1 Starship

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Spacecraft Control

Related terms:

Spacecraft

Fault Diagnosis

Fault Mode

Influencing Factor

System Structure

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The Basics of Analytical Mechanics, Optimization, Control and Estimation

Kyle T. Alfriend, ... Louis S. Breger, in Spacecraft Formation Flying, 2010

3.6 Control Lyapunov Functions

Our interest in Control Lyapunov Functions (CLFs) stems from their application to relative spacecraft control, to be discussed in Chapter 10. We consider autonomous nonlinear systems of the form

(3.36)ẋ=f(x)

to provide an elementary treatment of the CLF approach. It is assumed that the system of Eq. (3.36) satisfies the property

(3.37)f(0)=0,

i.e., the origin is an equilibrium point. The stability of the system of Eq. (3.36) at the origin can be verified by using Lyapunov's theorem[78], which can be stated as follows.

Theorem 3.1

Lyapunov's theorem [78]

If V(x) is a continuously differentiable, positive definite function, defined in a domain D containing the origin (i.e., V(0)=0 and V(x)>0 in D , except at x=0 ) and furthermore,

(3.38)V̇(0)=0andV̇(x)<0;x≠0

then the equilibrium point x=0 of Eq. (3.36)is asymptotically stable and V(x) is a Lyapunov function.

For our discussion, Eq. (3.36) can be considered to be the representation of a dynamical system under the action of a feedback control law, u=u(x). The CLF approach begins with the selection of a positive definite Lyapunov test function and the feedback control law is determined to satisfy Eq. (3.38) along the trajectories of Eq. (3.36), thus ensuring closed-loop stability. The choice of a CLF is not unique, but in many cases it may resemble the energy or the Hamiltonian of a closely-related open-loop (unforced) system.

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Collision Avoidance Systems

Michael J. Eiden Dipl. Ing., in Safety Design for Space Systems, 2009

Possible Methods of Docking Interruption in Off Nominal Situations

Methods for interrupting the docking process are dictated by the stage within which an anomaly occurs. For example, if a predefined capture time limit is exceeded, then the active spacecraft control system can fire its thrusters for retreat. This process is used to avoid collisions, thus ensuring safety in case of emergency.

In the case of a docking mechanism failure that occurs before interface closure, the spacecraft can separate after using the capture mechanism drives to open the latches. Should the spacecraft then be unable to separate, pyrocartridges, located at the attachment points of the docking mechanism to the active docking assembly body, are fired. Similarly, for the case of an interface closure failure, separation is performed by firing pyrocartridges located at the attachment points to the fastening elements.

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Selection and training

Tommaso Sgobba, ... Jean-Bruno Marciacq, in Space Safety and Human Performance, 2018

16.2.10.1 The Three Stages of Cosmonaut Training

A cosmonaut is an essential part of the spacecraft control loop. The cosmonaut-training task is to teach a cosmonaut to perform different flight operations, to get into the control loop, as required, in off-nominal situations, as well as to counteract emergencies.

In the spaceflight environment, a human being is subjected to stress. And, if an off-nominal or emergency situation occurs, the stress is doubled (or even tripled). Under exposure to stress factors, the wrong action probability increases that could affect the crew safety. The risk is becoming higher. The top priority is always given to the crew safety. Therefore, it is necessary to mitigate the risks related to the human factor. To accomplish this goal, cosmonauts are trained to counteract different off-nominal situations, which may be taking place in the spacecraft or station systems.

Tentatively, the cosmonaut-training course in Russia can be split in three stages:

(1)

Common training template: cosmonaut candidates are taking a course of the common training template, a course of spacecraft control principles and flight theory, astronomy and the essentials of nautical astronomy, as well as the basic course of the space research program;

(2)

Training within a group: cosmonauts are learning in details the spacecraft systems, their design, operation principles, and all possible types of failures, as well as gain a general idea of the systems operation logic and interoperability;

(3)

Training within a crew: the main task is to acquire skills in the interaction between the crew members, to study the forthcoming flight plan, to perfect the crew interaction in the off-nominal and emergency situations aboard the spacecraft or the space station.

