Cost Goals Research Articles

The Promotion Strategy Analysis for Green Degree of Railway Engineering Based on a System Dynamic Flow Diagram.

This article made a system dynamics flow diagram (SD flow diagram) to describe the green railway engineering (GRE) system, which provides a theoretical basis for discussing the source and change process of the green degree of railway engineering(GDR) and also provides a practical basis for accurate policy implementation and evaluation promotion of GRE management. Based on the definition of GDR and using “input-output” relationship to analyze system structure of GRE, set two green goals of environmental and resource cost decreases as the clue, deconstructed practice process based on the principle of construction to form GRE system dynamic flow diagram, which aims to reveal the key influencing factors and promotion path of GDR. The results of the research show that (1) the green schemes set the foundation of GDR, including 3 schemes of green planning, green design, green construction, and determine the expected control values (VE) of 4 status, namely ecological damage degree, environmental pollution degree, land occupation degree, and resources consume degree. (2) The deviation of expected control values (VE) and actual control values (VA) from 4 status is the premise of whether the GDR needs to be optimized or improved, and 2 practice achievements of green knowledge innovation and green culture creation provided different promotion paths for GDR. (3) According to the SD flow diagram constructed by research, the 3 schemes are influenced by regional ecological carrying capacity, social material resource reserve, green knowledge reserve, green talent reserve , reasonable goals setting, strengthening preliminary research, making full use of resources, deepening the connection of procedures, and so on are conducive to build a foundation for GDR. (4) The 4 status are directly controlled by seven rate variables, which promote the dynamic optimization of GDR by technology, equipment, institution management, and behavior management. The SD flow diagram of GRE provides 2 contributions. The first provides an analytical basis for the study of the promotion strategy of GDR, and the second provides a model basis for further quantitative study of GDR.

Improving Power Density of a Three-Level ANPC Structure Using the Electro-Thermal Model of Inverter and a Modified SPWM Technique

Multilevel inverter structures have become an interesting substitute for the well-known two-level inverters in a variety of applications due to their exceptional characteristics when compared to conventional inverters. One major issue regarding multilevel structures is the unequal junction temperature of the switches which deteriorates the power density and increase the cost of the inverter by reducing the maximum achievable output power with a given thermal model and cooling system. Hence, on account of the aggressive goals of power density and cost in a majority of power electronic applications, this article proposes a new technique for reducing the maximum junction temperature of switches in a three-level active neutral-point clamped (ANPC) inverter based on a junction temperature estimation method and a modified SPWM control scheme. This technique can ensure up to a 12% rise in the power density value when compared to basic SPWM techniques with no loss distribution algorithm. Moreover, the suggested approach can be used as a protection stage in the inverter which protects the inverter in transient loads, while allowing for reaching the maximum power capability of the inverter. Finally, the simulations of the proposed technique are validated with experimental results of a 400 V, 20 kW three-level ANPC inverter, controlled by the conventional and proposed techniques.

Human Resource Scheduling in Project Management Using the Simulated Annealing Algorithm with the Human Factors Engineering Approach

Manpower scheduling means assigning a work pattern (shift day) according to the wants and needs of the system and the workforce with the goal of minimum cost. Many production and service systems require multiple scheduling. This problem is generally NP‐complete, and it takes a long time to solve it through the current method. Today, several innovative methods have been proposed to solve such problems. In this paper, the simulated annealing method (SA) has been used. The problem in this study is in an oil extraction project for human resource scheduling, which consists of three human resource groups in four task types in oil exploration operations, including geology, geophysics, petrophysics, and oil engineering. To solve the human resource scheduling problem, a meta‐heuristic algorithm called simulated annealing algorithm was applied, and the result indicated that the allocated human resource was scheduled with the least fatigue implementing the proper job rotation.

Accelerator-level parallelism

Charging computer scientists to develop the science needed to best achieve the performance and cost goals of accelerator-level parallelism hardware and software.

