The cross disperser geneva drive mechanism has been analyzed to determine the loads on the drive motor as the turret moves from one observing position to another. The goal is to
determine the loads generated so that an appropriate stepper motor can be selected to drive the cross disperser turret. Two stepper motors rated for cryogenic, high‐vacuum use are being considered: the Phytron VSS 32, and VSS 42 which produce a maximum of 32N‐mm & 92N‐mm of torque respectively and a maximum speed of 600RPM. Since the cross disperser turret is the largest turret in the iSHELL instrument, this analysis may be considered as a worst case loading scenario and may be used as a guide for selecting motors for the other turret mechanisms in the instrument.
The results indicate that the VSS 42 stepper motor is more than adequate to drive the turret and produces a factor of ~3 more torque than what is needed to drive the turret. Additionally, the VSS 32 motor is sufficient to drive the turret if a 4:1 in‐line gearbox is mounted to the motor
Development of Hill Chart diagram for Francis turbine of Jhimruk Hydropower u...Suman Sapkota
The study is expected to provide a milestone for the study of performances of Francis Turbine at different loading conditions. It can also serve as a reference for the study of CFD analysis on Francis turbine for the development of performance characteristics curve and Hill chart.
Wind resources vary significantly in strength from one location to another over a wide geographical region. The major turbine manufacturers offer a family/series of wind tur- bines to suit the market needs of different wind regimes. The current state of the art in wind farm design however does not provide quantitative guidelines regarding what turbine feature combinations are suitable for different wind regimes, when turbines are operating as a group in an optimized layout. This paper provides a unique exploration of the best tradeoffs between the cost and the capacity factor of wind farms (of specified nameplate capacity), provided by the currently available turbines for different wind classes. To this end, the best performing turbines for different wind resource strengths are identified by minimizing the cost of energy through wind farm layout optimization. Exploration of the “cost - capacity factor” tradeoffs are then performed for the wind resource strengths cor- responding to the wind classes defined in the 7-class system. The best tradeoff turbines are determined by searching for the non-dominated set of turbines out of the pool of best performing turbines of different rated powers. The medium priced turbines are observed to provide the most attractive tradeoffs − 15% more capacity factor than the cheapest tradeoff turbines and only 5% less capacity factor than the most expensive tradeoff turbines. It was found that although the “cost - capacity factor” tradeoff curve expectedly shifted towards higher capacity factors with increasing wind class, the trend of the tradeoff curve remained practically similar. Further analysis showed that the “rated power - rotor diameter” com- bination and the “rotor diameter/hub height” ratios are very important considerations in the current selection and further evolution of turbine designs. We found that larger rotor diameters are not preferred for mid-range turbines with rated powers between 1.5 - 2.5 MW, and “rotor diameter/hub height” ratios greater than 1.1 are not preferred by any of the wind classes.
Hydraulic Turbines – II: Governing of turbines – Surge tanks – Unit and speci...Mood Naik
Hydraulic Turbines – II: Governing of turbines – Surge tanks – Unit and specific turbines – Unit speed
– Unit quantity – Unit power – Specific speed – Performance characteristics – Geometric similarity –
Cavitation. Selection of turbines
Development of Hill Chart diagram for Francis turbine of Jhimruk Hydropower u...Suman Sapkota
The study is expected to provide a milestone for the study of performances of Francis Turbine at different loading conditions. It can also serve as a reference for the study of CFD analysis on Francis turbine for the development of performance characteristics curve and Hill chart.
Wind resources vary significantly in strength from one location to another over a wide geographical region. The major turbine manufacturers offer a family/series of wind tur- bines to suit the market needs of different wind regimes. The current state of the art in wind farm design however does not provide quantitative guidelines regarding what turbine feature combinations are suitable for different wind regimes, when turbines are operating as a group in an optimized layout. This paper provides a unique exploration of the best tradeoffs between the cost and the capacity factor of wind farms (of specified nameplate capacity), provided by the currently available turbines for different wind classes. To this end, the best performing turbines for different wind resource strengths are identified by minimizing the cost of energy through wind farm layout optimization. Exploration of the “cost - capacity factor” tradeoffs are then performed for the wind resource strengths cor- responding to the wind classes defined in the 7-class system. The best tradeoff turbines are determined by searching for the non-dominated set of turbines out of the pool of best performing turbines of different rated powers. The medium priced turbines are observed to provide the most attractive tradeoffs − 15% more capacity factor than the cheapest tradeoff turbines and only 5% less capacity factor than the most expensive tradeoff turbines. It was found that although the “cost - capacity factor” tradeoff curve expectedly shifted towards higher capacity factors with increasing wind class, the trend of the tradeoff curve remained practically similar. Further analysis showed that the “rated power - rotor diameter” com- bination and the “rotor diameter/hub height” ratios are very important considerations in the current selection and further evolution of turbine designs. We found that larger rotor diameters are not preferred for mid-range turbines with rated powers between 1.5 - 2.5 MW, and “rotor diameter/hub height” ratios greater than 1.1 are not preferred by any of the wind classes.
Hydraulic Turbines – II: Governing of turbines – Surge tanks – Unit and speci...Mood Naik
Hydraulic Turbines – II: Governing of turbines – Surge tanks – Unit and specific turbines – Unit speed
– Unit quantity – Unit power – Specific speed – Performance characteristics – Geometric similarity –
Cavitation. Selection of turbines
The performance expectations for commercial wind turbines, from a variety of geograph- ical regions with differing wind regimes, present significant techno-commercial challenges to manufacturers. The determination of which commercial turbine types perform the best under differing wind regimes can provide unique insights into the complex demands of a concerned target market. In this paper, a comprehensive methodology is developed to explore the suitability of commercially available wind turbines (when operating as a group/array) to the various wind regimes occurring over a large target market. The three major steps of this methodology include: (i) characterizing the geographical variation of wind regimes in the target market, (ii) determining the best performing turbines (in terms of minimum COE accomplished) for different wind regimes, and (iii) developing a metric to investigate the performance-based expected market suitability of currently available tur- bine feature combinations. The best performing turbines for different wind regimes are determined using the Unrestricted Wind Farm Layout Optimization (UWFLO) method. Expectedly, the larger sized and higher rated-power turbines provide better performance at lower average wind speeds. However, for wind resources higher than class-4, the perfor- mances of lower-rated power turbines are fairly competitive, which could make them better choices for sites with complex terrain or remote location. In addition, turbines with direct drive are observed to perform significantly better than turbines with more conventional gear-based drive-train. The market considered in this paper is mainland USA, for which wind map information is obtained from NREL. Interestingly, it is found that overall higher rated-power turbines with relatively lower tower heights are most favored in the onshore US market.
