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This briefing will cover general and NSSB worker response actions for an evacuation. Operations Centers, first responders (e.g., LANL Haz-Mat), workers with unique Building Emergency Procedure (BEP) guidance (other than NSSB), etc. are outside the scope of this briefing.
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To meet evacuation needs from carless populations who need personalized assistance to evacuate safely, in this article we propose a ridesharing-based evacuation program that recruits volunteer drivers before a disaster strikes, and then matches volunteer drivers with evacuees once demand is realized. Here we optimize resource planning and evacuation operations under uncertain spatiotemporal demand, and construct a two-stage stochastic mixed-integer program to ensure high demand fulfillment rates. We consider three formulations to improve the number of evacuees served, by minimizing an expected penalty cost, imposing a probabilistic constraint, and enforcing a constraint on the conditional value at risk of the total number of unserved evacuees, respectively. We discuss the benefits and disadvantages of the different risk measures used in the three formulations, given certain carless population sizes and the variety of evacuation modes available. We also develop a heuristic approach to provide quick, dynamic and conservative solutions. We demonstrate the performance of our approaches using five different networks of varying sizes based on regions of Charleston County, South Carolina, an area that experienced a mandatory evacuation order during Hurricane Florence, and utilize real demographic data and hourly traffic count data to estimate the demand distribution.
Response strategies implemented in the first few hours to days after a nuclear detonation on the US homeland may save more than 100,000 lives. Effective planning and pre-event capability development can increase the response efficacy and so the number of lives saved. During such an event, the US Department of Defense, Joint Task Force Civil Support (DoD JTF-CS) provides command and control for the DoD forces supporting civil authority response operations. Among other activities, JTF-CS expects to support FEMA as well as State and local agencies in shelter and evacuation response activities. JTF-CS currently uses a manual method to generate evacuation decision making products. This method compares the dose that would be (a) acquired remaining at a given location to that (b) acquired during evacuation along a small number of routes. In these products, evacuation may be warranted when the projected “remain” dose is greater than the “evacuate” dose. This method only provides a limited consideration of the protection buildings provide their occupants. To inform the JTF-CS nuclear detonation response, the DoD Defense Threat Reduction Agency (DTRA) has tasked Lawrence Livermore National Laboratory (LLNL) to design a set of planning and response products to support shelter-evacuation activities. This report documents these products to facilitate DTRA and JTF-CS planning efforts. To provide context, we also provide appendices that summarize US planning guidance response zones, early (<72 h) response strategies, and other key topics.
Transparent and insulating materials having evacuated capsules are provided. According to an aspect of the invention, a method includes forming evacuated capsules within a solution, and dispersing and suspending the evacuated capsules within the solution such that a packing density of the evacuated capsules within the solution is greater than 30%, and a visible light transmission of the solution including the evacuated capsules is greater than 75%. According to another aspect of the invention, a layer includes a plurality of evacuated capsules distributed within a dried sol-gel. A thermal conductivity of the layer is between 0.02 W/m-K and 0.001 W/m-K, and the layer has a visible light transmission of greater than 30%.
The electric vehicle (EV) market has significantly expanded because EVs have lower operational costs while leaving less environmental footprint than internal combustion engine vehicles. However, EVs also come with drawbacks, including long charging time and short operational ranges. With these drawbacks and limited charging facilities, efficient long-distance EV evacuation management is challenging and has not been properly addressed. Without an efficient evacuation plan, serious congestion could happen at charging facilities, the evacuation process would be excessively long, and thus human lives may be put at risk. This study is motivated to investigate the optimal mass evacuation planning for EVs considering limited charging facilities. A three-stage method is proposed to efficiently approach this problem. A case study of Florida hurricane evacuation is conducted. Here, the method's effectiveness is verified by comparing it with a benchmark. Management insights and policy indications are drawn through sensitivity analysis of key parameters.
