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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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Metabolic engineering with multi-objective optimization of kinetic models
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Improved framework model to allocate optimal rainwater harvesting sites in small watersheds for agro-forestry uses
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Using an onion like neutron scattering instrument model to quickly optimize design parameters
When designing a neutron scattering instrument, one of the major challenges is the many parameters, generally interdependent, within the optimization. In this work we describe an approach using McStas 1-3 to quickly get a good overview of how changes in instrument parameters will impact neutron scattering instrument performance.
The impact of binary water–CO 2 isotherm models on the optimal performance of sorbent-based direct air capture processes
We outline mathematical descriptions of H 2 O–CO 2 co-adsorption on an amine-functionalised solid sorbent and support this with experimental data. We then show what impact this has on a solid sorbent based direct air capture process.
Two-dimensional quantum lattice models via mode optimized hybrid CPU-GPU density matrix renormalization group method
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Finding and proving the exact ground state of a generalized Ising model by convex optimization and MAX-SAT
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Analytical Model-Based Design Optimization of a Transverse Flux Machine
This paper proposes an analytical machine design tool using magnetic equivalent circuit (MEC)-based particle swarm optimization (PSO) for a double-sided, flux-concentrating transverse flux machine (TFM). The magnetic equivalent circuit method is applied to analytically establish the relationship between the design objective and the input variables of prospective TFM designs. This is computationally less intensive and more time efficient than finite element solvers. A PSO algorithm is then used to design a machine with the highest torque density within the specified power range along with some geometric design constraints. The stator pole length, magnet length, and rotor thickness are the variables that define the optimization search space. Finite element analysis (FEA) was carried out to verify the performance of the MEC-PSO optimized machine. The proposed analytical design tool helps save computation time by at least 50% when compared to commercial FEA-based optimization programs, with results found to be in agreement with less than 5% error.
System Modeling and Design Optimization for a Next-Generation Unattended Sensor
We are developing a next-generation unattendedsensor that can detect and identify radiation sources while operating on battery power for several weeks. The system achieves smaller size and weight over systems that use NaI:Tl and 3He detectors by using a relatively new scintillator, Cs2LiYCl6:Ce:Ce (CLYC). This material can detect both gamma rays and thermal neutrons, has energy resolution of ~4% full width at half maximum at 662 keV, and allows for particle discrimination by pulse amplitude as well as pulse shape. The overall design features an array of sixteen CLYC detectors, each read out by a photomultiplier tube and custom pulse processing electronics. A field-programmable gate array analyzes the energy spectra using computationally efficient algorithms for anomaly detection.
Model-Based Robust Optimal Control for Layer-By-Layer Ultraviolet Processing of Composite Laminates
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Model Reduction and Optimization of Natural Gas Pipeline Dynamics
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Thermodynamic and Kinetic Modeling Directs Pathway Optimization for Isopropanol Production in a Gas-Fermenting Bacterium
Highly efficient bioproduction from gaseous substrates (e.g., hydrogen and carbon oxides) will require systematic optimization of the host microbes. To date, the rational redesign of gas-fermenting bacteria is still in its infancy, due in part to the lack of quantitative and precise metabolic knowledge that can direct strain engineering.
The Art and Science of Climate Model Tuning
The process of parameter estimation targeting a chosen set of observations is an essential aspect of numerical modeling. This process is usually named tuning in the climate modeling community. In climate models, the variety and complexity of physical processes involved, and their interplay through a wide range of spatial and temporal scales, must be summarized in a series of approximate submodels. Most submodels depend on uncertain parameters. Tuning consists of adjusting the values of these parameters to bring the solution as a whole into line with aspects of the observed climate. Tuning is an essential aspect of climate modeling with its own scientific issues, which is probably not advertised enough outside the community of model developers. Optimization of climate models raises important questions about whether tuning methods a priori constrain the model results in unintended ways that would affect our confidence in climate projections. Here, we present the definition and rationale behind model tuning, review specific methodological aspects, and survey the diversity of tuning approaches used in current climate models. We also discuss the challenges and opportunities in applying so-called objective methods in climate model tuning. Here, we discuss how tuning methodologies may affect fundamental results of climate models, such as climate sensitivity. The article concludes with a series of recommendations to make the process of climate model tuning more transparent.
Machine-Learning for Rapid Optimization of Turbulence Models
1. Background and Motivation: What is turbulence; Why is turbulence important; How turbulence is modeled; BHR model; Motivation for a ML framework 2. DNS Database from OES-C4; 3. Machine learning framework: Formulation; Verification; Case scenarios; 4. Summary and Conclusions
Linearized three-phase optimal power flow models for distribution grids [Slides]
Abstract not provided.