Future Operating Conditions Research Articles

A multi-stage stochastic programming model for multi-mission selective maintenance optimization

This research introduces a novel selective maintenance model in the case of systems undergoing multiple consecutive missions. The model considers uncertainties related to future operating conditions during each mission. Within each maintenance break, various optional actions ranging from replacements which are perfect to imperfect and also minimal repairs can be chosen for individual components. Evaluating the probabilities of successful future mission accounts for uncertainties associated with component operational conditions. The selective maintenance problem is formulated as a nonlinear mixed-integer model for optimization, and computational challenges are addressed using the progressive hedging algorithm. Numerical examples validate the new proposed model and illustrate the benefits of the model by estimating a more realistic reliability level and lower maintenance cost.

Energy-efficient operation of the thermal management system in electric vehicles via integrated model predictive control

The thermal management system (TMS) in electric vehicles (EVs) is a comprehensive system that integrates an air conditioning system for the cabin, a temperature control system for the battery, and a cooling system for the motor. The currently used PI control strategy can only meet the basic TMS functions and cause high energy consumption. In this paper, we present a novel model predictive control (MPC) strategy for the TMS to optimize operational performance in real time. Different from the independent PI control for the individual components, MPC can predict future operation conditions and provide the optimal operating inputs in advance. A complete control-oriented model for MPC is developed, and the MPC strategy is designed to minimize the total power consumption of the TMS under the control-oriented model and constraints. The evaluation is carried out under several cases including the fixed ambient temperature, realistic ambient temperature, and different vehicle speeds. The results showed that the novel MPC strategy saved energy consumption by 5.9%–10.3% in these cases when compared to the PI strategy, demonstrating the effectiveness and feasibility of the proposed MPC control.

Engineering Approach to Assessing the Vulnerability of Water Abstraction

Variability in stream flow/discharge results in serious problems for engineers and difficulties in characterizing water systems under future climatic conditions. The management of water security in the engineering domain requires approaches aimed at minimizing the detrimental effects of the hydrological behavior of natural systems. Abstraction facilities must be strengthened to ensure sustainable supply and water security over time and at different scales. Several approaches and methodologies have been developed to translate water security into a framework that provides information on how to improve it. In this study, a scalar range idea is used to evaluate the sensitivity of a water resource system and cause–effect linkages define the vulnerability indicator as management-relevant information to address water security. This intuitively relates the extreme deviations of a particular streamflow to the average system response related to a particular hazard indicator. This determines the current stress in the operation of the abstraction facilities based on historical hydrometeorological changes, which is the basis for assessing future operational conditions and risks. This study uses streamflow extremes and averages as hazard-relevant indicators of water supply security. The results of the two case studies show that the applied approach fully appreciates the internal properties of water resource systems that affect the sensitivity/vulnerability of streamflow, as well as the derived streamflow vulnerability index and function. The obtained results were used to assess the vulnerability of water intake as well as the choice of safety factors and design parameters in accordance with the forecasted average annual and seasonal climate factors.

Selecting Optimum Dimensions for a Three-Phase Horizontal Smart Separator for Khor Mor Gas-Condensate Processing Plant

The Khor Mor gas-condensate processing plant in Iraq is currently facing operational challenges due to foaming issues in the sweetening tower caused by high-soluble hydrocarbon liquids entering the tower. The root cause of the problem could be liquid carry-over as the separation vessels within the plant fail to remove liquid droplets from the gas phase. This study employs Aspen HYSYS v.11 software to investigate the performance of the industrial three-phase horizontal separator, Bravo #2, located upstream of the Khor Mor sweetening tower, under both current and future operational conditions. The simulation results, regarding the size distribution of liquid droplets in the gas product and the efficiency gas/liquid separation, reveal that the separator falls short of eliminating all liquid droplets of specified sizes from the gas phase to meet efficiency requirements, weather with or without a mist extractor. Consequently, an analysis of various structural parameters of the vessel is undertaken to determine their impact on the carried-over liquid mass flow rate and the vessel’s gas/liquid efficiency. The findings recommend a new design concept termed the "smart separator" for Bravo #2, applicable to both current and anticipated operational scenarios. The smart separator demonstrates a remarkable enhancement in gas/liquid separation efficiency, showcasing improvements of 21.31% and 24.02% under existing and future operating conditions, respectively. This innovative design proves effective in controlling liquid carry-over and maintaining high-efficiency levels, even as vessel inlet flow rates increase over time, thus preventing foaming phenomena in downstream processes caused carried-over liquids.

