Results for “Verification and Validation methods” — Page 2

NASA NTRS2000

The NPARC Alliance Verification and Validation Archive

The NPARC Alliance (National Project for Applications oriented Research in CFD) maintains a publicly-available, web-based verification and validation archive as part of the development and support of the WIND CFD code. The verification and validation methods used for the cases attempt to follow the policies and guidelines of the ASME and AIAA. The emphasis is on air-breathing propulsion flow fields with Mach numbers ranging from low-subsonic to hypersonic.

Slater, John W.↗

NASA NTRS2007

An Approach to Improved Credibility of CFD Simulations for Rocket Injector Design

Computational fluid dynamics (CFD) has the potential to improve the historical rocket injector design process by simulating the sensitivity of performance and injector-driven thermal environments to. the details of the injector geometry and key operational parameters. Methodical verification and validation efforts on a range of coaxial injector elements have shown the current production CFD capability must be improved in order to quantitatively impact the injector design process.. This paper documents the status of an effort to understand and compare the predictive capabilities and resource requirements of a range of CFD methodologies on a set of model problem injectors. Preliminary results from a steady Reynolds-Average Navier-Stokes (RANS), an unsteady Reynolds-Average Navier Stokes (URANS) and three different Large Eddy Simulation (LES) techniques used to model a single element coaxial injector using gaseous oxygen and gaseous hydrogen propellants are presented. Initial observations are made comparing instantaneous results, corresponding time-averaged and steady-state solutions in the near -injector flow field. Significant differences in the flow fields exist, as expected, and are discussed. An important preliminary result is the identification of a fundamental mixing mechanism, accounted for by URANS and LES, but missing in the steady BANS methodology. Since propellant mixing is the core injector function, this mixing process may prove to have a profound effect on the ability to more correctly simulate injector performance and resulting thermal environments. Issues important to unifying the basis for future comparison such as solution initialization, required run time and grid resolution are addressed.

Tucker, Paul K.↗

NASA NTRSDate not supplied

Runtime Monitoring for Unmanned Aerospace Systems with Neural Network Components

AI components (e.g., Deep Neural Networks) are increasingly used in unmanned Aerospace systems for safety-relevant applications. Rigorous Verification and Validation methods for such components are still in their infancy and thus, monitoring of the AI's behavior during runtime is essential. In this paper, we will present a runtime-monitoring architecture, which combines the advanced statistical analysis framework SYSAI (System Analysis using Statistical AI) with temporal and probabilistic runtime monitoring carried out by R2U2 (Realizable, Responsive, and Unobtrusive Unit). Learned statistical models of complex systems with AI components are produced by the SYSAI framework and provide detailed information to enable the R2U2 runtime monitor to efficiently perform advanced safety and performance checks in nominal and off-nominal conditions. We will present initial results of our tool set and architecture on a case study, a DNN-based autonomous centerline tracking system (ACT).

Yuning He↗

NASA NTRSDate not supplied

Discovery and Analysis of Rare High-Impact Failure Modes using Adversarial RL-Informed Sampling

Adaptive learning agents have tremendous potential to handle critical tasks currently performed by humans. Unfortunately, due to their complexity, it can be difficult to verify that these learning agents do not have critical failure modes. Standard verification and validation methods often do not apply directly to learning agents and Monte Carlo methods have difficulty covering even a small fraction of the state space, especially in multiagent systems or over long time horizons. To overcome this difficulty, we demonstrate an adaptive stress-testing method based on reinforcement learning of correlations that raise the probability of failure. This approach has three key properties: (1) it is able to find rare failure modes with far greater sample efficiency than Monte Carlo methods, (2) it can estimate the true probability of a failure mode despite the inherent bias in the learning method, and (3) it is capable of learning and resampling compact representations of multimodal failure spaces. These properties are important in practice as we need to find disparate failure modes while accounting for their actual relevance. This is a significant advantage over traditional adaptive stress testing methods that give abstract likelihoods of particular failure instances, but cannot estimate the probability of a broader failure mode. We test our algorithm on a simple problem from the aviation domain where an autonomous aircraft lands in gusty wind conditions. The results suggest that we can find failure modes with far fewer samples than the Monte Carlo approach and simultaneously estimate the probability of failure.

