3.1. The Static Friction Coefficient (μs)
The direct measurement results of
β and
μs-pw between clam and SS, and AC are shown in
Table 2. Due to a
μs-pw-ss and
μs-pw-ac of 0.26 and 0.34, respectively, the resulting
μ′s-pw prediction range was 0.20–0.40. The quadratic polynomial fitting curve and equation y
s-ss, y
s-ac based on the DEM simulation test were fitted and are shown in
Figure 6a. The coordinates obtained by virtual line marking in
Figure 6a are the simulation contact parameters (
μ′s-pw) and their test results (
β’) in the DEM simulation calibration tests.
As shown in
Table 2, the directly measured
βac and
μs-pw-ac were 1.31 times more than the
βss and
μs-pw-ss, and a similar result occurred in the DEM simulation test. Due to the AC surface roughness, the static friction between clam and AC was sizeable. Therefore, in the clam seeding equipment design, AC would be the most appropriate material when increasing friction was required. For example, to avoid the clams dropping too quickly and causing congestion at the outlet of the dropping hopper, it would be necessary to add a high friction guide plate in the blanking hopper; hence, AC would be the recommended material. Conversely, SS would be the obvious choice when friction needs to be minimized.
The simulation value
μ′s-pw was significantly smaller than
μs-pw (as shown in
Table 2). The reason may be that the clam DEM model was composed of smooth particles; however, clams have a growth line on their surface that grows with increasing clam size. As the clam growth lines gradually deepened, the surface roughness and the static friction coefficient (
μs) also gradually increased, which concurs with previous studies that suggest that the larger the clam size, the greater the static friction coefficient is [
31]—hence why the
μs-pw was larger than
μ′s-pw, as also in the calibration case of panax notoginseng seeds [
36].
Furthermore, the value of the inclination angle relative error (δa) between β’ and β included: δa-ss = 4.0%, δa-ac = 1.8%, respectively. It indicated that the simulation inclination angle was consistent with the direct measurements. The DEM calibration test results of the static friction coefficient were accurate, which could then be applied to the clam simulation study.
3.2. The Coefficient of Restitution (e)
The coefficients of restitution (
e) for SS, AC, and Clam are shown in
Table 3. According to the calculated restitution coefficient results:
epw-ss = 0.30,
epp = 0.21,
epw-ac = 0.41, the predicted range of the simulation restitution coefficient was 0.25–0.55.
Figure 6b shows the relationship between the simulation restitution coefficient (
e’) and rebound height (
H’1) in the fitting curve and equation y
h-ss, y
h-ac, y
h-pp. The coordinates obtained by virtual line marking in
Figure 6b are the simulation contact parameters (
e’) and their test results (
H1’) in the DEM simulation test.
As illustrated in
Table 3, the
e varied in direct measurement, and the
epp <
epw-ss <
epw-ac. The lower the restitution coefficient, the lower rebound height is. In the process of clam seeding, when clams fall into the groove on the seeding wheel through the bottom of the blanking hopper, they rebound. When the rebound height of the clams is higher than the depth of the groove, the clams are crushed by the seeding wheel and the seeding traying. To avoid this affecting, SS, with a small restitution coefficient, should be selected as the surface contact material of the seeding wheel. AC with a high restitution coefficient would be an appropriate contact material if the impact of rebound height was negligible and the clam breakage rate could be reduced by modifying the equipment structure.
Additionally, the
e’ was greater than in
Table 3; specifically, the
e’pw-ac was 0.48, which is 17.1% larger than the
e’pw-ac. This may be because the center of gravity of the clam is different from that of its DEM model. The clam is composed of an external shell, internal flesh, and a small amount of water, which are heterogeneous granular materials. Due to the different shapes and water content between clams, the center of gravity of each clam also varies. Therefore, when each clam lands on the bottom plate, the impact position and rebound height are different. However, the clam DEM model in the simulated drop test was filled with solid homogeneous granular materials, and the gravity center and impact position were more fixed than the living clam. Therefore, the direct measurement rebound height was significantly lower than in the DEM simulation test result.
The rebound height relative error (δH1) between H’1 and H1 was: 1.7%, 1.7%, 2.1%, respectively. The DEM simulation test result was similar to the direct measurement, which could effectively replace the realistic drop test.
3.3. Response Surface Simulation Test and ANOVA
The results of the directly measured static repose angles of Clam-SS (
θss) and Clam-AC (
θac) were
θss = 31.75°,
θac = 38.07°. The range of the simulation contact parameters was predicted by a clam stacking simulation pre-test. With an SS wall, the simulation rolling coefficient of Clam-Clam (
μ′r-pp) was in the range of 0.14−0.22, the simulation statics coefficient of Clam-Clam (
μ′s-pp) was in the range of 1.04−1.12, and the rolling coefficient of Clam-SS (
μ′r-pw-ss) was in the range of 0.14−0.22. The simulation contact parameter range for an AC wall was also predicted. The factors and levels from the response surface simulation test are shown in
Table 4.
