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DOE OSTI2016

Fault Tolerance Assistant (FTA): An Exception Handling Programming Model for MPI Applications

Future high-performance computing systems may face frequent failures with their rapid increase in scale and complexity. Resilience to faults has become a major challenge for large-scale applications running on supercomputers, which demands fault tolerance support for prevalent MPI applications. Among failure scenarios, process failures are one of the most severe issues as they usually lead to termination of applications. However, the widely used MPI implementations do not provide mechanisms for fault tolerance. We propose FTA-MPI (Fault Tolerance Assistant MPI), a programming model that provides support for failure detection, failure notification and recovery. Specifically, FTA-MPI exploits a try/catch model that enables failure localization and transparent recovery of process failures in MPI applications. We demonstrate FTA-MPI with synthetic applications and a molecular dynamics code CoMD, and show that FTA-MPI provides high programmability for users and enables convenient and flexible recovery of process failures.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Final Technical Report Grant No. DE-FG02-97ER45653 Lance E. De Long, Principal Investigator, University of Kentucky Period of Performance: 09/01/97 to 05/14/15

Prior to 1997, the PI had studied the unusual upper critical magnetic field phase boundaries of several novel or exotic types of superconductors, including charge density wave materials such as NbSe 2 , organic superconductors such as κ-(ET) 2 Cu[N(CN) 2 ]Br, high-temperature oxide superconductors such as (Ba,K)BiO 3 and the cuprates, heavy fermion superconductors such as U 6 Fe, UBe 13 , URu 2 Si 2 and UPt 3 , and re-entrant Kondo alloys such as (La,Ce)Al 2 and ferromagnetic superconductors such as ErRh 4 B 4 . Most of these materials exhibited marked positive or negative curvature of H C2 (T) which could not be explained by traditional pair-breaking models. It became clear that many of these materials had very short coherence lengths that made quantized vortices highly mobile (depinned) near the phase boundary, and the fundamental, equilibrium H C2 (T) difficult to measure using finite field or current drives. These problems made the underlying physics obscure, and led to erroneous interpretations of experimental data in terms of models of exotic superconducting pairing mechanisms. Around 1995, these issues led the PI to take advantage of modern electron beam lithography techniques for patterning superconducting and ferromagnetic thin films on the nanoscale. Primarily due to strong magnetic shape anisotropy effects, EBL patterning has led to enhanced control of the spatial distribution and dynamics of topological defects such as domain walls and magnetic vortices, which can create serious energy dissipation and other limitations for modern devices. Moreover, finite size and interface effects also strongly alter phase transition temperatures and phase boundaries of superconducting and magnetic films, as well as introduce barriers to equilibration, enhanced fluctuations and alter magnetic relaxation. Geometrical frustration and spin ice behavior can also be systematically controlled in patterned film media. Film patterning thus provides an excellent tool for conducting highly-controlled, fundamental studies of cooperative phases and interactions in artificially structured condensed matter.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2016

2014 Runtime Systems Summit. Runtime Systems Report

This report summarizes runtime system challenges for exascale computing, that follow from the fundamental challenges for exascale systems that have been well studied in past reports, e.g., [6, 33, 34, 32, 24]. Some of the key exascale challenges that pertain to runtime systems include parallelism, energy efficiency, memory hierarchies, data movement, heterogeneous processors and memories, resilience, performance variability, dynamic resource allocation, performance portability, and interoperability with legacy code. In addition to summarizing these challenges, the report also outlines different approaches to addressing these significant challenges that have been pursued by research projects in the DOE-sponsored X-Stack and OS/R programs. Since there is often confusion as to what exactly the term “runtime system” refers to in the software stack, we include a section on taxonomy to clarify the terminology used by participants in these research projects. In addition, we include a section on deployment opportunities for vendors and government labs to build on the research results from these projects. Finally, this report is also intended to provide a framework for discussing future research and development investments for exascale runtime systems, and for clarifying the role of runtime systems in exascale software.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

The trends high-contrast imaging survey. VI. Discovery of a mass, age, and metallicity benchmark brown dwarf

