Update of the technical equipment of the grain-harvesting complex

Many options have been developed for the implementation of the algorithm for updating the fleet of grain harvesters to date. In accordance with the yield and other indicators, recommendations for the formation and renewal of the harvester fleet are proposed discretely in the form of tables or charts. This form of information does not always meet the requirements of operational correction and does not allow assessing the technological capabilities of the harvesting units, depending on the harvesting conditions. The method to improve the formation of the initial information for operational decision-making on the effective upgrading of technical means of grain harvesting complex taking into account the zonal features of a particular agricultural enterprise is proposed. A graph-analytical method for determining the main parameters of the basic harvesting tools depending on the predicted yield level is developed and the influence of the factors determining the composition of the grain harvesting fleet is assessed. This method makes it possible to identify the most rational basic parameters of alternative basic harvesting tools for a specific agricultural enterprise. The first step is to determine the basic parameters of the basic equipment, then select the appropriate size series of self-propelled threshers for combine harvesters and reapers. Further, alternative versions of various models of grain harvesting units and complexes are formed. For the subsequent selection of rational types of cleaning agents and their criterion assessment, technical and technological, environmental and other indicators are used. The expert-logical analysis of information resources makes it possible to identify and assess the factors that determine the quantitative composition of the technical means of the grain harvesting complex. The final stage in the formation of the initial information for making a decision on updating the technical means of the grain harvesting complex should be their economic assessment, which makes it possible to predict the competitiveness of the threshed grain.

Data Management Framework & UAV Simulation Testbed for the Study of System-level Prognostics Technologies

Prognostics-enabled technologies have emerged over the last few years, primarily for Condition Based Maintenance (CBM+) applications, which are used for maintenance and operational scheduling. However, due to the challenges that arise from real-world systems and safety concerns, they have not been adopted for operational decision making based on system end of life estimates. It is typically cost-prohibitive or highly unsafe to run a system to complete failure and, therefore, engineers turn to simulation studies for analyzing system performance. Prognostics research has matured to a point where we can start putting pieces together to be deployed on real systems, but this reveals new problems. First, a lack of standardization exists within this body of research that hinders our ability to compose various technologies or study their joint interactions when used together. The second hindrance lies in data management and creates hurdles when trying to reproduce results for validation or use the data as input to machine learning algorithms. We propose an end-to-end object-oriented data management framework & simulation testbed that can be used for a wide variety of applications. In this paper, we describe the requirements, design, and implementation of the framework and provide a detailed case study involving a stochastic data collection experiment.

Catchment Scale Evaluation of Multiple Global Hydrological Models from ISIMIP2a over North America

A satisfactory performance of hydrological models under historical climate conditions is considered a prerequisite step in any hydrological climate change impact study. Despite the significant interest in global hydrological modeling, few systematic evaluations of global hydrological models (gHMs) at the catchment scale have been carried out. This study investigates the performance of 4 gHMs driven by 4 global observation-based meteorological inputs at simulating weekly discharges over 198 large-sized North American catchments for the 1971–2010 period. The 16 discharge simulations serve as the basis for evaluating gHM accuracy at the catchment scale within the second phase of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2a). The simulated discharges by the four gHMs are compared against observed and simulated weekly discharge values by two regional hydrological models (rHMs) driven by a global meteorological dataset for the same period. We discuss the implications of both modeling approaches as well as the influence of catchment characteristics and global meteorological forcing in terms of model performance through statistical criteria and visual hydrograph comparison for catchment-scale hydrological studies. Overall, the gHM discharge statistics exhibit poor agreement with observations at the catchment scale and manifest considerable bias and errors in seasonal flow simulations. We confirm that the gHM approach, as experimentally implemented through the ISIMIP2a, must be used with caution for regional studies. We find the rHM approach to be more trustworthy and recommend using it for hydrological studies, especially if findings are intended to support operational decision-making.

Multistage Hydraulic Fracturing of the Tyumen Suite Reservoirs of Em-Yogovskoye Field: Frac-Design, Practice, Results

The paper describes a system of approaches to the design and engineering support of multistage hydraulic fracturing: A method of developing multiple-option modular design of multistage hydraulic fracturing which is a tool for operational decision-making in the process of hydraulic fracturing.Building a Hydraulic Fracturing Designs Matrix when optimizing field development plans. The result was used to build decision maps for finding well completion methods and selecting a baseline hydraulic fracturing design. The paper also describes how the systematization of approaches, methodological developments, and decision templates can help in optimizing field development by drilling directional and horizontal wells followed by multi-stage hydraulic fracturing. The sequence of events and tasks that led to the development of the methodology, as well as its potential, is briefly described. The methodologies were developed during the execution of a hydraulic fracturing project at JK 29 reservoirs of the Tyumen Suite of Em-Yogovskoye field, after which they were applied in a number of other projects for the development of hard-to-recover hydrocarbon reserves in West Siberia.

