Capacity Uncertainty Research Articles

Drivers of photovoltaic uncertainty

Energy systems scenarios project a wide range of uncertainty in solar photovoltaic capacity, often thought to stem from techno-economic assumptions. Now research shows that the underlying sources of this uncertainty might be different than expected.

Development of fragility functions for rigid-frame bridges subjected to tsunami-induced hydrodynamic forces

The rigid-frame bridges with a fully continuous moment connection between the superstructure and substructure piers are suitable for high-seismic regions; however, research on the response of these bridge types under tsunami loads is limited. This paper presents a framework for developing fragility functions of coastal rigid-frame bridges, and bridges with strong pier-to-deck connectivity, undergoing tsunami-induced hydrodynamic loads. The fragility analysis is demonstrated on a three-span reinforced concrete (RC) bridge structure with double-column bents. The Monte Carlo simulation is implemented to consider the uncertainties in capacity and demand parameters. The efficiency of single-parameter and two-parameter intensity measures for predicting the response of bridges is examined. The viability of strengthening bridge piers using fiber-reinforced polymer (FRP) is also investigated. The results indicate that the use of two-parameter intensity measures is a better option to estimate the structural response since the hydrodynamic forces are a function of both the flow depth and velocity. This also leads to a significant reduction in the scatter of fragility data. The comparative assessment of the initial and FRP-strengthened bridges highlights that the FRP-jacketing is an effective method in reducing the risk of failure of the rigid-frame bridges.

A robust possibilistic programming model for production-routing problem in a three-echelon supply chain

PurposeThis paper aims to provide an integrated production-routing model in a three-echelon supply chain containing a two-layer transportation system to minimize the total costs of production, transportation, inventory holding and expired drugs treatment. In the proposed problem, some specifications such as multisite manufacturing, simultaneous pickup and delivery and uncertainty in parameters are considered.Design/methodology/approachAt first, a mathematical model has been proposed for the problem. Then, one possibilistic model and one robust possibilistic model equivalent to the initial model are provided regarding the uncertain nature of the model parameters and the inaccessibility of their probability function. Finally, the performance of the proposed model is evaluated using the real data collected from a pharmaceutical production center in Iran. The results reveal the proper performance of the proposed models.FindingsThe results obtained from applying the proposed model to a real-life production center indicated that the number of expired drugs has decreased because of using this model, also the costs of the system were reduced owing to integrating simultaneous drug pickup and delivery operations. Moreover, regarding the results of simulations, the robust possibilistic model had the best performance among the proposed models.Originality/valueThis research considers a two-layer vehicle routing in a production-routing problem with inventory planning. Moreover, multisite manufacturing, simultaneous pickup of the expired drugs and delivery of the drugs to the distribution centers are considered. Providing a robust possibilistic model for tackling the uncertainty in demand, costs, production capacity and drug expiration costs is considered as another remarkable feature of the proposed model.

Express package routing problem with occasional couriers

This study investigates the viability of a “courier friendly” crowd-shipping (CS) model to carry out express package deliveries in an urban area. Imposing almost no managerial control over the occasional couriers (OC) to maximize their willingness to participate, the envisioned model suggests using transshipment points to enhance operational efficiency and a company-controlled backup delivery capacity to account for the uncertainty in the crowd-provided delivery capacity. A dynamic programming (DP) approach is developed to address the package routing problem that needs to be solved for the real-time management of the CS network. The suggested methodology does not assume any specific distributions for the courier and demand arrivals and provides the optimal package routing policy when the courier and service point capacities are not binding. Several extensions of the basic DP approach are studied to consider limited courier and service point capacities and take advantage of the extra information provided by those couriers that declare their arrivals in advance. To the best of the author’s knowledge, this study presents the first example of using Monte-Carlo simulations to determine “shadow costs” of capacity utilizations and use them in making the assignment (matching) decisions. An extensive computational study demonstrates the efficacy of the solution approach. It provides valuable insights into, among others, the potential benefits of delivering packages with a coordinated effort of OCs (with transshipments) under short delivery lead time restrictions, the impact of the spatial and temporal distribution of the demand and courier arrivals on the system performance, and the importance of the notice times for OC arrivals.