The mass and age of substellar objects are degenerate parameters leaving the evolutionary state of brown dwarfs ambiguous without additional information. Theoretical models are normally used to help distinguish between old, massive brown dwarfs and young, low-mass brown dwarfs but these models have yet to be properly calibrated. We have carried out an infrared high-contrast imaging program with the goal of detecting substellar objects as companions to nearby stars to help break degeneracies in inferred physical properties such as mass, age, and composition. Rather than using imaging observations alone, our targets are pre-selected based on the existence of dynamical accelerations informed from years of stellar radial velocity (RV) measurements. In this paper, we present the discovery of a rare benchmark brown dwarf orbiting the nearby (d = 18.69 ± 0.19 pc), solar-type (G9V) star HD 4747 ([Fe/H] = −0.22 ± 0.04) with a projected separation of only ρ = 11.3 ± 0.2 au (θ = 0.″6). Precise Doppler measurements taken over 18 years reveal the companion’s orbit and allow us to place strong constraints on its mass using dynamics (msin i=55.3±1.9M{sub Jup}). Relative photometry (ΔK {sub s} = 9.05 ± 0.14, M{sub K{sub s}}=13.00±0.14, K{sub s}−L{sup ′}=1.34±0.46) indicates that HD 4747 B is most likely a late-type L-dwarf and, if near the L/T transition, an intriguing source for studying cloud physics, variability, and polarization. We estimate a model-dependent mass of m=72{sub −13}{sup +3} M{sub Jup} for an age of 3.3{sub −1.9}{sup +2.3} Gyr based on gyrochronology. Combining astrometric measurements with RV data, we calculate the companion dynamical mass (m=60.2±3.3M{sub Jup}) and orbit (e = 0.740 ± 0.002) directly. As a new mass, age, and metallicity benchmark, HD 4747 B will serve as a laboratory for precision astrophysics to test theoretical models that describe the emergent radiation of brown dwarfs.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2013

A Generic Biogeochemical Module for Earth System Models: Next Generation BioGeoChemical Module (NGBGC), Version 1.0

Physical and biogeochemical processes regulate soil carbon dynamics and CO2 flux to and from atmosphere, influencing global climate changes. Integration of these processes into earth system models (e.g., community land models (CLM)), however, currently faces three major challenges: 1) extensive efforts are required to modify modeling structures and to rewrite computer programs to incorporate new or updated processes as new knowledge is being generated, 2) computational cost is prohibitively expensive to simulate biogeochemical processes in land models due to large variations in the rates of biogeochemical processes, and 3) various mathematical representations of biogeochemical processes exist to incorporate different aspects of fundamental mechanisms, but systematic evaluation of the different mathematical representations is difficult, if not possible. To address these challenges, we propose a new computational framework to easily incorporate physical and biogeochemical processes into land models. The new framework consists of a new biogeochemical module with a generic algorithm and reaction database so that new and updated processes can be incorporated into land models without the need to manually set up the ordinary differential equations to be solved numerically. The reaction database consists of processes of nutrient flow through the terrestrial ecosystems in plants, litter and soil. This framework facilitates effective comparison studies of biogeochemical cycles in an ecosystem using different conceptual models under the same land modeling framework. The approach was first implemented in CLM and benchmarked against simulations from the original CLM-CN code. A case study was then provided to demonstrate the advantages of using the new approach to incorporate a phosphorus cycle into the CLM model. To our knowledge, the phosphorus-incorporated CLM is a new model that can be used to simulate phosphorus limitation on the productivity of terrestrial ecosystems.

