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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Online real-time learning of dynamical systems from noisy streaming data

Abstract Recent advancements in sensing and communication facilitate obtaining high-frequency real-time data from various physical systems like power networks, climate systems, biological networks, etc. However, since the data are recorded by physical sensors, it is natural that the obtained data is corrupted by measurement noise. In this paper, we present a novel algorithm for online real-time learning of dynamical systems from noisy time-series data, which employs the Robust Koopman operator framework to mitigate the effect of measurement noise. The proposed algorithm has three main advantages: (a) it allows for online real-time monitoring of a dynamical system; (b) it obtains a linear representation of the underlying dynamical system, thus enabling the user to use linear systems theory for analysis and control of the system; (c) it is computationally fast and less intensive than the popular extended dynamic mode decomposition (EDMD) algorithm. We illustrate the efficiency of the proposed algorithm by applying it to identify the Van der Pol oscillator, the chaotic attractor of the Henon map, the IEEE 68 bus system, and a ring network of Van der Pol oscillators.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Density functional theory-based surrogate kinetic models for heterogeneous reactions of hydrocarbon intermediates on silicon carbide

The increasing demand for high-performance materials in advanced technologies highlights the importance of achieving a fundamental understanding and potential control of silicon carbide (SiC) deposition processes. However, existing models often lack sufficient theoretical detail, relying heavily on empirical data and offering limited predictive capability. In particular, the complex surface chemistry governing SiC growth remains poorly understood. This study addresses these challenges by employing density functional theory (DFT) to investigate key heterogeneous reactions involving hydrocarbon intermediates on SiC surfaces, including dehydrogenation, hydrogenation, and carbon deposition. Transition state searches were conducted to identify reaction pathways and energy barriers. While first-principles calculations offer high accuracy, they are computationally intensive. To extend the utility of these first-principles results, vibrational analyses were performed using phonon-based statistical thermochemistry to compute temperature-dependent reaction rates which were used to develop Arrhenius-type surrogate kinetic models. Furthermore, the resulting framework provides a more rigorous, physically grounded basis for integrating atomistic insights into continuum-scale modeling, ultimately enabling improved prediction and optimization of SiC film growth in high-performance material systems.

Density Functional Theory↗
DOE OSTI2019

Completed coarse-grained model for Pu hydriding that has quantum accuracy (Progress Summary)

Our team has developed a highly computationally efficient coarse-grained model for PuH 2 formation, based on high-level quantum mechanical calculations. Kohn-Sham Density Functional Theory (DFT) is a quantum mechanical approach that can be highly accurate for modeling condensed phases, including reactive materials that are subject to the breaking and forming of chemical bonds. DFT calculations, though, are extremely computationally intensive, and are generally limited to exceedingly small system sizes and time scales. This is particularly true for f-electron solids such as plutonium, where the high computational effort due to electron correlations and minimizing to specific electronic spin-states (e.g., anti-ferromagnetic vs. ferromagnetic) limits standard calculations to systems of tens of atoms and precludes the determination of time-dependent properties (e.g., chemical kinetic data). In this regard, we have created a simplified Pu + H 2 /PuH 2 equilibrium model based on a small number of interaction energies determined from DFT calculations. We have determined that Pu-H bond energetics in the bulk are largely defined by nearest-neighbor interactions, only. In other words, a hydrogen atom in a d-Pu tetrahedral interstitial site will only experience perturbations to its H-Pu bonded interactions when there is an additional hydrogen ion in a directly adjacent adsorption site. This is due to the strong electronic screening from the Pu atoms, which essentially limits the spatial extent of how hydrogen ions interact with each other within the Pu lattice. As a result, we are able to pre-tabulate all essential Pu-H interaction energies and model PuH 2 formation via a Monte Carlo simulation approach. This allows us to greatly expand the spatial and time scales of our calculations while maintaining most of the accuracy of quantum methods. Our model can thus be extended to system sizes if 100,000 atoms or greater and can be used to model hydride equilibrium processes, all of which are computationally cost prohibitive with any standard quantum code. We have used our approach to compute a two-dimensional free energy heat map and temperature dependence at sub-atmospheric pressures for PuH 2 formation. Our results compare well to the limited available experimental data, in particular for the enthalpy of formation (DH). Our goal is to extend these results to compute nucleation parameters such as the critical nucleus size and nucleation growth rates for continuum models.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Analysis and Integration of the Hydraulic Fracturing Test Site-2 (HFTS-2) Comprehensive Dataset