reinforcement learning↗

NASA NTRSDate not supplied

Discovery and Analysis of Rare High-Impact Failure Modes using Adversarial RL-Informed Sampling

Adaptive learning agents have tremendous potential to handle critical tasks currently performed by humans. Unfortunately, due to their complexity, it can be difficult to verify that these learning agents do not have critical failure modes. Standard verification and validation methods often do not apply directly to learning agents and Monte Carlo methods have difficulty covering even a small fraction of the state space, especially in multiagent systems or over long time horizons. To overcome this difficulty, we demonstrate an adaptive stress-testing method based on reinforcement learning of correlations that raise the probability of failure. This approach has three key properties: (1) it is able to find rare failure modes with far greater sample efficiency than Monte Carlo methods, (2) it can estimate the true probability of a failure mode despite the inherent bias in the learning method, and (3) it is capable of learning and resampling compact representations of multimodal failure spaces. These properties are important in practice as we need to find disparate failure modes while accounting for their actual relevance. This is a significant advantage over traditional adaptive stress testing methods that give abstract likelihoods of particular failure instances, but cannot estimate the probability of a broader failure mode. We test our algorithm on a simple problem from the aviation domain where an autonomous aircraft lands in gusty wind conditions. The results suggest that we can find failure modes with far fewer samples than the Monte Carlo approach and simultaneously estimate the probability of failure.

Validation↗

NASA NTRSDate not supplied

Discovery and Analysis of Rare High-Impact Failure Modes using Adversarial RL-Informed Sampling

Adaptive learning agents have tremendous potential to handle critical tasks currently performed by humans. Unfortunately, due to their complexity, it can be difficult to verify that these learning agents do not have critical failure modes. Standard verification and validation methods often do not apply directly to learning agents and Monte Carlo methods have difficulty covering even a small fraction of the state space, especially in multiagent systems or over long time horizons. To overcome this difficulty, we demonstrate an adaptive stress-testing method based on reinforcement learning of correlations that raise the probability of failure. This approach has three key properties: (1) it is able to find rare failure modes with far greater sample efficiency than Monte Carlo methods, (2) it can estimate the true probability of a failure mode despite the inherent bias in the learning method, and (3) it is capable of learning and resampling compact representations of multimodal failure spaces. These properties are important in practice as we need to find disparate failure modes while accounting for their actual relevance. This is a significant advantage over traditional adaptive stress testing methods that give abstract likelihoods of particular failure instances, but cannot estimate the probability of a broader failure mode. We test our algorithm on a simple problem from the aviation domain where an autonomous aircraft lands in gusty wind conditions. The results suggest that we can find failure modes with far fewer samples than the Monte Carlo approach and simultaneously estimate the probability of failure.

Validation↗

NASA NTRS1994

Fracture mechanics life analytical methods verification testing

Verification and validation of the basic information capabilities in NASCRAC has been completed. The basic information includes computation of K versus a, J versus a, and crack opening area versus a. These quantities represent building blocks which NASCRAC uses in its other computations such as fatigue crack life and tearing instability. Several methods were used to verify and validate the basic information capabilities. The simple configurations such as the compact tension specimen and a crack in a finite plate were verified and validated versus handbook solutions for simple loads. For general loads using weight functions, offline integration using standard FORTRAN routines was performed. For more complicated configurations such as corner cracks and semielliptical cracks, NASCRAC solutions were verified and validated versus published results and finite element analyses. A few minor problems were identified in the basic information capabilities of the simple configurations. In the more complicated configurations, significant differences between NASCRAC and reference solutions were observed because NASCRAC calculates its solutions as averaged values across the entire crack front whereas the reference solutions were computed for a single point.

