Experimental Input Data Research Articles

Parametric analysis of LIBRETTO-4 and 5 in-pile tritium transport model on EcosimPro

A new model for LIBRETTO-4/1, 4/2 and 5 experiments have been developed on ECOSIMPro© tool to simulate tritium in-pile breeding and transport into two separate purge gas channels with He+0.1%H2. Release from lead lithium eutectic plenum with coupled permeation through an austenitic steel wall on the first and single permeation through EUROFER-97 in the temperature ranges of 300–550°C can be simulated tuning the transport parameters involved. A parametric study has been performed to reduce the degrees of freedom and to determine the error caused in the simulation due to the uncertainty in experimental input data. The information obtained is essential for the experimental benchmarking.The Tritium Permeation Percentage (TPP) is an output calculated parameter with low variations between 2 and 6% along the whole experimental time easy to compare (730 Full Power Days for LIBRETTO-4 and 520 for 5). Tritium transport parameter ranges verifying this output are defined herein.

On the calibration strategies of the Johnson–Cook strength model: Discussion and applications to experimental data

The present paper aims at assessing the various procedures adoptable for calibrating the parameters of the so-called Johnson–Cook strength model, expressing the deviatoric behavior of elastoplastic materials, with particular reference to the description of High Strain Rate (HSR) phenomena. The procedures rely on input experimental data corresponding to a set of hardening functions recorded at different equivalent plastic strain rates and temperatures.After a brief review of the main characteristics of the Johnson–Cook strength model, five different calibration strategies are framed and widely described. The assessment is implemented through a systematic application of each calibration strategy to three different real material cases, i.e. a DH-36 structural steel, a commercially pure niobium and an AL-6XN stainless steel. Experimental data available in the literature are considered. Results are presented in terms of plots showing the predicted Johnson–Cook hardening functions against the experimental trends, together with tables describing the fitting problematics which arise in each case, by assessing both lower yield stress and overall plastic flow introduced errors.The consequences determined by each calibration approach are then carefully compared and evaluated. A discussion on the positive and negative aspects of each strategy is presented and some suggestions on how to choose the best calibration approach are outlined, by considering the available experimental data and the objectives of the following modeling process. The proposed considerations should provide a useful guideline in the process of determining the best Johnson–Cook parameters in each specific situation in which the model is going to be adopted.A last section introduces some considerations about the calibration of the Johnson–Cook strength model through experimental data different from those consisting in a set of hardening functions relative to different equivalent plastic strain rates and temperatures. In particular, the opportunity of using experimental data coming from Taylor impact tests is assessed, together with an evaluation of the possibility of using other less popular and somehow innovative ways for obtaining the Johnson–Cook strength model parameters.

Measuring similarity between dynamic ensembles of biomolecules.

Methods for comparing ensembles of biomolecules assess the population overlap between distributions but fail to fully quantify structural similarity. We present a simple and general approach for quantifying population overlap and structural similarity between ensembles. This approach captures improvements in the quality of ensembles determined using increasing input experimental data that go undetected using conventional methods and reveals unexpected similarities between RNA ensembles determined using NMR and molecular dynamics simulations.

Complementary tribometers for the analysis of contact phenomena in grinding

The characterization of friction and tribological phenomena occurring in grinding requires reliable experimental input data. Up to now, existing test benches exhibit important limitations. Steep thermal gradients and elastic deformations of both machine structure, and rubbing elements are the main sources of inaccuracy cited in the literature. In this paper, an alternative approach is presented for the study and characterization of contact phenomena in grinding. It consists of two complementary test benches: the On-Machine Test Bench and the High Speed Tribometer. They are designed to overcome the limitations of current grinding experimental studies.On the one hand, the On-Machine Test Bench enables accurate control of process parameters. This allows assessing the effect of the different grinding mechanisms on process forces, and specific energy. In addition, its configuration avoids steep thermal gradients; consequently, it is possible to obtain stable and accurate temperature measurements that can be directly related to process variables. On the other hand, the High Speed Tribometer enables to isolate adhesive effect because of its predominance. This test bench allows an accurate control of contact pressure and contact area. Then, adhesive phenomena can be characterized and related to process parameters using force and temperature measurements.Experimental results obtained in both test benches are presented. A wide range of data (forces, specific energy, temperatures, wear…) concerning contact conditions in grinding can be obtained from this complementary approach.