Solving unbounded knapsack problem using evolutionary algorithms with bound constrained strategy

ABSTRACT Unbound Knapsack Problems (UKP) are important research topics in many fields like portfolio and asset selection, selection of minimum raw materials to reduce the waste, and generating keys for cryptosystems. Given the uncertainty in data, capacity, and time constraints, users have to look at the possible combination of data to get maximum benefit. This paper uses UKP as a numerical model to represent different industrial combination problems. It applies Evolutionary Algorithms (EA) with Bound Constrained Strategy (BCS) to construct a search space and algorithm parameters for finding the optimal solution. Evolutionary Algorithms (EA) like Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) are designed based on reusable components for the algorithms to converge faster. Simulation for various objectives indicates that the GA and PSO can find the near-optimal solution in all cases. The execution time of GA and PSO for different goals and the variations in the algorithm parameters are measured. The measurement result shows the performance of GA and PSO is the same on an average for the differences in bounded constraints and parameter settings.

Fragility and sensitivity analysis of steel frames with bolted-angle connections under progressive collapse

Robustness is a key factor in the safety of structures and structural components or sub-assemblies against progressive or disproportionate collapse, the latter term being classically used to indicate a significant disproportion in size between the initial and final damage configurations. Particularly for a steel moment-resisting frame (MRF), the connections play a pivotal role in the robustness and redundancy of the structural system because an alternative load path could be mobilised when/if the beam-column joints have enough capacity to sustain the abnormal extra-loads developed following the loss or notional removal of a support element as consequence of a triggering event.Although the latter is usually but not exclusively related to terrorist attacks (e.g. faulty construction, uncontrolled gas releases, etc.) and the structural behaviour inherently involves uncertainties in loads and system capacity, progressive collapse has been probabilistically assessed in a few studies, mainly focussed on reinforced concrete structures. In light of this, delving more into probabilistic approaches could be timely, and this paper presents the main results of sensitivity and fragility analysis of steel MRFs featuring top-and-seat with web angle connections, leading to the derivation of both tornado diagrams as well as fragility models. The former ones quantify the progressive collapse sensitivity to design parameters, whilst the latter ones provide the probability of failure as a function of a given intensity measure. Uncertainties in material and geometrical properties of building portfolios were modelled and propagated through Monte Carlo simulation, whereas component-based modelling was integrated with the pushdown methodology for damage analysis of the randomly sampled building realisations. The obtained results show the influence of the studied parameters/random variables on progressive collapse behaviour/fragility and provide insights into the effect of changing the seismic intensity for the design and the column-removal scenario for the assessment of a bolted-angle joint system that has not been probabilistically treated up until now.

Experimental study on isobaric specific heat capacity of subcritical and supercritical fluids using flow calorimetry

Accurate measurement of isobaric specific heat capacity of supercritical fluids is essential for many scientific researches and engineering applications. Based on the flow calorimetry method, an online experiment system for measuring isobaric specific heat capacity for subcritical and supercritical fluids was established. The relative expanded uncertainty of isobaric specific heat capacity was estimated to 2.44%–2.70% (coverage factor k ​= ​2). Based on the verification of the reliability of the measurement system by using pure water, cyclohexane and pentane, the isobaric specific heat capacity of carbon dioxide (CO2) in the temperatures range of (25.0–60.0) °C and pressures range of (6.7–12.0) MPa was measured. The experiment platform can provide accurate data measurement support for further scientific investigations and engineering designs.