Actions to be taken in off-nominal situations shall be multiple refined and at times brought to automatic performance. To accomplish this, regular training on training facilities and program simulators are carried out, including hands-on training with the emergency equipment and rescue aids.

However regular training is only a half of success. The second required component is the psychological stability and assertion, the ability of grasping the situation and making a decision. To acquire these skills, special cosmonaut-training templates are employed.

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Failure Tolerance Design

Gregg John Baumer, in Safety Design for Space Systems, 2009

19.2.2 Design to Tolerate Failures

Failure tolerance is the primary safety process that designers should use to control identified hazards. Many safety specialists and safety requirements documents use the terms failure and fault as if they are interchangeable. In reliability circles, there is a distinct difference. A failure is the inability of a system, subsystem, component, or part to perform its required function under specified conditions for a specified duration. The focus is clearly on the hardware. Fault, on the other hand, is an undesired system state and, therefore, is nonspecific about the cause. In this chapter, fault tolerance and failure tolerance are considered synonymous. The concept of fault or failure tolerance means that the design must endure a minimum number of credible failures or operator errors without resulting in a hazardous consequence. Credible failures are those that can occur or are reasonably likely to occur. The term operator error is not meant to include all possible crew actions or deliberate malicious acts but rather addresses mistakes in procedure, such as an out of sequence step, an inadvertent single switch throw, or other action that has not been addressed specifically during proficiency training. The severity of the hazardous consequence determines the minimum number of failures or operator errors that the designer must protect against. The failure tolerance design concept applies to the control of hazardous functions. A hazardous function is an operational event or service, such as an appendage deployment, firing of a liquid propellant motor, control of a habitable atmosphere, or control of the thermal environment, whose inadvertent operation or loss can result in a hazardous consequence. The concept includes operations that are single-failure tolerant, two-failure tolerant, fail operational/fail operational (fail op/fail op), fail operational/fail safe (fail op/fail safe), fail safe/fail safe, and fail safe.

Single-Failure Tolerant

For a design to be single-failure tolerant, a minimum of two failures are required for the hazardous consequence to occur. This concept typically is applied to hazards with a severity category of critical (Table 19.1).

Two-Failure Tolerant

A two-failure tolerant design requires a minimum of three failures for the hazardous consequence to occur. This concept typically is applied to hazards with a severity category of catastrophic (Table 19.1).

The Fail Op/Fail Op Concept

The fail op/fail op concept is one in which the system or function to which it is being applied maintains functionality after the first and the second failure. This concept is applied to systems that are especially critical. Here, the goal is to maintain safety and achieve mission success with the greatest degree of operational flexibility. In building a spacecraft computer control system, an implementation of this concept is a system of four or five voting computers to control spacecraft functions, where as least two computers must be operational and online to provide safe control of the spacecraft.

The Fail Op/Fail Safe Concept

For designs utilizing a fail op/fail safe concept, the system or function to which it is being applied maintains functionality after the first failure but not after the second. However, the system remains safe even after two failures have occurred. An implementation of this concept might be a spacecraft with a fully redundant design to maintain performance and mission objectives and that possesses a separate and independent watchdog system to monitor primary and secondary systems and take control when they fail. This watchdog system reconfigures the vehicle to a survival mode in the event both primary and secondary systems fail. This watchdog system action also prevents the failure conditions from propagating, possibly affording ground controllers an opportunity to take corrective action.

The Fail Safe/Fail Safe Concept

Designs incorporating the fail safe/fail safe concept do not maintain functionality after any failure but ensure that no hazardous consequence will result after even two failures. Designers should apply this concept to hardware where the impact of mission failure does not warrant the cost of adding redundancy to the design. A typical application of this concept would be for low priority payloads where the cost of developing a highly reliable redundant design is too high, considering the payload priority. Another interesting aspect of this concept is that it might be adopted by the host vehicle as the minimum requirement to be certified for potential payloads. In this case, however, the payload is free to develop and incorporate as much reliability and redundancy as is affordable without incurring the cost of certifying that reliability and redundancy to the host vehicle.