A mixed-integer linear programming (MILP) model for the evaluation of CHP system in the context of hospital structures

In this paper an optimal planning method is proposed for sizing a cogeneration plant to be installed within a public/commercial micro-grid. The optimization tool is based on a mixed integer linear programming model which uses a set of experimental input data in terms of both thermal and electric power consumption for determining the annual operational strategy, able to reach a twofold objective: minimizing the total operational cost of the micro-grid and finding the optimal size of the CHP to be installed. A numerical example arisen from a case study is proposed with the aim of investigating the economic advantages coming from the adoption of an optimal cogeneration operation strategy. Two different scenario problems exploiting a set of experimental data have been investigated. The former focuses on the comparison between a micro-grid wherein the cooling energy demand coming from final users is neglected and a micro-grid wherein the cooling energy demand is taken into account. The latter assesses the effect of a heat storage properly introduced into the micro-grid, once the cooling energy demand is taken into account.

Kinematic Algorithms and Robust Controller Design for Inertially Stabilized System

This paper describes a controller design method for the inertially stabilized system of a tracking radar. Its aim is to track a reference target trajectory with high accuracy while isolating rigid body rotational motions of a host ship. First, we investigate the trajectory generation problem to make the control input for a target tracking on the moving base. Second, dynamic equations of motion are formulated by the spring-mass-damper system to include rigid body dynamics as well as structural flexibility. The unknown parameters of dynamic equations are estimated with experimental input and output data by minimizing a predicted error. Third, mixed sensitivity H∞ robust controllers are designed to meet the conflict requirements of robustness and performance in the face of uncertainty. Finally, the proposed optimal controllers demonstrate the effectiveness of design methodology, and show high performance by numerical and experimental results.

Service life assessment of historical building envelopes constructed using different types of sandstone: a computational analysis based on experimental input data.

Service life assessment of three historical building envelopes constructed using different types of sandstone is presented. At first, experimental measurements of material parameters of sandstones are performed to provide the necessary input data for a subsequent computational analysis. In the second step, the moisture and temperature fields across the studied envelopes are calculated for a representative period of time. The computations are performed using dynamic climatic data as the boundary conditions on the exterior side of building envelope. The climatic data for three characteristic localities are experimentally determined by the Czech Hydrometeorological Institute and contain hourly values of temperature, relative humidity, rainfalls, wind velocity and direction, and sun radiation. Using the measured durability properties of the analyzed sandstones and the calculated numbers of freeze/thaw cycles under different climatic conditions, the service life of the investigated building envelopes is assessed. The obtained results show that the climatic conditions can play a very significant role in the service life assessment of historical buildings, even in the conditions of such a small country as the Czech Republic. In addition, the investigations reveal the importance of the material characteristics of sandstones, in particular the hygric properties, on their service life in a structure.

Equilibrium Sampling Approach to the Interpretation of Electron Density Maps

The derivation of molecular models from spatial density data generated by X-ray crystallography or electron microscopy is an active field of research. Here, we introduce and evaluate an approach relying on the equilibrium sampling of energy landscapes describing restraints to experimental input data. Our procedure combines density restraints with replica exchange methodologies in the parameter space of the restraints, and we demonstrate its applicability to both flexible polymers and the assembly of protein complexes from rigid components. For the most difficult system studied, we highlight the importance of advanced data analysis techniques in mining poorly converged data further. Successful and unbiased interpretation of input density maps is a prerequisite for using this approach as an auxiliary restraint term in molecular simulations. Because these simulations will also utilize physical interaction potentials, we hope that they will contribute to deriving families of structural models for input data that are ambiguous per se.

The Design of Free Structure Granular Mappings: The Use of the Principle of Justifiable Granularity

