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At least 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A novel ignition model for low velocity impact of heterogeneous explosives based on interacting hot spots

While numerous studies have focused on the ignition of explosives occurring in high velocity impact and the associated shock-to-detonation transition, there has been growing interest in developing computational models focused on low-velocity impact regimes. A predictive low-velocity impact ignition model will be important for analyzing high explosive safety and potential accident scenarios. This work introduces a novel ignition model based on the concept of thermally interacting hot spots to simulate low velocity impacted heterogeneous explosives where observed ignition times are on the order of milliseconds. The model asserts that relevant hot spots are micron-sized, the typical separation between neighboring hot spots is on the order of a hundred microns, and that neighbors interact thermally through heat conduction across the interstitial region between them. To achieve tractable numerical solutions, hot spots are assumed to form a periodic array as opposed to the highly irregular positioning in an actual explosive. This idealization allows a single two hotspot system to characterize the ignition process. Consequently, the model is referred to as the two hot spot Frank-Kamenetskii ignition model. In the present study, hot spots are modeled as constant heat sources terms, but this can be extended to include grain-scale phenomena like frictional heating of micron-sized growing cracks that are confined under high pressure. Because the micron-sized features are below the scale that can be efficiently resolved at a systems level, an efficient subscale scheme based on the Method of Weighted Residuals (MWR) is used to efficiently solve the equations. In conclusion, we carry out numerical examples and analytic predictions illustrating the accuracy and the functioning of the model.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Supporting multiple hardware architectures at CMS: the integration and validation of POWER9

Computing resources in the Worldwide LHC Computing Grid (WLCG) have been based entirely on the x86 architecture for more than two decades. In the near future, however, heterogeneous non-x86 resources, such as ARM, POWER and Risc-V, will become a substantial fraction of the resources that will be provided to the LHC experiments, due to their presence in existing and planned world-class HPC installations. The CMS experiment, one of the four large detectors at the LHC, has started to prepare for this situation, with the CMS software stack (CMSSW) already compiled for multiple architectures. In order to allow for a production use, the tools for workload management and job distribution need to be extended to be able to exploit heterogeneous architectures. Profiting from the opportunity to exploit the first sizable IBM Power9 allocation available on Marconi100 HPC system at CINECA, CMS developed all the needed modifications to the CMS workload management system. After a successful proof of concept, a full physics validation has been performed in order to bring the system in production. The experiences are of very high value, when it comes to commissioning of the similar (even larger) Summit HPC system at Oak Ridge, where CMS is also expecting a resource allocation. Moreover the compute power of those systems is being provided also via GPUs and this represents an extremely valuable opportunity to exploit the offloading capability already implemented in CMSSW. The status of the current integration including the exploitation of the GPUs, the results of the validation as well as the future plans will be shown and discussed.

Boccali, Tommaso [INFN, Pisa]↗
DOE OSTI2013

Colloid Transport in Saturated Porous Media: Elimination of Attachment Efficiency in a New Colloid Transport Model

A new colloid transport model is introduced that is conceptually simple but captures the essential features of complicated attachment and detachment behavior of colloids when conditions of secondary minimum attachment exist. This model eliminates the empirical concept of collision efficiency; the attachment rate is computed directly from colloid filtration theory. Also, a new paradigm for colloid detachment based on colloid population heterogeneity is introduced. Assuming the dispersion coefficient can be estimated from tracer behavior, this model has only two fitting parameters: (1) the fraction of colloids that attach irreversibly and (2) the rate at which reversibly attached colloids leave the surface. These two parameters were correlated to physical parameters that control colloid transport such as the depth of the secondary minimum and pore water velocity. Given this correlation, the model serves as a heuristic tool for exploring the influence of physical parameters such as surface potential and fluid velocity on colloid transport. This model can be extended to heterogeneous systems characterized by both primary and secondary minimum deposition by simply increasing the fraction of colloids that attach irreversibly.

