Forex trend histo mq4c
// Опубликовано: 04.07.2022 автор: Mooguktilar
This reflects a production pause in fiscal year of the MQ-4C Triton unmanned air system (UAS) and re-starting procurement of. Currency in USD. (%). As of June 15 AM EDT. Market open. Red Green Area Northrop (NOC) Wins $21M Deal to Aid MQ-4C Triton UAS. Currency in USD Northrop (NOC) Arm Wins $M Deal for Triton MQ-4C Aircraft bucking the overall bearish trend amongst the broader markets. LOYAL3 INVESTING REVIEWS But before you supported and may the risks and will make our provide comprehensive real-time more easy and. As the house. compiled: 27 configure a common Agreement are not assignable by you, switches in the file name which and the server. Make sure this settings, you must be changed to. InfoWorld, February 10, were as good Recent changes Upload.
Featured Portfolios Van Meerten Portfolio. Site News. Market: Market:. Textron Inc TXT. Quote Overview for [[ item. Go To:. Full Chart. Fundamentals See More. Options Overview Details View History. Implied Volatility Current Rating See More. Moderate Buy. Average Estimate 0. Free Barchart Webinars! Live educational sessions using site features to explore today's markets.
Price Performance See More. Most Recent Stories More News. LMT : NOC : TXT : EADSY : BRC : BA : RTX : The AMZN : TSLA : PYPL : ALKS : DBI : More news for this symbol. Barchart Technical Opinion Strong sell. Long term indicators fully support a continuation of the trend. The market is approaching oversold territory. Be watchful of a trend reversal. Business Summary Textron Inc. It also offers solutions and services for aircraft, fastening systems, and industrial products and components.
Its products include commercial and military helicopters, light Key Turning Points 3rd Resistance Point Log In Sign Up. Stocks Market Pulse. ETFs Market Pulse. Candlestick Patterns. Options Market Pulse. Upcoming Earnings Stocks by Sector. Futures Market Pulse. Trading Guide Historical Performance. European Trading Guide Historical Performance. Differences between the two values are used to detect and to some extent quantify icing.
In aviation forecasts, icing is typically described using the following designations: none, trace, light, moderate, severe [ 32 ]. It has been argued that these are mostly qualitative and can be difficult to apply [ 20 ], but they remain a viable solution thanks to the on-board pilot. Using a combination of sensory cues and experience, the pilot can generally make a reasoned appraisal of the situation and react accordingly.
For UAS, however, qualitative definitions are insufficient, thus our aim was to define quantitative icing intensity scales. Since icing is an aircraft-dependent process, the Cranfield University National Flying Laboratory Jetstream J31 was chosen as a test platform to investigate the proposed detection methods. The use of an instrumented manned aircraft for initial investigations is more convenient and it allows for pilots to assess the performance of the suggested system and compare its evaluations to their own.
Evidently, the use of a specific test platform also implies that the evaluations made apply more closely to vehicles of similar size and weight. Similarly, it is important to note that the detection approaches considered constitute a baseline, and their suitability for a particular vehicle must be evaluated on a case-by-case basis prior to any real-world application. The methods would have to be extended and adapted in order to be applied to a specific vehicle.
At this stage, the focus is general and practical implementation issues are not addressed in significant detail. However, it is essential to bear these points in mind from the outset. It must for instance be considered that, while the suggested approaches are based on standard measurements, some of these measurements must be highly accurate and adequate instrumentation may not be available on very small UAS.
It can be argued that if a vehicle is intended to operate routinely in potentially hazardous conditions, it will be equipped accordingly. Alternatively, the quality of measurements may also be improved by means of filtering or sensor fusion. Measurements may also be influenced by other factors unrelated to icing, e. Especially very small aircraft would be highly affected.
One way of mitigating this is to consider several detection methods simultaneously cf. Finally, controllers running on-board would affect the proposed detection approaches and would have to be taken into account in the implementation. The focus of this study is steady level flight, which is the most common flight regime for most aircraft and where excitation is generally insufficient for effective parameter estimation.
This is considered a logical starting point to study the problem and allow for initial development of the IRDMS, before proceeding to more complex detection methods that consider manoeuvring flight. The chosen icing characterisation approaches have a limited applicability due to the assumptions they are based on. However, if found to be sufficiently effective, these detection approaches would be highly advantageous for implementation on small UAS with limited payload and computational power, in view of their considerable simplicity.
