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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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PARC and PNNL will develop advanced carbon fiber-reinforced polymer (CFRP) materials and manufacturing process that will equip automotive manufacturers with a scalable innovation that reduces costs, improves performance, and accelerates the deployment of CFRP materials across vehicle classes.
The report describes the development of scalable manufacturing methods for high-performance, stable perovskite solar modules using flexographic printing. The project developed self-leveling perovskite inks that exploit Marangoni flows to reduce coating defects and improve large-area film uniformity. Bayesian optimization was integrated with high-throughput photoluminescence mapping and photovoltaic measurements to efficiently optimize ink formulations and printing conditions. The resulting printed perovskite solar cells achieved champion power conversion efficiencies above 21.6%, with median efficiencies exceeding 20% across large device batches. At the module scale, printed devices achieved active-area efficiencies up to approximately 17.3% on 25 cm² substrates. The project also demonstrated improved performance and stability using additively patterned interconnections compared with laser-scribed controls. Overall, the work establishes a data-driven, roll-compatible pathway toward high-throughput, low-capital-cost manufacturing of uniform and stable perovskite photovoltaics.
Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants that resist conventional water treatment due to the strength of their carbon fluorine bonds. Electron-beam irradiation has shown potential for PFAS degradation; however, its effectiveness is limited by shallow energy penetration, restricting treatment to the upper region of the water surface. This project addresses this limitation through the design of a compact mixing chamber and recirculation system to enhance surface renewal and improve treatment efficiency. The system, constrained to approximately 10 cm 20 cm 20 cm, was developed using computer-aided design, computational fluid dynamics (CFD), and MATLAB-based hydraulic analysis to evaluate flow behavior, head loss, and pump compatibility. A perforated-baffle geometry and controlled flow range of 1 3 GPM were implemented to promote uniform mixing while maintaining a stable free surface for electron-beam exposure. Modeling results indicate improved flow distribution and reduced dead zones, supporting more consistent interaction between contaminated water and the beam. The final design enhances hydrodynamic performance within system constraints, contributing to scalable electron-beam water treatment technologies for improved PFAS degradation.
This paper presents a novel approach to power sharing between direct connected grid-forming converters, utilizing virtual cable impedance and droop-based outer loop control. To enhance stability, resistive droop is implemented, while virtual cable impedance with non-zero resistive and inductive components ensures improved power sharing. The inner loop controller employs a Lyapunov energy function to achieve superior dynamic performance. The proposed control architecture is validated through comprehensive modeling and real-time processor-in-the-loop simulations, demonstrating its robustness and efficiency under various operating conditions. The results highlight the potential of this control strategy to improve the reliability and efficiency of renewable energy systems. Additionally, a comparative analysis with traditional methods underscores the advantages of the proposed approach in terms of stability and performance. The proposed control architecture offers a scalable and flexible solution for grid-forming converters, enabling seamless integration of renewable energy sources. Its robustness and adaptability make it an attractive solution for real-world applications. Furthermore, the approach can be extended to other power electronic systems, enhancing overall system performance and efficiency. By providing a reliable and efficient control strategy, this paper contributes to the advancement of renewable energy systems and their adoption in the energy sector. The proposed control strategy has far-reaching implications for the widespread adoption of renewable energy sources, enabling a more sustainable and efficient energy future. The overall system is modeled in MATLAB/Simulink and PLECS software domain.
We present AMR-Wind, a verified and validated high-fidelity computational-fluid-dynamics code for wind farm flows. AMR-Wind is a block-structured, adaptive-mesh, incompressible-flow solver that enables predictive simulations of the atmospheric boundary layer and wind plants. It is a highly scalable code designed for parallel high-performance computing with a specific focus on performance portability for current and future computing architectures, including graphical processing units (GPUs). In this paper, we detail the governing equations, the numerical methods, and the turbine models. Establishing a foundation for the correctness of the code, we present the results of formal verification and validation. The verification studies, which include a novel actuator line test case, indicate that AMR-Wind is spatially and temporally second-order accurate. The validation studies demonstrate that the key physics capabilities implemented in the code, including actuator disk models, actuator line models, turbulence models, and large eddy simulation (LES) models for atmospheric boundary layers, perform well in comparison to reference data from established computational tools and theory. We conclude with a demonstration simulation of a 12-turbine wind farm operating in a turbulent atmospheric boundary layer, detailing computational performance and realistic wake interactions.