Experimental evaluation of a francis turbineeSAT Journals
Abstract Laboratory-Scale Francis Turbine has been used to generate experimental data for performance evaluation. The analysis presented here is based on turbine shaft load resulting in utilization of water power and includes overall efficiency, specific speed, unit quantities and critical cavitation factor. A modified critical cavitation factor relation has been obtained and verified with the existing relation. Keywords: Hydro Power, Francis Turbine, Turbine Performance, Cavitation Factor
Wind resources vary significantly in strength from one location to another over a wide geographical region. The major turbine manufacturers offer a family/series of wind tur- bines to suit the market needs of different wind regimes. The current state of the art in wind farm design however does not provide quantitative guidelines regarding what turbine feature combinations are suitable for different wind regimes, when turbines are operating as a group in an optimized layout. This paper provides a unique exploration of the best tradeoffs between the cost and the capacity factor of wind farms (of specified nameplate capacity), provided by the currently available turbines for different wind classes. To this end, the best performing turbines for different wind resource strengths are identified by minimizing the cost of energy through wind farm layout optimization. Exploration of the “cost - capacity factor” tradeoffs are then performed for the wind resource strengths cor- responding to the wind classes defined in the 7-class system. The best tradeoff turbines are determined by searching for the non-dominated set of turbines out of the pool of best performing turbines of different rated powers. The medium priced turbines are observed to provide the most attractive tradeoffs − 15% more capacity factor than the cheapest tradeoff turbines and only 5% less capacity factor than the most expensive tradeoff turbines. It was found that although the “cost - capacity factor” tradeoff curve expectedly shifted towards higher capacity factors with increasing wind class, the trend of the tradeoff curve remained practically similar. Further analysis showed that the “rated power - rotor diameter” com- bination and the “rotor diameter/hub height” ratios are very important considerations in the current selection and further evolution of turbine designs. We found that larger rotor diameters are not preferred for mid-range turbines with rated powers between 1.5 - 2.5 MW, and “rotor diameter/hub height” ratios greater than 1.1 are not preferred by any of the wind classes.
Ijmet 08 02_029NUMERICAL SOLUTIONS FOR PERFORMANCE PREDICTION OF CENTRIFUGAL ...IAEME Publication
An attempt is made in the present study to investigate the superior turbulence model for
simulating three dimensional flows in centrifugal compressor. The strong channelled curvature and
intensive rotations prevalent in centrifugal compressor resulting high swirling and secondary flow
nictitates choosing appropriate turbulence model for accurate performance predictions. The
various turbulence models offered in FLUENT viz Spalart Allmaras (curvature correction),
Transition SST (curvature correction), Scaled Adaptive Simulations (Curvature correction with
compressibility effect), Reynolds stress model (compressibility effect) were investigated presently
for Eckardt Impeller. Reynolds stress model though involves higher computational time was found
to be the superior model. It is essential to investigate the onset of surge and choke for completely
understanding the performance of a centrifugal compressor. Choking phenomena was observed
when the speed reached 16000 rpm with relative Mach number reaching unity in the impeller
region. The maximum flow rate at 16000 rpm was 0.4 kg/s per blade and remained constant then
16500 rpm. Surging was founded to initiate when the back pressure has to reach 1.8 bar resulting
in zero discharge
NUMERICAL SOLUTIONS FOR PERFORMANCE PREDICTION OF CENTRIFUGAL COMPRESSORIAEME Publication
An attempt is made in the present study to investigate the superior turbulence model forsimulating three dimensional flows in centrifugal compressor. The strong channelled curvature andintensive rotations prevalent in centrifugal compressor resulting high swirling and secondary flownictitates choosing appropriate turbulence model for accurate performance predictions. Thevarious turbulence models offered in FLUENT viz Spalart Allmaras (curvature correction),Transition SST (curvature correction), Scaled Adaptive Simulations (Curvature correction withcompressibility effect), Reynolds stress model (compressibility effect) were investigated presentlyfor Eckardt Impeller. Reynolds stress model though involveshigher computational time was found
to be the superior model. It is essential to investigate the onset of surge and choke for completelyunderstanding the performance of a centrifugal compressor. Choking phenomena was observedwhen the speed reached 16000 rpm with relative Mach number reaching unity in the impellerregion. The maximum flow rate at 16000 rpm was 0.4 kg/s per blade and remained constant then16500 rpm. Surging was founded to initiate when the back pressure has to reach 1.8 bar resultingin zero discharge
The performance expectations for commercial wind turbines, from a variety of geograph- ical regions with differing wind regimes, present significant techno-commercial challenges to manufacturers. The determination of which commercial turbine types perform the best under differing wind regimes can provide unique insights into the complex demands of a concerned target market. In this paper, a comprehensive methodology is developed to explore the suitability of commercially available wind turbines (when operating as a group/array) to the various wind regimes occurring over a large target market. The three major steps of this methodology include: (i) characterizing the geographical variation of wind regimes in the target market, (ii) determining the best performing turbines (in terms of minimum COE accomplished) for different wind regimes, and (iii) developing a metric to investigate the performance-based expected market suitability of currently available tur- bine feature combinations. The best performing turbines for different wind regimes are determined using the Unrestricted Wind Farm Layout Optimization (UWFLO) method. Expectedly, the larger sized and higher rated-power turbines provide better performance at lower average wind speeds. However, for wind resources higher than class-4, the perfor- mances of lower-rated power turbines are fairly competitive, which could make them better choices for sites with complex terrain or remote location. In addition, turbines with direct drive are observed to perform significantly better than turbines with more conventional gear-based drive-train. The market considered in this paper is mainland USA, for which wind map information is obtained from NREL. Interestingly, it is found that overall higher rated-power turbines with relatively lower tower heights are most favored in the onshore US market.
