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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.

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At least 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Inexact Newton-CG algorithms with complexity guarantees

Abstract We consider variants of a recently developed Newton-CG algorithm for nonconvex problems (Royer, C. W. & Wright, S. J. (2018) Complexity analysis of second-order line-search algorithms for smooth nonconvex optimization. SIAM J. Optim., 28, 1448–1477) in which inexact estimates of the gradient and the Hessian information are used for various steps. Under certain conditions on the inexactness measures, we derive iteration complexity bounds for achieving $\epsilon $-approximate second-order optimality that match best-known lower bounds. Our inexactness condition on the gradient is adaptive, allowing for crude accuracy in regions with large gradients. We describe two variants of our approach, one in which the step size along the computed search direction is chosen adaptively, and another in which the step size is pre-defined. To obtain second-order optimality, our algorithms will make use of a negative curvature direction on some steps. These directions can be obtained, with high probability, using the randomized Lanczos algorithm. In this sense, all of our results hold with high probability over the run of the algorithm. We evaluate the performance of our proposed algorithms empirically on several machine learning models. Our approach is a first attempt to introduce inexact Hessian and/or gradient information into the Newton-CG algorithm of Royer & Wright (2018, Complexity analysis of second-order line-search algorithms for smooth nonconvex optimization. SIAM J. Optim., 28, 1448–1477).

Mathematics↗
DOE OSTI2021

On the non-existence of stepped-pressure equilibria far from symmetry

The Stepped Pressure Equilibrium Code (SPEC) (Hudson et al 2012 Phys. Plasmas 19 112502) has been successful in the construction of equilibria in 3D configurations that contain a mixture of flux surfaces, islands and chaotic magnetic field lines. In this model, the plasma is sliced into sub-volumes separated by ideal interfaces, and in each volume the magnetic field is a Beltrami field. In the cases where the system is far from possessing a continuous symmetry, such as in stellarators, the existence of solutions to a stepped-pressure equilibrium with given constraints, such as a multi-region relaxed MHD minimum energy state, is not guaranteed but is often taken for granted. Using SPEC, we have studied two different scenarios in which a solution fails to exist in a slab with analytic boundary perturbations. Here we found that with a large boundary perturbation, a certain interface becomes fractal, corresponding to the break up of a Kolmogorov–Arnold–Moser (KAM) surface. Moreover, an interface can only support a maximum pressure jump while a solution of the magnetic field consistent with the force balance condition can be found. An interface closer to break-up can support a smaller pressure jump. We discovered that the pressure jump can push the interface closer to being non-smooth through force balance, thus significantly decreasing the maximum pressure it can support. Our work shows that a convergence study must be performed on a SPEC equilibrium with interfaces close to break-up. These results may also provide insights into the choice of interfaces and have applications in finding out the maximum pressure a machine can support.

3D equilibrium↗
DOE OSTI2023

Domain-aware Control-oriented Neural Models for Autonomous Underwater Vehicles

Conventional physics-based modeling is a time-consuming bottleneck in control design for complex nonlinear systems like autonomous underwater vehicles (AUVs). In contrast, purely data-driven models, require a large number of observations and lack operational guarantees for safety-critical systems. Data-driven models leveraging available partially characterized dynamics have potential to provide reliable systems models in a typical data-limited scenario for high value complex systems, thereby avoiding months of expensive expert modeling time. In this work we explore this middle-ground between expert-modeled and pure data-driven modeling. We present control-oriented parametric models with varying levels of domain-awareness that exploit known system structure and prior physics knowledge to create constrained deep neural dynamical system models. We employ universal differential equations to construct data-driven blackbox and graybox representations of the AUV dynamics. In addition, we explore a hybrid formulation that explicitly models the residual error related to imperfect graybox models. We compare the prediction performance of the learned models for different distributions of initial conditions and control inputs to assess their suitability for control.

