Decompression sickness (DCS), which is caused by inert gas bubbles in tissues, is an injury of concern for scuba divers, compressed air workers, astronauts, and aviators. Case reports for 3322 air ...and N2-O2 dives, resulting in 190 DCS events, were retrospectively analyzed and the outcomes were scored as (1) serious neurological, (2) cardiopulmonary, (3) mild neurological, (4) pain, (5) lymphatic or skin, and (6) constitutional or nonspecific manifestations. Following standard U.S. Navy medical definitions, the data were grouped into mild-Type I (manifestations 4-6)-and serious-Type II (manifestations 1-3). Additionally, we considered an alternative grouping of mild-Type A (manifestations 3-6)-and serious-Type B (manifestations 1 and 2). The current U.S. Navy guidance allows for a 2% probability of mild DCS and a 0.1% probability of serious DCS. We developed a hierarchical trinomial (3-state) probabilistic DCS model that simultaneously predicts the probability of mild and serious DCS given a dive exposure. Both the Type I/II and Type A/B discriminations of mild and serious DCS resulted in a highly significant (p << 0.01) improvement in trinomial model fit over the binomial (2-state) model. With the Type I/II definition, we found that the predicted probability of 'mild' DCS resulted in a longer allowable bottom time for the same 2% limit. However, for the 0.1% serious DCS limit, we found a vastly decreased allowable bottom dive time for all dive depths. If the Type A/B scoring was assigned to outcome severity, the no decompression limits (NDL) for air dives were still controlled by the acceptable serious DCS risk limit rather than the acceptable mild DCS risk limit. However, in this case, longer NDL limits were allowed than with the Type I/II scoring. The trinomial model mild and serious probabilities agree reasonably well with the current air NDL only with the Type A/B scoring and when 0.2% risk of serious DCS is allowed.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Synopsis The humpback whale (Megaptera novaeangliae) is exceptional among the large baleen whales in its ability to undertake aquabatic maneuvers to catch prey. Humpback whales utilize extremely ...mobile, wing-like flippers for banking and turning. Large rounded tubercles along the leading edge of the flipper are morphological structures that are unique in nature. The tubercles on the leading edge act as passive-flow control devices that improve performance and maneuverability of the flipper. Experimental analysis of finite wing models has demonstrated that the presence of tubercles produces a delay in the angle of attack until stall, thereby increasing maximum lift and decreasing drag. Possible fluid-dynamic mechanisms for improved performance include delay of stall through generation of a vortex and modification of the boundary layer, and increase in effective span by reduction of both spanwise flow and strength of the tip vortex. The tubercles provide a bio-inspired design that has commercial viability for wing-like structures. Control of passive flow has the advantages of eliminating complex, costly, high-maintenance, and heavy control mechanisms, while improving performance for lifting bodies in air and water. The tubercles on the leading edge can be applied to the design of watercraft, aircraft, ventilation fans, and windmills.
Hydrodynamic flow control in marine mammals Fish, Frank E.; Howle, Laurens E.; Murray, Mark M.
Integrative and comparative biology,
12/2008, Letnik:
48, Številka:
6
Journal Article
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The ability to control the flow of water around the body dictates the performance of marine mammals in the aquatic environment. Morphological specializations of marine mammals afford mechanisms for ...passive flow control. Aside from the design of the body, which minimizes drag, the morphology of the appendages provides hydrodynamic advantages with respect to drag, lift, thrust, and stall. The flukes of cetaceans and sirenians and flippers of pinnipeds possess geometries with flexibility, which enhance thrust production for high efficiency swimming. The pectoral flippers provide hydrodynamic lift for maneuvering. The design of the flippers is constrained by performance associated with stall. Delay of stall can be accomplished passively by modification of the flipper leading edge. Such a design is exhibited by the leading edge tubercles on the flippers of humpback whales (Megaptera novaeangliae). These novel morphological structures induce a spanwise flow field of separated vortices alternating with regions of accelerated flow. The coupled flow regions maintain areas of attached flow and delay stall to high angles of attack. The delay of stall permits enhanced turning performance with respect to both agility and maneuverability. The morphological features of marine mammals for flow control can be utilized in the biomimetic design of engineered structures for increased power production and increased efficiency.
Population estimates of the critically endangered North Atlantic right whale (Eubalaena glacialis) put the number of individuals at 458 with the actual number likely being lower due to a recent ...unusual mortality event. Entanglement with fixed fishing gear is the most significant cause of mortality of North Atlantic right whales. There remains little documentation of how North Atlantic right whales become enwrapped during an encounter with fixed fishing gear. In order to gain a better understanding of how entanglements might occur, an interactive simulator was developed that allows the user to swim a virtual whale model using a standard game controller through a gear field in an attempt to re‐create an entanglement. The morphologically accurate right whale model produces realistic swimming motions and is capable of pectoral fin motions in response to user input. Using the simulator, gear entanglements involving the pectoral flippers including ropes wrapping around the body and entanglements involving the tailstock were re‐created. Entanglements involving the pectoral flippers with body wraps were more easily generated than entanglements involving the tailstock only. The simulator should aid scientists, fisheries experts, fishing gear designers, and bycatch reduction scientists in understanding entanglement dynamics and testing potential new gear configurations.