Robust Water Supply Chain Network Design under Uncertainty in Capacity

This paper focuses on the capacity uncertainty in water supply chains that occurs when facilities face disruption. A combination of scenario-based two-stage stochastic programming with the min-max robust optimization approach is proposed to optimize the water supply chain network design problem. In the first stage, the decisions are made on locations and capacities of reservoirs and water-treatment plants while recourse decisions including amount of water extraction, amount of water refinement, and consequently amount of water held in reservoirs are made at the second stage. The proposed robust two-stage stochastic programming model can help decision makers consider the impacts of uncertainties and analyze trade-offs between system cost and stability. The literature reveals that most exact methods are not able to tackle the computational complexity of mixed integer non-linear two-stage stochastic problems at large scale. Another contribution of this study is to propose two metaheuristics - a particle swarm optimization (PSO) and a bat algorithm (BA) - to solve the proposed model in large-scale networks efficiently in a reasonable time. The developed model is applied to several hypothetical cases of water resources management systems to evaluate the effectiveness of the model formulation and solution algorithms. Sensitivity analyses are also carried out to analyze the behavior of the model and the robustness approach under parameters variations.

Isobaric heat capacity of the binary liquid (water + 1,2–propanediol) mixtures at high-temperatures and high-pressures

Isobaric heat capacity, CP, of binary aqueous 1,2 − propanediol solutions have been measured over the temperature range from room temperature 298.15 K to 473.15 K at pressures up to 30 MPa using differential scanning calorimeter (DSC, ITS-400). The measurements were performed for three concentrations of 0.0731, 0.1914, and 0.4153 mol fraction of 1,2 − propanediol. The expanded uncertainty of the heat capacity (CP), pressure (P), temperature (T), and concentration (x) measurements at the 95% confidence level with a coverage factor of k = 2 is estimated to be 2.4%, 0.05%, 15 mK, and 0.0001, respectively. The measuredCPPTx data have been used to calculate excess molar heat capacities (CPmE) of the mixture. The second temperature derivatives of specific volume or curvature of the Vm-T isobars have been calculated using the pressure dependence measured heat capacities along the isotherms as ∂2Vm/∂T2Px=-T-1∂CPm/∂PTx. Some unusual behaviour of CP in the low concentration range (below 0.2 mol fraction, dilute mixture) along the each measured isotherms-isobars was found.

Electric vehicles participation in load frequency control based on mixed H2/H∞

Besides being new loads that would call for further capacity from fossil-fuel based Conventional Sources (CS), managing Electric Vehicles (EVs) smartly i.e. using Vehicle-to-Grid (V2G), holds the opportunity to be used for Load Frequency Control (LFC). However, there exists uncertainty in available dispatch capacity and time delay linked to charging/discharging control of individual EVs. Assessing this uncertain capacity and related delay of its response for LFC is presented first in this paper. Next, for the EVs participating in multi-objective LFC (V2G) along with CSs, a robust mixed H2/H∞ controller based on static output feedback is adopted. The system stability, i.e. bounding frequency and tie-line power to their nominal values in the presence of internal and external disturbance, is ensured by providing better reference tracking to both EVs and CSs. Case studies of a single and three-area system with EVs are tested for LFC, where the efficacy of the mixed H2/H∞ is also endorsed by the comparison with only H∞ controller. The results showed that the EVs and the adopted controller can respond effectively to the scenarios i.e. load variation, parameters uncertainties, effects of dead bands and time delays of both controller and EVs aggregator responses. It caused a decrease in the capacity burden on CS while this study open ways for the future grid to be pollution-free.

Predicting capacity model and seismic fragility estimation for RC bridge based on artificial neural network

The uncertainty of a structural capacity plays an important role in regional fragility, risk, and resilient estimation. This paper proposes an artificial neural network (ANN)-based predicting capacity model to consider the uncertainty of the seismic performance of the reinforced concrete (RC) bridge columns. The seismic fragility of three typical RC bridges is studied. A database that includes 78 testing results is used to train, validate, and test the ANN model. The capacity measures of RC bridges are predicted using the constructed ANN model. Case studies are conducted for three typical simply supported T-beam bridges, and nonlinear time history analysis is performed to obtain the structural responses of the RC bridges. Seismic fragility models are established and a comparison is performed to study the discrepancies of damage probability for RC bridges with and without considering the uncertainty of the capacity model. The main results of the paper are as follows: (1) the ANN-based predicting capacity model can provide an acceptable capacity measure for seismic fragility estimation and (2) the damage probability of RC bridges is related to the uncertainty of seismic performance that needs to be considered for structural fragility estimation.