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Software System Safety

Nancy G. Leveson, Kathryn Anne Weiss Ph.D., in Safety Design for Space Systems, 2009

15.2.1 System Accidents

The introduction of embedded software to control physical systems has led to a new safety concern—system accidents. Traditionally, accidents have been treated as the result of single or multiple component failures, usually with an assumption that these failures exhibit some random behavior. Consequently, the logical approach to preventing component failure accidents is to provide redundancy or enhanced component integrity, thereby reducing the probability of component failure or the impact of the failure on the overall system.

With the proliferation of software control of physical systems and system components, a different type of accident is taking on increasing importance. In these accidents, labeled system accidents, losses arise from dysfunctional interactions among system components in which no components have failed. The MPL loss provides an example. In this case, it is believed that the software turned off the spacecraft descent engines prematurely, 40 m above the Mars surface (Euler, Jolly, and Curtis 2001). The software did not "fail" in the traditional sense; it satisfied all requirements as provided to the software designers. However, the designers were not expressly informed about the possibility of the landing leg sensors prematurely emitting signals before the spacecraft had reached the surface. Although this description is somewhat oversimplified, the example demonstrates the basic characteristics of a system accident where each component operates as specified, i.e., does not fail, but the combined behavior leads to a system loss.

System accidents arise in the interactions among components and are related to interactive complexity and tight coupling (Perrow 1984), both of which are usually increased when software is used to control spacecraft (or systems in general). Software allows the construction of systems with much greater complexity than permitted by analog components alone, which, ironically, is a major reason it is used. Whereas a simple system might be defined as having a small number of unknowns in its interactions within the system and with its environment, a system becomes interactively complex when the level of interactions reaches the point where they cannot be thoroughly anticipated, planned, understood, and guarded against. The MPL loss resulted from an unexpected interaction between the Hall effect sensors in the landing legs and the descent engine control software that was not handled correctly by the software logic (Jet Propulsion Laboratory Special Review Board 2000). Although system accidents can occur in systems without software, the interactions among components in these systems are usually simple and few enough that all the interactions can be understood, anticipated, and tested.

A second common factor in system accidents is tight coupling. A tightly coupled system is one that is highly interactive, with each part linked to many other parts. Failure or unplanned behavior in one can rapidly affect the status of others. Often the processes are time dependent and cannot wait, i.e., there is little slack in the system, and sequences are invariant so that there is only one way to achieve a goal. System accidents arise from unplanned interactions among the coupled components. Coupling creates an increased number of interfaces and potential interactions, which again raises the difficulty of handling them during test and operations.

There are, of course, many other types of complexity such as structural complexity (the structural decomposition is not consistent with the functional decomposition), nonlinear complexity (cause and effect are not related in an obvious way), and dynamic complexity (the system and environment change over time).

The potential for building systems with all these types of complexity is enhanced by using software and digital components and leads to systems that are intellectually unmanageable and hence to accidents where the cause is not a component failure but a design flaw. Intellectually unmanageable complexity is, of course, not required in system design; however, eliminating it requires discipline on the part of the system designer. This fact leads to the first basic solution for enhancing safety of software-intensive space applications—reduce the complexity of the system design. This solution, however, is not readily accepted by spacecraft designers, particularly because one of the reasons for introducing software is to allow functionality not easily achievable by using analog components alone.

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Crew Training Safety

Jean-Bruno Marciacq, Loredana Bessone, in Safety Design for Space Systems, 2009

25.2.3 Training Environments and Facilities

"Space crewmembers, like pilots, are considered operators who must function in unfamiliar environments" (Dietlein and Pestov 2004). During the early years of spaceflight, the crew received extensive exposure to endurance training. When it became clear that the environment was not affecting crewmembers adversely, a lot of the emphasis in training was shifted toward the use of simulators to develop operational skills.