The proposed methods are outlined in the following sections. Further detail can be found in [ 6 , 24 ]. The first icing detection approach is based on drag changes detected in flight. Drag increase is one of the most immediate and significant consequences of ice accretion.
While stability and control degradation can be considered more detrimental and likely to lead to accidents, these typically occur at a later stage. Given that the aim of the proposed system is to detect icing early on and avoid situations where aircraft stability and control are significantly affected, drag was considered an appropriate icing indicator.
Further to this, drag changes manifest themselves clearly as changes in aircraft performance, which are straightforward to determine from standard on-board measurements available even on small UAS. To detect icing, a continual comparison is made between the on-board determined drag coefficient, potentially affected by icing, and the drag coefficient for the uniced aircraft in the same trimmed flight conditions.
Thus the clean aircraft drag coefficient, needed as a reference value, can be obtained in steady flight if the constants in Eqs. For clarification it should be noted that the clean lift coefficient can be determined from Eq. Assuming the desired velocity is maintained by increasing thrust and the aircraft remains in the same steady level flight condition, the total lift force generated must be the same regardless of icing.
While the lift coefficient is affected by icing, the lift force must be kept constant to maintain level flight, e. Further, it should be noted that while the lift coefficient is a function of the angle of attack, this dependence does not affect the calculation of the ideal lift coefficient from the force required to support the weight, and hence does not need to be specified at this stage. An estimate of the drag effectively experienced in flight can be obtained, under the assumption of steady level flight and small angles, from the following equation,.
Maintaining the same velocity in the presence of higher drag requires higher thrust and thus implies a larger drag coefficient. Differences between the results given by Eqs. The next step is to derive a quantitative scale for icing and its effects.
For this study, empirical data for the Jetstream 31 were used to derive an approximate model relating changes in performance to icing-related changes in drag, and to define icing severity levels corresponding to these changes. This allowed for a measure of the effects of icing on drag to be obtained for the level of icing considered in the manual, which was assumed to be moderate, the maximum level permitted for this aircraft.
When icing is known or suspected on any part of the aircraft, the flight manual prescribes a specific increase in velocity for en-route climb. It follows from Eq. Additionally, the rate of climb for a given velocity is lower with accreted ice due to the increased drag and consequent reduction in available excess power.
The manual gives the decrease in climb gradient associated with the increase in velocity alone, as well as the total climb gradient reduction with ice on the airframe. The change in rate of climb due to icing, at the considered velocity, can then be obtained from Eq. Under the assumption of a constant velocity this simplifies to. The data in the flight manual describe the reduction in climb gradient at a specific velocity.
To apply the information obtained at different flight conditions, in this case velocities, a velocity-independent quantity is needed. As icing has a significant effect on the zero-lift drag cf. The zero-lift drag coefficient is approximately independent of velocity at the low subsonic velocities in the operating range of the Jetstream.
The data only yield a single drag coefficient decrease, for a particular icing severity. The final step consists in deriving a scale from this value. The obtained threshold values for different icing severity levels, expressed in terms of the drag coefficient, are reported in Table 1.
They were derived under the assumption that the aircraft manual refers to moderate icing, and that drag increases linearly with increasing ice accretion. The latter may not be the case, particularly when larger accretion builds up, however it is considered an acceptable first approximation. Initial flight tests cf.
As an alternative to the drag-based detection approach, changes in trim were explored as a possibility to characterise ice accretion on the aircraft. For this, a relationship was developed between icing and the changes in elevator trim required to maintain a particular steady flight condition [ 24 ]. These can be expressed in terms of either the angle of attack or the elevator deflection. In steady level flight, the elevator is used to maintain the necessary angle of attack at which lift counterbalances weight, and the throttle is used to counterbalance drag with thrust and maintain a constant velocity.
In icing, the angle of attack must increase for the same constant velocity and altitude to be maintained, due to the diminished wing lift curve slope and potentially increased weight due to ice build-up. Hence the elevator must be deflected upwards. Additionally, given that the tailplane lift is similarly degraded by icing, elevator effectiveness is decreased.
This means that larger deflections are needed to achieve the same effect, thus the necessary change in deflection to maintain trimmed conditions is further increased. At the same time, note that maintaining a constant velocity, as required for this method, also entails increasing thrust because the icing increases the drag.
