Slope Constraints Research Articles

Nominal state determination and its effect on remaining useful life prediction

Abstract In machinery operation, a prolonged healthy or nominal state often lacks prognostic significance, causing challenges like data overload, biased predictions, and complex models. Moreover, many prediction methods utilize the complete history of monitoring data from the machine’s startup to its failure; however, prognostics mostly relies on data from the degradation stage. To address this, this study proposes a method to identify and exclude the prolonged period of the nominal state, thereby enhancing the prediction performance of remaining useful life (RUL). A health index (HI) is formulated by integrating acceleration signals from multiple time windows, with deviations computed as the disparity between the HI and its root mean squares. The identification of start and end times for the nominal state, determined by the intersection of consecutive deviation curves, leads to its exclusion from degradation behaviour modelling. The utilization of polynomial degradation trends from HI data after the nominal state’s end time, incorporating a positive slope constraint, aids in mitigating extrapolation uncertainty. The method’s efficiency is demonstrated in three defect cases, highlighting improved RUL predictions without the nominal state’s inclusion.

Low conservative stability criteria for discrete-time Lur’e systems with sector and slope constrained nonlinearities

Lur’e system is an important nonlinear system in engineering, which is composed of a feedback connection of a linear dynamic system and a nonlinear system that satisfies the sector constraint. In this paper, for nominal and uncertain discrete Lur’e systems with time-varying delays, the absolute and robustly absolute stability are studied in detail with the nonlinearity satisfying the sector and slope constraints. Firstly, an augmented Lyapunov–Krasovskii functional (LKF) is designed, in which some augmented vectors are selected to increase the coupling information between the delay intervals and other system state variables. At the same time, some effective integral terms of nonlinear state variables are extended in the LKF according to the characteristics of sector constraint and slope constraint. Secondly, a general lemma of summation inequality for free-weighted matrices is given, which adds some coupling information between vectors and extends the search range for solving linear matrix inequalities (LMIs). Thirdly, by synthesizing the general summation inequality and the newly constructed LKF, different absolute stability criteria are derived respectively, and the absolute stability criteria are extended to robustly absolute stability cases with uncertain parameters. The stability criteria reduce the conservatism of some previously proposed results. Finally, some common numerical examples and simulations are used to verify the results of this paper.

Joint stochastic optimisation of open-pit mine production scheduling with ramp design

ABSTRACT Stochastic optimisation of open-pit mine production scheduling maximises the NPV of a mine’s extraction sequence while satisfying production constraints and minimising deviations from targets. Past approaches consider mining block access based on slope constraints, thus providing no guarantee that the infrastructure required for equipment access is feasible. To address this issue, a joint stochastic optimisation of life-of-mine production scheduling and ramp design is proposed, ensuring feasible equipment access to the related mining blocks. An application at a gold mine demonstrates the value of considering the complex relationship that exists between ramp design, equipment access and production scheduling during optimisation.

Adaptive energy management for fuel cell hybrid power system with power slope constraint and variable horizon speed prediction

An adaptive energy management strategy (EMS) is proposed to improve the economy and reliability of the fuel cell vehicle. Firstly, a variable horizon speed prediction method based on the principal component analysis and the K-means clustering is constructed. Then, an adaptive equivalent consumption minimization strategy (AECMS) with power slope constraints was designed to minimize the hydrogen consumption while ensuring reliability. Finally, a proportional-integral controller is used to track the air flow and pressure of the fuel cell engine (FCE) under energy distribution. Simulation results under West Virginia University Suburban (WVUSUB) show that the proposed strategy can improve the speed prediction accuracy by 2.80% and 25.57%, and reduce the hydrogen consumption by 2.79% and 2.66%, respectively, compared with the fixed 12 s and 15 s horizon. Moreover, the control error of oxygen excess ratio and the cathode pressure under energy distribution are 0.0102 (0.51%) and 189.4 Pa (0.0935%), respectively, indicating better reliability than the strategy without constraint.

A hybrid dynamic economics emissions dispatch model: Distributed renewable power systems based on improved COOT optimization algorithm

This study proposes a hybrid dynamic economics emissions dispatch (HDEED) model for a distributed power system containing thermal generating units, wind farms and photovoltaic plants. The construction of novel distributed power system has been a significant means of tackling the energy crisis. (1) the relationship between each generation unit of the power generation system is analyzed, as well as the power balance constraint, transmission loss constraint, output capacity of each generation unit and slope constraint of the power system; and (2) the operating cost objective function is established with the objective of minimizing unit's generation cost, pollutant emission objective function, and the satisfaction weight coefficient. A novel and improved COOT optimization algorithm is presented to enhance convergence performance and solution speed in solving the problem by introducing a chaotic initialization strategy. A mutation strategy and an improved chain movement of the model solution are verified. The result shows that for the HDEED problem, the ICOOT algorithm reduces the operating cost targets by 1.28%, 6.99% and 7.44% and the pollutant emission targets by 2.98%, 5.46% and 10.88% compared to other algorithms. The developed model provides an effective solution for improving the operational stability, economy and cleanliness of system.

