Estimation of Extreme Loads of the Mi-24 Helicopter During Maneuvers Using Simulation Method

The aim of the study is to assess the loads that are transferred from the main rotor and the tail rotor to the helicopter fuselage. These loads change in the various phases of the transient flight as a result of the variable control of the maneuver and as a result of the variable flow around the blades. The knowledge of the loads allows for the proper selection of the level of excitations that should load the fuselage structure during fatigue and strength tests. The simulation model describing the helicopter flight is discussed. This model takes into account the motion of each blade relative to its hinges. Results are shown for two maneuvers - pullup/pushover and diving. The values of extreme loads transferred to the fuselage were obtained.

Synthesizing the impacts of baseflow contribution on concentration–discharge (<i>C</i>–<i>Q</i>) relationships across Australia using a Bayesian hierarchical model

Understanding concentration–discharge (C–Q) relationships can inform catchment solute and particulate export processes. Previous studies have shown that the extent to which baseflow contributes to streamflow can affect C–Q relationships in some catchments. However, the current understanding on the effects of baseflow contribution in shaping the C–Q patterns is largely derived from temperate catchments. As such, we still lack quantitative understanding of these effects across a wide range of climates (e.g. arid, tropical and subtropical). The study aims to assess how baseflow contributions, as defined by the median and the range of daily baseflow indices within individual catchments (BFI_m and BFI_range, respectively), influence C–Q slopes across 157 catchments in Australia spanning five climate zones. This study focuses on six water quality variables: electrical conductivity (EC), total phosphorus (TP), soluble reactive phosphorus (SRP), total suspended solids (TSS), the sum of nitrate and nitrite (NOx) and total nitrogen (TN). The impact of baseflow contributions is explored with a novel Bayesian hierarchical model. For sediments and nutrient species (TSS, NOx, TN and TP), we generally see largely positive C–Q slopes, which suggest a dominance of mobilization export patterns. Further, for TSS, NOx and TP we see stronger mobilization (steeper positive C–Q slopes) in catchments with higher values in both the BFI_m and BFI_range, as these two metrics are positively correlated for most catchments. The enhanced mobilization in catchments with higher BFI_m or BFI_range is likely due to the more variable flow pathways that occur in catchments with higher baseflow contributions. These variable flow pathways can lead to higher concentration gradients between low flows and high flows, where the former is generally dominated by groundwater/slow subsurface flow while the latter by surface water sources, respectively. This result highlights the crucial role of flow pathways in determining catchment exports of solutes and particulates. Our study also demonstrates the need for further studies on how the temporal variations of flow regimes and baseflow contributions influence flow pathways and the potential impacts of these flow pathways on catchment C–Q relationships.

Female football specific energy availability questionnaire and menstrual cycle hormone monitoring

Objectives The purpose of this study was to assess the energy availability status of professional female football players with an online Female Football Energy Availability Questionnaire (FFEAQ), combined with the clinical tool to model menstrual cycle hormones using artificial intelligence (AI) techniques. Methods The Female Football Energy Availability (FFEAQ) was developed based on published questionnaires, with a weighted scoring system to assess risk of Relative Energy Deficiency in Sport (RED-S). For menstrual cycle hormones AI techniques modelled hormone variation over a cycle, using the results from capillary blood samples taken at two time points. Results 21 female footballers of professional club level participated in this study, with mean age 22 years [range 16 to 30]. 20 athletes recorded positive scores on the FFEAQ, suggesting a low risk of Relative Energy Deficiency in Sport (RED-S). No players had experienced primary amenorrhoea. 5 athletes reported previous history of secondary amenorrhoea. Amongst the 15 players not taking hormonal contraception, 2 reported current oligomenorrhoea. The application of AI techniques to model menstrual cycle hormones found that in 1 of the 3 players, subclinical hormone disruption was occurring with this player reporting variable flow of menstruation. Although the other 2 players showed expected menstrual hormone variation, 1 player reported variable flow according to training load, suggestive of subclinical anovulation. At the time of testing training load was low due to pandemic lock down. Conclusions The professional female football athletes in this study were found to be at low risk of RED-S from the FFEAQ. Modelling menstrual cycle hormones using AI techniques indicated that this has the potential to be an effective clinical tool in identifying subtle hormone dysfunction such as subclinical anovulatory cycles in female athletes.

