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NASA NTRSDate not supplied

Exploitation of a Validation Hierarchy for Modeling and Simulation

Across engineering there is an evolving need to increase reliance on physics-based simulation to develop, design and optimize engineering systems. This increased reliance on modeling and simulation has highlighted a growing need to transform the confidence that modeling and simulation analysts have in their results into credibility for systems engineers to design and field systems more quickly and with less physical testing. For isolated components of a complex system, where a single discipline may drive product design, this is less of a concern as the relationship is often straightforward and easy to explain. However, when these isolated components are integrated, and are expected to operate in a multi-disciplinary context in which safety critical systems are involved, new concepts and model assurance standards are required. In this paper we address this challenge by showing how a model validation hierarchy can be exploited to identify those model validation experiments that will contribute most to increasing confidence and credibility of modeling and simulation predictions. The approach that is adopted contains four main steps. The first step is the construction of a model validation hierarchy that links subsystems, assemblies, and components to a hierarchy of physical experiments that can be used support model validation. This hierarchy connects the concerns of systems engineers to those of the modeling and simulation analyst in a clear and logical way. The structure and content of this hierarchy is then used in a second step to establish which physical phenomena have the greatest impact on overall system performance metrics. A gap analysis technique, based upon modeling and simulation concerns, is then used to prioritize the important physical phenomenon. Unfortunately, a common outcome of such gap analyses is the identification of many important gaps and so, in the final step of our process, we advocate the use of a global sensitivity analysis as a means to complete the prioritization.

Verification and Validation↗
NASA NTRSDate not supplied

Exploitation of a Validation Hierarchy for Modeling and Simulation

Across engineering there is an evolving need to increase reliance on physics-based simulation to develop, design and optimize engineering systems. This increased reliance on modeling and simulation has highlighted a growing need to transform the confidence that modeling and simulation analysts have in their results into credibility for systems engineers to design and field systems more quickly and with less physical testing. For isolated components of a complex system, where a single discipline may drive product design, this is less of a concern as the relationship is often straightforward and easy to explain. However, when these isolated components are integrated, and are expected to operate in a multi-disciplinary context in which safety critical systems are involved, new concepts and model assurance standards are required. In this paper we address this challenge by showing how a model validation hierarchy can be exploited to identify those model validation experiments that will contribute most to increasing confidence and credibility of modeling and simulation predictions. The approach that is adopted contains four main steps. The first step is the construction of a model validation hierarchy that links subsystems, assemblies, and components to a hierarchy of physical experiments that can be used support model validation. This hierarchy connects the concerns of systems engineers to those of the modeling and simulation analyst in a clear and logical way. The structure and content of this hierarchy is then used in a second step to establish which physical phenomena have the greatest impact on overall system performance metrics. A gap analysis technique, based upon modeling and simulation concerns, is then used to prioritize the important physical phenomenon. Unfortunately, a common outcome of such gap analyses is the identification of many important gaps and so, in the final step of our process, we advocate the use of a global sensitivity analysis as a means to complete the prioritization.

Verification and Validation↗
NASA NTRSDate not supplied

Medical Resource Set Bulky Item Trade Space Analysis for Spaceflight Medical Risk

The NASA engineering community utilizes event-driven and fault-tree probabilistic techniques to classify risks in the space environment by taking advantage of the inherent knowledge of complex spaceflight system design and testing to quantify failure risk. In harmonizing the risk of human space flight, answering the question of ‘How do we balance health, performance and resource risks with other engineering risks on long duration space missions?’ remains a deeply challenging and largely qualitative practice. The Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT) is one aspect of the efforts by NASA’s Human Research Program (HRP) to quantitatively assess the impact of health and performance risk. One of MEDPRAT’s key features is its high degree of computational efficiency. Coupled with the HRP High Performance Compute cluster located at NASA’s Glenn Research Center, MEDPRAT runs millions of simulated missions in a matter of minutes. This degree of computational efficiency provides the novel opportunity to explore the relationship between medical set mass, volume, and medical resource size. Of particular interest for future human spaceflight missions are ‘bulky’ items, medical resources like devices, which occupy a large portion of the small, allocated mass and volume for the medical set leaving less room for other resources. This talk will present results showing the quantitative impact of forced inclusion of several bulky items across a variety of medical kit constraints, and the effect that a potential research investment into reducing the bulky item mass and volume may have on risk.

