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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Efficient, Compact, and Smooth Variable Propulsion Motor (Final Report)

In this project, a new architecture of highly efficient hydraulic motor was developed for the propulsion of off-highway vehicles. The motor uses an adjustable linkage driving a cam to vary the displacement of the piston, resulting in a Variable Displacement Linkage Motor (VDLM). The motor uses low friction rolling element bearings to significantly reduce mechanical friction, especially in the demanding low-speed high-torque conditions experienced by off-highway vehicles. The VDLM has high torque capabilities for its size due to the radial piston packaging and use of a multi-lobe cam. A VDLM is very smooth due to the ability to tune the torque ripple through the design of the cam profile. The project was divided into three periods. During the first period, a dynamic model was constructed of the motor to predict the performance of the motor and the vehicle. During the second period, a single-cylinder learning prototype was designed, built, and tested to validate the models constructed in the first period. In the third period, a multi-cylinder prototype motor was optimized, designed, fabricated, and tested. The motor demonstrated excellent mechanical efficiency (above 92.5% across the range of displacements), but the experimentally measure volumetric efficiency was lower than expected due to higher leakage rates created by the poor tolerance control on the prototype. To validate the dynamic models developed in the first period and better understand design trade-offs. In the third period a multi-cylinder concept demonstration prototype will be designed, fabricated, and tested. The final prototype will be tested on a motor dynamometer and will be utilized in hardware-in-the-loop testing to demonstrate its efficiency and performance impacts on the overall drive train. The experimental results were used in a drive train simulation of a compact track loader operating through a drive cycle. Using the VDLM in a hydrostatic circuit yielded 17.1% reduction in fuel consumption and 36.5% reduction in a series hybrid transmission.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Hybrid geological modeling: Combining machine learning and multiple-point statistics

Accurately modeling and constructing a geologically realistic subsurface model remains an outstanding problem as the morphology controls the flow behaviors. Particularly, one of the pattern-based methods, namely cross-correlation based simulation, has been proved to be an effective way to reconstruct a realistic model, at both small and large scales. However, conditioning to point data in the large-scale problems is still a crucial issue in these algorithms, since there is always a trade-off between the quality of the realizations and the degree of point data reproduction. Specifically, it is not practical to build a training image (TI) which includes all the possibilities and variabilities. Therefore, finding a pattern that can represent the point data and, at the same time, preserving the connectivities is difficult. This leads to producing highly-connected realizations with a significant mismatch or poor models with a reasonable degree of point data reproduction. To accurately reproduce the densely distributed hard data, pixel-based methods can also produce some unrealistic artifacts around the hard data. In this paper, to overcome this challenge, however, we use pattern-based methods as they often produce more disconnected geobodies when dealing with dense hard data, and proposed a hybrid algorithm using the pattern-based methods and convolutional neural network (CNN). The trained CNN model is utilized to improve the quality of conditioning to point data for the original realizations generated by the pattern-based algorithm. As such, the mismatch locations are identified, and the same regions are used in the training of CNN to mimic the procedure through which a missing region can be filled. To evaluate the performance of the proposed hybrid algorithm, it is tested on cases with different dimensions and different numbers of facies. Then, the newly improved realizations are compared with the initial realizations generated by the pattern-based algorithm. The comparison is also conducted by the flow simulation test. And it indicates that the proposed hybrid algorithm can better reproduce the point data, while the connectivities are better preserved.

58 GEOSCIENCES↗
DOE OSTI2023

Parallelized domain decomposition for multi-dimensional Lagrangian random walk mass-transfer particle tracking schemes

Lagrangian particle tracking schemes allow a wide range of flow and transport processes to be simulated accurately, but a major challenge is numerically implementing the inter-particle interactions in an efficient manner. This article develops a multi-dimensional, parallelized domain decomposition (DDC) strategy for mass-transfer particle tracking (MTPT) methods in which particles exchange mass dynamically. We show that this can be efficiently parallelized by employing large numbers of CPU cores to accelerate run times. In order to validate the approach and our theoretical predictions we focus our efforts on a well-known benchmark problem with pure diffusion, where analytical solutions in any number of dimensions are well established. In this work, we investigate different procedures for “tiling” the domain in two and three dimensions (2-D and 3-D), as this type of formal DDC construction is currently limited to 1-D. An optimal tiling is prescribed based on physical problem parameters and the number of available CPU cores, as each tiling provides distinct results in both accuracy and run time. We further extend the most efficient technique to 3-D for comparison, leading to an analytical discussion of the effect of dimensionality on strategies for implementing DDC schemes. Increasing computational resources (cores) within the DDC method produces a trade-off between inter-node communication and on-node work. For an optimally subdivided diffusion problem, the 2-D parallelized algorithm achieves nearly perfect linear speedup in comparison with the serial run-up to around 2700 cores, reducing a 5 h simulation to 8 s, while the 3-D algorithm maintains appreciable speedup up to 1700 cores.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

