Efficient Architectural Design Space Exploration via Predictive Modeling

Efficiently exploring exponential-size architectural design spaces with many interacting parameters remains an open problem: the sheer number of experiments required renders detailed simulation intractable.We attack this via an automated approach that builds accurate predictive models. We simulate sampled points, using results to teach our models the function describing relationships among design parameters. The models can be queried and are very fast, enabling efficient design tradeoff discovery. We validate our approach via two uniprocessor sensitivity studies, predicting IPC with only 1–2% error. In an experimental study using the approach, training on 1% of a 250-K-point CMP design space allows our models to predict performance with only 4–5% error. Our predictive modeling combines well with techniques that reduce the time taken by each simulation experiment, achieving net time savings of three-four orders of magnitude.

TLM2.0 based timing accurate modeling method for complex NoC systems

Scalability and efficiency of on-chip communication of emerging Multiprocessor System-on-Chip (MPSoC) are critical design considerations. Conventional bus based interconnection schemes no longer fit for MPSoC with a large number of cores. Networks-on-Chip (NoC) is widely accepted as the next generation interconnection scheme for large scale MPSoC. The increase of MPSoC complexity requires fast and accurate system-level modeling techniques for rapid modeling and veri-fication of emerging MPSoCs. However, the existing modeling methods are limited in delivering the essentials of timing accuracy and simulation speed. This paper proposes a novel system-level Networks-on-Chip (NoC) modeling method, which is based on SystemC and TLM2.0 and capable of delivering timing accuracy close to cycle accurate modeling techniques at a significantly lower simulation cost. Experimental results are presented to demonstrate the proposed method. ©2010 IEEE.

Modeling target bulk heating resulting from ultra-intense short pulse laser irradiation of solid density targets

Isochoric heating of solid-density matter up to a few tens of eV is of interest for investigating astrophysical or inertial fusion scenarios. Such ultra-fast heating can be achieved via the energy deposition of short-pulse laser generated electrons. Here, we report on experimental measurements of this process by means of time-and space-resolved optical interferometry. Our results are found in reasonable agreement with a simple numerical model of fast electron-induced heating. (C) 2013 AIP Publishing LLC.

Advances in GPCR modeling evaluated by the GPCR Dock 2013 assessment:Meeting new challenges

Despite tremendous successes of GPCR crystallography, the receptors with available structures represent only a small fraction of human GPCRs. An important role of the modeling community is to maximize structural insights for the remaining receptors and complexes. The community-wide GPCR Dock assessment was established to stimulate and monitor the progress in molecular modeling and ligand docking for GPCRs. The four targets in the present third assessment round presented new and diverse challenges for modelers, including prediction of allosteric ligand interaction and activation states in 5-hydroxytryptamine receptors 1B and 2B, and modeling by extremely distant homology for smoothened receptor. Forty-four modeling groups participated in the assessment. State-of-the-art modeling approaches achieved close-to-experimental accuracy for small rigid orthosteric ligands and models built by close homology, and they correctly predicted protein fold for distant homology targets. Predictions of long loops and GPCR activation states remain unsolved problems.

Status of GPCR modeling and docking as reflected by community-wide GPCR Dock 2010 assessment

The community-wide GPCR Dock assessment is conducted to evaluate the status of molecular modeling and ligand docking for human G protein-coupled receptors. The present round of the assessment was based on the recent structures of dopamine D3 and CXCR4 chemokine receptors bound to small molecule antagonists and CXCR4 with a synthetic cyclopeptide. Thirty-five groups submitted their receptor-ligand complex structure predictions prior to the release of the crystallographic coordinates. With closely related homology modeling templates, as for dopamine D3 receptor, and with incorporation of biochemical and QSAR data, modern computational techniques predicted complex details with accuracy approaching experimental. In contrast, CXCR4 complexes that had less-characterized interactions and only distant homology to the known GPCR structures still remained very challenging. The assessment results provide guidance for modeling and crystallographic communities in method development and target selection for further expansion of the structural coverage of the GPCR universe.

Modeling study of woody biomass:Interactions of cellulose, hemicellulose, and lignin

Lignocellulosic biomass pretreatment and the subsequent thermal conversion processes to produce solid, liquid, and gas biofuels are attractive solutions for today's energy challenges. The structural study of the main components in biomass and their macromolecular complexes is an active and ongoing research topic worldwide. The interactions among the three main components, cellulose, hemicellulose, and lignin, are studied in this paper using electronic structure methods, and the study includes examining the hydrogen bond network of cellulose-hemicellulose systems and the covalent bond linkages of hemicellulose-lignin systems. Several methods (semiempirical, Hartree-Fock, and density functional theory) using different basis sets were evaluated. It was shown that theoretical calculations can be used to simulate small model structures representing wood components. By comparing calculation results with experimental data, it was concluded that B3LYP/6-31G is the most suitable basis set to describe the hydrogen bond system and B3LYP/6-31G(d,p) is the most suitable basis set to describe the covalent system of woody biomass. The choice of unit model has a much larger effect on hydrogen bonding within cellulose-hemicellulose system, whereas the model choice has a minimal effect on the covalent linkage in the hemicellulose-lignin system. © 2011 American Chemical Society.