Lauren Mcintyre↗
NASA NTRSDate not supplied

The Future of Integrated Performance Modeling in the Crew Health and Performance – Probabilistic Risk Assessment Project

The NASA engineering community utilizes event-driven and fault-tree probabilistic techniques to classify risks in the space environment by taking advantage of the inherent knowledge of complex spaceflight system design and testing to quantify failure risk. In harmonizing the risk of human space flight, answering the question of ‘How do we balance health, performance and resource risks with other engineering risks on long duration space missions?’ remains a deeply challenging and largely qualitative practice. The Human Research Program’s Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT) was a significant step forward in efforts to robustly quantify the risk to crew health for exploration missions. However, there remains a significant gap in the ability to comprehensively assess and characterize risk across the disparate functionalities and capabilities which comprise the Crew Health and Performance (CHP) system. The Crew Health and Performance – Probabilistic Risk Assessment (CHP-PRA) project seeks to characterize CHP risks by expanding beyond the foundation established by its PRA predecessors like IMM and MEDPRAT, that simulate medical risk metrics like loss of crew life and evacuations. One of the new risk measures in the CHP-PRA system is embodied in our Performance Risk Model (PRisM). PRisM provides a novel way of assessing crew performance on mission tasks, using a generalized framework which relates back to NASA-STD-3001. This approach allows PRisM to capture and integrate data from a variety of different domains into a single, unified, reproducible representation of astronaut performance. In this presentation, we discuss the motivation for the CHP-PRA work and give a high level overview of the goals of the project, outline the forward work for PRisM, and discuss collaboration opportunities for the community who might explore if their domain knowledge and data could be represented, integrated, and quantified with these tools, whose outcomes are metrics useful for supporting operational mission planning and decision making.

Lauren McIntyre↗
DOE OSTI2025

System Engineers and Decisions: It?s All about Knowledge

In order to guarantee that a system meets adequate levels of reliability and availability, system performances are continuously monitored and analyzed thanks to the technological advancements driving the Industry 4.0 revolution. An Industry 4.0 approach is typically based on advanced statistical, big data mining, machine learning, and internet-of-things methods designed to detect anomalies in the behavior of system, detect the most likely failure modes, and provide indications to system engineers on when maintenance activities should be performed before system performance are deemed unacceptable (which can be generated by diagnostic and prognostic methods). However, these analyses, which are designed to automatize and increase the efficacy of the system maintenance program, require large amount of data which can come in various forms: numeric, textual, images, sounds etc. Such data constitutes the historic knowledge benchmark to track system performances and support system engineer decisions. Here we claim that data is not sufficient to support this kind of analyses when applied to systems characterized by complex architectures and behaviors. Robust system engineer decisions require the ability to understand the system operational context that lies behind the observed data elements. In this respect, system models are in fact necessary to “put data in context” and capture relationships between data elements. Industry 4.0 methods require in fact contextual knowledge as a basis upon which hypotheses can be generated and assumptions tested. In our view, for complex systems, model-based system engineering (MBSE) models can afford this contextual knowledge, as they are typically used to describe systems architecture and dynamic behaviors. System knowledge is here intended as the blending of collected data and system architecture which takes the form of a “knowledge graph”. A knowledge graph is a database which consists of a large set of nodes (in our case an entity can be either a data or an MBSE element) which are linked to each other. The types of nodes and links follow a pre-defined topology, sometimes also refers as an ontology, that is designed to fit the actual decisions that needs to be performed. We show here how a knowledge graph can be defined to support system engineer maintenance decisions and how the same graph can be built based on system MBSE models and pre-processed data from numeric (through anomaly detections and diagnostic methods) and textual elements (through technical language processing TLP).

97 - MATHEMATICS AND COMPUTING↗
NASA NTRS1970

Safe suspension of specimens or clusters during dynamic testing - A concept

Support system without point supports permits dynamic testing of complex or delicate mechanical units without excessive movement which might cause damage. Motion-sensor pressure-control system regulates inflation of molded rubber bags surrounding test specimen, so that any excessive motion is smothered until test is terminated.

Hoch, G. C.↗
DOE OSTI2025

Multiphysics Modeling of Microreactors with NEAMS codes, and Validation Based on KRUSTY Reactivity Insertion

