SEARCH · Engineering Papers
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Gaseous benzene-iodine and diethyl ether-iodine spectra, extinction coefficients, formation constants and enthalpies for charge transfer complexes of gaseous benzene-iodine and diethyl ether-iodine
The present investigation is concerned with an eight-day episode, during which a series of mesoscale convective complexes (MCC) developed and moved across the country, producing heavy rain and some flooding over an extensive region. An overview of the considered period from August 3 to August 10, 1977 is presented, and the evolution of the August 4 storm is examined. The structure of the mature MCC is discussed, taking into account the August 4-5 storm, a comparative case involving the August 3-4 storm, and an evaluation of the observed phenomena. It is concluded that MCCs are basically tropical in nature and that their dynamics are dominated by buoyant accelerations. It was found that the MCCs developed a warm-core, divergent anticyclonic flow pattern in the upper troposphere which was not present prior to the development of convection. A similar structure is observed in tropical cloud clusters.
Complex refractive index and dielectric permittivity studies of presently used Space Shuttle tile materials at millimeter wavelengths reveal these tiles to exhibit similar absorption characteristics to those of fused silica materials. This absorption is mainly related to the water content in the specimen. A strong birefringence is observed at least in one of these fibrous refractory composite materials.
Electronic parts (integrated circuits) have grown in complexity such that determining all failure modes and risks from single particle event testing is impossible. In this presentation, the authors will present why this is so and provide some realism on what this means. Its all about understanding actual risks and not making assumptions.
As part of Task 1, we have started by testing our modeling capabilities by reproducing isothermal DFIT simulations presented in the literature. To start, we have been working towards adjusting numerical parameters to reproduce DFIT tests with different fracture geometries. Then, we have focused modeling the DFIT conducted at well 58-32 (cycle 4 in zone 2). Specifically, we have carefully analyzed the influence of the aperture-contact stress relationship and investigated how two different laws affect model results. We have also introduced thermal effects in the model and rerun the simulations to estimate the effect of cooling on the DFIT response. Due to the small amount of fluid injected during a DFIT, thermal effects seem to be moderate.
Performance period 2 of the project spanned from April to December 2024. During this period, our efforts were primarily focused on advancing Tasks 2, 3, and 4 of the project.
As part of Task 1, we have started by testing our modeling capabilities by reproducing isothermal DFIT simulations presented in the literature. To start, we have been working towards adjusting numerical parameters to reproduce DFIT tests with different fracture geometries. Then, we have focused modeling the DFIT conducted at well 58-32 (cycle 4 in zone 2). Specifically, we have carefully analyzed the influence of the aperture-contact stress relationship and investigated how two different laws affect model results.
A great deal of effort is now being devoted to the study, analysis, prediction and minimization of software maintenance expected cost, long before software is delivered to users or customers.
The national airspace has evolved over many years to accommodate increased traffic demand [1] while simultaneously maintaining one of the safest forms of transportation [2], [3]. One of the reasons for this success is the ability of the system and the operators to adapt and accommodate to situations that routinely disrupt optimal operations. These situations may include: adverse weather, delays, early arrivals, equipment outages, and other factors that are outside the operators ability to control. These factors can lead to states where automation is unable to properly handle these issues and therefore air traffic controllers and pilots have to intervene, ultimately increasing communication between operators resulting in higher workload. As controller workload increases to handle sub-optimal operating conditions this can be viewed as an increase in complexity. The reasoning for this is because humans are now required to make tactical decisions in response to external factors, resulting in a departure from the strategic plan where operations would be more efficiently managed. Human operators control airspace complexity under rigid regulations that are constantly changing. The airspace is divided into sectors and the number of aircraft assigned to each controller is limited for safe handling. There has been past work that devised airspace complexity metrics in commercial aviation and related these metrics to controller workload (e.g., [4],[5]). The upper bounds on the system load are pre-determined. Such bounds on complexity make for a safe system, but the system cannot scale and adapt to autonomous, dense, and heterogeneous traffic, including the many types of Unmanned Aerial Vehicles (UAVs) envisioned to be added to the operations. We hypothesize that, as traffic density and heterogeneity grow, and other key metrics change, there will be phase transitions at which the way traffic should be managed changes