Search NASA⌕ Search

SEARCH · Search NASA

Results for “complex system modeling”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
NASA NTRS2006

A Generic Multibody Parachute Simulation Model

Flight simulation of dynamic atmospheric vehicles with parachute systems is a complex task that is not easily modeled in many simulation frameworks. In the past, the performance of vehicles with parachutes was analyzed by simulations dedicated to parachute operations and were generally not used for any other portion of the vehicle flight trajectory. This approach required multiple simulation resources to completely analyze the performance of the vehicle. Recently, improved software engineering practices and increased computational power have allowed a single simulation to model the entire flight profile of a vehicle employing a parachute.

Neuhaus, Jason Richard↗
NASA NTRS2000

Theoretical Modeling and Computer Simulations for the Origins and Evolution of Reproducing Molecular Systems and Complex Systems with Many Interactive Parts

Our research effort has produced nine publications in peer-reviewed journals listed at the end of this report. The work reported here are in the following areas: (1) genetic network modeling; (2) autocatalytic model of pre-biotic evolution; (3) theoretical and computational studies of strongly correlated electron systems; (4) reducing thermal oscillations in atomic force microscope; (5) transcription termination mechanism in prokaryotic cells; and (6) the low glutamine usage in thennophiles obtained by studying completely sequenced genomes. We discuss the main accomplishments of these publications.

Liang, Shoudan↗
DOE OSTI2024

Modeling performance of data collection systems for high-energy physics

Exponential increases in scientific experimental data are outpacing silicon technology progress, necessitating heterogeneous computing systems—particularly those utilizing machine learning (ML)—to meet future scientific computing demands. The growing importance and complexity of heterogeneous computing systems require systematic modeling to understand and predict the effective roles for ML. We present a model that addresses this need by framing the key aspects of data collection pipelines and constraints and combining them with the important vectors of technology that shape alternatives, computing metrics that allow complex alternatives to be compared. For instance, a data collection pipeline may be characterized by parameters such as sensor sampling rates and the overall relevancy of retrieved samples. Alternatives to this pipeline are enabled by development vectors including ML, parallelization, advancing CMOS, and neuromorphic computing. By calculating metrics for each alternative such as overall F1 score, power, hardware cost, and energy expended per relevant sample, our model allows alternative data collection systems to be rigorously compared. We apply this model to the Compact Muon Solenoid experiment and its planned high luminosity-large hadron collider upgrade, evaluating novel technologies for the data acquisition system (DAQ), including ML-based filtering and parallelized software. The results demonstrate that improvements to early DAQ stages significantly reduce resources required later, with a power reduction of 60% and increased relevant data retrieval per unit power (from 0.065 to 0.31 samples/kJ). However, we predict that further advances will be required in order to meet overall power and cost constraints for the DAQ.

Olin-Ammentorp, Wilkie (ORCID:0000000224729862)↗
NASA NTRS2015

ISS-CREAM Thermal and Fluid System Design and Analysis

Thermal and Fluids Analysis Workshop (TFAWS), Silver Spring MD NCTS 21070-15. The ISS-CREAM (Cosmic Ray Energetics And Mass for the International Space Station) payload is being developed by an international team and will provide significant cosmic ray characterization over a long time frame. Cold fluid provided by the ISS Exposed Facility (EF) is the primary means of cooling for 5 science instruments and over 7 electronics boxes. Thermal fluid integrated design and analysis was performed for CREAM using a Thermal Desktop model. This presentation will provide some specific design and modeling examples from the fluid cooling system, complex SCD (Silicon Charge Detector) and calorimeter hardware, and integrated payload and ISS level modeling. Features of Thermal Desktop such as CAD simplification, meshing of complex hardware, External References (Xrefs), and FloCAD modeling will be discussed.

Thermal analysis↗
DOE OSTI2025

SCEPTRE: A Cyber-Physical Emulation Capability

Cyber-physical systems form a critical but vulnerable backbone to US critical infrastructure. Recent high-profile cyber-attacks have shown the need for increased assessment and hardening of these systems. However, such assessments and investigations into advanced technologies to harden these systems is difficult due to their operational nature. Instead, modeling of these systems is heavily leveraged. Investigation into these complex systems and their potential cascading failures requires comprehensive modeling of both the cyber and physical components of the system. This paper introduces SCEPTRE, an emulation capability to address this need.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
NASA NTRS2005

Enabling Requirements-Based Programming for Highly-Dependable Complex Parallel and Distributed Systems

The manual application of formal methods in system specification has produced successes, but in the end, despite any claims and assertions by practitioners, there is no provable relationship between a manually derived system specification or formal model and the customer's original requirements. Complex parallel and distributed system present the worst case implications for today s dearth of viable approaches for achieving system dependability. No avenue other than formal methods constitutes a serious contender for resolving the problem, and so recognition of requirements-based programming has come at a critical juncture. We describe a new, NASA-developed automated requirement-based programming method that can be applied to certain classes of systems, including complex parallel and distributed systems, to achieve a high degree of dependability.