Fuzzy Approaches and Simulation-Based Reliability Modeling to Solve a Road–Rail Intermodal Routing Problem with Soft Delivery Time Windows When Demand and Capacity are Uncertain

In this study, a freight routing problem considering both soft delivery time windows and demand and capacity uncertainty in a road–rail intermodal transportation system is investigated. According to fuzzy set theory, uncertain demands and capacities are formulated as trapezoidal fuzzy numbers. Soft delivery time windows under a fuzzy environment is established, in which fuzzy periods caused by early and late deliveries that lead to penalty are modeled based on maximum functions. To solve the routing problem yielding the above characteristics, this study designs a fuzzy mixed-integer nonlinear programming model whose objective is to minimize the total costs created in the road–rail intermodal transportation activities. After using the fuzzy expected value method to address the fuzzy objective, two fuzzy approaches, i.e., fuzzy chance-constrained programming method and fuzzy ranking method, are separately adopted to undertake the defuzzification of the fuzzy constraints. Improved linear formulations of the model are then produced to make it easier to solve. A simulation-based reliability modeling is developed to quantify the reliability of the optimization results given by different fuzzy approaches under different parameter settings in a simulation environment. Finally, an empirical case is presented to verify the feasibility of the proposed methods. The effects of demand and capacity fuzziness on the routing optimization are revealed, and an optimization procedure that helps decision-makers to select a more suitable fuzzy approach and determine the best parameter setting for a given case is demonstrated. Some insights that are helpful for organizing a reliable transportation are also drawn.

A Simultaneous Optimization Model for Airport Network Slot Allocation under Uncertain Capacity

Serious congestion and delay problems exist in most of the busiest airports worldwide because of imbalance between scarce airport slot resources and increasing traffic demand. Various factors, especially weather conditions, exacerbate the demand–capacity imbalance. This paper presents a robust model for simultaneous slot allocation on an airport network in multiple calendar days, considering airport capacity uncertainty. The idea of robust optimization is conducive to sustainable and stable decision-making. Robustness is represented through reducing the potential scheduling conflicts in the worst case. Then the model links the strategic decisions and pre-tactical decisions in air traffic management (ATM) through the tradeoff between strategic discrepancy cost and operational congestion cost. Under the support of the Cplex solver, numerical analyses are taken to validate the characteristics and effectiveness of the proposed model. The results show that the proposed model effectively eliminates the existing and potential scheduling conflicts, and makes effective tradeoffs between airline preference and potential airport congestion risk.

The role of prior assumptions in carbon budget calculations

Abstract. Cumulative emissions budgets and net-zero emission target dates are often used to frame climate negotiations (Frame et al., 2014; Millar et al., 2016; Van Vuuren et al., 2016; Rogelj et al., 2015b; Matthews et al., 2012). However, their utility for near-term policy decisions is confounded by uncertainties in future negative emissions capacity (Fuss et al., 2014; Smith et al., 2016; Larkin et al., 2018; Anderson and Peters, 2016), in the role of non-CO2 forcers (MacDougall et al., 2015) and in the long-term Earth system response to forcing (Rugenstein et al., 2019; Knutti et al., 2017; Armour, 2017). Such uncertainties may impact the utility of an absolute carbon budget if peak temperatures occur significantly after net-zero emissions are achieved, the likelihood of which is shown here to be conditional on prior assumptions about the long-term dynamics of the Earth system. In the context of these uncertainties, we show that the necessity and scope for negative emissions deployment later in the century can be conditioned on near-term emissions, providing support for a scenario framework which focuses on emissions reductions rather than absolute budgets (Rogelj et al., 2019b).