Bdellovibrio predation in the presence of decoys:Three-way bacterial interactions revealed by mathematical and experimental analyses

Bdellovibrio bacteriovorus is a small, gram-negative, motile bacterium that preys upon other gram-negative bacteria, including several known human pathogens. Its predation efficiency is usually studied in pure cultures containing solely B. bacteriovorus and a suitable prey. However, in natural environments, as well as in any possible biomedical uses as an antimicrobial, Bdellovibrio is predatory in the presence of diverse decoys, including live nonsusceptible bacteria, eukaryotic cells, and cell debris. Here we gathered and mathematically modeled data from three-member cultures containing predator, prey, and nonsusceptible bacterial decoys. Specifically, we studied the rate of predation of planktonic late-log-phase Escherichia coli S17-1 prey by B. bacteriovorus HD100, both in the presence and in the absence of Bacillus subtilis nonsporulating strain 671, which acted as a live bacterial decoy. Interestingly, we found that although addition of the live Bacillus decoy did decrease the rate of Bdellovibrio predation in liquid cultures, this addition also resulted in a partially compensatory enhancement of the availability of prey for predation. This effect resulted in a higher final yield of Bdellovibrio than would be predicted for a simple inert decoy. Our mathematical model accounts for both negative and positive effects of predator-prey-decoy interactions in the closed batch environment. In addition, it informs considerations for predator dosing in any future therapeutic applications and sheds some light on considerations for modeling the massively complex interactions of real mixed bacterial populations in nature.

Modeling of titanium alloys by using artificial neural networks

Titanium alloy exhibits an excellent combination of bio-compatibility, corrosion resistance, strength and toughness. The microstructure of an alloy influences the properties. The microstructures depend mainly on alloying elements, method of production, mechanical, and thermal treatments. The relationships between these variables and final properties of the alloy are complex, non-linear in nature, which is the biggest hurdle in developing proper correlations between them by conventional methods. So, we developed artificial neural networks (ANN) models for solving these complex phenomena in titanium alloys.

In the present work, ANN models were used for the analysis and prediction of the correlation between the process parameters, the alloying elements, microstructural features, beta transus temperature and mechanical properties in titanium alloys. Sensitivity analysis of trained neural network models were studied which resulted a better understanding of relationships between inputs and outputs. The model predictions and the analysis are well in agreement with the experimental results. The simulation results show that the average output-prediction error by models are less than 5% of the prediction range in more than 95% of the cases, which is quite acceptable for all metallurgical purposes.

Microphysiological modeling of the reproductive tract: A fertile endeavor

Preclinical toxicity testing in animal models is a cornerstone of the drug development process, yet it is often unable to predict adverse effects and tolerability issues in human subjects. Species-specific responses to investigational drugs have led researchers to utilize human tissues and cells to better estimate human toxicity. Unfortunately, human cell-derived models are imperfect because toxicity is assessed in isolation, removed from the normal physiologic microenvironment. Microphysiological modeling often referred to as 'organ-on-a-chip' or 'human-on-a-chip' places human tissue into a microfluidic system that mimics the complexity of human in vivo physiology, thereby allowing for toxicity testing on several cell types, tissues, and organs within a more biologically relevant environment. Here we describe important concepts when developing a repro-on-a-chip model. The development of female and male reproductive microfluidic systems is critical to sex-based in vitro toxicity and drug testing. This review addresses the biological and physiological aspects of the male and female reproductive systems in vivo and what should be considered when designing a microphysiological human-on-a-chip model. Additionally, interactions between the reproductive tract and other systems are explored, focusing on the impact of factors and hormones produced by the reproductive tract and disease pathophysiology.

Deep Salience: Visual Salience Modeling via Deep Belief Propagation

Visual salience is an intriguing phenomenon observed in biological neural systems. Numerous attempts have been made to model visual salience mathematically using various feature contrasts, either locally or globally. However, these algorithmic models tend to ignore the problem’s biological solutions, in which visual salience appears to arise during the propagation of visual stimuli along the visual cortex. In this paper, inspired by the conjecture that salience arises from deep propagation along the visual cortex, we present a Deep Salience model where a multi-layer model based on successive Markov random fields (sMRF) is proposed to analyze the input image successively through its deep belief propagation. As a result, the foreground object can be automatically separated from the background in a fully unsupervised way. Experimental evaluation on the benchmark dataset validated that our Deep Salience model can consistently outperform eleven state-of-the-art salience models, yielding the higher rates in the precision-recall tests and attaining the best F-measure and mean-square error in the experiments.