Conducting long-term simulations of small molecules using Molecular Dynamics (MD) is crucial in drug design. However, traditional methods to accelerate the process including ASICs or GPUs, have limitations. ASICs are difficult to access, while publicly accessible GPU packages for MD have issues with more GPUs when processing small molecules, causing decreased performance. Conversely, FPGAs are renowned for their scalability and have been commonly used as accelerators for various applications, making them suitable for the task. Moreover, with the availability of FPGAs in the cloud, pharmaceutical developers can now access FPGA devices. The only missing piece is a design deployed for MD acceleration. Therefore, we present FASDA, the first FPGA-based MD accelerator available for community development. FASDA is designed to evaluate range-limited MD, which is the most resource-intensive and computationally demanding component in MD. It outperforms the state of-the-art GPU solution by 4.67x, significantly reducing the time for MD in drug design.
Algebraic multigrid (AMG) is a widely used scalable solver and preconditioner for large-scale linear systems resulting from the discretization of a wide class of elliptic PDEs. While AMG has optimal computational complexity, the cost of communication has become a significant bottleneck that limits its scalability as processor counts continue to grow on modern machines. This article examines the design, implementation, and parallel performance of a novel algorithm, algebraic multigrid domain decomposition (AMG-DD), designed specifically to limit communication. The goal of AMG-DD is to provide a low-communication alternative to standard AMG V-cycles by trading some additional computational overhead for a significant reduction in communication cost. Numerical results show that AMG-DD achieves superior accuracy per communication cost compared with AMG, and speedup over AMG is demonstrated on a large GPU cluster.
As GPU-accelerated high-performance computing (HPC) systems approach exascale performance, controlling energy consumption without compromising throughput is essential. Architectures such as the AMD MI250X-based Frontier supercomputer provide runtime mechanisms like frequency and power capping, enabling energy tuning without modifying application code. Although both target energy reduction, they operate via distinct hardware control paths and influence workloads differently. We present a comprehensive evaluation of these strategies on a leadership-class system using diverse HPC proxy applications representative of production workloads. Our study analyzes performance–energy trade-offs across multiple capping levels, node counts (1 and 32), and application profiles. Results show that frequency capping generally achieves higher energy efficiency and scalability, with gains of up to 13.2% without performance loss, while power capping is more effective for single-node runs or bursty GPU utilization. We also provide practical guidelines to help system administrators and users balance energy efficiency and performance in large-scale scientific workloads.
The main contribution of this work is to increase the coding productivity of GPU programming by using the concept of Static Graphs. GPU capabilities have been increasing significantly in terms of performance and memory capacity. However, there are still some problems in terms of scalability and limitations to the amount of work that a GPU can perform at a time. To minimize the overhead associated with the launch of GPU kernels, as well as to maximize the use of GPU capacity, we have combined the new CUDA Graph API with the CUDA programming model (including CUDA math libraries) and the OpenACC programming model. We use as test cases two different, well-known and widely used problems in HPC and AI: the Conjugate Gradient method and the Particle Swarm Optimization. In the first test case (Conjugate Gradient) we focus on the integration of Static Graphs with CUDA. In this case, we are able to significantly outperform the NVIDIA reference code, reaching an acceleration of up to 11x thanks to a better implementation, which can benefit from the new CUDA Graph capabilities. In the second test case (Particle Swarm Optimization), we complement the OpenACC functionality with the use of CUDA Graph, achieving again accelerations of up to one order of magnitude, with average speedups ranging from 2x to 4x, and performance very close to a reference and optimized CUDA code. Our main target is to achieve a higher coding productivity model for GPU programming by using Static Graphs, which provides, in a very transparent way, a better exploitation of the GPU capacity. The combination of using Static Graphs with two of the current most important GPU programming models (CUDA and OpenACC) is able to reduce considerably the execution time w.r.t. the use of CUDA and OpenACC only, achieving accelerations of up to more than one order of magnitude. Finally, we propose an interface to incorporate the concept of Static Graphs into the OpenACC Specifications.