Shaw Cortez, Wenceslao E.↗
NASA NTRS2006

Case Study: Test Results of a Tool and Method for In-Flight, Adaptive Control System Verification on a NASA F-15 Flight Research Aircraft

Adaptive control technologies that incorporate learning algorithms have been proposed to enable autonomous flight control and to maintain vehicle performance in the face of unknown, changing, or poorly defined operating environments [1-2]. At the present time, however, it is unknown how adaptive algorithms can be routinely verified, validated, and certified for use in safety-critical applications. Rigorous methods for adaptive software verification end validation must be developed to ensure that. the control software functions as required and is highly safe and reliable. A large gap appears to exist between the point at which control system designers feel the verification process is complete, and when FAA certification officials agree it is complete. Certification of adaptive flight control software verification is complicated by the use of learning algorithms (e.g., neural networks) and degrees of system non-determinism. Of course, analytical efforts must be made in the verification process to place guarantees on learning algorithm stability, rate of convergence, and convergence accuracy. However, to satisfy FAA certification requirements, it must be demonstrated that the adaptive flight control system is also able to fail and still allow the aircraft to be flown safely or to land, while at the same time providing a means of crew notification of the (impending) failure. It was for this purpose that the NASA Ames Confidence Tool was developed [3]. This paper presents the Confidence Tool as a means of providing in-flight software assurance monitoring of an adaptive flight control system. The paper will present the data obtained from flight testing the tool on a specially modified F-15 aircraft designed to simulate loss of flight control faces.

Jacklin, Stephen A.↗
DOE OSTI2022

Using advanced data structures to enable responsive security monitoring

Write-optimized data structures (WODS), offer the potential to keep up with cyberstream event rates and give sub-second query response for key items like IP addresses. These data structures organize logs as the events are observed. To work in a real-world environment and not fill up the disk, WODS must efficiently expire older events. As the basis for our research into organizing security monitoring data, we implemented a tool, called Diventi, to index IP addresses in connection logs using RocksDB (a write-optimized LSM tree). In this work, we extended Diventi to automatically expire data as part of the data structures’ normal operations. We guarantee that Diventi always tracks the N most recent events and tracks no more than N + k events for a parameter k < N, while ensuring the index is opportunistically pruned. To test Diventi at scale in a controlled environment, we used anonymized traces of IP communications collected at SuperComputing 2019. We synthetically extended the 2.4 billion connection events to 100 billion events. We tested Diventi vs. Elasticsearch, a common log indexing tool. In our test environment, Elasticsearch saw an ingestion rate of at best 37,000 events/s while Diventi sustained ingestion rates greater than 171,000 events/s. Our query response times were as much as 100 times faster, typically answering queries in under 80 ms. Furthermore, we saw no noticeable degradation in Diventi from expiration. We have deployed Diventi for many months where it has performed well and supported new security analysis capabilities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

El-CID: a filter for gravitational-wave electromagnetic counterpart identification

ABSTRACT As gravitational-wave (GW) interferometers become more sensitive and probe ever more distant reaches, the number of detected binary neutron star mergers will increase. However, detecting more events farther away with GWs does not guarantee corresponding increase in the number of electromagnetic counterparts of these events. Current and upcoming wide-field surveys that participate in GW follow-up operations will have to contend with distinguishing the kilonova (KN) from the ever increasing number of transients they detect, many of which will be consistent with the GW sky-localization. We have developed a novel tool based on a temporal convolutional neural network architecture, trained on sparse early-time photometry and contextual information for Electromagnetic Counterpart Identification (El-CID). The overarching goal for El-CID is to slice through list of new transient candidates that are consistent with the GW sky localization, and determine which sources are consistent with KNe, allowing limited target-of-opportunity resources to be used judiciously. In addition to verifying the performance of our algorithm on an extensive testing sample, we validate it on AT2017gfo – the only EM counterpart of a binary neutron star merger discovered to date – and AT2019npv – a supernova that was initially suspected as a counterpart of the GW event, GW190814, but was later ruled out after further analysis.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2015