Partner Innovation Collaboration and Management Support Toward Innovation Speed and New Product Performance in Small Manufacturing Firms

The purpose of this study was to examine and analyze the role of partner innovation collaboration and management support on innovation speed, and its implications towards new product performance in small manufacturing firms. This study took samples in the category of small manufacturing firms in the food & beverage sector, shoes & bags sector, craft sector, and diverse sector. Then, this study uses primary data using a questionnaire distributed to 95 owners or managers of small manufacturing firms in the Bandung and surrounding areas. Data analysis uses structural equation modeling based on partial least square with the help of SmartPLS 3.0. The results showed that partner innovation collaboration had a positive and significant effect on innovation speed, then management support had a positive and significant effect on innovation speed, then innovation speed had a positive and significant effect on new product performance, most recently the role of partner innovation collaboration also had a positive effect and significant to new product performance. The R-Square calculation results show that partner innovation collaboration and management support influence 0.432 on innovation speed, then partner innovation collaboration and innovation speed have an effect of 0.637 on new product performance. This study implied that small manufacturing firms to utilize an innovation collaboration with partners and allocate more time, effort, thought, costs in the effort to innovation, and be able to create an atmosphere of work together with employees that support innovation activities. Further research is to include moderation variables that support the improvement of new product performance such as absorptive capacity and technological uncertainty. Keywords: management support, partner innovation collaboration, innovation speed, new product performance

The seismic reliability analysis of moment resisting frames with bolted end-plate connection

This paper presents an algorithm to determine the reliability index of moment-resisting steel frames with bolted end-plate connections, by considering the effect of the uncertainties of capacity and seismic demand. To reflect the probabilistic response of structures, the horizontal and vertical distance of the bolt from edge of end-plate, the pretension force and the ultimate strength of the bolts, yield strength and the elastic modulus of plates for beam and column, which are the main parameters of construction errors in bolted end-plate connection, considered as a random variables for the moment-rotation behavior of connections. All the components of the connections have been regarded as random parameters. Then, the probabilistic moment-rotation curve of connections was determined by means of the Latin Hypercube sampling method. Afterward, by conducting a nonlinear dynamic analysis in the 2-dimensional 5, 10, and 15 stories frames and considering the idealized random behavior of the connections as well as the random effect of different records of the earthquake, the results obtained with the aid of Monte Carlo sampling technique. Finally, the appropriate probability density function (PDF) fitted to the results, which this figure for reliability index and probability of failure for the limit state function is 1.62 and 0.052 respectively and also the mean value state is 1.85 and 0.033. This procedure has been named as the Simplified Response Surface Method (SRSM). Results show the structural reliability index is acceptable at the performance level of Life Safety, yet it exceeded this level only by a small amount.

Anchorage capacity reliability and redundancy optimization research in coastal ports

ABSTRACT Influenced by various random factors, such as a ship's size, arrival time and anchoring spot, the number of ships that can be practically accommodated by the anchorage of a coastal port is generally far lower than the designed anchorage capacity. To address the shortage problem, this article applies a redundancy optimization theory to realize a reasonable and practical design of the anchorage capacity. A new concept of anchorage capacity reliability and its calculation method, via an anchorage capacity probability evaluation model, are proposed. A multi-objective optimization model with maximum anchorage capacity reliability and minimum anchorage redundant area is established. Furthermore, a case study of a container terminal in northern China is adopted to verify the performance of the proposed concept and methodology. Model results show that anchorage capacity reliability can effectively reflect the anchorage capacity uncertainty, and the redundancy optimization model can help to reasonably determine the anchorage capacity.

Isobaric heat capacity of 1,2-propanediol at high temperatures and high pressures

Abstract Present paper reports new high-temperature and high-pressure isobaric heat capacities (CPPT relationship) of 1,2-propanediol. Measurements were made over the temperature range from (298 to 473) K and at pressures up to 147 MPa using two different methods, differential scanning and isothermal heat-conduction calorimeters. The expanded uncertainty of the heat capacity, pressure, and temperature measurements at the 95% confidence level with a coverage factor of k = 2 is estimated to be 2.4%, 0.05%, and 15 mK, respectively. The measured CPPT data were used to calculate the second temperature derivatives of specific volume or curvature of the V-T isobars as (∂2V/∂T2)P = − T−1(∂CP/∂P)T. The measured data were also used to check accuracy and reliability existing Tait-type ρ(P, T) equations of state for liquid 1,2-propanediol at high temperatures and high pressures.