FMECA and FTA analysis applied to the manufacturing process of pulsating heat pipes

Pulsating heat pipes (PHPs) offer significant advantages for the thermal control of electronic components due to their simple manufacturing and high heat transfer rates. The reliability of PHPs has traditionally been assessed through long-life testing, but detailed reliability analyses from an equipment perspective are limited. The study of PHP reliability is essential due to its application and operational conditions. For instance, in aerospace applications these devices operate under severe conditions, and maintenance or replacement is impossible during operation, making them critical components in system functionality. The reliability analysis of PHPs focuses on the manufacturing process, considering future operating conditions. Although preliminary PHP testing will be conducted on Earth, laboratory conditions are less stringent due to the difficulty of replicating launch acceleration and space conditions for long-term testing under microgravity. This study presents an FMECA (Failure Modes, Effects, and Criticality Analysis) of the pulsating heat pipe manufacturing process, breaking down the production of each component. The results indicate that the most critical point is concentrated in the assembly of these components, leading to a higher incidence of welding failures. It recommends further work to improve welding and analyze mechanical stresses within the heat pipe.

Substantiation of a rational program for the running-in of tribosystems

The paper presents the results of studies on the justification of a rational program for running-in of tribosystems. It is shown that the first component of the program is the fulfillment of the condition on the verge of loss of stability due to the appearance of accelerated wear, with the maximum load on the tribosystem and the minimum sliding speed. This mode performs the function of "training" the surface layers for future operating conditions due to deformation processes and changes in the roughness of the friction surfaces. The mode is characterized by minimum wear rate values and maximum friction coefficient values.
 The second component of the running-in program is the fulfillment of the condition on the verge of loss of stability due to the appearance of burrs on the friction surfaces, with minimal load on the tribosystem and maximum sliding speed. This mode performs the function of "adaptation" of the surface layers to the future operating conditions by increasing the rate of deformation of the materials of the surface layers on the spots of actual contact. The mode is characterized by maximum wear rate values and minimum friction coefficient values.
 The third mode of the program aims to form surface structures and roughness on the friction surfaces of tribosystems that correspond to operational modes. This mode performs the function of "sufficient adaptation" of the surface layers to the future operating conditions, and corresponds to the condition with the maximum value of the stability margin. The final result of the running-in process is the transition of the tribosystem from an unbalanced, thermodynamically unstable state to a stationary, equilibrium state, as a result of which such parameters as wear rate, friction coefficient, temperature and roughness of the friction surfaces are stabilized. Such a step-by-step transition is associated with the formation of a special, dissipative structure of the surface layers of triboelements as a result of self-organization.
 The use of the three-mode program will reduce the time for tribosystems to run in by 23.0 - 38.4% compared to other programs. The effectiveness of the developed three-mode program is proven by experimental studies with the calculation of the modeling error.

Indirect prediction of remaining discharge energy of lithium-ion batteries: Looking into future complex non-constant temperature operating conditions

Remaining discharge energy (RDE) is the basis for estimating the remaining driving mileage of electric vehicles. The prediction of RDE is affected by various factors, such as the future discharge current and temperature of the battery. The simplified indirect method predicts the RDE by piecewise averaging the future current under ideal conditions at a constant temperature. Based on the simplified indirect method, this study considers the influence of temperature and proposes an indirect method with a wider range of applications. The proposed indirect method predicts the RDE based on the difference between the remaining chemical energy and future heat loss using both the future current and future environment temperatures as inputs. The battery RDE under no isothermal conditions can be predicted without calculating the terminal voltage across the full state-of-charge range. The proposed indirect method predicts the RDE values under various working conditions with a maximum prediction error and root-mean-square error of ±0.4 Wh and 0.3 Wh, respectively. This suggests that the proposed method exhibits good accuracy and robustness, and demonstrates the potential for practical applications.

Energy Performance Comparison of a Chiller Plant Using the Conventional Staging and the Co-Design Approach in the Early Design Phase of Hotel Buildings

As part of the design process of a chiller plant, one of the final stages is the energy testing of the system in relation to future operating conditions. Recent studies have suggested establishing robust solutions, but a conservative approach still prevails at this stage. However, the results of some recent studies suggest the application of a new co-design (control–design) approach. The present research involves a comparative analysis between the use of conventional staging and the co-design approach in the design phase of a chiller plant. This paper analyzes the energy consumption estimations of six different chiller plant combinations for a Cuban hotel. For the conservative approach using on/off traditional staging, the results suggest that the best option would be the adoption of a chiller plant featuring a symmetrical configuration. However, the outcomes related to the co-design approach suggest that the best option would be an asymmetrical configuration. The energy savings results were equal to 24.8% and the resulting coefficient of performance (COP) was 59.7% greater than that of the symmetrical configuration. This research lays firm foundations for the correct choice and design of a suitable chiller plant configuration for a selected hotel, allowing for significant energy savings in the tourism sector.