The Role of Margin in Link Design and Optimization

Link analysis is a system engineering process in the design, development, and operation of communication systems and networks. Link models that are mathematical abstractions representing the useful signal power and the undesirable noise and attenuation effects (including weather effects if the signal path transverses through the atmosphere) that are integrated into the link budget calculation that provides the estimates of signal power and noise power at the receiver. Then the link margin is applied which attempts to counteract the fluctuations of the signal and noise power to ensure reliable data delivery from transmitter to receiver. (Link margin is dictated by the link margin policy or requirements.) A simple link budgeting approach assumes link parameters to be deterministic values typically adopted a rule-of-thumb policy of 3 dB link margin. This policy works for most S- and X-band links due to their insensitivity to weather effects. But for higher frequency links like Ka-band, Ku-band, and optical communication links, it is unclear if a 3 dB link margin would guarantee link closure. Statistical link analysis that adopted the 2-sigma or 3-sigma link margin incorporates link uncertainties in the sigma calculation. (The Deep Space Network (DSN) link margin policies are 2-sigma for downlink and 3-sigma for uplink.) The link reliability can therefore be quantified statistically even for higher frequency links. However in the current statistical link analysis approach, link reliability is only expressed as the likelihood of exceeding the signal-to-noise ratio (SNR) threshold that corresponds to a given bit-error-rate (BER) or frame-error-rate (FER) requirement. The method does not provide the true BER or FER estimate of the link with margin, or the required signalto-noise ratio (SNR) that would meet the BER or FER requirement in the statistical sense. In this paper, we perform in-depth analysis on the relationship between BER/FER requirement, operating SNR, and coding performance curve, in the case when the channel coherence time of link fluctuation is comparable or larger than the time duration of a codeword. We compute the "true" SNR design point that would meet the BER/FER requirement by taking into account the fluctuation of signal power and noise power at the receiver, and the shape of the coding performance curve. This analysis yields a number of valuable insights on the design choices of coding scheme and link margin for the reliable data delivery of a communication system - space and ground. We illustrate the aforementioned analysis using a number of standard NASA error-correcting codes.

Cheung, K.↗
NASA NTRS2020

Mitigating Fading in Cislunar Communications: Application to the Human Landing System

NASA’s human exploration program is currently working towards landing astronauts on the surface of the Moon by 2024, close the lunar South Pole. To guarantee astronaut safety and maximize science data return, NASA is in the process of defining the communication architecture that will support all astronaut activities from launch to surface operations. Of particular interest to this paper are links from the lunar surface back to Earth without any intermediate relays. We show that the system geometry is such that antennas on the landing system will need to be pointed at low elevation angles, thus potentially causing multi-path fading effects not typically encountered in space communications. This paper is organized in three parts. First, we characterize the multi-path fading effects expected in links between the lunar South Pole and Earth and show that for moderate data rates (less than 1 Mbps) the links suffer from slow fading. We then show that for this operations regime the performance of forward error correction schemes is significantly worse for traditional Additive White Gaussian Noise channels. Finally, we investigate multi-copy mechanisms to mitigate the effects of fading, most notably repetition schemes and Automatic Repeat Request.

Sanchez Net, Marc↗
DOE OSTI2022

Towards Fully Secure 5G Ultra-Low Latency Communications: A Cost-Security Functions Analysis

Future components to enhance the basic, native security of 5G networks are either complex mechanisms whose impact in the requiring 5G communications are not considered, or lightweight solutions adapted to ultra-reliable low-latency communications (URLLC) but whose security properties remain under discussion. Although different 5G network slices may have different requirements, in general, both visions seem to fall short at provisioning secure URLLC in the future. In this work we address this challenge, by introducing cost-security functions as a method to evaluate the performance and adequacy of most developed and employed non-native enhanced security mechanisms in 5G networks. We categorize those new security components into different groups according to their purpose and deployment scope. We propose to analyze them in the context of existing 5G architectures using two different approaches. First, using model checking techniques, we will evaluate the probability of an attacker to be successful against each security solution. Second, using analytical models, we will analyze the impact of these security mechanisms in terms of delay, throughput consumption, and reliability. Finally, we will combine both approaches using stochastic cost-security functions and the PRISM model checker to create a global picture. Our results are first evidence of how a 5G network that covers and strengthened all security areas through enhanced, dedicated non-native mechanisms could only guarantee secure URLLC with a probability of ~55%.