The study introduces a concept of mappings realized in presence of information granules and offers a design framework supporting the formation of such mappings. Information granules are conceptually meaningful entities formed on a basis of a large number of experimental input–output numeric data available for the construction of the model. We develop a conceptually and algorithmically sound way of forming information granules. Considering the directional nature of the mapping to be formed, this directionality aspect needs to be taken into account when developing information granules. The property of directionality implies that while the information granules in the input space could be constructed with a great deal of flexibility, the information granules formed in the output space have to inherently relate to those built in the input space. The input space is granulated by running a clustering algorithm; for illustrative purposes, the focus here is on fuzzy clustering realized with the aid of the fuzzy C-means algorithm. The information granules in the output space are constructed with the aid of the principle of justifiable granularity (being one of the underlying fundamental conceptual pursuits of Granular Computing). The construct exhibits two important features. First, the constructed information granules are formed in the presence of information granules already constructed in the input space (and this realization is reflective of the direction of the mapping from the input to the output space). Second, the principle of justifiable granularity does not confine the realization of information granules to a single formalism such as fuzzy sets but helps form the granules expressed any required formalism of information granulation. The quality of the granular mapping (viz. the mapping realized for the information granules formed in the input and output spaces) is expressed in terms of the coverage criterion (articulating how well the experimental data are “covered” by information granules produced by the granular mapping for any input experimental data). Some parametric studies are reported by quantifying the performance of the granular mapping (expressed in terms of the coverage and specificity criteria) versus the values of a certain parameters utilized in the construction of output information granules through the principle of justifiable granularity. The plots of coverage–specificity dependency help determine a knee point and reach a sound compromise between these two conflicting requirements imposed on the quality of the granular mapping. Furthermore, quantified is the quality of the mapping with regard to the number of information granules (implying a certain granularity of the mapping). A series of experiments is reported as well.

Direct Control Parameter Tuning Method for Disturbance Attenuation Based on Variance Evaluation

Abstract The direct control parameter tuning methods derive the control parameters from the one-shot experimental input and output data with no need of help from the plant model. Most prior methods have been based on the perfect model matching design strategy so that the output of the closed loop system should follow the prescribed reference model output. In considering the disturbance attenuation problem, however, the model following criterion is a rather demanding specification because the disturbance response is just required to be suppressed around the target value. Therefore, the paper proposes a direct control parameter tuning method which employs the variance evaluation for the performance criterion to solve the disturbance attenuation problem. The proposed method tunes the control parameters so that the variance of the plant output approaches the variance of the reference model output. The proposed method can avoid an additive experiment where test signals are fed to the closed loop system for collecting the initial input and output data. The feature facilitates applying the proposed method to the industrial applications. Finally, the efficiency of the proposed method is demonstrated from a numerical example.

Nonlinear system identification of large-scale smart pavement systems

This paper proposes a novel model for predicting complex behavior of smart pavements under a variety of environmental conditions. The mathematical model is developed through an adaptive neuro fuzzy inference system (ANFIS). To evaluate the effectiveness of the ANFIS model, the temperature fluctuations at different locations in smart pavement systems equipped with pipe network systems under solar radiations is investigated. To develop the smart pavement ANFIS model, various sets of input and output field experimental data are collected from large-scale experimental test beds. The solar radiation and the inlet water flow are used as input signals for training complex behavior of the smart pavement ANFIS model, while the temperature fluctuation of the smart pavement system is used for the output signal. The trained model is validated using 20 different data sets that are not used for the training process. It is demonstrated from the simulation that the ANFIS identification approach is effective in modeling complex behavior of the pavement-fluid system under a variety of environmental conditions. Comparison with high fidelity data proves the viability of the proposed approach in pavement health monitoring setting, as well as automatic control systems.

Numerical investigation and experimental validation of a plasticity model for sheet steel forming

This paper investigates a recently developed elasto-plastic constitutive model. For this purpose, the model was implemented in a commercial finite element code and was used to simulate the cross-die deep drawing test. Deep drawing experiments and numerical simulations were conducted for five interstitial-free steels and seven dual-phase (DP) steels, each of them having a different thickness and strength. The main interest of the adopted model is a very efficient parameter identification procedure, due to the physical background of the model and the physical significance of some of its parameters and state variables. Indeed, the dislocation density, grain size and martensite volume fraction explicitly enter the model's formulation, although the overall approach is macroscopic. For the DP steels, only the chemical composition and the average grain sizes were measured for the martensite and ferrite grains, as well as the martensite volume fraction. The mild steels required three additional tensile tests along three directions, in order to describe the plastic anisotropy. Information concerning the transient mechanical behavior after strain-path changes (reverse and orthogonal) was not collected for each material, but for only one material of each family of steels (IF, DP), based on previous works available in the literature. This minimalistic experimental base was used to feed the numerical simulations for the twelve materials that were confronted to deep drawing experiments in terms of thickness distributions. The results suggested that the accuracy of the numerical simulations is very satisfactory in spite of the scarce experimental input data. Additional investigations indicated that the modeling of the transient behavior due to strain-path changes may have a significant impact on the simulation results, and that the adopted approach provides a simple and efficient alternative in this regard.