significantly [6]. We offer a method for in-time detection of contributing factors that lead to phase transitions, characterized by increased complexity. To the best of our knowledge, there is no tool similar to our proposed effort that identifies such contributing factors or precursor patterns. To define the scope we are proposing to measure complexity from the viewpoint of the Terminal Radar Approach Control Facilities (TRACON) controller’s perspective. In particular we are analyzing arrivals into KSFO. With safety as the top concern for airspace operators, it is important to recognize that as density and heterogeneity grow, the focus of the system will change. Times of the day when the airspace has low density and heterogeneity, the flights will follow more efficient paths where the aircraft move on established routes that are more or less directly to the destination. However, when density and heterogeneity increases, the system will begin changing focus to avoiding conflicts and collisions and route the flights in a more flexible way. Higher flexibility requires more communication and coordination between controllers and pilots which the current automation is unable to handle. This paper proposes a novel approach that monitors airspace complexity at multiple scales, uses a Machine Learning-based tool that predicts when operations will transition to a regime of greater complexity, and identifies actions that can reduce the complexity while still maintaining efficient and safe operations. We demonstrate our proposed approach using data from multiple complementary sources. This includes, but is not limited to: historical aircraft surveillance data from NASA’s Sherlock Data Warehouse [7], METAR weather data, and airport configuration data from Aviation System Performance Metrics (ASPM). The surveillance data flight paths are sampled at a variable sample rate — increasing as the aircraft approaches the airport. This is due to how Sherlock manages flight track stitching between different radar facilities which have different sampling rates. The weather and performance data are logged at defined intervals throughout the day at a courser refresh rate. In addition to the logged data and metrics, we leverage pre-defined Standard Terminal Arrival Routes (STARs) procedures to characterize the path of each flight. Each flight files for one of these routes in the flight plan well before entering the terminal airspace, and approximately follows the route until it leaves the STAR, typically on the final fix of a runway transition. However, most flights do not always fly the full STAR procedure to completion [8], but the majority do adhere to the fixes within the common route of the procedure. Our approach leverages fixes in the common route of each of the STARs to build a reference path to the airport. This allows us to characterize the flight paths in what we are defining as the “maneuvering area” (the airspace between the STAR and before the flight is lined up on the runway’s final approach) to determine how off nominal the flights are to calculate its complexity score. Determining airspace complexity is a concept that does not have a concrete answer. In designing this metric, we consider what increases the workload for the air traffic controllers. Consequently more specialized vectoring maneuvers results in higher workload. Accordingly, we start with a theory: each flight has a direct path it takes from the STAR’s common route to the final approach’s outer marker fix for the flight’s landing runway. It is important to note that the direct path is only used as a reference. If the majority of the flights have a large consistent offset as compared to other routes it does not necessarily mean that those flights have higher complexity. We are merely building a distribution based on this direct path for that particular STAR and runway pair to determine the normal mode of operations for that route. Flights that are in the upper tail of these distributions will result in higher complexity scores and flights that fly in the median will represent the normal mode of operations and therefore will have lower complexity scores. Since flights following each STAR route take different paths to the airport, we have a different distribution for each STAR route and therefore can model these distributions to compute a complexity score from their respective normalized distributions. To evaluate the effectiveness of our proposed airspace complexity metric we will compare against an established approach based on trajectory clustering [9]. This unsupervised learning technique consists of the following steps: (1) identify the general maneuvering areas (waypoints) by performing $\kappa$-means or DBSCAN clustering on locations where aircraft frequently turn based on the surveillance radar track data, (2) map flight trajectories onto sequences of waypoints, and (3) cluster the sequences based on their common subsequences. From a high-level perspective, this baseline model learns nominal operations in the airspace through the sequence of waypoints that are representative of where aircraft change direction and defines deviations from the nominal operations as “complex.” Therefore, more deviations from the nominal operations correspond to higher complexity values. For our validation, we re-implemented this technique and tune model hyper-parameters to correctly detect waypoints for the arrival traffic into the San Francisco bay area. We will compute the complexity