Chemical Plume Tracing Edwin A. Cowen† and Keith B. Ward§
1. Introduction Given a measurable concentration of a dissolved substance at a point within a fluid flow, where is the source? As physicists and engineers we often look at this problem from a more traditional perspective given a point source release of a substance, we seek to describe the temporal and spatial evolution of the effluent plume. Animals, seeking to evade predators or to locate mates or sources of food, contend with this problem routinely every day, and they have evolved a keen ability to locate these sources. Thus whereas our traditional approach when studying point source releases in the field of fluid mechanics is to seek to understand the fate of the source material and hence to study the forward problem, animals, and hence the biologists who study them, traditionally approach the study of the point-source-in-a-fluid as an inverse problem. This Special Issue on chemical plume tracing presents the work of an interdisciplinary research team, assembled by the Defense Advanced Research Projects Agency (DARPA) and the Office of Naval Research (ONR) to develop the ability to track chemical plumes to their sources. The fundamental approach to this research has been biomimetic. Both ONR and DARPA currently support research programs that seek either to integrate biological sensors or materials directly into new technology, or to capitalize on our knowledge about biological systems and processes to design new technological approaches to meet the demanding capability requirements of many armed services applications. This “biocentric” approach spans many disciplines and length scales. Some workers seek to enhance the performance of Navy surface and underwater platforms by a careful hydrodynamic analysis of swimming animals (Triantafyllou and Triantafyllou, 1995; Gordon et al., 2000; Bandyopadhyay, 2002). Others attempt to harness the power of single molecular motors to provide new energy sources (Montemagno and †
Asst. Professor and Director, DeFrees Hydraulics Laboratory, Hollister Hall, Cornell University, Ithaca NY 14853-3501.
[email protected] § Program Officer, Code 342 BB, Chair, Biomolecular and Biosystems Sciences and Technology Group, 800 N. Quincy Street, Office of Naval Research , Arlington, VA 22217-5660.
[email protected] c 2002 Kluwer Academic Publishers. Printed in the Netherlands.
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Bachand, 1999). Some even propose to take advantage of the power of molecular biology to engineer and control precisely the structure of plant viruses in order to develop a new class of “molecular electronics” (Wang et al., 2002). The work reported in this Special Issue takes advantage of our latest understanding of how animals behave when faced with the problem of locating sources of chemical plumes. DARPA and ONR approached the chemical plume tracing research program from the perspective that a careful understanding of how animals track plumes to their sources would lead to the development of practical plume tracing systems. Clearly the ability to track a plume to its source would be of great significance, both for military and for civilian applications. This Special Issue comprises six papers that are products of the DARPA/ONR research program on chemical plume tracing. Several of the papers are themselves interdisciplinary efforts while others are heavily influenced by the interdisciplinary interactions developed between these research groups throughout the two-year DARPA/ONR research program. The ability of animals to track odor plumes to their source is well known and is clearly documented in each of the six papers included in this Special Issue. At first thought the problem of locating the source of a chemical signature within a fluid might seem a fairly simple task. As relatively unsophisticated olfactory-based searchers we have all experienced the intuitive reaction to turn to face up-wind when presented with the chemical signature of barbecue on the grill or a bread bakery. This tendency, coupled with a movement up-wind, is known as odor gated rheotaxis or OGR to biologists and is perhaps the simplest of search strategies for locating a chemical source. However, as Grasso and Atema demonstrate in their contribution to this Issue, we are far from having an understanding of the complete process by which even a relatively simple organism, such as the American lobster, conducts an OGR style search. Even in the most straightforward of turbulent plumes (spatially and temporally steady state), we lack sufficient understanding to enable us to develop a biomimetic surrogate that can conduct the search as efficiently as the lobster itself. Why is the task of chemical plume tracing complicated? The starting point is of course the chemical signature itself. Chemical plumes of interest are almost always turbulent and despite the linear nature of scalar (e.g., passive chemical signature) turbulence there is an incredible richness of scales, both in space and time, that rapidly develops once a chemical signature is released into a fluid flow. Weissburg et al. demonstrate that the correlated spatial structure within a plume offers chemical cues that can be used to guide a searcher to the source. Liao and Cowen explore turbulence from a fundamental stochastic per-
