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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Fully implicit Particle-in-cell algorithms for multiscale plasma simulation

The outline of the paper is as follows: Particle-in-cell (PIC) methods for fully ionized collisionless plasmas, explicit vs. implicit PIC, 1D ES implicit PIC (charge and energy conservation, moment-based acceleration), and generalization to Multi-D EM PIC: Vlasov-Darwin model (review and motivation for Darwin model, conservation properties (energy, charge, and canonical momenta), and numerical benchmarks). The author demonstrates a fully implicit, fully nonlinear, multidimensional PIC formulation that features exact local charge conservation (via a novel particle mover strategy), exact global energy conservation (no particle self-heating or self-cooling), adaptive particle orbit integrator to control errors in momentum conservation, and canonical momenta (EM-PIC only, reduced dimensionality). The approach is free of numerical instabilities: ω pe Δt >> 1, and Δx >> λ D . It requires many fewer dofs (vs. explicit PIC) for comparable accuracy in challenging problems. Significant CPU gains (vs explicit PIC) have been demonstrated. The method has much potential for efficiency gains vs. explicit in long-time-scale applications. Moment-based acceleration is effective in minimizing N FE , leading to an optimal algorithm.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Advanced Turbulence Models for Large-Scale Atmospheric Boundary Layer Flows

We present high-fidelity large-eddy-simulation (LES) modeling approaches for the turbulent atmospheric boundary layer (ABL) flows. Wind energy is a prime example of an application driven by ABL. Generation of electrical energy from farms of wind turbines at night in the stable ABL is a particularly interesting situation. In this report, we consider the well-known GEWEX (Global Energy and Water Cycle Experiment) Atmospheric Boundary Layer Study (GABLS) stably stratified benchmark LES case. We use a high-order spectral element code Nek5000/RS, which is supported under the DOE's Exascale Computing Project (ECP) Center for Efficient Exascale Discretizations (CEED) project, targeting application simulations on various acceleration-device based exascale computing platforms. In our earlier ANL report, we demonstrated our newly developed subgrid-scale (SGS) models based on high-pass filter (HPF), mean-field eddy viscosity (MFEV), and Smagorinsky (SMG) with no-slip and traction boundary conditions, provided with low-order statistics, convergence and turbulent structure analysis. In this report, we extend the range of our SGS modeling approaches in the context of the mean-field eddy viscosity (MFEV), to include the solution of an SGS turbulent kinetic energy equation (TKE). We demonstrate the model fidelity of Nek5000/RS in comparison to that of AMR-Wind, a block-structured second-order finite-volume code with adaptive-mesh-refinement capabilities, with which we studied scaling performance for both codes in comparison on DOE's leadership computing platforms.

17 WIND ENERGY↗
NASA NTRSDate not supplied

GPU Acceleration of VULCAN-CFD

This work presents a comprehensive overview of recent advancements in the application of GraphicsProcessing Units (GPUs) to accelerate the NASA-developed VULCAN-CFD code for hypersonic flow sim-ulations. The unstructured solver in VULCAN-CFD is undergoing a significant rewrite from modern Fortranto C++, enabling its execution on both GPUs and CPUs through the utilization of Kokkos, a programmingmodel for performance portability developed by Sandia National Labs. The paper outlines some modifica-tions that were made in the original implementation of VULCAN-CFD in order to harness the computationalpower of GPUs. Finally, we demonstrate performance improvements achieved through GPU acceleration.The accelerated code throughput on one GPU is shown to match approximately 200 CPU cores for bothsingle species and multi-species reacting cases.

Matthew O'Connell↗
DOE OSTI2025

Even Higher-Level Synthesis: An Exploration of AI Hardware Accelerators using HLS4ML