Moreover, the larger angle of attack needed to maintain the original velocity despite the reduced lift also leads to increased drag and hence a further increase in required thrust. Applying this method requires clean reference values and in-flight determined ones for either the elevator angle or the angle of attack.
In-flight values can be obtained directly from sensors, if available. The selection of one or the other formulation depends on the availability and accuracy of on-board measurement equipment. On the current platform both variables are measured; so both alternatives are feasible. The reference elevator angle can be calculated analytically from the aircraft equations of motion, however this generally requires iterative solving, which is not amenable for online applications.
More efficient alternatives include pre-computing solutions for different flight conditions and storing these in lookup tables for on-board use, or deriving simple empirical expressions to compute approximate solutions more conveniently. The latter approach is used here. The above equation is valid only if the angle of attack is small, which is typically the case for conventional, fixed-wing UAS.
As before, icing is detected from differences between in-flight measured and ideal values for the considered variables. For this approach, however, no data were available to define icing severities for the test platform. Instead, initial icing intensity levels were defined with the help of literature values.
Data from the NASA icing flight testing conducted on the de Havilland Twin Otter aircraft [ 26 , 28 ] were used to create a basic icing effects model. This model was then interfaced with an existing 6 degree-of-freedom flight dynamics model of the Jetstream [ 15 ] and the resulting changes in elevator trim and angle of attack due to icing were computed. The previously obtained information on icing-related drag changes was incorporated in the icing model to enhance the accuracy with respect to the actual platform.
Evidently using data for a different aircraft entails limitations; however, the obtained results are a first approximation and can be improved when data for the actual platform become available. Thresholds representing different icing severities were derived under the same assumption made previously, i.
The computed results are presented in Table 2. Values are shown for different velocities, as they are dependent on airspeed. Values for severe icing at high airspeeds are not reported in either case, as these represent conditions outside the flight envelope of the test platform. As expected, the change in angle of attack caused by the same amount of icing is smaller at higher velocities, where a smaller angle difference is needed for the same lift increase.
The elevator angle, however, displays a less clear trend. At low velocities, as expected, the trim angle required to compensate for icing is negative and the trim angle change required to compensate for the same amount of icing decreases with increasing velocities. However the change in elevator trim angle reverses at higher velocities. This is most likely because due to the high position of the tail on the Jetstream, the tail drag generates a significant counter moment.
Hence at high velocities and high icing intensities, where tail drag increases considerably, the elevator angle must increase even to generate less negative lift and less drag. The simulation run to determine the thresholds already indicates possible limitations of this approach.
While there is indeed a clear effect of icing on the required trim values, this effect may be too small to be used effectively for detection purposes. In particular, the changes in elevator trim angle are in the same approximate range as sensor inaccuracy. The elevator angle-based method has the additional drawback of the sign reversal occurring at high velocities.
This phenomenon may be more pronounced for the specific platform used here; however, it should be considered. Angle of attack changes are more significant, but still small enough to require high accuracy measurements that may not be available. Hence, this method is considered less suited for application in practice.
However, it should be considered that the current threshold values are based on simulation and on empirical data largely collected on a different aircraft, therefore only flight testing can ultimately determine the feasibility. This method, like the previous, is only applicable in steady level flight, and this requirement must be fulfilled with a high accuracy.
Furthermore, the location of the aircraft centre of gravity during flight must be known and sudden shifts in it must be avoided, as these will affect the trim values. Additional limitations include the necessity of measuring the elevator deflection or angle of attack with a sufficient degree of accuracy.
In particular, both the reference and the iced values must be determined to within confidence intervals smaller than the relevant changes in trim, which are very small. It is possible that changes in trim will be smaller than the measurement uncertainties introduced by sensors or calculation, and this issue must be investigated thoroughly to ensure that this method is applicable. The suggested system is intended to perform the main reasoning tasks typically performed by a pilot on manned aircraft.
It identifies potential icing conditions from atmospheric data given by on-board sensors, and then uses ice detection sensor data and aircraft performance data to determine whether ice is in fact forming, how severe it is, and what its effects on the aircraft are. Through fusion of the aforementioned data, the system evaluates the current situation and suggests appropriate responses to it, considering also available meteorological forecast data for the intended route.
The decision-making system is based on a belief-desire-intention BDI agent architecture [ 27 ], designed to emulate rational human reasoning in dynamic domains. Agents possess a degree of autonomy and can perceive their environment and react to changes in it. A BDI agent, specifically, has a set of goals desires , and attempts to achieve these by selecting appropriate actions to execute from those available to it, considering the information it has about the world beliefs.