Favenesi, J. A.↗

NASA NTRS2015

Survey of Verification and Validation Techniques for Small Satellite Software Development

The purpose of this paper is to provide an overview of the current trends and practices in small-satellite software verification and validation. This document is not intended to promote a specific software assurance method. Rather, it seeks to present an unbiased survey of software assurance methods used to verify and validate small satellite software and to make mention of the benefits and value of each approach. These methods include simulation and testing, verification and validation with model-based design, formal methods, and fault-tolerant software design with run-time monitoring. Although the literature reveals that simulation and testing has by far the longest legacy, model-based design methods are proving to be useful for software verification and validation. Some work in formal methods, though not widely used for any satellites, may offer new ways to improve small satellite software verification and validation. These methods need to be further advanced to deal with the state explosion problem and to make them more usable by small-satellite software engineers to be regularly applied to software verification. Last, it is explained how run-time monitoring, combined with fault-tolerant software design methods, provides an important means to detect and correct software errors that escape the verification process or those errors that are produced after launch through the effects of ionizing radiation.

Small Satellite Technology↗

NASA NTRS2020

Risk considerations for autonomy software

This paper summarizes key findings related to methods for the risk considerations of autonomy software. Existing methods for Verification and Validation (V&V) of autonomy software are summarized and a method for the assurance of autonomy software is suggested and demonstrated on a command execution use case. Risk and reliability are defined in the context of autonomy and an approach for risk assessment of autonomy is presented using an example use case. Key insights regarding areas of uncertainty for autonomy are provided, along with a suggested architecture for the systematic consideration of reliability within the context of a given autonomous planner.

Meshkat, Leila↗

NASA NTRS1988

Constrained structural dynamic model verification using free vehicle suspension testing methods

Verification of the validity of a spacecraft's structural dynamic math model used in computing ascent (or in the case of the STS, ascent and landing) loads is mandatory. This verification process requires that tests be carried out on both the payload and the math model such that the ensuing correlation may validate the flight loads calculations. To properly achieve this goal, the tests should be performed with the payload in the launch constraint (i.e., held fixed at only the payload-booster interface DOFs). The practical achievement of this set of boundary conditions is quite difficult, especially with larger payloads, such as the 12-ton Hubble Space Telescope. The development of equations in the paper will show that by exciting the payload at its booster interface while it is suspended in the 'free-free' state, a set of transfer functions can be produced that will have minima that are directly related to the fundamental modes of the payload when it is constrained in its launch configuration.

Blair, Mark A.↗

NASA NTRSDate not supplied

Verification and Validation of Progressive Damage Analysis Methods for Laminated Composite Structures

Explore the source record for details and available documents.

Prabhakar Rao↗

NASA NTRS2017

Verification and Validation Process for Progressive Damage and Failure Analysis Methods in the NASA Advanced Composites Consortium

The Advanced Composites Consortium is a US Government/Industry partnership supporting technologies to enable timeline and cost reduction in the development of certified composite aerospace structures. A key component of the consortium's approach is the development and validation of improved progressive damage and failure analysis methods for composite structures. These methods will enable increased use of simulations in design trade studies and detailed design development, and thereby enable more targeted physical test programs to validate designs. To accomplish this goal with confidence, a rigorous verification and validation process was developed. The process was used to evaluate analysis methods and associated implementation requirements to ensure calculation accuracy and to gage predictability for composite failure modes of interest. This paper introduces the verification and validation process developed by the consortium during the Phase I effort of the Advanced Composites Project. Specific structural failure modes of interest are first identified, and a subset of standard composite test articles are proposed to interrogate a progressive damage analysis method's ability to predict each failure mode of interest. Test articles are designed to capture the underlying composite material constitutive response as well as the interaction of failure modes representing typical failure patterns observed in aerospace structures.

Wanthal, Steven↗

DOE OSTI2024

A Causal Approach to Model Validation and Calibration

This poster presents a novel method for validation and verification that focuses on identifying causal relationships between data elements, moving beyond traditional statistical and machine learning approaches. These methods employ causal discovery techniques to reveal the underlying mechanisms of data generation. The research utilizes structural causal models and directed acyclic graphs to depict causal relationships. This approach assists in achieving alignment between simulation models and reality.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2004

Robust Requirements Tracing via Internet Search Technology: Improving an IV and V Technique

