Performance Of Individual Components Research Articles

Seismic design of buildings in the context of regions: A case study of a site in Semnan city, Iran

The seismic codes focus on single buildings and ignore the interdependencies between various sectors and infrastructure. This approach cannot ensure resilient communities and ignores essential aspects of seismic risk (e.g., exposure element). This paper proposes a regional-based design (RBD) method that considers the performance of individual components as well as the interactive consequences of their design on the communities. Unlike most studies, the proposed RBD method considers a dynamic behavior for a region/city and utilizes its master plan to model the stochastic variations in the future built environment. The method determines buildings' seismic design requirement (SDR) by considering the building-level and regional goals. Building-level goals address the most important performance objectives (e.g., safety in residential buildings) and should be satisfied for each structural system and occupancy. However, the method satisfies the regional goals for any structural system. This implies that if (hypothetically) all the buildings in a region/city were designed and constructed with only one structural system (e.g., special steel moment resisting frames, SMRFs), the regional goals should be satisfied. The method starts with the determination of the SDRs by satisfying equal building-level objectives for each occupancy in various locations of the region with one structural system. Then, to achieve the regional goals, the calculated SDRs are uniformly raised to improve the performance of the buildings (if required). The method also considers factors other than the buildings/facilities' performance that could affect the achievement of the regional goals (e.g., emergency response capacity). As a proof-of-concept, the proposed method is used in a site in Semnan City, Iran, to determine the SDR of steel SMRFs. The case study shows that there might be situations where elevating the seismic design requirements of the buildings could not guarantee the achievement of some regional goals. Moreover, the results indicate that factors like the design of the built environment could affect regional risks. Therefore, attention to factors other than the buildings' seismic performance is also essential in achieving the regional goals.

(Invited) Looking Beyond the Stack: A Systems Engineering Approach to Optimize Stack and System Design of Electrolysers

In the coming decade green hydrogen production is expected to grow at an unparalleled rate. Much of the international focus is currently on the Gigawatt scale plants, with regular announcement of new projects. From a European perspective, the rapid scale-up is necessary to reduce the reliance on fossil fuels and achieve CO₂ reduction targets. Importing green hydrogen or derivatives such as ammonia from regions where renewable electricity cost are low, is key driver for investments for the energy intensive industry, especially in the North Western part of Europe. This will require hydrogen production on a very large-scale. In contrast to the differentiation on application level, a one-size fits all approach for stack manufacturing appears to be attractive, with a high degree of standardisation and volume of production.However, as the case for the Netherlands illustrates, there are more drivers for green hydrogen production, such as addressing (regional) grid congestion, improving the business case for offshore wind farm developments, and providing green energy to small and medium enterprises. For all these use cases the requirements for the electrolyser system differ. Aspects such as the variability of the energy supply, capacity factors, logistics for operation and maintenance, available footprint, are application specific. Therefore, electrolyser system designs may need to be optimized for different markets and applications.At TNO we focus on the development, integration and validation of novel materials and innovative cell components for the different generations of electrolyser technology, both for low temperature (liquid alkaline, PEM, AEM) and high temperature electrolysers. To understand and assess the requirements for future generations of electrolyser technology, a systems engineering approach is developed together with the industrial partners. This allows to translate the requirements from the application, to the system and stack design, down to the requirements on a cell and component level.Results will be presented from several studies carried out by TNO and industrial partners in the field of low temperature electrolyser systems to understand: How to optimize a stack design for a specific application. Should an electrolyser be designed to maximize flexible operation and achieve a broad operating range? Or will a high efficiency be the most important target? And how application specific is such an optimisation.How to bring down the cost of the total system. What impact do the design choices for the electrolyser stack have on the complexity of the system around the electrolyser, the balance-of-plant? What can we learn from this for the future stack requirements?How different operating strategies impact a certain design. Which stack requirements change in large multi-stack systems powered by renewable electricity supply? Can different strategies improve overall system performance and bring down the overall cost of hydrogen production?How can the desired safety level be achieved against the lowest cost by optimizing component and stack design? As both the electrolyser technology and the value chains become more mature, a systems engineering approach will be indispensable, bringing together the requirements of the different levels: from application, electrolyser system and stack design, down to the performance of individual components.