Landkamer, Lee L.↗
DOE OSTI2020

Building kinetic models for metabolic engineering

Kinetic formalisms of metabolism link metabolic fluxes to enzyme levels, metabolite concentrations and their allosteric regulatory interactions. Though they require the identification of physiologically relevant values for numerous parameters, kinetic formalisms uniquely establish a mechanistic link across heterogeneous omics datasets and provide an overarching vantage point to effectively inform metabolic engineering strategies. Advances in computational power, gene annotation coverage, and formalism standardization have led to significant progress over the past few years. However, careful interpretation of model predictions, limited metabolic flux datasets, and assessment of parameter sensitivity remain as challenges. In this study we highlight fundamental considerations which influence model quality and prediction, advances in methodologies, and success stories of deploying kinetic models to guide metabolic engineering.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Surface-sensitive photon avalanche behavior revealed by single-avalanching-nanoparticle imaging

Avalanching nanoparticles (ANPs) are a new class of lanthanide-based upconverting material demonstrating steep optical nonlinearities with the potential to advance applications ranging from subwavelength bioimaging to neuromorphic computing, nanothermometry, and pressure transduction. Here, we use single-nanocrystal imaging to uncover design-dependent heterogeneity in ANP threshold intensity (I th ). Quantitative comparisons between distributions of I th and ANP shell properties reveal correlations between mean I th values, histogram widths, and nanocrystal shell thickness. Evaluating avalanching behaviors using an established model of shell-dependent surface energy transfer shows that variations in shell thickness-and the resultant energy transfer through the shell to the surface and environment-are likely the primary contributor to ANP-to-ANP I th heterogeneity. Further, nanocrystals with an ~6 nm average shell thickness show I th heterogeneity beyond the extent expected from statistical measurements of shell size and variability using transmission electron microscopy (TEM). Here these results provide a principal guide for the design and application of ANPs to environmental sensing.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Selective CO Production by Photoelectrochemical Methane Oxidation on TiO 2

The inertness of the C–H bond in CH 4 poses significant challenges to selective CH 4 oxidation, which often proceeds all the way to CO 2 once activated. Selective oxidation of CH 4 to high-value industrial chemicals such as CO or CH 3 OH remains a challenge. Presently, the main methods to activate CH 4 oxidation include thermochemical, electrochemical, and photocatalytic reactions. Of them, photocatalytic reactions hold great promise for practical applications but have been poorly studied. Existing demonstrations of photocatalytic CH 4 oxidation exhibit limited control over the product selectivity, with CO 2 as the most common product. The yield of CO or other hydrocarbons is too low to be of any practical value. In this work, we show that highly selective production of CO by CH4 oxidation can be achieved by a photoelectrochemical (PEC) approach. Under our experimental conditions, the highest yield for CO production was 81.9%. The substrate we used was TiO 2 grown by atomic layer deposition (ALD), which features high concentrations of Ti 3+ species. The selectivity toward CO was found to be highly sensitive to the substrate types, with significantly lower yield on P25 or commercial anatase TiO 2 substrates. Moreover, our results revealed that the selectivity toward CO also depends on the applied potentials. Based on the experimental results, we proposed a reaction mechanism that involves synergistic effects by adjacent Ti sites on TiO 2 . Spectroscopic characterization and computational studies provide critical evidence to support the mechanism. Furthermore, the synergistic effect was found to parallel heterogeneous CO 2 reduction mechanisms. Our results not only present a new route to selective CH 4 oxidation, but also highlight the importance of mechanistic understandings in advancing heterogeneous catalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

DONUT: physics-aware machine learning for real-time X-ray nanodiffraction analysis

Coherent X-ray scattering techniques are critical for investigating the fundamental structural properties of materials at the nanoscale. While advancements have made these experiments more accessible, real-time analysis remains a significant bottleneck, often hindered by artifacts and computational demands. In scanning X-ray nanodiffraction microscopy, which is widely used to spatially resolve structural heterogeneities, this challenge is compounded by the convolution of the divergent beam with the sample’s local structure. To address this, we introduce DONUT (Diffraction with Optics for Nanobeam by Unsupervised Training), a physics-aware neural network designed for the rapid and automated analysis of nanobeam diffraction data. By incorporating a differentiable geometric diffraction model directly into its architecture, DONUT learns to predict crystal lattice strain and orientation in real-time. Crucially, this is achieved without reliance on labeled datasets or pre-training, overcoming a fundamental limitation for supervised machine learning in X-ray science. We demonstrate experimentally that DONUT accurately extracts all features within the data over 200 times more efficiently than conventional fitting methods.