Constraints on OPF Surrogates for Learning Stable Local Volt/Var Controllers

We consider the problem of learning local Volt/Var controllers in distribution grids (DGs). Our approach starts from learning separable surrogates that take both local voltages and reactive powers as arguments and predict the reactive power setpoints that approximate optimal power flow (OPF) solutions. We propose an incremental control algorithm and identify two different sets of slope conditions on the local surrogates such that the network is collectively steered toward desired configurations asymptotically. Our results reveal the trade-offs between each set of conditions, with coupled voltage-power slope constraints allowing an arbitrary shape of surrogate functions but risking limitations on exploiting generation capabilities, and reactive power slope constraints taking full advantage of generation capabilities but constraining the shape of surrogate functions. AC power flow simulations on the IEEE 37-bus feeder illustrate their guaranteed stability properties and respective advantages in two DG scenarios.

RETRACTED: Delineation of potential managed aquifer recharge sites of Kuchlak sub-basin, Balochistan, using remote sensing and GIS

In the Kuchlak Sub-Basin (Pakistan), groundwater is overexploited, resulting in growing stress on groundwater resources. The water table level has declined rapidly due to intensive pumping. Artificial recharge methods and good management strategies are vital for the sustainable production of groundwater resources. Managed aquifer recharge is an artificial way of recharging the subsurface aquifers using surplus surface water, treated wastewater, and stormwater. It is a potential strategy for increasing freshwater supply and adapting to climate change. The present study proposes a method to delineate potential zones for MAR suitability in the Kuchlak Sub-Basin. INOWAS, a web-based tool, is utilized for narrowing down the available MAR techniques based on the hydrogeologic parameter and objectives of the study area. A geographic information system (GIS) coupled with the multi-criteria decision analysis (MCDA), commonly known as GIS-MCDA, is used to develop the MAR suitability map. Six criterion maps, including geology, land use, slope analysis, drainage density, soil, and rainfall, were created in ArcGIS for suitability mapping. The criterion maps are ranked and weighted based on their relative contribution to the groundwater recharge and published literature using the Multi Influence Factor (MIF) method. The final suitability map was developed by overlaying all the criterion maps using a weighted linear combination (WLC) technique. The MAR suitability map was divided into five zones, namely, very high, high, moderate, very low, and low. The unsuitable zones reflect the urban and slope constraints that reduce surface infiltration. The suitability map reveals that 45% of the Kuchlak Sub-Basin exists in a very high-high suitability zone, 33% in moderate, and 17% in a very low-low suitability zone, while 5% of the study area was unsuitable due to the urban and slope constraints. The MAR suitability map developed in this study can serve as a basis for conducting a focused analysis of MAR implementation. Furthermore, the technique and results of this study may aid in mapping MAR suitability in any arid or semi-arid region.

Suitability modelling: an application to forest road development planning in Peninsular Malaysia

The development of modelling applications with Geographic Information System (GIS) has been widely used for planning and analysis. It is a vital technology that can be integrated with multi-criteria evaluation decision support system for problems solving process. This paper presents the model development for suitability forest road planning for the forestry department of Peninsular Malaysia’s application. Multiple significance preferences factors concerned with suitable forest road allocation were selected for the model development, such as slope constraint, elevation factor, the buffer zone of the river, and distance from the existing road. In conclusion, this model is developed to help forest managers sustainably manage the forest, particularly in commencing the forest harvesting operation, which has been widely reported as the main cause of negative environmental impact.

Topology optimization of thermophotonic problem for daytime passive radiative cooling

Passive radiative cooling during the daytime requires efficient control of energy exchange in heat conduction, convection and radiation in the cooling system. In the paper, topology optimization formulations are proposed to design thermophotonic materials for passive radiative cooling. The first formulation solves pure photonic problem to achieve ideal absorption coefficients for radiation cooling. In the second formulation, we solve the coupled thermophotonic problem to maximize heat flux from the thermal load for efficient control of heat conduction, convection and radiation in passive radiative cooling. For better manufacturability, density gradient based boundary slope constraints are proposed to achieve layer-wise design. Sensitivity is derived through the adjoint method. While the objective function in the thermophotonic problem is independent of the density field, a derivation strategy is suggested to reduce the computational cost in sensitivity analysis. Numerical results demonstrate that passive radiative cooling can be achieved through topology optimization of thermophotonic materials.