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spective and investigated the convergence rate of candidate statistics that could form the basis of a chemical plume tracing algorithm. They find that for an Eulerian sensor the scalar intermittency can be used to determine the direction of a chemical source with a given level of confidence in significantly less time than more common metrics (e.g., differences in the mean concentration field). Both of these contributions suggest that search strategies based on following the ‘edge’ of a plume (as opposed to the centerline) may be particularly robust and each group cites the blue crab as an organism evincing this search strategy. No sensor detects a chemical signature exactly as it is presented in the fluid flow. In biological sensors the signature is detected only after the flow is disturbed, intentionally or unintentionally, by the animal. The chemical signal must then diffuse through the sensor structure to the point where transduction occurs (Koehl et al., 2001). The signal may then be either combined with other sensor signals and/or preprocessed before finally being received and acted on by the brain. Analogous processes come into play for any sensor one can imagine - they are not unique to biologically based sensors. In their contribution to this Special Issue, Crimaldi et al. demonstrate how animals use olfactory appendages to modify the signature of a chemical signal, before the signature first contacts a sensor. They report that movement of a sensor array through the flow (e.g., the flicking of an antennule) alters the frequency content of the scalar signal while increasing the probability of encountering high concentration events. The time constant of typical biological transducers is an area of active research. Indications are that biological transducers are effective binary detectors at maximum frequencies of about 20 Hz while they have appreciable concentration bandwidth at frequencies of up to 5 Hz (Gomez and Atema, 1996a). This puts a limit on the detectable temporal frequency content, at least for biological searchers, that is well below the Batchelor frequency for typical chemical signatures in the environment (which under typical conditions, characterized by high Schmidt number, is of order 1 kHz, where the Schmidt number is the ratio of the kinematic viscosity to the molecular diffusivity of the dissolved chemical species). However, as pointed out by Crimaldi et al., the spatial extent of a sensor, coupled with any sensor motion within the fluid, also influences the temporal resolution of a sensor. Consideration of typical biological sensors suggests it is in fact the sensor length scale that quickly becomes the limiting factor in temporal resolution. Hence, and perhaps not surprisingly, biological sensor responses appear to be at or near the limit of temporal resolution set by the sensor length. Biological constraints, however, are not a fundamental limit as we look to develop plume tracking capabilities. If sensors with small spatial
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scale and rapid temporal response can be developed, they may have distinct advantages over larger sensors with lower frequency response. Chemical plume tracing has three distinct phases: Detecting the plume, tracing the plume to the source, and determining the source location. Detection of the plume often relies on the measurement of low level chemical signatures at distances that are potentially far from the source. In this scenario high frequency capability increases the probability of encountering relatively high concentration events (discussed in some detail in the contributions by Crimaldi et al. and Liao and Cowen). Additionally, as suggested by Liao and Cowen, anisotropy at the small scales may contain information about the direction to the source location. While it is not yet clear what aspect of the chemical signature is being used, it is well known that insects track plumes optimally under conditions that present relatively high frequency (5 - 10 Hz) temporal structure to their sensors, as discussed in the contribution by Justus et al. These authors investigate the effect of near-source release conditions on plume structure and demonstrate that fluid mechanicians are not the only researchers to think about statistical descriptions of turbulence. These entomologists recognize the importance of scalar intermittency to chemotactic strategies for source location. Their contribution elucidates an important arena the DARPA/ONR research team grappled within during this interdisciplinary research program – a common language. Entomologists consider zero intermittency to mean that pure source material is present whereas fluid mechanicians consider intermittency values of one to indicate pure source material. This is but one simple example of a complex communication hurdle the team members