The performance of a wind farm is affected by several key factors that can be classified into two cate- gories: the natural factors and the design factors. Hence, the planning of a wind farm requires a clear quantitative understanding of how the balance between the concerned objectives (e.g., socia-economic, engineering, and environmental objectives) is affected by these key factors. This understanding is lacking in the state of the art in wind farm design. The wind farm capacity factor is one of the primary perfor- mance criteria of a wind energy project. For a given land (or sea area) and wind resource, the maximum capacity factor of a particular number of wind turbines can be reached by optimally adjusting the layout of turbines. However, this layout adjustment is constrained owing to the limited land resource. This paper proposes a Bi-level Multi-objective Wind Farm Optimization (BMWFO) framework for planning effective wind energy projects. Two important performance objectives considered in this paper are: (i) wind farm Capacity Factor (CF) and (ii) Land Area per MW Installed (LAMI). Turbine locations, land area, and nameplate capacity are treated as design variables in this work. In the proposed framework, the Capacity Factor - Land Area per MW Installed (CF - LAMI) trade-off is parametrically represented as a function of the nameplate capacity. Such a helpful parameterization of trade-offs is unique in the wind energy literature. The farm output is computed using the wind farm power generation model adopted from the Unrestricted Wind Farm Layout Optimization (UWFLO) framework. The Smallest Bounding Rectangle (SBR) enclosing all turbines is used to calculate the actual land area occupied by the farm site. The wind farm layout optimization is performed in the lower level using the Mixed-Discrete Particle Swarm Optimization (MDPSO), while the CF - LAMI trade-off is parameterized in the upper level. In this work, the CF - LAMI trade-off is successfully quantified by nameplate capacity in the 20 MW to 100 MW range. The Pareto curves obtained from the proposed framework provide important in- sights into the trade-offs between the two performance objectives, which can significantly streamline the decision-making process in wind farm development.
The performance of a wind farm is affected by several key factors that can be classified into two cate- gories: the natural factors and the design factors. Hence, the planning of a wind farm requires a clear quantitative understanding of how the balance between the concerned objectives (e.g., socia-economic, engineering, and environmental objectives) is affected by these key factors. This understanding is lacking in the state of the art in wind farm design. The wind farm capacity factor is one of the primary perfor- mance criteria of a wind energy project. For a given land (or sea area) and wind resource, the maximum capacity factor of a particular number of wind turbines can be reached by optimally adjusting the layout of turbines. However, this layout adjustment is constrained owing to the limited land resource. This paper proposes a Bi-level Multi-objective Wind Farm Optimization (BMWFO) framework for planning effective wind energy projects. Two important performance objectives considered in this paper are: (i) wind farm Capacity Factor (CF) and (ii) Land Area per MW Installed (LAMI). Turbine locations, land area, and nameplate capacity are treated as design variables in this work. In the proposed framework, the Capacity Factor - Land Area per MW Installed (CF - LAMI) trade-off is parametrically represented as a function of the nameplate capacity. Such a helpful parameterization of trade-offs is unique in the wind energy literature. The farm output is computed using the wind farm power generation model adopted from the Unrestricted Wind Farm Layout Optimization (UWFLO) framework. The Smallest Bounding Rectangle (SBR) enclosing all turbines is used to calculate the actual land area occupied by the farm site. The wind farm layout optimization is performed in the lower level using the Mixed-Discrete Particle Swarm Optimization (MDPSO), while the CF - LAMI trade-off is parameterized in the upper level. In this work, the CF - LAMI trade-off is successfully quantified by nameplate capacity in the 20 MW to 100 MW range. The Pareto curves obtained from the proposed framework provide important in- sights into the trade-offs between the two performance objectives, which can significantly streamline the decision-making process in wind farm development.
This work was aimed at developing a computational model following certain standards that are important to turbo machinery. Numerical and experimental investigations have been carried out on a two bladed savonius rotor by varying certain parameters of the turbine namely blade shape, blade profile, aspect ratio of the turbine and position of vent on the blade. For numerical investigation, commercial computational fluid dynamic (CFD) software ANSYS-FLUENT has been used. The results obtained have been validated with established experimental results. Investigations involving the variation of Aspect ratio have been done completely through experimentation. For the other cases, the obtained numerical results have been validated with the established experimental values. For the investigation regarding variation of blade shape, the length of semi minor axis has been changed and simulations have been carried out. Also, in the blade a vent has been introduced and its best position determined. Finally, new blade shapes have been designed and simulations carried out to find the optimum one. All these cases were computed at two different Reynolds number specifically 150000 and 80000. The new configurations gave better results than that for the conventional one.
DESIGN AND FABRICATION OF SINGLE REDUCTION GEARBOX WITH INBOARD BRAKINGabdul mohammad
An inboard braking system is an automobile technology where in the disc brakes are mounted on the chassis or to the gearbox of the vehicle, rather than directly on the wheel hubs.
The main advantages are a reduction in the unsprung weight of the wheel hubs, as this no longer includes the brake discs and calipers; also, braking torque applies directly to the chassis or the gear box , rather than being taken through the suspension arms.
Inboard brakes are fitted to a driven axle of the car, as they require a drive shaft to link the wheel to the brake. Most have thus been used for rear-wheel drive cars, although four-wheel drive and some front-wheel drives have also used them.
this presentation will give brief idea about Drillship(Dhirubhai Deep-water KG2).And what kind of technologies are used to build this drill ship, its design,Drilling Equipment's,Draw works,Motion Compensator,Rotary Table,Top Drive,Mud pump,Shale shaker,Riser handling,BOP and subsea equipment.
