Computational dynamics and virtual dragline simulation for extended rope service life

The dragline machinery is one of the largest equipment for stripping overburden materials in surface mining operations. Its effectiveness requires rigorous kinematic and dynamic analyses. Current dragline research studies are limited in computational dynamic modeling because they eliminate important structural components from the front-end assembly. Thus, the derived kinematic, dynamic and stress intensity models fail to capture the true response of the dragline under full operating cycle conditions. This research study advances a new and robust computational dynamic model of the dragline front-end assembly using Kane\u27s method. The model is a 3-DOF dynamic model that describes the spatial kinematics and dynamics of the dragline front-end assembly during digging and swinging. A virtual simulator, for a Marion 7800 dragline, is built and used for analyzing the mass and inertia properties of the front-end components. The models accurately predict the kinematics, dynamics and stress intensity profiles of the front-end assembly. The results showed that the maximum drag force is 1.375 MN, which is within the maximum allowable load of the machine. The maximum cutting resistance of 412.31 KN occurs 5 seconds into digging and the maximum hoist torque of 917. 87 KN occurs 10 seconds into swinging. Stress analyses are carried out on wire ropes using ANSYS Workbench under static and dynamic loading. The FEA results showed that significant stresses develop in the contact areas between the wires, with a maximum von Mises stress equivalent to 7800 MPa. This research study is a pioneering effort toward developing a comprehensive multibody dynamic model of the dragline machinery. The main novelty is incorporating the boom point-sheave, drag-chain and sliding effect of the bucket, excluded from previous research studies, to obtain computationally dynamic efficient models for load predictions --Abstract, page iii

Models for Estimating Energy and Protein Utilization for Feeds

Data on the proximate nutrient content of feedstuffs , digestibility and energy utilization available from the International Feedstuffs Institute (Utah State University) were used to develop mathematical models for estimating energy and protein utilization of five classes of feedstuffs for various kinds of animals. Classes of feedstuffs were subdivided into more related subclasses. Furthermore, data from all feeds were pooled together then subgrouped into more related subgroups in an attempt to gain high precision in prediction of digestible proximate nutrients and TDN from a single chemical entity by the use of simple regression models (Y = bo + b1x1). Digestible percentages (Y) of crude protein , ether extract, crude fiber and nitrogen free extract were highly correlated with their proximate contents (Xs) of most classes, subclasses and subgroups of feedstuffs for various kinds of animals. However, the use of linear multiple regression equation resulted in more precision in estimating each digestible nutrient (Y) from proximate analysis (Xl ; CP%, Xz ; EE% , x3 ; CF% and X4 ; NFE%) of the different classes of feedstuffs for various kinds of animals. Prediction of digestible proximate nutrients made it possible to calculate Tn~ by the conventional equation: TDN ; DCP% + DCF% + DNFE% + 2.25 x DEE%. And to calculate digestible energy (DE) from the following equation : DE(Mcal/kg); 5.72 (DCP%) + 9.5 (DEE%)+ 4.79 (DCF%) + 4. 03 (NFE%)/100 TDN, DE and ME (Ys) were highly correlated with the digestible proximate nutrients (X1 ; DCP%, x2 ; DEE% , X3 ; DCF% and X4 = DNFE%) and with proximate analysis (upon the use of multiple regression models). However, TDN, DE and ME (Ys) were not predictable with high precision from any one single chemical entity (Xs) in most cases of the different classes of feedstuffs for various kinds of animals. DE (Y) was highly correlated with TDN values (X), and ME (Y) was highly correlated with DE and TDN (Xs) values of t he different classes of feedstuffs for various kinds of animals. The inclusion of physical descriptions (qualitative factors) of feedstuffs along with chemical analysis (quantitative factors) gave promising results predicting TDN content of feedstuffs. MEn and NEP for poultry were highly correlated with proximate analysis of the different classes of feedstuffs. NEP was also estimated with high precision from MEn. However, both MEn and NEP were not highly associated with single chemical entities. The dissertation contains an extensive literature review on systems of evaluating nutritive value, and factors affecting digestibility of feedstuffs. This dissertation also contains numerous equations which predict each digestible nutrient from its proximate content and from proximate analysis; TDN, DE and ME from each proximate nutrient, digestible proximate nutrients and proximate analysis; DE and~~ from TDN; and ME from DE contents of different classes of feedstuffs for various kinds of animals. Moreover, there are complex equations to predict TDN from proximate analysis and their interactions and from proximate analysis plus physical descriptions of feedstuffs for various kinds of animals

Kinematic Analysis of an Under-Actuated, Closed-Loop Front-End Assembly of a Dragline Manipulator

Dragline excavators are closed-loop mining manipulators that operate using a rigid multilink framework and rope and rigging system, which constitute its front-end assembly. The arrangements of dragline front-end assembly provide the necessary motion of the dragline bucket within its operating radius. The assembly resembles a five-link closed kinematic chain that has two independent generalized coordinates of drag and hoist ropes and one dependent generalized coordinate of dump rope. Previous models failed to represent the actual closed loop of dragline front-end assembly, nor did they describe the maneuverability of dragline ropes under imposed geometric constraints. Therefore, a three degrees of freedom kinematic model of the dragline front-end is developed using the concept of generalized speeds. It contains all relevant configuration and kinematic constraint conditions to perform complete digging and swinging cycles. The model also uses three inputs of hoist and drag ropes linear and a rotational displacement of swinging along their trajectories. The inverse kinematics is resolved using a feedforward displacement algorithm coupled with the Newton-Raphson method to accurately estimate the trajectories of the ropes. The trajectories are solved only during the digging phase and the singularity was eliminated using Baumgarte\u27s stabilization technique (BST), with appropriate inequality constraint equations. It is shown that the feedforward displacement algorithm can produce accurate trajectories without the need to manually solve the inverse kinematics from the geometry. The research findings are well in agreement with the dragline real operational limits and they contribute to the efficiency and the reduction in machine downtime due to better control strategies of the dragline cycles