Hydraulic Fracturing Test Site-2 (HFTS-2) is a field-based research experiment performed in the Permian (Delaware) Basin. The unique aspect of this program was the acquisition of a unique, comprehensive, diagnostic dataset. Additionally, shorter parent wells drilled three years before the child wells offered clear distinction between the stages influenced by parent-child effects and the stages without any effects. The goal of this study was to analyze and integrate this comprehensive diagnostic dataset to understand the areal and vertical extent of hydraulic fractures (HF). The paper also provides insights on the effects of parent wells’ depletion on child well HF geometry based on various monitoring methods and subsurface models. Areal and vertical coverage for all HFTS-2 wells during stimulation and depletion was estimated based on analysis and interpretation of diagnostics and advanced modeling results. HFTS-2 diagnostics included microseismic (MS), pre- and post-stimulation logs and cores, bottomhole gauges, and fiber optic (FO) data. The diagnostics results (MS, FO, image logs) were integrated and used to calibrate subsurface models. Additional field tests were designed and implemented for depletion monitoring. The tailored program for monitoring depletion included vertical and slant well pressures, interference testing, and a vertical strain depletion trial. Areal Coverage: Conventional MS (and FO MS) were used to compute HF dimensions, which were compared with diagnostics (FO strain, gauge, image logs) observations and calibrated subsurface models. A post-production interference test did not show offset well communication. Vertical Coverage: Vertical coverage during stimulation was monitored using a vertical monitoring well. The stronger mechanical strain signals showed good correlation with MS event intensities, geomechanical properties, and gauge inferences. Vertical depletion was estimated based on vertical/slant well gauges and strain depletion tests. Parent-Child Effects: Diagnostics and calibrated subsurface models show asymmetry in child well fracture geometries for stages that overlap parent wells. Child well image logs serve as a good indicator for parent Downloaded from http://onepetro.org/URTECONF/proceedings-pdf/21URTC/2-21URTC/D021S031R004/2477423/urtec-2021-5241-ms.pdf/1 by Carol Worster on 28 February 2022 URTeC 5241 well HF tracking. Child well MS events had an eastward bias, in line with pre-stimulation image logs, and was confirmed by parent well frac hits. Novel/Additive Information: The dataset presents a unique, over-constrained problem space to compare independent techniques to arrive at HF metrics (i.e., stimulation height and/or half-length), unlike a single source dataset, in which calibration is done using available data to guide predictions. Here, the asymmetry in HF geometry seen in the stages influenced by parent-child effects offers unique insights into well spacing and landing, which are key capital decisions the unconventional resources industry is seeking to optimize.

58 GEOSCIENCES↗
DOE OSTI2024

Physics-constrained machine learning for electrodynamics without gauge ambiguity based on Fourier transformed Maxwell’s equations

We utilize a Fourier transformation-based representation of Maxwell’s equations to develop physics-constrained neural networks for electrodynamics without gauge ambiguity, which we label the Fourier–Helmholtz–Maxwell neural operator method. In this approach, both of Gauss’s laws and Faraday’s law are built in as hard constraints, as well as the longitudinal component of Ampère–Maxwell in Fourier space, assuming the continuity equation. An encoder–decoder network acts as a solution operator for the transverse components of the Fourier transformed vector potential, $\hat{A}_⟂(k,t)$, whose two degrees of freedom are used to predict the electromagnetic fields. This method was tested on two electron beam simulations. Among the models investigated, it was found that a U-Net architecture exhibited the best performance as it trained quicker, was more accurate and generalized better than the other architectures examined. We demonstrate that our approach is useful for solving Maxwell’s equations for the electromagnetic fields generated by intense relativistic charged particle beams and that it generalizes well to unseen test data, while being orders of magnitude quicker than conventional simulations. We show that the model can be re-trained to make highly accurate predictions in as few as 20 epochs on a previously unseen data set.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Filtering with Marked Point Process Observations via Poisson Chaos Expansion