Characterization of disordered proteins with ENSEMBLE

ENSEMBLE is a computational approach for determining a set of conformations that represents the structural ensemble of a disordered protein based on input experimental data. The disordered protein can be an unfolded or intrinsically disordered state. Here, we introduce the latest version of the program, which has been enhanced to facilitate its general release and includes an intuitive user interface, as well as new approaches to treat data and analyse results. ENSEMBLE is a program implemented in C and embedded in a Perl wrapper. It is supported on main Linux distributions. Source codes and installation files, including a detailed example, can be freely downloaded at http://abragam.med.utoronto.ca/∼JFKlab.

The Ame2012 atomic mass evaluation

This paper is the first of two articles (Part I and Part II) that presents the results of the new atomic mass evaluation, Ame2012. It includes complete information on the experimental input data (including not used and rejected ones), as well as details on the evaluation procedures used to derive the tables with recommended values given in the second part. This article describes the evaluation philosophy and procedures that were implemented in the selection of specific nuclear reaction, decay and mass-spectrometer results. These input values were entered in the least-squares adjustment procedure for determining the best values for the atomic masses and their uncertainties. Calculation procedures and particularities of the Ame are then described. All accepted and rejected data, including outweighed ones, are presented in a tabular format and compared with the adjusted values (obtained using the adjustment procedure). Differences with the previous Ame2003 evaluation are also discussed and specific information is presented for several cases that may be of interest to various Ame users. The second Ame2012 article, the last one in this issue, gives a table with recommended values of atomic masses, as well as tables and graphs of derived quantities, along with the list of references used in both this Ame2012 evaluation and the Nubase2012 one (the first paper in this issue).

Application of Abraham solvation parameters for surface tension prediction of mono-solvents and solvent mixtures at various temperatures

A combined form of the Jouyban–Acree model and the Abraham solvation parameters was used to predict the surface tension of binary and ternary solvent mixtures at various temperatures. The new equations have been tested by comparisons with the experimental results for 364 binary and 79 ternary mixtures reported in the literature. After the training procedures, the models were used for the prediction of the surface tension of mixed solvent systems. The mean relative deviations (MRDs) between predicted and experimental surface tensions are used as an accuracy criterion. The overall MRD for binary mixtures was 6.5% and the corresponding value for ternary mixtures was 11.2%. These versions of the models require the experimental surface tension of the mono-solvents as input data. An attempt has been made here to correlate the surface tension of the mono-solvents at various temperatures using Abraham parameters. The model calculates the surface tension with the MRD of 11.0%. A combined version of the models was presented to predict the surface tension of binary and ternary mixtures without using any experimental input data in which the MRDs of 14.6 and 11.7% were obtained. Results show that the addition of solvation parameters may lead to an improvement in the calculated surface tension and show good agreement between theory and experiment.

Frequency analysis of micropillar structured surfaces: A characterization and design tool for surface texturing

Various systematically controlled, ordered and randomized micropillar structures were fabricated onto polypropylene by injection molding. Mold inserts were prepared by a micro-working robot. The advantages of this simple surface texturing application include the rigorous control of the pillar positions and the use of a variety of polymers. The textured surfaces were characterized both in horizontal and vertical directions. Power spectral density (PSD) curves combined with filtered root-mean-square (RMS) values provide structure periodicities and roughness differences between various spatial frequencies, respectively. The developed characterization tool is useful in texture mimicking when substantial periodicities and roughness is obtained and imitated in the input data of the micro-working robot. Hence, injection molded micropillar surfaces can possess similar spatial frequency and roughness features to that of mimicked surfaces.The developed characterization method was also connected to the texture design. The height profiles of structures corresponding to the fabricated surfaces were predicted from the experimental input data of the micro-working robot, and the PSD curves were generated to obtain the connection between experimental and predicted structures. The rigorous correspondence between the experimental and predicted structures and the filtered RMS values indicates that the developed design tool enables the theoretical inspection of desired structures. This can also be used in texture mimicking to expedite experimental work.