The NEAMS Multiphysics Applications team continues to assess code usability and functionality for microreactor design and safety analyses, while demonstrating that NEAMS tools capture both steady-state and transient behavior across distinct microreactor concepts. In FY2025, the team advanced full-core, high-fidelity, multiphysics models that solve more complex problems and strengthen verification/validation for several microreactor systems: heat-pipe microreactor (HPMR), gas-cooled microreactor (GCMR), and the KRUSTY experiment. These models employ the MOOSE MultiApp/Transfers architecture with Griffin for neutronics, BISON for heat conduction/thermomechanics, Sockeye for heat pipes, SAM/THM for coolant channels and loops, and SWIFT for hydride behavior, with meshes generated via the MOOSE Reactor Module. The graphite models available in the Grizzly code were also investigated for future analyses. For the HPMR, a Na-HPMR variant was constructed to align with recently validated heat-pipe experiments and Sockeye’s LCVF capability, enabling mechanistic heat-pipe transients and startup modeling. The Na-HPMR will serve as the primary model for HPMR investigations in upcoming tasks. The load-following and single heat-pipe failure scenarios (Griffin/BISON/Sockeye), which were previously modeled for the K-HPMR, were replicated for the Na-HPMR, showing strong negative temperature feedback and highly localized thermal effects, respectively, while the startup case captured vapor-front progression and heat-removal activation. Solid mechanics was added to the previously built K-HPMR full-core model in BISON, showing minimal impact on steady-state reactivity yet enabling stress-field predictions that prepare the path for full-core TRISO performance analyses. For the GCMR, automated steady-state and four transient scenarios were executed using Griffin/BISON/SAM/SWIFT. Results confirm robust inherent safety: power collapses promptly in loss-of-cooling events, the inlet-temperature drop settles to a new equilibrium, and a single-channel blockage yields only a ~30 K local fuel-temperature rise with <0.4% power decrease. SWIFT-predicted hydrogen redistribution affects reactivity during both steady-state and transient conditions, underscoring its importance. A Brayton-cycle balance of plant (BOP) model in SAM/THM demonstrated stable startup behavior, and xenon-driven reactivity during load following was analyzed. To improve TRISO-compact temperature fidelity, a fast multiscale Heat Source Decomposition (HSD) treatment was implemented. Against heterogeneous benchmarks, HSD reduces underprediction of kernel temperatures and lowers predicted peak powers in reactivity-insertion transients compared to previous homogenized models. KRUSTY warm-critical validation progressed from FY2024 baselines: the 15Ȼ insertion shows excellent agreement in peak power (~2% high) and temperature trends, and the 30Ȼ case was automated via a feedback controller that maintained power near 3 kW for ~150 s with close agreement to data. The successful modeling of the warm critical tests has laid a strong foundation for simulating more complex nuclear system tests in the years ahead. Throughout FY2025, developer feedback was provided (e.g., MOOSE batch mesh generation, distributed pre-split meshes, Griffin sweeper on displaced meshes), several new models were contributed to the Virtual Test Bed, and an OECD-NEA WPRS multiphysics benchmark based on the HPMR was initiated to enable broader cross-comparison and best-practice development with the nuclear community at large.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2006

Structure Detection of Nonlinear Aeroelastic Systems with Application to Aeroelastic Flight Test Data

This viewgraph presentation reviews the 1. Motivation for the study 2. Nonlinear Model Form 3. Structure Detection 4. Least Absolute Shrinkage and Selection Operator (LASSO) 5. Objectives 6. Results 7. Assess LASSO as a Structure Detection Tool: Simulated Nonlinear Models 8. Applicability to Complex Systems: F/A-18 Active Aeroelastic Wing Flight Test Data. The authors conclude that 1. this is a novel approach for detecting the structure of highly over-parameterised nonlinear models in situations where other methods may be inadequate 2. that it is a practical significance in the analysis of aircraft dynamics during envelope expansion and could lead to more efficient control strategies and 3. this could allow greater insight into the functionality of various systems dynamics, by providing a quantitative model which is easily interpretable

Kukreja, Sunil L.↗
NASA NTRS2004

A General Overview of the Data Acquisition and Controls Systems (DACS) of the E-Complex at NASA's Stennis Space Center

The John C. Stennis Space Center (SSC) provides test operations services to a variety of customers, including NASA, DoD, and commercial enterprises for the development of current and next-generation rocket propulsion systems. Many of these testing services are provided in the E-Complex test facilities composed of three active test stands (E1, E2, & E3) and 7 total test positions. Each test position is outfitted with unique sets of data acquisition and controls hardware and software that record both facility and test article data and enable safe operation of the test facility. This paper addresses each system in more detail including efforts to upgrade hardware and software.

Hughes, Mark S.↗
NASA NTRS1993

Software For Graphical Representation Of A Network

System Visualization Tool (SVT) computer program developed to provide systems engineers with means of graphically representing networks. Generates diagrams illustrating structures and states of networks defined by users. Provides systems engineers powerful tool simplifing analysis of requirements and testing and maintenance of complex software-controlled systems. Employs visual models supporting analysis of chronological sequences of requirements, simulation data, and related software functions. Applied to pneumatic, hydraulic, and propellant-distribution networks. Used to define and view arbitrary configurations of such major hardware components of system as propellant tanks, valves, propellant lines, and engines. Also graphically displays status of each component. Advantage of SVT: utilizes visual cues to represent configuration of each component within network. Written in Turbo Pascal(R), version 5.0.