Identification of asset life cycle stage: case study in heavy-duty truck fleet

PurposeThis paper aims to determine the stage of the life cycle where the trucks of a waste collection fleet from a Colombian city are located through a reliability approach. The reliability analysis and the evaluation of curve of operational costs allow to know the moment in which it is necessary to make decisions regarding an asset, its maintenance or possible replacement.Design/methodology/approachFor a dataset presented as maintenance work orders, the time to failures (TTFs) for each vehicle in the fleet were calculated. Then, a probability density function for those TTFs was fitted to locate each vehicle in a region of the bathtub curve and to calculate the reliability of the whole fleet. A general functional analysis was also developed to understand the function of the vehicles.FindingsIt was possible to determine that the largest proportion of the fleet was in the final stage of the life cycle, in this sense, the entire fleet represent critical assets which in most of cases could be worth replacement or overhaul.Originality/valueIn this study, an address is exposed for the identification of critical equipment by reliability and statistical analysis. This analysis is also integrated with the maintenance management process. This is a broadly interested topic since it allows to support the maintenance and operational decision-making process, indicating the focus of resource allocation all over the entire asset life cycle.

Using Metocean Forecast Verification Information to Effectively Enhance Operational Decision-Making

Metocean forecast verification statistics (or ‘skill scores’), for variables such as significant wave height, are typically computed as a means of assessing the (past) weather model performance over the particular area of interest. For developers, this information is important for the measurement of model improvement, while for consumers this is commonly applied for the comparison/evaluation of potential service providers. However, an opportunity missed by many is also its considerable benefit to users in enhancing operational decision-making on a real-time (future) basis, when combined with an awareness of the context of the specific decision being made. Here, we present two categorical verification techniques and demonstrate their application in simplifying the interpretation of ensemble (probabilistic) wave forecasts out to 15 days ahead, as pioneered – in operation – in Summer 2020 to support the recent weather sensitive installation of the first phase of a 36 km subsea pipeline in the Fenja field in the North Sea. Categorical verification information (based on whether forecast and observations exceed the user-defined operational weather limits) was constructed from 1460 archive wave forecasts, issued for the two-year period 2017 to 2018, and used to characterise the past performance of the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system (EPS) in the form of Receiver Operating Characteristic (ROC) and Relative Economic Value (REV) analysis. These data were then combined with a bespoke parameterization of the impact of adverse weather on the planned operation, allowing the relevant go/no-go ensemble probability threshold (i.e. the number of individual/constituent forecast members that must predict favourable/unfavourable conditions) for the interpretation of future forecasts to be determined. Following the computation of the probability thresholds for the Fenja location, trials on an unseen nine-month period of data from the site (Spring to Autumn 2019) confirm these approaches facilitate a simple technique for processing/interpreting the ensemble forecast, able to be readily tailored to the particular decision being made. The use of these methods achieves a considerably greater value (benefit) than equivalent deterministic (single) forecasts or traditional climate-based options at all lead times up to 15 days ahead, promising a more robust basis for effective planning than typically considered by the offshore industry. This is particularly important for tasks requiring early identification of long weather windows (e.g. for the Fenja tie-ins), but similarly relevant for maximising the exploitation of any ensemble forecast, providing a practical approach for how such data are handled and used to promote safe, efficient and successful operations.

Factors Contributing to Accidents During Aerobatic Flight Operations

INTRODUCTION: Aerobatic flight operations involve a higher level of risk than standard flight operations. Aerobatics imposes considerable stresses on both the aircraft and the pilot. The purpose of this study was to analyze civilian aerobatic aircraft accidents in Australia, with particular emphasis on the underlying accident causes and survival outcomes.METHODS: The accident and incident database of the Australian Transport Safety Bureau was searched for all events involving aerobatic flight for the period 19802010.RESULTS: A total of 51 accidents involving aircraft undertaking aerobatic operations were identified, with 71 aircraft occupants. Of the accidents, 27 (52.9) were fatal, resulting in a total of 36 fatalities. There were 24 nonfatal accidents. In terms of injury outcomes, there were 4 serious and 9 minor injuries, and 22 accidents in which no injuries were recorded. Fatal accidents were mainly due to loss of control by the pilot (44.4), in-flight structural failure of the airframe (25.9), and terrain impact (25.9). G-LOC was considered a possible cause in 11.1 of fatal accidents. Nonfatal accidents were mainly due to powerplant failure (41.7) and noncatastrophic airframe damage (25). Accidents involving aerobatic maneuvering have a significantly increased risk of a fatal outcome (odds ratio 26).DISCUSSION: The results of this study highlight the risks involved in aerobatic flight. Exceeding the operational limits of the maneuver and the design limits of the aircraft are major factors contributing to a fatal aerobatic aircraft accident. Improved awareness of G physiology and better operational decision-making while undertaking aerobatic flight may help prevent further accidents.Newman DG. Factors contributing to accidents during aerobatic flight operations. Aerosp Med Hum Perform. 2021; 92(8):612618.