Generic Relationships between Field Uses and Their Geographical Characteristics in Mountain-Area Dairy Cattle Farms

In mountain farms, challenges posed by the degree of land slope, altitude and harsh climate further compound multiple other possible constraints, particularly in relation to the distance of the farm from the farmstead. This study focused on how mountain-area dairy farmers factor the geographical characteristics of their fields into their field-use decisions. To that end, we surveyed 72 farmers who farm the traditional Salers breed of cattle and 28 specialised dairy system farmers in the central Massif region, France. Information was collected on the uses and geographical characteristics of all grassland fields (n = 2341) throughout the entire outdoor grazing season, without identifying farmers’ rationales for their field-use decisions. Field-use classes were constructed for the traditional Salers system per group of fields (grazed-only, cut-only, grazed-and-cut) and then used to classify fields in the specialized dairy system. The geographical characteristics, which were associated afterwards, were significantly different between the field groups and between field-use classes. Grazed-only fields were found to be more sloping and cut-only fields were smaller and further from the farmstead. Distance/area combinations were different according to field use (animal category, earliness of first cut, grazing and cutting sequence) and were decisive for all field-use classes. This study allowed the identification of generic relationships between field uses and their geographical characteristics in mountain-area dairy cattle farms.

Multisource high-resolution optical remote sensing image registration based on point–line spatial geometric information

High-resolution multisource optical remote sensing images often have considerable non-linear radiation and scale differences that become more prominent as the image resolution increases. Since this problem may cause low registration accuracy, we propose an automatic registration algorithm based on point–line spatial geometric information (PLSGI). First, we propose an improved scale invariant feature transform algorithm using the quadtree uniformization, the Bhattacharyya distance, and the slope constraint to obtain many correct point pairs. Then we generate a new global line descriptor using PLSGI. In particular, the descriptor can resist the non-linear radiation and scale differences of multisource remote sensing images. Finally, we use a piecewise linear model to warp the sensed images based on a set of tie points that consist of corresponding features and corresponding intersections. The effectiveness of the proposed algorithm was validated using multiple groups of high-resolution remote sensing images. The experimental results indicated that our algorithm is highly generalized and robust for high-resolution multisource remotely sensed images.

Power system hybrid dynamic economic emission dispatch with wind energy based on improved sailfish algorithm

This study proposes an improved sailfish optimization algorithm to deal with the problems of high operation cost and large pollution emission in hybrid dynamic economic emission dispatch (HDEED) of the power system. The algorithm also solves the problem of power dispatching difficulty caused by the randomness of wind energy when considering wind energy. The randomness of wind energy is modeled by Weibull distribution and combined with the dynamic economic emission dispatch; hence, the proposed model needs to be established. The traditional sailfish optimization (SFO) algorithm is improved by introducing weight inertia, global search formula, and Levy flight strategy to improve the search performance and solution speed. The improved sailfish optimization (ISFO) algorithm is to deal with the constraint conditions such as valve point effect, power balance constraint, and slope constraint of thermal power unit involved. Two test systems test the proposed ISFO algorithm performance. The ISFO algorithm operation cost is 9% and 6% lower than other optimization algorithms and the pollution emission is reduced by 30% and 4%. The proposed algorithm provides a competitive scheduling scheme on the premise of ensuring the flexibility of power system scheduling and effectively deals with the randomness of wind energy while reducing the operation cost and pollution emissions of the power system. This study has a positive impact on improving power supply reliability and reducing environmental pollution.

Centrifugal Compressor Polytropic Performance—Improved Rapid Calculation Results—Cubic Polynomial Methods

This paper presents a new improved approach to calculation of polytropic performance of centrifugal compressors. This rapid solution technique is based upon a constant efficiency, temperature-entropy polytropic path represented by cubic polynomials. New thermodynamic path slope constraints have been developed that yield highly accurate results while requiring fewer computing resources and reducing computing elapsed time. Applying this thermodynamically sound cubic polynomial model would improve accuracy and shorten compressor performance test duration at a vendor’s shop. A broad range of example case results verify the accuracy and ease of use of the method. The example cases confirm the cubic polynomial methods result in lower calculation uncertainty than other methods.