Mcallister, R. William↗
DOE OSTI2023

Deuteron off-resonance rotating frame relaxation for the characterization of slow motions in rotating and static solid-state proteins

Here, we demonstrate the feasibility of deuterium solid-state NMR off-resonance rotating frame relaxation measurements for studies of slow motions in biomolecular solids. The pulse sequence, which includes adiabatic pulses for magnetization alignment, is illustrated for static and magic-angle spinning conditions away from rotary resonances. We apply the measurements for three systems with selective deuterium labels at methyl groups: a) a model compound, Fluorenylmethyloxycarbonyl methionine-D 3 amino acid, for which the principles of the measurements and corresponding motional modeling based on rotameric interconversions are demonstrated; b) amyloid-β 1-40 fibrils labeled at a single alanine methyl group located in the disordered N-terminal domain. This system has been extensively studied in prior work and here serves as a test of the method for complex biological systems. The essential features of the dynamics consist of large-scale rearrangements of the disordered N-terminal domain and the conformational exchange between the free and bound forms of the domain, the latter one due to transient interactions with the structured core of the fibrils. and c) a 15-residue helical peptide which belongs to the predicted α-helical domain near the N-terminus of apolipoprotein B. The peptide is solvated with triolein and incorporates a selectively labeled leucine methyl groups. The method permits model refinement, indicating rotameric interconversions with a distribution of rate constants.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2005

Requirements to Design to Code: Towards a Fully Formal Approach to Automatic Code Generation

A general-purpose method to mechanically transform system requirements into a provably equivalent model has yet to appear. Such a method represents a necessary step toward high-dependability system engineering for numerous possible application domains, including distributed software systems, sensor networks, robot operation, complex scripts for spacecraft integration and testing, and autonomous systems. Currently available tools and methods that start with a formal model of a system and mechanically produce a provably equivalent implementation are valuable but not sufficient. The gap that current tools and methods leave unfilled is that their formal models cannot be proven to be equivalent to the system requirements as originated by the customer. For the classes of systems whose behavior can be described as a finite (but significant) set of scenarios, we offer a method for mechanically transforming requirements (expressed in restricted natural language, or in other appropriate graphical notations) into a provably equivalent formal model that can be used as the basis for code generation and other transformations.

Hinchey, Michael G.↗
NASA NTRS2005

Requirements to Design to Code: Towards a Fully Formal Approach to Automatic Code Generation

A general-purpose method to mechanically transform system requirements into a provably equivalent model has yet to appear. Such a method represents a necessary step toward high-dependability system engineering for numerous possible application domains, including distributed software systems, sensor networks, robot operation, complex scripts for spacecraft integration and testing, and autonomous systems. Currently available tools and methods that start with a formal model of a: system and mechanically produce a provably equivalent implementation are valuable but not sufficient. The "gap" that current tools and methods leave unfilled is that their formal models cannot be proven to be equivalent to the system requirements as originated by the customer. For the ciasses of systems whose behavior can be described as a finite (but significant) set of scenarios, we offer a method for mechanically transforming requirements (expressed in restricted natural language, or in other appropriate graphical notations) into a provably equivalent formal model that can be used as the basis for code generation and other transformations.

Hinchey, Michael G.↗
NASA NTRS2001

High Speed PC Based Data Acquisition and Instrumentation for Measurement of Simulated Low Earth Orbit Thermally Induced Disturbances

The Hubble Space Telescope (HST) Disturbance Verification Test (DVT) was conducted to characterize responses of the Observatory's new set of rigid solar array's (SA3) to thermally induced 'creak' or stiction releases. The data acquired in the DVT were used in verification of the HST Pointing Control System on-orbit performance, post-Servicing Mission 3B (SM3B). The test simulated the on-orbit environment on a deployed SA3 flight wing. Instrumentation for this test required pretest simulations in order to select the correct sensitivities. Vacuum compatible, highly accurate accelerometers and force gages were used for this test. The complexity of the test, as well as a short planning schedule, required a data acquisition system that was easy to configure, highly flexible, and extremely robust. A PC Windows oriented data acquisition system meets these requirements, allowing the test engineers to minimize the time required to plan and perform complex environmental test. The SA3 DVT provided a direct practical and complex demonstration of the versatility that PC based data acquisition systems provide. Two PC based data acquisition systems were assembled to acquire, process, distribute, and provide real time processing for several types of transducers used in the SA3 DVT. A high sample rate digital tape recorder was used to archive the sensor signals. The two systems provided multi-channel hardware and software architecture and were selected based on the test requirements. How these systems acquire and processes multiple data rates from different transducer types is discussed, along with the system hardware and software architecture.

