Calculating component temperatures in gearboxes for transient operation conditions

The knowledge of component temperatures during transient operation conditions is essential for an optimal design of a gearbox. This is because critical peak temperatures limit the transferable power as well as the load capacity. Moreover, understanding the thermal behavior of the gearbox is key to improving its efficiency. Therefore, the Thermal Network Method (TNM) of the calculation program WTplus was extended to calculate component temperatures in gearboxes for transient operation conditions. Specifically, the TNM considers the component masses and specific heat capacities of each node modelling the gearbox structure. This enables the algorithm to compute a corresponding system of differential equations and thus determine the temperature change over time. Therefore, WTplus can be used to identify critical gearbox component temperatures during load cycles. The applied method was validated with measurements collected at the FZG gear efficiency test rig.

Gear efficiency estimation method through finite element and curve fitting

The inclusion of high energy density permanent magnet (PM) in MG contributes to the high eddy current loss in magnetic gear and reduces its efficiency. There was limited research done that focused on gear efficiency behavior over a broader range of speed and in different gear ratios. In this paper, the function of gear efficiency concerning gear ratio and rotational speed is proposed. Torque and eddy current loss data were obtained through transient magnetic analysis using finite element software at several rotational ranges and gear ratios. The analytical approach through mathematical substitution was discussed to confirm the finding in the simulation. The result showed that the gear efficiency decreases as the speed increases. Nonetheless, the gear efficiency revealed improvement in efficiency as the gear ratio increases. Finally, gear efficiency behavior was modeled using the curve fitting method. Subsequently, based on the correlation study, an equation was proposed, yielding a 1% error compared to the new simulated data. With this proposed method and equation, the analysis and estimation of gear efficiency behavior over wider speed and gear ratios are simplified, thus reducing the need to perform simulation over different speeds and gear ratios.

Combining fisheries surveys to inform marine species distribution modelling

Ecosystem-scale examination of fish communities typically involves creating spatio-temporally explicit relative abundance distribution maps using data from multiple fishery-independent surveys. However, sampling performance varies by vessel and sampling gear, which may influence estimated species distribution patterns. Using GAMMs, the effect of different gear–vessel combinations on relative abundance estimates at length was investigated using European fisheries-independent groundfish survey data. We constructed a modelling framework for evaluating relative efficiency of multiple gear–vessel combinations. 19 northeast Atlantic surveys for 254 species-length combinations were examined. Space-time variables explained most of the variation in catches for 181/254 species-length cases, indicating that for many species, models successfully characterized distribution patterns when combining data from disparate surveys. Variables controlling for gear efficiency explained substantial variation in catches for 127/254 species-length data sets. Models that fail to control for gear efficiencies across surveys can mask changes in the spatial distribution of species. Estimated relative differences in catch efficiencies grouped strongly by gear type, but did not exhibit a clear pattern across species’ functional forms, suggesting difficulty in predicting the potential impact of gear efficiency differences when combining survey data to assess species’ distributions and highlighting the importance of modelling approaches that can control for gear differences.

Bearing Power Losses with Water-Containing Gear Fluids

Lubricants have a large influence on gearbox power losses. Recent investigations at a gear efficiency test rig have shown the high potential of water-containing gear fluids in drastically reducing load-dependent gear losses and temperatures. In this study, the bearing power losses with water-containing gear fluids were evaluated at a specific bearing power loss test rig explicitly and compared with mineral and polyalphaolefine oils. For all investigated lubricants, a Stribeck curve behavior of the load-dependent losses is observed. The water-containing gear fluids demonstrate lower no-load bearing losses and higher load-dependent bearing losses at higher rotational speeds. The comparison of measured bearing losses with typical calculation procedures showe partially large differences. The results underline the importance of having detailed knowledge of bearing losses when evaluating gear losses in gearboxes.

An algorithm to determining the gear efficiency to a simple planetary train

The paper presents an original algorithm composed by authors in order to determine through exact calculations the efficiency value of a simple planetary mechanism, increasingly used in aerospace, robotics, mechatronics, machine building, and various automation. The calculation program is written in excel and for its simple automation four sign-type switches, are used plus or minus 1, and a logic function for checking the status and choosing the corresponding value. In this way, the program is generalized to be used for any type of simple planetary mechanism for the purpose of accurately determining its yield.