In this study, 17 experiments were carried out to find the best combination of simulation contact parameters and to study the effect of the
μ′r-pp,
μ′s-pp, and
μ′r-pw on the clam simulation static repose angle, based on the BBD method [
37]. The corresponding simulation results are shown in
Table 5.
In ANOVA, the
p-value represents the significance of the factors. The
F-value represents the primary and secondary order of influence that the factors had on the response. The larger the
F-value, the stronger the influence on the response was. The ANOVA results for the quadratic polynomial model are shown in
Table 6. With an SS bottom plate, the simulation contact parameters:
μ′r-pp,
μ′s-pp,
μ′r-pw,
μ′r-pp μ′s-pp,
μ′r-ppμ′r-pw,
μ′s-pp2, and
μ′r-pp 2 showed highly significant influence (
p < 0.01), whereas
μ′s-ppμ′r-pw and
μ′r-pw2 showed insignificant influence. The influence order of the factors was
μ′s-pp >
μ′s-pp2 >
μ′r-pp >
μ′r-ppμ′s-pp >
μ′r-pp2 >
μ′r-ppμ′r-pw >
μ′r-pw >
μ′r-pw2 >
μ′s-ppμ′r-pw. With an AC bottom plate, the simulation contact parameters:
μ′r-pp,
μ′r-pw2 showed highly significant influence and
μ′s-pp,
μ′s-ppμ′r-pw,
μ′s-pp2 showed significant influence (
p < 0.05), whereas
μ′r-ppμ′r-pw and
μ′r-pw2 showed insignificant influence. The influence order of the factors was
μ′r-pp >
μ′r-pw2 >
μ′s-pp >
μ′s-pp2 >
μ′s-ppμ′r-pw >
μ′r-ppμ′s-pp >
μ′r-pp2 >
μ′r-pw >
μ′r-ppμ′r-pw.
In both SS and AC regression models, the parameters such as the lack of fit
p value, the coefficient of variation (
CV), determination coefficient (
Rs2), correction determination coefficient (
Adj−Rs2), and the
Adeq−Precision demonstrated good predictability with the multiple regression equation (Equations (5) and (6)).
Some simulation contact parameters, obtained through the multiple regression Equations (5) and (6), included: μ′r-pp-ss = 0.33, μ′r-pp-ac = 0.20, μ′s-pp-ss = 1.25, μ′s-pp-ac = 1.12, μ′r-pw-ss = 0.34, μ′r-pw-ac = 0.17. The clam simulation static repose angles included: θ’ss = 31.55° and θ’ac = 37.90°, and the relative error between θ and θ’ included: δa-ss = 0.04% and δa-ac = 0.06%, respectively. As there was no obvious difference between the DEM simulation test and the direct measurement results; the accuracy of the clam simulation contact parameters was high. Therefore, the clam DEM model could be used for EDEM simulation for clam seeding.
The static repose angle in the stacking test was determined as
θss <
θac by comparing the direct measurement AC and SS results. This may be because the roughness of the AC surface is greater than that of smoother SS. The larger the
μs-pw-ac, the larger the
θac with the AC bottom plate is. It could be inferred that the
μs-pw,
μs-pw,
μr-pp, and
μs-pp have a significant effect on the static repose angle of clams, as illustrated in
Table 6. Consequently, it could be inferred that the clam contact parameters
μs-pw,
μr-pp,
μs-pp had a significant effect on the application of the clam DEM model, and the response surface simulation test conclusions were similar to the published result [
11]. Therefore, the influence of
μs-pw,
μr-pp, and
μs-pp on the seeding effect should be fully considered when designing and optimizing clam seeding equipment.
3.4. Clam Seeding Verification Test
As shown in
Figure 7, as the rotation speed of the seeding wheel increases to 30 r min
−1, the
δt value and the realistic and simulated values of the coefficient of variation decrease; however, when this rotation speed increases from 30 r min
−1 to 45 r min
−1, the said values start to increase. When the seeding wheel speed was around 30 r min
−1, all the variation coefficients were at their lowest values. The smaller the variation coefficient, the more uniform the distribution of clams is. Therefore, an optimal working rotation speed of around 30 r min
−1, which is the best matching relationship with the feeding speed and the equipment advancing speed in this study, for clam seeding equipment is recommended. With a slower seeding wheel rotation speed, the seeding continuity was reduced, consequently increasing the clam seeding variation coefficient. On the contrary, when the rotation speed of the seeding wheel was higher, the groove of the seeding wheel directly below the clam blanking hopper was not full; the groove had already rotated away. Therefore, the clam seeding variation coefficient and test error were large. When the seeding wheel rotation speed was 30 r min
−1, the feeding speed and advancing speed were more appropriate. Moreover, the verification results were also helpful in the design and optimization of the clam seeding equipment and could effectively help to understand the motion state of clams for future designers, whilst providing a technical reference.
As shown in
Figure 7, the minimum and maximum values of the variation coefficient error (
δt) were 9.65%, and 1.60%, and the average value of
δt was 4.98%. The results showed that the clam seeding realistic and simulated tests were highly similar, so it could be inferred that the accuracy of the clam DEM model was high. Therefore, the clam seeding simulation could effectively simulate actual clam seeding.