Fang, Yilin↗

DOE OSTI2015

SB6.0: The 6th International meeting on Synthetic Biology, July 9-11, 2013

The Synthetic Biology conference series (SBx.0) is the preeminent academic meeting in synthetic biology. Organized by the BioBricks Foundation, the SBx.0 conference series brings together leading researchers, students, industry executives, and policy makers from around the world to share, consider, debate, and plan efforts to make biology easier to engineer. Historically held every two years, the SBx.0 conferences are held in alternating locations in the United States, Europe, and Asia to encourage global participation and collaboration so that the ramifications of synthetic biology research and development are most likely to be safe ethical, and beneficial. On 9-11 July 2013, the 6th installment of the synthetic biology conference series (SB6.0) was held on the campus of Imperial College London (http://sb6.biobricks.org). The SB6.0 conference was attended by over 700 people, and many more were able to participate via video digital conference (http://sb6.biobricks.org/digital-conference/). Over the course of three days, the SB6.0 conference agenda included plenary sessions, workshops, and poster presentations covering topics ranging from the infrastructure needs arising when “Systematic Engineering Meets Biological Complexity” and design-led considerations for “Connecting People and Technologies” to discussions on “Engineering Biology for New Materials,” “Assessing Risk and Managing Biocontainment,” and “New Directions for Energy and Sustainability.” The $10,150 grant awarded by the U.S. Department of Energy (DE-SC0010233) to the BioBricks Foundation was used to provide partial reimbursement for the travel expenses of leading researchers from the United States to speak at the SB6.0 conference. A total of $9,450 was used to reimburse U.S. speakers for actual expenses related to the SB6.0 conference, including airfare (economy or coach only), ground transportation, hotel, and registration fees. In addition, $700 of the grant was used to offset direct administrative costs associated with selecting speakers (preparing announcements, evaluating abstract submissions) and handling travel arrangements. Leading U.S. researchers selected to speak at the SB6.0 conference included: Adam Arkin, Ph.D. Division Director of the Physical Biosciences Division at the Lawrence Berkeley National Laboratory and Professor in the Department of Bioengineering at UC Berkeley Jay Keasling, Ph.D. Professor in the Department of Bioengineering at Berkeley, Senior Faculty Scientist and Associate Laboratory Director of the Lawrence Berkeley National Laboratory, and Chief Executive Officer of the Joint BioEnergy Institute. Debra Mathews, Ph.D. Assistant Director for Science Programs for the Johns Hopkins Berman Institute of Bioethics, Assistant Professor in the Department of Pediatrics, and Affiliate Faculty in the McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins School of Medicine. Richard Murray, Ph.D. Thomas E. and Doris Everhart Professor of Control & Dynamical Systems and Bioengineering at Caltech. Sarah Richardson, Ph.D. Distinguished Postdoctoral Fellow in Genomics at the Lawrence Berkeley National Laboratory and the Department of Energy Joint Genome Institute. and others (for a complete listing of speakers presenting at the SB6.0 conference see http://sb6.biobricks.org/speakers/) The SB6.0 conference was the largest synthetic biology conference to date, and highlights of the SB6.0 conference have been published in a special issue of ACS Synthetic Biology (http://pubs.acs.org/toc/asbcd6/3/3). The BioBricks Foundation appreciates the support of the U.S. Department of Energy in helping to make this most influential and important conference in the field of synthetic biology a success.

42 ENGINEERING↗

DOE OSTI2025

An open-source hybrid unstructured mesh - CAD fusion multiphysics analysis workflow in SALAMANDER

Plasma facing components in fusion devices will endure extreme neutron and heat fluxes. To facilitate their design using simulation tools, the open-source Fusion Module, Fusion ENergy Integrated multiphys-X (FENIX) framework is being developed to model these components with a high-fidelity multi-physics multi-dimensional approach. It can iteratively resolve couplings between all the physics at play, from neutron radiation, to thermomechanics, to near-wall plasma dynamics. This framework is based on the Multiphysics Object Oriented Simulation Environment (MOOSE), which is developed by a collaboration of US National Laboratories since 2008, for advanced nuclear, geomechanics simulations and other applications. FENIX couples numerous simulation tools, including OpenMC, the Tritium Migration Analysis Program v8, the NekRS CFD software, and most MOOSE modules. For the coupling of radiation transport and other physics, FENIX supports a hybrid workflow between Computer Assisted Design (CAD) and unstructured mesh geometries. The CAD can be generated from skinning the unstructured mesh, to enable a coarse geometry for efficient particle transport, but still resolving the local material compositions and temperature gradients. Neutron transport is performed using DAGMC on the CAD, and Cardinal, integrated in FENIX, maps tallied quantities, such as the heat deposition or the tritium generation rates, from a tally volumetric mesh to the other physics’ unstructured mesh. This coupling was exercised on a simplified tokamak geometry, coupling neutron transport with the heat conduction equation, and on a monoblock divertor problem, coupling additionally with tritium migration. Mesh convergence studies highlight the importance of the mapping conservativeness. Coupling with thermo-mechanics is further enabled by the generalization of the approach to moving meshes. The presentation will include these coupled analysis as well as an update on status of the FENIX framework.