With the rise of artificial intelligence, the popularization of deep learning, and a constantly evolving industry, the demand for flexible and efficient tools has never been greater. As algorithms grow more complex, their runtime and energy consumption increase exponentially. Customized hardware accelerators, long used for specific mathematical operations, remain essential for managing modern applications' computational and power demands. Hardware accelerators can speed up complex computations by orders of magnitude, but their manual design and verification processes are often challenging and time-consuming. High-Level Synthesis (HLS) provides a solution by transforming high-level algorithm descriptions, typically written in C++ or SystemC, into synthesizable RTL suitable for hardware implementation. This approach reduces development time for RTL engineers while offering flexibility beyond what traditional handwritten RTL can provide. We extended this capability to the machine-learning domain with the open-source framework hls4ml, which allows neural networks trained in Python frameworks like Tensorflow or PyTorch to be synthesized into efficient hardware representations for the traditional FPGA and ASIC flows. This breakthrough addresses the growing need for reduced design turnaround and easy verification of ML hardware accelerators with low latency and power efficiency constraints. During this tutorial, we will demonstrate how Python complements HLS by simplifying the ML design process, bridging the gap between software and hardware development. Attendees will explore how we translate neural networks modeled in Python into fixed-point C++ models suitable for HLS workflows. We will dive into strategies like Value-Range Analysis and Quantization-Aware Training, which optimize these designs for deployment and evaluate their accuracy, power consumption, and energy efficiency. To exemplify these concepts, experts from Fermilab will share their experiences applying this technology to high-energy physics experiments, where real-time, low-latency processing is critical. Over the years, Fermilab engineers have demonstrated how deep neural networks, optimized for hardware using hls4ml, can meet the stringent requirements of trigger systems at the CERN Large Hadron Collider. These systems rely on rapid decision-making to process immense data volumes while retaining only the most relevant events for further analysis. The application of hls4ml has also been extended to innovative technologies like smart pixel arrays. These smart pixels integrate ML inference capabilities directly into sensor devices, enabling localized data processing at the pixel level. This approach drastically reduces the need to transmit raw data to external processing units, significantly decreasing power consumption and latency. By embedding neural networks within the pixel architecture, the smart pixels can identify and prioritize relevant data in real time, providing a highly efficient solution for edge computing in scenarios such as particle detectors and imaging systems. Fermilab's work highlights the potential of hardware-accelerated ML in scenarios where both speed and power efficiency are mission-critical. Through this tutorial, attendees will gain valuable insights into the challenges and solutions of deploying ML in hardware. Understanding how HLS and hls4ml streamline the development of neural network-based hardware accelerators is fundamental for the industry's future. Participants will learn how these technologies are shaping the future of AI and scientific computing.

Di Guglielmo, Giuseppe [Fermilab]↗
DOE OSTI2020

Quasi‐Binary Transition Metal Dichalcogenide Alloys: Thermodynamic Stability Prediction, Scalable Synthesis, and Application

Abstract Transition metal dichalcogenide (TMDCs) alloys could have a wide range of physical and chemical properties, ranging from charge density waves to superconductivity and electrochemical activities. While many exciting behaviors of unary TMDCs have been demonstrated, the vast compositional space of TMDC alloys has remained largely unexplored due to the lack of understanding regarding their stability when accommodating different cations or chalcogens in a single‐phase. Here, a theory‐guided synthesis approach is reported to achieve unexplored quasi‐binary TMDC alloys through computationally predicted stability maps. Equilibrium temperature–composition phase diagrams using first‐principles calculations are generated to identify the stability of 25 quasi‐binary TMDC alloys, including some involving non‐isovalent cations and are verified experimentally through the synthesis of a subset of 12 predicted alloys using a scalable chemical vapor transport method. It is demonstrated that the synthesized alloys can be exfoliated into 2D structures, and some of them exhibit: i) outstanding thermal stability tested up to 1230 K, ii) exceptionally high electrochemical activity for the CO 2 reduction reaction in a kinetically limited regime with near zero overpotential for CO formation, iii) excellent energy efficiency in a high rate Li–air battery, and iv) high break‐down current density for interconnect applications. This framework can be extended to accelerate the discovery of other TMDC alloys for various applications.

Hemmat, Zahra↗
DOE OSTI2017

Considerations on the Use of Custom Accelerators for Big Data Analytics

Accelerators, including Graphic Processing Units (GPUs) for gen- eral purpose computation, many-core designs with wide vector units (e.g., Intel Phi), have become a common component of many high performance clusters. The appearance of more stable and reliable tools tools that can automatically convert code written in high-level specifications with annotations (such as C or C++) to hardware de- scription languages (High-Level Synthesis - HLS), is also setting the stage for a broader use of reconfigurable devices (e.g., Field Pro- grammable Gate Arrays - FPGAs) in high performance system for the implementation of custom accelerators, helped by the fact that new processors include advanced cache-coherent interconnects for these components. In this chapter, we briefly survey the status of the use of accelerators in high performance systems targeted at big data analytics applications. We argue that, although the progress in the use of accelerators for this class of applications has been sig- nificant, differently from scientific simulations there still are gaps to close. This is particularly true for the ”irregular” behaviors exhibited by no-SQL, graph databases. We focus our attention on the limits of HLS tools for data analytics and graph methods, and discuss a new architectural template that better fits the requirement of this class of applications. We validate the new architectural templates by mod- ifying the Graph Engine for Multithreaded System (GEMS) frame- work to support accelerators generated with such a methodology, and testing with queries coming from the Lehigh University Benchmark (LUBM). The architectural template enables better supporting the task and memory level parallelism present in graph methods by sup- porting a new control model and a enhanced memory interface. We show that out solution allows generating parallel accelerators, pro- viding speed ups with respect to conventional HLS flows. We finally draw conclusions and present a perspective on the use of reconfig- urable devices and Design Automation tools for data analytics.