Performance Improvement of Gallium‐Doped Passivated Emitter and Rear Cells by Two‐Step Bias Application

Passivated emitter and rear cell (PERC) solar cells have dominated the photovoltaic market in recent years. Continuously improving the efficiency of PERC solar cells is of great importance to enable the goal of low electricity cost, which is cheaper than the cost of thermal power generation. Herein, it is demonstrated that a two‐step postfiring bias treatment is able to evidently enhance the efficiency of commercial gallium‐doped PERC solar cells by up to 0.1% absolute. In detail, the first‐step bias treatment is done by forward biasing the PERC solar cells at 12 A and 200 °C for 60 min, resulting in an average efficiency enhancement at around 0.05% absolute. The second‐step bias treatment is done by reverse biasing the PERC solar cells at −0.1 or −0.2 V and at the elevated temperatures for certain times, leading to another average efficiency enhancement at around 0.05% absolute. To explore the mechanism underlying the two‐step bias treatments on improving cell efficiency, a new model in light of hydrogen behavior under electric field is proposed to explain this phenomenon.

PFAS treatment with granular activated carbon and ion exchange resin: Comparing chain length, empty bed contact time, and cost

Per- and polyfluoroalkyl substances (PFASs) are among the most recalcitrant chemicals ever manufactured and are associated with substantial global environmental challenges. PFAS removal performance of granular activated carbon (GAC) and ion exchange resin (IX) was evaluated and compared using a segmented column system with full scale media under continuous flow conditions. Results from the PFAS breakthrough analysis established via column operation under different empty bed contact times (EBCTs) was used to characterize the mass transfer zone (MTZ) of multiple PFASs and evaluate optimum EBCTs for GAC and IX treatment. The height of the MTZ corresponded to PFAS chain length and functional group, where the poorly removed short chain perfluoro-carboxylic acids exhibited the longest MTZ. Elongated PFAS MTZs justified longer EBCTs required for treatment of short chain PFAAs compared to long chain PFAAs for GAC and IX. In addition, a cost sensitivity analysis was employed to demonstrate how unit media cost, changeout frequency, and treatment goal feasibility dictated annual operation and maintenance (O&M) costs associated with GAC and IX installations. IX was found to the most cost-effective adsorbent for the removal of long and short chain PFASs compared to GAC. Results from the cost analysis indicated that adsorbent selection was the most critical factor dictating treatment cost with marginal impact from unit media cost or expected changeout frequency.

An Effective Project Management Simulation for Distance Education

The challenge in distance education for project management courses is how to maintain quality when team projects, traditional lectures and in-class exercises normally conducted in face-to-face courses are not practical. This research explores the desirability and effectiveness of computer-based project management simulation as a teaching tool for industrial technology students in a distance education (DE) degree program. The purpose of using the simulation was to enhance learning outcomes related to project management processes and techniques taught in the course. A survey of approximately 100 students over four semesters was used to determine how online students responded to the use of simulation to teach specific project management concepts. Additionally, the research sought to determine which project management concepts were most enhanced through this active learning activity. The results established that the SimProject® computer-based simulation improved project management education for distance students and added value to the course by reinforcing text-based content. The data established a positive relationship between the simulation and specific project management knowledge areas for scope, time, cost, risk, human resource, communication, stakeholder, and quality management. The simulation provided students with the challenge to execute a virtual project, allowing up to three separate attempts, each with the same defined goals for time, cost, and quality. Dynamic, simulation- induced, life-like obstacles based on student choices presented unique management decisions for each trial. A successful outcome required students to meet time, cost, and quality goals within 10 percent of target. Student motivation for this pedagogy was apparent as most used all three attempts and provided positive survey feedback.

