for weight reduction via metal substitution, it also offers cost reduction through minimization of material scrap and reduced labor. This reduction in component weight leads to increased fuel efficiency, and reduced production energy consumption, thereby, helping to achieve the stated IACMI technical goals. This technology leverages centuries of manufacturing development in support of the textile and embroidery industry. One major drawback to this technology is the lack of commercial or non- proprietary structural performance data and robust analytical tools used to optimize fiber architecture and predict performance. This project was structured to utilize common sub-element features to validate analytical performance tools, generate performance data, and gather cost and performance data on components of interest. This project was designed to give industry sponsors the confidence and ability to take full advantage of TFP to fabricate primary, highly loaded structure and integrate features such as metallic fasteners. The project focused principally on the use of high strength carbon fiber, such as T700, and the use of aerospace epoxy resin matrix to primarily support development of new composite applications in vehicle, aerospace, and industrial markets. This project applied previously developed analytical tools to predict the performance of TFP produced parts. This work focused on developing the pipeline to characterize material in order to accurately predict component performance when modifying the TFP print paths and stitch density. This focused on experimental characterization via standardized ASTM testing, alongside experimental testing of more representative service components by testing curved beam strength, beam shear performance, a large scale TFP lug, and ultimately designing a fully TFP clip bracket that reduced weight and cost compared to a traditional metallic component. The new knowledge gained from this program included: 1) development and demonstration of novel analytical tools applied to analysis of TFP preforms; 2) development and demonstration of a building block approach using coupons and sub-elements to optimize the design of a more complex component; 3) demonstration that optimized fiber orientation using TFP can exceed performance of conventional textile composite materials and can open new applications currently limited to metallic components; 4) Demonstration of performance and cost benefits of the TFP process as compared to metallic and conventional textile composites. Recommendations for follow-on work include development of design allowables to assess the impact of high temperature/moisture exposure or saturation during loading, tracking the impact of stitching needle wear on the performance of parts and ability to stitch thicker preforms, using TFP preforms as local reinforcement at areas of bearing or complex loading, and topology optimization of components by tow steering. The expertise developed during the course of this project can be leveraged to provide commercial engineering design and fabrication services using TFP. UDRI is in the process of formalizing their partnership with Spintech, who will serve as the commercialization partner for this technology and provide molding services and deliver finished components to the end user. UDRI will continue to produce the preforms until the economics allow Spintech to procure its own TFP equipment or lease UDRI equipment, at which point UDRI will step away from manufacture and serve as the engineering and design lead on product development.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Ionic/electronic conductivity regulation of n-type polyoxadiazole lithium sulfonate conductive polymer binders for high-performance silicon microparticle anodes

Low-cost silicon microparticles (SiMP), as a substitute for nanostructured silicon, easily suffer from cracks and fractured during the electrochemical cycle. A novel n-type conductive polymer binder with excellent electronic and ionic conductivities as well as good adhesion, has been successfully designed and applied for high-performance SiMP anodes in lithium-ion batteries to address this problem. Its unique features are attributed to the strong electron-withdrawing oxadiazole ring structure with sulfonate polar groups. The combination of rigid and flexible components in the polymer ensures its good mechanical strength and ductility, which is beneficial to suppress the expansion and contraction of SiMP s during the charge/discharge process. By fine-tuning the monomer ratio, the conjugation and sulfonation degrees of the polymer can be precisely controlled to regulate its ionic and electronic conductivities, which has been systematically analyzed with the help of an electrochemical test method, filling in the gap on the conductivity measurement of the polymer in the doping state. The experimental results indicate that the cell with the developed n-type polymer binder and SiMP (~0.5 μm) anodes achieves much better cycling performance than traditional non-conductive binders. It has been considered that the initial capacity of the SiMP anode is controlled by the synergetic effect of ionic and electronic conductivity of the binder, and the capacity retention mainly depends on its electronic conductivity when the ionic conductivity is sufficient. Here, it is worth noting that the fundamental research of this work is also applicable to other battery systems using conductive polymers in order to achieve high energy density, broadening their practical applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Co-design of Advanced Architectures for Graph Analytics using Machine Learning

A graph is an excellent way of representing relationships among entities. We can use graph analytics to synthesize and analyze such relational data, and extract relevant features that are useful for various tasks such as machine learning. Considering the crucial role of graph analytics in various domains, it is important and timely to investigate the right hardware configurations that can achieve optimal performance for graph workloads on future high-performance computing systems. Design space exploration studies facilitate the selection of appropriate configurations (e.g. memory) to achieve a desired system performance. Recently, the approach of accelerating graph analytics using persistent non-volatile memory has gained a lot of attention. Traditional system simulators such as Gem5 and NVMain can be used to explore the design space of these advanced memory architectures for graph workloads. However, these simulators are slow in execution thus limiting the efficiency of design space exploration studies. To overcome this challenge, we proposed a machine learning based approach to co-design advanced memory architectures for graph workloads. We tested our approach with DRAM, non-volatile memory, and hybrid memory (DRAM+NVM) using a breadth first search benchmark algorithm. Our results showed the applicability of the proposed machine learning based approach to the co-design of the advanced memory architectures. In this paper, we provide recommendations on selecting advanced memory architectures to achieve desired performance for graph workloads. We also discuss the performances of different machine learning models that were considered in this study.

