Long-duration Flows Research Articles

Impacts of climate change on flood volumes over North American catchments

Climate change is expected to increase the frequency and intensity of extreme events, such as droughts and floods. Assessing the impacts of climate change on flood volumes is crucial to provide better management of flooding disasters given the devastating consequences they can cause. Using flood volume instead of flood peak is critical because the latter focuses on the highest possible discharge observed during a flood event, while flood volume also considers flow duration, which is an important factor in terms of the hazard caused to the surrounding environment. This study aims to evaluate the overall impact of climate change on floods caused by long-duration flows exceeding synthetic flooding thresholds. These flows are used to compute flood storage with various flow thresholds increasing from the 50th to 95th percentile of annual maximum observed discharge over a large sample of 1403 North American catchments. This study also aims to evaluate the contribution of each uncertainty source of the ensemble approach (climate models, bias-correction methods, and hydrological models) on future flood volumes. The results show that flood volumes are expected to decrease in western mountainous areas, the Great Lakes region, and the Maritimes, while increases are expected over most of eastern North America. The study finds that climate models contribute the most to the variance of flood volume uncertainty, followed by hydrological models. Overall, this study provides projected flood volume changes for North American catchments from a comprehensive ensemble that includes eleven climate models driven by two RCP scenarios and four hydrological models of varying complexity. This leads to a large-sample assessment of future flood volumes that could be useful to policymakers for making better-informed decisions in flood risk management.

Hybrid Cooling-Based Thermal Management of Containerised Vanadium Flow Battery Systems in Photovoltaic Applications

The integration of industrial batteries with photovoltaic applications is a common practice to charge the batteries using solar energy. Long-duration flow batteries are useful in dealing with the intermittency of renewable energy sources and offer a great opportunity for total fossil fuel replacement. In this study, the effects of different battery operation time and load profiles on the temperature dynamics of a containerised vanadium flow battery system are modelled and simulated for a range of locations and seasons to identify active cooling or heating requirements that might be needed to maintain safe operating temperatures. This paper explores and analyses the stack, tank, and container temperature dynamics of 6 h and 8 h containerised vanadium flow batteries (VFBs) during periods of higher charge and discharge current using computer simulations that apply insulation with passive or active hybrid cooling thermal management where needed to keep the battery temperature within a safe operating range under a range of climate conditions. According to the simulation results, when adopting the hybrid cooling strategy as described in the case study, for a 30 kW–240 kWh VFB system with ambient temperatures fluctuating between 25 °C and 45 °C, the monthly electricity consumption of the air conditioning system, calculated using average power, can be maintained at a relatively low level of approximately 330 kWh. By employing an air conditioning system with an airflow rate of 0.2 m3/s and a suitable thermal management strategy, it is sufficient to keep an 8 h system operating within a safe temperature range when the ambient temperature is between 15 °C and 35 °C. This study presents the first application of our previously developed containerised VFB thermodynamic model to explore the necessity of active cooling or heating in PV (photovoltaic) applications across different geographical locations and seasons. This analysis provides valuable insights for battery designers and manufacturers to understand the performance of containerised battery systems under various climate conditions. Furthermore, this paper is the first to apply this model for simulating 6 and 8 h batteries and to adopt a hybrid thermal management strategy. The simulation data offer guidance on whether active cooling or heating is required for industrialised vanadium batteries with capacities exceeding 6 h.

Malicious Overtones

A method for detecting electronic data theft from computer networks is described, capable of recognizing patterns of remote exfiltration occurring over days to weeks. Normal traffic flow data, in the form of a host’s ingress and egress bytes over time, is used to train an ensemble of one-class learners. The detection ensemble is modular, with individual classifiers trained on different traffic features thought to characterize malicious data transfers. We select features that model the egress to ingress byte balance over time, periodicity, short timescale irregularity, and density of the traffic. The features are most efficiently modeled in the frequency domain, which has the added benefit that variable duration flows are transformed to a fixed-size feature vector, and by sampling the frequency space appropriately, long-duration flows can be tested. When trained on days or weeks worth of traffic from individual hosts, our ensemble achieves a low false-positive rate (<2%) on a range of different system types. Simulated exfiltration samples with a variety of different timing and data characteristics were generated and used to test ensemble performance on different kinds of systems: When trained on a client workstation’s external traffic, the ensemble was generally successful at detecting exfiltration that is not simultaneously ingress-heavy, connection-sparse, and of short duration—a combination that is not optimal for attackers seeking to transfer large amounts of data. Remote exfiltration is more difficult to detect from egress-heavy systems, like web servers, with normal traffic exhibiting timing characteristics similar to a wide range of exfiltration types.