Hinchey, Michael G.↗
NASA NTRS1998

Model Based Autonomy for Robust Mars Operations

Space missions have historically relied upon a large ground staff, numbering in the hundreds for complex missions, to maintain routine operations. When an anomaly occurs, this small army of engineers attempts to identify and work around the problem. A piloted Mars mission, with its multiyear duration, cost pressures, half-hour communication delays and two-week blackouts cannot be closely controlled by a battalion of engineers on Earth. Flight crew involvement in routine system operations must also be minimized to maximize science return. It also may be unrealistic to require the crew have the expertise in each mission subsystem needed to diagnose a system failure and effect a timely repair, as engineers did for Apollo 13. Enter model-based autonomy, which allows complex systems to autonomously maintain operation despite failures or anomalous conditions, contributing to safe, robust, and minimally supervised operation of spacecraft, life support, In Situ Resource Utilization (ISRU) and power systems. Autonomous reasoning is central to the approach. A reasoning algorithm uses a logical or mathematical model of a system to infer how to operate the system, diagnose failures and generate appropriate behavior to repair or reconfigure the system in response. The 'plug and play' nature of the models enables low cost development of autonomy for multiple platforms. Declarative, reusable models capture relevant aspects of the behavior of simple devices (e.g. valves or thrusters). Reasoning algorithms combine device models to create a model of the system-wide interactions and behavior of a complex, unique artifact such as a spacecraft. Rather than requiring engineers to all possible interactions and failures at design time or perform analysis during the mission, the reasoning engine generates the appropriate response to the current situation, taking into account its system-wide knowledge, the current state, and even sensor failures or unexpected behavior.

Kurien, James A.↗
DOE OSTI2026

Advancing Concentrating Solar Thermal Modeling Using System Advisor Model (SAM)

Concentrating solar thermal (CST) technologies play a critical role in enabling dispatchable power and high-temperature industrial heat applications. Accurate and flexible modeling tools are essential for evaluating system performance, guiding technology research and development, and informing investment decisions. The National Laboratory of the Rockies's System Advisor Model (SAM) is a widely used techno-economic simulation platform for CST systems, providing detailed performance and financial modeling capabilities for multiple CST system configurations. SAM integrates physics-based performance models with financial analysis to simulate the behavior of complex energy systems under realistic operating conditions. For CST technologies (including tower, parabolic trough, and linear Fresnel), SAM enables hourly simulations using site-specific weather data that ensure feasible operating conditions and convergence of mass and energy between core system components (i.e., solar field, receiver, thermal energy storage, and power cycle). These capabilities allow researchers and developers to evaluate annual energy production, capacity factors, levelized cost of energy (LCOE), and system dispatch strategies. A key advantage of SAM lies in its flexibility for parametric analysis and large-scale computational studies. Users can vary system design parameters such as heliostat field layout, receiver dimensions, thermal energy storage capacity, power block sizing, and installation cost assumptions to investigate their impact on system performance and financial metrics. When combined with automated scripting through LK, SDKTool, or Python interfaces, SAM enables high-throughput simulation workflows that support sensitivity analysis, technology benchmarking, and optimization studies. These approaches are particularly valuable for next-generation CST concepts, where design spaces are large and system interactions are complex. Another important capability of SAM is its support for dispatch optimization and thermal energy storage modeling, which are central to the value proposition of CST technologies. The ability to simulate integrated storage and flexible power generation allows researchers to explore strategies that maximize grid value, improve capacity utilization, and enhance integration with variable resources such as photovoltaic and wind generation. This poster will present an overview of SAM's thermal system modeling capabilities including concentrating solar. Additionally, we will highlight new feature developments including: 1) implementing Google's OR-Tools optimization platform for faster and more robust dispatch optimization, 2) developing a new power load following controller for modeling behind-the-meter applications, 3) enabling direct modeling of CSP-PV hybrid systems with the inclusion of battery storage, and 4) developing a multi-receiver falling particle Gen3 system model.