Data‐Led Policy Design Using Medicare Shared Savings Program (MSSP) Health Care Cost Trajectories

Research ObjectiveThe Medicare Shared Savings Program (MSSP) encourages participating Accountable Care Organizations (ACOs) to deliver quality care at lower cost through shared savings models. While the MSSP model offers ACOs and the Centers for Medicare and Medicaid Services (CMS) an opportunity to reduce spending growth while promoting quality goals, ACOs need explicit guidance about those activities over which they have control (e.g. program offerings, care pathways) to improve upon cost and quality goals. Variation amongst geographically‐adjusted and risk‐adjusted total per beneficiary spending amongst ACOs suggests room for ACOs to consider further cost saving efforts. Existing studies do not clearly point to how to achieve those ends. This study presents a methodology for understanding MSSP cost trajectories and identifying those factors that may impact them.Study DesignThis study seeks to characterize and identify trajectories of health care spending for individual patients with type 2 diabetes who experience hospitalization for a major acute cardiovascular event (MACE) (e.g. myocardial infarction, stroke). This analysis uses a retrospective cohort design with the cohort created by an index event (MACE). We examine spending variability for at least six months following the acute event to estimate variability pre and post the event and to cluster patients into post‐event cost trajectories. We use a nonparametric random forest model to identify characteristics from prior to the index event that predict changes in cost trajectories post‐event. We test finding robustness using a RE‐EM model that allows for autocorrelation and combines the structure of mixed effects models for longitudinal data with the flexibility of tree‐based estimation methods. These models allow for testing a broad set of characteristics. These include patient characteristics (e.g. gender, age, percent in zipcode below federal poverty line), comorbid conditions (e.g. dementia, Charlson co‐morbidity index), service use (e.g. prescription count, eye exam, skilled nursing facility use), preventive activities (e.g. flu shot, mammogram) and clinical indicators (e.g. blood pressure, BMI).Population StudiedThis study uses claims and electronic medical record (EMR) data from 2015–2017 for an MSSP population from the largest ACO in South Carolina. In 2017, there were 58,472 attributed beneficiaries. 2015–2017 was one contract period for the ACO and an upside risk‐only arrangement (Track 1).Principal Findings4064 individuals met exclusionary criteria prior to the event (e.g. continuously enrolled from 2015–2017, no prior MACE, index event is non‐fatal). Fisher's F‐test finds strong evidence of variation (P value <2.2e‐16 [F = 19.516]) in costs for the sample before and after the index event. Further work to be presented will cluster patients by post‐event cost trajectory and examine the impact of characteristics (e.g eye exam, falls risk assessment) prior to the index event on changes in post‐event health care spending.ConclusionsVariability exists in our sample pre‐ and post‐index event suggesting reason to investigate drivers of differences in patient characteristics prior to their MACE across cost trajectory clusters.Implications for Policy or PracticeThis work contributes a methodology for examining patient cost trajectories that allows health systems and policy makers to point to specific services and programs for interventions that suggests ways for that cost trajectory to be intervened upon.Primary Funding SourceUniversity of South Carolina internal funding.

Scheduling Optimization of Prefabricated Buildings under Resource Constraints

Different from traditional construction project management, the prefabricated building (PB) engineering has a complicated restricted relationship in scheduling scheme, and the entire project is accomplished by multi-stage collaboration in construction process. Consequently, it is of great importance for project managers to make reasonable resources scheduling to avoid disruptions caused by resource unavailability. However, the poor interoperability and interactivity still results in diverse constraints, which limit the PB construction progress. Therefore, the aim of this paper is to establish a PB resource scheduling model that satisfying resource constraints and strengthening the PB dispatch time connection. The PB construction process is divided into assembly work space, logistics work space, and production work space where the construction time note is regarded as connection constraint and the three work spaces are restrained mutually. What's more, the optimal total amount of resources determination technology is presented to arrange the resource schedule in assembly work space to ensure the optimal resource quantity with the goals of the shortest construction time and the lowest cost. The dynamic scheduling coordination technology is put forward to logistics work space and production work space where the resource schedule is arranged with time node constraint. A high-rise building is presented as an example to illustrate the implementation of the proposed model. Results show that the presented method could effectively solve the problem of resource tension problem under the goal of the lowest cost as well as alleviate the shortage of multi-resource scheduling.