The power generation of a wind farm is significantly less than the summation of the power generated by each turbine when operating as a standalone entity. This power reduction can be attributed to the energy loss due to the wake effects − the resulting velocity deficit in the wind downstream of a turbine. In the case of wind farm design, the wake losses are generally quantified using wake models. The effectiveness of wind farm design (seeking to maximize the farm output) therefore depends on the accuracy and the reliability of the wake models. This paper compares the impact of the following four analytical wake
models on the wind farm power generation: (i) the Jensen model, (ii) the Larsen model, (iii) the Frandsen model, and (iv) the Ishihara model. The sensitivity of this impact to the Land Area per Turbine (LAT) and the incoming wind speed is also investigated. The wind farm power generation model used in this paper is adopted from the Unrestricted Wind Farm Layout Optimization (UWFLO) methodology. Single wake case studies show that the velocity deficit and the wake diameter estimated by the different analytical wake models can be significantly different. A maximum difference of 70% was also observed for the wind farm capacity factor values estimated using different wake models.
The performance expectations for commercial wind turbines, from a variety of geograph- ical regions with differing wind regimes, present significant techno-commercial challenges to manufacturers. The determination of which commercial turbine types perform the best under differing wind regimes can provide unique insights into the complex demands of a concerned target market. In this paper, a comprehensive methodology is developed to explore the suitability of commercially available wind turbines (when operating as a group/array) to the various wind regimes occurring over a large target market. The three major steps of this methodology include: (i) characterizing the geographical variation of wind regimes in the target market, (ii) determining the best performing turbines (in terms of minimum COE accomplished) for different wind regimes, and (iii) developing a metric to investigate the performance-based expected market suitability of currently available tur- bine feature combinations. The best performing turbines for different wind regimes are determined using the Unrestricted Wind Farm Layout Optimization (UWFLO) method. Expectedly, the larger sized and higher rated-power turbines provide better performance at lower average wind speeds. However, for wind resources higher than class-4, the perfor- mances of lower-rated power turbines are fairly competitive, which could make them better choices for sites with complex terrain or remote location. In addition, turbines with direct drive are observed to perform significantly better than turbines with more conventional gear-based drive-train. The market considered in this paper is mainland USA, for which wind map information is obtained from NREL. Interestingly, it is found that overall higher rated-power turbines with relatively lower tower heights are most favored in the onshore US market.
Experimental evaluation of a francis turbineeSAT Journals
Abstract Laboratory-Scale Francis Turbine has been used to generate experimental data for performance evaluation. The analysis presented here is based on turbine shaft load resulting in utilization of water power and includes overall efficiency, specific speed, unit quantities and critical cavitation factor. A modified critical cavitation factor relation has been obtained and verified with the existing relation. Keywords: Hydro Power, Francis Turbine, Turbine Performance, Cavitation Factor
Wind resources vary significantly in strength from one location to another over a wide geographical region. The major turbine manufacturers offer a family/series of wind tur- bines to suit the market needs of different wind regimes. The current state of the art in wind farm design however does not provide quantitative guidelines regarding what turbine feature combinations are suitable for different wind regimes, when turbines are operating as a group in an optimized layout. This paper provides a unique exploration of the best tradeoffs between the cost and the capacity factor of wind farms (of specified nameplate capacity), provided by the currently available turbines for different wind classes. To this end, the best performing turbines for different wind resource strengths are identified by minimizing the cost of energy through wind farm layout optimization. Exploration of the “cost - capacity factor” tradeoffs are then performed for the wind resource strengths cor- responding to the wind classes defined in the 7-class system. The best tradeoff turbines are determined by searching for the non-dominated set of turbines out of the pool of best performing turbines of different rated powers. The medium priced turbines are observed to provide the most attractive tradeoffs − 15% more capacity factor than the cheapest tradeoff turbines and only 5% less capacity factor than the most expensive tradeoff turbines. It was found that although the “cost - capacity factor” tradeoff curve expectedly shifted towards higher capacity factors with increasing wind class, the trend of the tradeoff curve remained practically similar. Further analysis showed that the “rated power - rotor diameter” com- bination and the “rotor diameter/hub height” ratios are very important considerations in the current selection and further evolution of turbine designs. We found that larger rotor diameters are not preferred for mid-range turbines with rated powers between 1.5 - 2.5 MW, and “rotor diameter/hub height” ratios greater than 1.1 are not preferred by any of the wind classes.