Salt Marsh Migration Potential at Cape Cod National Seashore (Massachusetts, U.S.A.) in Response to Sea-Level Rise

Smith, S.M., 2020. Salt marsh migration potential at Cape Cod National Seashore (Massachusetts, U.S.A.) in response to sea-level rise. Journal of Coastal Research, 36(4), 771–779. Coconut Creek (Florida), ISSN 0749-0208.Salt marshes can adjust to sea-level rise (SLR) through vertical elevation gain and horizontal expansion into terrestrial environments. The latter depends on the topography of adjacent uplands and the availability of suitable substrate for halophyte colonization. Within Cape Cod National Seashore (CCNS), calculations of marsh migration potential in response to 1-m of SLR were completed using ArcGIS (version 10.4) based on real-time kinematic GPS marsh elevations, local tide data, land-use, and upland topography derived from 2011 LIDAR scans of CCNS. These estimates were combined with marsh-loss predictions from a previous study within their present-day footprint under the same SLR scenario. The results suggest that individual marshes will respond quite disparately, as dictated by their position in the tidal frame, adjacent terrestrial slopes, land-use, and barrier-beach losses. Most sites are expected to gain or lose relatively small amounts of marsh area under few or no migration constraints. However, there could be a large increase (∼144–240 ha) at one site and an almost a complete loss at another (∼260–290 ha). Where migration was constrained to slopes ≤1%, opportunities for marsh expansion diminished the most—even more so when barrier-beach losses are simulated. Overall, losses at five marsh sites were offset by gains at Hatches Harbor and Pleasant Bay, Massachusetts, such that the total area of salt marsh actually increases with 1-m of SLR, except under the strictest slope constraints. Changes in the spatial distribution and total extent of salt marsh within the CCNS will influence both the quality and quantity of ecosystem services they provide. For coastal land managers, understanding the potential for overland marsh migration is critical for the kind of land-use planning that accommodates these transitioning salt marshes.

Enforcing a Force–Displacement Curve of a Nonlinear Structure Using Topology Optimization with Slope Constraints

The objective of this study is to develop an optimization methodology to find a layout that traces a prescribed force–displacement curve through a topology optimization approach. To this end, we propose an objective function to minimize the difference between a prescribed force–displacement curve and the curve calculated at each iteration of the optimization process. Slope constraints are introduced to solve issues encountered when using a small number of target points. In addition, a projection filter is employed to suppress the gray region observed between the solid and void regions, which generally occurs when using a density-based filter. A recently proposed energy interpolation scheme is implemented to stabilize the instability in the nonlinear analysis, which generally results from excessive distortion in the void region when the structure is modeled on a fixed mesh in the topology optimization process. To validate the outlined methodology, several case studies with different types of nonlinearity and structural features of the obtained layouts are investigated.

Ultra-light Dark Matter Is Incompatible with the Milky Way’s Dwarf Satellites

Abstract The density profiles of dwarf galaxies are a highly varied set. If the dark matter is an ultra-light particle such as axions, then simulations predict a distinctive and unique profile. If the axion mass is large enough to fit the ultra-faint dwarf (UFD) satellites ( eV), then the models do not fit the density profile of Fornax and Sculptor and are ruled out by more than confidence. If the axion mass is in the mass range that can fit mass profiles of Fornax and Sculptor dwarf spheroidals, then its extended profile implies enormous masses ( ) for the UFDs. These large masses for the UFDS are ruled out by more than confidence by dynamical friction arguments. The tension would increase further considering star formation histories and stellar masses of the UFDs. Unless future ultra-light dark matter (ULDM) simulations with baryonic feedback show a significant change in the density structure of the halos, the current data is incompatible with the ULDM scenario. Relaxing the slope constraint from classical dwarf galaxies would lead to excluding ULDM with mass less than .

Boundary Slope Control in Topology Optimization for Additive Manufacturing: For Self-Support and Surface Roughness

This paper studies how to control boundary slope of optimized parts in density-based topology optimization for additive manufacturing (AM). Boundary slope of a part affects the amount of support structure required during its fabrication by additive processes. Boundary slope also has a direct relation with the resulting surface roughness from the AM processes, which in turn affects the heat transfer efficiency. By constraining the minimal boundary slope, support structures can be eliminated or reduced for AM, and thus, material and postprocessing costs are reduced; by constraining the maximal boundary slope, high-surface roughness can be attained, and thus, the heat transfer efficiency is increased. In this paper, the boundary slope is controlled through a constraint between the density gradient and the given build direction. This allows us to explicitly control the boundary slope through density gradient in the density-based topology optimization approach. We control the boundary slope through two single global constraints. An adaptive scheme is also proposed to select the thresholds of these two boundary slope constraints. Numerical examples of linear elastic problem, heat conduction problem, and thermoelastic problems demonstrate the effectiveness and efficiency of the proposed formulation in controlling boundary slopes for additive manufacturing. Experimental results from metal 3D printed parts confirm that our boundary slope-based formulation is effective for controlling part self-support during printing and for affecting surface roughness of the printed parts.