14 SOLAR ENERGY↗
NASA NTRS1971

A system debugging model.

Consideration of the nature of the 'debugging' process applied to a new complex system during the initial period of its life. During this period failures and errors are corrected as they occur, with resulting improvement in the subsequent performance of the system. One mathematical idealization of this process leads to the assumption that system failure rate is decreasing with time. In practice, the debugging phase is considered completed when the failure rate reaches an equilibrium or constant value. Models are formulated for this phenomenon. Maximum likelihood estimates are obtained for relevant failure rate functions and for the end of the debugging period. A conservative upper confidence bound on the stable failure rate is obtained.

Barlow, R. E.↗
DOE OSTI2024

Machine-learned closure of URANS for stably stratified turbulence: connecting physical timescales & data hyperparameters of deep time-series models

Stably stratified turbulence (SST), a model that is representative of the turbulence found in the oceans and atmosphere, is strongly affected by fine balances between forces and becomes more anisotropic in time for decaying scenarios. Moreover, there is a limited understanding of the physical phenomena described by some of the terms in the Unsteady Reynolds-Averaged Navier–Stokes (URANS) equations—used to numerically simulate approximate solutions for such turbulent flows. Rather than attempting to model each term in URANS separately, it is attractive to explore the capability of machine learning (ML) to model groups of terms, i.e. to directly model the force balances. We develop deep time-series ML for closure modeling of the URANS equations applied to SST. We consider decaying SST which are homogeneous and stably stratified by a uniform density gradient, enabling dimensionality reduction. We consider two time-series ML models: long short-term memory and neural ordinary differential equation. Both models perform accurately and are numerically stable in a posteriori (online) tests. Furthermore, we explore the data requirements of the time-series ML models by extracting physically relevant timescales of the complex system. We find that the ratio of the timescales of the minimum information required by the ML models to accurately capture the dynamics of the SST corresponds to the Reynolds number of the flow. The current framework provides the backbone to explore the capability of such models to capture the dynamics of high-dimensional complex dynamical system like SST flows.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2017

The Underpinnings of Workload in Unmanned Vehicle Systems

This paper identifies and characterizes factors that contribute to operator workload in unmanned vehicle systems. Our objective is to provide a basis for developing models of workload for use in design and operation of complex human-machine systems. In 1986, Hart developed a foundational conceptual model of workload, which formed the basis for arguably the most widely used workload measurement technique—the NASA Task Load Index. Since that time, however, there have been many advances in models and factor identification as well as workload control measures. Additionally, there is a need to further inventory and describe factors that contribute to human workload in light of technological advances, including automation and autonomy. Thus, we propose a conceptual framework for the workload construct and present a taxonomy of factors that can contribute to operator workload. These factors, referred to as workload drivers, are associated with a variety of system elements including the environment, task, equipment and operator. In addition, we discuss how workload moderators, such as automation and interface design, can be manipulated in order to influence operator workload. We contend that workload drivers, workload moderators, and the interactions among drivers and moderators all need to be accounted for when building complex, human-machine systems.

Hooey, Becky L.↗
NASA NTRS1988

Obstacles to high fidelity multibody dynamics simulation

The authors take a wide ranging look at issues involved in the dynamic modeling of complex, multibodied orbiting space systems. The nature of the multibody problem is reviewed and capabilities and limitations of two major codes (DISCOS, TREETOPS) are assessed. Problem areas that limit accuracy or contribute to uncertainty in the modeling, simulation process are identified. As a specific case, the significance of including, or of not including, second-degree geometric nonlinearity in the elastic displacement field (foreshortening) is evaluated. Conclusions and recommendations are made concerning the direction future development should take to achieve higher-fidelity and more computationally efficient multibody software solutions.

Lips, K. W.↗
NASA NTRS2016

Architecture for Integrated Medical Model Dynamic Probabilistic Risk Assessment

Probabilistic Risk Assessment (PRA) is a modeling tool used to predict potential outcomes of a complex system based on a statistical understanding of many initiating events. Utilizing a Monte Carlo method, thousands of instances of the model are considered and outcomes are collected. PRA is considered static, utilizing probabilities alone to calculate outcomes. Dynamic Probabilistic Risk Assessment (dPRA) is an advanced concept where modeling predicts the outcomes of a complex system based not only on the probabilities of many initiating events, but also on a progression of dependencies brought about by progressing down a time line. Events are placed in a single time line, adding each event to a queue, as managed by a planner. Progression down the time line is guided by rules, as managed by a scheduler. The recently developed Integrated Medical Model (IMM) summarizes astronaut health as governed by the probabilities of medical events and mitigation strategies. Managing the software architecture process provides a systematic means of creating, documenting, and communicating a software design early in the development process. The software architecture process begins with establishing requirements and the design is then derived from the requirements.