measure over a one-year period using our proposed technique as well as the baseline. Our validation will be based on each technique’s ability to detect a set of undesirable outcomes (e.g., go-arounds, holding patterns, average time in the airspace, etc.). Since our current complexity metric is derived from the offset from the direct reference path, it’s important to understand what causes these offsets. In many of the flights with high offset distance, flights performing holding patterns and S turns can be observed. These maneuvering tactics are utilized to add distance between the aircraft and the destination runway to prevent multiple flights from having conflicting arrival times. In order to predict a rise in complexity (or the precursor to complexity), it’s necessary to be able to identify these potential conflicts (which in turn, result in higher offsets). To do this, we define a “representative flight” for each STAR route and runway pair. This flight is approximately the path the flight would take if there was a clear path with no other flights in the airspace — including the time remaining to the airport. We first identify the flights for a given STAR runway pair using the offset to the reference path distributions that fall between the 44-55 percentiles. This yields the flights that conform to the most normal mode of operation. Each of these flights is partitioned based on the percent complete from the entry point into the maneuvering areas from 0\% – 100\% complete. Then for each percent “bin”, we take the median value of the flight’s latitude/longitude coordinates, airspeed, and (non causal) time remaining to the airport to construct a lookup table for each percent complete bin on a given route. As a flight enters the maneuvering area, we can find the estimated arrival time of a flight to the airport by finding the closest point to the representative path’s percent complete bin (relative to the flight’s current position at any snapshot in the airspace) and therefore retrieve the corresponding remaining time left on the “representative path”. We assume that the flight will follow the representative path to completion when deriving these estimates. We can then compare these estimated arrival times against other flights for the same snapshot in time to identify potential conflicts. If more flights are estimated to arrive within a tolerance window than there are runways available, then we have a potential conflict. We can use this derived measure along with other factors expected to add disruption to the operation such as weather and runway configuration changes as an input to machine learning tools to detect precursors that increases in our complexity measure. This novel method will assist in uncovering insights into the contributing factors that lead to increased complexity that may allow for in-time responses to avoid reaching a high complexity state in the airspace.
Occam's razor is often used in science to define the minimum criteria to establish a physical or philosophical idea or relationship. Albert Einstein is attributed the saying "everything should be made as simple as possible, but not simpler". These heuristic ideas are based on a belief that there is a minimum state or set of states for a given system or phenomena. In looking at system complexity, these heuristics point us to an idea that complexity can be reduced to a minimum. How then, do we approach a reduction in complexity? Complexity has been described as a subjective concept and an objective measure of a system. Subjective complexity is based on human cognitive comprehension of the functions and inter relationships of a system. Subjective complexity is defined by the ability to fully comprehend the system. Simplifying complexity, in a subjective sense, is thus gaining a deeper understanding of the system. As Apple's Jonathon Ive has stated," It's not just minimalism or the absence of clutter. It involves digging through the depth of complexity. To be truly simple, you have to go really deep". Simplicity is not the absence of complexity but a deeper understanding of complexity. Subjective complexity, based on this human comprehension, cannot then be discerned from the sociological concept of ignorance. The inability to comprehend a system can be either a lack of knowledge, an inability to understand the intricacies of a system, or both. Reduction in this sense is based purely on a cognitive ability to understand the system and no system then may be truly complex. From this view, education and experience seem to be the keys to reduction or eliminating complexity. Objective complexity, is the measure of the systems functions and interrelationships which exist independent of human comprehension. Jonathon Ive's statement does not say that complexity is removed, only that the complexity is understood. From this standpoint, reduction of complexity can be approached in finding the optimal or 'best balance' of the system functions and interrelationships. This is achievable following von Bertalanffy's approach of describing systems as a set of equations representing both the system functions and the system interrelationships. Reduction is found based on an objective function defining the system output given variations in the system inputs and the system operating environment. By minimizing the objective function with respect to these inputs and environments, a reduced system can be found. Thus, a reduction of the system complexity is feasible.