Specific training environments (Table 25.8) are used to simulate spaceflight conditions. This allows crews to become familiar with and perform operations in such environments. These environments carry with them specific hazards, and the safety of crew training is guaranteed by very strict hazard prevention and control processes and adherence to strict operational and safety rules.

Table 25.8. Typical Training Environments by Category

CategorySubcategoryNotesGround1-g training facilitiesMock-upsSuspension/air cushion/pogoArtificial zero-g facilities reproducing the absence of fixation by artificial suspension or airliftHypobaric/hyperbaric chambersUsed for extravehicular activity suit tests and medical testing or recovery following nitrogen saturation sicknessesMedical environmentCentrifuges and baseline data collection facilitiesLaunch site (for evacuation training)Very hazardous or critical because of the presence of the launcher itself and relative isolation from emergency servicesOpen airWoods, ice, snow, or simulators for survival trainingFitness trainingFitness rooms and facilities, such as stadiumWaterSubaquaticNeutral buoyancy facilities, such as neutral buoyancy laboratory, neutral buoyancy facility, hydrolab, weightless environmentsWeightless environmental test system, neutral buoyancy training facility, and others; extravehicular activity training facilities, splashdown, diving (proficiency and fitness)WetSurvival training in sea, lakes, or in neutral buoyancy facilitiesAirParabolic flightFlight intravehicular environments reproduced in wide bodied aircraft: the NASA KC-135, Ilyushin Il-76 MDK, Novespace AIRBUS A-300 zero-g, equipped with soft padding, handholds, and netsAir flight trainingVarious aircraft: military jet fighters, such as the MiG-25, used to train Buran reentries, or any aircraft in the inventory of military and test pilot astronauts, such as Panavia Tornado, Mirage 2000, or General Dynamics F-16; jet trainers, such as the Northrop T-38, Let L-39 Albatross or the Breguet-Dornier Alpha Jet; and general aviation modified aircraft, such as the NASA Grumman Gulftstream II with in-flight extendable thrust reversers and airbrakes to train Space Shuttle steep approaches; or business jets, such as Dassault Falcon 20 and propeller aircraft

Specific Training Facilities

In this subsection, a synopsis of typical training and the respective facilities in which it is conducted is presented (Stromme 1998; Dietlein and Pestov 2004; NASA 2007).

Parabolic Flights

The crews must be trained to function, move, and operate in microgravity. Such training is best done in the simulated zero-g environment afforded by parabolic flight. These flights are conducted using modified wide bodied commercial or military transport aircraft, such as the NASA KC-135, known as the vomit comet, the Gagarin cosmonaut training center, the Ilyushin Il-76 MDK, and the Novespace Airbus A-300 zero-g (Figure 25.14). These aircraft, which serve as training facilities, have been modified to permit flying parabolic arc patterns, that is, dive/pull-up/parabola/pull-up, and they are equipped with soft padding, handholds, and nets for use by and to protect the crew during training.

Figure 25.14. Novespace Airbus A-300

(Courtesy of Novespace/CNES).

Centrifuge Training

Training on a large radius centrifuge that simulates the g-loading the crew experiences during launch and landing develops skills required to control spacecrafts during acceleration. The Gagarin cosmonaut training center centrifuge is used to train for Soyuz reentry and the Brooks Air Force Base centrifuge facility at the Armstrong laboratory for Space Shuttle launch and reentry simulations.

Vestibular Training

Training to develop tolerance to vestibular stimulation is conducted using short radius centrifuges and rotating chairs (Figure 25.15).

Figure 25.15. ESA astronaut Claudie Haigneré on a rotating chair during biomedical tests at the Gagarin cosmonaut training center

(Courtesy of ESA).

Parachute Training

Largely associated with the development of stress management and decision making skills, parachute training also requires an acute attention to safety because it replicates the risk factor of spaceflight. Parachute training also is indicated as part of bailout training.

High Altitude Training

Using a barochamber as the test facility, high altitude training develops the skills required for the effective use of preventive measures to tolerate hypoxia caused by a reduction in atmospheric pressure. High altitude training also is used to practice donning space suits and for air lock operations.