Kurte, Kuldeep↗
DOE OSTI2020

Surface-Mediated Interconnections of Nanoparticles in Cellulosic Fibrous Materials toward 3D Sensors

Fibrous materials serve as an intriguing class of 3D materials to meet the growing demands for flexible, foldable, biocompatible, biodegradable, dis- posable, inexpensive, and wearable sensors and the rising desires for higher sensitivity, greater miniaturization, lower cost, and better wearability. The use of such materials for the creation of a fibrous sensor substrate that interfaces with a sensing film in 3D with the transducing electronics is however difficult by conventional photolithographic methods. Here, a highly effective pathway featuring surface-mediated interconnection (SMI) of metal nanoclusters (NCs) and nanoparticles (NPs) in fibrous materials at ambient conditions is demon- strated for fabricating fibrous sensor substrates or platforms. Bimodally distrib- uted gold–copper alloy NCs and NPs are used as a model system to demonstrate the semiconductive-to-metallic conductivity transition, quantized capacitive charging, and anisotropic conductivity characteristics. Upon coupling SMI of NCs/NPs as electrically conductive microelectrodes and surface-mediated assembly (SMA) of the NCs/NPs as chemically sensitive interfaces, the resulting fibrous chemiresistors function as sensitive and selective sensors for gaseous and vaporous analytes. Finally, this new SMI–SMA strategy has significant implications for manufacturing high-performance fibrous platforms to meet the growing demands of the advanced multifunctional sensors and biosensors.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Process for ultra-sensitive quantification of target analytes in complex biological systems

Antibody-free processes are disclosed that provide accurate quantification of a wide variety of low-abundance target analytes in complex samples. The processes can employ high-pressure, high-resolution chromatographic separations for analyte enrichment. Intelligent selection of target fractions may be performed via on-line Selected Reaction Monitoring (SRM) or off-line rapid screening of internal standards. Quantification may be performed on individual or multiplexed fractions. Applications include analyses of, e.g., very low abundance proteins or candidate biomarkers in plasma, cell, or tissue samples without the need for affinity-specific reagents.

Shi, Tujin↗
DOE OSTI2024

Improving and Automating Building Model Data Exchange

There are many instances throughout a project’s lifecycle where there arises a need for quick and accurate risk assessment of building designs. For example, an unexpected design change during construction may necessitate structural engineers to perform a seismic risk assessment on analytical models of the updated building design using high fidelity structural analysis software, such as ANSYS or Abaqus. However, the efficiency of such workflows often depends upon the interoperability of architectural design software and structural analysis software. When the quality of this interoperability is lacking or even non-existent, the efficiency of virtual engineering workflows is hampered, which increases project costs. A McGraw Hill industry survey of professional users of Building Information Modeling (BIM) technologies found that there is high demand for BIM interoperability for structural analysis, but that the value/difficulty ratio is currently too low for practical use. There have been efforts by the academic community to facilitate model data exchange between the architectural design and structural analysis domains, but such solutions have not been widely adopted by industry, face technical challenges, and oftentimes are limited in applicability for users of various BIM software. Therefore, INL is developing capabilities to improve, automate, and generalize model data exchange between architectural BIM software (e.g., Revit) and structural analysis software (e.g., SAP2000, ANSYS). The goal is to help expedite and automate as much of the pre-processing step for creating analytical models in finite element analysis software as reasonably as possible. Such a "BIM-to-FEA" conversion tool should provide direct benefit to end-users through accuracy, automation, quick turn-around, and wide applicability. To generalize the application of this BIM-to-FEA conversion tool and increase its useability among the many different commercial BIM software currently used by industry, the program is being developed with the concept of openBIM. OpenBIM is the application of non-proprietary, open data standards that allow for BIM model data exchange in a format that is accessible, retainable, and useable for all users. The most widely used open, non-proprietary data exchange format for BIM is the Industry Foundation Classes (IFC) schema. IFC is developed by buildingSMART international and is ISO certified (ISO 16739-1:2018). The BIM-to-FEA conversion tool is being developed for compatibility with typical commercial building designs of steel framed structures. The tool is currently capable of importing architectural BIM data of framed building structures, recognizing and extracting the aspects of the model that are required for structural analysis, adjusting the connectivity of frame members, and finally exporting to an analytical model stored in the IFC format. The exported IFC analytical model can then be imported into various openBIM compliant software, such as SAP2000. Such capabilities have already been tested on commercial software, as shown above, and continue to be improved. Work is underway to test the conversion on various commercial BIM software, develop a user-friendly interface, incorporate the program into the broader DeepLynx data warehouse project being developed by INL, and to eventually open-source the tool for the benefit of the community. Future development of the tool envisions the ability for efficient iterative risk assessment of generative building designs, all within a workflow utilizing open-source tools. One such open-source tool will be MOOSE, an advanced finite element analysis tool developed at INL. The conversion tool will also branch out from typical commercial building designs and will aim to incorporate nuclear construction. The aim will be to convert both structural and non-structural components of nuclear facilities, such as curved concrete containment structures and piping systems, respectively.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