CERN Linac4 Chopper Dump: operational experience and future upgrades

The Chopper Dump in the Linac4 accelerator at CERN is a beam-intercepting device responsible for the absorption of the 3 MeV H− ion beam produced by the Linac4 source and deflected upstream by an electromagnetic chopper. It allows a portion of the beam, which would otherwise fall into the unstable region of the radiofrequency buckets in the Proton Synchrotron Booster, to be dumped at low energy with minimal induced radiation. It may also be used to absorb the entire beam. With peak currents of 25 to 45 mA and shallow penetration, this results in large deposited energy densities, thermal gradients and mechanical stresses. Additional constraints arise from geometric integration, vacuum and radiation protection requirements. Material selection, beam-matter interaction studies and thermo-structural analyses are important aspects of the design process. The Chopper Dump underwent modification in 2019 following observed material degradation in the original version of the device. The experience gained, modifications made and observations noted since then are detailed herein. Against this background, the design and analysis of an upgraded device, intended to cope with future operational conditions, is outlined and discussed.

Increasing the Carrying Capacity of the Solid-Body Rail Freight Car

In paper has been carried out to develop a load-bearing floor to increase the carrying capacity of the solid-body rail freight car. An analysis of the structures made it possible to formulate the hypothesis that the load-bearing floor of the solid-body rail freight car should have a different cross-sectional shape. As a result, the load-bearing floor of the solid-body rail freight car was proposed, making it possible to reduce the consumption of materials by 1 to 2%. The maximum equivalent stresses (according to Mises) of the proposed load-bearing floor of the solid-body rail freight car in static analysis are 210.7 MPa, which is less than the yield limit of simple carbon steel. The proposed technical solution allows one to increase the carrying capacity of the solid-body rail freight car by 1.4-1.7 tons. The proposed technical solutions for the future operating conditions of rail freight cars make it possible to reduce the number of units in a train by 1-2 units while maintaining the gross weight of the train.

Train-Induced Dynamic Behavior and Fatigue Analysis of Cable Hangers for a Tied-Arch Bridge Based on Vector Form Intrinsic Finite Element

This paper presents train-induced dynamic response and fatigue damage analyses for the hangers of a tied-arch railway bridge. A train–bridge interaction analysis is carried out using the vector form intrinsic finite element method, through which the inertial force effect of the moving train can be effectively analyzed. The responses of the bridge deck and its hangers are investigated at resonant train speeds. Significantly larger stresses are observed on the shorter hangers near the arch anchorages, which is primarily caused by the first two anti-symmetrical vibration modes. The fatigue damage to the hangers is estimated using the Palmgren-Miner model (PMM) for linear fatigue damage accumulation and the continuum damage mechanics (CDM) method for nonlinear accumulation. The mean stress effect is considered in the S–N curve primarily by Smith–Watson–Topper equation in terms of the effective stress range with a zero-mean stress. Two probability distributions for train speed are considered for the current and future operating conditions with mean speeds of 220 and 300[Formula: see text]km/h, respectively. This study found that the fatigue lives estimated by the nonlinear CDM are significantly shorter than those estimated by the linear PMM. It also found that the shortest hanger reflects the shortest fatigue life at the current operating speed, whereas the longer hanger near the third point of the bridge deck may have the shortest fatigue life at an increased speed in the future.

An Integrated Method for Locating Logistic Centers in a Rural Area

Transport has long been considered a concern for rural residents around the world. Rural express logistics, one of the transport topics, is associated with rural residents’ convenience as well as e-commerce development. As key nodes in the network, logistic centers need to be properly located to improve the delivery efficiency. This paper provides an integrated method for locating logistic centers which is of strong feasibility: on one hand, the proposed method seeks to maximize coverage of express service; on the other hand, considerations are given to operational profitability of the centers, ensuring overall viability for the plan. Specifically, the Holt–Winters model is used for demand prediction. Total predicted demand is then allocated to each town according to population, gross regional product, and some other indicators. A maximal covering model is applied to select logistic centers based on demand under different service radius and numbers of logistic centers. Revenue evaluation is then conducted for the selected logistic centers to evaluate their future operating conditions. The proposed method is applied in rural areas in Lhasa, China. Numerical analysis suggested that 12 of the selected 22 logistic centers could make a profit by 2025. Finally, policy recommendations are given for the development of rural logistic systems.