Reference-Electrode Configuration in CO2 Electrolysis (CO2E) and Anion-Exchange Membrane Water Electrolysis (AEMWE) Cells for Degradation and Stability Evaluation

CO2 electrolysis (CO2E) and anion-exchange membrane water electrolysis (AEMWE) have both been identified as promising technologies for reducing emissions from the industrial and energy sectors in pursuit of achieving net-zero goals.1,2,3,4 Recent developments of electrocatalysts, membranes, and electrolyser cell design have resulted in rapid progress of CO2E and AEMWE, achieving high current densities and faradaic efficiencies at the laboratory scale.1,2 However, the long-term stability of these technologies requires improvement if they are to be deployed at commercially relevant scales.2,5 Unfortunately, degradation phenomena such as; cathode flooding6, 10, catalyst degradation7, membrane instability8,9, and salt precipitation10 are difficult to diagnose electrochemically, without the ability to decouple resistance contributions in-situ.We have therefore developed a 3-electrode technique and explored protocols that allow for the decoupling of cathode and anode overpotentials during long-term stability measurements.By using an edge-type reference electrode connected to the membrane through a liquid electrolyte bridge, we successfully demonstrate the ability to assess the performance of individual components within different membrane-electrode assembly (MEA) configurations. We highlight instabilities with using edge-type reference electrodes such as changes in the pH of the electrolyte bridge, and discuss efforts to mitigate these issues to permit long-term stability testing. We then demonstrate how this method can be used to provide more insights into degradation phenomena in both CO2 and AEM water electrolyser cells, showing early diagnosis - up to 7 hours before detection from a conventional 2-electrode set-up - of failure mechanisms during stability testing.The proposed 3-electrode technique is capable of separating anode and cathode overpotentials over the course of extended testing periods and provides a platform to study the electrochemical response of individual electrolyser components to various degradation phenomena, and the durability of CO2E and AEMWE materials. Critical gaps in knowledge surrounding these issues can be better understood, which will enable these technologies to advance to the next stages of development.

Medium spatiotemporal characteristics based global optimization method for energy efficiency trade-off issue in variable flow rate HVAC system

During the optimization and control processes of variable flow rate heating, ventilation, and air conditioning (HVAC) systems, the medium temporal and spatial characteristics often significantly influence the control effectiveness and energy efficiency of the overall system. Unfortunately, much of the existing research predominantly concentrates on enhancing the performance of specific individual components, neglecting the impact of medium temporal and spatial characteristics on the overall HVAC system performance. Consequently, issues such as energy efficiency trade-offs in practical systems remain inadequately addressed. In this paper, the medium spatiotemporal characteristics of HVAC systems are systematically illustrated and modeled. A novel optimization method based on these characteristics is proposed to improve the overall performance of the HVAC system. This method adopts an event-driven optimization strategy to determine the optimal timing with consideration of temporal characteristics, and an adaptive hybrid algorithm is proposed to determine the optimal setpoints with consideration of spatial characteristics. By comparing to conventional methods with constant setpoints, genetic algorithm (GA) based setpoints, and differential evolution (DE) based setpoints in both simulation test and experiment, the proposed method demonstrates the ability to reduce electricity consumption by 21.2%, 13.1%, and 10.0%, respectively. The methodology in this research provides significant guidance for engineers to promote the decoupling control and overall optimization of HVAC systems. Additionally, the results have referable values for implementing the most appropriate algorithm in practice.

A real-time wearable AR system for egocentric vision on the edge

Real-time performance is critical for Augmented Reality (AR) systems as it directly affects responsiveness and enables the timely rendering of virtual content superimposed on real scenes. In this context, we present the DARLENE wearable AR system, analysing its specifications, overall architecture and core algorithmic components. DARLENE comprises AR glasses and a wearable computing node responsible for several time-critical computation tasks. These include computer vision modules developed for the real-time analysis of dynamic scenes supporting functionalities for instance segmentation, tracking and pose estimation. To meet real-time requirements in limited resources, concrete algorithmic adaptations and design choices are introduced. The proposed system further supports real-time video streaming and interconnection with external IoT nodes. To improve user experience, a novel approach is proposed for the adaptive rendering of AR content by considering the user’s stress level, the context of use and the environmental conditions for adjusting the level of presented information towards enhancing their situational awareness. Through extensive experiments, we evaluate the performance of individual components and end-to-end pipelines. As the proposed system targets time-critical security applications where it can be used to enhance police officers’ situational awareness, further experimental results involving end users are reported with respect to overall user experience, workload and evaluation of situational awareness.