AI-assisted detector design for the EIC (AID(2)E)

Artificial Intelligence is poised to transform the design of complex, large-scale detectors like ePIC at the future Electron Ion Collider. Featuring a central detector with additional detecting systems in the far forward and far backward regions, the ePIC experiment incorporates numerous design parameters and objectives, including performance, physics reach, and cost, constrained by mechanical and geometric limits. This project aims to develop a scalable, distributed AI-assisted detector design for the EIC (AID(2)E), employing state-of-the-art multiobjective optimization to tackle complex designs. Supported by the ePIC software stack and using G EANT 4 simulations, our approach benefits from transparent parameterization and advanced AI features. The workflow leverages the PanDA and iDDS systems, used in major experiments such as ATLAS at CERN LHC, the Rubin Observatory, and sPHENIX at RHIC, to manage the compute intensive demands of ePIC detector simulations. Tailored enhancements to the PanDA system focus on usability, scalability, automation, and monitoring. Ultimately, this project aims to establish a robust design capability, apply a distributed AI-assisted workflow to the ePIC detector, and extend its applications to the design of the second detector (Detector-2) in the EIC, as well as to calibration and alignment tasks. Additionally, we are developing advanced data science tools to efficiently navigate the complex, multidimensional trade-offs identified through this optimization process.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Instabilities and Mixing in Inertial Confinement Fusion

By imploding fuel of hydrogen isotopes, inertial confinement fusion (ICF) aims to create conditions that mimic those in the Sun's core. This is fluid dynamics in an extreme regime, with the ultimate goal of making nuclear fusion a viable clean energy source. The fuel must be reliably and symmetrically compressed to temperatures exceeding 100 million degrees Celsius. After the best part of a century of research, the foremost fusion milestone was reached in 2021, when ICF became the first technology to achieve an igniting fusion fuel (thermonuclear instability), and then in 2022 scientific energy breakeven was attained. A key trade-off of the ICF platform is that greater fuel compression leads to higher burn efficiency, but at the expense of amplified Rayleigh–Taylor and Richtmyer–Meshkov instabilities and kinetic-energy-wasting asymmetries. In extreme cases, these three-dimensional instabilities can completely break up the implosion. Even in the highest-yielding 2022 scientific breakeven experiment, high-atomic-number (high-Z) contaminants were unintentionally injected into the fuel. Here we review the pivotal role that fluid dynamics plays in the construction of a stable implosion and the decades of improved understanding and isolated experiments that have contributed to fusion ignition.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Chain Flexibility and Structure of a Polyimide Copolymer: Revisiting the Freely Rotating Chain Model

Poly(4–4′-oxydiphenylene-pyromellitimide)-based polyimides─trade name Kapton─have wide-ranging engineering applications owing to their thermal and mechanical stability, but little is known about underlying chain-level characteristics. While theoretical models have conceptualized Kapton as inflexible polycyclic rods separated by freely rotating diphenyl ether hinge groups, the model’s core predictions remain untested and subtleties of the relaxation behavior are missed, which atomistic modeling can resolve. To these ends, we examine all-atom Kapton structures in crystalline and glassy amorphous configurations using a DFT-validated class II force field. Constructing amorphous configurations is challenging, as the fused-ring-containing backbone has slow relaxation dynamics and scaling suggestive of entanglements even in oligomers. In conclusion, we find larger backbone rearrangements of the linear polycyclic segments about ether groups that are consistent with the rod-hinge picture on the monomer scale, whereas a ring rotation analysis suggests partially flexible rod-like segments and involves multiple facile rotational relaxation modes.