70 - PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2015

Trajectory Modulated Arc Therapy: A Fully Dynamic Delivery With Synchronized Couch and Gantry Motion Significantly Improves Dosimetric Indices Correlated With Poor Cosmesis in Accelerated Partial Breast Irradiation

Purpose: To develop planning and delivery capabilities for linear accelerator–based nonisocentric trajectory modulated arc therapy (TMAT) and to evaluate the benefit of TMAT for accelerated partial breast irradiation (APBI) with the patient in prone position. Methods and Materials: An optimization algorithm for volumetrically modulated arc therapy (VMAT) was generalized to allow for user-defined nonisocentric TMAT trajectories combining couch rotations and translations. After optimization, XML scripts were automatically generated to program and subsequently deliver the TMAT plans. For 10 breast patients in the prone position, TMAT and 6-field noncoplanar intensity modulated radiation therapy (IMRT) plans were generated under equivalent objectives and constraints. These plans were compared with regard to whole breast tissue volume receiving more than 100%, 80%, 50%, and 20% of the prescription dose. Results: For TMAT APBI, nonisocentric collision-free horizontal arcs with large angular span (251.5 ± 7.9°) were optimized and delivered with delivery time of ∼4.5 minutes. Percentage changes of whole breast tissue volume receiving more than 100%, 80%, 50%, and 20% of the prescription dose for TMAT relative to IMRT were −10.81% ± 6.91%, −27.81% ± 7.39%, −14.82% ± 9.67%, and 39.40% ± 10.53% (P≤.01). Conclusions: This is a first demonstration of end-to-end planning and delivery implementation of a fully dynamic APBI TMAT. Compared with IMRT, TMAT resulted in marked reduction of the breast tissue volume irradiated at high doses.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2013

Climate-Weather Modeling Studies Using a Prototype Global Cloud-System Resolving Model: ALCF-2 Early Science Program

Clouds remain the largest source of uncertainty in our understanding of global climate change. Different aspects of the planetary cloud field can provide positive and negative feedback to the Earth’s energy balance, and clouds of course are directly implicated in changes to the planetary distribution of precipitation. A fundamental problem in our current understanding of the role of clouds in the dynamics of climate are that current resolutions do not resolve the fundamental length scales associated with clouds. We expect our understanding of the role of clouds in climate to undergo a qualitative change as the resolutions of global models begin to encompass clouds. At these resolutions (which roughly scale with the tropopause height of 10km) non-hydrostatic dynamics become significant and deep convective processes are resolved. We are poised at the threshold of being able to run global scale simulations that include direct, nonparameterized, simulations of deep convective clouds. The goal of this research is to use the Argonne Leadership Computing Facility to explore the frontier of weather prediction and climate modeling with the newly developed Geophysical Fluid Dynamics (GFDL) global cloud-resolving model. A single unified atmospheric modeling system with a cubed-sphere dynamical core and bulk cloud micro-physics running at 3.5km resolutions was run with the goal of capturing the climatology of clouds and severe storms in a warming world. The ability to reproduce historical tropical storm statistics will be used as a test of this ground-breaking model.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