In rapid-onset disaster scenarios such as wildfires, evacuation traffic often significantly deviates from historical patterns, rendering conventional data-driven forecasting methods less effective. To address this challenge, we propose an improved algebraic derivative estimation (ADE) incorporating particle swarm optimization (PSO) for real-time traffic flow prediction. Our approach dynamically adjusts the ADE prediction time window at each step by minimizing a cost function based on the mean and variance of accumulated forecasting errors within the window, thereby balancing bias and variability. We evaluate the method using traffic data from the January 2025 California wildfires, focusing on key road segments critical for large-scale evacuations. The results demonstrate that our approach surpasses established machine learning and deep learning models—XGBoost, LSTM, and GRU—in predictive accuracy and maintains high computational efficiency. Notably, the proposed method eliminates the need for offline model training. Moreover, rapid PSO-based tuning enables real-time deployment, which provides a crucial advantage in scenarios where evacuation timings and road closures change dynamically. In conclusion, these findings highlight the benefits of the PSO-enhanced ADE framework for emergency traffic management, where rapid, data-sparse forecasts are essential for effective evacuation planning.
On April 1, 1946, an earthquake off Unimak Island, Alaska, generated a tsunami that raced across the Pacific. One of the islands in danger was Kwajalein, the logistical hub for Operation Crossroads. Commodore Ben Wyatt, the U.S. naval commander of the Marshall Islands, ordered the evacuation of “10 Red Cross workers, 19 USO women, 17 native women, 16 male [hospital] patients, four tiny babies, one small boy, and two navy doctors,” to the USS Rockbridge, which then steamed in deep water off the atoll until the danger passed. One of the Red Cross workers, Reva Hurwitz, played bridge in the ship’s wardroom until 2 a.m., a luxury since the island curfew, strictly enforced, was 10:30 pm. The tsunami bypassed Kwajalein, and everyone returned to the island less than twenty-four hours after being evacuated. Although the evacuation proved unnecessary, the women were grateful, said Hurwitz, because they had the opportunity “to use hot water and eat fresh fruit.”
This dataset contains de-identified data from human subjects experiments, along with the images and code that were used to run the experiments (as a crowdsourced online study). In this study, participants were shown the probability of a house being in the burn zone of a wildfire. They were asked if they would stay in the house or evacuate in that scenario. The probability information was presented in different ways, including text and maps. The studies tested the impact of different visual cues on the participants' patterns of decisions.
Augmented Reality is a promising technology to support safe pedestrian evacuation from buildings during earthquakes. The widespread availability of phone cameras makes this idea feasible. QR codes, for instance, can place virtual three-dimensional objects to guide people along a prescribed route. In this work, a prototypical system based on this premise was created in response to the 2020 sudden surge in seismic activity in Puerto Rico. The results point towards feasibility as well as to a series of areas that require improvement for a real-life implementation.
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Supercritical carbon dioxide is becoming a hot research topic as a potential heat transfer fluid in parabolic trough concentrators since it enables operating the solar system at high temperatures for a higher quality of energy. However, the corresponding inflated thermal losses necessitate alternative receiver designs. This work examines four double-glazed receivers, with each annular space being evacuated or non-evacuated, in terms of the absorber tube's diameter (53-80 mm) and the diameter ratios of the two glass shells (1.2-2.0). An analytical model is developed and validated for this purpose, and the four designs are further examined using ground-level solar and meteorological measurements. The results demonstrate higher performance in the case of fully evacuating the receiver and using the smallest possible diameters of the three concentric cylinders, where the energy and exergy efficiencies reach 65.3 and 40.3%, respectively. Yet, evacuating only the inner annular space is sufficient to achieve virtually the same performance level. This energy efficiency decreases to 62% in case of increasing the tube diameter to 80 mm. As the operating temperature increases from 423 to 850 K, the specific thermal losses increase by 3.98-4.34 folds, depending on the receiver design. Double glazing the receivers is favorable only at high operating temperatures of sCO2, where the reduction in thermal losses overcomes the drop in optical efficiency. For an inlet sCO2 temperature of 850 K, thermal losses are reduced by 33.64 and 53.92%, compared to evacuated and non-evacuated single-glazed receivers, respectively. Throughout the year, the fully evacuated and fully non-evacuated double-glazed receivers have energy efficiencies of 54.96 and 52.39%, exergy efficiencies of 33.64 and 32.06%, and thermal losses of 348.8 and 402.5 W/m, respectively.
