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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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This note argues that parallel-in-time methods will be necessary for doing high-fidelity time-dependent simulations in the future. A “proof” is given to support the argument and to provide a framework for debate. The effect of a parallel-in-time paradigm on scientific computing practice is also discussed.
Geospatial Artificial Intelligence (GeoAI), as the integration of geospatial studies and AI, has become one of the fastest-developing research directions in spatial data science and geography. This rapid change in the field calls for a deeper understanding of the recent developments and envision where the field is going in the near future. In this work, we provide a quantitative analysis of the GeoAI literature from the spatial, temporal, and semantic aspects. We briefly discuss the history of AI and GeoAI by highlighting some pioneering work. Then we discuss the current landscape of GeoAI by selecting five representative subdomains including remote sensing, urban computing, Earth system science, cartography, and geospatial semantics. Finally, we highlight several unique future research directions of GeoAI which are classified into two groups: GeoAI method development challenges and GeoAI Ethics challenges. Topics include heterogeneity-aware GeoAI, knowledge-guided GeoAI, spatial representation learning, geo-foundation models, fairness-aware GeoAI, privacy-aware GeoAI, as well as interpretable and explainable GeoAI. We hope our review of GeoAI’s past, present, and future is comprehensive and can enlighten the next generation of GeoAI research.
As chemistry expands to more complex and interdisciplinary areas, a new generation of diverse researchers must engage with science and learn effective cross-disciplinary collaboration and communication. To these ends, we designed and implemented In the Mix, a graduate student-led, two-day workshop for undergraduate students promoting collaborative science in the context of energy storage innovations. Here, the interactive workshop was designed for future and emerging researchers to gain hands-on experience with data science, computational chemistry, and electrochemistry techniques that are critical for developing materials for battery technologies. Participants also visited commercial renewable energy facilities to help them connect discovery-based research with industry and broader societal considerations. The workshop content and structure ensured that participants experienced the interrelatedness of the fields and understood the importance of collaborative research to yield scientific advances with real-world applications. An external team evaluated the workshop and participants’ perceptions of their experiences. While our research context was energy storage, the workshop goals and outcomes are applicable to other contexts. Interdisciplinary, experiential workshops are a key avenue to broadening participation in science and research, and the ideas presented here can be readily modified for other scientific contexts and/or incorporated as broader impact activities.
The dawn of quantum computing brings on a revolution in the way combinatorially complex power system problems such as Unit Commitment are solved. The Unit Commitment problem complexity is expected to increase in the future because of the trend toward the increase of penetration of intermittent renewables. Even though quantum computing has proven effective for solving a host of problems, its applications for power systems’ problems have been rather limited. Here, in this paper, a quantum unit commitment is innovatively formulated and the quantum version of the decomposition and coordination alternate direction method of multipliers (ADMM) is established. The above is achieved by devising quantum algorithms and by exploiting the superposition and entanglement of quantum bits (qubits) for solving subproblems, which are then coordinated through ADMM to obtain feasible solutions. The main contributions of this paper include: 1) the innovative development of a quantum model for Unit Commitment; 2) development of decomposition and coordination-supported framework which paves the way for the utilization of limited quantum resources to potentially solve the large-scale discrete optimization problems; 3) devising the novel quantum distributed unit commitment (QDUC) to solve the problem in a larger scale than currently available quantum computers are capable of solving. The QDUC results are compared with those from its classical counterpart, which validate the efficacy of quantum computing.
An intensive R&D and programming effort is required to accomplish new challenges posed by future experimental high-energy particle physics (HEP) programs. The GeantV project aims to narrow the gap between the performance of the existing HEP detector simulation software and the ideal performance achievable, exploiting latest advances in computing technology. The project has developed a particle detector simulation prototype capable of transporting in parallel particles in complex geometries exploiting instruction level microparallelism (SIMD and SIMT), task-level parallelism (multithreading) and high-level parallelism (MPI), leveraging both the multi-core and the many-core opportunities. We present preliminary verification results concerning the electromagnetic (EM) physics models developed for parallel computing architectures within the GeantV project. In order to exploit the potential of vectorization and accelerators and to make the physics model effectively parallelizable, advanced sampling techniques have been implemented and tested. In this paper we introduce a set of automated statistical tests in order to verify the vectorized models by checking their consistency with the corresponding Geant4 models and to validate them against experimental data.