Evaluation of an improved atomic data basis for carbon in UEDGE emission modeling for L-mode plasmas in DIII-D

New scaled carbon atomic electron-impact excitation data is utilized to evaluate comparisons between experimental measurements and fluid emission modeling of detached plasmas at DIII-D. The C I and C II modeled emission lines for 909.8 and 514.7 nm were overestimated by a factor of 10-20 than observed experimentally for the inner leg, while the outer leg was within a factor of 2. Due to higher modeled emissions, a previous study using the UEDGE code predicted that a higher amount of carbon was required to achieve a detached outboard divertor plasma in L-mode at DIII-D. The line emission predicted by using the new scaled carbon data yields closer results when compared against experiment. We also compare modeling and measurements of Dα emission from neutral deuterium against predictions from newly calculated R-Matrix with pseudostates data available at the ADAS database. © 2013 Published by Elsevier B.V.

Data-Driven Dynamic Modeling of Coupled Thermal and Electric Outputs of Microturbines

Microturbines are among the most successfully commercialized distributed energy resources, especially when they are used for combined heat and power generation. However, the interrelated thermal and electrical system dynamic behaviors have not been fully investigated. This is technically challenging due to the complex thermo-fluid-mechanical energy conversion processes which introduce multiple time-scale dynamics and strong nonlinearity into the analysis. To tackle this problem, this paper proposes a simplified model which can predict the coupled thermal and electric output dynamics of microturbines. Considering the time-scale difference of various dynamic processes occuring within microturbines, the electromechanical subsystem is treated as a fast quasi-linear process while the thermo-mechanical subsystem is treated as a slow process with high nonlinearity. A three-stage subspace identification method is utilized to capture the dominant dynamics and predict the electric power output. For the thermo-mechanical process, a radial basis function model trained by the particle swarm optimization method is employed to handle the strong nonlinear characteristics. Experimental tests on a Capstone C30 microturbine show that the proposed modeling method can well capture the system dynamics and produce a good prediction of the coupled thermal and electric outputs in various operating modes.

Avoiding Overfitting in Inverse Modeling of Chloride Migration in Concrete

Inverse analysis for reactive transport of chlorides through concrete in the presence of electric field is presented. The model is solved using MATLAB’s built-in solvers “pdepe.m” and “ode15s.m”. The results from the model are compared with experimental measurements from accelerated migration test and a function representing the lack of fit is formed. This function is optimised with respect to varying amount of key parameters defining the model. Levenberg-Marquardt trust-region optimisation approach is employed. The paper presents a method by which the degree of inter-dependency between parameters and sensitivity (significance) of each parameter towards model predictions can be studied on models with or without clearly defined governing equations. Eigen value analysis of the Hessian matrix was employed to investigate and avoid over-parametrisation in inverse analysis. We investigated simultaneous fitting of parameters for diffusivity, chloride binding as defined by Freundlich isotherm (thermodynamic) and binding rate (kinetic parameter). Fitting of more than 2 parameters, simultaneously, demonstrates a high degree of parameter inter-dependency. This finding is significant as mathematical models for representing chloride transport rely on several parameters for each mode of transport (i.e., diffusivity, binding, etc.), which combined may lead to unreliable simultaneous estimation of parameters.

Modeling of Turbulent Natural Gas and Biogas Flames of the Delft Jet-in-Hot-Coflow Burner: Effects of Coflow Temperature, Fuel Temperature and Fuel Composition on the Flame Lift-Off Height

The structure of a turbulent non-premixed flame of a biogas fuel in a hot and diluted coflow mimicking moderate and intense low dilution (MILD) combustion is studied numerically. Biogas fuel is obtained by dilution of Dutch natural gas (DNG) with CO2. The results of biogas combustion are compared with those of DNG combustion in the Delft Jet-in-Hot-Coflow (DJHC) burner. New experimental measurements of lift-off height and of velocity and temperature statistics have been made to provide a database for evaluating the capability of numerical methods in predicting the flame structure. Compared to the lift-off height of the DNG flame, addition of 30 % carbon dioxide to the fuel increases the lift-off height by less than 15 %. Numerical simulations are conducted by solving the RANS equations using Reynolds stress model (RSM) as turbulence model in combination with EDC (Eddy Dissipation Concept) and transported probability density function (PDF) as turbulence-chemistry interaction models. The DRM19 reduced mechanism is used as chemical kinetics with the EDC model. A tabulated chemistry model based on the Flamelet Generated Manifold (FGM) is adopted in the PDF method. The table describes a non-adiabatic three stream mixing problem between fuel, coflow and ambient air based on igniting counterflow diffusion flamelets. The results show that the EDC/DRM19 and PDF/FGM models predict the experimentally observed decreasing trend of lift-off height with increase of the coflow temperature. Although more detailed chemistry is used with EDC, the temperature fluctuations at the coflow inlet (approximately 100K) cannot be included resulting in a significant overprediction of the flame temperature. Only the PDF modeling results with temperature fluctuations predict the correct mean temperature profiles of the biogas case and compare well with the experimental temperature distributions.