Liesen, Nicholas T. [Lawrence Livermore National L↗
DOE OSTI2023

Principled Schedulability Analysis for Distributed Storage Systems Using Thread Architecture Models

In this article, we present an approach to systematically examine the schedulability of distributed storage systems, identify their scheduling problems, and enable effective scheduling in these systems. We use Thread Architecture Models (TAMs) to describe the behavior and interactions of different threads in a system, and show both how to construct TAMs for existing systems and utilize TAMs to identify critical scheduling problems. We specify three schedulability conditions that a schedulable TAM should satisfy: completeness, local enforceability, and independence; meeting these conditions enables a system to easily support different scheduling policies. We identify five common problems that prevent a system from satisfying the schedulability conditions, and show that these problems arise in existing systems such as HBase, Cassandra, MongoDB, and Riak, making it difficult or impossible to realize various scheduling disciplines. We demonstrate how to address these schedulability problems using both direct and indirect solutions, with different trade-offs. To show how to apply our approach to enable scheduling in realistic systems, we develop Tamed-HBase and Muzzled-HBase, sets of modifications to HBase that can realize the desired scheduling disciplines, including fairness and priority scheduling, even when presented with challenging workloads.

Computer Science↗
DOE OSTI2025

Deep Learning–Assisted Multiobjective Optimization of Geological CO 2 Storage Performance under Geomechanical Risks

In geological CO 2 storage, designing the optimal well control strategy for CO 2 injection to maximize CO 2 storage while minimizing the associated geomechanical risks is not trivial. This challenge arises due to pressure buildup, CO 2 plume migration, the highly nonlinear nature of geomechanical responses to rock-fluid interaction, and the high computational cost associated with coupled flow and geomechanics simulations. In this paper, we introduce a novel optimization framework to address these challenges. The optimization problem is formulated as follows: maximize total CO 2 storage while minimizing geomechanical risks by adjusting the injection schedules within bounded constraints. The geomechanical risks are primarily driven by injection-induced pressure build-up, which is characterized by ground displacement and the induced microseismicity. We used the Fourier neural operator (FNO)-based deep learning model to construct surrogate models, replacing the time-consuming coupled flow and geomechanics simulations for evaluating the aforementioned objective functions. The developed surrogate models have been incorporated into a multiobjective optimization framework through a genetic algorithm to reduce the computational burden. The proposed optimization framework reduces the computational cost from approximately 2,400 hours, when using objective function evaluations based on physics-based simulations, to around 20 minutes. A set of Pareto-optimal solutions of the proposed workflow yields nontrivial optimal decisions, reducing the microseismicity potential and the vertical displacement. This Pareto front highlights the optimal trade-offs between CO 2 storage amount, safety, and ground displacement, emphasizing the need for careful optimization and management of injection strategies to achieve a balanced outcome. The novelty of this work is twofold. First, we demonstrate the importance of incorporating the minimization of the geomechanical risks as objective functions into the CO 2 storage optimization workflow to mitigate the potential risk of induced microseismicity and ground displacement. Second, we leverage the FNO-based surrogate models to optimize a real-field CO 2 storage operation.

42 ENGINEERING↗
DOE OSTI2023

Evaluating and Countering the Insider Threat to the Radioactive Source Supply Chain

The modern supply chain is a global enterprise and little drove this home more than the global COVID-19 pandemic which sent economic shockwaves throughout the world. Many goods became scarce, as products were delayed, in limited supply, or simply not available. The suddenly diminished supply collided with still high demand and led to greatly increased costs. This was particularly true for the radioactive source supply chain. The pandemic introduced extensive delays for construction projects, slowed the transport of radiological materials to facilities, interrupted treatment deliveries, and impaired the mobility of contractors across the industry. All of these concerns not only adversely affected the economy, but also impacted the safety and security of radiological material, potentially raising national security concerns. The vulnerability of the supply chain, a critical element in an increasingly interconnected world, was exposed. One example that challenged the adaptive capacity of the overall supply chain is the Ever Given container ship, which became stuck in the Suez Canal in 2020. This accident immediately shut down shipments that accounted for 12% of global trade, with long-term impacts estimated to be much larger. Developing the ability to anticipate and react in real-time to sudden changes has quickly become a necessity, particularly in industries that deal with the transport of hazardous material. The reaction to these dramatic incidents was to largely focus attention and resources on protecting the supply chain from external threats. However, the threat to the radioactive material supply chain from insiders intimately involved in the process may be even greater and remains a blind spot that requires increased attention. Recent events revealed the blueprint for targeting and disrupting that supply chain, so the potential for a malicious insider—or a manipulated, unwitting insider—to take advantage of this vulnerability is elevated, creating security concerns for radiological industries. This paper examines and analyzes the potential insider threat to the radioactive source supply chain and recommends steps to take to counter this possibility.