Ijmet 08 02_029NUMERICAL SOLUTIONS FOR PERFORMANCE PREDICTION OF CENTRIFUGAL ...IAEME Publication
An attempt is made in the present study to investigate the superior turbulence model for
simulating three dimensional flows in centrifugal compressor. The strong channelled curvature and
intensive rotations prevalent in centrifugal compressor resulting high swirling and secondary flow
nictitates choosing appropriate turbulence model for accurate performance predictions. The
various turbulence models offered in FLUENT viz Spalart Allmaras (curvature correction),
Transition SST (curvature correction), Scaled Adaptive Simulations (Curvature correction with
compressibility effect), Reynolds stress model (compressibility effect) were investigated presently
for Eckardt Impeller. Reynolds stress model though involves higher computational time was found
to be the superior model. It is essential to investigate the onset of surge and choke for completely
understanding the performance of a centrifugal compressor. Choking phenomena was observed
when the speed reached 16000 rpm with relative Mach number reaching unity in the impeller
region. The maximum flow rate at 16000 rpm was 0.4 kg/s per blade and remained constant then
16500 rpm. Surging was founded to initiate when the back pressure has to reach 1.8 bar resulting
in zero discharge
NUMERICAL SOLUTIONS FOR PERFORMANCE PREDICTION OF CENTRIFUGAL COMPRESSORIAEME Publication
An attempt is made in the present study to investigate the superior turbulence model forsimulating three dimensional flows in centrifugal compressor. The strong channelled curvature andintensive rotations prevalent in centrifugal compressor resulting high swirling and secondary flownictitates choosing appropriate turbulence model for accurate performance predictions. Thevarious turbulence models offered in FLUENT viz Spalart Allmaras (curvature correction),Transition SST (curvature correction), Scaled Adaptive Simulations (Curvature correction withcompressibility effect), Reynolds stress model (compressibility effect) were investigated presentlyfor Eckardt Impeller. Reynolds stress model though involveshigher computational time was found
to be the superior model. It is essential to investigate the onset of surge and choke for completelyunderstanding the performance of a centrifugal compressor. Choking phenomena was observedwhen the speed reached 16000 rpm with relative Mach number reaching unity in the impellerregion. The maximum flow rate at 16000 rpm was 0.4 kg/s per blade and remained constant then16500 rpm. Surging was founded to initiate when the back pressure has to reach 1.8 bar resultingin zero discharge
The performance expectations for commercial wind turbines, from a variety of geograph- ical regions with differing wind regimes, present significant techno-commercial challenges to manufacturers. The determination of which commercial turbine types perform the best under differing wind regimes can provide unique insights into the complex demands of a concerned target market. In this paper, a comprehensive methodology is developed to explore the suitability of commercially available wind turbines (when operating as a group/array) to the various wind regimes occurring over a large target market. The three major steps of this methodology include: (i) characterizing the geographical variation of wind regimes in the target market, (ii) determining the best performing turbines (in terms of minimum COE accomplished) for different wind regimes, and (iii) developing a metric to investigate the performance-based expected market suitability of currently available tur- bine feature combinations. The best performing turbines for different wind regimes are determined using the Unrestricted Wind Farm Layout Optimization (UWFLO) method. Expectedly, the larger sized and higher rated-power turbines provide better performance at lower average wind speeds. However, for wind resources higher than class-4, the perfor- mances of lower-rated power turbines are fairly competitive, which could make them better choices for sites with complex terrain or remote location. In addition, turbines with direct drive are observed to perform significantly better than turbines with more conventional gear-based drive-train. The market considered in this paper is mainland USA, for which wind map information is obtained from NREL. Interestingly, it is found that overall higher rated-power turbines with relatively lower tower heights are most favored in the onshore US market.
The performance of a wind farm is affected by several key factors that can be classified into two cate- gories: the natural factors and the design factors. Hence, the planning of a wind farm requires a clear quantitative understanding of how the balance between the concerned objectives (e.g., socia-economic, engineering, and environmental objectives) is affected by these key factors. This understanding is lacking in the state of the art in wind farm design. The wind farm capacity factor is one of the primary perfor- mance criteria of a wind energy project. For a given land (or sea area) and wind resource, the maximum capacity factor of a particular number of wind turbines can be reached by optimally adjusting the layout of turbines. However, this layout adjustment is constrained owing to the limited land resource. This paper proposes a Bi-level Multi-objective Wind Farm Optimization (BMWFO) framework for planning effective wind energy projects. Two important performance objectives considered in this paper are: (i) wind farm Capacity Factor (CF) and (ii) Land Area per MW Installed (LAMI). Turbine locations, land area, and nameplate capacity are treated as design variables in this work. In the proposed framework, the Capacity Factor - Land Area per MW Installed (CF - LAMI) trade-off is parametrically represented as a function of the nameplate capacity. Such a helpful parameterization of trade-offs is unique in the wind energy literature. The farm output is computed using the wind farm power generation model adopted from the Unrestricted Wind Farm Layout Optimization (UWFLO) framework. The Smallest Bounding Rectangle (SBR) enclosing all turbines is used to calculate the actual land area occupied by the farm site. The wind farm layout optimization is performed in the lower level using the Mixed-Discrete Particle Swarm Optimization (MDPSO), while the CF - LAMI trade-off is parameterized in the upper level. In this work, the CF - LAMI trade-off is successfully quantified by nameplate capacity in the 20 MW to 100 MW range. The Pareto curves obtained from the proposed framework provide important in- sights into the trade-offs between the two performance objectives, which can significantly streamline the decision-making process in wind farm development.
The performance of a wind farm is affected by several key factors that can be classified into two cate- gories: the natural factors and the design factors. Hence, the planning of a wind farm requires a clear quantitative understanding of how the balance between the concerned objectives (e.g., socia-economic, engineering, and environmental objectives) is affected by these key factors. This understanding is lacking in the state of the art in wind farm design. The wind farm capacity factor is one of the primary perfor- mance criteria of a wind energy project. For a given land (or sea area) and wind resource, the maximum capacity factor of a particular number of wind turbines can be reached by optimally adjusting the layout of turbines. However, this layout adjustment is constrained owing to the limited land resource. This paper proposes a Bi-level Multi-objective Wind Farm Optimization (BMWFO) framework for planning effective wind energy projects. Two important performance objectives considered in this paper are: (i) wind farm Capacity Factor (CF) and (ii) Land Area per MW Installed (LAMI). Turbine locations, land area, and nameplate capacity are treated as design variables in this work. In the proposed framework, the Capacity Factor - Land Area per MW Installed (CF - LAMI) trade-off is parametrically represented as a function of the nameplate capacity. Such a helpful parameterization of trade-offs is unique in the wind energy literature. The farm output is computed using the wind farm power generation model adopted from the Unrestricted Wind Farm Layout Optimization (UWFLO) framework. The Smallest Bounding Rectangle (SBR) enclosing all turbines is used to calculate the actual land area occupied by the farm site. The wind farm layout optimization is performed in the lower level using the Mixed-Discrete Particle Swarm Optimization (MDPSO), while the CF - LAMI trade-off is parameterized in the upper level. In this work, the CF - LAMI trade-off is successfully quantified by nameplate capacity in the 20 MW to 100 MW range. The Pareto curves obtained from the proposed framework provide important in- sights into the trade-offs between the two performance objectives, which can significantly streamline the decision-making process in wind farm development.
This work was aimed at developing a computational model following certain standards that are important to turbo machinery. Numerical and experimental investigations have been carried out on a two bladed savonius rotor by varying certain parameters of the turbine namely blade shape, blade profile, aspect ratio of the turbine and position of vent on the blade. For numerical investigation, commercial computational fluid dynamic (CFD) software ANSYS-FLUENT has been used. The results obtained have been validated with established experimental results. Investigations involving the variation of Aspect ratio have been done completely through experimentation. For the other cases, the obtained numerical results have been validated with the established experimental values. For the investigation regarding variation of blade shape, the length of semi minor axis has been changed and simulations have been carried out. Also, in the blade a vent has been introduced and its best position determined. Finally, new blade shapes have been designed and simulations carried out to find the optimum one. All these cases were computed at two different Reynolds number specifically 150000 and 80000. The new configurations gave better results than that for the conventional one.