A user's guide for the Air Traffic Complexity Measurement Environment (ACME) software is presented. The ACME consists of two major components, a complexity analysis tool and user interface. The Complexity Analysis Tool (CAT) analyzes complexity off-line, producing data files which may be examined interactively via the Complexity Data Analysis Tool (CDAT). The Complexity Analysis Tool is composed of three independently executing processes that communicate via PVM (Parallel Virtual Machine) and Unix sockets. The Runtime Data Management and Control process (RUNDMC) extracts flight plan and track information from a SAR input file, and sends the information to GARP (Generate Aircraft Routes Process) and CAT (Complexity Analysis Task). GARP in turn generates aircraft trajectories, which are utilized by CAT to calculate sector complexity. CAT writes flight plan, track and complexity data to an output file, which can be examined interactively. The Complexity Data Analysis Tool (CDAT) provides an interactive graphic environment for examining the complexity data produced by the Complexity Analysis Tool (CAT). CDAT can also play back track data extracted from System Analysis Recording (SAR) tapes. The CDAT user interface consists of a primary window, a controls window, and miscellaneous pop-ups. Aircraft track and position data is displayed in the main viewing area of the primary window. The controls window contains miscellaneous control and display items. Complexity data is displayed in pop-up windows. CDAT plays back sector complexity and aircraft track and position data as a function of time. Controls are provided to start and stop playback, adjust the playback rate, and reposition the display to a specified time.
The complexities of chemical composition and crystal structure are fundamental characteristics of minerals that have high relevance to the understanding of their stability, occurrence and evolution. This review summarises recent developments in the field of mineral complexity and outlines possible directions for its future elaboration. The database of structural and chemical complexity parameters of minerals is updated by H-correction of structures with unknown H positions and the inclusion of new data. The revised average complexity values (arithmetic means) for all minerals are 3.54(2) bits/atom and 345(10) bits/cell (based upon 4443 structure reports). The distributions of atomic information amounts, chemIG and strIG, versus the number of mineral species fit the normal modes, whereas the distributions of total complexities, chemIG,total and strIG,total, along with numbers of atoms per formula and per unit cell are log normal. The three most complex mineral species known today are ewingite, morrisonite and ilmajokite, all either discovered or structurally characterised within the last five years. The most important complexity-generating mechanisms in minerals are: (1) the presence of isolated large clusters; (2) the presence of large clusters linked together to form three-dimensional frameworks; (3) formation of complex three-dimensional modular frameworks; (4) formation of complex modular layers; (5) high hydration state in salts with complex heteropolyhedral units; and (6) formation of ordered superstructures of relatively simple structure types. The relations between symmetry and complexity are considered. The analysis of temporal dynamics of mineralogical discoveries since 1875 with the step of 25 years show the increasing chemical and structural complexities of human knowledge of the mineral kingdom in the history of mineralogy. In the Earth’s history, both diversity and complexity of minerals experience dramatic increases associated with the formation of Earth’s continental crust, initiation of plate tectonics and the Great Oxidation event.
Additions of two equivalents of (CF 3 ) 3 COH (R F9 OH) or (CF 3 ) 2 MeCOH (R F6 OH) to W(NAr) 2 R 2 complexes (Ar = 2,6-diisopropylphenyl, R = n-propyl or i-propyl) offer the opportunity to synthesize propylene or isopropylidene olefin metathesis-active complexes in the absence of free propylene. Propylene and isopropylidene complexes (W(NAr)(ArNH 2 )(OR F9 ) 2 (propylene) and W(NAr)(ArNH 2 )(OR F9 ) 2 (CMe 2 )) are formed at room temperature from both W(NAr) 2 (i-propyl) 2 and W(NAr) 2 (n-propyl) 2 complexes upon addition of two equivalents of R F9 OH; no W = CHCH 2 Me complexes are observed. Similar results are found for W(NAd) 2 (propyl) 2 complexes (Ad = 1-adamantyl). Both RNH 2 and RNH 2 B(C 6 F 5 ) 3 (R = Ar or Ad) catalyze the interconversion of propylene and isopropylidene complexes. Addition of R F6 OH to W(NAr) 2 R 2 or W(NAd) 2 R 2 complexes leads to mixtures that contain largely propylene complexes. Addition of (CF 3 )Me 2 COH (R F3 OH) to W(NAr) 2 (i-Pr) 2 yields only propylene complexes. One propylene complex, W(NAd)(OR F9 ) 2 (CH 2 ═CHMe)(dme), was isolated, structurally characterized, and found to react with AdNH 2 to reform W(NAd)(OR F9 ) 2 (CMe 2 )(AdNH 2 ). Furthermore, it is proposed that propylene and isopropylidene complexes interconvert through the formation of an intermediate isopropyl complex.