The layer-structured Li[Ni x Co y Mn 1-x-y ]O 2 (NCM) cathode materials have been the best choice for increasing electric vehicle driving distance per charge. The high Ni layered oxide represents successfully commercialized NCM cathodes (such as NCM622 and NCA) in lithium-ion batteries (LIBs) for EV applications due to their high energy density and acceptable cycling stability. However, the price of cobalt, the key element within LIBs for stability, has nearly tripled over the past few years due to increased demand from the cell phone industry. As mentioned in the DOE Funding Opportunity Announcement, the current materials shortage will also cause speculation for a future global shortage. Therefore, to meet the requirement and sustainability of the next-generation long-range and low-cost EVs, developing cathode materials with low-Co content to achieve higher energy density and lower cost is both essential and urgent. The overarching objective of this work is to develop stabilized NCM cathode materials with low Co content (namely LiNi x Co y Mn 1-x-y O 2 , y ≤ 0.04) to meet DOE’s goal of reducing Co loading below 50 mg Wh -1 while maintaining energy density greater than 600 Wh kg -1 based on cathode material. Via various dopings and coatings scalable methods, we explored and enhanced the cycling performance of low-cobalt cathodes. The final obtained NCM cathodes paired with graphite anode aim to deliver batteries with a high initial specific energy density of over 240 Wh kg -1 and a low capacity fading rate of less than 20% in 1000 cycles under a C/3 discharge rate. To accomplish this goal, a multidisciplinary team with several co-investigators has been formed from three organizations: The Pennsylvania State University (PSU), Oak Ridge National Laboratory (ORNL), and Pacific Northwest National Laboratory (PNNL). The PI and co-investigators are Dr. Donghai Wang (PI) from PSU with expertise in the synthesis of nanostructured materials and manipulation of interfacial properties of electrochemically active materials, Dr. Jagjit Nanda, with substantial knowledge of and expertise in state-of-the-art cathodes from ORNL, Dr. Chao-Yang Wang with significant experience in advanced cell design and fabrication and cell diagnostics from PSU, and Dr. Chongmin Wang with world-wide known expertise of advance atomic scale characterization of electrode materials from PNNL. Furthermore, this project will leverage and synergistically work with the current DOE-funded programs on battery materials at PSU and ORNL and electrode materials characterization at PNNL. During this funded period, we have accomplished milestones stated as follows: • Scale up production of LiFePO 4 (LFP) coated NCM811 with a production of 300g/batch. Fifteen (≥2 Ah) pouch cells with LFP-coated NCM811 cathode are delivered. Self-evaluated pouch cells in PSU show superior over 80% capacity retention performance even after 1500 cycles at C/3 rate. • Various cations (Al, Ti, Zr, and Mo) substitute cobalt in low-cobalt partially and Co-free cathodes. Their effects on crystal structure and electrochemical behavior are explored. • Phosphate compounds as coating materials represent promising surface protection precursors for low-cobalt cathode materials. Therefore, several metal phosphates were selected for improving the NMC cycling performance and are regarded as effective approaches for a scalable and practical surface protection method. • Production of NCM92, where Nickel content is 92% among transition metals, is scaled up from synthesis to coating and heat treatment procedures. Fifteen 2.7Ah pouch cells with Ti-doped NCM92 cathodes and industrial graphite anode are delivered to Idaho National Lab for testing.
CRADL captures performance metrics of machine learning algorithms operating on mesh data from multiphysics codes This proxy application is a tool to explore scalability of inference on HPC platforms, and also gather performance metrics for inference on new machine learning specific hardware. CRADL is designed to give users as fine a control as possible over an inference simulation. Users may select the number of cycles, amount of data, and batch size to pass to the accelerator of choice. Additionally the user may select a number of performance optimization libraries and flags. CRADL comes packaged with a repository of anonymized multi-physics simulation data, as well as a pretrained model for inference. The code allows a user to load their own pre-trained model and data if they wish. The code can operate in multiple parallelization schemes, with performance enhancing options such as half-precision libraries, PyTorch benchmarking, and pinned memory with non-blocking data transfers.
ScalES is a high performance software package for performing large scale electronic structure calculations on high performance supercomputers.
Obtaining and analyzing high performance computing (HPC) telemetry in real time is a complex task that can impact algo- rithmic performance, operating costs, and ultimately scientific outcomes. If your organization operates multiple HPC systems, filesystems, and clusters, telemetry streams can be synthesized in order to ease operational and analytics burden. In order to collect this telemetry, the Oak Ridge Leadership Computing Facility (OLCF) has deployed STREAM (Streaming Telemetry for Resource Events, Analytics, and Monitoring), which is a distributed and high-performance message bus based on Apache Kafka. STREAM collects center-wide performance information and must interface with many sources, including five HPE deployed supercomputers, each with their own Kafka cluster which is managed by HPCM. OLCF Supercomputers and their attached scratch filesystems currently send more than 300 million messages to over 200 topics producing around 1.3 Terabytes per day of telemetry data to STREAM. This paper describes the architectural principles that enable STREAM to be both resilient and highly performant while supporting multiple upstream Kafka clusters and other data sources. It also discusses the design challenges and decisions faced in adapting our existing system- monitoring infrastructure to support the first Exascale computing platform.
