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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Analyzing Cyber Security Threats on Cyber-Physical Systems Using Model-Based Systems Engineering

The spectre of cyber attacks on aerospace systems can no longer be ignored given that many of the components and vulnerabilities that have been successfully exploited by the adversary on other infrastructures are the same as those deployed and used within the aerospace environment. An important consideration with respect to the mission/safety critical infrastructure supporting space operations is that an appropriate defensive response to an attack invariably involves the need for high precision and accuracy, because an incorrect response can trigger unacceptable losses involving lives and/or significant financial damage. A highly precise defensive response, considering the typical complexity of aerospace environments, requires a detailed and well-founded understanding of the underlying system where the goal of the defensive response is to preserve critical mission objectives in the presence of adversarial activity. In this paper, a structured approach for modeling aerospace systems is described. The approach includes physical elements, network topology, software applications, system functions, and usage scenarios. We leverage Model-Based Systems Engineering methodology by utilizing the Object Management Group's Systems Modeling Language to represent the system being analyzed and also utilize model transformations to change relevant aspects of the model into specialized analyses. A novel visualization approach is utilized to visualize the entire model as a three-dimensional graph, allowing easier interaction with subject matter experts. The model provides a unifying structure for analyzing the impact of a particular attack or a particular type of attack. Two different example analysis types are demonstrated in this paper: a graph-based propagation analysis based on edge labels, and a graph-based propagation analysis based on node labels.

MBSE↗
NASA NTRSDate not supplied

Developing Deep Learning Models for System Remaining Useful Life Predictions: Application to Aircraft Engines

Prognostics and health management (PHM) is an important part of ensuring reliable operations of complex safety- critical systems. System-level remaining useful life (RUL) estimation is a much more complex problem than making estimations at the component level, and system-level RUL methodologies remain sparse in the literature. Model-based approaches have traditionally worked in the past for components such as capacitors, MOSFETs, batteries, or hard-drives (to name a few examples), but developing high fidelity dynamics models of cyber physical systems that can be used to study the effects of multiple degrading components in the system remains a challenging task. Some initial work on model-based System RUL predictions was demonstrated in Khorasgani, et al [1], but, to generalize the system-level prognostics problem, we have to resort to pure data driven and hybrid approaches. In this work, we propose an end-to-end data- driven framework for developing deep learning models to predict remaining useful life of cyber physical systems operating under unknown faulty conditions. The raw data is organized with a data schema that improves the model development process and down stream data analysis tasks. Due to the unknown faulty conditions, the raw sensor data is transformed into signals that expose the underlying degradation processes, which are then used for model development. Bayesian Optimization is used to tune the model parameters prior to training and validation. We show that this approach results in accurate predictions within 3 cycles to end of life (EOL). We demonstrate the effectiveness of our approach by applying it to the N-CMAPSS turbofan engine dataset recently released by NASA, which includes high fidelity degradation modeling, real world operating conditions, and a large set of fault operating modes.

Prognostics↗
NASA NTRS2010

SimCheck: An Expressive Type System for Simulink

MATLAB Simulink is a member of a class of visual languages that are used for modeling and simulating physical and cyber-physical systems. A Simulink model consists of blocks with input and output ports connected using links that carry signals. We extend the type system of Simulink with annotations and dimensions/units associated with ports and links. These types can capture invariants on signals as well as relations between signals. We define a type-checker that checks the wellformedness of Simulink blocks with respect to these type annotations. The type checker generates proof obligations that are solved by SRI's Yices solver for satisfiability modulo theories (SMT). This translation can be used to detect type errors, demonstrate counterexamples, generate test cases, or prove the absence of type errors. Our work is an initial step toward the symbolic analysis of MATLAB Simulink models.

Roy, Pritam↗
NASA NTRS2021

Analysis and Monitoring of Cyber-Physical Systems via Environmental Domain Knowledge & Modeling

While verifying adherence to a specification (i.e., specification-based testing) is important, the results are only as valid as the specification itself. Problematically, verifying a system specification must be done within the context of changing or even unknown environmental domain knowledge that could render the specification ineffective or incorrect. This issue is even more apparent in the context of self-adaptive systems, where uncertainty in both the system configuration and environment can impact the validity of the system. This paper introduces a method to explicitly model domain knowledge of the environment as a secondary system to enable design-time verification against documented environmental assumptions (i.e., those elements external to the system). In addition, run-time monitors are used to detect scenarios in the actual environment not specified by the modeled environmental domain knowledge. Rather than simply identifying unexpected inputs, our approach is able to identify run-time violations of the environmental domain knowledge, even when inputs appear valid based on the domain assumptions embedded in the system specification. These violations can then be used to correspondingly update the system and environmental specifications via automated run-time adaptation or subsequent design-time revisions. We illustrate our approach by applying our method to a running example of a goal-based model of a baby monitor.