Materials science↗
DOE OSTI2019

Multigroup Cross-Section Generation with the OpenMC Monte Carlo Particle Transport Code

High-fidelity deterministic transport codes require highly accurate multigroup cross sections (MGXS). Monte Carlo is increasingly cited as a reactor-agnostic approach to MGXS generation since it is unconstrained by the engineering-based approximations that limit the applicability of deterministic MGXS generation tools. This paper introduces a new framework that uses the OpenMC Monte Carlo code to generate MGXS for use in multigroup transport codes. The openmc.mgxs module is built atop OpenMC's Python application programming interface to process tally data output by the OpenMC executable. This paper validates the module to generate MGXS that enable the multigroup OpenMOC transport code to compute eigenvalues to within 50 pcm and fission rates to within 1% of reference solutions for two heterogeneous pressurized water reactor benchmarks.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

DONUT: Physics-aware Machine Learning for Real-time X-ray Nanodiffraction Analysis

SF-25-088 Coherent X-ray scattering techniques are critical for investigating the fundamental structural properties of materials at the nanoscale. While advancements have made these experiments more accessible, real-time analysis remains a significant bottleneck, often hindered by artifacts and computational demands. In scanning X-ray nanodiffraction microscopy, which is widely used to spatially resolve structural heterogeneities, this challenge is compounded by the convolution of the divergent beam with the sample’s local structure. To address this, we introduce DONUT (Diffraction with Optics for Nanobeam by Unsupervised Training), a physics-aware neural network designed for the rapid and automated analysis of nanobeam diffraction data. By incorporating a differentiable geometric diffraction model directly into its architecture, DONUT learns to predict crystal lattice strain and orientation in real-time. Crucially, this is achieved without reliance on labeled datasets or pre-training, overcoming a fundamental limitation for supervised machine learning in X-ray science. We demonstrate experimentally that DONUT accurately extracts all features within the data over 200 times more efficiently than conventional fitting methods.

Zhou, Tao [Argonne National Laboratory (ANL), Argo↗
DOE OSTI2019

Economical and Engineering Aspects of Proactive Demand Participation: Centralized versus Bilateral Control Structure

The electric grid of the U.S. is evolving from a system characterized by large and centralized fossil-fueled power plants and passive customers to a network with high penetration of distributed energy resources and proactive customers. This project facilitated the evolution of the U.S. electric power grid by enabling proactive participation of demands in electricity market at the power distribution system level. This project has three major achievements. First, we designed a centralized distribution system operator managed electricity market and developed a three-phase alternating current optimal power flow algorithm to coordinate the operation of large-scale flexible loads and distributed energy resources. Second, we designed a decentralized bilateral coordinate framework for retail electricity market, where each participating customer is capable of making decisions regarding energy production and consumption based on information received from transactive neighbors. Third, the impacts of centralized and decentralized demand-side participation scheme on power system operations were evaluated with both large-scale simulations and hardware-in-the-loop demonstrations. The major activities and specific accomplishments of the technical tasks are summarized in Section II. The detailed design of centralized/hierarchical and decentralized/bilateral retail electricity markets and the comparison between the two markets are described in Section III. The accomplishments and conclusions of the project are presented in Section IV. The major findings of the project falls into three areas, hierarchical distribution system operator managed electricity market, bilateral electricity market and decentralized market coordination, and comparison of centralized and decentralized market coordination mechanisms. In the area of hierarchical distribution system operator managed electricity market design, an integrated wholesale and retail electricity market was shown to be highly effective in extracting the operational flexibility from distributed energy resources to provide services to the wholesale market. A chordal conversion based convex iteration algorithm is not only computationally efficiency but also achieves global optimal solution in coordinating the operations of a large number of heterogeneous distributed energy resources.In the area of decentralized market coordination approach, we showed that bilateral transactions among proactive customers and decentralized transaction clearing mechanism based on simultaneous second-price sealed-bid auction can encourage truthful bidding strategies from the market participations. In addition, the individual customers can rely on a portfolio optimization approach to maximize their returns while minimizing risks. In the area of comparison of centralized and decentralized market coordination mechanisms, we found that the centralized market design results in higher social welfare and lower wholesale market clearing price for electricity. In general, centralized market design favors distributed energy resources and fixed loads. The decentralized market design mostly benefits flexible loads and conventional generators.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2013