5G networks↗
NASA NTRS2015

Ethernet for Aerospace Applications - Ethernet Heads for the Skies

One of the goals of aerospace applications is to reduce the cost and complexity of avionic systems. Ethernet is a highly scalable, flexible, and popular protocol. The aerospace market is large, with a forecasted production of over 50,000 turbine-powered aircraft valued at $1.7 trillion between 2012 and 2022. Boeing estimates demand for commercial aircraft by 2033 to total over 36,000 with a value of over $5 trillion. In 2014 US airlines served over 750 million passengers and this is growing over 2% yearly. Electronic fly-by-wire is now used for all airliners and high performance aircraft. Although Ethernet has been widely used for four decades, its use in aerospace applications is just beginning to become common. Ethernet is the universal solution in commercial networks because of its high bandwidths, lower cost, openness, reliability, maintainability, flexibility, and interoperability. However, when Ethernet was designed applications with time-critical, safety relevant and deterministic requirements were not given much consideration. Many aerospace applications use a variety of communication architectures that add cost and complexity. Some of them are SpaceWire, MIL-STD-1553, Avionics Full Duplex Switched Ethernet (AFDX), and Time-Triggered Ethernet (TTE). Aerospace network designers desire to decrease the number of networks to reduce cost and effort while improving scalability, flexibility, openness, maintainability, and reliability. AFDX and TTE are being considered more for critical aerospace systems because they provide redundancy, failover protection, guaranteed timing, and frame priority and are based on Ethernet IEEE 802.3. This paper explores the use of AFDX and TTE for aerospace applications.

Ethernet↗
DOE OSTI2026

QProR: An Efficient Framework for Quantity-of-Interest Based Progressive Retrieval with Guaranteed Error Control

Scientific applications generate an unprecedented volume of data, overwhelming the network and file systems’ bandwidth and posing challenges for efficient and scalable data retrieval and analysis. Progressive data compression offers a promising solution by enabling on-demand retrieval at reduced size. However, existing progressive methods either fail to bound the errors in essential quantities of interest (QoIs) derived from raw data or suffer from suboptimal retrieval efficiency. In this work, we propose QProR, an efficient QoI-based progressive framework that optimizes progressive retrieval for target QoIs. Our key contributions include: (1) a systematic framework that integrates error-controlled lossy compressors with bitplane encoding while decoupling the two processes for high flexibility and adaptability; (2) a novel weighted bitplane encoding method which incorperates QoI knowledge into data refactoring to enhance retrieval efficiency; (3) an optimized retrieval strategy that accounts for the varying impacts of different variables on multivariate QoIs; (4) comprehensive evaluations using six real-world datasets from multiple scientific applications and thorough comparisons against state of the arts. Experimental results demonstrate that QProR achieves up to 80.38% reduction in the retrieval size under the same requested QoI error tolerance, when compared with the best-performing existing methods. When transferring 384 GB of scientific data to remote sites, QProR delivers up to 1.68 × speedup in the end-to-end data transfer performance.

Li, Wenbo [University of Kentucky]↗
DOE OSTI2022

Optimizing Error-Bounded Lossy Compression for Scientific Data With Diverse Constraints

Vast volumes of data are produced by today's scientific simulations and advanced instruments. These data cannot be stored and transferred efficiently because of limited I/O bandwidth, network speed, and storage capacity. Error-bounded lossy compression can be an effective method for addressing these issues: not only can it significantly reduce data size, but it can also control the data distortion based on user-defined error bounds. In practice, many scientific applications have specific requirements or constraints for lossy compression, in order to guarantee that the reconstructed data are valid for post hoc analysis. For example, some datasets contain irrelevant data that should be isolated in particular and users often have intuition regarding value ranges, geospatial regions, and other data subsets that are crucial for subsequent analysis. Existing state-of-the-art error-bounded lossy compressors, however, do not consider these constraints during compression, resulting in inferior compression ratios with respect to user's post hoc analysis, due to the fact that the data itself provides little or no value for post hoc analysis. In this work we address this issue by proposing an optimized framework that can preserve diverse constraints during the error-bounded lossy compression, e.g., cleaning the irrelevant data, efficiently preserving different precision for multiple value intervals, and allowing users to set diverse precision over both regular and irregular regions. We perform our evaluation on a supercomputer with up to 2,100 cores. Experiments with six real-world applications show that our proposed diverse constraints based error-bounded lossy compressor can obtain a higher visual quality or data fidelity on reconstructed data with the same or even higher compression ratios compared with the traditional state-of-the-art compressor SZ. Furthermore, our experiments also demonstrate very good scalability in compression performance compared with the I/O throughput of the parallel file system.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2020

U.S. Coast Guard Boat Recovery Simulation at the NASA Ames Vertical Motion Simulator