There are three major objectives to this phase of the work. (1) Improvement of Information Retrieval (IR) methods for Independent Verification and Validation (IV&V) requirements tracing. Information Retrieval methods are typically developed for very large (order of millions - tens of millions and more documents) document collections and therefore, most successfully used methods somewhat sacrifice precision and recall in order to achieve efficiency. At the same time typical IR systems treat all user queries as independent of each other and assume that relevance of documents to queries is subjective for each user. The IV&V requirements tracing problem has a much smaller data set to operate on, even for large software development projects; the set of queries is predetermined by the high-level specification document and individual requirements considered as query input to IR methods are not necessarily independent from each other. Namely, knowledge about the links for one requirement may be helpful in determining the links of another requirement. Finally, while the final decision on the exact form of the traceability matrix still belongs to the IV&V analyst, his/her decisions are much less arbitrary than those of an Internet search engine user. All this suggests that the information available to us in the framework of the IV&V tracing problem can be successfully leveraged to enhance standard IR techniques, which in turn would lead to increased recall and precision. We developed several new methods during Phase II; (2) IV&V requirements tracing IR toolkit. Based on the methods developed in Phase I and their improvements developed in Phase II, we built a toolkit of IR methods for IV&V requirements tracing. The toolkit has been integrated, at the data level, with SAIC's SuperTracePlus (STP) tool; (3) Toolkit testing. We tested the methods included in the IV&V requirements tracing IR toolkit on a number of projects.

Hayes, Jane↗

NASA NTRSDate not supplied

Verification and Validation of Progressive Damage and Failure Analysis Methods for Intralaminar Failure Modes in Thermoplastic Composites

Current progressive damage and failure analysis (PDFA) methods were developed around thermoset material systems and their physical behavior. However, the applicability of these analysis methods has not been evaluated for use in modeling thermoplastic material systems. As part of the Hi-Rate Composite Aircraft Manufacturing (HiCAM) Project sponsored by NASA, intralaminar failure characterization was completed on a thermoplastic material system, AS4D/PEKK-FC, with off-axis tension (OAT) and off-axis compression (OAC) testing. The experiments were able to capture matrix non-linear shear behavior and the failure envelope of the material system. Two PDFA methods, LS-DYNA MAT299 and NASA’s CompDam-DGD, were then employed to simulate individual coupon behavior and simulation results were compared to published verification and validation guidelines from the CMH-17 crashworthiness working group. The results found that the two modeling methods provided suitable representation of material behavior of thermoplastics.

PDFA↗

NASA NTRSDate not supplied

Verification and Validation of Progressive Damage and Failure Analysis Methods for Intralaminar Failure Modes in Thermoplastic Composites

Current progressive damage and failure analysis (PDFA) methods were developed around thermoset material systems and their physical behavior. However, the applicability of these analysis methods has not been evaluated for use in modeling thermoplastic material systems. As part of the Hi-Rate Composite Aircraft Manufacturing (HiCAM) Project sponsored by NASA, intralaminar failure characterization was completed on a thermoplastic material system, AS4D/PEKK-FC, with off-axis tension (OAT) and off-axis compression (OAC) testing. The experiments were able to capture matrix non-linear shear behavior and the failure envelope of the material system. Two PDFA methods, LS-DYNA MAT299 and NASA’s CompDam-DGD, were then employed to simulate individual coupon behavior and simulation results were compared to published verification and validation guidelines from the CMH-17 crashworthiness working group. The results found that the two modeling methods provided suitable representation of material behavior of thermoplastics.