had to overcome. A general consensus of the investigator team assembled by DARPA/ONR was that it took 12 - 18 months before a sufficiently potent common language emerged enabling the team to effectively address the task with its full intellectual resources. A common theme of each of the papers in this Special Issue is sensor capability. Grasso and Atema work with a binary sensor while Liao and Cowen suggest that ultimately sensors capable of detecting sharp gradients (high temporal and/or spatial resolution) and subtle concentration changes (high concentration resolution) may be required. The actual capability of biological sensors is an area of active research. The frequency response of some of these sensors was described briefly above but the picture is more complex than initially stated. While a temporal response of 5 - 10 Hz can be observed for individual sensors, this is a function of the concentration history (Gomez and Atema, 1996b) as sensor saturation can lead to a temporary cessation of sensor response. The sensors themselves have an extraordinarily large overall dynamic
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range that typically exceeds order 105 (e.g., Borroni and Atema, 1988) and for certain chemical species may be much greater. Although sensors at any instant in time have a usable dynamic range only of order 102 , they can adapt to local changes in the background concentration, effectively utilizing their entire dynamic range, albeit at time scales that are slower than their typical sample rate time scale. While the continuous frequency response is slow with respect to the Batchelor frequency (the smallest temporal or spatial frequency present in a turbulent concentration field), there is evidence that some biological sensors can detect rise slopes (e.g., Zettler and Atema, 1999). However, the utility of this ability to the animal’s behavior remains an open question. Even if the sensor can detect the rise slope it is not clear that this signal ever reaches or can be processed by the brain. For example, Webster and Weissburg (2001) find that the blue crab is unable to react to concentration rise slopes. However, as new experimental search platforms are developed and instrumented with sensor payloads, high frequency sensor response capabilities may prove advantageous both for plume detection and chemotaxis. Sensor capability is not restricted to the properties of single-sensor transducers. Organisms are equipped with sensor arrays - often having thousands of chemosensors spatially distributed over an antennule or other portion of their body. Several of the contributions in this Special Issue discuss the potential impact of these arrays on sampling (e.g., Crimaldi et al., Weissburg et al.). Still an open question about the neuronal system of these animals is the level of spatio-temporal detail that the brain receives from these sensor arrays. Is the information combined or filtered in some way before it is processed by the brain? Much remains to be learned about how animals manage to pick out specific chemical signals at low concentration within a complex chemical background. Although that was not a subject addressed by this research team, DARPA and ONR continue to support important work in this area. Much current “artificial nose” technology has relied upon principles derived from biological olfaction, which has been shown to use large arrays of less selective but highly cross-reactive sensors (Dickinson et al., 1999; Kauer and White, 2001; Stizel et al., 2001). We expect this research soon will lead to improved sensors for explosives and other weapons of mass destruction. The focus of this Special Issue is the tracing of a plume after it has been detected. However, Grasso and Atema, Justus et al., and Weissburg et al. also addressed the final stage of the chemical plume tracing problem - locating the source. Biological literature describes a well-documented behavioral change from plume tracing to sourcelocating behavior when a searcher gets within a certain distance of
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the source. This dramatic behavioral change poses an interesting fluid mechanics question because the searcher clearly detects a different physio-chemical signature near the source compared to the signature far from the source. The mechanism for behavioral change might be as simple as the fact that crustaceans such as the American lobster and blue crab lose contact with the chemical plume at their antennules (which move outward with respect to their position in the concentration boundary layer) as the searcher approaches the source. At the same time, the chemosensor arrays along their legs remain within the concentration boundary layer’s inner layer and hence remain in contact or even experience increased contact with the plume as the source is approached. Conducting physical simulations such as those described in the majority of papers in this issue is time consuming and expensive. Algorithm developers desire the ability to test algorithms under appropriate conditions quickly and inexpensively. Grasso and