Review of Metal AM Simulation Validation Techniques

Due to the complexity of metal AM （additive manufacturing）, it can require many trial runs to obtain processing parameters which produce a quality build. Because of this trial and error process, the drive for simulations of AM has grown significantly. A simulation only becomes useful to researchers if it can be shown that it is a true representation of the physical process being simulated. Each process being simulated has a different method of validation to show it is an accurate representation of the process. This paper explores the various methodologies for validation of laser-based metal AM simulations, focusing mainly on the modeling of the thermal processes and other characteristics derived from the thermal history. It will identify and explain the various validation techniques used, specifically looking at the frequency of reported use of each technique.

Optimization of Shanghai marine environment monitoring sites by integrating spatial correlation and stratified heterogeneity

The water quality grades of phosphate（PO4-P） and dissolved inorganic nitrogen（DIN） are integrated by spatial partitioning to fit the global and local semi-variograms of these nutrients. Leave-one-out cross validation is used to determine the statistical inference method. To minimize absolute average errors and error mean squares,stratified Kriging（SK） interpolation is applied to DIN and ordinary Kriging（OK） interpolation is applied to PO4-P.Ten percent of the sites is adjusted by considering their impact on the change in deviations in DIN and PO4-P interpolation and the resultant effect on areas with different water quality grades. Thus, seven redundant historical sites are removed. Seven historical sites are distributed in areas with water quality poorer than Grade IV at the north and south branches of the Changjiang（Yangtze River） Estuary and at the coastal region north of the Hangzhou Bay. Numerous sites are installed in these regions. The contents of various elements in the waters are not remarkably changed, and the waters are mixed well. Seven sites that have been optimized and removed are set to water with quality Grades III and IV. Optimization and adjustment of unrestricted areas show that the optimized and adjusted sites are mainly distributed in regions where the water quality grade undergoes transition.Therefore, key sites for adjustment and optimization are located at the boundaries of areas with different water quality grades and seawater.

Validity of virtual reality simulator in training of transurethral resection of prostate

Objective To assess the face and construct validity of a full procedural transurethral prostate resection simulator ( TURPSimTM ) in training of transurethral resection of prostate. Methods Ten experienced and thirteen inexperienced urologists ( TURP experience ≥ 30 and

Simulating urban land use change by incorporating an autologistic regression model into a CLUE-S model

The Conversion of Land Use and its Effects at Small regional extent （CLUE-S） model is a widely used method to simulate land use change. An ordinary logistic regression model was integrated into the CLUE-S model to identify explanatory variables without considering the spatial autocorrelation effect. Using image-derived maps of the Changsha- Zhuzhou-Xiangtan urban agglomeration, the CLUE-S model was integrated with the ordinary logistic regression and autologistic regression models in this paper to simulate land use change in 2000, 2005 and 2009 based on an observation map from 1995. Significant positive spatial autocorrelation was detected in residuals of ordinary logistic models. Some variables that were much more significant than they should be were selected. Autologistic regression models, which used autocovariate incorporation, were better able to identify driving factors. The Receiver Operating Characteristic Curve （ROC） values of autologistic regression models were larger than 0.8 and the pseudo R^2 values were improved, compared with results of logistic regression model. By overlapping the observation maps, the Kappa values of the ordinary logistic regression model （OL）-CLUE-S and autologistic regression model （AL）-CLUE-S models were larger than 0.75. The results showed that the simulation results were indeed accurate. The Kappa fuzzy （Kfuzzy） values of the AL-CLUE-S models （0.780, 0.773, 0.606） were larger than the values of the OL-CLUE-S models （0.759, 0.760, 0.599） during the three periods. The AL-CLUE-S models performed better than the OL-CLUE-S models in the simulation of land use change. The results showed that it is reasonable to integrate autocovariates into CLUE-S models. However, the Kfuzzy values decreased with prolonged duration of simulation and the maximum range of time was not discussed in this paper.

Investigation of longitudinal aerodynamic parameters identifcation method for fly-by-wire passenger airliners

The flight control system of a fly-by-wire （FBW） passenger airliner with a complex frame-work and high feedback gain augmentation would change the original characteristic of a loaded signal and suppress the excitation of an airplane＇s pertinent motion modes. Taking a research example of an FBW passenger airliner model with longitudinal relaxed-static-stability, a new method of signal type selection and signal parameter design is proposed, through analysis of signal energy distribution and plane body＇s frequency response. According to CCAR60--the Appraisal and Use Regulation of Flight Simulator Device, the simulation validation of the FBW passenger airliner＇s longitudinal aerodynamic parameters identification is put forward. The validation result indicates that the designed signal could excite the longitudinal motion mode of the FBW passenger airliner adequately and the multiparameter comparison in simulation meets the objective test request of CCAR60. Meanwhile, the relative errors of aerodynamic parameters are less than 10%.

Stochastic model updating using distance discrimination analysis

This manuscript presents a stochastic model updating method, taking both uncertainties in models and variability in testing into account. The updated finite element（FE） models obtained through the proposed technique can aid in the analysis and design of structural systems. The authors developed a stochastic model updating method integrating distance discrimination analysis（DDA） and advanced Monte Carlo（MC） technique to（1） enable more efficient MC by using a response surface model,（2） calibrate parameters with an iterative test-analysis correlation based upon DDA, and（3） utilize and compare different distance functions as correlation metrics. Using DDA, the influence of distance functions on model updating results is analyzed. The proposed stochastic method makes it possible to obtain a precise model updating outcome with acceptable calculation cost. The stochastic method is demonstrated on a helicopter case study updated using both Euclidian and Mahalanobis distance metrics. It is observed that the selected distance function influences the iterative calibration process and thus, the calibration outcome, indicating that an integration of different metrics might yield improved results.