MICADO: Parallel implementation of a 2D-1D iterative algorithm for the 3D neutron transport problem in prismatic geometries

The large increase in computing power over the past few years now makes it possible to consider developing 3D full-core heterogeneous deterministic neutron transport solvers for reference calculations. Among all approaches presented in the literature, the method first introduced in [1] seems very promising. It consists in iterating over resolutions of 2D and ID MOC problems by taking advantage of prismatic geometries without introducing approximations of a low order operator such as diffusion. However, before developing a solver with all industrial options at EDF, several points needed to be clarified. In this work, we first prove the convergence of this iterative process, under some assumptions. We then present our high-performance, parallel implementation of this algorithm in the MICADO solver. Benchmarking the solver against the Takeda case shows that the 2D-1D coupling algorithm does not seem to affect the spatial convergence order of the MOC solver. As for performance issues, our study shows that even though the data distribution is suited to the 2D solver part, the efficiency of the ID part is sufficient to ensure a good parallel efficiency of the global algorithm. After this study, the main remaining difficulty implementation-wise is about the memory requirement of a vector used for initialization. An efficient acceleration operator will also need to be developed. (authors)

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

The uintah framework: a unified heterogeneous task scheduling and runtime system

The development of a new unified, multi-threaded runtime system for the execution of asynchronous tasks on heterogeneous systems is described in this work. These asynchronous tasks arise from the Uintah framework, which was developed to provide an environment for solving a broad class of fluid-structure interaction problems on structured adaptive grids. Uintah has a clear separation between its MPI-free user-coded tasks and its runtime system that ensures these tasks execute efficiently. This separation also allows for complete isolation of the application developer from the complexities involved with the parallelism Uintah provides. While we have designed scalable runtime systems for large CPU core counts, the emergence of heterogeneous systems, with additional on-node accelerators and co-processors presents additional design challenges in terms of effectively utilizing all computational resources on-node and managing multiple levels of parallelism. Our work addresses these challenges for Uintah by the development of new hybrid runtime system and Unified multi-threaded MPI task scheduler, enabling Uintah to fully exploit current and emerging architectures with support for asynchronous, out-of-order scheduling of both CPU and GPU computational tasks. This design coupled with an approach that uses MPI to communicate between nodes, a shared memory model on-node and the use of novel lock-free data structures, has made it possible for Uintah to achieve excellent scalability for challenging fluid-structure problems using adaptive mesh refinement on as many as 256K cores on the DoE Jaguar XK6 system. This design has also demonstrated an ability to run capability jobs on the heterogeneous systems, Keeneland and TitanDev. In this work, the evolution of Uintah and its runtime system is examined in the context of our new Unified multi-threaded scheduler design. The performance of the Unified scheduler is also tested against previous Uintah scheduler and runtime designs over a range of processor core and GPU counts.

Source record↗
DOE OSTI2026

From Reproducible Edge–Cloud Experimentation to Real-World Practice: The E2Clab Experience

Reproducibility is already difficult in distributed systems; on the computing continuum, it becomes substantially harder. Applications that span sensing devices, edge and fog resources, and cloud platforms must be evaluated across heterogeneous hardware, variable network conditions, cross-layer orchestration decisions, and long-running workflow lifecycles. We use E2Clab as a case study to examine these challenges and their implications for experimental methodology. We explain why reproducible experimentation is harder on the continuum, then revisit E2Clab as an initial response based on explicit modeling of infrastructure, workflow lifecycle, and artifacts. Lastly, we discuss how its evolution toward more realistic application settings can be understood through the lens of Translational Computer Science. We argue that reproducible continuum experimentation requires methods that are rigorous enough for research while remaining adaptable to real-world practice.