Complex systems have characteristics that challenge traditional systems engineering processes and methods. These characteristics have been defined in various ways. INCOSE has previously identified characteristics of complex systems and potential methods to deal with complexity in system development. The purpose of this paper is to provide definitions and describe distinguishing characteristics of complexity using example systems to illustrate approaches to assessing the extent of complexity. The paper applies Appreciative Inquiry to identify and assess complex system characteristics. The characteristics are used to examine several different examples of systems to illuminate areas of complexity. These examples range from seemingly simple systems to complicated systems to complex systems. Different tiers of complexity are identified as a result of the assessment. The paper also identified and introduces topics on managing complexity and the integrating system perspective that represent new directions for the engineering of complex systems. The Appreciative Inquiry approach provides a method for systems engineering practitioners to more readily identify complexity when they encounter it, and to deal more effectively with this complexity once it has been identified.
Launches of Delta, Atlas, and Titan rockets from Cape Canaveral Air Station (CCAS) have potential environmental effects. These could occur from direct impacts of launches or indirectly from habitat alterations. This report summarizes a three-year study (1995-1998) characterizing the environment, with particular attention to threatened and endangered species, near Delta, Atlas, and Titan launch facilities. Cape Canaveral has been modified by Air Force development and by 50 years of fire suppression. The dominant vegetation type around the Delta and Atlas launch complexes is coastal oak hammock forest. Oak scrub is the predominant upland vegetation type near the Titan launch complexes. Compositionally, these are coastal scrub communities that has been unburned for greater than 40 years and have developed into closed canopy, low-stature forests. Herbaceous vegetation around active and inactive facilities, coastal strand and dune vegetation near the Atlantic Ocean, and exotic vegetation in disturbed areas are common. Marsh and estuarine vegetation is most common west of the Titan complexes. Launch effects to vegetation include scorch, acid, and particulate deposition. Discernable, cumulative effects are limited to small areas near the launch complexes. Water quality samples were collected at the Titan, Atlas, and Delta launch complexes in September 1995 (wet season) and January 1996 (dry season). Samples were analyzed for heavy metals, chloride, total organic carbon, calcium, iron, magnesium, sodium, total alkalinity, pH, and conductivity. Differences between fresh, brackish, and saline surface waters were evident. The natural buffering capacity of the environment surrounding the CCAS launch complexes is adequate for neutralizing acid deposition in rainfall and launch deposition. Populations of the Florida Scrub-Jay (Aphelocoma coerulescens), a Federally- listed, threatened species, reside near the launch complexes. Thirty-seven to forty-one scrub-jay territories were located at Titan, Atlas, and Delta launch complexes between 1995 and 1997. No direct impacts to scrub-jays were observed as a result of normal launches. The explosion of the Delta rocket in January 1997 caused direct impacts to the habitat of several scrub-jays families, from fire and debris; however, no scrub-jay mortality was observed. Mortality exceeded reproductive output at all areas over the course of the study. Populations of the southeastern beach mouse (Peromyscus polionotus niveiventris) populations, a Federally listed, threatened species, reside near all the launch complexes. Hurricane Erin and several other tropical storms impacted several areas at the inception of the study in 1995 causing coastal habitat alterations as a result of salt-water intrusion. Both the habitat and the beach mice populations recovered during the course of the study. No direct impacts to southeastern beach mice were observed as a result of normal launch operations. Direct impacts were observed to the habitat as a result of the explosion of the Delta rocket in January 1997. This alteration of the habitant resulted in a shift in use with the mice moving on to the newly burned part of the site. Waterbirds use wetlands and aquatic systems near the launch complexes. Species include the Federally-listed, endangered Wood Stork (Mycteria americana) and several state-listed species of special concern including the Snowy Egret (Egretta thula thula), Reddish Egret (Egretta rufescens rufescens), White Ibis (Eudocimus albus), Roseate Spoonbill (Ajaia ajaja), Tricolored Heron (Egretta tricolor ruficolis), and Little Blue Heron (Egretta caerulea). No impacts to these populations resulting from any launch operations were observed. Gopher tortoises (Gopherus polyphemus) also occur around the launch complexes. Most of those observed appeared to be in good condition; however, upper respiratory tract disease is known to occur in the population. Cape Canaveral Air Station, including areas near active launch complexes, remains important habitat for a variety of native plants and animals including threatened and endangered species. Direct negative effects of current launch systems appear limited. Additional monitoring of these populations and habitats is required to determine if subtle, long-term changes are occurring, to determine if new launch systems and facilities cause other effects, and to determine the effects of habitat restoration and management.