We study a general filtering problem with marked point process observations. The motivation comes from modeling financial ultra-high frequency data. First, we rigorously derive the unnormalized filtering equation with marked point process observations under mild assumptions, especially relaxing the bounded condition of stochastic intensity. Then, we derive the Poisson chaos expansion for the unnormalized filter. Based on the chaos expansion, we establish the uniqueness of solutions of the unnormalized filtering equation. Moreover, we derive the Poisson chaos expansion for the unnormalized filter density under additional conditions. To explore the computational advantage, we further construct a new consistent recursive numerical scheme based on the truncation of the chaos density expansion for a simple case. The new algorithm divides the computations into those containing solely system coefficients and those including the observations, and assign the former off-line.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Multi-reference approach to the calculation of photoelectron spectra including spin-orbit coupling

X-ray photoelectron spectra provide a wealth of information on the electronic structure. The extraction of molecular details requires adequate theoretical methods, which in case of transition metal complexes has to account for effects due to the multi-configurational and spin-mixed nature of the many-electron wave function. Here, the restricted active space self-consistent field method including spin-orbit coupling is used to cope with this challenge and to calculate valence- and core-level photoelectron spectra. The intensities are estimated within the frameworks of the Dyson orbital formalism and the sudden approximation. Thereby, we utilize an efficient computational algorithm that is based on a biorthonormal basis transformation. The approach is applied to the valence photoionization of the gas phase water molecule and to the core ionization spectrum of the [Fe(H{sub 2}O){sub 6}]{sup 2+} complex. The results show good agreement with the experimental data obtained in this work, whereas the sudden approximation demonstrates distinct deviations from experiments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2011

Dynamic estimation of three-dimensional cerebrovascular deformation from rotational angiography

Purpose: The objective of this study is to investigate the feasibility of detecting and quantifying 3D cerebrovascular wall motion from a single 3D rotational x-ray angiography (3DRA) acquisition within a clinically acceptable time and computing from the estimated motion field for the further biomechanical modeling of the cerebrovascular wall. Methods: The whole motion cycle of the cerebral vasculature is modeled using a 4D B-spline transformation, which is estimated from a 4D to 2D+t image registration framework. The registration is performed by optimizing a single similarity metric between the entire 2D+t measured projection sequence and the corresponding forward projections of the deformed volume at their exact time instants. The joint use of two acceleration strategies, together with their implementation on graphics processing units, is also proposed so as to reach computation times close to clinical requirements. For further characterizing vessel wall properties, an approximation of the wall thickness changes is obtained through a strain calculation. Results: Evaluation on in silico and in vitro pulsating phantom aneurysms demonstrated an accurate estimation of wall motion curves. In general, the error was below 10% of the maximum pulsation, even in the situation when substantial inhomogeneous intensity pattern was present. Experiments on in vivo data provided realistic aneurysm and vessel wall motion estimates, whereas in regions where motion was neither visible nor anatomically possible, no motion was detected. The use of the acceleration strategies enabled completing the estimation process for one entire cycle in 5-10 min without degrading the overall performance. The strain map extracted from our motion estimation provided a realistic deformation measure of the vessel wall. Conclusions: The authors' technique has demonstrated that it can provide accurate and robust 4D estimates of cerebrovascular wall motion within a clinically acceptable time, although it has to be applied to a larger patient population prior to possible wide application to routine endovascular procedures. In particular, for the first time, this feasibility study has shown that in vivo cerebrovascular motion can be obtained intraprocedurally from a 3DRA acquisition. Results have also shown the potential of performing strain analysis using this imaging modality, thus making possible for the future modeling of biomechanical properties of the vascular wall.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-T-620: Development of a Convolution/Superposition Dose Engine for CyberKnife System