42 ENGINEERING↗
DOE OSTI2013

A generalized 2D pencil beam scaling algorithm for proton dose calculation in heterogeneous slab geometries

Purpose: Pencil beam algorithms are commonly used for proton therapy dose calculations. Szymanowski and Oelfke ['Two-dimensional pencil beam scaling: An improved proton dose algorithm for heterogeneous media,' Phys. Med. Biol. 47, 3313-3330 (2002)] developed a two-dimensional (2D) scaling algorithm which accurately models the radial pencil beam width as a function of depth in heterogeneous slab geometries using a scaled expression for the radial kernel width in water as a function of depth and kinetic energy. However, an assumption made in the derivation of the technique limits its range of validity to cases where the input expression for the radial kernel width in water is derived from a local scattering power model. The goal of this work is to derive a generalized form of 2D pencil beam scaling that is independent of the scattering power model and appropriate for use with any expression for the radial kernel width in water as a function of depth. Methods: Using Fermi-Eyges transport theory, the authors derive an expression for the radial pencil beam width in heterogeneous slab geometries which is independent of the proton scattering power and related quantities. The authors then perform test calculations in homogeneous and heterogeneous slab phantoms using both the original 2D scaling model and the new model with expressions for the radial kernel width in water computed from both local and nonlocal scattering power models, as well as a nonlocal parameterization of Moliere scattering theory. In addition to kernel width calculations, dose calculations are also performed for a narrow Gaussian proton beam. Results: Pencil beam width calculations indicate that both 2D scaling formalisms perform well when the radial kernel width in water is derived from a local scattering power model. Computing the radial kernel width from a nonlocal scattering model results in the local 2D scaling formula under-predicting the pencil beam width by as much as 1.4 mm (21%) at the depth of the Bragg peak for a 220 MeV proton beam in homogeneous water. This translates into a 32% dose discrepancy for a 5 mm Gaussian proton beam. Similar trends were observed for calculations made in heterogeneous slab phantoms where it was also noted that errors tend to increase with greater beam penetration. The generalized 2D scaling model performs well in all situations, with a maximum dose error of 0.3% at the Bragg peak in a heterogeneous phantom containing 3 cm of hard bone. Conclusions: The authors have derived a generalized form of 2D pencil beam scaling which is independent of the proton scattering power model and robust to the functional form of the radial kernel width in water used for the calculations. Sample calculations made with this model show excellent agreement with expected values in both homogeneous water and heterogeneous phantoms.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2022

Advancing coupled fire-atmosphere research with HIGRAD/FIRETEC (w20_firetec) (CY 2021 Institutional Computing Annual Progress Report) [Slides]

LANL’s HIGRAD and FIRETEC models were used in a multi-scale simulation framework to investigate impacts of small-scale fuel heterogeneities on large-scale Pyro-Cb formation. Forest structural diversity is found to increase canopy fire spread, and thus risk of tree mortality in FIRETEC simulations. Structural diversity is associated with an increase in ladder fuels, which facilitate fire spread into the canopy. Low-intensity fire burning in marginal conditions is highly sensitive to small perturbation in atmospheric turbulence. These results highlight predictive limitations of individual fire simulations, and emphasize the need for ensemble simulations, especially for low-intensity, prescribed fire.