The U.S. Coast Guard routinely uses the Over-The-Horizon (OTH-IV), a cutter deployed, rigid-hulled inflatable boat for rescue and law enforcement operations. The term "cutter" refers to a Coast Guard vessel 65 feet in length or greater with accommodations for crew to live aboard and the ability to deploy smaller boats including the OTH-IV. A deployment method employed by some cutter class vessels is a davit crane system and specialized hook mechanism to connect the smaller boat. The launch and recovery operation requires several crewmembers, where one is tasked with releasing and connecting the hook to the small boat. Manipulating the hook can pose a risk to the crewmember depending on sea conditions combined with fatigue level or task expertise. Equipment and crewmembers are tested extensively due to the inherent risk of the task, but the sea conditions cannot be controlled. To address the sea condition variable, the U.S. Coast Guard and NASA leveraged the Vertical Motion Simulator (VMS) as a platform to test new equipment or train crewmembers in varying sea conditions. With 6 degrees of freedom and a vertical displacement of +/- 22ft, the VMS is capable of simulating conditions up to sea-state 5. The proof of concept experiment took place in September 2018 and demonstrated that the VMS can accurately simulate varying sea-states, collect performance data, and design a reliable and safe system for participants. The U.S. Coast Guard provided boat displacement data from the Large Amplitude Motion Program (LAMP). The program generates multi-directional waves with a cosine squared spreading function which produced a time history response of displacement data (surge, sway, heave, roll, pitch, yaw) of the OTH-IV's center of gravity (C.G). The displacement data was differentiated into acceleration evaluated at the crewmember's position at the front (bow) of the boat. Those accelerations were transformed to the VMS coordinate system and used as command inputs to the motion system's washout filters. The VMS uses Interchangeable Cabs (ICab) to perform flight simulations on a variety of aircraft or spacecraft configurations. An ICab typically contains seats, restraints, pilot controls, instrument panel, and out-the-window projectors all enclosed in a familiar cockpit configuration. To replicate the launch and recovery of a small boat, all the conventional features of a flight simulator and canopy were removed to be replaced with a to-scale OTH-IV bow mockup. The bow section was constructed from aluminum, and a connection point known as the davit ring. Additionally, a hook replica was suspended by a cable from the ceiling as if it were hanging from a davit crane aboard a cutter vessel. The last step was providing an auxiliary control to close the distance between suspended hook and the davit ring on the bow mockup. Without control the boat would simulate the wave motion, but with no guarantee that the crewmember will be in reach of the hook. Since the wave motion was repeatable, an additional acceleration command sent to the washout filters to move the simulator in the surge, sway, and heave directions, independently. The commands were rate limited such that the additional movement was not detectable with respect to the wave motion. The results showed that for each wave profile, a unique set of acceleration commands provided opportunities for crewmembers to gain control of the hook and connect it to the davit ring.

Riccobono, Nicholas S.↗
DOE OSTI2020

Thermal Management for FPGA Nodes in HPC Systems

The integration of FPGAs into large-scale computing systems is gaining attention. In these systems, real-time data handling for networking, tasks for scientific computing, and machine learning can be executed with customized datapaths on reconfigurable fabric within heterogeneous compute nodes. At the same time, thermal management, particularly battling the cooling cost and guaranteeing the reliability, is a continuing concern. The introduction of new heterogeneous components into HPC nodes only adds further complexities to thermal modeling and management. The thermal behavior of multi-FPGA systems deployed within large compute clusters is less explored. Here, we first show that the thermal behaviors of different FPGAs of the same generation can vary due to their physical locations in a rack and process variation, even though they are running the same tasks. We present a machine learning–based model to capture the thermal behavior of each individual FPGA in the cluster. We then propose two thermal management strategies guided by our thermal model. First, we mitigate thermal variation and hotspots across the cluster by proactive thermal-aware task placement. Under the tested system and benchmarks, we achieve up to 26.4° C and on average 13.3° C system temperature reduction with no performance penalty. Second, we utilize this thermal model to guide HLS parameter tuning at the task design stage to achieve improved thermal response after deployment.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Generalization in quantum machine learning from few training data

Modern quantum machine learning (QML) methods involve variationally optimizing a parameterized quantum circuit on a training data set, and subsequently making predictions on a testing data set (i.e., generalizing). In this work, we provide a comprehensive study of generalization performance in QML after training on a limited number N of training data points. We show that the generalization error of a quantum machine learning model with T trainable gates scales at worst as $\sqrt{T/N}$. When only K$\ll$T gates have undergone substantial change in the optimization process, we prove that the generalization error improves to $\sqrt{K/N}$. Our results imply that the compiling of unitaries into a polynomial number of native gates, a crucial application for the quantum computing industry that typically uses exponential-size training data, can be sped up significantly. We also show that classification of quantum states across a phase transition with a quantum convolutional neural network requires only a very small training data set. Other potential applications include learning quantum error correcting codes or quantum dynamical simulation. Our work injects new hope into the field of QML, as good generalization is guaranteed from few training data.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2011