Impact of Lean Manufacturing Practices on Operational and Business Performance: Evidence from Sri Lankan Food Industry

Lean is a strategy that aims to achieve smooth production flow by eliminating waste and hence helps manufacturing firms realize their time, cost, and quality goals. Food manufacturers not only have limited ability to offer uniform quality but also a greater chance of generating wastes due to the perishable nature of materials. This study investigates the degree to which the Sri Lankan food sector is practicing lean manufacturing and examines the direct and indirect relationships between lean manufacturing, business performance, and operational performance. Data were collected through a questionnaire distributed to managers who have lean experience in the food industry. Using a sample of 86, the hypotheses were tested applying Partial Least Square Structural Equation Modelling with SmartPLS software. Developing a new construct to represent four lean manufacturing practices, namely, Just-in-time (JIT), Total Quality Management (TQM), Total Preventive Maintenance (TPM), and Human Resource Management (HRM), this study empirically confirms that lean manufacturing has a significant positive impact on both operational and business performance. Furthermore, operational performance partially mediates the relationship between lean manufacturing and business performance. The findings offer valuable implications for mangers, particularly in the food industry to achieve sustainability through practicing lean in their manufacturing and supply chain operations.

Multiobjective optimisation and cluster analysis in placement of best management practices in an urban flooding scenario

Abstract This research is being carried out to study how best management practices (BMPs) can mitigate the negative effects of urban floods during extreme rainfall events. Strategically placing BMPs throughout open areas and rooftops in urban areas serves multiple purposes of storage of rainwater, removal of pollutants from surface runoff and sustainable utilisation of land. This situation is framed as a multiobjective optimisation problem to analyse the trade-offs between multiple goals of runoff reduction, construction cost and pollutant load reduction. Output includes a wide range of choices to choose from for decision makers. Proposed methodology is demonstrated with a case study of Greater Hyderabad Municipal Corporation (GHMC), India. A historical extreme rainfall event of 237.5 mm which occurred in 2016 and extreme rainfall event of 1,740.62 mm corresponding to representative concentration pathway (RCP) 2.6 were considered for analysis. Two multiobjective optimisation algorithms, namely non-dominated sorting genetic algorithm-III (NSGA-III) and constrained two-archive evolutionary algorithm (C-TAEA) are used to solve the BMP placement problem, following which the resulting Pareto-fronts are ensembled. K-Medoids-based cluster analysis is performed on the resulting ensembled Pareto-front. The proposed ensembled approach identified ten possible BMP configurations, with costs ranging from Rs. to surface runoff reduction ranging from to and pollutant load removal ranging from tonnes. Use of BMPs in future events has the potential to reduce surface runoff from , while simultaneously removing tonnes of pollutants for cost ranging from The proposed framework forms an effective and novel way to characterise and solve BMP optimisation problems in context of climate change, presenting a view of the urban flooding scenario today, and the likely course of events in the future.

Probabilistic optimal power flow calculation method considering sequential features and correlations of multi-uncertain source-load scenarios

In order to accurately evaluate the influence of the correlation and sequential feature of wind power, PV power, and load on the economic operation of a power system, a sequential probabilistic optimal power flow calculation method based on multi-uncertain source-load scenarios generated by a multi-generative adversarial network (MGAN) is proposed. First, the historical datasets of wind power, PV power, and load in adjacent areas were collected, and their probability distributions in 24 time periods were analyzed. The results showed that the probability distribution of different periods had obvious differences, which were defined as time sequence differences in this paper. Second, a novel neural network called MGAN for scenario generation was designed. Based on the historical data of each time period, the correlated wind power, photovoltaic (PV) power, and load scenarios were generated by MGAN. Finally, in the modified IEEE 118-bus system, in which two wind power sources, two PV power sources, and two loads with correlation were integrated, probabilistic optimal power flow calculations were carried out for 24 periods with the goal of the lowest power generation cost of thermal power units. The experimental results showed that the power generation cost fluctuation ranges that considered the time sequence difference were reduced by 60% on average compared with the nonconsidered. The proposed method can effectively reduce the power generation cost budget.

