Academic literature on the topic 'Performance ratio; capacity utilization factor; THD'

Applications of Unmanned Aerial Vehicles (UAVs) are on the rise. Particularly within the healthcare sector the potential is huge as its cited as the most accepted application. This paper introduces an agent-based simulation to evaluate the network performance of UAV-based logistics networks in healthcare. The simulation is applied to a hypothetical real-world network. During a simulated day, the UAV fleet performs 212 flights, including 97 delivery flights, amounting to 4264 minutes enroute and covering a distance of 5941 kilometers. The analysis reveals average non-idle and mission utilization of 66% and 33%, respectively. The study also calculates annual network costs of EUR 2.23Mn, with a majority of it being direct costs (54.5%). Further sensitivity analysis identifies the biggest influences of battery capacity, C-Rate, and operator-to-UAV ratio on network performance and costs, highlighting these factors as critical for future optimization. Additionally, the benefit of incorporating various different UAV types into the network is only given if each UAV provides a unique value proposition to enhance the network performance.

Abstract CO2-enhanced oil recovery (CO2-EOR) technology can achieve the dual purposes of oil production and CO2 storage, which has attracted wide attention. However, due to the economic benefits of CO2 capture, utilization, and storage (CCUS) technology, how to maximize enhanced oil recovery and CO2 storage during the CO2 displacement process remains open. Besides, there is little research on the mechanisms of CO2 displacement and storage in low-permeability oil reservoirs under different conditions. This study investigates the synergistic effects of CO2-EOR and geological CO2 storage efficiency under varying injection stages and rates. A comprehensive synergy factor is proposed to evaluate the trade-offs between oil recovery and CO2 storage. The factor incorporates weighted contributions of the oil recovery factor, determined by stage-average recovery degree and oil displacement rates, and the CO2 storage factor, derived from cumulative storage ratio and storage volume. Weight assignments adapt dynamically to project objectives, prioritizing oil recovery during early stages, balanced considerations during intermediate stages, and CO2 storage during later stages. Results reveal distinct performance characteristics across three stages: the CO2-EOR and storage synergy stage, the oil displacement stage, and the CO2 storage stage. At an injection rate of 20000 m3/d, the synergy stage demonstrates peak efficiency with enhanced oil recovery and CO2 storage capacity, attributed to increased formation pressure and improved crude oil mobilization. The displacement stage emphasizes oil recovery, facilitated by reduced viscosity through CO2 dissolution, with optimal performance observed at 15000 m3/d. During the storage stage, CO2 solubility and storage efficiency improve under higher pressure conditions, with reduced gas channeling and effective long-term CO2 storage achieved at 5000 m3/d. Numerical simulations indicate that CO2 injection velocities significantly influence both oil recovery and CO2 storage outcomes. Higher injection rates enhance crude oil displacement and CO2 storage indices, extending reservoir productivity while addressing climate mitigation goals. However, excessive rates lead to gas channeling, necessitating well shut-in strategies to optimize pressure and suppress flow along high-permeability pathways. This study provides critical insights into optimizing CO2-EOR and geological storage strategies, supporting sustainable hydrocarbon recovery and carbon storage initiatives.

Abstract In frontal impacts, the usage of thin-walled box structures in the front-end of vehicles is crucial for effectively absorbing collision energy. However, achieving maximum energy absorption while considering constraints such as available space, weight, cost, and maintaining occupant safety is a challenging task. Previous studies have indicated that the structural shape, utilization of high strength materials, and incorporating foam-filled structures can significantly enhance energy absorption capacity. This paper presents a novel approach to optimize the energy absorption capacity with a combination of conventional and auxetic materials. Auxetic materials possess a unique characteristic of exhibiting a negative Poisson’s ratio, which means they contract when compressed and expand when subjected to tension. These distinctive properties enable auxetic structures to achieve higher stiffness and improved impact resistance while remaining lightweight. In this paper, a Finite Element Analysis (FEA) methodology has been formulated with a simple crush box, and with multiple materials combinations of conventional materials such as high strength steels, Aluminum and auxetic materials, to improve the energy absorption capacity. The significant factors affecting the energy absorption which are critical to occupant safety have been compared between the simulations. It is demonstrated that the optimal blend of conventional materials such as Aluminum and Ultra High Strength Steels, when integrated with auxetic structures, exhibits a higher potential for maximizing the specific energy absorption capacity of these structures. The findings reveal that the optimized combination of the above-mentioned materials can increase the specific energy absorption (SEA) capacity by up to 256%. Additionally, the practical application of these findings is demonstrated through full vehicle level crash analyses aimed at achieving light weighting. The research findings were implemented on the Ford Taurus FE model, available online and validated experimentally that shows benefits of 7.83kg weight reduction for the same crash performance of a standard material taken as baseline.