Stream Lab 2008

StreamLab08 experiments were specifically designed to understand and quantify the effect of migrating gravel-bed topography on velocity fluctuations and sediment transport. These experiments provide a much higher resolution and longer duration flow field, bed topography and sediment transport measurements in the case of gravel-bed conditions compared to those of StreamLab06.

Generation, ascent and eruption of magma on the Moon: New insights into source depths, magma supply, intrusions and effusive/explosive eruptions (Part 2: Predicted emplacement processes and observations)

Generation, ascent and eruption of magma on the Moon: New insights into source depths, magma supply, intrusions and effusive/explosive eruptions (Part 2: Predicted emplacement processes and observations)

Virtual vectors and network traffic analysis

In a high-speed network, traffic monitoring modules should be compact in size to fit into a fast but small memory (e.g., SRAM). We propose two compact algorithms for network traffic monitoring and analysis, for the purposes of per-flow traffic measurement and long-duration flow detection. The proposed schemes are based on the data structure of a virtual vector that was recently invented, but limited to the purpose of estimating spread value. We found that the virtual vector can be applied to a range of different problems in the area of network traffic monitoring and analysis. In this article, we propose a counting virtual vector that counts the number of packets for per-flow traffic measurement. For long-duration flow detection, we observe that the attackers can easily evade the previous work and propose a new detection scheme to catch even evasive flows. Through experiments on real Internet traffic traces, we show that the proposed schemes outperform previous work or make up for its weaknesses.

Detecting Long Duration Flows without False Negatives

A new network measurement primitive was recently proposed, known as long duration flows (LDF). LDF deserves special attention for network management and security monitoring. This kind of traffic appears periodically and persistently through a long period, but its total amount of traffic is not necessarily large. This feature makes detection difficult especially when the resources of detection system are limited or the detection should cover high-speed networks. In this paper, we propose a new lightweight data structure and streaming algorithm to detect such traffic.

Modeling the Effect of Flow and Sediment Transport on White Sturgeon Spawning Habitat in the Kootenai River, Idaho

Kootenai River white sturgeon spawn in an 18-km reach of the Kootenai River, Id. Since completion of Libby Dam upstream from the spawning reach in 1972, 1974 is the only year with documented significant recruitment of juvenile fish. Where successful in other rivers, white sturgeon spawn over clean coarse material of gravel size or larger. The channel substrate in the current (2008) 18-km spawning reach is composed primarily of sand and some buried gravel; within a few kilometers upstream there is an extended reach of clean gravel, cobble, and bedrock. We used a quasi-three-dimensional flow and sediment-transport model along with the locations of collected sturgeon eggs as a proxy for spawning location from 1994 to 2002 to gain insight into spawning-habitat selection in a reach which is currently unsuitable due to the lack of coarse substrate. Spatial correlations between spawning locations and simulated velocity and depth indicate fish select regions of higher velocity and greater depth within any river cross section to spawn. These regions of high velocity and depth occur in the same locations regardless of the discharge magnitude as modeled over a range of pre- and postdam flow conditions. A flow and sediment-transport simulation shows high discharge, and relatively long-duration flow associated with predam flow events is sufficient to scour the fine sediment overburden, periodically exposing existing lenses of gravel and cobble as lag deposits in the current spawning reach. This is corroborated by video observations of bed surface material following a significant flood event in 2006, which show gravel and cobble present in many locations in the current spawning reach. Thus, both modeling and observations suggest that the relative rarity of extremely high flows in the current regulated flow regime is at least partly responsible for the lack of successful spawning; in the predam flow regime, frequent high flows removed the fine sediment overburden, unveiling coarse material and providing suitable substrate in the current spawning reach.