Optimum Kinematics Crack Download

We are a first year team and we were wondering if it is absolutely necessary to use simulation softwares like optimum kinematics to design our suspension geometry. I have been told it would be better not to do too much optimisation as a first year team. Would it be realistic to design a suspension without these tools?

In this paper, the design of a planar three-degree-of-freedom parallel manipulator is considered from a kinematic viewpoint. Four different design criteria are established and used to produce designs having optimum characteristics. These criteria are (a) symmetry (b) the existence of a nonvanishing workspace for every orientation of the gripper (c) the maximization of the global workspace, and (d) the isotropy of the Jacobian of the manipulator. The four associated problems are formulated and their solutions are derived. Two of these require to resort to numerical methods for nonlinear algebraic systems. Results of optimum designs are also included.

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Hovering flight for flapping wing vehicles requires rapid and relatively complex reciprocating movement of a wing relative to a stationary surrounding fluid. This note develops a compact analytical aero-kinematic model that can be used for optimization of flapping wing kinematics against aerodynamic criteria of effectiveness (maximum lift) and efficiency (minimum power for a given amount of lift). It can also be used to make predictions of required flapping frequency for a given geometry and basic aerodynamic parameters. The kinematic treatment is based on a consolidation of an existing formulation that allows explicit derivation of flapping velocity for complex motions whereas the aerodynamic model is based on existing quasi-steady analysis. The combined aero-kinematic model provides novel explicit analytical expressions for both lift and power of a hovering wing in a compact form that enables exploration of a rich kinematic design space. Good agreement is found between model predictions of flapping frequency and observed results for a number of insects and optimal hovering kinematics identified using the model are consistent with results from studies using higher order computational models. For efficient flight, the flapping angle should vary using a triangular profile in time leading to a constant velocity flapping motion, whereas for maximum effectiveness the shape of variation should be sinusoidal. For both cases the wing pitching motion should be rectangular such that pitch change at stroke reversal is as rapid as possible.

Gait pattern affects the quality of a walking robot. In this paper, an optimum human-like gait generation method is proposed for a seven-link biped robot. This method utilizes kinematic constraints based on human-like motion. At first, the constraints are parameterized with unknown parameters. Then, an optimization problem is defined with an objective function and the kinematic constraints. The objective function includes the energy consumption and walking stability. Unknown parameters appearing in the constraints are determined by solving the optimization problem. Joint variables are obtained by integrating a velocity form of the kinematic constraints. This approach is carried out for gait planning of the seven-link biped robot and its results are presented. This method allows studying some effective parameters on the gait such as constant knee angle duration, and torso inclination is optimized for different speeds. Our studies indicate that as walking speed increases the knee should extend and the torso should lean forward further in order to maintain better stability. Moreover, energy consumption increases as the torso leans forward.

In addition, a difference in the mean values of the gait parameters recorded through the two measurement methods was detected, which could represent a measurement error that is consistent between trials in one or both of the acquisition methods. In this study, one possible factor involved in this possible measurement error of the kinetic measure was the imprecise manual determination of the FC obtained from the anteroposterior or the mediolateral center of the foot pressure displacement curve. Indeed, while a kinematic analysis can spatially detect the exact position of the foot in relation to the ground and therefore calculate the L and the BI, the FC is manually estimated on the force platform through the analysis of the curves representing the forces during gait, and this could depend on how the foot hits the ground. This led to a variation in V2 and the BI. The imprecision related to kinetic measurements has already been brought to light [26], and the use of kinematics has already been suggested to avoid errors linked to extrapolations of the calculus of the CoM position [17]. Regardless, this evaluation of FC is validated in the literature [9,10,11,12,14,15,22,26].

Based on these background studies, we assess the spatial and temporal characteristics of the optimum process noise settings of unknown tropospheric parameters for kinematic PPP data analysis. In this paper, we initially focus on the spatial distribution of the optimum parameter settings between ZWD and tropospheric gradient across the Japanese nationwide GNSS network, which comprises more than 1300 stations. We also discuss the long-term stability of the optimum tropospheric parameters for specific sites. Finally, we discuss the effects of optimizing process noise for kinematic GNSS data analysis.