Optimal Dispatching of Power System by Introducing Concentrating Solar Power Station to Promote Large-scale Wind Power and Photovoltaic Accommodation

Background: With respect to the problem of wind power and photovoltaic (PV) gridconnected accommodation, a power system optimization dispatching method is proposed that introduces a concentrating solar power (CSP) station to promote wind power and PV accommodation, considering the dispatch-ability of the thermal storage system (TSS) and the accommodation capacity of the electric heating device (EHD) in the CSP station. Methods: The method analyzes an internal simplified model of a CSP station and a short-timescale identification method of wind and PV power ramping events based on a CSP-Wind-PV system structure. Furthermore, the operating characteristics and constraints of various units are considered with the goal of the lowest comprehensive system cost to establish a CSP-Wind-PV optimization model for dispatching power system. Results: The method is simulated and verified on the IEEE-RTS24 node system by using the MATLAB optimization toolbox. Conclusion: The results demonstrate that the participation of a CSP station can decrease the number of power ramping events and reduce the curtailment of wind and PV power while ensuring the economical operation of the power system.

Use Linear Weighted Genetic Algorithm to Optimize the Scheduling of Fog Computing Resources

This paper establishes a mathematical model for the resource management and scheduling of the fog node cluster and establishes the optimization goals of delay, communication load, and service cost. According to the idea of genetic algorithm for single-objective optimization, this paper proposes a linear weighted genetic algorithm based on linear weighting. The optimization weight is established according to the user’s preference for the target. We normalize the optimization objective function and merge it into one target, and then we proceed with genetic manipulation to get a better solution. The experimental results show that when the user specifies the preference weight, the optimal solution can be obtained by the genetic algorithm based on linear weighting, and the algorithm execution efficiency is high. With the increase of the single-objective weight, the optimization effect of this objective is better. When the preference weight tends to be average, its overall optimization effect is not ideal. When the user does not specify the preference weight, a set of optimal solutions can be obtained through the improved nondominated sorting genetic algorithm with elite strategy. Compared with the traditional algorithm, in addition to the overall optimization effect of the target being better, the algorithm itself also has higher efficiency.

DEVELOPMENT OF A MULTI-OBJECTIVE PRODUCTION PLANNING MODEL FOR A SAWMILL

Based on the objectives set out for a Sawmill, a goal programming model was developed to simultaneously consider the production volumes goal, sales revenue goal, production cost goal, and machine utilization goal in order to develop its production plans for a horizon. The unwanted deviations from the goals served as the objective function to be optimized subject to the goals constraints, operational constraints, and non-negativity constraints. Three independent pre-emptive goal priority structures, GP1, GP2, and GP3, were considered with each prioritizing the objectives differently. The goal programming model was tested for its utility using data gathered from the mill to the three-goal priority structures. The results obtained indicated that, for GP1, the product volume goals for all products were achieved, and all but one, volume goals were achieved for both GP2 and GP3. The viability test showed that all priority structures used were profitable with GP1, GP2, and GP3 recording 1.099, 1.102, and 1.095 respectively. This indicates that the three priority structures considered are approximately profitable at the same level. The goal programming model for production planning offers the decision-maker a variety of options as to its application.

Modeling and configuration optimization of the natural gas-wind-photovoltaic-hydrogen integrated energy system: A novel deviation satisfaction strategy