Thermal Vacuum Chambers

Replication of the real flight environment requires the use of thermal vacuum chambers. This facility (Figure 25.16) provides a realistic environment within which to train the crew in the use of actual flight tools for extravehicular activity.

Figure 25.16. NASA astronaut and extravehicular activity specialist Scott Parazynski within the extravehicular mobility unit in the NASA extravehicular training assembly

(Courtesy of NASA).

All extravehicular activity crewmembers test their flight extravehicular activity suits in vacuum chambers that resemble the extravehicular activity air lock of the Space Shuttle. Here, they can practice their extravehicular activity preparation procedures and post-extravehicular activity tasks. More important, astronauts can feel and hear an actual suit purge, experience the difference in stiffness of their flight suit as compared to their pool suits, and conduct actual suit leak checks. The chamber runs are a definite confidence builder for extravehicular activity crews, demonstrating that their suits actually do work in a vacuum and will take care of them when they are called on to conduct an extravehicular activity.

The suits are exposed to a vacuum for about 1 h during this training. Extravehicular activity crewmembers must prebreathe 100% oxygen for 4 h prior to the exercise to reduce the nitrogen content of their bloodstream. Without this procedure, there is considerable risk of developing decompression sickness (Stromme 1998).

Neutral Buoyancy Training Facilities

Microgravity conditions for operating extravehicular activity suits are simulated by the neutral buoyancy training facilities (Figure 25.17), which are simply large water tanks. Extravehicular activity training in these facilities allows the crew to develop spatial awareness and orientation in simulated microgravity conditions. In these facilities, crewmembers learn to move themselves and equipment efficiently and operate while restrained by tethers, the bulky space suits, and related equipment that constitute their life support and protection from the void of space.

Figure 25.17. The ESA European astronaut center neutral buoyancy facility located in Cologne, Germany. The overall dimensions of the water tank are 22 m × 17 m × 10 m deep, for a total volume of 3747 m3

(Courtesy of ESA).

Flight Training

Stress management skills, decision making, communication, tolerance to the forces of acceleration, and control of the aircraft during acceleration are developed during flight training. To maintain and demonstrate proficiency, flight training must be performed on a regular and periodic basis; for example, a T-38 flight is required of the crew every 45 to 90 d.

Escape and Survival Training

The skills required to achieve a successful emergency escape from a spacecraft (Figure 25.18) and survive after an emergency landing using equipment and resources from the vehicle and the environment are developed through escape and survival training. This training addresses escapes and survival in water, on land, and under all weather conditions. Survival training also tests the psychological endurance of individuals and the functioning of the team. Survival environments are used for human behavior and performance training.

Figure 25.18. Bailout from the Space Shuttle cockpit is trained at the Space Shuttle training facility

(Courtesy of NASA).

Spacecraft and Module Simulators

Simulators provide for the training of operational skills in a highly representative environment (Figure 25.19 and Figure 25.20). The spacecraft and modules training models are divided into three categories:

Figure 25.19. ESA astronaut Pedro Duque in the left hand seat (board engineer 1) of the Soyuz TMA simulator during a training session at the Gagarin cosmonaut training center. The commander (holding a pen) sits in the middle, and the flight engineer 2 (whose hand wearing a watch is visible) in the right hand seat

(Courtesy of ESA).

Figure 25.20. ESA astronaut Pedro Duque practices reentry on the manual controls of the Soyuz TMA trainer. Note that this trainer is an extraction of the main instruments panel of the Soyuz TMA but located in a more comfortable classroom environment

(Courtesy of ESA).

▪

Mock-ups, such as passive mechanical devices, for the training of mechanical operations.

▪

Trainers, such as panels or laptops with active indications, instruments, or displays for functional simulations.

▪

Full fledged simulators combining both functions that provide the crew with a more realistic simulation of the flight.

Another example of the variety of training systems is shown in Table 25.9, which lists a sample of all facilities used to train the crew on the Space Shuttle (Stromme 1998).