Graphic Processing Units (GPUs) are unanimously considered as powerful computational resources. General-purpose computing on GPU (GPGPU), as well, is the de facto infrastructure for most of the today computationally intensive problems that researchers all over the globe dill with. High Performance Computing (HPC) facilities use state of the art GPUs. Many domains like deep learning, machine learning, and computational finance uses GPU's for decreasing the execution time. GPUs are widely used in data centers for high performance computing where virtualization techniques are intended for optimizing the resource utilization (e.g. GPU cloud computing). The GPU programming model requires for all the data to be stored in a global memory before it is used. This limits the dimension of the problem a GPU can handle. A system utilizing a cluster of GPU would have a bigger level of parallelization but also would eliminate the memory limitation imposed by a single GPU. These being just a few of the problems a programmer needs to handle. However, the ratio between specialists that are able to efficiently program such processors and the rest of programmers is very small. One important reason for this situation is the steepness of the GPU programming learning curve due to the complex parallel architecture of the processor. Therefore, the tool presented in this article aims to provide visual support for a better understanding of the execution on GPU. With it, the programmers can easily observe the trace of the parallel execution on their own algorithm and, from that, they could determine the unused GPU capacity that could be better exploited.

Considering the importance of the transportation network and bridge structures, the associated seismic design philosophy is shifting from the basic collapse prevention objective to maintaining functionality on the community scale in the aftermath of moderate to strong earthquakes (i.e., resiliency). In addition to performance, the associated construction philosophy is also being modernized, with the utilization of accelerated bridge construction (ABC) techniques to reduce impacts of construction work on traffic, society, economy, and on-site safety during construction. Recent years have seen several developments towards the design of low-damage bridges and ABC. According to the results of conducted tests, these systems have significant potential to achieve the intended community resiliency objectives. Taking advantage of such potential in the standard design and analysis processes requires proper modeling that adequately characterizes the behavior and response of these bridge systems. To evaluate the current practices and abilities of the structural engineering community to model this type of resiliency-oriented bridges, the Pacific Earthquake Engineering Research Center (PEER) organized a blind prediction contest of a two-column bridge bent consisting of columns with enhanced response characteristics achieved by a well-balanced contribution of self-centering, rocking, and energy dissipation. The parameters of this blind prediction competition are described in this report, and the predictions submitted by different teams are analyzed. In general, forces are predicted better than displacements. The post-tension bar forces and residual displacements are predicted with the best and least accuracy, respectively. Some of the predicted quantities are observed to have coefficient of variation (COV) values larger than 50%; however, in general, the scatter in the predictions amongst different teams is not significantly large. Applied ground motions (GM) in shaking table tests consisted of a series of naturally recorded earthquake acceleration signals, where GM1 is found to be the largest contributor to the displacement error for most of the teams, and GM7 is the largest contributor to the force (hence, the acceleration) error. The large contribution of GM1 to the displacement error is due to the elastic response in GM1 and the errors stemming from the incorrect estimation of the period and damping ratio. The contribution of GM7 to the force error is due to the errors in the estimation of the base-shear capacity. Several teams were able to predict forces and accelerations with only moderate bias. Displacements, however, were systematically underestimated by almost every team. This suggests that there is a general problem either in the assumptions made or the models used to simulate the response of this type of bridge bent with enhanced response characteristics. Predictions of the best-performing teams were consistently and substantially better than average in all response quantities. The engineering community would benefit from learning details of the approach of the best teams and the factors that caused the models of other teams to fail to produce similarly good results. Blind prediction contests provide: (1) very useful information regarding areas where current numerical models might be improved; and (2) quantitative data regarding the uncertainty of analytical models for use in performance-based earthquake engineering evaluations. Such blind prediction contests should be encouraged for other experimental research activities and are planned to be conducted annually by PEER.