Microfluidic chip with integrated separation, mixing, and concentration operations for rapid and sensitive bacterial detection utilizing synthetic inorganic antibodies

In this paper, we presented a microfluidic chip that integrates bacterial separation, mixing, and concentration solely through fluid dynamics. Size-based bacterial separation was achieved through Dean flow and inertial focusing by harnessing the attributes of a spiral-shaped fluidic channel. Furthermore, Dean flow was capitalized upon to enable the mixing of bacteria with the detection substance, complemented by the integration of PDMS membrane filter for bacterial concentration, all unified within the chip architecture. The performance of individual components was validated using E. coli. In the separation experiment between 4.1 µm particles and E. coli, a separation efficiency of 87.07 ± 2.21% was measured for E. coli, and the mixer demonstrated a mixing efficiency of 92.8% at 30 μL/min flow rate. Concentrating E. coli through the filter for 30 min yielded an average loss rate of 7.47%. Moreover, the proposed microfluidic device was applied for bacterial detection using both our in-house-developed inorganic synthetic antibodies and commercially available fluorescent antibodies. Real-time optical measurements demonstrated the potential of the chip for cost-effective, rapid, and sensitive bacterial detection. The versatility of the chip was substantiated by its successful implementation for the E. coli detection at a concentration of 102 CFU/mL in spiked urine samples.

Machine-learning based optimization of a biomimiced herringbone microstructure for superior aerodynamic performance

Biomimicry involves drawing inspiration from nature’s designs to create efficient systems. For instance, the unique herringbone riblet pattern found in bird feathers has proven effective in minimizing drag. While attempts have been made to replicate this pattern on structures like plates and aerofoils, there has been a lack of comprehensive optimization of their overall design and of their constituent individual repeating structures. This study attempts to enhance the performance of individual components within the herringbone riblet pattern by leveraging computational fluid dynamics (CFD) and supervised machine learning to reduce drag. The paper outlines a systematic process involving the creation of 107 designs, parameterization, feature selection, generating targets using CFD simulations, and employing regression algorithms. From CFD calculations, the drag coefficients (C d ) for these designs are found, which serve as an input to train supervised learning models. Using the trained transformed target regressor model as a substitute to CFD, C d values for 10,000 more randomly generated herringbone riblet designs are predicted. The design with the lowest predicted C d is the optimized design. Notably, the regressed model exhibited an average prediction error rate of 6% on the testing data. The prediction of C d for the optimized design demonstrated an error of 4% compared to its actual C d value calculated through CFD. The study also delves into the mechanics of drag reduction in herringbone riblet structures. The resulting optimized microstructure design holds the potential for reducing drag in various applications such as aerospace, automotive, and marine crafts by integrating it onto their surfaces. This innovative approach could significantly transform drag reduction and open pathways to more efficient transportation systems.

Using the multiphase optimization strategy to adapt cognitive processing therapy (CPT MOST): study protocol for a randomized controlled factorial experiment

BackgroundApproximately ten percent of US military veterans suffer from posttraumatic stress disorder (PTSD). Cognitive processing therapy (CPT) is a highly effective, evidence-based, first-line treatment for PTSD that has been widely adopted by the Department of Veterans Affairs (VA). CPT consists of discrete therapeutic components delivered across 12 sessions, but most veterans (up to 70%) never reach completion, and those who discontinue therapy receive only four sessions on average. Unfortunately, veterans who drop out prematurely may never receive the most effective components of CPT. Thus, there is an urgent need to use empirical approaches to identify the most effective components of CPT so CPT can be adapted into a briefer format.MethodsThe multiphase optimization strategy (MOST) is an innovative, engineering-inspired framework that uses an optimization trial to assess the performance of individual intervention components within a multicomponent intervention such as CPT. Here we use a fractional factorial optimization trial to identify and retain the most effective intervention components to form a refined, abbreviated CPT intervention package. Specifically, we used a 16-condition fractional factorial experiment with 270 veterans (N = 270) at three VA Medical Centers to test the effectiveness of each of the five CPT components and each two-way interaction between components. This factorial design will identify which CPT components contribute meaningfully to a reduction in PTSD symptoms, as measured by PTSD symptom reduction on the Clinician-Administered PTSD Scale for DSM-5, across 6 months of follow-up. It will also identify mediators and moderators of component effectiveness. DiscussionThere is an urgent need to adapt CPT into a briefer format using empirical approaches to identify its most effective components. A brief format of CPT may reduce attrition and improve efficiency, enabling providers to treat more patients with PTSD. The refined intervention package will be evaluated in a future large-scale, fully-powered effectiveness trial. Pending demonstration of effectiveness, the refined intervention can be disseminated through the VA CPT training program.Trial registrationClinicalTrials.gov NCT05220137. Registration date: January 21, 2022.