Purpose: Current CyberKnife treatment planning system (TPS) provided two dose calculation algorithms: Ray-tracing and Monte Carlo. Ray-tracing algorithm is fast, but less accurate, and also can’t handle irregular fields since a multi-leaf collimator system was recently introduced to CyberKnife M6 system. Monte Carlo method has well-known accuracy, but the current version still takes a long time to finish dose calculations. The purpose of this paper is to develop a GPU-based fast C/S dose engine for CyberKnife system to achieve both accuracy and efficiency. Methods: The TERMA distribution from a poly-energetic source was calculated based on beam’s eye view coordinate system, which is GPU friendly and has linear complexity. The dose distribution was then computed by inversely collecting the energy depositions from all TERMA points along 192 collapsed-cone directions. EGSnrc user code was used to pre-calculate energy deposition kernels (EDKs) for a series of mono-energy photons The energy spectrum was reconstructed based on measured tissue maximum ratio (TMR) curve, the TERMA averaged cumulative kernels was then calculated. Beam hardening parameters and intensity profiles were optimized based on measurement data from CyberKnife system. Results: The difference between measured and calculated TMR are less than 1% for all collimators except in the build-up regions. The calculated profiles also showed good agreements with the measured doses within 1% except in the penumbra regions. The developed C/S dose engine was also used to evaluate four clinical CyberKnife treatment plans, the results showed a better dose calculation accuracy than Ray-tracing algorithm compared with Monte Carlo method for heterogeneous cases. For the dose calculation time, it takes about several seconds for one beam depends on collimator size and dose calculation grids. Conclusion: A GPU-based C/S dose engine has been developed for CyberKnife system, which was proven to be efficient and accurate for clinical purpose, and can be easily implemented in TPS.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Generalized and Mechanistic PV Module Performance Prediction From Computer Vision and Machine Learning on Electroluminescence Images

Electroluminescence (EL) imaging of photovoltiac (PV) modules offers high-speed, high-resolution information about device performance, affording opportunities for greater insight and efficiency in module characterization across manufacturing, research and development, and power plant operations and management. Predicting module electrical properties from EL image features is a critical step toward these applications. In this article, we demonstrate quantification of both generalized and performance mechanism-specific EL image features, using pixel intensity-based and machine learning classification algorithms. From EL image features, we build predictive models for PV module power and series resistance, using time-series current-voltage (I-V) and EL data obtained stepwise on five brands of modules spanning three Si cell types through two accelerated exposures: damp heat (DH) (85°C/85% RH) and thermal cycling (TC) (IEC 61215). Overall, 195 pairs of EL images and I-V characteristics were analyzed, yielding 11700 individual PV cell images. A convolutional neural network was built to classify cells by the severity of busbar corrosion with high accuracy (95%). Generalized power predictive models estimated the maximum power of PV modules from EL images with high confidence and an adjusted-R 2 of 0.88, across all module brands and cell types in extended DH and TC exposures. Mechanistic degradation prediction was demonstrated by quantification of busbar corrosion in EL images of three module brands in DH, and subsequent modeling of series resistance using these mechanism-specific EL image features. For modules exhibiting busbar corrosion, we demonstrated series resistance predictive models with adjusted-R 2 of up to 0.73.

14 SOLAR ENERGY↗
DOE OSTI2025

Real-Time Wave Energy Converter Control Using Instantaneous Frequency

Wave Energy Converters (WECs) rely on effective Power Take-Off (PTO) control strategies to maximize energy absorption under dynamic sea conditions. Traditional hydrodynamic modeling techniques may require computationally intensive convolution calculations, making real-time control implementation challenging. This paper presents an alternative approach by leveraging instantaneous frequency estimation to dynamically adjust PTO damping in response to varying wave frequencies. Two real-time frequency estimation methods are explored: the Hilbert Transform (HT) and Phase-Locked Loop (PLL). The Hilbert Transform method provides accurate frequency tracking but introduces a delayed response due to its dependence on causal data. Conversely, the PLL approach demonstrates strong potential in frequency tracking but requires careful gain tuning, particularly in complex sea states. Comparative evaluations across multiple test cases—including sinusoidal variations, amplitude steps, frequency step changes, and real-world JONSWAP spectrum waves—highlight the strengths and limitations of each method. The two different PTO control techniques across the various frequency estimation methods were tested under real-sea states using a state-space model of a point-absorbing Wave Energy Converter. The Capture Width Ratio (CWR) is used as a performance metric, with results showing that the HT achieves a 10.6% improvement, while the PLL estimation yields a 0.9% improvement relative to the fixed parameter control baseline. These results highlight the effectiveness of real-time frequency estimation in improving energy absorption compared to static control parameters.