On the measurement of hardness at high strain rates by nanoindentation impact testing

Recent advances in electronics have enabled nanomechanical measurements with very low noise, fast time constants and high data acquisition rates. Furthermore, these capabilities open the door for a wide range of ultra-fast nanomechanical testing. Given the inherent dynamic nature of high-speed testing, a thorough understanding of the testing system's dynamics and electronics is extremely important for accurate measurements. In this work, an analytical framework that includes the mechanical and electronic contributions of the instrument and the material constitutive response is presented to provide guidelines for performing high strain rate measurements of hardness by nanoindentation testing. Simple closed-form solutions that provide insights on the choice of test methodology, test parameters and instrument design are presented along with the strain rate range over which accurate measurements can be performed with the commercially available nanoindenters.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Artificial Intelligence for Data Center Operations (AIOps): Cooperative Research and Development (Final Report)

High performance computing data centers will increasingly need to rely on automation to keep pace with exascale growth in compute capability and to manage and optimize the data center environment and facility resources. Artificial intelligence and machine learning approaches provide the means to improve HPC data center operational efficiency, by learning historical trends and training models to operate on real-time data collected from both IT and facilities sources. NREL has developed methods of real-time collection, aggregation and streaming of these data in the ESIF HPC Data Center and has collected a significant dataset of relevant metrics across computer systems, racks, environmental, building and utility sources for research into various predictive analytics problems. HPE's Advanced Technology Group (ATG) is doing comprehensive research into exascale monitoring and management for High Performance Computing (HPC) systems (hereinafter HPE's Data Monitoring/ Management Technology). NREL and HPE will collaborate to add Artificial Intelligence (AI) to NREL's real-time data collection/ aggregation/ streaming system and HPE's Data Monitoring/ Management System, with the goal of improving the operational efficiency of NREL's Energy Systems Integration Facility (ESIF) HPC Data Center through data analytics on both historical and real-time data from IT systems and facilities operations. This collaboration will consist of efforts in Data Management, Data Analytics, and AI/ML Optimization for both manual and autonomous intervention in data center operations. This will be a multi-year, multi-staged effort with a goal towards building capabilities for an Advanced Smart Facility, and demonstration of these techniques in the NREL ESIF HPC Data Center.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Special Topic on High Performance Computing in Chemical Physics

Computational modeling and simulation have become indispensable scientific tools in virtually all areas of chemical, biomolecular, and materials systems research. Computation can provide unique and detailed atomic level information that is difficult or impossible to obtain through analytical theories and experimental investigations. In addition, recent advances in micro-electronics have resulted in computer architectures with unprecedented computational capabilities, from the largest supercomputers to common desktop computers. In conclusion, combined with the development of new computational domain science methodologies and novel programming models and techniques, this has resulted in modeling and simulation resources capable of providing results at or better than experimental chemical accuracy and for systems in increasingly realistic chemical environments.

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

Biopolymer‐assisted Synthesis of P‐doped TiO 2 Nanoparticles for High‐performance Lithium‐ion Batteries: A Comprehensive Study

Abstract TiO 2 material has gained significant attention for large‐scale energy storage due to its abundant, low‐cost, and environmentally friendly properties, as well as the availability of various nanostructures. Phosphorus doping has been established as an effective technique for improving electronic conductivity and managing the slow ionic diffusion kinetics of TiO 2 . In this study, non‐doped and phosphorus doped TiO 2 materials were synthesized using sodium alginate biopolymer as chelating agent. The prepared materials were evaluated as anode materials for lithium‐ion batteries (LIBs). The electrodes exhibit remarkable electrochemical performance, including a high reversible capacity of 235 mAh g −1 at 0.1 C and excellent first coulombic efficiency of 99 %. An integrated approach, combining operando XRD and ex‐situ XAS, comprehensively investigates the relationship between phosphorus doping, material structure, and electrochemical performance, reinforced by analytical tools and first principles calculations. Furthermore, a full cell was designed using 2 %P‐doped TiO 2 anode and LiFePO 4 cathode. The output voltage was about 1.6 V with high initial specific capacity of 148 mAh g −1 , high rate‐capability of 120 mAh g −1 at 1 C, and high‐capacity retention of 96 % after 1000 cycles at 1 C.

El Halya, Nabil↗