We used data from GEONET, which is a dense nationwide GNSS network established by the Geospatial Information Authority of Japan (GSI) comprising more than 1300 stations, to assess the spatial and temporal dependency of the optimum process noise values. Thirty-second dual-frequency phase data were used for processing. This study used only GPS satellites, and Fig. 1 shows the distribution of the GNSS stations used.

To assess the long-term stability of the optimum tropospheric parameters for a specific site, we analyzed data recorded continuously at stations 0098 and 0032 throughout the year 2010. The locations of these sites are indicated in Fig. 1. We estimated the optimum combination of tropospheric process noise values for each day, using the same procedure as for the spatial characteristic assessment described above.

Frequency distribution of the optimum parameter combinations in TROP and GRAD parameter space, based on data from the entire GEONET network on March 10, 2011. Regions indicated by the dashed red squares were used for the common optimum parameter combinations for all components. The colors indicate the ratio of the frequency within each parameter combination relative to the number of GEONET sites. The solid squares indicated the optimum combination value in each component

In this section, we show the spatial distribution of the optimum TROP and GRAD parameters for each calculated day. Firstly, we show the general characteristics, based on the results of the 3 days. Secondly, we show the results for sub-divided region based on the results for March 10, 2011.

Histograms showing the estimated optimum process noise value for each component based on data from the entire GEONET network on March 10, 2011. Left (a, c, e) and right columns (b, d, f) show the TROP and GRAD parameter histograms, respectively

In comparison with the TROP parameter, the spatial characteristics of the GRAD parameter are less well defined (Fig. 5; Additional file 3: Figure S3; Additional file 4: Figure S4). The frequency histograms, however, show clear characteristics in each day. For example, in the case of March 10, the frequency histograms of the optimum GRAD parameter for the horizontal components clearly show broader distributions compared to the TROP parameter for the vertical component (Fig. 6). In contrast, the frequency histogram for July 4 clearly shows the steep characteristic for the horizontal components (Additional file 5: Figure S5(b, d)). Weather conditions on July 4 were strongly influenced by the passage of a cold front, such that the obtained results should reflect these weather conditions.

The TROP parameter distribution shows a second important characteristic, namely the influence of recording station elevation. Additional file 8: Figure S8 shows the ratio of each optimum TROP parameter within each ellipsoidal GNSS station elevation range for the case of March 10. It is clear that the ratio of low TROP parameter values increased with site elevation. This is a reasonable result because higher elevation is associated with less integration of water vapor. These results suggest that the optimum ZWD process noise parameter might depend on each sub-divided region and the elevation of each site. At this time, the scale of the sub-divided region is several hundred kilometers. Thus, the parameter at least depends on such spatial expansion.

Figure 7 shows the time series for the estimated optimum process noise values at stations 0098 and 0032 for each coordinate time series component. The gray and red lines denote the daily optimum value and 11-day moving average, respectively.

Estimated optimum process noise value time series at station 0098 (left side) and 0032 (right side) throughout the year 2010. Gray and red lines indicate the daily optimum process noise value and 11-day moving average time series, respectively. Left and right columns for each site indicate the TROP and GRAD parameter for each coordinate component, respectively

Interestingly, the optimum TROP time series for the vertical component indicates limited disturbance compared with the horizontal components. Furthermore, the moving average time series obtained at station 0098 shows stability throughout the year (Fig. 7) with small annual pattern. Similarly, at station 0032 the obtained time series is stable despite the minor long-term pattern that developed following day of year (DOY) 100 (Fig. 7). In contrast, the optimum GRAD parameters for the horizontal components show a different tendency. It is clear that the obtained time series did not stabilize during the year, and shows a clear annual pattern in the moving average time series. In the previous section, we suggested that the GRAD parameter might not have a significant spatial characteristic within the scale of the GEONET on that specific day. The obtained time series, however, suggests that the optimum GRAD parameter might vary following an annual pattern, despite the relatively large disturbance compared with the optimum TROP parameter for the vertical component. ff782bc1db