Launches of Delta, Atlas, and Titan rockets from Cape Canaveral Air Station (CCAS) have potential environmental effects. These could occur from direct impacts of launches or indirectly from habitat alterations. This report summarizes a three-year study (1 995-1 998) characterizing the environment, with particular attention to threatened and endangered species, near Delta, Atlas, and Titan launch facilities. Cape Canaveral has been modified by Air Force development and by 50 years of fire suppression. The dominant vegetation type around the Delta and Atlas launch complexes is coastal oak hammock forest. Oak scrub is the predominant upland vegetation type near the Titan launch complexes. Compositionally, these are coastal scrub communities that has been unburned for > 40 years and have developed into closed canopy, low-stature forests. Herbaceous vegetation around active and inactive facilities, coastal strand and dune vegetation near the Atlantic Ocean, and exotic vegetation in disturbed areas are common. Marsh and estuarine vegetation is most common west of the Titan complexes. Launch effects to vegetation include scorch, acid, and particulate deposition. Discernable, cumulative effects are limited to small areas near the launch complexes. Water quality samples were collected at the Titan, Atlas, and Delta launch complexes in September 1995 (wet season) and January 1996 (dry season). Samples were analyzed for heavy metals, chloride, total organic carbon, calcium, iron, magnesium, sodium, total alkalinity, pH, and conductivity. Differences between fresh, brackish, and saline surface waters were evident. The natural buffering capacity of the environment surrounding the CCAS launch complexes is adequate for neutralizing acid deposition in rainfall and launch deposition. Populations of the Florida Scrub-Jay (Aphelocoma coerulescens), a Federally-listed, threatened species, reside near the launch complexes. Thirty-seven to forty-one scrub-jay territories were located at Titan, Atlas, and Delta launch complexes between 1995 and 1997. No direct impacts to scrub-jays were observed as a result of normal launches. The explosion of the Delta rocket in January 1997 caused direct impacts to the habitat of several scrub-jays families, from fire and debris; however, no scrub-jay mortality was observed. Mortality exceeded reproductive output at all areas over the course of the study. Populations of the southeastern beach mouse (Peromyscus polionotus niveiventris) populations, a Federally listed, threatened species, reside near all the launch complexes. Hurricane Erin and several other tropical storms impacted several areas at the inception of the study in 1995 causing coastal habitat alterations as a result of salt-water intrusion. Both the habitat and the beach mice populations recovered during the course of the study. No direct impacts to southeastern beach mice were observed as a result of normal launch operations. Direct impacts were observed to the habitat as a result of the explosion of the Delta rocket in January 1997. This alteration of the habitat resulted in a shift in use with the mice moving on to the newly burned part of the site. Waterbirds use wetlands and aquatic systems near the launch complexes. Species include the Federally-listed, endangered Wood Stork (Mycferia americana) and several state-listed species of special concern including the Snowy Egret (Egretfa thula fhula), Reddish Egret (Egreffa rufescens rufescens), White Ibis (Eudocimus albus), Roseate Spoonbill (Ajaia ajaja), Tricolored Heron (Egreffa tricolor ruficolis), and Little Blue Heron (Egreffa caerulea). No impacts to these populations resulting from any launch operations were observed. Gopher tortoises (Gopherus polyphemus) also occur around the launch complexes. Most of those observed appeared to be in good condition; however, upper respiratory tract disease is known to occur in the population. Cape Canaveral Air Station, including areas near active launch colexes, remains important habitat for a variety of native plants and animals including threatened and endangered species. Direct negative effects of current launch systems appear limited. Additional monitoring of these populations and habitats is required to determine if subtle, long-term changes are occurring, to determine if new launch systems and facilities cause other effects, and to determine the effects of habitat restoration and management.