MLCommons Science Benchmarks

Benchmarks are a cornerstone of modern machine learning practice, providing standardized eval- uations that enable reproducibility, comparison, and scientific progress. Yet, as AI systems particularly deep learning models become increasingly dynamic, traditional static benchmarking approaches are losing their relevance. Models rapidly evolve in architecture, scale, and capability; datasets shift; and deployment contexts continuously change, creating a moving target for evaluation. Without adaptive benchmarking frame- works, both scientific assessment and real-world de- ployment risk becoming misaligned with actual system behavior. Drawing on our experience from MLCommons, educa- tional initiatives, and government programs such as the DOE s Million Parameter Consortium, we identify key barriers that hinder the broader adoption and utility of benchmarking in AI. These include substantial resource demands, limited access to specialized hardware, lack of expertise in benchmark design, and uncertainty among practitioners about how to relate benchmark results to their own application domains. Moreover, current benchmarks often emphasize peak performance on leadership-class hardware, offering limited guidance for more diverse, real-world deployment scenarios. We argue that benchmarking itself must become dy- namic in order to incorporate evolving models, updated data, and heterogeneous computational platforms while maintaining transparency, reproducibility, and inter- pretability. Democratizing this process requires not only technical innovation, but also systematic educational efforts spanning undergraduate to professional levels to develop sustained expertise in benchmark design and use. Finally, benchmarks should be framed and com- municated to support application-relevant comparisons, enabling both developers and users to make informed, context-sensitive decisions. Advancing dynamic and inclusive benchmarking practices will be essential to ensure that evaluation keeps pace with the evolving AI landscape and supports responsible, reproducible, and accessible AI deployment.

Hawks, Benjamin G. [Fermilab]↗

DOE OSTI2026

Final DOE-ASR Report for the Project “Using LASSO to bridge the gap between model and observations and to learn about atmospheric convection”

Atmospheric convection spans a wide range of spatial and temporal scales and involves complex interactions with the surrounding dynamic and thermodynamic environment, particularly over tropical continental regions. These processes remain a major source of uncertainty in weather and climate models, including persistent biases in the diurnal cycle of convective precipitation that directly affect estimates of climate sensitivity. Addressing these challenges requires the combined use of high-resolution observations and cloud-resolving modeling frameworks. In this context, the DOE Atmospheric Radiation Measurement (ARM) program’s Large-Eddy Simulation ARM Symbiotic Simulation and Observation (LASSO) activity provides a powerful platform that pairs comprehensive observations with numerical simulations to enable process-level understanding of atmospheric convection. Within this context, this Research and Development Partnership Pilot (RDPP) project was designed to initiate and expand DOE ARM/ASR research capacity at minority-serving institutions, while advancing scientific understanding of convective processes over the Amazon rainforest. Consistent with the RDPP mission, the project emphasized partnership development, training, and workforce capacity building alongside exploratory research activities. On the scientific side, the project produced two peer-reviewed journal articles, and one manuscript currently under review (see list in section 3.1). Together, these studies combine long-term ARM observations and cloud-resolving and convection-permitting modeling to investigate the environmental controls on the shallow-to-deep convective transition during the Amazon wet season. The results demonstrate the central role of early-day moisture preconditioning and large-scale dynamical forcing in regulating isolated deep convection, provide mechanistic insight into convective evolution, and establish physically informed modeling frameworks for future sensitivity experiments. These scientific outcomes are described in sections 2.1 to 2.3 and were disseminated in 8 conference presentations (see section 3.2) and 5 invited talks (see section 3.3), reflecting broad engagement with our community. Equally important, the project achieved its RDPP capacity-building objectives (see section 2.4). A sustained research partnership was established among the University of Maryland, Baltimore County (UMBC), Morgan State University (MSU), and Howard University (HU), and extended to include collaboration with Pacific Northwest National Laboratory (PNNL). The project organized multiple multi-day training events focused on ARM data, LASSO simulations, and quantitative analysis methods, directly engaging students, postdoctoral researchers, and faculty across institutions. These activities broadened participation in ASR research and led to independent adoption of LASSO workflows by students beyond the immediate project team. Finally, the project successfully positioned the participating institutions to pursue future DOE research. Preliminary scientific results, coupled with strengthened partnerships and technical capacity, enabled the submission of follow-on proposals to DOE ASR funding opportunities. In this way, the project fulfilled the RDPP goal of seeding durable research capacity and laying the foundation for larger-scale, sustained engagement with DOE ARM and ASR programs.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

hPIC2: A hardware-accelerated, hybrid particle-in-cell code for dynamic plasma-material interactions