Hybridizing ant colony optimization algorithm with nonlinear programming method for effective optimal design of sewer networks

The ant colony optimization algorithm (ACOA) is hybridized with nonlinear programming (NLP) for the optimal design of sewer networks. The resulting problem is a highly constrained mixed integer nonlinear problem (MINLP) presenting a challenge even to the modern heuristic search methods. In the proposed hybrid method, The ACOA is used to determine pipe diameters while the NLP is used to determine the pipe slopes of the network by proposing two different formulations. In the first formulation, named ACOA-NLP1, a penalty method is used to satisfy the problem constraints while in the second one, named ACOA-NLP2, the velocity and flow depth constraints are expressed in terms of the slope constraints which are easily satisfied as box constraint of the NLP solver leading to a considerable reduction of the search space size. In addition, the assumption of minimum cover depth at the network inlets is used to calculate the nodal cover depths and the pump and drop heights at the network nodes, if required, leading to a complete solution. The total cost of the constructed solution is used as the objective function of the ACOA, guiding the ant toward minimum cost solutions. Proposed hybrid methods are used to solve three test examples, and the results are presented and compared with those produced by a conventional application of ACOA. The results indicate the effectiveness and efficiency of the proposed formulations and in particular the ACOA-NLP2 to optimally solve the sewer network design optimization problems. PRACTITIONER POINTS: ACOA is hybridized with NLP for the effective optimal design of sewer networks. Here, ACOA is used to determine pipe diameters and NLP is used to determine the network pipe slopes with predefined pipe diameters. In ACOA-NLP1, a penalty method is used to enforce the problem constraints. In ACOA-NLP2, velocity and flow depth constrains are expressed in terms of slope constraint.

ANT INTELLIGENT APPLIED TO SEWER NETWORK DESIGN OPTIMIZATION PROBLEM: USING FOUR DIFFERENT ALGORITHMS

In this paper, four different kinds of the Ant Colony Optimization Algorithm (ACOA) are used to find optimal solution for sewer network design optimization problem proposing two different formulations for each of them. In both proposed formulations, the decision variables of the problem are cover depths of sewer network nodes. In the second formulation, the constrained version of ACOA is used to find optimal cover depths of the sewer network nodes. The constrained version of ACOA is used here to satisfy slope constraints explicitly leading to reduction of search space of the problem. The Ant System, Elitist Ant System, Elitist-Rank Ant System and Max-Min Ant System are used here to solve two benchmark test examples and the results are presented and compared with other available results. The results show the superiority of the Max-Min Ant System over than other ACOAs in which the trade-off between the two contradictory search characteristic of exploration and exploitation is managed better using this algorithm. Furthermore, best results are obtained with second proposed formulation in comparison with other available results in which they are shown the capability of the proposed formulation to find optimal solution for sewer network design optimization. In other words, the second formulation of Max-Min Ant System has been able to produce results 0.3% and 0.15% cheaper than those obtained by first formulation of Max-Min Ant System for the first and second benchmark test examples, respectively. Furthermore, the average solution cost value of second formulation of Max-Min Ant System is reduced 10.6% (8.1%), 0.43% (0.6%) and 0.34% (0.02%) in comparison with second formulation of Ant System, Elitist Ant System and Elitist-Rank Ant System for first (second) benchmark test examples, respectively.

Coupled analysis method of a mooring system and a floating crane based on flexible multibody dynamics considering contact with the seabed

The mooring system keeps the position of a floating body in the ocean under environmental loads. Mooring analysis should be performed based on the accurate mathematical model in order to predict the behaviors of the floating body and the mooring system. For this purpose, the finite element method (FEM)-based model has been widely used in recent years to model the mooring line numerically. However, existing studies were only applicable to a single floating body, and solved the equations of motion for the floating body and the mooring lines separately. For the application to a floating crane, which consists of several bodies connected to each other, the coupling effect due to the relative motion of each body should be taken into account. Therefore, in this study, the FEM-based mooring line model applicable to a multibody system is introduced based on flexible multibody dynamics. Then, to realize the interaction between the mooring line and the seabed, the non-interpenetration and slope constraints are suggested, and the friction model including sticking are adopted. For the verification of the suggested model, comparisons with the analytic solution and the commercial software are performed. Finally, the applications for a floating crane under various environmental loads are simulated. The results show that the suggested model can be properly adapted for mooring analysis.