Experimental investigation, modeling and optimization of membrane separation using artificial neural network and multi-objective optimization using genetic algorithm

Abstract In this work, treatment of oily wastewaters with commercial polyacrylonitrile (PAN) ultrafiltration (UF) membranes was investigated. In order to do these experiments, the outlet wastewater of the API (American Petroleum Institute) unit of Tehran refinery, is used as the feed. The purpose of this paper was to predict the permeation flux and fouling resistance, by applying artificial neural networks (ANNs), and then to optimize the operating conditions in separation of oil from industrial oily wastewaters, including trans-membrane pressure (TMP), cross-flow velocity (CFV), feed temperature and pH, so that a maximum permeation flux accompanied by a minimum fouling resistance, was acquired by applying genetic algorithm as a powerful soft computing technique. The experimental input data, including TMP, CFV, feed temperature and pH, permeation flux and fouling resistance as outputs, were used to create ANN models. This fact that there is an excellent agreement between the experimental data and the predicted values was shown by the modeling results. Eventually, by multi-objective optimization, using genetic algorithm (GA), an optimization tool was created to predict the optimum operating parameters for desired permeation flux (i.e. maximum flux) and fouling resistance (i.e. minimum fouling) behavior. The accuracy of the model is confirmed by the comparison between the predicted and experimental data.

A New Algorithm for Reliable and General NMR Resonance Assignment

The new FLYA automated resonance assignment algorithm determines NMR chemical shift assignments on the basis of peak lists from any combination of multidimensional through-bond or through-space NMR experiments for proteins. Backbone and side-chain assignments can be determined. All experimental data are used simultaneously, thereby exploiting optimally the redundancy present in the input peak lists and circumventing potential pitfalls of assignment strategies in which results obtained in a given step remain fixed input data for subsequent steps. Instead of prescribing a specific assignment strategy, the FLYA resonance assignment algorithm requires only experimental peak lists and the primary structure of the protein, from which the peaks expected in a given spectrum can be generated by applying a set of rules, defined in a straightforward way by specifying through-bond or through-space magnetization transfer pathways. The algorithm determines the resonance assignment by finding an optimal mapping between the set of expected peaks that are assigned by definition but have unknown positions and the set of measured peaks in the input peak lists that are initially unassigned but have a known position in the spectrum. Using peak lists obtained by purely automated peak picking from the experimental spectra of three proteins, FLYA assigned correctly 96-99% of the backbone and 90-91% of all resonances that could be assigned manually. Systematic studies quantified the impact of various factors on the assignment accuracy, namely the extent of missing real peaks and the amount of additional artifact peaks in the input peak lists, as well as the accuracy of the peak positions. Comparing the resonance assignments from FLYA with those obtained from two other existing algorithms showed that using identical experimental input data these other algorithms yielded significantly (40-142%) more erroneous assignments than FLYA. The FLYA resonance assignment algorithm thus has the reliability and flexibility to replace most manual and semi-automatic assignment procedures for NMR studies of proteins.

Systematic Identification of Functional Plant Modules through the Integration of Complementary Data Sources

A major challenge is to unravel how genes interact and are regulated to exert specific biological functions. The integration of genome-wide functional genomics data, followed by the construction of gene networks, provides a powerful approach to identify functional gene modules. Large-scale expression data, functional gene annotations, experimental protein-protein interactions, and transcription factor-target interactions were integrated to delineate modules in Arabidopsis (Arabidopsis thaliana). The different experimental input data sets showed little overlap, demonstrating the advantage of combining multiple data types to study gene function and regulation. In the set of 1,563 modules covering 13,142 genes, most modules displayed strong coexpression, but functional and cis-regulatory coherence was less prevalent. Highly connected hub genes showed a significant enrichment toward embryo lethality and evidence for cross talk between different biological processes. Comparative analysis revealed that 58% of the modules showed conserved coexpression across multiple plants. Using module-based functional predictions, 5,562 genes were annotated, and an evaluation experiment disclosed that, based on 197 recently experimentally characterized genes, 38.1% of these functions could be inferred through the module context. Examples of confirmed genes of unknown function related to cell wall biogenesis, xylem and phloem pattern formation, cell cycle, hormone stimulus, and circadian rhythm highlight the potential to identify new gene functions. The module-based predictions offer new biological hypotheses for functionally unknown genes in Arabidopsis (1,701 genes) and six other plant species (43,621 genes). Furthermore, the inferred modules provide new insights into the conservation of coexpression and coregulation as well as a starting point for comparative functional annotation.

Comparative technical and economical parameters of gas diesel and gas-spark-ignited converted diesels to run on CNG

On the basis of experimental input data the commercial efficiency of conversion of automotive diesels in gas-diesel and gas-spark-ignition engines is calculated. Analysis of both options' performance is given, taking into account the load factor of engine.