Battery degradation prediction against uncertain future conditions with recurrent neural network enabled deep learning

Accurate degradation trajectory and future life are the key information of a new generation of intelligent battery and electrochemical energy storage systems. It is very challenging to obtain accurate predictions against uncertain application conditions by using only a few known historical data. In this article, we extend the widely studied remaining useful life (RUL) prediction to the prediction of charge and discharge capacity trajectories under both fixed and random future operating conditions. This is achieved by developing a general deep learning framework cored by recurrent neural network (RNN) which integrates future current plan and few early capacity-voltage data as inputs. As a case study, we have experimented with 77 commercial batteries cycled under fixed and random operating conditions. We demonstrate that the median root mean square error (RMSE) of prediction can be within 2.4% for NMC/graphite batteries and 2.3% for LFP/graphite batteries by using 3.8% of the whole life data only. Compared with the existing methods, the proposed framework predicts more accurately and has a very balanced performance for both fixed and random future conditions. This work highlights the promise of actively forecasting the future of batteries based on RNN.

A two-stage stochastic programming model for selective maintenance optimization

The problem of selective maintenance exists in many multicomponent systems carrying out an alternating sequence of missions, with scheduled breaks where only a limited number of components can be maintained due to time limit. In this paper, a stochastic programming approach is proposed for determining an optimum maintenance plan to minimize maintenance costs and expected failure costs, while maximizing the probability of successful accomplishment of the next mission under uncertainties in future operating conditions. Traditionally, future operating conditions that affect failure time distribution when calculating reliability in selective maintenance models were assumed as deterministic. In this study, future operating conditions are assumed to be uncertain. The system is subject to several uncertain condition scenarios of exposure, conditional, usage, stress, etc. Each scenario is modeled with its associated occurrence probability. The presented model is a two-stage stochastic mixed-integer nonlinear programming model with fixed recourse, where the first stage is associated with maintenance decisions made before uncertainties are revealed, and the second stage is modeled as a recourse function which is related to the occurrence probability of system failure. A numerical example of a series-parallel system is used to demonstrate the effectiveness of the suggested model.

A vehicle-cloud collaboration strategy for remaining driving range estimation based on online traffic route information and future operation condition prediction

Due to the complexity of real driving and operating conditions of Battery Electric Vehicles, it is challenging to accurately estimate the remaining driving range of the vehicle. Relying only on traditional energy consumption prediction based on the historical data shows obvious low-fidelity and hysteresis, especially when the traffic route is unknown. The accuracy of future travel energy consumption prediction fails to be guaranteed once the switching of operating conditions is involved. For this reason, a map named “Driving Route Planning” Application Programming Interface server is built on the cloud, receiving online traffic route information, and the Hidden Markov Model is applied for prediction optimization of future operating conditions. The remaining driving range of Battery Electric Vehicles is finally estimated according to the future energy consumption and the remaining dischargeable energy. The results show that the prediction of operating condition combined with traffic route information and Hidden Markov Model reflects the switching of future operating conditions more accurately and quickly. The relative error of the remaining driving range estimation proposed keeps within 5% under the real operating verification.

Characterization and Evolution of a Digital Economy Ecosystem Based on an Interspecies Competition Model

This paper provides an in‐depth study and analysis of the characterization of the digital economy ecosystem and the mechanism of eye‐flowering through the method of interspecies competition. The evolutionary game model of symbiotic decision‐making in the entrepreneurial ecosystem is constructed, the evolutionary process of symbiotic decision‐making of subjects is analyzed through mathematical derivation, and the symbiotic decision‐making process of subjects is simulated through computer simulation to answer how the subjects of the entrepreneurial ecosystem make symbiotic decisions and explore the mechanism of symbiotic formation of the entrepreneurial ecosystem. Then, based on the ecological perspective, the symbiotic evolution model of entrepreneurial ecosystem subjects is constructed from the subject level, the equilibrium point of the evolution of entrepreneurial ecosystem subjects, the stability conditions, and the relationship between the equilibrium point and the symbiosis model are analyzed, and the symbiotic evolution paths of entrepreneurial ecosystem subjects under different symbiosis modes, initial population size, maximum size, and natural growth rate are presented with simulation experiments, respectively. The main characteristics and manifestations of the dynamic evolution of the platform ecosystem are analyzed, and the key competitive factors that determine the dynamic evolution of the platform ecosystem are depicted. Then, according to the inherent characteristic laws of the platform ecosystem, the complex network approach is applied to construct a dynamic evolution model with originality and wide applicability for the change of bilateral user scale. Based on the dynamic evolution process, the relationship between model parameters and business performance is explored, and the trajectory of bilateral user size change over time and the range of parameters are derived by numerical calculation. Finally, using Monte Carlo simulation methods, the dynamic evolution model is used to predict the future operating conditions of platform enterprises, providing a valuation basis for investors to make investment decisions and helping platform managers to formulate business strategies.