Data Analytics↗
DOE OSTI2015

An Investigation of Catastrophic Failure in Solid-State Lamps Exposed to Harsh Environment Operational Conditions

Today’s lighting technology is steadily becoming more energy efficient and less toxic to the environment since the passing of the Energy Independence and Security Act of 2007 (EISA) [1]. EISA has mandated a higher energy efficiency standard for lighting products and the phase out of the common incandescent lamp. This has led lighting manufacturers to pursue solid-state lighting (SSL) technologies for consumer lighting applications. However, two major roadblocks are hindering the transition process to SSL lamps: cost and quality. In order to cut cost, manufactures are moving towards cheaper packaging materials and a variety of package architecture construction techniques which may potentially erode the quality of the lamp and reduce its survivability in everyday applications. Typically, SSL lamps are given product lifetimes of over twenty years based off of the IES TM-21-11 lighting standard which does not include moisture effects or large operational temperatures [2]. A group of recently released off-the-shelf lamps have undergone a steady-state temperature humidity bias life test of 85°C/85%RH (85/85) to investigate the reliability in harsh environment applications. The lack of accelerated test methods for lamps to assess reliability prior to introduction into the marketplace does not exist in literature. There is a need for SSL physics based models for the assessment and prediction of a lamp’s lifetime which is being spearheaded by the DOE [3]. In order to be fully accepted in the marketplace, SSL lamps must be able to perform similarly to incandescent lamps in these environments, as well as live up to the lifetime claims of manufacturers. A lamp’s package architecture must be designed with performance factors in mind, as well as address some of the known and published package related failure mechanisms, such as carbonization of the encapsulant material, delamination, encapsulant yellowing, lens cracking, and phosphor thermal quenching [4]. Each failure mechanism produces the similar failure mode of lumen degradation predominately due to two contributing factors: high junction temperature and moisture ingress. The current state-of-the-art has focused on individual areas of the lamp, such as the LED chip, substrate material, electrical driver design and thermal management techniques. [5] – [16] Looking at the lamp as a whole is a novel approach and has not been seen before in literature. This work followed the JEDEC standard JESD22-A101C of 85/85 with a one hour interval of applied voltage followed by a one hour interval of no applied voltage [17]. This test was performed continuously for each SSL lamp until it became nonoperational, i.e. did not turn on. Periodically, photometric measurements were taken following the IES LM-79-08 standard at room temperature using an integrating sphere, a spectrometer, and lighting software. The overall health of the SSL lamps was assed using the relative luminous flux (RLF), correlated color temperature (CCT) and the color difference (Δu'v') using the Euclidean distance of the CIE 1976 color space coordinates. Finally, a Weibull analysis was completed to compare the characteristic lifetime of the SSL lamp to the actual rated lifetime. An important result from this work shows that the rated lifetime does not come close to the actual lifetime when the SSL lamps are used in a harsh humid environment which is fairly common in outdoor applications across the U.S. Also, the photometric results are presented for the entire lifetime of each SSL lamp under test.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Machine Learning for Mapping Multipactor Susceptibility in RF Systems: Capabilities and Generalization Constraints

Multipactor is a surface-driven electron avalanche phenomenon that degrades the performance and reliability of radio-frequency (RF) systems in particle accelerator and vacuum electronics applications. Multipactor behavior in a given device structure is conventionally assessed through susceptibility charts, which provide a parameter-space characterization of the instability. In this work, we assess the capabilities of machine-learning (ML) models to learn and predict such susceptibility charts and analyze the constraints governing their generalization across materials. Using a simulation-derived dataset spanning six distinct secondary-electron-yield material profiles in a canonical two-surface planar geometry, we train supervised regression models and artificial neural networks to predict the time-averaged electron growth rate, δavg, across the relevant parameter space. Model performance is evaluated using metrics that explicitly probe the structure of susceptibility charts, including Intersection over Union, Structural Similarity Index, and correlation analysis. Tree-based ensemble models outperform neural-network models in reconstructing susceptibility regions and in generalizing across material domains. Principal-component analysis reveals disjoint material feature distributions, indicating that the piecewise mode structure of multipactor susceptibility is difficult to represent with a single global model and that generalization is constrained by data coverage rather than by model complexity. An exhaustive reduced-coverage study further shows that sparse material-space coverage can yield mean performance in the same general range but producing large variability in the susceptibility-region overlap. These results clarify the capabilities of ML-based surrogate models for parameter-space characterization of multipactor discharge. They also provide guidance for their appropriate use in RF system design.