Table 25.9. Sample Simulator and Mock-up Facilities for the Space Shuttle Program Used During the STS-90 Mission

CategoryNameDescriptionStatic mock-upsFull fuselage trainerA full-scale mock-up of the Space Shuttle without wings, used for training crew escape procedures, in-cabin and payload bay photography, Spacelab ingress and tunnel operations, as well as stowageCrew compartment trainerAn accurate representation of the front end of the Space Shuttle, including the flight deck and middeck but without the payload bayVertically tiltable mock-upTo train in the launch configurationSimulatorsSingle system trainerMedium fidelity simulators with very close representations of the Space Shuttle flight deck, used for basic Space Shuttle systems instruction and malfunction training. They are used early in the flight training flow to help refresh knowledge of each system and for some of the qualification lessons.Shuttle mission simulatorMotion based, high fidelity simulator used for training the dynamic phases of Space Shuttle flight. These are fixed based, fixed high fidelity simulators (without motion)Vertical motion simulatorHighest fidelity Space Shuttle landing simulator, the vertical motion simulator allows the crew to go all the way from flying down final approach to landing and rollout of the vehicleOthersShuttle training aircraftGulftstream II with modified cockpit and thrust reversers, airbrakes used for steep approach trainingExternal tank doors1-g extravehicular activity simulatorKu-band antenna1-g extravehicular activity manual retraction training facilityNeutral buoyancy laboratorySpace Shuttle wet mock-up

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A review of the diagnosability of control systems with applications to spacecraft

Dayi Wang, ... Wenjing Liu, in Annual Reviews in Control, 2020

3.3 Application of diagnosability-based design to spacecraft control systems

The autonomous fault diagnosis capabilities of spacecraft control systems are important guarantees for ensuring their safe and reliable autonomous operation. Improving a system's fault diagnosis capability by implementing diagnosability-based control system design is a fundamental way to transform the traditional manual interpretation approach at ground stations into an autonomous interpretation protocol in fault diagnosis systems. The summary presented above shows that design schemes based on diagnosability analysis include the design of both the system structure and the fault diagnosis method. During the design phase of the spacecraft control system structure, the structure can be modified by multiobjective optimization by combining the diagnosability evaluation results with existing design requirements including the required control performance. During the design phase of a fault diagnosis method for a spacecraft control system, by fully considering the effects of the control system's resource arrangement and operating conditions, the diagnosability evaluation results can be used to optimize the residuals, thresholds, and other aspects of the diagnosis method, thereby improving the degree of fit of the diagnosis method with the system structure and the practical environment, thereby scientifically and purposefully improving the spacecraft control system's fault diagnosis capability. In summary, performing diagnosability-based design on a spacecraft's control system can effectively improve the allocation efficiency and optimize the allocation scheme during the system design phase, alleviate the effects of limited onboard resources, and better align the diagnosis method with the spacecraft control systems, thereby improving the system's fault diagnosis capability. This method can offset the high costs of improving spacecraft reliability, alleviates the difficulty of implementation, and reduces the detrimental effects of limited improvements, all of which fundamentally improve the autonomous fault diagnosis capability of the spacecraft. To further clarify the difference between the reliability-based design mentioned in Section 2 and the diagnosability-based design, a comparison is given in Table 3.