DESIGN AND FABRICATION OF SINGLE REDUCTION GEARBOX WITH INBOARD BRAKINGabdul mohammad
An inboard braking system is an automobile technology where in the disc brakes are mounted on the chassis or to the gearbox of the vehicle, rather than directly on the wheel hubs.
The main advantages are a reduction in the unsprung weight of the wheel hubs, as this no longer includes the brake discs and calipers; also, braking torque applies directly to the chassis or the gear box , rather than being taken through the suspension arms.
Inboard brakes are fitted to a driven axle of the car, as they require a drive shaft to link the wheel to the brake. Most have thus been used for rear-wheel drive cars, although four-wheel drive and some front-wheel drives have also used them.
this presentation will give brief idea about Drillship(Dhirubhai Deep-water KG2).And what kind of technologies are used to build this drill ship, its design,Drilling Equipment's,Draw works,Motion Compensator,Rotary Table,Top Drive,Mud pump,Shale shaker,Riser handling,BOP and subsea equipment.
The power generation of a wind farm is significantly less than the summation of the power generated by each turbine when operating as a standalone entity. This power reduction can be attributed to the energy loss due to the wake effects − the resulting velocity deficit in the wind downstream of a turbine. In the case of wind farm design, the wake losses are generally quantified using wake models. The effectiveness of wind farm design (seeking to maximize the farm output) therefore depends on the accuracy and the reliability of the wake models. This paper compares the impact of the following four analytical wake
models on the wind farm power generation: (i) the Jensen model, (ii) the Larsen model, (iii) the Frandsen model, and (iv) the Ishihara model. The sensitivity of this impact to the Land Area per Turbine (LAT) and the incoming wind speed is also investigated. The wind farm power generation model used in this paper is adopted from the Unrestricted Wind Farm Layout Optimization (UWFLO) methodology. Single wake case studies show that the velocity deficit and the wake diameter estimated by the different analytical wake models can be significantly different. A maximum difference of 70% was also observed for the wind farm capacity factor values estimated using different wake models.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
CW RADAR, FMCW RADAR, FMCW ALTIMETER, AND THEIR PARAMETERSveerababupersonal22
It consists of cw radar and fmcw radar ,range measurement,if amplifier and fmcw altimeterThe CW radar operates using continuous wave transmission, while the FMCW radar employs frequency-modulated continuous wave technology. Range measurement is a crucial aspect of radar systems, providing information about the distance to a target. The IF amplifier plays a key role in signal processing, amplifying intermediate frequency signals for further analysis. The FMCW altimeter utilizes frequency-modulated continuous wave technology to accurately measure altitude above a reference point.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
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NUMERICAL SIMULATIONS OF HEAT AND MASS TRANSFER IN CONDENSING HEAT EXCHANGERS...ssuser7dcef0
Power plants release a large amount of water vapor into the
atmosphere through the stack. The flue gas can be a potential
source for obtaining much needed cooling water for a power
plant. If a power plant could recover and reuse a portion of this
moisture, it could reduce its total cooling water intake
requirement. One of the most practical way to recover water
from flue gas is to use a condensing heat exchanger. The power
plant could also recover latent heat due to condensation as well
as sensible heat due to lowering the flue gas exit temperature.
Additionally, harmful acids released from the stack can be
reduced in a condensing heat exchanger by acid condensation. reduced in a condensing heat exchanger by acid condensation.
Condensation of vapors in flue gas is a complicated
phenomenon since heat and mass transfer of water vapor and
various acids simultaneously occur in the presence of noncondensable
gases such as nitrogen and oxygen. Design of a
condenser depends on the knowledge and understanding of the
heat and mass transfer processes. A computer program for
numerical simulations of water (H2O) and sulfuric acid (H2SO4)
condensation in a flue gas condensing heat exchanger was
developed using MATLAB. Governing equations based on
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predict variables such as flue gas exit temperature, cooling
water outlet temperature, mole fraction and condensation rates
of water and sulfuric acid vapors. The equations were solved
using an iterative solution technique with calculations of heat
and mass transfer coefficients and physical properties.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
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A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
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In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
13. User inputs that affect the motor torque required to drive the turret.
Enter the desired cycle time. This is the time to move the turret from one position to an adjacent position. The
maximum number of cycles for the longest move is 6 cycles.
tcycle 5sec Desired cycle time
Select a desired compression spring for the detent preload force.
The following spring is assumed: Associated Spring Part No. C0360-035-2500-S.
Lfree 2.50in Free Length of spring Fmax_comp_spr .833 5.73 lbf Fmax_comp_spr 4.773lbf
kspr 4.3 .833
lbf
in
Spring rate with .833 factor for stainless steel kspr 3.582
lbf
in
Select a desired preload force for seating the detent into the vee-groove.
Fpre 3.5lbf Desired initial preload of compression spring when the detent is seated into the vee-groove.
The following two stepper motors are considered for driving the turret.
Motor Parameters for Phytron Steppers
Tvss42_max 92N mm VSS42 Stepper motor drive (stall) torque
Tvss32_max 32N mm VSS 32.200.1.2-VGPL 32 4-UHVC stepper motor with 4:1 reduction gear box (stall) torque
Ratiomot_gearbox 4
Select a desired gear reduction ratio between the motor and the crank of the geneva mechanism (default ratio is 3:1).
Ratiomitergear 3 Miter gear reduction ratio between stepper and crank
Define crank shaft bearing friction (torque).
Tcrank_friction .10in lbf
Define motor drive shaft bearing friction (torque).
Tfric_shaft1 Tcrank_friction
Define turret hub bearing friction (torque).