Chemically stable metal–organic frameworks (MOFs) featuring interconnected hierarchical pores have proven to be promising for a remarkable variety of applications. Nevertheless, the framework’s susceptibility to capillary-force-induced pore collapse, especially during water evacuation, has often limited practical applications. Methodologies capable of predicting the relative magnitudes of these forces as functions of the pore size, chemical composition of the pore walls, and fluid loading would be valuable for resolution of the pore collapse problem. Here, we report that a molecular simulation approach centered on evacuation-induced nanocavitation within fluids occupying MOF pores can yield the desired physical-force information. Here, the computations can spatially pinpoint evacuation elements responsible for collapse and the chemical basis for mitigation of the collapse of modified pores. Experimental isotherms and difference-electron density measurements of the MOF NU-1000 and four chemical variants validate the computational approach and corroborate predictions regarding relative stability, anomalous sequence of pore-filling, and chemical basis for mitigation of destructive forces.
Abstract The 2011 nuclear accident in Fukushima, Japan caused the evacuation of > 100,000 people and prompted studies on environmental impacts of radiological contamination. However, few researchers have explored how the human evacuation has affected ecosystem processes. Despite contamination, one common scavenger (wild boar, Sus scrofa ) is 2–3× more abundant inside the Fukushima Exclusion Zone (FEZ). Shifts in abundance of some scavenger species can have cascading effects on ecosystems, so our objective was to investigate impacts of the evacuation and the resulting increase in wild boar on vertebrate scavenger communities. We deployed cameras at 300 carcasses in the FEZ and a nearby inhabited area, and quantified carcass fate, scavenger species, and detection/persistence times. We also tested effects of carcass size and habitat on scavenger community composition and efficiency by balancing trials across two carcass sizes and habitats in each zone. Overall scavenger richness and carcass removal rates (73%) were similar in the FEZ and inhabited area, but species-specific carcass removal rates and occurrence differed between zones. Wild boar removed substantially more carcasses inside the FEZ, with implications for nutrient and contaminant distribution. Our results suggest carcass size affects scavenging dynamics more than human activity or habitat, and abundance changes of common scavengers can influence carrion resource allocation.
Thermal insulation materials are crucial to improve the energy performance of buildings and industrial applications. We report an approach to create hermetically vacuum-sealed silica-based hollow microspheres that can lower the thermal conductivity of closed-cell insulation materials. The wall structure of these hollow microspheres includes a reticulated network of pores or channels that extend through the thickness of the wall. When a thin layer of glass material is applied to the wall exterior, followed by a vacuum-assisted thermal treatment process, the coated microsphere surfaces display a highly dense conformal coverage and near-complete elimination of surface porosity. The sealing efficiency of these microspheres is verified by trapping argon within their cavities as well as through evacuating their hollow cores. Notably, incorporating the evacuated microspheres into a polymer matrix resulted in ~27% enhancement in its thermal insulation performance and no notable loss of performance was observed following three months of exposure to ambient conditions. Thus, we believe that the present study offers a commercially viable strategy that opens the door to applications of such inorganic hollow particles in areas ranging from vacuum-based thermal insulation systems to catalysis, separation technologies, and medical fields.
This paper presents a multidisciplinary analysis of the Fukushima Dai-ichi Nuclear Power Plant accident. Along with the latest observations and simulation studies, we synthesize the time-series and event progressions during the accident across multiple disciplines, including in-plant physics and engineering systems, operators’ actions, emergency responses, meteorology, radionuclide release and transport, land contamination, and health impacts. We identify three key factors that exacerbated the consequences of the accident: (1) the failure of Unit 2 containment venting, (2) the insufficient integration of radiation measurements and meteorology data in the evacuation strategy, and (3) the limited risk assessment and emergency preparedness. We conclude with new research and development directions to improve the resilience of nuclear energy systems and communities, including (1) meteorology-informed proactive venting, (2) machine learning-enabled adaptive evacuation zones, and (3) comprehensive risk-informed emergency planning while leveraging the experience from responses to other disasters.