Byron DeVries↗
NASA NTRS1980

Development of alternative data analysis techniques for improving the accuracy and specificity of natural resource inventories made with digital remote sensing data

An investigation was conducted into ways to improve the involvement of state and local user personnel in the digital image analysis process by isolating those elements of the analysis process which require extensive involvement by field personnel and providing means for performing those activities apart from a computer facility. In this way, the analysis procedure can be converted from a centralized activity focused on a computer facility to a distributed activity in which users can interact with the data at the field office level or in the field itself. A general image processing software was developed on the University of Minnesota computer system (Control Data Cyber models 172 and 74). The use of color hardcopy image data as a primary medium in supervised training procedures was investigated and digital display equipment and a coordinate digitizer were procured.

Lillesand, T. M.↗
NASA NTRS2016

Mission-centric cyber security assessment of critical systems

We present a novel model-based, mission-centric approach to perform cyber security assessments for evaluating the impact of low-level cyber events on high-level mission objectives.We demonstrate the benefits of our approach using a system model and attack trees specific to the command-and-control system of a spacecraft. Specifically, we demonstrate how our approach enables a decision-maker to assess the security posture of the system, identify necessary mitigations and prioritize their deployment.

Tan, Kymie↗
NASA NTRSDate not supplied

Computational Models of Trustworthiness and Trust in Autonomous Cyber-Physical-Human Systems

In this paper, we propose an approach to developing a concept of actionable trust in multi-agent,cyber-physical-human systems in safety-critical and time-critical environment of air transportation. Actionable trust requires computational models of trustworthiness and trust, for use during system design and in real time, during operations. We describe the models, examine their computability and scalability, as well as what remains to be done.

Autonomous Systems↗
NASA NTRSDate not supplied

A Verification Framework for Runtime Assurance of Autonomous UAS

Runtime Assurance (RTA) is a design-time architecture for safety-critical systems where an internal monitor acts upon detecting a violation of a property. The simplex architecture is an instance of RTA, where the action taken is to hand control of the overall system to a trusted controller when an untrusted one violates a safety property. Simplex RTA is emerging as a method for allowing AI/ML and other unverified software to be integrated into safety-critical applications like aircraft. To this end, the American Society for Testing and Materials (ASTM) and NASA have each published guidelines on the use of RTA in such systems. In the simplex RTA framework, a system has an advanced controller (AC) and a reversionary controller (RC). The system is allowed to operate with the AC until a runtime monitor detects that some property has been violated and then the RC takes over. Assuming that the sample rate of the monitor will detect improper functioning with enough time for the RC to correct the impending problem, and that the RC is trusted, the system will operate as intended. This use of the simplex RTA framework can allow for the integration of untrusted, but possibly more performant, controllers in a safe way. This paper presents a formalization of a simplex RTA framework in the Prototype Verification System (PVS) theorem prover using an embedding of differential dynamic logic (DDL) called Plaidypvs. A novel feature of this framework is that it can be instantiated at different levels of abstraction. This feature allows for the formal verification of a system with an untrusted black box component, such as an AI/ML controller. This paper does not address the many difficulties in deploying RTA in an industrial-level system. Instead, the focus is on the formal verification of the simplex RTA framework in the language of hybrid programs. Hybrid programs are programs that include both discrete and continuous dynamics and can be used to model complex cyber-physical systems. Plaidypvs is a tool that enables formalization of hybrid programs in the PVS theorem prover. Plaidypvs enables the verification of the general simplex RTA framework and then, by specializing some components of the hybrid program, verifying instances of the framework while treating the untrusted component as a black box. A selection of Unmanned Aircraft Systems (UAS) operations are shown as instances of the general RTA framework in PVS. This offers the benefit of design time verification of relevant safety properties to the system, and it also gives requirements on the sample rate of sensors that determine the time interval in which the ‘switch’ property of the RTA framework is checked.

PVS↗
NASA NTRS1984

Computer simulations of space-borne meteorological systems on the CYBER 205

Because of the extreme expense involved in developing and flight testing meteorological instruments, an extensive series of numerical modeling experiments to simulate the performance of meteorological observing systems were performed on CYBER 205. The studies compare the relative importance of different global measurements of individual and composite systems of the meteorological variables needed to determine the state of the atmosphere. The assessments are made in terms of the systems ability to improve 12 hour global forecasts. Each experiment involves the daily assimilation of simulated data that is obtained from a data set called nature. This data is obtained from two sources: first, a long two-month general circulation integration with the GLAS 4th Order Forecast Model and second, global analysis prepared by the National Meteorological Center, NOAA, from the current observing systems twice daily.