The exascale era of high performance computing promises to bring the field of computational plasma physics ever closer to the goal of accurate multiscale modeling. Such computers will rely on hardware acceleration to offload work to dedicated components, notably general-purpose graphics processing units (GPUs). However, devices from different manufacturers require software to be written with different parallel programming models, greatly increasing the code maintenance burden of applications designed to perform on more than one such device. hPIC2 is a hybrid plasma simulation code developed with the Kokkos performance portability framework to target the architectures that will drive exascale computing for the foreseeable future. As a hybrid simulation code, hPIC2 investigates the simultaneous use of various plasma models on the same domain, at the same time. hPIC2 also optionally couples to RustBCA, which accurately models ion-material interactions using the binary collision approximation (BCA) method. In conclusion, hPIC2 therefore achieves scalable performance on a variety of computing architectures when simulating complex and diverse plasmas, particularly near plasma-material interfaces.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

A shared-mobility-based framework for evacuation planning and operations under forecast uncertainty

To meet evacuation needs from carless populations who need personalized assistance to evacuate safely, in this article we propose a ridesharing-based evacuation program that recruits volunteer drivers before a disaster strikes, and then matches volunteer drivers with evacuees once demand is realized. Here we optimize resource planning and evacuation operations under uncertain spatiotemporal demand, and construct a two-stage stochastic mixed-integer program to ensure high demand fulfillment rates. We consider three formulations to improve the number of evacuees served, by minimizing an expected penalty cost, imposing a probabilistic constraint, and enforcing a constraint on the conditional value at risk of the total number of unserved evacuees, respectively. We discuss the benefits and disadvantages of the different risk measures used in the three formulations, given certain carless population sizes and the variety of evacuation modes available. We also develop a heuristic approach to provide quick, dynamic and conservative solutions. We demonstrate the performance of our approaches using five different networks of varying sizes based on regions of Charleston County, South Carolina, an area that experienced a mandatory evacuation order during Hurricane Florence, and utilize real demographic data and hourly traffic count data to estimate the demand distribution.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

Impact of Dutch nuclear fuel cycle scenarios on radionuclide inventory intended for disposal - 15201

One of the main subjects of Dutch five-year research program into geological disposal of radioactive waste, OPERA, is the post-closure radiological safety assessment. Reliable estimates of the radionuclide inventory and matrix composition are an important input for the safety assessment. The amounts and categories of waste foreseen to be disposed in 2130 the Netherlands have been compiled by the Dutch waste management organisation COVRA. Additionally, NRG has made a characterisation of the waste (radionuclides and matrix). This reference inventory is based on the Dutch base nuclear energy scenario of operating the Dutch single reactor, the Borssele NPP, until its presently scheduled shut-down in 2033. However, changes in the presently adopted nuclear fuel cycle strategy in the Netherlands may impact both the quantities of generated radioactive waste and its composition, and therefore the source term of the radiological safety assessment. To get a grip on this uncertainty a set of alternative future fuel cycle scenarios in the Netherlands, which are in compliance with scenarios formulated in the Dutch vision documents, has been considered and evaluated. The scenarios have been analysed with the computer code DANESS, 'Dynamic Analysis of Nuclear Energy System Strategies'. The result of the DANESS analyses concerns the amount and type of the radioactive waste to be finally disposed in a deep geological repository. This also includes the inventories of the individual radionuclides that are relevant for the post-closure safety assessment to be performed in a subsequent stage of the OPERA program. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

PyCDFT: A Python package for constrained density functional theory

In this paper, we present PyCDFT, a Python package to compute diabatic states using constrained density functional theory (CDFT). PyCDFT provides an object-oriented, customizable implementation of CDFT, and allows for both single-point self-consistent-field calculations and geometry optimizations. PyCDFT is designed to interface with existing density functional theory (DFT) codes to perform CDFT calculations where constraint potentials are added to the Kohn–Sham Hamiltonian. Here, we demonstrate the use of PyCDFT by performing calculations with a massively parallel first-principles molecular dynamics code, Qbox, and we benchmark its accuracy by computing the electronic coupling between diabatic states for a set of organic molecules. We show that PyCDFT yields results in agreement with existing implementations and is a robust and flexible package for performing CDFT calculations. The program is available at https://dx.doi.org/10.5281/zenodo.3821097.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2017