Performance evaluation of the cloud computing application for IoT-based public transport systems

The object of research is cloud computing as an element of the server infrastructure for intelligent public transport systems. Given the increasing complexity and requirements for modern transportation, the application of the Internet of Things concept has a high potential to improve efficiency and passenger comfort. Since the load generated in IoT systems is dynamic and difficult to predict, the use of traditional infrastructure with dedicated servers is suboptimal. This study considers the use of cloud computing as the main server infrastructure for the above systems. The paper investigates the main cloud platforms that can be used to develop such systems and evaluates their advantages and disadvantages. The authors developed the overall architecture of the system and evaluated the performance and scalability of individual components of the server infrastructure. To test the system, a software emulator was developed that simulates the controller module installed in vehicles. Using the developed emulator, stress tests were conducted to analyze and confirm the ability to scale and process input data by the proposed architecture. The test scenarios were developed and conducted on the basis of the existing public transportation system in Kyiv, Ukraine. The experimental results showed that the proposed IoT architecture is able to scale efficiently according to the load generated by the connected devices. It has been found that when the number of incoming messages increases from 40 to 6000, the average message processing time remains unchanged, and the error rate does not increase, which is an indicator of stable system operation. The obtained results can be used in the development of modern public transport systems, as well as for the modernization of existing ones

Inverse design of nanophotonic devices using dynamic binarization.

The complexity of applications addressed with photonic integrated circuits is steadily rising and poses increasingly challenging demands on individual component functionality, performance and footprint. Inverse design methods have recently shown great promise to address these demands using fully automated design procedures that enable access to non-intuitive device layouts beyond conventional nanophotonic design concepts. Here we present a dynamic binarization method for the objective-first algorithm that lies at the core of the currently most successful inverse design algorithms. Our results demonstrate significant performance advantages over previous implementations of objective first algorithms, which we show for a fundamental TE00 to TE20 waveguide mode converter both in simulation and in experiments with fabricated devices.

Proposed Enhancements to the Risk-Informed and Performance-Based Regulatory Framework for Seismic Hazard Design at NRC-Regulated Nuclear Power Plants

The commercial nuclear power plant industry initiated the licensing modernization project (LMP) to enhance the risk-informed and performance-based (RIPB) regulatory basis for advanced nuclear power reactors. The LMP framework relies heavily on RIPB concepts and approaches that together integrate the defense-in-depth philosophy. One example approach for seismic design is to align the LMP concepts with the performance targets described in the American Society of Civil Engineers (ASCE) standard, ASCE 43-19. The underlying strategy of this approach is to consider the performance of individual structures, systems, and components (SSCs) in seismic design, as well as the role they play in an accident event sequence. This approach contrasts with current regulations, in which every individual safety-related SSC is designed to the same seismic criteria irrespective of the role the SSC plays in the overall system performance. This new philosophy envisions more flexible seismic design options for each SSC, such that the overall seismic design can meet system-level acceptability criteria as well as plant-level acceptability criteria. The objective of this paper is to illustrate the flexibility and benefits of this proposed approach to the seismic design of SSCs in terms of reduced SSC demands (by reducing the design ground motions for SSCs) and improved SSC capacities (by allowing for alternative damage state limits). A simple shear wall was designed using ASCE 43-19 for a hard rock site in the Central Eastern United States considering alternate seismic design category and limit state combinations to examine the physical designs and functional fragilities of these combinations and their impact on seismic performance. The flexibility of this proposed approach is illustrated by an example that shows reduced SSC demands, while the SSC capacities and margins remain consistent with the required safety performance without any loss in overall plant safety.

