Computational Modeling of Terry Turbine Airflow Testing to Support the Expansion of Operating Band in Beyond Design Basis Conditions.
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Soldered joints can be made with a wide range of base materials and filler metals that allow the assembly to meet its performance and reliability requirements. Structural solder joints have, as their foremost requirement, to provide mechanical attachment between base material structures. The joint is typically subjected to one, or a combination of, three loading configurations: (a) tensile or compressive force, (b) shear force, or (c) peel force. Solder filler metals and in particular, the so-called “soft solders” based on tin (Sn), lead (Pb), and indium (In), generally have a bulk strength that is less than that of the base materials. Finally, deformation occurs largely in the solder when the joint is subjected to an applied force.
AIChE: 3rd Natural Gas Utilization Workshop, Virtual, April 24-25, 2021
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Numerical Modeling of Proppant Transport and Coverage in Rock Fractures
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For presentation at the ACS 2023 Fall Meeting, San Francisco, CA, August 14-18, 2023.
For presentation at the 244th ECS Meeting, Gothenburg, Sweden, October 8-12, 2023.
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Computational modeling of the excited states of molecular aggregates faces significant computational challenges and size heterogeneity. Current machine learning (ML) models, typically trained on specific-sized aggregates, struggle with scalability. We found that the exciton model Hamiltonian of large aggregates can be decomposed into dimer pairs, allowing an ML model trained on dimers to reconstruct Hamiltonians for aggregates of any size. We also proposed a new method to address the phase-correction problem by introducing coupling terms’ approximations. Our model accurately predicted the excitation energies of the trimer and tetramer of perylene and tetracene and estimated S1 oscillator strengths of perylene aggregates. Leveraging our ML model, the optical gaps of nanosized perylene aggregates with up to 50 monomers are analyzed, qualitatively revealing the role of different couplings on their size dependency. Future work will explore transferability across different monomers to predict optical properties in heterogeneous assemblies.
Computational models play an increasingly vital role in scientific research by enabling the numerical simulation of complex processes. Such models are also fundamental in geosciences. For instance, they offer critical insights into the impacts of global change on the Earth system today and in the future. Beyond their value as research tools, models are also software products and should therefore adhere to certain established software engineering standards. However, scientists are rarely trained as software developers, which can lead to potential deficiencies in software quality like unreadable, inefficient, or erroneous code. The complexity of models, coupled with their integration into broader workflows, also often makes it challenging to reproduce results, evaluate processes, and build upon them. In this paper, we review the state and current practices of the development processes of the state-of-the-art land surface models used by the Global Carbon Budget. We combine the experience of modelers from the respective research groups with the expertise of software engineers from tech companies to outline key principles and tools for improving software quality in research. We explore four main areas: (1) model testing and validation, (2) scientific, technical, and user documentation, (3) version control, continuous integration, and code review, and (4) the portability and reproducibility of workflows. Our review reveals that while modeling communities are incorporating many best practices, significant room for improvement remains in areas such as automated testing, automated documentation, and reproducibility. Therefore, we here identify and promote essential software engineering practices, including numerous examples of practices from within the community that can serve as guidelines for other models and could help streamline processes across the entire community. We conclude with an open-source example implementation of these principles, demonstrating portable and reproducible data flows, a continuous integration setup, and web-based visualizations. This example may serve as a practical resource for model developers, users, and all scientists engaged in scientific programming.
The ever increasing demands placed upon machine performance have resulted in the need for more comprehensive particle accelerator modeling. Computer simulations are key to the success of particle accelerators. Many aspects of particle accelerators rely on computer modeling at some point, sometimes requiring complex simulation tools and massively parallel supercomputing. Examples include the modeling of beams at extreme intensities and densities (toward the quantum degeneracy limit), and with ultra-fine control (down to the level of individual particles). In the future, adaptively tuned models might also be relied upon to provide beam measurements beyond the resolution of existing diagnostics. Much time and effort has been put into creating accelerator software tools, some of which are highly successful. However, there are also shortcomings such as the general inability of existing software to be easily modified to meet changing simulation needs. In this paper possible mitigating strategies are discussed for issues faced by the accelerator community as it endeavors to produce better and more comprehensive modeling tools. This includes lack of coordination between code developers, lack of standards to make codes portable and/or reusable, lack of documentation, among others.
As the nuclear energy sector advances toward next-generation reactors, the need for high-performance fuel cladding materials has become increasingly urgent. Traditional alloys like zirconium and stainless steel are reaching their performance limits under higher temperatures, more corrosive coolants, and extended irradiation. This report presents the development of a new class of fuel cladding materials based on Non-Concentrated Alloys (NCAs) ? multi-element systems designed to deliver enhanced mechanical strength, corrosion resistance, and radiation tolerance. Through a combination of computational modeling (Computer Coupling of Phase Diagrams and Thermochemistry: CALPHAD), simulation-guided alloy selection, and experimental fabrication via arc melting and spark plasma sintering (SPS), three strategic alloy design paths were explored: (1) FeCrAl-based NCAs, (2) refractory-lean neutron-efficient alloys, and (3) equimolar high-entropy compositions. Microstructural analysis confirmed the formation of stable body-centered cubic BCC_A2 phases, while mechanical testing demonstrated hardness values significantly exceeding those of conventional cladding materials. The results highlight the tunability of NCA systems and their potential for balancing strength and ductility ? a critical consideration for in-reactor performance. Looking forward, future work will focus on thermomechanical optimization, CALPHAD refinement, and benchmarking against industry standards to enable scalable deployment. This work not only advances the science of nuclear materials but also supports broader goals in nuclear safety, performance, and nuclear energy innovation.
A computational model for the interaction of a high-pressure microplasma with an electromagnetic wave is presented. A one-dimensional particle-in-cell Monte Carlo collision model is used to investigate the plasma non-ideality effects in a second-stage laser-heated xenon plasma with a comprehensive chemistry mechanism, including excited species. A xenon microplasma at a temperature of 300 K and pressure of 10 bar becomes non-ideal after the laser heating stage with lower ionization and electronic excitation barriers, which makes the presence of excited species important to the plasma generation process. For these conditions, two-step ionization via excited species becomes a major contributor to plasma ionization. We find that full ionization of the plasma is obtained in ~2 ps when excited species are included in the chemistry model, as opposed to ~2.5 ps for a chemistry mechanism that does not consider their effect. With excited species, the ionization mode transitions from direct ionization via electron-neutral collisions to direct and stepwise ionization as the plasma generation progresses.
A number of previously published studies apply extended-MHD computations to model macroscopic dynamics of tokamak vertical displacement events (VDEs). The characteristic plasma-wall contact and resistive-wall diffusion imply sensitivity to boundary conditions in a general sense, but which conditions have significant influence depends on how the plasma is modeled. This work examines these dependencies by comparing results of axisymmetric extended-MHD computations with different sets of boundary conditions, plasma thermal-conduction models, and wall resistivity values. The geometry and plasma parameters of the computations represent a modest-sized tokamak. The forced-displacement scenario and computational setup are similar to those used in previous 3D computations. The results show that for a given wall resistivity, the VDE time-scale is most sensitive to variations in the boundary and thermal-conduction parameters that affect electron thermal transport. The electrical conductivity depends on electron temperature, and the dependence on thermal transport stems from its influence on the electrical circuit that includes the open-field halo current. Lastly, conditions that lead to hotter, broader halo regions slow the evolution. Significant sensitivity to the boundary condition on plasma flow-velocity exists when electron thermal conduction is restricted and electron energy loss is convective, which is expected for conditions at the entrance of the magnetic presheath.