Kinney, Justin↗
DOE OSTI2026

Two-Tower Quantum Matrix Chain Multiplication: Trading Qubits for Depth

Matrix chain multiplication -- computing $\mathcal{W} = M^{(0)}\cdots M^{(K-1)}$ where $M^{(k)} \in \mathbb{R}^{P_k \times P_{k+1}}$-- arises in scientific computing, machine learning, and graph analysis. Despite the importance of this problem, for chains of distinct matrices, the classical number of operations grows linearly with the chain length $K$ and polynomially in the matrix dimensions. We present \emph{Two-Tower Matrix Multiplication}, a quantum subroutine that encodes the product $\mathcal{W}$ of the $K$ matrices into a quantum state in circuit depth $\mathcal{O}(\max_{k} \mathrm{polylog} (P_k P_{k+1}))$, which is independent of~$K$ within the QRAM-based state-preparation model, whereas the qubit count is $\mathcal{O}\bigl(\sum_{k} \log P_k \bigr)$; the total gate count remains linear in $K$, so the gain is in the circuit depth. The construction interleaves state-preparation operators across two layers; within each layer, all operators act on disjoint registers and execute in parallel. This subroutine can be specialized for the chain-vector case, which computes the product of $K-1$ matrices applied to a vector. We prove the correctness of the subroutine for all $K$ and provide two implementations using the Qiskit and QCLAB frameworks. The subroutine is applicable to any downstream quantum algorithm that operates on a matrix encoded in the statevector, including norm estimation, graph-matrix powers, linear system solving, and quantum machine learning kernels.

Antonioli, Giacomo [Pisa U.] (ORCID:00090000668703↗
DOE OSTI2024

Carbon Capture, Transport, And Storage (CTS) Cost Modeling Of The Onshore Gulf Coast

The onshore Gulf of Mexico region presents significant opportunities for CO2 capture, transport, and storage due to its numerous CO2 sources, such as power plants, refineries, and its substantial CO2 storage potential. However, operators face critical decisions in designing an efficient and cost-effective CO2 pipeline network. This study examines the economic implications of two primary strategies: constructing a trunkline with excess initial capacity versus developing dedicated pipelines incrementally as new CO2 sources come online. Building a trunkline first offers the advantage of future-proofing the network, allowing for the accommodation of increased CO2 volumes from various sources over time. However, this approach incurs higher upfront costs and risks underutilizing the transport capacity in the initial stages, potentially resulting in economic inefficiencies. Conversely, constructing dedicated pipelines for each new CO2 source as it becomes operational may avoid the initial overcapacity issue but fails to capitalize on the economies of scale. This could lead to higher overall costs due to the duplication of infrastructure and increased complexity in network management. This research employs a comprehensive cost-benefit analysis, integrating factors such as capital expenditure, operational costs, projected CO2 volumes, and potential economies of scale. Through this analysis, we aim to provide operators with insights into the most economically viable strategy for CO2 pipeline network design in the region. The findings underscore the importance of strategic planning and highlight the trade-offs between immediate capacity utilization and long-term cost savings, ultimately guiding stakeholders towards informed decision-making in the development of CO2 transport infrastructure. Presented at the 41st USAEE/IAEE North American Conference, 3-6 November 2024, Baton Rouge, LA, United States.

Shih, Chung Yan↗
DOE OSTI2025

Life-Cycle Analysis of Residential Windows Retrofits: Net GHG Emission Reduction and Payback Periods

Windows are a critical envelope component that plays an important role in the overall performance and environmental impact of a building life cycle. These implications can be embedded in the window lifecycle related to its design, manufacturing, raw materials and transportation, performance during the building’s use (operational), replacements, maintenance and end-of-life. Windows may impact 25% of the heating and cooling energy use, 10% of total building energy use and 45% of the envelope heat transfer (Harris 2022). The impacts of windows on the energy consumption of buildings have been extensively discussed, however, its embodied life-cycle impacts, such as greenhouse gas (GHG) emissions, and the trade-offs between the embodied and its operational emissions are less explored. Understanding the life cycle impacts of windows may subsidize decision making process and inform the development of emerging windows technologies. BTO’s Windows Program has played an important role to increase the adoption of emerging technologies as high-performance windows in the U.S. (Harris 2022) and to consider the GHG emission impacts of the those windows is an important aspect that can support the strategic objectives and the performance targets from the national blueprint for decarbonizing the buildings sector and to reduce the on-site emissions and embodied life cycle emissions from building materials and construction (US DOE 2024).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