Scalable risk assessment of large infrastructure systems with spatially correlated components

Risk assessment of spatially distributed infrastructure systems under natural hazards shall treat the performance of individual components as stochastically correlated due to the common engineering practice in the community including similarities in building design code, regulatory practices, construction materials, construction technologies, and the practices of local contractors. Modelling the spatially correlated damages of an infrastructure system with many components can be computationally expensive. This study addresses the scalability issue of risk analysis of large-scale systems by developing an interpolation technique. The basic idea is to sample a portion of components in the systems and evaluate their correlated damages accurately, while the damages of remaining components are interpolated from the sampled components. The new method can handle not only linear systems, but also systems with complex connectivity such as utility networks. Two examples are presented to demonstrate the proposed method, including cyclone loss assessment of the building portfolios in a virtual community, and connectivity analysis of an electric power system under a scenario cyclone event.

Senti-202, a Selective, Off-the-Shelf, Preclinical CAR-NK Cell Therapy with CD33 and/or FLT3 Activating CAR, Healthy Cell Protection from Endomucin (EMCN) Inhibitory CAR and Calibrated Release IL-15 for Hematologic Malignancies Including AML

Background: Patients with CD33 and/or FLT3 malignancies, which encompass most myeloid malignancies such as acute myeloid leukemia (AML), have a grim prognosis and a high unmet need. There are no approved cell therapies for patients with AML due to paucity of targets and an immunosuppressive bone marrow milieu. SENTI-202, a preclinical CAR-NK cell is engineered with a logic-gated gene circuit to overcome these challenges. Methods: SENTI-202 is engineered with a FLT3 OR CD33 NOT EMCN logic-gated gene circuit and a proprietary calibrated release IL-15 (crIL-15). These 3 chimeric proteins are engineered onto a natural killer (NK) cell (healthy adult peripheral blood-derived) to create an off-the-shelf chimeric antigen receptor (CAR) NK cell therapy. The bivalent CD33 OR FLT33 (OR GATE) activating CAR (aCAR) enables concurrent targeting of CD33+ and/or FLT3+ AML cells which kills both AML blasts and leukemic stem cells (LSCs) in vitro and in vivo. This concurrent targeting of two tumor antigens could potentially provide longer remission and less chance of relapse. The NOT EMCN (NOT GATE) inhibitory CAR (iCAR) protects healthy EMCN+ hematopoietic stem cells (HSCs) and early hematopoietic progenitor cells (HPCs) from off-tumor toxicity to potentially aid in post-treatment reconstitution of a healthy hematopoietic system. The crIL-15 provides both autocrine and paracrine IL-15 stimulation to the CAR-NK cells (and surrounding immune cells) to promote cell expansion, persistence, and tumor killing. While previous presentations (Garrison et al., 2021) focused on the performance of individual SENTI-202 components in a cell, this presentation focuses on functional data from the complete logic-gated gene circuit and the crIL-15 together in a cell (i.e., the final CAR-NK with all 3 genetic elements). Results: We demonstrated delivery of all circuit components using a single retroviral vector. We then investigated activity of the resulting CAR-NK cells using a traditional in vitro cytotoxicity assay (E:T=1:2; 20hrs) in which SENTI-202 showed >90% killing of leukemia cells (e.g. SEM; P≤0.001), maintained significant serial killing potential (3 rounds; P≤0.0001; P≤0.001; P≤0.001), as well as robust killing against primary AML blasts and LSC-enriched target cell populations. Importantly, SENTI-202 also exhibited significant tumor cell killing and improved survival (P≤0.001) in an MV4-11 xenograft AML mouse model. We investigated the ability of SENTI-202 to reduce off-tumor toxicity toward EMCN+ healthy cells, including primary HSCs. Within in vitro assays, SENTI-202 provided >50% protection to FLT3+ CD33+ model healthy cells that also expressed the EMCN safety antigen, and similar significant protection was maintained through serial protection assays (3 rounds; P≤0.0001; P≤0.001; P≤0.0001). As further evidence of the selectivity imparted using the OR/NOT logic gating approach, in vitro assays with SENTI-202 resulted in killing of leukemia cells but not primary healthy human HSCs and HPCs (~42% protection) (P≤0.01). Finally, in the presence of SENTI-202 in vivo, the NOT GATE protected and enabled significant (P≤0.0001) expansion of EMCN+ FLT3+ CD33+ model healthy cells. Conclusions: In summary, preclinical evaluation of SENTI-202 demonstrated selective killing of CD33 and/or FLT3 expressing AML cell lines while protecting healthy HSCs and HPCs. This selectivity is imparted by the novel logic-gated gene circuit engineered into healthy adult NK cells. Clinical evaluation of SENTI-202 is planned to evaluate the safety and efficacy in patients with hematologic malignancies with high unmet need, including AML.