Investigation of Forecast Accuracy and its Impact on the Efficiency of Data-Driven Forecast-Based Reservoir Operating Rules

Today, variable flow pattern, which uses static rule curves, is considered one of the challenges of reservoir operation. One way to overcome this problem is to develop forecast-based rule curves. However, managers must have an estimate of the influence of forecast accuracy on operation performance due to the intrinsic limitations of forecast models. This study attempts to develop a forecast model and investigate the effects of the corresponding accuracy on the operation performance of two conventional rule curves. To develop a forecast model, two methods according to autocorrelation and wrapper-based feature selection models are introduced to deal with the wavelet components of inflow. Finally, the operation performances of two polynomial and hedging rule curves are investigated using forecasted and actual inflows. The results of applying the model to the Dez reservoir in Iran visualized that a 4% improvement in the correlation coefficient of the coupled forecast model could reduce the relative deficit of the polynomial rule curve by 8.1%. Moreover, with 2% and 10% improvement in the Willmott and Nash—Sutcliffe indices, the same 8.1% reduction in the relative deficit can be expected. Similar results are observed for hedging rules where increasing forecast accuracy decreased the relative deficit by 15.5%. In general, it was concluded that hedging rule curves are more sensitive to forecast accuracy than polynomial rule curves are.

A Brief History of the Kaplan Turbine Invention

One of the most important milestones in the history of hydropower is the invention of the Kaplan turbine. It is a machine stemming from the Francis turbine, which Viktor Kaplan was originally trying to improve. However, it gradually developed into the creation of a completely new solution of an impeller with an axial flow rate and adjustable blades. The first patent relating to the new invention dates from 1913. Shortly afterwards, the Kaplan turbine became the most widely used type of device for the use of low heads and variable flow rates. That meant a significant expansion of the potential of economically usable hydropower. The article briefly introduces the history of turbine development. The overall picture is then completed by a few less-known historical documents.

Design of an Intelligent Variable-Flow Recirculating Aquaculture System Based on Machine Learning Methods

A recirculating aquaculture system (RAS) can reduce water and land requirements for intensive aquaculture production. However, a traditional RAS uses a fixed circulation flow rate for water treatment. In general, the water in an RAS is highly turbid only when the animals are fed and when they excrete. Therefore, RAS water quality regulation technology based on process control is proposed in this paper. The intelligent variable-flow RAS was designed based on the circulating pump-drum filter linkage working model. Machine learning methods were introduced to develop the intelligent regulation model to maintain a clean and stable water environment. Results showed that the long short-term memory network performed with the highest accuracy (training set 100%, test set 96.84%) and F1-score (training 100%, test 93.83%) among artificial neural networks. Optimization methods including grid search, cuckoo search, linear squares, and gene algorithm were proposed to improve the classification ability of support vector machine models. Results showed that all support vector machine models passed cross-validation and could meet accuracy standards. In summary, the gene algorithm support vector machine model (accuracy: training 100%, test 98.95%; F1-score: training 100%, test 99.17%) is suitable as an optimal variable-flow regulation model for an intelligent variable-flow RAS.

INTELLIGENT LIQUID LEVEL CONTROL OF A COUPLED NONLINEAR THREE TANK SYSTEM SUBJECTED TO VARIABLE FLOW PARAMETERS

In this paper, an intelligent control system technique is proposed to model and control of a nonlinear coupled three tank system. Two pumps fed the tank 1 and tank 2 and a fractional flow of these two pumps fed tank 3. The main aim of this paper is to make a set point tracking experiments of the tanks level using a nonlinear autoregressive moving average L-2 (NARMA L-2) and neural network predictive controllers. The proposed controllers are designed with the same neural network architecture and algorithm. Comparison of the system with the proposed controllers for tracking a step and random level set points for a fixed and variable flow parameter and some good results have been obtained.

ENERGY AND COST SAVING POTENTIAL OF HOTEL AIR CONDITIONING USING MAGNETIC BEARING CHILLER IN JAKARTA

The use of magnetic bearing chillers in hotel air conditioning systems is an opportunity for energy or cost savings. This study will compare the electrical energy consumption and cost analysis of the centralized air conditioning system using magnetic bearing chiller that uses variable flow to another air conditioning system such as the centralized air conditioning using constant flow chiller and the VRF split air conditioning system at Hotel A in Jakarta. The calculation of energy consumption for each air conditioning system is carried out for a year. Meanwhile, the cost analysis will be carried out using the life cycle cost method for 20 years. The air conditioning system which has the least energy consumption and has the lowest life cycle cost is the best air conditioning system for this hotel building. The maximum cooling load that occurs in Hotel A is 3,281 kW. From the results of energy calculations and cost analysis, a centralized air conditioning system with magnetic bearing chiller with variable flow is the best choice to Hotel A or similar building to Hotel A, with IKE (Intensitas Konsumsi Energi) value of 84 kWh/(m2.year), and a total cost of 78,873,678,478.00 IDR for a period of 20 years.