Decompression sickness (DCS) is a condition associated with reductions in ambient pressure during underwater diving and altitude exposure. Determining the risk of DCS from a dive exposure remains an ...active area of research, with the goal of developing safe decompression schedules to mitigate the occurrence of DCS. This work develops a probabilistic model for the trinomial outcome of full, marginal, and no DCS. The model treats full DCS and marginal DCS as separate, fully weighted hierarchical events. Six variants of exponential-exponential (EE) and linear-exponential (LE) decompression models were optimized to fit dive outcomes from the BIG292 empirical human dive trial data of 3322 exposures. Using the log likelihood difference test, the LE1 trinomial marginal model was determined to provide the best fit for the data. The LE1 trinomial marginal model can be used to better understand decompression schedules, expanding upon binomial models which treat marginal DCS as either a fractionally weighted event or a non-event. Future work could investigate whether the use of marginal DCS cases improves multinomial probabilistic DCS model performance. Model improvement could include the addition of a fourth outcome, where full DCS is split and categorized as serious or mild DCS, creating a tetranomial model with serious, mild, marginal, and no DCS outcomes for comparison with the presently developed model.
Background: Decompression sickness (DCS) is a condition associated with reductions in ambient pressure during underwater diving. The signs and symptoms of DCS can range from mild, such as joint pain or rash, to more severe, such as paralysis and death. Marginal DCS is defined as symptoms associated with DCS lasting only for a short duration and resolving spontaneously without recompression treatment. There are two categories of decompression modeling used to mitigate risk of DCS: deterministic and probabilistic; neither address DCS symptom severity. Symptom severity is considered during U.S. Navy dive planning, as the Navy has established limits on the number of allowable cases of DCS for a given dive that vary based on symptom severity. Previously, Howle et al. explored multinomial probabilistic models, in which the probabilities of multiple separate events are calculated simultaneously during model calibration. Howle derived and tested a trinomial model, which predicted probabilities of serious DCS, mild DCS, and no DCS (which included marginal cases). The observed cases of serious and mild DCS were treated hierarchically, as a diagnosis of serious DCS would take precedence over and mask mild DCS if both types of symptoms were present. In this work, we explore an alternative trinomial model, the trinomial marginal model, in which the three states are full DCS, marginal DCS, and no DCS. Methods: The BIG292 standard DCS data set available from two Naval Medical Research Institute reports was used in the fitting of all models in this work. This work explores six variants of exponential-exponential (EE) and linear-exponential (LE) decompression models. All six models consist of three parallel, well-mixed compartments, each with a unique half-time, and have a baseline of six adjustable parameters (three tissue half-times and three gain parameters). The classification of an observed DCS outcome is dictated by the most severe symptom present. To reconcile this hierarchical system of classification with the competitive probabilities used in modeling, Howle et al.‘s definition of hierarchical probabilities is applied here. Model optimization via maximum likelihood is extended to a three-state log likelihood function. Results: Using the log likelihood difference test, we can conclude that the LE1 variant is the best performing trinomial marginal model. The model's predictions for the total number of full DCS and marginal DCS cases do match the observed data within their 95% confidence intervals. When compared with a binomial LE1 model, it is evident that for some dives, the trinomial marginal model predicts a lower probability of no DCS and a higher probability of full DCS than the binomial model. Conclusions: Analysis of the LE1 trinomial marginal model indicated there is room for improvement. The model's predicted probabilities of DCS do not align directly with observed probabilities of DCS. The lack of recorded symptom onset times for many of the marginal DCS cases and subsequent right censoring is unfavorable during the optimization process, as these large event windows make model parameters more difficult to refine than cases with recorded onset times. In our future work, we will explore a four-state (tetranomial) model, that simultaneously predicts mild, serious, marginal, and no DCS outcomes. This model can be compared directly against the trinomial marginal model, with the eventual goal of determining which multinomial probabilistic model performs best, and whether these models provide a significant improvement over the binomial probabilistic model.
Abstract Decompression sickness (DCS) is a disease known to be related to inert gas bubble formation originating from gases dissolved in body tissues. Probabilistic DCS models, which employ survival ...and hazard functions, are optimized by fitting model parameters to experimental dive data. In the work reported here, I develop methods to find the survival function gain parameter analytically, thus removing it from the fitting process. I show that the number of iterations required for model optimization is significantly reduced. The analytic gain method substantially improves the condition number of the Hessian matrix which reduces the model confidence intervals by more than an order of magnitude.