A new view into three-dimensional excitation-emission matrix fluorescence spectroscopy for dissolved organic matter

Three-dimensional excitation-emission matrix fluorescence spectroscopy (3D EEMs) has been extensively used for dissolved organic matter (DOM) characterization. However, the application of 3D EEMs is constantly limited by issues such as contradictory component identification, confusing interpretation of spectral indicators, and inability to establish biodegradability. In this study, some improvements were proposed by investigating the 3D EEMs, spectral indicators, and degradability of the standard and representative DOM. To overcome the unclear identification of DOM components, it was recommended to partition 3D EEMs into three subareas: aromatic protein (New-I), humic-like (New-II), and soluble microbial by-product-like (New-III). Significant strong positive correlations (ρ = 0.727, P < 0.001) were observed between fluorescence index (FI) and biological index (BIX), and (R = 0.809, P < 0.001) humification index (HIX) and specific ultraviolet absorbance of 254 nm (SUVA254). Except for FI (R = -0.483, P = 0.023), no other spectral indicators (P > 0.05) were found to be significantly correlated with molecular weight. As thence results, the FI and HIX were the most suitable indicators for evaluating DOM. The half-life (20 < 21 < 26 < 29 < 46 days) revealed that the degradability of individual DOM components was in the order of tyrosine > tryptophan > fulvic acid > protein > humic acid. The degradation dynamics were governed by first-order decay kinetics (R2 = 0.91–0.99). This study clarified the fluorescence properties and degradability of DOM, as well as the reliability of spectral indicators. The degradation performance of individual DOM components engaged in the carbon cycling process was revealed, paving the path for further applications of 3D EEMs in DOM research.

ВПЛИВ ТЕМПЕРАТУРИ НА ПРАЦЕЗДАТНІСТЬ ПАЛИВНОЇ СИСТЕМИ КОСМІЧНИХ ЛІТАЛЬНИХ АПАРАТІВ

The tasks that modern spacecraft must solve during the flight task are constantly becoming more complicated. Accordingly, the conditions for the functioning of all systems and, above all, the fuel system are complicated. This system should provide the engines of the aircraft with fuel components when they start and operate under conditions of action of a variable field of mass forces, that is, in conditions of practical weightlessness. In these conditions, the nature of the impact on the performance of the fuel system of various external and internal factors changes significantly.&#x0D; The paper analyzes the degree of influence of the temperature of the structural elements of the spacecraft and liquid fuel on the performance of individual components of the fuel system. Some existing approaches to this issue are considered and it is concluded that it is necessary to conduct further research in this direction in order to improve existing and create new, more modern, fuel systems of multipurpose spacecraft and acceleration units.

The Advantage Analysis of 3-D Inversion of Airborne Gravity Gradiometry Data With Larger Sampling Interval

Airborne gravity gradiometry is an effective tool for its high efficiency and sensitivity to the interesting buried targets, which has been increasingly introduced in mineral and petroleum exploration. When compared to gravimetry, full tensor gradient (FTG) measurements provide higher resolution and signal-to-noise ratio. In this article, we build and test models with different sampling intervals and compare the performances of individual components or their combinations in minimum structure inversion to evaluate the benefits of FTG combinations. As part of a theoretical study, the singular value spectrum (SVS) and depth-resolution plot (DRP) are examined to determine how much information and resolution different datasets provide in the underdetermined inverse problem. The synthetic examples show that integrating more components in inversion improves the resolution of the recovered model, and the inversion result of gravity or individual component cannot accurately restore the distribution of anomalous bodies. With the increasing sampling interval, joint inversion of FTG data still provides enough information corresponding to the source density distribution, and the limit survey interval is tested through models. In addition, we use a real survey over the Vinton dome as a case study and discover that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$V_{\mathrm {zz}}$ </tex-math></inline-formula> component fails to reconstruct the caprock model at coarse line spacing, whereas FTG combinations recover the acceptable geometry flawlessly. It also reflects the benefit of FTG combinations for inversion processing, which reduces the interference of measured noise on inversion results and allows for longer sampling intervals during the survey, effectively lowering the cost of an airborne survey and improving exploration efficiency

Dynamic Analysis of a Series Hybrid–Electric Powertrain for an Unmanned Aerial Vehicle