United States plastics: Large flows, short lifetimes, and negligible recycling

Given increasing concerns for the marine environment and human health, as well as trade restrictions from Asian countries, plastics have become a great challenge for the United States. This study addresses the seven commonly used plastics: low-density polyethylene/linear low-density polyethylene, high-density polyethylene, polyethylene terephthalate (PET), polypropylene, polystyrene, polyvinyl chloride, and other plastics. Material flows of the seven polymers were tracked from production into fabrication, manufacturing, flow into use, waste management, and recycling in the United States in 2015. Low- and high-density polyethylene and polypropylene were found to be the largest in both production and product manufacture. More than 88% of the plastics went into three end-use sectors: Packaging, Consumer and Institutional Products, and Building and Construction. In-use lifetimes across the plastics are generally short. Virgin plastics were mainly exported, while intermediate plastic products were largely imported. The actual end-of-life recycling rate of the plastics as a group was no more than 6.2%, with PET and the polyethylene family the most recycled. Here, the high yearly plastic throughput and low recycling rate pose a serious challenge to the sustainability goals of the United States and is in stark contrast to the vision of a circular economy of plastics.

02 PETROLEUM↗
DOE OSTI2025

Enhancing the accuracy of XPS calculations: Exploring hybrid basis set schemes for CVS-EOMIP-CCSD calculations

Reliable computational methodologies and basis sets for modeling x-ray spectra are essential for extracting and interpreting electronic and structural information from experimental x-ray spectra. In particular, the trade-off between numerical accuracy and computational cost due to the size of the basis set is a major challenge, since molecular orbitals undergo extreme relaxation in the core-hole state. To gain clarity on the changes in electronic structure induced by the formation of a core-hole, the use of sufficiently flexible basis for expanding the orbitals, particularly for the core region, has been shown to be essential. This work focuses on the refinement of core-hole ionized state calculations using the equation-of-motion coupled cluster family of methods through an extensive analysis on the effectiveness of “hybrid” and mixed basis sets. In this investigation, we utilize the CVS-EOMIP-CCSD method in combination and construct hybrid basis sets piecewise from readily available Dunning’s correlation consistent basis sets in order to calculate x-ray ionization energies (IEs) for a set of small gas phase molecules. Our results provide insights into the impact of basis sets on the CVS-EOMIP-CCSD calculations of K-edge IEs of first-row p-block elements. Furthermore, these insights enable us to understand more about the basis set dependence of the core IEs computed and allow us to establish a protocol for deriving reliable and cost-effective theoretical estimates for computing IEs of small molecules containing such elements.

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

A Framework for Assessing Economic and Environmental Trade-offs of Internalized Emission Costs in ERCOT Grid Planning

The power grid is on the cusp of a massive transition driven by three major areas: 1) the growth in demand for electricity, 2) efforts to decarbonize the United States economy, and 3) a desire to mitigate social disparities from the impact of electricity generation on local populations. However, most studies of the electricity sector do not include equity impacts in their models. This study seeks to do so by developing a comprehensive and generalizable model tailored to the Electric Reliability Council of Texas (ERCOT) grid, designed to incorporate the equity impacts of electricity generation in a decarbonized and resilient framework. To integrate equity into our research, we incorporate environmental externalities into our capacity expansion model of ERCOT. Specifically, we factor in intermediate-level local marginal damages of precursor pollutants (NH3, NOx, primary PM2.5, SO2, and VOC) and global pollutant CO2 into the cost of generating electricity. We do this by taking into account county population, county ambient pollution concentration, and generator emission rates. Leveraging open-source modeling tools, such as PowerGenome, pyGRETA, and GenX we construct a county-level model to account for these costs. We integrate these marginal damages into the variable operations and maintenance costs of generators, for both existing and potential future builds. This study’s findings suggest that the value of a dynamic social cost of carbon (SSC) will cover criteria pollutant marginal damages within the ERCOT grid and solar and wind is expected to increase out to 2035. Key metrics evaluated within this research include fuel mix distribution across technologies, transmission and grid infrastructure costs, CO2 emissions, local pollutants marginal damages, and the variation in generation capacity built by the model. These results and framework can be used to support grid decisions that explicitly include distributional and procedural equity within a decarbonized and sustainable grid framework.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Programmable Heisenberg interactions between Floquet qubits