Atema describe such a test of OGR and derivative algorithms in a physically modeled flow. In addition to their expense, physically modeled flows can be severely constrained in spatial scale, and hence physical simulations with potentially significant larger scale features of the flow, such as plume meander, have not generally been conducted. Because the intermittent temporal and spatial structure of chemical plumes is an important feature that organisms may take advantage of when conducting searches, these features must be resolved if one desires to develop a computational plume simulation tool appropriate for chemical plume tracing algorithm development. However, resolution of the requisite spatial and temporal structure would require at minimum a large eddy simulation approach to solving the Navier-Stokes equations coupled with appropriate transport equations. This approach requires intensive computation, and is simply not feasible for researchers wanting to simulate environmental scale problems. In their contribution to this Special Issue, Farrell et al. offer a clever approach to plume simulation at environmental scales that captures key features of turbulent chemical plumes yet is computationally inexpensive to implement. Although the cost and space constraints of using physical simulations for optimizing search algorithms is prohibitive, physical simulations and further insight into the information content of chemical plumes continue to be essential if we are to develop search platforms capable of locating chemical sources with the speed, efficiency and robustness of animal searchers who have, over tens of millions of years, evolved sensor systems, locomotion and navigation systems, and modes of behavior that are optimized for this task. As the information content of chemical plumes is further understood, simulation tools such as the
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one developed by Farrell et al. can be extended to model the critical physics at more reasonable costs. It is clear that we still have much to learn at this exciting interface between biological behavior and fluid mechanics if we are ultimately to develop practical chemical plume tracking systems that meet or exceed biomimetic criteria. . The papers that follow offer a sound technical basis for this development. Recent increased demands for enhanced plume tracing capabilities now required for Homeland Defense and CounterTerrorism missions strengthen our resolve to pursue vigorously Science and Technology development efforts in this area. Acknowledgements The authors, on behalf of all the DARPA/ONR Team members, thank Dr. Regina Dugan, DARPA, for stimulating discussions and technical direction during the course of the Chemical Plume Tracing program.
References Bandyopadhyay, P. R.: 2002, ‘Maneuvering Hydrodynamics of Fish and Small Underwater Vehicles’. Journal of Integrative and Comparative Biology 42, in press. Borroni, P. and J. Atema: 1988, ‘Adaptation in chemoreceptor cells: I. Self-adapting backgrounds determine threshold and cause parallel shift of response function’. J. Comp. Physiol A 164, 67–74. Dickinson, T. A., K. L. Michael, J. S. Kauer, and D. R. Walt: 1999, ‘Convergent, self-encoded bead sensor arrays in the design of an artificial nose’. Analytical Chemistry 71, 2192–2198. Gomez, G. and J. Atema: 1996b, ‘Temporal resolution in olfaction II: Time course of recovery from adaptation in lobster chemoreceptor cells’. J. Neurophys 76, 1340–1343. Gomez, G. and J. Atema: 1996a, ‘Temporal resolution in olfaction: Stimulus integration time of lobster chemoreceptor cells’. J. Exp. Biol. 199, 1771–1779. Gordon, M. S., J. R. Hove, P. W. Webb, and D. Weihs: 2000, ‘Boxfishes as unusually well-controlled autonomous underwater vehicles’. Phys. Biochem. Zool. 73, 63– 671. Kauer, J. S. and J. White: 2001, ‘Imaging and coding in the olfactory system’. Annual Reviews of Neuroscience 24, 963–979. Koehl, M. A. R., J. R. Koseff, J. P. Crimaldi, M. G. McCay, T. Cooper, M. B. Wiley, and P. A. Moore: 2001, ‘Lobster Sniffing: Antennule Design and Hydrodynamic Filtering of Information in an Odor Plume’. Science 294, 1948–1951. Montemagno, C. and G. Bachand: 1999, ‘Constructing nanomechanical devices powered by biomolecular motors’. Nanotechnology 10, 225–231. Stizel, S. E., L. J. Cohen, K. J. Albert, and D. R. Walt: 2001, ‘Array-to-array transfer of an artifcial nose classifier’. Analytical Chemistry 73, 5266–5271.
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Triantafyllou, M. S. and G. S. Triantafyllou: 1995, ‘An Efficient Swimming Machine’. Scientific American 272, 64–70. Wang, Q., T. Lin, L. Taing, and J. E. Johnson: 2002, ‘Icosahedral Virus Plant Particles as Addressable Nanoscale Building Blocks’. Angew. Chem. Int. Ed. 41, 459–462. Webster, D. R. and M. J. Weissburg: 2001, ‘Chemosensory guidance cues in a turbulent odor plume’. Limnol. Oceanogr. 46, 1048 – 1053. Zettler, E. and J. Atema: 1999, ‘Chemoreceptor cells as concentration slope detectors: preliminary evidence from the lobster nose’. Biol. Bull 197, 252–253.
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