At present, with the continuous publication of theoretical research on diagnosability and the gradual increase in requirements for the safe and reliable autonomous operation of spacecraft, diagnosability-based design technology has been regarded with rising importance and has gradually become a research hotspot in the field of fault diagnosis for spacecraft control systems. For example, Chen et al. Chen, Ng, Speyer, Guntur, and Carpenter (2006) used a reduced-order measurement matrix in the dynamic equation of defined new states to obtain the fault directions of a star sensor, a horizon sensor, and a sun sensor; based on the obtained fault directions, the authors designed a fault detection filter with a stronger fault diagnosis capability by analyzing the relationships between the magnitudes and directions of different faults. In Jiang and Khorasani (2007), the influences of a single fault and two concurrent faults in a reaction wheel on residuals are analyzed based on a reaction wheel matrix. Moreover, according to the fault analysis, residual analysis is performed to differentiate the fault corresponding to each residual; upon applying these analyses, reaction wheel faults can be detected, isolated and reconstructed effectively. In Meskin and Khorasani (2006), a filter for spacecraft FDI using only local/decentralized signals is investigated for spacecraft formation flying systems, and a sufficient condition for the existence of the fault detection filter with acceptable performance is presented based on the unobservability subspace, which can be used for filter design without other subsystem measurements and input signals. De Persis and Isidori (De Persis & Isidori, 2001) investigated a filter for detecting and isolating fault signals based on the formulation of the nonlinear fundamental problem of residual generation (ℓNLFPRG) and analyzed the existence of residual generators by solving the ℓNLFPRG. The results in De Persis and Isidori (2001) are further extended in Baldi, Castaldi, Simani, and Bertoni (2010) to design an FDI method for spacecraft reaction wheels. Furthermore, in Hu, Sarosh, and Dong (2012), a distance coefficient is proposed to measure the distance from a new data point to the center of a class of historical data, providing a threshold used for fault diagnosis in satellite reaction wheels.

A summary of diagnosability-based design methods applied to spacecraft is provided in Table 4. The first conclusion reached from Table 4 is that most diagnosability-based design methods are model-based, which is similar to the review of research on the evaluation of diagnosability. Furthermore, Table 4 shows that research on the design of diagnosability-based system structures focuses primarily on optimizing the sensor placement, whereas the design of dynamic characteristics of spacecraft from a fault diagnosis perspective is lacking. In addition, few existing works have taken multiobjective optimization into consideration; that is, only diagnosability performance has been considered in the design process, which significantly reduces the practicability of the design results. This shortcoming is worth considering in future work (as shown in Section 6).

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A review of methods for input/output selection

Marc van de Wal, Bram de Jager, in Automatica, 2001

4.3 Flight control

Specialized IO selection methods have also been used for aircraft and spacecraft control purposes. There is no method that is clearly used the most frequently. Based on the complexity and the nature of the control problem, the preferred IO selection method should be considered for each application separately. For a process-control-like problem (e.g., controlling fluids, temperatures, and pressures in engines) IO controllability measures are a suitable starting point, whereas state controllability and observability and efficiency of manipulation and estimation may be preferred for mechanical structures (e.g., active vibration control of wings and antennas). The following applications of flight control are encountered:

•

Hoskin et al. (1991, combined RS and NP), Reeves (1991, Section 7.2, combined RS and NP), and Samar and Postlethwaite (1994, state controllability and observability, RHP zeros, IO controllability) determine suitable actuators and sensors for controlling high-performance aircraft engines.

•

Compressors are often used in aircraft engines and, in this context, IO selection for compression systems could also be mentioned, see Hendricks and Gysling (1994), Montazeri-Gh, Allerton, and Elder (1996, efficiency of manipulation and estimation), Simon, Valavani, Epstein, and Greitzer (1993), and Van de Wal et al. (1997, combined RS and NP).

•

The optimal location of actuators for attitude control of satellites is considered by Müller and Weber (1972, state controllability and observability).

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Heat pipe heat exchangers and heat sinks: Opportunities, challenges, applications, analysis, and state of the art

Hamidreza Shabgard, ... Theodore L. Bergman, in International Journal of Heat and Mass Transfer, 2015

4.4.2 Spacecraft thermal management

Kim et al. [90] numerically studied the potential use of a PCM-HPHS for thermal management of a spacecraft control system. The proposed design implemented an integrated HS with an underlying heated baseplate. The HS contained two internal channels, of which the lower was partially filled with an internal working fluid and acted as a HP, and the upper channel contained PCM to absorb heat from the baseplate. Addition of PCM with a mass less than 10% of the total radiator mass resulted in a reduction of the component operating temperature range by 28 °C due to redistribution of the temporal peak heat over the whole orbit period.