WEC control↗
DOE OSTI2017

On the arithmetic intensity of high-order finite-volume discretizations for hyperbolic systems of conservation laws

It has been conjectured that higher-order discretizations for partial differential equations will have advantages over the lower-order counterparts commonly used today. The reasoning is that the increase in arithmetic operations will be more than offset by the reduction in data transfers and the increase in concurrent floating-point units. To evaluate this conjecture, the arithmetic intensity of a class of high-order finite-volume discretizations for hyperbolic systems of conservation laws is theoretically analyzed for spatial discretizations from orders three through eight in arbitrary dimensions. Additionally, three cache models are considered: the limiting cases of no cache and infinite cache as well as a finite-sized cache model. Models are validated experimentally by measuring floating-point operations and data transfers on an IBM Blue Gene/Q node. Theory and experiments demonstrate that high-order finite-volume methods will be able to provide increases in arithmetic intensity that will be necessary to make better utilization of on-node floating-point capability.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Investigating power capping toward energy-efficient scientific applications: Investigating Power Capping toward Energy-Efficient Scientific Applications

The emergence of power efficiency as a primary constraint in processor and system design poses new challenges concerning power and energy awareness for numerical libraries and scientific applications. Power consumption also plays a major role in the design of data centers, which may house petascale or exascale-level computing systems. At these extreme scales, understanding and improving the energy efficiency of numerical libraries and their related applications becomes a crucial part of the successful implementation and operation of the computing system. In this paper, we study and investigate the practice of controlling a compute system's power usage, and we explore how different power caps affect the performance of numerical algorithms with different computational intensities. Further, we determine the impact, in terms of performance and energy usage, that these caps have on a system running scientific applications. This analysis will enable us to characterize the types of algorithms that benefit most from these power management schemes. Our experiments are performed using a set of representative kernels and several popular scientific benchmarks. Lastly, we quantify a number of power and performance measurements and draw observations and conclusions that can be viewed as a roadmap to achieving energy efficiency in the design and execution of scientific algorithms.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2012

The Impact of New Technologies on Radiation Oncology Events and Trends in the Past Decade: An Institutional Experience

Purpose: To review the type and frequency of patient events from external-beam radiotherapy over a time period sufficiently long to encompass significant technology changes. Methods and Materials: Ten years of quality assurance records from January 2001 through December 2010 were retrospectively reviewed to determine the frequency of events affecting patient treatment from four radiation oncology process steps: simulation, treatment planning, data entry/transfer, and treatment delivery. Patient events were obtained from manual records and, from May 2007 onward, from an institution-wide database and reporting system. Events were classified according to process step of origination and segregated according to the most frequently observed event types. Events from the institution-wide database were evaluated to determine time trends. Results: The overall event rate was 0.93% per course of treatment, with a downward trend over time led by a decrease in treatment delivery events. The frequency of certain event types, particularly in planning and treatment delivery, changed significantly over the course of the study, reflecting technologic and process changes. Treatments involving some form of manual intervention carried an event risk four times higher than those relying heavily on computer-aided design and delivery. Conclusions: Although the overall event rate was low, areas for improvement were identified, including manual calculations and data entry, late-day treatments, and staff overreliance on computer systems. Reducing the incidence of pretreatment events is of particular importance because these were more likely to occur several times before detection and were associated with larger dosimetric impact. Further improvements in quality assurance systems and reporting are imperative, given the advent of electronic charting, increasing reliance on computer systems, and the potentially severe consequences that can arise from mistakes involving complex intensity-modulated or image-guided treatments.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

MultispeQ Beta: a tool for large-scale plant phenotyping connected to the open PhotosynQ network