Optimal Control via Self-Generated Stochasticity

The problem of global maxima of functionals has been examined. Mathematical roots of local maxima are the same as those for a much simpler problem of finding global maximum of a multi-dimensional function. The second problem is instability even if an optimal trajectory is found, there is no guarantee that it is stable. As a result, a fundamentally new approach is introduced to optimal control based upon two new ideas. The first idea is to represent the functional to be maximized as a limit of a probability density governed by the appropriately selected Liouville equation. Then, the corresponding ordinary differential equations (ODEs) become stochastic, and that sample of the solution that has the largest value will have the highest probability to appear in ODE simulation. The main advantages of the stochastic approach are that it is not sensitive to local maxima, the function to be maximized must be only integrable but not necessarily differentiable, and global equality and inequality constraints do not cause any significant obstacles. The second idea is to remove possible instability of the optimal solution by equipping the control system with a self-stabilizing device. The applications of the proposed methodology will optimize the performance of NASA spacecraft, as well as robot performance.

Zak, Michail↗
NASA NTRS2020

In-flight Testing of the Injection of the LISA Pathfinder Test Mass into a Geodesic

LISA Pathfinder is a technology demonstrator space mission, aimed at testing key technologies for detecting gravitational waves in space. The mission is the precursor of LISA, the first space gravitational waves observatory, whose launch is scheduled for 2034. The LISA Pathfinder scientific payload includes two gravitational reference sensors (GRSs), each one containing a test mass (TM), which is the sensing body of the experiment. A mission critical task is to set each TM into a pure geodesic motion, i.e. guaranteeing an extremely low acceleration noise in the sub-Hertz frequency bandwidth. The grabbing positioning and release mechanism (GPRM), responsible for the injection of the TM into a geodesic trajectory, was widely tested on ground, with the limitations imposed by the 1-g environment. The experiments showed that the mechanism, working in its nominal conditions, is capable of releasing the TM into free-fall fulfilling the very strict constraint imposed on the TM residual velocity, in order to allow its capture on behalf of the electrostatic actuation. However, the first in-flight releases produced unexpected residual velocity components, for both the TMs. Moreover, all the residual velocity components were greater than maximum value set by the requirements. The main suspect is that unexpected contacts took place between the TM and the surroundings bodies. As a consequence, ad hoc manual release procedures had to be adopted for the few following injections performed during the nominal mission. These procedures still resulted in non compliant TM states which were captured only after impacts. However, such procedures seem not practicable for LISA, both for the limited repeatability of the system and for the unmanageable time lag of the telemetry/telecommand signals (about 4400 s). For this reason, at the end of the mission, the GPRM was deeply tested in-flight, performing a large number of releases, according to different strategies. The tests were carried out in order to understand the unexpected dynamics and limit its effects on the final injection. Some risk mitigation maneuvers have been tested aimed at minimizing the vibration of the system at the release and improving the alignment between the mechanism and the TM. However, no overall optimal release strategy to be implemented in LISA could be found, because the two GPRMs behaved differently.

LISA↗
NASA NTRS2021

Compositional Realizability Checking within FRET

A set of requirements for a reactive system is realizable if, for any sequence of inputs that satisfy the assumptions on the environment, the guarantees always hold. Realizability checking is essential to ensure that an implementation can be constructed that satisfies the requirements. We propose a framework that supports users in the non-trivial task of developing realizable requirements. Our framework uses architectural information to automatically de-compose a set of requirements into subsets that can be analyzed separately, and therefore more efficiently. It then integrates existing algorithms in order to detect unrealizability, identify minimal sets of conflicting requirements, and compute counterexamples. The capability to focus on minimal conflict sets is key for localizing and correcting the sources of unrealizability. Our approach supports this process by enabling users to interactively visualize and explore the produced conflict sets and counterexamples. We have implemented our framework in the open-source Formal Requirements Elicitation Tool (FRET), and have used it on a variety of industrial-level case studies, showcasing the strengths of our approach in terms of raw performance, as well as diagnostic potential.

FRET↗