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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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A Computational Information Criterion for Particle-Tracking with Sparse or Noisy Data
Traditional probabilistic methods for the simulation of advection-diffusion equations (ADEs) often overlook the entropic contribution of the discretization, e.g., the number of particles, within associated numerical methods. Many times, the gain in accuracy of a highly discretized numerical model is outweighed by its associated computational costs or the noise within the data. Herein, we address the question of how many particles are needed in a simulation to best approximate and estimate parameters in one-dimensional advective-diffusive transport. To do so, we use the well-known Akaike Information Criterion (AIC) and a recently-developed correction called the Computational Information Criterion (COMIC) to guide the model selection process. Random-walk and mass-transfer particle tracking methods are employed to solve the model equations at various levels of discretization. Numerical results demonstrate that the COMIC provides an optimal number of particles that can describe a more efficient model in terms of parameter estimation and model prediction compared to the model selected by the AIC even when the data is sparse or noisy, the sampling volume is not uniform throughout the physical domain, or the error distribution of the data is non-IID Gaussian.
Genome-scale metabolic model accurately predicts fermentation of glucose by Chromochloris zofingiensis
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Application of polyhedron model to predict heat capacity of mixed oxides
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Process structure-based recurrent neural network modeling for predictive control: A comparative study
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Predictive interactome modeling for precision microbiome engineering
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Evaluating generalized feature importance via performance assessment of machine learning models for predicting elastic properties of materials
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Structure-based QSAR models to predict repeat dose toxicity points of departure
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A graph-theoretic approach for multiscale modeling and prediction of crack propagation in polycrystalline materials
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Developing statistical and machine learning models for predicting CO2 solubility in live crude oils
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Influence of functional groups on low-temperature combustion chemistry of biofuels
Ongoing progress in synthetic biology, metabolic engineering, and catalysis continues to produce a diverse array of advanced biofuels with complex molecular structure and functional groups. In order to integrate biofuels into existing combustion systems, and to optimize the design of next-generation combustion systems, understanding connections between molecular structure and ignition at low-temperature conditions (< 1000 K) remains a priority that is addressed in part using chemical kinetics modeling. The development of predictive models relies on detailed information, derived from experimental and theoretical studies, on molecular structure and chemical reactivity, both of which influence the balance of chain reactions that occur during combustion – propagation, termination, and branching. In broad context, three main categories of reactions affect ignition behavior: (i) initiation reactions that generate a distribution of organic radicals, ; (ii) competing unimolecular decomposition of and bimolecular reaction of with O2; (iii) decomposition mechanisms of peroxy radical adducts (RO), including isomerization via RO ⇌ OOH. Furthermore, all three categories are influenced by functional groups in different ways, which causes a shift in the balance of chain reactions that unfold over complex temperature- and pressure-dependent mechanisms.
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