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A review of flow boiling heat transfer of nanofluids

Xiande Fang, ... Chunxiang Ma, in Applied Thermal Engineering, 2015

1 Introduction

Flow boiling heat transfer is used in a variety of industrial sectors, such as air conditioning, refrigeration, power generation, chemical engineering, aircraft environmental control, spacecraft thermal management, high-power electronics component cooling, and nuclear reactor cooling [1–4]. Enhancements in flow boiling heat transfer processes are vital to make these industrial applications more energy efficient to achieve significant reduction of energy consumption.

For flow boiling heat transfer, an important issue is the critical heat flux (CHF). The CHF is also called a boiling crisis. It describes the thermal limit of a phenomenon where a phase change occurs during heating. After the CHF point, the efficiency of heat transfer suddenly decreases, thus causing localized overheating of the heating surface. The most serious problem is that the boiling limitation is directly related to the physical burnout of the materials of a heated surface. Therefore, it is very important to enhance the CHF to make a system compact and energy efficient and to ensure system safety, and for the use of boiling in practical applications it is imperative that the CHF is not exceeded.

One of the state-of-the-art methods to enhance flow boiling heat transfer is to use nanofluids [5–8], which offer exciting new possibilities to enhance heat transfer performance compared to pure liquids and thus are promising next-generation heat transfer fluids. Nanofluids are a new class of fluids engineered by dispersing nanometer-size solid particles (1–100 nm) in base fluids and were first reported by Choi [9]. Since then, a number of studies have been conducted, with water-based nanofluids being paid much more attention. Most of the available studies deal with the thermal conductivity of nanofluids rather than their heat transfer characteristics, and most of the available heat transfer studies are related to single-phase flow heat transfer. The boiling heat transfer of nanofluids began to draw research interest from 2003 and has become a hot spot of the study of nanofluids. Most of the available studies of boiling heat transfer of nanofluids deal with pool boiling and some with flow boiling, following which is natural convective boiling in heat pipes.

Flow boiling heat transfer has more applications in engineering thermal systems than pool boiling. For most potential applications of nanofluids, flow boiling should still be an important research topic, and it has the potential to remarkably improve heat transfer and energy efficiency [6,10–13]. However, the flow boiling heat transfer of nanofluids began to attract research interest only from 2007, about four years later than the pool boiling. The author's intensive literature search only found one related paper published before 2007 [14]. It should be a main reason for this later start that nanofluid flow boiling heat transfer is far more complicated than nanofluid pool boiling. In flow boiling, apart from fluid thermal physical properties and surface characteristics, the particular flow regime also produces different effects on bubble generation, growth, and wall detachment, as well as motion and clustering within the fluid [8].

Most of the available studies of the flow boiling heat transfer of nanofluids deal with the heat transfer coefficient (HTC), CHF, pressure drop, and nanofluid stability. The base fluids involved in the investigations include water, refrigerants, and refrigerant/oil mixtures, and the materials used as nanoparticles include metaloxides (Al2O3, CuO, ZnO, TiO2, Fe3O4), metals (Cu, Al, Ag), carbon (nanotubes, diamond), and other materials (SiC, graphene oxide). The study of nanofluid flow boiling provides many opportunities to investigate new frontiers of flow boiling heat transfer but also poses great challenges.

In recent years, some review articles involving the progress of nanofluid heat transfer were published [5–8,15–17], but their main concerns are the single-phase convective heat transfer or pool boiling heat transfer. This review summarizes the fundamental and applied research of nanofluid flow boiling heat transfer, with an emphasis on the HTC, CHF, pressure drop, and nanofluid stability, because most of the available studies are related to these topics. Researches on nanofluid flow and heat transfer mechanisms, flow patterns, and bubble dynamics of nanofluid flow boiling heat transfer are also reviewed because of their importance and relevance, though the related studies are rare. Based on reviewing the available studies on the topics, important results, inconsistence, and contradictions in the available literature are identified, and the future research directions are suggested.

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