We demonstrate a Bayesian method that can be used to calibrate computationally expensive 3D RANS (Reynolds Av- eraged Navier Stokes) models with complex response surfaces. Such calibrations, conditioned on experimental data, can yield turbulence model parameters as probability density functions (PDF), concisely capturing the uncertainty in the parameter estimates. Methods such as Markov chain Monte Carlo (MCMC) estimate the PDF by sampling, with each sample requiring a run of the RANS model. Consequently a quick-running surrogate is used instead to the RANS simulator. The surrogate can be very difficult to design if the model's response i.e., the dependence of the calibration variable (the observable) on the parameter being estimated is complex. We show how the training data used to construct the surrogate can be employed to isolate a promising and physically realistic part of the parameter space, within which the response is well-behaved and easily modeled. We design a classifier, based on treed linear models, to model the "well-behaved region". This classifier serves as a prior in a Bayesian calibration study aimed at estimating 3 k - ε parameters ( C μ, C ε2 , C ε1 ) from experimental data of a transonic jet-in-crossflow interaction. The robustness of the calibration is investigated by checking its predictions of variables not included in the cal- ibration data. We also check the limit of applicability of the calibration by testing at off-calibration flow regimes. We find that calibration yield turbulence model parameters which predict the flowfield far better than when the nomi- nal values of the parameters are used. Substantial improvements are still obtained when we use the calibrated RANS model to predict jet-in-crossflow at Mach numbers and jet strengths quite different from those used to generate the ex- perimental (calibration) data. Thus the primary reason for poor predictive skill of RANS, when using nominal values of the turbulence model parameters, was parametric uncertainty, which was rectified by calibration. Post-calibration, the dominant contribution to model inaccuraries are due to the structural errors in RANS.
Under shock compression, most porous materials exhibit lower densities for a given pressure than that of a full-dense sample of the same material. However, some porous materials exhibit an anomalous, or enhanced, densification under shock compression. We demonstrate a molecular mechanism that drives this behavior. We also present evidence from atomistic simulation that silicon belongs to this anomalous class of materials. Atomistic simulations indicate that local shear strain in the neighborhood of collapsing pores nucleates a local solid-solid phase transformation even when bulk pressures are below the thermodynamic phase transformation pressure. This metastable, local, and partial, solid-solid phase transformation, which accounts for the enhanced densification in silicon, is driven by the local stress state near the void, not equilibrium thermodynamics. This mechanism may also explain the phenomenon in other covalently bonded materials.
Adult neurogenesis in the hippocampus is a notable process due not only to its uniqueness and potential impact on cognition but also to its localized vertical integration of different scales of neuroscience, ranging from molecular and cellular biology to behavior. Our review summarizes the recent research regarding the process of adult neurogenesis from these different perspectives, with particular emphasis on the differentiation and development of new neurons, the regulation of the process by extrinsic and intrinsic factors, and their ultimate function in the hippocampus circuit. Arising from a local neural stem cell population, new neurons progress through several stages of maturation, ultimately integrating into the adult dentate gyrus network. Furthermore, the increased appreciation of the full neurogenesis process, from genes and cells to behavior and cognition, makes neurogenesis both a unique case study for how scales in neuroscience can link together and suggests neurogenesis as a potential target for therapeutic intervention for a number of disorders.
This report summarizes the result of a NEAMS project focused on sensitivity analysis of the heat transfer model in the gap between the fuel rod and the cladding used in the BISON fuel performance code of Idaho National Laboratory. Using the gap heat transfer models in BISON, the sensitivity of the modeling parameters and the associated responses is investigated. The study results in a quantitative assessment of the role of various parameters in the analysis of gap heat transfer in nuclear fuel.
A method is described for applying a sequence of peridynamic models with different length scales concurrently to subregions of a body. The method allows the smallest length scale, and therefore greatest spatial resolution, to be focused on evolving defects such as cracks. The peridynamic horizon in each of the models is half of that of the next model in the sequence. The boundary conditions on each model are provided by the solution predicted by the model above it. Material property characterization for each model is derived by coarse-graining the more detailed resolution in the model below it. Implementation of the multiscale method in the PDMS code is described. Examples of crack growth modeling illustrate the ability of the method to reproduce the main features of crack growth seen in a model with uniformly small resolution. Comparison of the multiscale model results with XFEM and cohesive elements is also given for a crack growth problem.
The peridynamic theory is an extension of traditional solid mechanics in which the field equations can be applied on discontinuities, such as growing cracks. This paper proposes a bond damage model within peridynamics to treat the nucleation and growth of cracks due to cyclic loading. Bond damage occurs according to the evolution of a variable called the "remaining life" of each bond that changes over time according to the cyclic strain in the bond. It is shown that the model reproduces the main features of S-N data for typical materials and also reproduces the Paris law for fatigue crack growth. Extensions of the model account for the effects of loading spectrum, fatigue limit, and variable load ratio. A three-dimensional example illustrates the nucleation and growth of a helical fatigue crack in the torsion of an aluminum alloy rod.
Adult neurogenesis in the hippocampus region of the brain is a neurobiological process that is believed to contribute to the brain's advanced abilities in complex pattern recognition and cognition. Here, we describe how realistic scale simulations of the neurogenesis process can offer both a unique perspective on the biological relevance of this process and confer computational insights that are suggestive of novel machine learning techniques. First, supercomputer based scaling studies of the neurogenesis process demonstrate how a small fraction of adult-born neurons have a uniquely larger impact in biologically realistic scaled networks. Second, we describe a novel technical approach by which the information content of ensembles of neurons can be estimated. Finally, we illustrate several examples of broader algorithmic impact of neurogenesis, including both extending existing machine learning approaches and novel approaches for intelligent sensing.