Generating mocks for future sky surveys requires large volumes and high resolutions, which is computationally expensive even for fast simulations. Here we try to develop numerical schemes to calibrate various halo and matter statistics in fast low resolution simulations compared to high resolution N-body and hydrodynamic simulations. For the halos, we improve the initial condition resolution and develop a halo finder "relaxed-FoF", where we allow different linking lengths for different halo mass and velocity dispersions. We show that our relaxed-FoF halo finder improves the common statistics, such as halo bias, halo mass function, halo auto power spectrum, cross correlation coefficient with the reference halo catalog, and halo-matter cross power spectrum. We also calibrate small-scale velocities of small halos to improve the power spectrum in redshift space. For the matter statistics, we incorporate the potential gradient descent (PGD) method into fast simulations to improve the matter distribution at nonlinear scales. By building a lightcone output, we show that the PGD method significantly improves the weak lensing convergence tomographic power spectrum. With these improvements FastPM is comparable to the high resolution full N-body simulation of the same mass resolution, with two orders of magnitude fewer time steps. These techniques can be used to improve the halo and matter statistics of FastPM simulations for mock catalogs of future surveys such as DESI and LSST.
Cardiovascular pathologies, such as a brain aneurysm, are affected by the global blood circulation as well as by the local microrheology. Hence, developing computational models for such cases requires the coupling of disparate spatial and temporal scales often governed by diverse mathematical descriptions, e.g., by partial differential equations (continuum) and ordinary differential equations for discrete particles (atomistic). However, interfacing atomistic-based with continuum-based domain discretizations is a challenging problem that requires both mathematical and computational advances. We present here a hybrid methodology that enabled us to perform the first multiscale simulations of platelet depositions on the wall of a brain aneurysm. The large scale flow features in the intracranial network are accurately resolved by using the high-order spectral element Navier–Stokes solver NεκTαr. The blood rheology inside the aneurysm is modeled using a coarse-grained stochastic molecular dynamics approach (the dissipative particle dynamics method) implemented in the parallel code LAMMPS. The continuum and atomistic domains overlap with interface conditions provided by effective forces computed adaptively to ensure continuity of states across the interface boundary. A two-way interaction is allowed with the time-evolving boundary of the (deposited) platelet clusters tracked by an immersed boundary method. The corresponding heterogeneous solvers (NεκTαr and LAMMPS) are linked together by a computational multilevel message passing interface that facilitates modularity and high parallel efficiency. Results of multiscale simulations of clot formation inside the aneurysm in a patient-specific arterial tree are presented. We also discuss the computational challenges involved and present scalability results of our coupled solver on up to 300 K computer processors. Validation of such coupled atomistic-continuum models is a main open issue that has to be addressed in future work.
The CMS experiment at CERN employs a distributed computing infrastructure to satisfy its data processing and simulation needs. The CMS Submission Infrastructure team manages a dynamic HTCondor pool, aggregating mainly Grid clusters worldwide, but also HPC, Cloud and opportunistic resources. This CMS Global Pool, which currently involves over 70 computing sites worldwide and peaks at 350k CPU cores, is employed to successfully manage the simultaneous execution of up to 150k tasks. While the present infrastructure is sufficient to harness the current computing power scales, CMS latest estimates predict a noticeable expansion in the amount of CPU that will be required in order to cope with the massive data increase of the High-Luminosity LHC (HL-LHC) era, planned to start in 2027. This contribution presents the latest results of the CMS Submission Infrastructure team in exploring and expanding the scalability reach of our Global Pool, in order to preventively detect and overcome any barriers in relation to the HL-LHC goals, while maintaining high effciency in our workload scheduling and resource utilization.
In the large LHC experiments the majority of computing resources are provided by the participating countries. These resource pledges account for more than three quarters of the total available computing. The experiments are asked to give indications of their requests three years in advance and to evolve these as the details and constraints become clearer. In this paper we will discuss the resource planning techniques used in CMS to predict the computing resources several years in advance. We will discuss how we attempt to implement the activities of the computing model in spread-sheets and formulas to calculate the needs. We will talk about how those needs are reflected in the 2012 running and how the planned long shutdown of the LHC in 2013 and 2014 impacts the planning process and the outcome. In the end we will speculate on the computing needs in the second major run of LHC.
In FY-2015, the development of MOOSE was driven by the needs of the NEAMS MOOSE-based applications, BISON, MARMOT, and RELAP-7. An emphasis was placed on the continued upkeep and improvement MOOSE in support of the product line integration goals. New unified documentation tools have been developed, several improvements to regression testing have been enforced and overall better software quality practices have been implemented. In addition the Multiapps and Transfers systems have seen significant refactoring and robustness improvements, as has the “Restart and Recover” system in support of Multiapp simulations. Finally, a completely new “Control Logic” system has been engineered to replace the prototype system currently in use in the RELAP-7 code. The development of this system continues and is expected to handle existing needs as well as support future enhancements.