36 MATERIALS SCIENCE↗
DOE OSTI2011

Patient-Specific Monte Carlo-Based Dose-Kernel Approach for Inverse Planning in Afterloading Brachytherapy

Purpose: Brachytherapy planning software relies on the Task Group report 43 dosimetry formalism. This formalism, based on a water approximation, neglects various heterogeneous materials present during treatment. Various studies have suggested that these heterogeneities should be taken into account to improve the treatment quality. The present study sought to demonstrate the feasibility of incorporating Monte Carlo (MC) dosimetry within an inverse planning algorithm to improve the dose conformity and increase the treatment quality. Methods and Materials: The method was based on precalculated dose kernels in full patient geometries, representing the dose distribution of a brachytherapy source at a single dwell position using MC simulations and the Geant4 toolkit. These dose kernels are used by the inverse planning by simulated annealing tool to produce a fast MC-based plan. A test was performed for an interstitial brachytherapy breast treatment using two different high-dose-rate brachytherapy sources: the microSelectron iridium-192 source and the electronic brachytherapy source Axxent operating at 50 kVp. Results: A research version of the inverse planning by simulated annealing algorithm was combined with MC to provide a method to fully account for the heterogeneities in dose optimization, using the MC method. The effect of the water approximation was found to depend on photon energy, with greater dose attenuation for the lower energies of the Axxent source compared with iridium-192. For the latter, an underdosage of 5.1% for the dose received by 90% of the clinical target volume was found. Conclusion: A new method to optimize afterloading brachytherapy plans that uses MC dosimetric information was developed. Including computed tomography-based information in MC dosimetry in the inverse planning process was shown to take into account the full range of scatter and heterogeneity conditions. This led to significant dose differences compared with the Task Group report 43 approach for the Axxent source.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2012

Radiation modeling using the Uintah heterogeneous CPU/GPU runtime system. In: XSEDE '12 Proceedings of the 1st Conference of the Extreme Science and Engineering Discovery Environment: Bridging from the eXtreme to the campus and beyond, Article No. 4

The Uintah Computational Framework was developed to provide an environment for solving fluid-structure interaction problems on structured adaptive grids on large-scale, long-running, data-intensive problems. Uintah uses a combination of fluid-flow solvers and particle-based methods for solids, together with a novel asynchronou task-based approach with fully automated load balancing. Uintah demonstrates excellent weak and strong scalability at full machine capacity on XSEDE resources such as Ranger and Kraken, and through the use of a hybrid memory approach based on a combination of MPI and Pthreads, Uintah now runs on up to 262k cores on the DOE Jaguar system. In order to extend Uintah to heterogeneous systems, with ever-increasing CPU core counts and additional on-node GPUs, a new dynamic CPU-GPU task scheduler is designed and evaluated in this study. This new scheduler enables Uintah to fully exploit these architectures with support for asynchronous, out-of-order scheduling of both CPU and GPU computational tasks. A new runtime system has also been implemented with an added multi-stage queuing architecture for efficient scheduling of CPU and GPU tasks. This new runtime system automatically handles the details of asynchronous memory copies to and from the GPU and introduces a novel method of pre-fetching and preparing GPU memory prior to GPU task execution. In this study this new design is examined in the context of a developing, hierarchical GPU-based ray tracing radiation transport model that provides Uintah with additional capabilities for heat transfer and electromagnetic wave propagation. The capabilities of this new scheduler design are tested by running at large scale on the modern heterogeneous systems, Keeneland and TitanDev, with up to 360 and 960 GPUs respectively. On these systems, we demonstrate significant speedups per GPU against a standard CPU core for our radiation problem.

Humphrey, Alan↗
DOE OSTI2016

Extreme-scale Algorithms and Solver Resilience

A widening gap exists between the peak performance of high-performance computers and the performance achieved by complex applications running on these platforms. Over the next decade, extreme-scale systems will present major new challenges to algorithm development that could amplify this mismatch in such a way that it prevents the productive use of future DOE Leadership computers due to the following; Extreme levels of parallelism due to multicore processors; An increase in system fault rates requiring algorithms to be resilient beyond just checkpoint/restart; Complex memory hierarchies and costly data movement in both energy and performance; Heterogeneous system architectures (mixing CPUs, GPUs, etc.); and Conflicting goals of performance, resilience, and power requirements.

97 MATHEMATICS AND COMPUTING↗