43 PARTICLE ACCELERATORS↗
DOE OSTI2017

Nb3Sn SRF Cavities for Nuclear Physics Applications

Nuclear physics experiments rely increasingly on accelerators, which employ superconducting RF (SRF) technology. CEBAF, SNS, FRIB, ESS, among others exploit the low surface resistance of SRF cavities to efficiently accelerate particle beams towards experimental targets. Niobium is the cavity material of choice for all current or planned SRF accelerators, but it has been long recognized that other superconductors with high superconducting transition temperatures have the potential to surpass niobium for SRF applications. Among the alternatives, Nb3Sn coated cavities are the most advanced on the path to practical applications: Nb3Sn coatings on R&D cavities have Tc consistently close the optimal 18 K, very low RF surface resistances, and very recently were shown to reach above Hc1 without anomalous RF surface resistance increase. In my talk I will discuss the prospects of Nb3Sn SRF cavities and the research efforts to realize Nb3Sn coatings on practical multi-cell accelerating structures.

Eremeev, Grigory↗
DOE OSTI2024

Multiphysics modeling of accelerators through code integration

This work aims to improve the ability of particle accelerator researchers to develop high-performance accelerator cavity designs by creating an overall multiphysics framework that integrates and couples existing application codes. This framework will allow accelerator researchers to build multiphysics models that will optimize cavity design, improve understanding of whole-device performance, and reduce the development and fabrication costs of accelerator research. We utilize the open-source VizSchema data standard as an intermediate data structure interface layer to standardize interfaces between individual application codes. VizScema is extensively documented online, and plugins for VizSchema are available for popular visualization packages, including VisIt and ParaView. Currently, the work focuses on coupling the EM field solver COMSOL and the electron gun code MICHELLE to allow COMSOL field-solve results to be seamlessly used by MICHELLE for particle-solve. Later work will extend this integration to include other fields, particles, and thermodynamics simulation codes.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Controlling Selectivity in Plasmonic Catalysis: Switching Reaction Pathway from Hydrogenation to Homocoupling Under Visible‐Light Irradiation

Abstract Plasmonic catalysis enables the use of light to accelerate molecular transformations. Its application to the control reaction selectivity is highly attractive but remains challenging. Here, we have found that the plasmonic properties in AgPd nanoparticles allowed different reaction pathways for tunable product formation under visible‐light irradiation. By employing the hydrogenation of phenylacetylene as a model transformation, we demonstrate that visible‐light irradiation can be employed to steer the reaction pathway from hydrogenation to homocoupling. Our data showed that the decrease in the concentration of H species at the surface due to plasmon‐enhanced H 2 desorption led to the control in selectivity. These results provide important insights into the understanding of reaction selectivity with light, paving the way for the application of plasmonic catalysis to the synthesis of 1,3‐diynes, and bringing the vision of light‐driven transformations with target selectivity one step closer to reality.

Peiris, Erandi↗
DOE OSTI2022

Controlling Selectivity in Plasmonic Catalysis: Switching Reaction Pathway from Hydrogenation to Homocoupling Under Visible–Light Irradiation

Plasmonic catalysis enables the use of light to accelerate molecular transformations. Its application to the control reaction selectivity is highly attractive but remains challenging. Here, we have found that the plasmonic properties in AgPd nanoparticles allowed different reaction pathways for tunable product formation under visible-light irradiation. By employing the hydrogenation of phenylacetylene as a model transformation, we demonstrate that visible-light irradiation can be employed to steer the reaction pathway from hydrogenation to homocoupling. Our data showed that the decrease in the concentration of H species at the surface due to plasmon-enhanced H2 desorption led to the control in selectivity. Furthermore, these results provide important insights into the understanding of reaction selectivity with light, paving the way for the application of plasmonic catalysis to the synthesis of 1,3-diynes, and bringing the vision of light-driven transformations with target selectivity one step closer to reality.