Large-scale high-throughput plant phenotyping (sometimes called phenomics) is becoming increasingly important in plant biology and agriculture and is essential to cutting-edge plant breeding and management approaches needed to meet the food and fuel needs for the next century. Currently, the application of these approaches is severely limited by the availability of appropriate instrumentation and by the ability to communicate experimental protocols, results and analyses. To address these issues, we have developed a low-cost, yet sophisticated open-source scientific instrument designed to enable communities of researchers, plant breeders, educators, farmers and citizen scientists to collect high-quality field data on a large scale. The MultispeQ provides measurements in the field or laboratory of both, environmental conditions (light intensity and quality, temperature, humidity, CO 2 levels, time and location) and useful plant phenotypes, including photosynthetic parameters—photosystem II quantum yield (Φ II ), non-photochemical exciton quenching (NPQ), photosystem II photoinhibition, light-driven proton translocation and thylakoid proton motive force, regulation of the chloroplast ATP synthase and potentially many others—and leaf chlorophyll and other pigments. Plant phenotype data are transmitted from the MultispeQ to mobile devices, laptops or desktop computers together with key metadata that gets saved to the PhotosynQ platform (https://photosynq.org) and provides a suite of web-based tools for sharing, visualization, filtering, dissemination and analyses. We present validation experiments, comparing MultispeQ results with established platforms, and show that it can be usefully deployed in both laboratory and field settings. We present evidence that MultispeQ can be used by communities of researchers to rapidly measure, store and analyse multiple environmental and plant properties, allowing for deeper understanding of the complex interactions between plants and their environment.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Strangers in a foreign land: ‘Yeastizing’ plant enzymes

Abstract Expressing plant metabolic pathways in microbial platforms is an efficient, cost‐effective solution for producing many desired plant compounds. As eukaryotic organisms, yeasts are often the preferred platform. However, expression of plant enzymes in a yeast frequently leads to failure because the enzymes are poorly adapted to the foreign yeast cellular environment. Here, we first summarize the current engineering approaches for optimizing performance of plant enzymes in yeast. A critical limitation of these approaches is that they are labour‐intensive and must be customized for each individual enzyme, which significantly hinders the establishment of plant pathways in cellular factories. In response to this challenge, we propose the development of a cost‐effective computational pipeline to redesign plant enzymes for better adaptation to the yeast cellular milieu. This proposition is underpinned by compelling evidence that plant and yeast enzymes exhibit distinct sequence features that are generalizable across enzyme families. Consequently, we introduce a data‐driven machine learning framework designed to extract ‘yeastizing’ rules from natural protein sequence variations, which can be broadly applied to all enzymes. Additionally, we discuss the potential to integrate the machine learning model into a full design‐build‐test cycle.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

MaDaTS: Managing Data on Tiered Storage for Scientific Workflows

Scientific workflows are increasingly used in High Performance Computing (HPC) environments to manage complex simulation and analyses, often consuming and generating large amounts of data. However, workflow tools have limited support for managing the input, output and intermediate data. The data elements of a workflow are often managed by the user through scripts or other ad-hoc mechanisms. Technology advances for future HPC systems is redefining the memory and storage subsystem by introducing additional tiers to improve the I/O performance of data-intensive applications. These architectural changes introduce additional complexities to managing data for scientific workflows. Thus, we need to manage the scientific workflow data across the tiered storage system on HPC machines. In this paper, we present the design and implementation of MaDaTS (Managing Data on Tiered Storage for Scientific Workflows), a software architecture that manages data for scientific workflows. We introduce Virtual Data Space (VDS), an abstraction of the data in a workflow that hides the complexities of the underlying storage system while allowing users to control data management strategies. We evaluate the data management strategies with real scientific and synthetic workflows, and demonstrate the capabilities of MaDaTS. Our experiments demonstrate the flexibility, performance and scalability gains of MaDaTS as compared to the traditional approach of managing data in scientific workflows.

Ghoshal, Devarshi↗
DOE OSTI2016

Draco,Version 6.x.x

Draco is an object-oriented component library geared towards numerically intensive, radiation (particle) transport applications built for parallel computing hardware. It consists of semi-independent packages and a robust build system. The packages in Draco provide a set of components that can be used by multiple clients to build transport codes. The build system can also be extracted for use in clients. Software includes smart pointers, Design-by-Contract assertions, unit test framework, wrapped MPI functions, a file parser, unstructured mesh data structures, a random number generator, root finders and an angular quadrature component.

Thompson, Kelly↗