The integrated energy system (IES) coupled with renewable energy power generation and hydrogen energy storage (HES) is an effective way to achieve clean and low-carbon energy consumption, with great development potential. How to reasonably determine the system configuration scheme is a critical issue that needs to be solved urgently. This paper constructs a configuration optimization model. First, the mathematical model, objective function, optimization strategy, and constraints are given. Then, this study innovatively proposes an optimization strategy based on deviation satisfaction to construct the objective function, which can simultaneously optimize the two goals of annual comprehensive cost (ACC) and annual carbon emissions (ACE); the weight coefficients are introduced to reflect the different optimization preferences of decision-makers. The entire optimization model belongs to mixed integer linear programming and the case study gives the results under the same emphasis on ACC and ACE (W1 = 0.5, W2 = 0.5). The initial purchase cost structure shows that the cost of gas turbines, hydrogen storage tanks, and photovoltaic accounts for more than 70%. The cost of HES is high but the carbon emissions reduction effect of the system is considerable, and the ACE is only 6.63% of the traditional supply system. In the third part, sensitivity analysis results show that different optimization preferences could significantly affect the configuration results and the “cost-effectiveness” performance corresponding to each preference is different. The optimal optimization strategy interval is (W1 = 0.3, W2 = 0.7) to (W1 = 0.4, W2 = 0.6). The comparative analysis shows that the simulation results are reliable. Further analysis reveals that the current carbon tax base price is extremely unsatisfactory for the stimulus effect of the IES. Therefore, it is recommended to increase the base price of the current carbon tax.

Bilevel interactive optimisation for rebatching scheduling problem with selectivity banks in high variety flow line production

Mass customization enables the integration of traditional flow line production with product platforms to accommodate abundant product-process varieties. These platform-based flow lines explore common process routes while highlighting rebatching scheduling with selectivity banks (RBS) to handle large process varieties across production stages at minimum setup cost. Given the inherent coupling between decision making in job diverging and retrieval quality, an interactive optimization approach is necessary for the RBS problem. This study proposes a bilevel interactive optimization (BIO) model for RBS to accommodate high variety flow line production. The model addresses the conflicting goals of lane occupancy cost, process setup cost, and job divergence and retrieval efficiency. Regarding job divergence at the leader-level, a vehicle routeing problem with precedence constraints is formulated and solved by a constructed genetic algorithm (GA). Concerning job retrieval at the follower-level and the ongoing characteristic of selectivity banks, a dispatching problem with various batch size preference and dynamic time window is established and dealt with a restricted dynamic programming (RDP) algorithm after balancing search efficiency and accuracy. Thus, to solve the BIO, a hybrid GA-RDP is developed and implemented. A practical application to an automotive painting shop illustrates the operational benefits of the BIO model for the RBS problem.

Synchrotron X-Ray Tomography Investigation of Fundamental Degradation Mechanisms in Polymer Electrolyte Membrane Water Electrolyzers

Electrochemical energy conversion devices such as fuel cells and electrolyzers provide the potential to meet global energy demands, particularly as a renewable alternative to fossil fuels or as part of a more environmentally friendly combination. In both polymer electrolyte membrane (PEM) fuel cells (PEMFCs) and water electrolyzers (PEMWEs), degradation studies are extremely important. Degradation mechanisms will become even more problematic as electrolyzers transition towards intermittent operation; additionally, there is a need to reduce noble metal loadings, used to combat slow kinetics of the anodic oxygen evolution reaction (OER), to meet cost goals at scale.1,2 A shift towards scalable, efficient low-loading catalyst layers operated with intermittent power will lead to significant degradation concerns, but the extent and severity are not yet fully understood.2–4 Advanced physicochemical characterization techniques are required to interpret electrochemical data and fully understand how the device components degrade. Synchrotron X-ray based tomography and spectroscopy techniques in particular can provide sensitive analysis of chemical states, elemental distribution, and visual information to elucidate large-scale changes in catalyst layer (CL) chemistry, morphology, and structure after relevant testing protocols. These methods are highly complementary to lab-based characterization methods, such as electron microscopy and X-ray spectroscopy.In this study, commercial Ir and IrO2 black catalysts were obtained and studied as catalyst powders, fresh catalyst-coated membranes (CCMs), and tested CCMs. Electrochemical durability testing was performed on PEMWEs to replicate either the start-up/shut-down process (intermittent operation) or an extended hold (steady-state operation). Transmission X-ray microscopy (TXM) was used to visualize distribution and morphology of iridium throughout the CL, as well as CL porosity. By collecting microscopy images while scanning the X-ray energy across the Ir absorption edge, X-ray absorption near-edge structure (XANES) chemical maps were generated to track iridium oxidation state to compare Ir and IrO2 CLs as well as the impact of testing. These synchrotron methods were supplemented with electron microscopy of the CCM surface as well as cross-sectional analysis to investigate changes in morphology, structure, and elemental distribution, and surface-sensitive X-ray photoelectron spectroscopy (XPS) to track relevant surface species after testing. Together, these techniques provide an unparalleled look into degradation of PEMWE catalyst layers at multiple scales to isolate the impact of individual CL constituents on overall electrode structure and stability.(1) Ayers, K. E. Curr. Opin. Electrochem. 2019, 18, 9–15.(2) Alia, S. M.; Stariha, S.; Borup, R. L. J. Electrochem. Soc. 2019, 166 (15), F1164–F1172.(3) Ayers, K. E.; Danilovic, N.; Ouimet, R.; Carmo, M.; Pivovar, B. S. Annu. Rev. Chem. Biomol. Eng. 2019, 10, 219–239.(4) Ayers, K. E.; Renner, J. N.; Danilovic, N.; Wang, J. X.; Zhang, Y.; Maric, R.; Yu, H.