Hydrophilic domain structure in polymer exchange membranes: Simulations of NMR spin diffusion experiments to address ability for model discrimination

Here, we detail the development of a flexible simulation program (NMR_DIFFSIM) that solves the nuclear magnetic resonance (NMR) spin diffusion equation for arbitrary polymer architectures. The program was used to explore the proton ( 1 H) NMR spin diffusion behavior predicted for a range of geometrical models describing polymer exchange membranes. These results were also directly compared with the NMR spin diffusion behavior predicted for more complex domain structures obtained from molecular dynamics (MD) simulations. The numerical implementation and capabilities of NMR_DIFFSIM were demonstrated by evaluating the experimental NMR spin diffusion behavior for the hydrophilic domain structure in sulfonated Diels-Alder Poly(Phenylene) (SDAPP) polymer membranes. The impact of morphology variations as a function of sulfonation and hydration level on the resulting NMR spin diffusion behavior were determined. These simulations allowed us to critically address the ability of NMR spin diffusion to discriminate between different structural models, and to highlight the extremely high fidelity experimental data required to accomplish this. A direct comparison of experimental double-quantum-filtered 1 H NMR spin diffusion in SDAPP membranes to the spin diffusion behavior predicted for MD-proposed morphologies revealed excellent agreement, providing experimental support for the MD structures at low to moderate hydration levels.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Model Predictive Control of Discrete-Continuous Energy Systems via Generalized Disjunctive Programming

Generalized Disjunctive Programming (GDP) provides an alternative framework to model optimization problems with both discrete and continuous variables. The key idea behind GDP involves the use of logical disjunctions to represent discrete decisions in the continuous space, and logical propositions to denote algebraic constraints in the discrete space. Compared to traditional mixed-integer programming (MIP), the inherent logic structure in GDP yields tighter relaxations that are exploited by global branch and bound algorithms to improve solution quality. In this paper, we present a general GDP model for optimal control of hybrid systems that exhibit both discrete and continuous dynamics. Specifically, we use GDP to formulate a model predictive control (MPC) model for piecewise-affine systems with implicit switching logic. As an example, the GDP-based MPC approach is used as a supervisory control to improve energy efficiency in residential buildings with binary on/off, relay-based thermostats. A simulation study is used to demonstrate the validity of the proposed approach, and the improved solution quality compared to existing MIPbased control approaches.

Bhattacharya, Arnab↗

DOE OSTI2021

3D simulations of vertical displacement events in tokamaks: A benchmark of M3D-C 1 , NIMROD, and JOREK

In recent years, the nonlinear 3D magnetohydrodynamic codes JOREK, M3D-C1, and NIMROD developed the capability of modeling realistic 3D vertical displacement events (VDEs) including resistive walls. In this paper, a comprehensive 3D VDE benchmark is presented between these state-of-the-art codes. The simulated case is based on an experimental NSTX plasma but with a simplified rectangular wall. There are differences between the physics models and numerical methods, and the VDE evolution leads to sensitivities on the initial conditions that cannot be avoided as can be done in edge localized modes (ELM) and sawtooth simulations (due to the non-cyclical nature of VDEs). Nonetheless, the comparison serves to quantify the level of agreement in the relevant quantities used to characterize disruptions, such as the 3D wall forces and energy decay. The results bring confidence regarding the use of the mentioned codes for disruption studies, and they distinguish aspects that are specific to the models used (e.g., reduced vs full MHD models). Here, the simulations show important 3D fea- tures for a NSTX plasma, such as the self-consistent evolution of the halo current and the origin of the wall forces. In contrast to other reduced MHD models based on an ordering in the aspect ratio, the ansatz-based JOREK reduced MHD model allows capturing many aspects of the 3D dynamics even in the spherical tokamak limit considered here.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