Accurate estimates of drag on marine animals are required to investigate the locomotive cost, propulsive efficiency, and the impacts of entanglement if the animal is carrying fishing gear. In this ...study, we performed computational fluid dynamics analysis of a 10 m (length over all) right whale to obtain baseline measurements of drag on the animal. Swimming speeds covering known right whale speed range (0.125 m/s to 8 m/s) were tested. We found a weak dependence between drag coefficient and Reynolds number. At a swimming speed of 2 m/s, we analyzed the boundary layer thicknesses, the flow regimes, and drag components. We found the thickest boundary layer at the lateral sides of the peduncle, whereas the boundary layer thickness over the outer part of the flukes was less than 1.7 cm. Laminar flow occurred over the anterior ~0.6 LoA and turbulent flow from ~0.8 LoA to the fluke notch. On the surfaces of the flukes outside of the body wake region, flow was laminar. Our most significant finding is that the drag coefficient (0.0071–0.0059) of a right whale for swimming speeds ranging from 0.25 m/s to 2 m/s is approximately twice that of many previous estimates for cetaceans.
Human decompression sickness (DCS) is a condition associated with depressurization during underwater diving. Human research dive trial data containing dive outcome (DCS, no-DCS) and symptom ...information are used to calibrate probabilistic DCS models. DCS symptom onset time information is visualized using occurrence density functions (ODF) which plot the DCS onset rate per unit time. For the BIG292 human dive trial data set, a primary U.S. Navy model calibration set, the ODFs are bimodal, however probabilistic models do not produce bimodal ODFs. We investigate the source of bimodality by partitioning the BIG292 data based on dive type, DCS event severity, DCS symptom type, institution, and chronology of dive trial. All but one variant of data partitioning resulted in a bimodal or ambiguously shaped ODF, indicating that ODF bimodality is not related to the dive type or the DCS event severity. Rather, we find that the dive trial medical surveillance protocol used to determine DCS symptom onset time may have biased the reported event window. Thus, attempts to develop probabilistic DCS models that reproduce BIG292 bimodality are unlikely to result in an improvement in model performance for data outside of the calibration set.
Bio-logging tags are an important tool for the study of cetaceans, but superficial tags inevitably increase hydrodynamic loading. Substantial forces can be generated by tags on fast-swimming animals, ...potentially affecting behavior and energetics or promoting early tag removal. Streamlined forms have been used to reduce loading, but these designs can accelerate flow over the top of the tag. This non-axisymmetric flow results in large lift forces (normal to the animal) that become the dominant force component at high speeds. In order to reduce lift and minimize total hydrodynamic loading this work presents a new tag design (Model A) that incorporates a hydrodynamic body, a channel to reduce fluid speed differences above and below the housing and wing to redirect flow to counter lift. Additionally, three derivatives of the Model A design were used to examine the contribution of individual flow control features to overall performance. Hydrodynamic loadings of four models were compared using computational fluid dynamics (CFD). The Model A design eliminated all lift force and generated up to ~30 N of downward force in simulated 6 m/s aligned flow. The simulations were validated using particle image velocimetry (PIV) to experimentally characterize the flow around the tag design. The results of these experiments confirm the trends predicted by the simulations and demonstrate the potential benefit of flow control elements for the reduction of tag induced forces on the animal.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
122,129 dives by 10,358 recreational divers were recorded by dive computers from 11 manufacturers in an exploratory study of how dive profile, breathing gas (air or nitrox N2/O2 mixes), repetitive ...diving, gender, age, and dive site conditions influenced observed decompression sickness (DCSobs). Thirty-eight reports were judged as DCS. Overall DCSobs was 3.1 cases/10⁴ dives.
Three dive groups were studied: Basic (live-aboard and shore/dayboat), Cozumel Dive Guides, and Scapa Flow wreck divers. A probabilistic decompression model, BVM(3), controlled dive profile variability. Chi-squared test, t-test, logistic regression, and log-rank tests evaluated statistical associations.
(a) DCSobs was 0.7/10⁴ (Basic), 7.6/10⁴ (Guides), and 17.3/104 (Scapa) and differed after control for dive variability (p ≺ 0.001). (b) DCSobs was greater for 22%-29% nitrox (12.6/10⁴) than for 30%-50% nitrox (2.04/10⁴) (p ≤ 0.0064) which did not differ from air (2.97/1010⁴). (c) For daily repetitive dives (≺12-hour surface intervals (SI)), DCS occurred only following one or two dives (4.3/1010⁴ DCSobs; p ≺ 0.001) where SIs were shorter than after three or more dives. (d) For multiday repetitive dives (SIs ≺ 48 hours), DCS was associated with high multiday repetitive dive counts only for Guides (p = 0.0018). (e) DCSobs decreased with age at 3%/year (p ≤ 0.0144). (f) Males dived deeper (p ≺ 0.001) but for less time than females (p ≺ 0.001).
Collecting dive profiles with dive computers and controlling for profile variability by probabilistic modeling was feasible, but analytical results require independent confirmation due to limited observed DCS. Future studies appear promising if more DCS cases are gathered, stakeholders cooperate, and identified data collection problems are corrected.
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