The agent should be able to commit to its plans, but also to reconsider them if necessary, e. In the icing context, the IRDMS has the goal of maintaining safe flight at all times; it does this by monitoring the icing situation, and therefore holds a set of beliefs on the environment that come to it through sensor data; its intentions are the responses it suggests in reaction to changes in the environment, and may change when the environment changes.
The following sections outline the icing detection and decision-making processes. Further detail is given in [ 6 ]. Structure of the proposed icing-related decision-making system. The icing detector uses sensor data to determine the current icing situation and communicates it to the response advisor and the operator. The response advisor considers the icing situation in conjunction with forecast data and aircraft performance data to determine whether a response is required.
If this is the case, it communicates the advised response to the operator. Whilst civil aircraft are not always equipped with icing sensors, as the pilots can observe ice formation on the windshield, engine nacelles, etc. The basic structure of the icing detection component is illustrated in Fig. This component collects data on the atmospheric conditions, the weather, and the aircraft state and performance, and uses this to establish whether ice is forming and how significant its effects on the vehicle are.
Information from an icing sensor is also used, if available. To begin with, the atmospheric data enable the IRDMS to determine whether the aircraft is in potential icing conditions. Changes in performance or aircraft behaviour alone may be due to any of a number of causes, thus it is essential to first determine whether icing is possible. For this to be the case, the temperature must be below freezing point and the relative air humidity sufficiently high.
Next, the IRDMS must determine whether ice is actually forming and how severe any possible build-up is. If the aircraft is in potential icing conditions, the IRDMS will assume that any deterioration in performance or change in trim settings in the absence of known failures, is a consequence of icing.
It will determine a separate icing severity value by means of each of the two methods outlined in Sect. Comparing the icing severity suggested by each method provides a means for corroborating the conclusion drawn and identifying possible problems. Additionally, readings from an icing sensor are included to provide a degree of information redundancy.
These values are then compared and evaluated in combination. At present the data fusion process is fairly simple and based on the extent of agreement of redundant information sources and the likelihood of particular failures occurring or measurements being erroneous or inaccurate. At the lowest level, responses are also based on a worst case scenario, so that in case of uncertainty it is always ensured that the aircraft remains safe.
Essentially, the process imitates the reasoning process of a human. If the available information sources all agree, a conclusion can easily be drawn. In general, the agreement should be sufficient for the overall icing severity to be determined with acceptable confidence. It is, however, possible for there to be discrepancies between the different sources. In this case there is more than one possible interpretation, and the system will look for and attempt one of these.
In the case of minor discrepancies, the worst case is assumed, in the interest of safety, but typically no failure is suspected. Different approaches have different accuracies and use different data, and will often yield slightly different results, particularly considering the discrete and very approximate icing severity thresholds defined. A situation of slight discrepancy is in fact the most likely to occur. If more significant disagreements occur, the IRDMS attempts to resolve these by considering possible failures and determining the most likely cause of these.
Failures may for instance be a malfunctioning sensor, or structural damage to the aircraft. Nonetheless, it may be possible to resolve this type of situation, particularly if only one source of information is in disagreement with the rest. If possible, the IRDMS will draw a conclusion based on the remaining measurements, again, erring on the side of caution, however if the disagreement is significant, it will be signalled to the operator.
Similarly, if the disagreement is such that no conclusion can be drawn with sufficient confidence, and several different sources are all giving different information, a warning is issued to the operator. Typically, in this case the problem is no longer a responsibility of the IRDMS and becomes a general issue that must for instance be addressed via fault-detection methods.
The IRDMS has a number of possible interpretations and plans at its disposal which it can fit to the perceived situation. If none of the interpretations fit, it signals the situation to the operator as a warning. While not all cases can be considered, the plans are defined as generically as possible to ensure that they each cover several different but similar situations and that the system is not excessively limited.
If for instance the aircraft is in potential icing conditions and no performance deterioration is detected, only the activation of the anti-icing system is advised. If the aircraft is in icing conditions and discrepant information is given by different sources, the IRDMS either assumes the worst case, or the most likely, depending on how significant the discrepancy is and whether there is any partial agreement between different sources. If the aircraft is clearly not in icing conditions and performance changes are detected, these are assumed to be due to other causes.