This study looks at the high-level trade-offs for interconnect architectures that are likely to be available in HPC machines in the 2021-2022 timeframe. The study uses multiple simulation models to look at the effects of varying machine size, injection bandwidth and topology for a selected set of benchmark communication patterns. The studies show the sensitivity of various applications to these network parameters. This study can help inform decisions on node size versus number of nodes, which topology is most efficient for our applications, how much injection bandwidth to provision, etc. These simulations only look at interconnects with maximally configured global bandwidth, and studies of networks configured with less will be the subject of future milestones.
This document presents a Tribal Telecom/Internet Service Provider (ISP) internet protocol, version 6 (IPv6) business planning advisory. It includes examples of business areas, services, challenges, benefits, and a vision for the future.
RASM is a multi-disciplinary project, which brings together researchers from six state universities, one military postgraduate school, and one DoE laboratory to address the core modeling objectives of the arctic research community articulated in the Arctic System Modeling report by Roberts et al. (2010b). This report advocates the construction of a regional downscaling tool to generate probabilistic decadal projections of Greenland ice sheet retreat, evolution of arctic sea ice cover, changes in land surface vegetation, and regional processes leading to arctic amplification. Unified coupled models such as RASM are ideal for this purpose because they simulate fine-scale physics, essential for the realistic representation of intra-annual variability, in addition to processes fundamental to long term climatic shifts (Hurrell et al. 2009). By using RASM with boundary conditions from a global model, we can generate many-member ensembles essential for understanding uncertainty in regional climate projections (Hawkins and Sutton 2009). This probabilistic approach is computationally prohibitive for high-resolution global models in the foreseeable future, and also for regional models interactively nested within global simulations. Yet it is fundamental for quantifying uncertainty in decadal forecasts to make them useful for decision makers (Doherty et al. 2009). For this reason, we have targeted development of ensemble generation techniques as a core project task (Task 4.5). Environmental impact assessment specialists need high-fidelity regional ensemble projections to improve the accuracy of their work (Challinor et al. 2009; Moss et al. 2010). This is especially true of the Arctic, where economic, social and national interests are rapidly reshaping the high north in step with regional climate change. During the next decade, considerable oil and gas discoveries are expected across many parts of the marine and terrestrial Arctic (Gautier et al. 2009), the economics of the Northern Sea Route will steadily improve (Arctic Council 2009), and sovereign claims over the Arctic Ocean will increasingly be subject to international negotiations (Proelss 2009). Issues such as these have led to an expanding demand for Arctic climate projections to aid national and commercial decisions. However, detailed information from existing models is lacking. RASM will enhance the existing Arctic system modeling capabilities and align them with the scientific and societal needs outlined above. The science involved in the development of RASM will be integrated with teaching and training at a few different levels. RASM PIs will supervise at least six postdoctoral and doctoral students with additional involvement of undergraduate research assistants. RASM postdoctoral fellows and graduate trainees will benefit from being a part of a large, collaborative, and multi-disciplinary research projects employing state-of-the-art modeling and computational tools. They will also have the opportunity to contribute to the UTEP-led education and outreach effort developed specifically for RASM. The UTEP team will use RASM as a platform for education and outreach. First, we will develop products for public dissemination, such as curriculum units, lesson plans and other materials (simulations, movies, and images) for use by students and teachers in high school and university classrooms. Second, we will facilitate bringing RASM PIs, postdocs, and graduate students into the classroom, through electronic mentorship and by contributing content via online lectures and presentations. Third, by the use of problem-based learning (PBL) approaches, we will provide real-world scenarios and problems enabling students to do research and develop position papers or presentations on topics related to RASM. (Problem-based learning is a student-centered, inquiry-based approach in which students work in teams to solve challenging, open-ended problems.) Fourth, we will develop teacher-training materials that will be developed into workshops (face-to-face or online) to help teachers understand how to use the materials we supply. Overall, we will provide a broad, and long-term RASM program for education and outreach. RASM education and outreach activities will target underrepresented populations - in particular, Hispanic students and teachers. For example, the UTEP student body is more than 70 percent Hispanic and 55 percent female, and the majority of students are the first in their families to attend college. Students from underrepresented groups will be given the chance to expand their understanding of the different scientific knowledge and processes associated with RASM. We will develop an eye-catching RASM website to include information about the project, model results and educational materials, and lists of RASM publications and presentations.