We present hipBone, an open-source performance-portable proxy application for the Nek5000 (and NekRS) computational fluid dynamics applications. HipBone is a fully GPU-accelerated C++ implementation of the original NekBone CPU proxy application with several novel algorithmic and implementation improvements which optimize its performance on modern fine-grain parallel GPU accelerators. Our optimizations include a conversion to store the degrees of freedom of the problem in assembled form in order to reduce the amount of data moved during the main iteration and a portable implementation of the main Poisson operator kernel. We demonstrate near-roofline performance of the operator kernel on three different modern GPU accelerators from two different vendors. We present a novel algorithm for splitting the application of the Poisson operator on GPUs which aggressively hides MPI communication required for both halo exchange and assembly. Our implementation of nearest-neighbor MPI communication then leverages several different routing algorithms and GPU-Direct RDMA capabilities, when available, which improves scalability of the benchmark. We demonstrate the performance of hipBone on three different clusters housed at Oak Ridge National Laboratory, namely, the Summit supercomputer and the Frontier early-access clusters, Spock and Crusher. Our tests demonstrate both portability across different clusters and very good scaling efficiency, especially on large problems.
Edge computing methods allow devices to efficiently train a high-performing, robust, and personalized model for predictive tasks. However, these methods succumb to privacy and scalability concerns such as adversarial data recovery and expensive model communication. Furthermore, edge computing methods unrealistically assume that all devices train an identical model. In practice, edge devices have varying computational and memory constraints which may not allow certain devices to have the space or speed to train a specific model. To overcome these issues, we propose MIDDLE: a model independent distributed learning algorithm which allows heterogeneous edge devices to assist each other’s training while communicating only non-sensitive information. MIDDLE unlocks the ability for edge devices, regardless of computational or memory constraints, to assist each other even with completely different model architectures. Furthermore, MIDDLE does not require model or gradient communication which greatly reduces communication size and time. We prove that MIDDLE attains the optimal convergence rate O(1/sqrt(TM)) of stochastic gradient descent for convex and non-convex smooth optimization (for total iterations T and batch size M). Finally, our experimental results demonstrate that MIDDLE (even in non-IID data settings) attains robust and high-performing models without model or gradient communication.
The aviation industry is increasingly turning to modernize freight facilities by integrating electric Ground Support Equipment (eGSE) to enhance operational efficiency of freight facility moving vehicles and equipment. Airports worldwide are adopting eGSE to streamline cargo movement, reduce fuel and maintenance costs, and improve logistics coordination.1 North America, with its advanced aviation infrastructure, leads this transition, leveraging Internet of things (IoT)-enabled automation and zero emission technologies to boost reliability and reduce human errors.2 Electrification of freight facility moving vehicles and equipment boosts turnaround times, improves equipment reliability, and optimizes logistics coordination, giving operators a competitive advantage. With rising fuel price volatility and the pressure to meet stringent performance benchmarks, airports are focusing on cost-effective, scalable solutions for long-term financial and operational gains. To further accelerate electrification, airports are integrating Zero Emission Vehicles (ZEVs) into rental car fleets and deploying electric baggage carts, requiring strategic investments in charging infrastructure. 3 The shift, however, presents challenges, such as limited technical expertise, high capital costs, and complex procurement processes. By forging strategic partnerships, leveraging advanced technologies, and optimizing infrastructure investments, airports can create a resilient, future-ready ecosystem that enhances the movement of people and goods through electrification-driven efficiency. Supported by the U.S. Department of Energy (DOE) Vehicle Technologies Office (VTO), this electrification effort provides a scalable, cost-effective solution to improve airport freight operations. Through targeted investments and innovation, airports enhance efficiency, reduce costs, and meet performance benchmarks while advancing toward a resilient, electrified future.
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The defect density present at the dielectric-semiconductor interface in an MOS structure directly influences the channel carrier characteristics in semiconductor devices, especially in wide bandgap material systems used in power devices. While these trap defects are typically quantified through electrical characterization of MOS-capacitor test structures, this treatment offers very little insight into the physical nature of interface defects. Such shortcomings demand a physical characterization strategy to guide fabrication optimization. X-ray photoelectron spectroscopy (XPS) is suggested as a viable technique to determine chemical data for dielectric interfaces formed using atomic layer deposition (ALD) on GaN substrates. Previously, 1-D XPS characterization has confirmed the presence of a Ga x O y interlayer between ALD dielectrics and the GaN substrate. In this work, XPS data is serially collected to form 2-D images of an ALD-Al 2 O 3 /GaN interface as a proof-of-concept experiment for in-situ XPS quality monitoring during ALD processing. The information provided by this work reveals some of the challenges for incorporating XPS characterization as an in-situ strategy during fabrication of GaN-based devices. Separately, electrical mapping of a 2-D array of ALD-Al 2 O 3 /GaN MOS-capacitor devices provide a means to quantify the spatial variations in interface quality across a single wafer. Physical characterization techniques, such as time-of-flight secondary ion mass spectroscopy, provide additional chemical information about the Al 2 O 3 /Ga x O y /GaN structure that complement the electrical mapping results. This analysis shows that a higher Ga x O y content correlates with higher interface state defects for trap energies deep in the band gap.