Study on Design Method of Combustion Reference Values for Model-Based Control of Advanced Diesel Engine

Abstract Model-based control systems are drawing attention in relation to implementing advanced combustion technologies with high thermal efficiency and low emissions, such as homogeneous charge compression ignition and premixed charge compression ignition combustion, having low robustness. A model-based control system for engine combustion derives control inputs based on reference values and operating conditions in each cycle. Thus, it can replace the conventional control map, which necessitates a significant number of experiments. However, model-based control for combustion requires reference values, such as heat-release-rate peak timing and value, which represent the combustion state. In addition, because model-based control systems derive control inputs cycle by cycle, the combustion reference for the transient condition is critical for maximizing the benefit of such systems. Therefore, this study describes a design method of the combustion reference values for transient operation. The combustion method targeted in this study is a premixed compression ignition combustion that exhibits two-stage heat releases and can reduce combustion noise. Particularly, a method using predicted future operating conditions considering the driving characteristics is proposed. Designed combustion reference values with the proposed method were evaluated with engine control experiments in a certain part of worldwide harmonized light vehicles test cycle. Results indicate that the rate of achieving the desired two-stage heat releases combustion improved from 57% to 81% and combustion noise reduced. Thus, designing the combustion reference values for transient operation by considering future operating conditions is effective to ensure advanced combustion. In addition, such a method can consider the driving characteristics.

Remaining useful life prediction for complex systems considering varying future operational conditions

AbstractRemaining useful life (RUL) prediction technology is important for optimizing maintenance schedules. With the advancement of sensing technology, several deep learning approaches have been proposed to predict RUL without relying on prior knowledge about systems. However, previous deep learning‐based approaches rarely consider the future operational conditions, which can be known according to the future work plan and is an important influential factor for RUL prediction. This paper proposes a multi‐input neural network based on long short‐term memory for RUL prediction considering the temporal dependencies among the measurements when the future operational conditions are known. The sliding window approach is employed for determining the input time sequences of previous monitoring data (including operational condition and sensor measurements), and the length of input time sequences of the future operational conditions are determined based on the prior estimated RUL. Fine‐tuning strategy is proposed to make the training of the multi‐input network more effective. To illustrate the effectiveness of the proposed methods, a case study referring to the C‐MAPSS dataset is used and a sensitivity analysis is also conducted on the future operational conditions.

Clustering Technique for Scenario Reduction in Post-Energy Transition Voltage Dips Assessment

The Dutch transmission system operator makes multiple scenarios to predict the future developments. These scenarios will help to define the risk factors and constraints in the grid, for which reinforcement planning is necessary. The developed grid after these reinforcements should continue to fulfil the power quality assessment criteria specified in the Dutch grid code. The reduction in system strength due to partial phase out of the conventional generation may have an adverse impact on the PQ, especially the voltage dips. Precise assessment criteria for voltage dips have been stipulated by the Dutch grid code that also need to be met after the energy transition. Evaluating all possible grid future scenarios can provide insight in possible future operating conditions. In practice, due to various combinations of network configurations, loading scenarios and dispatch scenarios, it is not possible to analyze all operating scenarios in detail. This paper presents a method to determine the most important scenarios for voltage dip assessments using a clustering technique. The proposed clustering technique reduces the number of scenarios that are needed to be assessed that makes the whole process doable in practice.

Integrated Process Re-Design with Operation in the Digital Era: Illustration through an Industrial Case Study

This work discusses what should be the desirable path and correct tools for the optimal re-design and operation of processes in the Industry 4.0 framework, as illustrated in a challenging case study corresponding to a complex network of evaporation plants in a viscose-fiber factory. The goal is to integrate optimal design, to improve the existing cooling systems, together with the optimal operation of the whole network, balancing the initial investment with the potentially achievable savings. A rigorous mathematical model for such optimization purpose has been built. The model explicitly considers different structural alternatives as a superstructure for the incorporation of new equipment into the network. The uncertainty associated to future operating conditions is also considered by using a two-stage stochastic formulation. Furthermore, the model is also the base from which a deterministic real-time optimization (RTO) builds upon to support the daily management of the future network operation. The RTO tool suggests the allocation of different products to evaporation plants, the distribution of the cooling water and the suitable number of heat pumps to switch on for optimal economic operation. Design and operation problems are formulated and solved via mixed-integer non-linear programming and the results have been tested with historical plant data.