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

Evaluation of dual-weighted residual and machine learning error estimation for projection-based reduced-order models of steady partial differential equations

Projection-based reduced-order models (pROMs) show great promise as a means to accelerate many-query applications such as forward error propagation, solving inverse problems, and design optimization. In order to deploy pROMs in the context of high-consequence decision making, accurate error estimates are required to determine the region(s) of applicability in the parameter space. The following paper considers the dual-weighted residual (DWR) error estimate for pROMs and compares it to another promising pROM error estimate, machine learned error models (MLEM). Here, we show how DWR can be applied to ROMs and then evaluate DWR on two partial differential equations (PDEs): a two-dimensional linear convection–reaction–diffusion equation, and a three-dimensional static hyper-elastic beam. It is shown that DWR is able to estimate errors for pROMs extrapolating outside of their training set while MLEM is best suited for pROMs used to interpolate within the pROM training set.

42 ENGINEERING↗
DOE OSTI2014

Accelerating dissipative particle dynamics simulations on GPUs: Algorithms, numerics and applications

We present a scalable dissipative particle dynamics simulation code, fully implemented on the Graphics Processing Units (GPUs) using a hybrid CUDA/MPI programming model, which achieves 10-30 times speedup on a single GPU over 16 CPU cores and almost linear weak scaling across a thousand nodes. A unified framework is developed within which the efficient generation of the neighbor list and maintaining particle data locality are addressed. Our algorithm generates strictly ordered neighbor lists in parallel, while the construction is deterministic and makes no use of atomic operations or sorting. Such neighbor list leads to optimal data loading efficiency when combined with a two-level particle reordering scheme. A faster in situ generation scheme for Gaussian random numbers is proposed using precomputed binary signatures. We designed custom transcendental functions that are fast and accurate for evaluating the pairwise interaction. The correctness and accuracy of the code is verified through a set of test cases simulating Poiseuille flow and spontaneous vesicle formation. Computer benchmarks demonstrate the speedup of our implementation over the CPU implementation as well as strong and weak scalability. A large-scale simulation of spontaneous vesicle formation consisting of 128 million particles was conducted to further illustrate the practicality of our code in real-world applications.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2013

Multilevel Monte Carlo for two phase flow and Buckley–Leverett transport in random heterogeneous porous media

Monte Carlo (MC) is a well known method for quantifying uncertainty arising for example in subsurface flow problems. Although robust and easy to implement, MC suffers from slow convergence. Extending MC by means of multigrid techniques yields the multilevel Monte Carlo (MLMC) method. MLMC has proven to greatly accelerate MC for several applications including stochastic ordinary differential equations in finance, elliptic stochastic partial differential equations and also hyperbolic problems. In this study, MLMC is combined with a streamline-based solver to assess uncertain two phase flow and Buckley–Leverett transport in random heterogeneous porous media. The performance of MLMC is compared to MC for a two dimensional reservoir with a multi-point Gaussian logarithmic permeability field. The influence of the variance and the correlation length of the logarithmic permeability on the MLMC performance is studied.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Recent progress on coarse graining simulations

We focus on coarse graining simulations based on the primary conservation equations, effectively codesigned physics and algorithms, and low-Mach-number corrected (LMC) hydrodynamics. Simulation methods involve LANL’s x-Radiation-Adaptive-Grid-Eulerian Large-Eddy Simulation, Besnard-Harlow-Rauenzahn (BHR) Reynolds-Averaged Navier-Stokes (RANS) approach, and Dynamic BHR – a paradigm bridging RANS and LES. A relevant question addressed relates to whether 3D RANS and RANS/LES hybrids – the industry standards for aerospace and automotive research, are presently relevant for practical variable-density applications involving shocked and accelerated interface instabilities. Furthermore, recent simulations of the GaTECH inclined mixing-layer shock-tube and NIF ICF-capsule experiments are used to demonstrate issues, challenges, and potential for 3D coarse grained LMC simulation strategies for robustly simulating complex transitional and coupled hydrodynamics-multiphysics with coarser resolution. Present LES readiness to provide accurate predictions at scale is demonstrated – whereas 3D RANS and RANS/LES bridging do not appear impactful in this context.

42 ENGINEERING↗
DOE OSTI2023

Transferring a Molecular Foundation Model for Polymer Property Predictions

Transformer-based large language models have remarkable potential to accelerate design optimization for applications such as drug development and material discovery. Self-supervised pretraining of transformer models requires large-scale data sets, which are often sparsely populated in topical areas such as polymer science. Further, state-of-the-art approaches for polymers conduct data augmentation to generate additional samples but unavoidably incur extra computational costs. In contrast, large-scale open-source data sets are available for small molecules and provide a potential solution to data scarcity through transfer learning. In this work, we show that using transformers pretrained on small molecules and fine-tuned on polymer properties achieves comparable accuracy to those trained on augmented polymer data sets for a series of benchmark prediction tasks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