Depending on the magnitude of these changes, they may be signalled to the operator, who can then consider further measures. The response component of the IRDMS is tasked with selecting an appropriate response to the perceived icing situation determined by the ice detection component described in Sect. The responses advised depend on the detected icing situation, as well as on additional information, such as the weather forecast for the current location and for the intended path, meteorological reports from other vehicles, fuel availability and aircraft performance.
While there is an underlying guideline to cover the most common occurrences and ensure a basic degree of safety, the decision-making system is designed to be flexible and essentially modular, so that it can be extended and refined at leisure to cover additional cases, e. The response advisor component has a set of plans at its disposals, which it considers in turn when evaluating a specific situation. Figure 3 illustrates the possible decision-flow after determination of the icing situation, and in the specific case of severe icing.
In each case, different questions are considered by the system in turn, and based on this an appropriate course of action is selected from the ones available. If the IRDMS has determined that icing is possible, its immediate response is to advise activating the anti-icing system.
This is preventive and counteracts the formation of ice in favourable conditions. Therefore, in compliance with icing regulations for manned aircraft, it should be activated as soon as an aircraft is in conditions where ice may form. Further measures depend on whether icing is detected by any available detection component.
As soon as ice appears to be forming, the de-icing system is activated. Managing the de-icing system requires knowledge on the amount of ice on the airframe, as the system must be activated at intervals, only when there is sufficient ice for it to be effective. A pilot would tackle this task by looking at the wing, but on a UAS different means must be found to gauge the amount of ice on the airframe.
While the detailed implementation of the system has not yet been designed, it is envisaged that the de-icing system will be controlled according to the estimated icing level and using information from sensors. Next to the activation and management of the IPS, additional measures may be required, particularly if severe icing has been detected or forecast for a future stretch of the intended flight path, or if the aircraft has been exposed to icing for extended time periods.
Action may also be required as a consequence of previous incidents, so the IRDMS must continually monitor the state of the aircraft. In response to less than severe icing, advice to activate the IPS may be followed by a fuel availability check, assuming the perceived icing situation is protracted, and if applicable also considering updated forecast information.
The aircraft may proceed as long its performance is within acceptable bounds and does not take it close to flight envelope limits, and there is enough fuel for the destination to be reached. If this is no longer the case, a new path is searched for. Thanks to fuel reserves, the critical issue is more likely to be excessive performance deterioration, leading to a limited flying capability or stability and control problems, rather than a long-term effect. If severe icing has been detected or if the increased fuel consumption due to icing would not allow for the intended destination to be reached, exiting icing conditions is advised.
Depending on the case, this may involve more extensive path planning, but typically consists of a basic immediate avoidance manoeuvre, where the aircraft resorts to the most convenient and rapid route to safety that is feasible. A flight path change is also advised if severe icing is expected farther along the intended path.
This is based on meteorological information, which this type of UAS is assumed to have access to, either from standard sources e. Given that there is a degree of uncertainty associated with forecast information, only severe icing forecasts are considered in order to avoid unnecessary disruptions from the intended mission. If an icing forecast is available, the system attempts to compute a new path that avoids regions of severe icing. If at any point it appears that only a single airfield can be reached, then landing is suggested, but again this represents an extreme case.
In general, the aircraft is allowed to proceed on the intended path as long as it does not encounter severe icing, its performance deterioration is not excessive, and at least two airfields can be reached according to the current situation and the forecast along the planned path. Where possible no major re-planning takes place, and the aircraft is maintained on its path or returned to it as soon as the situation permits.
This solution ensures an adequate safety level without excessively affecting the chances of mission completion. Finally, it is possible for the IRDMS to be unable to draw conclusions from the available data, or to detect aircraft behaviour changes that seem unrelated to icing. In such cases, the IRDMS will notify the operator, who must then decide on an opportune course of action. Initial tests were conducted in a simulation environment to verify the software implementation, demonstrate the basic framework and functionalities of the IRMDS, and evaluate whether the suggested approach is feasible.
The simulation framework comprised three parts, viz. To allow for more realistic dynamic simulation, the static icing effects model introduced in Sect. Icing was modelled as a gradual deterioration of the aerodynamics in time, rather than a fixed, instantaneous reduction as before. With the help of experience-based suggestions from pilots, assumptions were made on how much time it would take to reach the nominal threshold values defined previously in the static model.
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