Ttrt_fric 1in lbf
Part 1. Cam Move Profile Calculations
This calculations assumes a trapezoidal move profile for the Geneva crank. The crank starts at θ1= 0deg, which corrseponds to the detent mechanism fully
seated in the vee-groove. The crank acclerates from θ1 to θ2 with constant acceleration. From θ2 toθ3, the angular velocity is constant. From θ3 to θ4, the
crank decelerates back to a stop, completing one cycle of the crank.
θ0 0deg Starting position of the crank, which corresponds to the detent mechanism seated in the vee-groove.
θ1 10deg Cam angle when follower begins to lift detent off of vee-groove.
θ2 105deg Position of crank when the detent mechanism completely clears the vee-groove. The crank accelerates to full speed from
θ1 to this position and begin to engage with the geneva wheel to begin cycling the turret to the next position.
θ3 360deg θ2 θ3 255 deg Position of crank when the geneva has completed a move of the turret to the next position. The crank rotates at constant
speed from this position to θ4.
θ4 360deg θ1 θ4 350 deg Cam angle when follower finishes seating the detent onto the vee-groove.
θ5 360deg Ending position of the crank, which corresponds to the detent mechanism seated in the vee-groove.
θ θ0 .1 deg θ5 Define range for crank angle.
Cross Disperser 5 Drive Torque Calculations
Prepared by Gary Muller 05/13/2011 Page 13 of 21
14. Calculate the corresponding times for the angular positions above.
ωcrank_max
1
tcycle
2θ5 θ3 θ2 ωcrank_max 19
rev
min
Calculated maximum crank velocity for the desired cycle time
t2 2
θ2
ωcrank_max
t2 1.842 s Calculated time to get to the θ2 position.
t1
2t2 θ1
ωcrank_max
t1 0.568 sec
t3 t2
θ3 θ2
ωcrank_max
t3 3.158 s Calculated time to get to the θ3 position.
t5 t3 2
θ5 θ3
ωcrank_max
t5 5 s Calculated time to get to the θ5 position.
t4 t5 t1 t4 4.432 s
Calculate angular position of crank as a function of time.
t 0s .01s t5 Define time range
θx t( ) if t t2
ωcrank_max
2t2
t
2
ωcrank_max
2
t2 ωcrank_max t t2
θcrank t( ) if t t3 θx t( )
ωcrank_max
t5 t3
t5 t
1
2
t
2
θ5
ωcrank_max
t5 t3
t5
2
2
Calculate angular velocity of crank as a function of time.
ωt t( ) if t t2 ωcrank_max
t
t2
ωcrank_max
ωcrank t( ) if t t3 ωt t( ) ωcrank_max
t5 t
t5 t3
Calculate angular acceleration of crank as a function of time.
αcrank t( )
t
ωcrank t( )
d
d
Crank angular acceleration.
0 1 2 3 4 5
0
50
100
150
200
250
300
350
400
Crank Angular Position
Time (sec)
AngularPosition(deg)
0 1 2 3 4 5
0
2
4
6
8
10
12
14
16
18
20
Crank Angular Velocity
Time (sec)
CrankAngVelocity(RPM)
0 1 2 3 4 5
80
60
40
20
0
20
40
60
80
Crank Angular Accleration
Time (sec)
AngularAccel(deg/sec^2)
Cross Disperser 5 Drive Torque Calculations
Prepared by Gary Muller 05/13/2011 Page 14 of 21
15. Cam Follower Path Calculations
This calculation uses a sinusoidal cam follower path for a smooth transition between the seated to unseated positions of the detent mechanism.
Lift 4mm Cam lift (distance from detent fully seated to fully disengaged).
R2 16.63190451 mm R2 0.654799 in Radius of cam when detent is fully disengaged.
R1 R2 Lift R1 0.497319 in Radius of cam when detent is seated.
Define cam radius equation for three segments of cam from 0 deg to θ2, & θ2 to 180 deg.
Rzone1 θ( ) if θ θ1 R1 R1
R2 R1
2
1 sin
180deg
θ2 θ1
θ θ1 90deg
Radius of cam path in zone 1.
Rzone12 θ( ) if θ θ2 Rzone1 θ( ) R2 Radius of cam path in zones 1 & 2.
Rzone123 θ( ) if θ θ3 Rzone12 θ( ) R1
R2 R1
2
1 sin
180deg
θ4 θ3
θ θ3 90deg
Radius of cam path in zones 1, 2 & 3.
Rzone1234 θ( ) if θ θ4 Rzone123 θ( ) R1 Radius of cam path in zones 1, 2, 3 & 4.
Rcam θ( ) Rzone1234 θ( )
0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360
0.45
0.5
0.55
0.6
0.65
0.7
Follower Path
Cam Follower Path vs. Follower Angle
Theta (deg)
Radius(in)
θ2
deg
θ3
deg
0
30
60
90
120
150
180
210
240
270
300
330
0
0.2
0.4
0.6
Follower Path
Cam Follower Path
Rz1 t( ) if t t1 R1 R1
R2 R1
2
1 sin
180deg
θ2 θ1
θcrank t( ) θ1 90deg
Radius of cam path in zone 1.
Rz12 t( ) if t t2 Rz1 t( ) R2 Radius of cam path in zones 1 & 2.
Rz123 t( ) if t t3 Rz12 t( ) R1
R2 R1
2
1 sin
180deg
θ4 θ3
θcrank t( ) θ3 90deg
Radius of cam path in zones 1, 2 & 3.
Rz1234 t( ) if t t4 Rz123 t( ) R1 Rcm t( ) Rz1234 t( ) Radius of cam path in zones 1, 2, 3 & 4.