Sills, Joel W., Jr.↗
NASA NTRS1998

Open Architecture Data System for NASA Langley Combined Loads Test System

The Combined Loads Test System (COLTS) is a new structures test complex that is being developed at NASA Langley Research Center (LaRC) to test large curved panels and cylindrical shell structures. These structural components are representative of aircraft fuselage sections of subsonic and supersonic transport aircraft and cryogenic tank structures of reusable launch vehicles. Test structures are subjected to combined loading conditions that simulate realistic flight load conditions. The facility consists of two pressure-box test machines and one combined loads test machine. Each test machine possesses a unique set of requirements or research data acquisition and real-time data display. Given the complex nature of the mechanical and thermal loads to be applied to the various research test articles, each data system has been designed with connectivity attributes that support both data acquisition and data management functions. This paper addresses the research driven data acquisition requirements for each test machine and demonstrates how an open architecture data system design not only meets those needs but provides robust data sharing between data systems including the various control systems which apply spectra of mechanical and thermal loading profiles.

Lightfoot, Michael C.↗
NASA NTRS2003

Rockwell Automation PLC-5 Lands Stennis Space Center with a Reliable, Flexible Control System

Ever since the first rocket was launched, people have been infatuated with the vast and unchartered frontier of space. Whether it's visiting a space center or watching a shuttle launch, people are waiting to see what will be discovered next. And even though orbiting the Earth or taking soil samples form the Moon now seems effortless, decades worth of behind-the-scenes work have helped the U.S. space program get to this point. Even today, NASA must take every precaution to ensure equipment is up to the endeavor of setting foot on the moon. As part of the initial push to put the first man on the moon, NASA established the John C. Stennis Space Center, Hancock County, Mississippi in 1961 for space engine propulsion system development. Today, Stennis has three major test complexes where engine and component testing is carried out and integrated into full motion systems for space shuttles and vehicles as well as secondary testing facilities. With different products being tested throughout the facilities, Stennis was in need of an automation system that could link the operations. By integrating a control system based on a Rockwell Automation's flexible and reliable PLC-5 controller, Stennis was able to implement projects more efficiently and focus its efforts on getting the next generation of products ready for space.

Epperson, Dave↗
NASA NTRS2017

A Virtual Laboratory for Aviation and Airspace Prognostics Research

Integration of Unmanned Aerial Vehicles (UAVs), autonomy, spacecraft, and other aviation technologies, in the airspace is becoming more and more complicated, and will continue to do so in the future. Inclusion of new technology and complexity into the airspace increases the importance and difficulty of safety assurance. Additionally, testing new technologies on complex aviation systems and systems of systems can be challenging, expensive, and at times unsafe when implementing real life scenarios. The application of prognostics to aviation and airspace management may produce new tools and insight into these problems. Prognostic methodology provides an estimate of the health and risks of a component, vehicle, or airspace and knowledge of how that will change over time. That measure is especially useful in safety determination, mission planning, and maintenance scheduling. In our research, we develop a live, distributed, hardware- in-the-loop Prognostics Virtual Laboratory testbed for aviation and airspace prognostics. The developed testbed will be used to validate prediction algorithms for the real-time safety monitoring of the National Airspace System (NAS) and the prediction of unsafe events. In our earlier work1 we discussed the initial Prognostics Virtual Laboratory testbed development work and related results for milestones 1 & 2. This paper describes the design, development, and testing of the integrated tested which are part of milestone 3, along with our next steps for validation of this work. Through a framework consisting of software/hardware modules and associated interface clients, the distributed testbed enables safe, accurate, and inexpensive experimentation and research into airspace and vehicle prognosis that would not have been possible otherwise. The testbed modules can be used cohesively to construct complex and relevant airspace scenarios for research. Four modules are key to this research: the virtual aircraft module which uses the X-Plane simulator and X-PlaneConnect toolbox, the live aircraft module which connects fielded aircraft using onboard cellular communications devices, the hardware in the loop (HITL) module which connects laboratory based bench-top hardware testbeds and the research module which contains diagnostics and prognostics tools for analysis of live air traffic situations and vehicle health conditions. The testbed also features other modules for data recording and playback, information visualization, and air traffic generation. Software reliability, safety, and latency are some of the critical design considerations in development of the testbed.

LVC-DE↗