PDFA↗

NASA NTRS2004

A Survey of Formal Methods for Intelligent Swarms

Swarms of intelligent autonomous spacecraft, involving complex behaviors and interactions, are being proposed for future space exploration missions. Such missions provide greater flexibility and offer the possibility of gathering more science data than traditional single spacecraft missions. The emergent properties of swarms make these missions powerful, but simultaneously far more difficult to design, and to assure that the proper behaviors will emerge. These missions are also considerably more complex than previous types of missions, and NASA, like other organizations, has little experience in developing or in verifying and validating these types of missions. A significant challenge when verifying and validating swarms of intelligent interacting agents is how to determine that the possible exponential interactions and emergent behaviors are producing the desired results. Assuring correct behavior and interactions of swarms will be critical to mission success. The Autonomous Nano Technology Swarm (ANTS) mission is an example of one of the swarm types of missions NASA is considering. The ANTS mission will use a swarm of picospacecraft that will fly from Earth orbit to the Asteroid Belt. Using an insect colony analogy, ANTS will be composed of specialized workers for asteroid exploration. Exploration would consist of cataloguing the mass, density, morphology, and chemical composition of the asteroids, including any anomalous concentrations of specific minerals. To perform this task, ANTS would carry miniaturized instruments, such as imagers, spectrometers, and detectors. Since ANTS and other similar missions are going to consist of autonomous spacecraft that may be out of contact with the earth for extended periods of time, and have low bandwidths due to weight constraints, it will be difficult to observe improper behavior and to correct any errors after launch. Providing V&V (verification and validation) for this type of mission is new to NASA, and represents the cutting edge in system correctness, and requires higher levels of assurance than other (traditional) missions that use a single or small number of spacecraft that are deterministic in nature and have near continuous communication access. One of the highest possible levels of assurance comes from the application of formal methods. Formal methods are mathematics-based tools and techniques for specifying and verifying (software and hardware) systems. They are particularly useful for specifying complex parallel systems, such as exemplified by the ANTS mission, where the entire system is difficult for a single person to fully understand, a problem that is multiplied with multiple developers. Once written, a formal specification can be used to prove properties of a system (e.g., the underlying system will go from one state to another or not into a specific state) and check for particular types of errors (e.g., race or livelock conditions). A formal specification can also be used as input to a model checker for further validation. This report gives the results of a survey of formal methods techniques for verification and validation of space missions that use swarm technology. Multiple formal methods were evaluated to determine their effectiveness in modeling and assuring the behavior of swarms of spacecraft using the ANTS mission as an example system. This report is the first result of the project to determine formal approaches that are promising for formally specifying swarm-based systems. From this survey, the most promising approaches were selected and are discussed relative to their possible application to the ANTS mission. Future work will include the application of an integrated approach, based on the selected approaches identified in this report, to the formal specification of the ANTS mission.

Truszkowski, Walt↗

NASA NTRS2017

Validation of Test Methods for Air Leak Rate Verification of Spaceflight Hardware

As deep space exploration continues to be the goal of NASAs human spaceflight program, verification of the performance of spaceflight hardware becomes increasingly critical. Suitable test methods for verifying the leak rate of sealing systems are identified in program qualification testing requirements. One acceptable method for verifying the air leak rate of gas pressure seals is the tracer gas leak detector method. In this method, a tracer gas (commonly helium) leaks past the test seal and is transported to the leak detector where the leak rate is quantified. To predict the air leak rate, a conversion factor of helium-to-air is applied depending on the magnitude of the helium flow rate. The conversion factor is based on either the molecular mass ratio or the ratio of the dynamic viscosities. The current work was aimed at validating this approach for permeation-level leak rates using a series of tests with a silicone elastomer O-ring. An established pressure decay method with constant differential pressure was used to evaluate both the air and helium leak rates of the O-ring under similar temperature and pressure conditions. The results from the pressure decay tests showed, for the elastomer O-ring, that neither the molecular flow nor the viscous flow helium-to-air conversion factors were applicable. Leak rate tests were also performed using nitrogen and argon as the test gas. Molecular mass and viscosity based helium-to-test gas conversion factors were applied, but did not correctly predict the measured leak rates of either gas. To further this study, the effect of pressure boundary conditions was investigated. Often, pressure decay leak rate tests are performed at a differential pressure of 101.3 kPa with atmospheric pressure on the downstream side of the test seal. In space applications, the differential pressure is similar, but with vacuum as the downstream pressure. The same O-ring was tested at four unique differential pressures ranging from 34.5 to 137.9 kPa. Up to six combinations of upstream and downstream pressures for each differential pressure were compared. For a given differential pressure, the various combinations of upstream and downstream dry air pressures did not significantly affect the leak rate. As expected, the leak rate of the O-ring increased with increasing differential pressure. The results suggested that the current leak test pressure conditions, used to verify spacecraft sealing systems with elastomer seals, produce accurate values even though the boundary conditions do not model the space application.

helium leak detector↗