This summer I worked in the tools group of the variable-precision computing effort at Lawrence Livermore National Laboratory (LLNL). I coordinated an integration project that combined three existing tools into a single pipeline for automated mixed-precision tuning at the source code level. This involved coordinating efforts between several different project groups and making improvements to all three tools. Two of the tools are already available publicly as open-source projects, and the third has been submitted for release. This document serves as an overview of the summer's efforts, focusing on our contributions as well as ideas for future work.
ABSTRACT As we move towards future galaxy surveys, the three-point statistics will be increasingly leveraged to enhance the constraining power of the data on cosmological parameters. An essential part of the three-point function estimation is performing triplet counts of synthetic data points in random catalogues. Since triplet counting algorithms scale at best as $\mathcal {O}(N^2\log N)$ with the number of particles and the random catalogues are typically at least 50 times denser than the data; this tends to be by far the most time-consuming part of the measurements. Here, we present a simple method of computing the necessary triplet counts involving uniform random distributions through simple one-dimensional integrals. The method speeds up the computation of the three-point function by orders of magnitude, eliminating the need for random catalogues, with the simultaneous pair and triplet counting of the data points alone being sufficient.
This presentation on assessing the impact of sensitivity/uncertainty selection criteria on computational bias prediction starts with providing a motivation and scope. This presentation then gives the selection criteria description with the method and application. This presentation finishes with results, conclusions, and future work.
During the reporting period, research at PNNL focused on two tasks within the DOE Fusion Blanket and Fuel Cycle Program. Research on Task 1, Tritium Extraction from Pb-Li and He Using a Vacuum Permeator, focused on atomistic modeling to better understand tritium transport in Pd-coated V vacuum permeators. As a lower cost alternative to Pd permeators, thin coatings of Pd (or other noble metals) can be deposited over a substrate like V. However, the permeation performance of composite metal membranes degrades over time, due to the formation of intermetallics at the coating-substrate interface. Computational studies were performed to better understand tritium transport through these Pd-V intermetallics. The results of the FY22 Pd-V modeling study were recently submitted for publication in Computational Materials Science and presented at the Technology of Fusion Energy conference. Future work in this area will focus on interdiffusion barriers to prevent intermetallic formation that is deleterious to tritium transport. There are opportunities for collaboration with researchers at the Colorado School of Mines, who are manufacturing and testing candidate interdiffusion barriers. Research on Task 3, Solid Breeder Materials, included ion irradiation and post-irradiation characterization of lithium orthosilicate (Li 4 SiO 4 ) and lithium metasilicate (Li 2 SiO 3 ) to improve fundamental understanding of irradiation effects, in combination with atomistic modeling focused on the energetics of He clustering in these two ceramic phases. The results of the study suggested that the Li 4 SiO 4 phase, which is more desirable as a solid breeder due to its higher Li density, was amorphized during ion irradiation while the Li 2 SiO 3 phase appeared to be more resistant to irradiation damage. It is possible that Li loss contributed to the poor irradiation performance of the Li 4 SiO 4 , and some thoughts are provided regarding coatings that could be applied to solid breeders like this to prevent Li loss at elevated temperature while not hindering tritium diffusion. The results of the FY22 ion irradiation study were recently submitted for publication in Journal of Nuclear Materials and presented at the 22nd International Conference on Ion Beam Modification of Materials. Future work in this area will focus on Li-rich ceramics such as Li 5 AlO 4 and Li 8 ZrO 6 that have high Li density and should provide rapid tritium release based on previous work with less Li-rich ceramics.
Verifying the structural integrity of passive components in light water and advanced reactors will be necessary to ensure safe, long-term operations of the existing U.S. nuclear fleet. This objective can be achieved through nondestructive condition monitoring techniques, which can be integrated with plant operations to quantify the “state of health” of structural materials in real-time. While nondestructive methods for monitoring many classes of degradation (such as fatigue or stress corrosion cracking) are relatively advanced, this is not the case for degradation caused by irradiation. The development of nondestructive evaluation technologies for these types of degradation will require advanced materials characterization techniques and tools that enable comprehensive understanding of nuclear reactor material microstructural and behavioral changes under extreme operating environments. Irradiation-induced degradation of reactor steels causes changes in their microstructure that impacts their micro-magnetic properties. In this paper, we describe preliminary results of integrating advanced material characterization techniques with meso-scale computational models. In the future, this will help to provide an interpretive understanding of the state of degradation in structural materials. Microstructural data are presented from monocrystalline Fe and are correlated with variable-field magnetic force microscopy and micro-magnetic measurements. Ongoing research is focused on extending the measurements and models on thin films to gain insights into the structural state of irradiated materials and the resulting impact on magnetic properties. Preliminary conclusions from these correlations are presented, and next steps described.