Cross Disperser 5 Drive Torque Calculations
Prepared by Gary Muller 05/13/2011 Page 15 of 21
16. 0 1 2 3 4 5
0.45
0.5
0.55
0.6
0.65
0.7
Cam Follower Path vs. Follower Angle
Time (sec)
Radius(in)
t3t2
Convert cam follower path to cartesian coordinates
x θ( ) Rcam θ( ) cos θ( ) x coordinate xt t( ) Rcam θcrank t( ) cos θcrank t( ) x coordinate
y θ( ) Rcam θ( ) sin θ( ) y coordinate yt t( ) Rcam θcrank t( ) sin θcrank t( ) y coordinate
Rcam θ( )
0.497319
0.497319
0.497319
0.497319
0.497319
...
in
x θ( )
0.497319
0.497318
0.497316
0.497312
0.497307
...
in
y θ( )
0
-48.679851·10
-31.735968·10
-32.603945·10
-33.471914·10
...
in
Calculate angle beta. See figure of cam geometry above.
β θ( ) if θ θ2 90deg atan
θ
y θ( )
d
d
θ
x θ( )
d
d
θ
β1t t( ) if t t2 90deg atan
t
yt t( )
d
d
t
xt t( )
d
d
θcrank t( )
βt t( ) if t t3 β1t t( ) 270deg atan
t
yt t( )
d
d
t
xt t( )
d
d
Calculate angle η.
η θ( ) θ β θ( ) ηt t( ) θcrank t( ) βt t( )
Calculate moment arm BB
BB θ( ) Rcam θ( ) sin η θ( )( ) BBt t( ) Rcm t( ) sin ηt t( ) Moment arm length.
Motor & Drive Inertias
Ivss32rotor .00342lb in
2
Imot_gearbox .00854lb in
2
Icoupling .002 lb in
2
Ishaft1 .00298lb in
2
Icrank .0292lb in
2
Iturret 102.972lb in
2
Cross Disperser 5 Drive Torque Calculations
Prepared by Gary Muller 05/13/2011 Page 16 of 21
17.
Calculate loads on crank in Zone 1 & Zone 3.
(Cam angle from 0 degrees to 2 and 3 to 5).
The crank is subjected to the following loads in zones 1 & 3.
Parallel spring flexure loads from detent mechanism.1.
Preload force from detent mechanism compression spring.2.
Inertial forces caused by accelerating mass of detent mechansim when crank is moving.3.
Inertial force (torque) on crank shaft from accelerating crank (I x ).4.
Frictional forces (torque) from bearings.5.
1. Calculate Parallel Spring Flexure Load.
kprll .9985
lbf
Lift
kprll 6.34
lbf
in
The spring rate was determined by finite element analysis to be 1.47 lbf over a 4mm deflection.
Fprll t( ) kprll Rcm t( ) R1 Component of detent spring force provided by the parallel spring flexure.
2. Calculate detent mechanism preload compression spring forces.
Lpreload Lfree
Fpre
kspr
Lpreload 1.523 in Length at initial preload (seated in vee-groove).
Lmaxcomp Lpreload Lift Lmaxcomp 1.365 in Length at max compression (unseated)
Fcomp t( ) Fpre kspr Rcm t( ) R1 Component of detent spring force provided by the compression spring.
3. Calculate the inertial forces caused by accelerating mass of detent mechansim when crank is moving.
vdetent t( )
t
Rcm t( )
d
d
Linear velocity of parallel spring flexure when climbing the cam.
adetent t( )
2
t
Rcm t( )
d
d
2
Linear acceleration of parallel spring flexure when climbing the cam.
Mdetent .111lb Mass of moving portion of parallel spring flexure.
Fdetent_inertial t( ) Mdetent adetent t( ) Force component of parallel spring flexure due to the acceleration when climbing cam.
Calculate total force on cam from follower loads.
Ffollower t( ) Fcomp t( ) Fprll t( ) Fdetent_inertial t( ) Total detent spring force.
Fnormal t( )
Ffollower t( )
cos ηt t( )
Reaction force to the spring in the normal direction
Cross Disperser 5 Drive Torque Calculations
Prepared by Gary Muller 05/13/2011 Page 17 of 21
18. 0 1 2 3 4 5
1
0
1
2
3
4
5
6
7
8
Compression Spring
Parallel Flexure
Follower Inertial Force
Total Force
Cam Follower Forces vs. Time
Time (sec)
Force(lbf).
Calculate motor speed and acceleration
ωshaft1 t( ) ωcrank t( ) Ratiomitergear αshaft1 t( ) Ratiomitergear αcrank t( )
ωmotor t( ) ωshaft1 t( ) Ratiomot_gearbox αmotor t( ) αshaft1 t( ) Ratiomot_gearbox
Calculate loads on crank in Zone 2
(Cam angle from 2 to ).
0 1 2 3 4 5
0
100
200
300
228
0
ω.motor t( )
rev
min
ω.shaft1 t( )
rev
min
50 t
sec
The crank is subjected to the following loads in zone 2..
Inertial force (torque) of accelerating turret (I x ) as is cycles from one position to the next.1.
Frictional forces (torque) from turret bearings.2.
Rcrank 25.88190451mm Radius of Geneva crank
Dctr 100mm Distance between centers of Geneva wheel and crank
θgen2 t( ) if t t2 atan
Rcrank sin 180deg θ2
Dctr Rcrank cos 180deg θ2
atan
Rcrank sin 180deg θcrank t( )
Dctr Rcrank cos 180deg θcrank t( )
θgeneva t( ) if t t3 θgen2 t( ) atan
Rcrank sin 180deg θ3
Dctr Rcrank cos 180deg θ3
Rgeneva t( )
Rcrank sin 180deg θcrank t( )
sin θgeneva t( )
Geneva radius as a function of time
ωgeneva t( )
t
θgeneva t( )
d
d
αgeneva t( )
2
t
θgeneva t( )
d
d
2
Tturret t( ) Iturret αgeneva t( ) Ttrt_fric
Ftan_trt_1 t( ) if t t2 0lbf
Tturret t( )
Rgeneva t( )
Ftan_turret t( ) if t t3 Ftan_trt_1 t( ) 0lbf
Ftan_crank t( ) Ftan_turret t( ) sin θcrank t( ) θgeneva t( ) 90deg
Tcrank_z2 t( ) Ftan_crank t( ) Rcrank
Cross Disperser 5 Drive Torque Calculations
Prepared by Gary Muller 05/13/2011 Page 18 of 21