The cis-anti-cis and cis-syn-cis isomers of [Sm(dicyclohexano-18-crown-6)(H 2 O) 2 ]I 2 exhibiting trans water molecules bound to the Sm 2+ ion have been isolated and characterized. Sm 2+ possesses an electrochemical potential sufficient for water reduction, and thus these complexes add to the recent body of evidence that the oxidation of Sm 2+ by water can operate by a mechanism that is not straightforward. These complexes are obtained by the direct addition of stoichiometric amounts of water to solutions of the respective Sm(dicyclohexano-18-crown-6)I 2 isomers under an inert atmosphere. The parent complex, Sm(dicyclohexano-18-crown-6)I 2 , lacking coordinating water molecules can be obtained through rigorous exclusion of water. It was determined that the bulky cyclohexano-substituents deter intramolecular interactions between [Sm(dicyclohexano-18-crown-6)(H 2 O) 2 ]I 2 complexes and slow the oxidization of the metal centers. The extent of the stability of these complexes to the presence of water has been further probed through cyclic voltammetry, where it was found that the redox potential of both isomers of [Sm(dicyclohexano-18-crown-6)(H 2 O) 2 ]I 2 maintains quasi-reversible behavior with a 50,000-fold excess of water to Sm 2+ in solution with the cis-syn-cis complex being quasi-reversible at even higher concentrations of water. Solution-phase spectroscopy of these complexes in acetonitrile shows a corresponding hypsochromic shift of the Sm 2+ 4f → 5d transition typically observed in the visible region from Sm 2+ complexes. Here, the crystalline compounds obtained in this study support solid-state spectroscopic trends observed from other Sm 2+ crown-ether complexes containing iodide counterions, wherein the proximity of the iodide ions to the metal center determines whether the complex can exhibit 4f → 4f photoluminescence.
In this paper, we discuss a critical aspect of uncertainty in the operation of complex systems, such as the future air traffic: the ability of agents in the system to arrive at satisfactory decisions and the attendant actions as a function of problem complexity. Intuitively, when the problem complexity is manageable, given an appropriate decision problem formulation and solution tools, an agent (computational or human) has no trouble arriving at a solution that yields good outcomes for the agent and the system. Growing problem complexity results in progressively larger computational problems that may yield suboptimal solutions or even be intractable within required time limits or at all. We propose a measurable representation of complexity in terms of problem tractability and quality of solutions. We also propose a computational scheme, MAGE (Monitor, Anticipate, Guide, Evolve), for detecting approaching transitions from efficient decision-making states to inefficient to unsafe ones, so that operations based on decision-making can be reconfigured to forestall unfavorable transitions, returning to efficient modes when complexity diminishes. Maintaining tractable complexity reduces the uncertainty in the outcomes of decision-making. We describe the general scheme, an outline of MAGE applied to managing airspace complexity, and initial examples of investigating the tractability of problem-solving schemes.