Abstract The trade-off between robustness and tunability is a central challenge in the pursuit of quantum simulation and fault-tolerant quantum computation. In particular, quantum architectures are often designed to achieve high coherence at the expense of tunability. Many current qubit designs have fixed energy levels and consequently limited types of controllable interactions. Here by adiabatically transforming fixed-frequency superconducting circuits into modifiable Floquet qubits, we demonstrate an XXZ Heisenberg interaction with fully adjustable anisotropy. This interaction model can act as the primitive for an expressive set of quantum operations, but is also the basis for quantum simulations of spin systems. To illustrate the robustness and versatility of our Floquet protocol, we tailor the Heisenberg Hamiltonian and implement two-qubit iSWAP, CZ and SWAP gates with good estimated fidelities. In addition, we implement a Heisenberg interaction between higher energy levels and employ it to construct a three-qubit CCZ gate, also with a competitive fidelity. Our protocol applies to multiple fixed-frequency high-coherence platforms, providing a collection of interactions for high-performance quantum information processing. It also establishes the potential of the Floquet framework as a tool for exploring quantum electrodynamics and optimal control.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Argonne’s global critical materials agent-based model (GCMat)

Several studies have identified rare earths as critical materials. Although reasonably abundant in the Earth’s crust, rare earths typically occur in low concentrations of mined ores. Processes for recovering rare earth concentrates from these ores are complex and capital intensive. Further, lead times for deposit development, licensing, and construction are long, with reports of 10- 15 years. China is a major player in the rare earths supply chain, both in production capacity and technology innovation. In 2017, China supplied more than 80% of global rare earth oxide demand. Rare earth elements (REEs) have unique magnetic, catalytic, and phosphorescent properties that significantly improve performance of a wide range of technologies. These technologies span aerospace, energy, telecommunications, electronics, transportation, defense, and other diverse applications. Consequently, disruptions in rare earth supply can have a significant societal impact. Estimating that impact requires understanding of the dynamics across the supply chain, from rare earth oxide extraction to end use application. This report documents Argonne’s Global Critical Materials model (GCMat), first described by Riddle et al. 2015. GCMat provides capabilities to explore supply chain dynamics and uncertainty under scenarios of demand growth or shrinkage, technology adoption, supply disruptions, and trade policies and mitigation strategies of new supply sources, product substitution, consumer thrifting, and stockpiling. Supply chain participants from rare earth mining through final demand are modeled as interacting agents who make market decisions independently as time progresses. Since the version documented in Riddle et al. 2015, GCMat has been expanded to cover additional REEs, derived products and supply chains that use these REEs, and includes new agent behaviors and modeling capabilities. Section 2 provides a summary of the GCMat model design, including the structure of the model and key assumptions, section 3 summarizes methods used for model calibration and sensitivity analysis, and section 4 provides examples of model results.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A conservative, consistent, and scalable meshfree mimetic method

Mimetic methods discretize divergence by restricting the Gauss theorem to mesh cells. Because point clouds lack such geometric entities, construction of a compatible meshfree divergence remains a challenge. In this work, we define an abstract Meshfree Mimetic Divergence (MMD) operator on point clouds by contraction of field and virtual face moments. This MMD satisfies a discrete divergence theorem, provides a discrete local conservation principle, and is first-order accurate. We consider two MMD instantiations. The first one assumes a background mesh and uses generalized moving least squares (GMLS) to obtain the necessary field and face moments. This MMD instance is appropriate for settings where a mesh is available but its quality is insufficient for a robust and accurate mesh-based discretization. The second MMD operator retains the GMLS field moments but defines virtual face moments using computationally efficient weighted graph-Laplacian equations. This MMD instance does not require a background grid and is appropriate for applications where mesh generation creates a computational bottleneck. It allows one to trade an expensive mesh generation problem for a scalable algebraic one, without sacrificing compatibility with the divergence operator. We demonstrate the approach by using the MMD operator to obtain a virtual finite-volume discretization of conservation laws on point clouds. Finally, numerical results in the paper confirm the mimetic properties of the method and show that it behaves similarly to standard finite volume methods.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