Performance enhancement of spur gear formed through asymmetric tooth

Tooth fracture and surface wear are the major failure causes in a gearing system. With increasing demand for high power density gear applications, the need of effective gear design becomes an important requirement to improve gear life. This article presents a method to enhance the load carrying capacity in bending and contact, as well as wear resistance to increase gear efficiency through asymmetric tooth. Asymmetric gear is the one whose pressure angles at pitch circle on drive and coast sides are different. In the present investigation, the load shared by a teeth pair, fillet and contact stresses, wear resistance, frictional power losses and the respective mechanical efficiencies have been determined for comparative performance assessment of symmetric and asymmetric spur gears.

Assessment of Gear Efficiency for Harvesting Artisanal Giant Freshwater Prawn (Macrobrachium Rosenbergii de Man) Fisheries from the Sundarbans Mangrove Ecosystem in Bangladesh

The Sundarnbans mangrove forest has been an immense source of aquatic resources from time immemorial. Among the resources, harvesting of Giant Freshwater Prawn (Macrobrachium rosenbergii de Man 1879) plays an important role in the economy of the country, therefore, this investigation was carried out to assess the Gear Efficiency for Harvesting Artisanal Giant Freshwater Prawn Fisheries from the Sundarbans Mangrove Ecosystem in Bangladesh. Four types of non-mechanized crafts made of fourteen types of wood were found in harvesting Giant Freshwater Prawn. Crafts were varied from 6.25±0.35 to 8.2±0.71 m in length, 1.1±0.14 to 1.75±0.36 m in breadth and 0.65±1.07 to 1.55±3.16 m in depth with a carrying capacity of 0.40 - 1.00 MT with an economic life of 10±2.11 years. A total of eight types of gears, namely, Hookline, Behundijal, Badhajal, Charpata, Chandijal, Gillnet, Kathijal and KhalPata were found in Giant Freshwater Prawn fishing. Among them the Hookline was the only main primary active Giant Freshwater Prawn fishing gear while the rest of the seven gears were multi-species gears which harvest Giant Freshwater Prawn as a by-catch. Only 13.6% of 1,989 fishers were found to use Hookline; the rest, 86.4% of fishers used the seven other gears. They only harvest 3.24% Giant Freshwater Prawn as a bycatch. The highest daily harvest volume per boat (kg day-1 boat-1) was 1.00±0.96 for the Kathijal followed by 0.99±0.27 for the Badhajal and 0.99±0.42 for the Hookline. The highest annual catch per boat (kg boat-1 yr-1) which was 172.80±0.29 was recorded for the Hookline followed by 172.24±0.54 for the Chandijal and the lowest (152.08±0.21) for the Khalpata. Total annual catch by all the eight gears surveyed was estimated at 1318.60±0.37 kg from 1,428 unit gears, 545 individual boats and 1,989 individual fishers. Based on the above scenario, Hookline showed the best performance among the 8 gears used for Giant Freshwater Prawn harvesting in the Sundarbans by the small-scale artisanal fishery. It is believed that the findings and the recommendations of this study will be helpful to policy makers in improving the current status of Giant Freshwater Prawn fishery and relevant livelihood as well as conserving the SME ecosystem

A data-limited method for assessing cumulative fishing risk on bycatch

To assess fishing effects on data-poor species, impact can be derived from spatial overlap between species distribution and fishing effort and gear catchability. Here, we enhance the existing sustainability assessment for fishing effect method by estimating gear efficiency and heterogeneous density from sporadic catch data. We apply the method to two chondrichthyan bycatch species, Bight Skate and Draughtboard Shark in Australia, to assess cumulative fishing mortality (Fcum) from multiple fisheries. Gear efficiency is estimated from a Bayesian mixture distribution model and fish density is predicted by a generalized additive model. These results, combined with actual fishing effort, allow estimation of fishing mortality in each sector and subsequently, the Fcum. Risk is quantified by comparing Fcum with reference points based on life history parameters. When only the point estimates were considered, our result indicates that for the period 2009 and 2010 Bight Skate caught in 14 fisheries was at high cumulative risk (Fcum ≥ Flim) while Draughtboard Shark caught by 19 fisheries was at low cumulative risk (Fcum ≤ Fmsy). Because of the high cost of conducting cumulative risk assessments, we recommend examining the distribution of fishing effort across fisheries before carrying out the assessments.