Probability Theory↗
NASA NTRSDate not supplied

Efficient and Accurate Terrain Integration & Application to Modeling and Simulation of Planetary Landing Systems

As planetary spacecraft landing system complexity increases, pre-flight performance assessments include correspondingly more detailed and complex models that represent the various environmental elements and vehicle subsystems. Specifically, the relevance and criticality of planetary terrain modeling in the context of precision landing and hazard avoidance (PL&HA) has become evident as both commercial and national agencies return to the Lunar surface and beyond.

Terrain↗
NASA NTRS2013

Automated Generation of Fault Management Artifacts from a Simple System Model

Our understanding of off-nominal behavior - failure modes and fault propagation - in complex systems is often based purely on engineering intuition; specific cases are assessed in an ad hoc fashion as a (fallible) fault management engineer sees fit. This work is an attempt to provide a more rigorous approach to this understanding and assessment by automating the creation of a fault management artifact, the Failure Modes and Effects Analysis (FMEA) through querying a representation of the system in a SysML model. This work builds off the previous development of an off-nominal behavior model for the upcoming Soil Moisture Active-Passive (SMAP) mission at the Jet Propulsion Laboratory. We further developed the previous system model to more fully incorporate the ideas of State Analysis, and it was restructured in an organizational hierarchy that models the system as layers of control systems while also incorporating the concept of "design authority". We present software that was developed to traverse the elements and relationships in this model to automatically construct an FMEA spreadsheet. We further discuss extending this model to automatically generate other typical fault management artifacts, such as Fault Trees, to efficiently portray system behavior, and depend less on the intuition of fault management engineers to ensure complete examination of off-nominal behavior.

Spinup and Orient↗
DOE OSTI2025

The Martini 3 Lipidome: Expanded and Refined Parameters Improve Lipid Phase Behavior

Lipid membranes are central to cellular life. Complementing experiments, computational modeling has been essential in unraveling complex lipid-biomolecule interactions, crucial in both academia and industry. The Martini model, a coarse-grained force field for efficient molecular dynamics simulations, is widely used to study membrane phenomena but has faced limitations, particularly in capturing realistic lipid phase behavior. Here, we present refined Martini 3 lipid models with a mapping scheme that distinguishes lipid tails that differ by just two carbon atoms, enhancing the structural resolution and thermodynamic accuracy of model membrane systems including ternary mixtures. The expanded Martini lipid library includes thousands of models, enabling simulations of complex and biologically relevant systems. These advancements establish Martini as a robust platform for lipid-based simulations across diverse fields.

Lipids↗
NASA NTRS1984

The Solid Model Geometry Generator (SMGG)

The Solid Model Geometry Generator (SMGG) system provides the capacity to model complex solid objects through the composition of primitive parts. The parts are specified through input of dimension and orientation parameters. The parts may later be edited, the model may be displayed (through a variety of display options), and the resulting geometry formatted for subsequent analysis. The SMGG system was originally constructed for use in the preliminary stages of space station and other spacecraft design. Because the system models a design through the use of basic shapes common to many solid objects and provides limited capabilities for producing irregularly shaped parts, it can be used for a variety of applications. Currently, seven primitive parts may be specified and produced through SMGG. These include boxes, spheres, cones, paraboloids, tori, and translationally or rotationally swept parts. Each part may be specified by the user through input of basic part dimensions and a local orientation. For example, a box is specified by its length, width, height and orientation in space (rotation, translation, and scaling).

Jones, K. H.↗
NASA NTRS2005

A model-based executive for commanding robot teams

The paper presents a way to robustly command a system of systems as a single entity. Instead of modeling each component system in isolation and then manually crafting interaction protocols, this approach starts with a model of the collective population as a single system. By compiling the model into separate elements for each component system and utilizing a teamwork model for coordination, it circumvents the complexities of manually crafting robust interaction protocols. The resulting systems are both globally responsive by virtue of a team oriented interaction model and locally responsive by virtue of a distributed approach to model-based fault detection, isolation, and recovery.

domain compilations↗