Production of Topology-optimised Structural Nodes Using Arc-based, Additive Manufacturing with GMAW Welding Process

The desire to generate a stress optimised structural node with maximum stability is often coupled with the goal of low manufacturing costs and an adapted and minimal use of material. The complex, three-dimensional free-form structures, which are created by means of topology-optimisation, are only partially suitable for conventional manufacturing. The wire arc additive manufacturing (WAAM), by means of arc welding processes, offer a cost-effective and flexible possibility for the individual production of complex, metallic components. Gas metal arc welding (GMAW) is particularly suitable to produce large-volume, load-bearing structures due to build-up rates of up to 5 kg/h. The generation of strength and stiffness adapted support structures by means of the numerical simulation method of topology-optimisation was investigated in this study to generate topology-optimised structural nodes. The resulting node is transferred into a robot path using CAD/CAM software and manufactured from the filler material G4Si1 using WAAM with the GMAW process. Based on the boundary conditions of the WAAM process, the path planning and thus the manufacturability of the topology-optimised supporting structure nodes is evaluated and verified using a sample structure made of the welding filler material G4Si1. Depending on the path planning, an improvement of the mechanical properties could be achieved, due to changes in t8/5 times.

The Science Case for a Titan Flagship-class Orbiter with Probes

We outline a flagship-class mission concept focused on studying Titan as a global system, with particular emphasis on the polar regions. Investigating Titan from the unique standpoint of a polar orbit would enable comprehensive global maps to uncover the physics and chemistry of the atmosphere, and the topography and geophysical environment of the surface and subsurface. The mission includes two key elements: (1) an orbiter spacecraft, which also acts as a data relay, and (2) one or more small probes to directly investigate Titan's seas and make the first direct measurements of their liquid composition and physical environment. The orbiter would carry a sophisticated remote sensing payload, including a novel topographic lidar, a long-wavelength surface-penetrating radar, a sub-millimeter sounder for winds and for mesospheric/thermospheric composition, and a camera and near-infrared spectrometer. An instrument suite to analyze particles and fields would include a mass spectrometer to focus on the interactions between Titan's escaping upper atmosphere and the solar wind and Saturnian magnetosphere. The orbiter would enter a stable polar orbit around 1500 to 1800 km, from which vantage point it would make global maps of the atmosphere and surface. One or more probes, released from the orbiter, would investigate Titan's seas in situ, including possible differences in composition between higher and lower latitude seas, as well as the atmosphere during the parachute descent. The number of probes, as well as the instrument complement on the orbiter and probe, remain to be finalized during a mission study that we recommend to NASA as part of the NRC Decadal Survey for Planetary Science now underway, with the goal of an overall mission cost in the "small flagship" category of ~$2 bn. International partnerships, similar to Cassini-Huygens, may also be included for consideration.