Halem, M.↗
NASA NTRS2017

Automated Analysis of Stateflow Models

Stateflow is a widely used modeling framework for embedded and cyber physical systems where control software interacts with physical processes. In this work, we present a framework a fully automated safety verification technique for Stateflow models. Our approach is two-folded: (i) we faithfully compile Stateflow models into hierarchical state machines, and (ii) we use automated logic-based verification engine to decide the validity of safety properties. The starting point of our approach is a denotational semantics of State flow. We propose a compilation process using continuation-passing style (CPS) denotational semantics. Our compilation technique preserves the structural and modal behavior of the system. The overall approach is implemented as an open source toolbox that can be integrated into the existing Mathworks Simulink Stateflow modeling framework. We present preliminary experimental evaluations that illustrate the effectiveness of our approach in code generation and safety verification of industrial scale Stateflow models.

Stateflow↗
NASA NTRS1986

Airport-Noise Levels and Annoyance Model (ALAMO) user's guide

A guide for the use of the Airport-Noise Level and Annoyance MOdel (ALAMO) at the Langley Research Center computer complex is provided. This document is divided into 5 primary sections, the introduction, the purpose of the model, and an in-depth description of the following subsystems: baseline, noise reduction simulation and track analysis. For each subsystem, the user is provided with a description of architecture, an explanation of subsystem use, sample results, and a case runner's check list. It is assumed that the user is familiar with the operations at the Langley Research Center (LaRC) computer complex, the Network Operating System (NOS 1.4) and CYBER Control Language. Incorporated within the ALAMO model is a census database system called SITE II.

Deloach, R.↗
NASA NTRS1984

Spectral General Circulation Model Development

A spectral general circulation model was developed to run on the Cyber 205 computer system installed at the NASA Goddard Space Flight Center. This model should enhance the utility of satellite-based observation systems for accurate long-range weather prediction.

Orszag, S. A.↗
NASA NTRS2009

Towards Co-Engineering Communicating Autonomous Cyber-Physical Systems

In this paper, we sketch a framework for interdisciplinary modeling of space systems, by proposing a holistic view. We consider different system dimensions and their interaction. Specifically, we study the interactions between computation, physics, communication, uncertainty and autonomy. The most comprehensive computational paradigm that supports a holistic perspective on autonomous space systems is given by cyber-physical systems. For these, the state of art consists of collaborating multi-engineering efforts that prompt for an adequate formal foundation. To achieve this, we propose a leveraging of the traditional content of formal modeling by a co-engineering process.

Bujorianu, Marius C.↗
NASA NTRS1988

Applications products of aviation forecast models

A service called the Optimum Path Aircraft Routing System (OPARS) supplies products based on output data from the Naval Oceanographic Global Atmospheric Prediction System (NOGAPS), a model run on a Cyber-205 computer. Temperatures and winds are extracted from the surface to 100 mb, approximately 55,000 ft. Forecast winds are available in six-hour time steps.

Garthner, John P.↗
NASA NTRS1985

Atmospheric Modeling And Sensor Simulation (AMASS) study

A 4800 band synchronous communications link was established between the Perkin-Elmer (P-E) 3250 Atmospheric Modeling and Sensor Simulation (AMASS) system and the Cyber 205 located at the Goddard Space Flight Center. An extension study of off-the-shelf array processors offering standard interface to the Perkin-Elmer was conducted to determine which would meet computational requirements of the division. A Floating Point Systems AP-120B was borrowed from another Marshall Space Flight Center laboratory for evaluation. It was determined that available array processors did not offer significantly more capabilities than the borrowed unit, although at least three other vendors indicated that standard Perkin-Elmer interfaces would be marketed in the future. Therefore, the recommendation was made to continue to utilize the 120B ad to keep monitoring the AP market. Hardware necessary to support requirements of the ASD as well as to enhance system performance was specified and procured. Filters were implemented on the Harris/McIDAS system including two-dimensional lowpass, gradient, Laplacian, and bicubic interpolation routines.

Parker, K. G.↗
NASA NTRSDate not supplied

Differential Equation Approximation Using Gradient-Boosted Quantile Regression

The operation of cyber-physical-human (CPH) systems is subject to various epistemic and aleatory uncertainties. Overall trustworthiness of CPH systems relies on the trustworthiness of its components and their interactions. It is important that computational models comprising the cyber component of CPH provide predictions accompanied by a measure of confidence in model outcomes. Uncertainty quantification (UQ) and propagation are especially important in safety critical CPH systems. Gradient-boosted trees is a modeling approach capable both of learning the dynamics of a system and performing UQ. In this paper, we devise a method for using gradient boosting to learn the dynamics of a second order differential equation and estimate uncertainty at the same time. We do this by creating a custom loss function that trains the model to approximate the second derivative of a noisy time series, and to penalize based on a parameter that corresponds to the desired quantile. The resulting gradient boosting model can simulate stochastic trajectories of the system given a single starting point, that is, it can estimate both the expected trajectory and its uncertainty. We show that the uncertainty estimation is well calibrated and that the model can learn the dynamics even in the presence of noise. We demonstrate the approach on a simple cartpole system.

Autonomous systems↗