Measurement and analysis of specific heat capacity of lead-bismuth eutectic

The lead-cooled fast reactor is a fourth-generation nuclear system with great development potential. Lead-bismuth eutectic (LBE) is used as the coolant of lead-cooled fast reactor, and specific heat capacity of LBE is a necessary parameter for studying the heat transfer in the primary loop. However, the experimental data on specific heat capacity of LBE are limited. There are only three independent data could be found in open literature, and the measurement error of the existing data is about 5%~7%. In this paper, specific heat capacity of LBE at 30–600 °C is studied by differential scanning calorimetry and the reliability and uncertainty of the data are analyzed. The recommended value of LBE's specific heat capacity at 150–600 °C is 0.1342–0.1495 J kg−1 °C−1. The results show that the measurement error of this method is about 0.62%, the maximum composite uncertainty of LBE's specific heat capacity is 1.83%, and the expanded uncertainty is 3.66% (k = 2). The experimental data have high reliability and can provide data support for thermal-hydraulic design in lead-cooled fast reactor.

Analysis of ultra-low uncertainty gas turbine flow capacity measurement techniques

Accurate assessment of nozzle guide vane (NGV) capacity is essential for understanding engine performance data, and to achieve accurate turbine stage matching. In accelerated engine development programmes in particular, accurate and early assessment of NGV capacity is a significant advantage. Whilst the capabilities of computational methods have improved rapidly in recent years, the accuracy of absolute capacity prediction capability is lower than experimental techniques by some margin. Thus, experimental measurement of NGV capacity is still regarded as an essential part of many engine programmes. The semi-transient capacity measurement technique, developed and refined at the University of Oxford over the last 10 years, allows rapid and accurate measurement of engine component (typically fully cooled NGVs) capacity at engine-representative conditions of Mach and Reynolds numbers and coolant-to-mainstream pressure ratio. The technique has been demonstrated to offer considerable advantages over traditional (industrial steady-state) techniques in terms of accuracy, time and operating cost. Since the original facility was constructed, the facility has been modularised to allow for rapid interchange of test vane modules, and the instrumentation has been optimised to drive down the uncertainty in NGV capacity. In this paper, these improvements are described in detail, and a detailed uncertainty analysis is presented of the original facility, the current facility, and a proposed future facility in which the uncertainty of the measurement has been driven down to a practical limit. The bias errors of the three facilities are determined to be ±0.535%, ± 0.495% and ±0.301%, respectively (to 95% confidence). The corresponding precision uncertainties are ±0.028%, ±0.025% and ±0.025%, respectively. The extremely low precision uncertainty in particular allows very small changes in capacity to be resolved. This, combined with rapid interchangeability of test modules, allows studies of the sensitivity of capacity to secondary influences with much greater flexibility than was previously possible. Consideration is also given to the definition of vane capacity in systems with several streams at different conditions of inlet total pressure and temperature. A typical high pressure (HP) NGV has three distinct streams: a mainstream flow; coolant flow ejected from film cooling holes (distributed over the vane surface); and trailing edge coolant ejection. Whilst it is helpful for the coolant mass flow rates and inlet temperatures to be included in the definition, only a relatively small difference arises from the way in which this is achieved. Several definitions appear to share similar usefulness in terms of their robustness to changing inlet conditions of individual streams, but the favoured definition offers the possibility of isolating sensitivities to key effects such as trailing edge coolant ejection. This is achieved by explicitly expressing vane capacity as a function of two controlling pressure ratios. The overall purpose of this paper is to review and analyse in detail the current state-of-the-art in gas turbine flow capacity measurement.