Hybrid–electric powertrains offer the potential for performance improvements in unmanned aerial systems. However, for small unmanned aerial systems, potential gains in range and endurance can depend significantly on the aircraft flight profile and powertrain control logic. Subsequently, these impact the performance of individual powertrain components. This study uses dynamic simulations of an unmanned aerial system (UAS) with different powertrain control logic approaches to evaluate the performance of a series hybrid–electric powertrain. Component models generated using lookup table approaches and model parameterization are combined to generate a dynamic system model of the unmanned aerial system. The performance of the powertrain is evaluated for three representative mission profiles. Fuel consumption and battery state of charge form two metrics that are used to evaluate the performance of a baseline controller against an ideal operating line strategy. The ideal operating line strategy, which uses a performance map obtained by engine characterization on a specialized dynamometer, produces an average fuel economy improvement ranging from 0.5–2.0 g/km for a 30-min-long mission profile. The study demonstrates the need to consider a dynamic analysis aided by detailed component performance maps and a robust control strategy in evaluating hybridization approaches for UAS powertrains.

Removal of rubber, bitumen and other microplastic particles from stormwater by a gross pollutant trap - bioretention treatment train

Microplastic particles in stormwater pose significant threats to the environment. This study investigated how effective a stormwater treatment train was at removing rubber, bitumen and other microplastics (incl. fibers, fragments, and paint particles) in the 100–300 µm and >300 µm size fractions from highway runoff. The two treatment trains comprise a gross pollutant trap (GPT) followed by either a vegetated bioretention cell or non-vegetated sand filter. Flow-proportional composite samples were taken from the highway runoff, GPT outflow and the outflow from the two parallel filters during nine rain events to determine overall treatment performance, as well as the performance of individual system components. The identified rubber, bitumen and other microplastic particles mainly represented the 100–300 µm fraction and included high ratios of rubber (30%) and bitumen (60%). Overall, the treatment train efficiently removed rubber, bitumen and other microplastic particles in the 100–300 µm size fraction from the stormwater. The filter cells accounted for a major share of this removal, as the GPT did not reduce microplastic particle concentrations. This observation is likely explained by the fact that the rubber, bitumen and other microplastic particles have a density close to the density of water and thus removal by sedimentation is decreased. This identified an inherent weakness of the system; more specifically, the high microplastic concentrations in the surface water of the GPT means there can be a risk of microplastic release through overflow pits when inflows surpass the system capacity. Despite some differences, both the vegetated bioretention cell and the non-vegetated sand filter removed rubber, bitumen and other microplastic particles to similar extent.

Coordinated process scheduling algorithms for coupled earth system models

AbstractIt is becoming increasingly significant for humans to predict and understand future climate changes using coupled climate system models. Although the performance and scalability of individual physical components have improved over the past few years, coupled climate systems still suffer from low efficiency. This paper focuses on the process scheduling problem for the widely applied coupled earth system model (CESM). The proposed resource allocation strategies allow components to execute on a compromised suboptimal setup and still maintain approximately the best parallel speedup. With this flexible resource allocation strategy, we further propose a coordinated process scheduling algorithm (CPSA). More notably, we propose an upgraded version called CPSA‐B, which makes efficient resource sharing configurations, including resource allocation and process layout of components. We integrate CPSA and CPSA‐B as pre‐arrangement tools into the CESM program and deploy them on the Huawei Kunpeng platform. The speedup curves of the CESM components are prepared in advance, based on sampling tests. Experimental data show that CPSA‐B reduces up to 58% of the execution time compared with the CESM default strategy. The algorithm has low complexity and can efficiently find solutions for large input sizes.

Benchmark Concept for Industrial Pick&Place Applications

Robotic grasping and manipulation is a highly active research field. Typical solutions are usually composed of several modules, e.g. object detection, grasp selection and motion planning. However, from an industrial point of view, it is not clear which solutions can be readily used and how individual components affect each other. Benchmarks used in research are often designed with simplified settings in a very specific scenario, disregarding the peculiarities of the industrial environment. Performance in real-world applications is therefore likely to differ from benchmark results. In this paper, we present a concept for the design of general Pick&Place benchmarks, which help practitioners to evaluate the system and its components for an industrial scenario. The user specifies the workspace (obstacles, movable objects), the robot (kinematics, etc.) and chooses from a set of methods to realize a desired task. Our proposed framework executes the workflow in a physics simulation to determine a range of system-level performance measures. Furthermore, it provides introspective insights for the performance of individual components.