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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials Using MALAMUTE

Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy, aims to develop and qualify additively-manufactured materials for nuclear applications. The key challenges to these efforts are the microstructural variabilities observed on the AM products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-through-put experimental and modeling techniques to accelerate the qualification efforts. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the AM process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture the microstructural variabilities is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning modeling capabilities to develop a digital twin for AM that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation for AM materials. The melting and subsequent solidification that occurs during the AM process is a complex phenomenon that requires multiscale multiphysics analysis. Idaho National Laboratory’s (INL) Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for AM materials in an efficient, reliable, and cost-effective way. This work package focuses on understanding the role of process variabilities on the various microstructural characteristics of the AM materials. Microstructures unique to AM materials, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. In fiscal year (FY) 24, we significantly advanced upon our work in the last fiscal year, both on physics-based and ML models. The alloy solidification model available in MOOSE has been extended to incorporate the thermodynamic properties and free energy relevant to 316SS. The model demonstrates the Cr segregation that occurs during solidifcation. It is demonstrated that rate of solidification and solute segregation is primarily influence by the cooling rate dictating the level of freezing. This work captures the microstructural variabilities at the subgrain level that are often missing in the part-scale models. With an aim to connect the microstructural evolution model to realistic process conditions, a reduced order model is developed for predicting the thermal conditions around meltpool from high-fidelity process simulations. Furthermore, machine learning approach is used to accelerate the temperature prediction during the AM process. In the following years, MALAMUTE will be used to connect different aspects of the models and quantitatively predict the microstructural evolution. The developed ML-based surrogate model will consider the process conditions as the input to predict the microstructural features in a cost-effective way. The generated microstructures can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work help identify the key microstructural features at the subgrain level that are significant in property/performance prediction of the AM products. This work will provide inputs to the large-scale process variability models to reevaluate and validate assumptions/simplifications made in the part-scale models. Furthermore, through active learning this work will help identify the data need from both modeling and experimental sides for development of a robust digital twin for AM.

36 MATERIALS SCIENCE↗
NASA NTRS1992

The process group approach to reliable distributed computing

The difficulty of developing reliable distribution software is an impediment to applying distributed computing technology in many settings. Experience with the ISIS system suggests that a structured approach based on virtually synchronous process groups yields systems that are substantially easier to develop, exploit sophisticated forms of cooperative computation, and achieve high reliability. Six years of research on ISIS, describing the model, its implementation challenges, and the types of applications to which ISIS has been applied are reviewed.

Birman, Kenneth P.↗
NASA NTRS1992

HiRel - Reliability/availability integrated workstation tool

The HiRel software tool is described and demonstrated by application to the mission avionics subsystem of the Advanced System Integration Demonstrations (ASID) system that utilizes the PAVE PILLAR approach. HiRel marks another accomplishment toward the goal of producing a totally integrated computer-aided design (CAD) workstation design capability. Since a reliability engineer generally represents a reliability model graphically before it can be solved, the use of a graphical input description language increases productivity and decreases the incidence of error. The graphical postprocessor module HARPO makes it possible for reliability engineers to quickly analyze huge amounts of reliability/availability data to observe trends due to exploratory design changes. The addition of several powerful HARP modeling engines provides the user with a reliability/availability modeling capability for a wide range of system applications all integrated under a common interactive graphical input-output capability.

Bavuso, Salvatore J.↗
NASA NTRS2010

Optimization Testbed Cometboards Extended into Stochastic Domain

COMparative Evaluation Testbed of Optimization and Analysis Routines for the Design of Structures (CometBoards) is a multidisciplinary design optimization software. It was originally developed for deterministic calculation. It has now been extended into the stochastic domain for structural design problems. For deterministic problems, CometBoards is introduced through its subproblem solution strategy as well as the approximation concept in optimization. In the stochastic domain, a design is formulated as a function of the risk or reliability. Optimum solution including the weight of a structure, is also obtained as a function of reliability. Weight versus reliability traced out an inverted-S-shaped graph. The center of the graph corresponded to 50 percent probability of success, or one failure in two samples. A heavy design with weight approaching infinity could be produced for a near-zero rate of failure that corresponded to unity for reliability. Weight can be reduced to a small value for the most failure-prone design with a compromised reliability approaching zero. The stochastic design optimization (SDO) capability for an industrial problem was obtained by combining three codes: MSC/Nastran code was the deterministic analysis tool, fast probabilistic integrator, or the FPI module of the NESSUS software, was the probabilistic calculator, and CometBoards became the optimizer. The SDO capability requires a finite element structural model, a material model, a load model, and a design model. The stochastic optimization concept is illustrated considering an academic example and a real-life airframe component made of metallic and composite materials.

Patnaik, Surya N.↗
NASA NTRS2011

The Legacy of Space Shuttle Flight Software

The initial goals of the Space Shuttle Program required that the avionics and software systems blaze new trails in advancing avionics system technology. Many of the requirements placed on avionics and software were accomplished for the first time on this program. Examples include comprehensive digital fly-by-wire technology, use of a digital databus for flight critical functions, fail operational/fail safe requirements, complex automated redundancy management, and the use of a high-order software language for flight software development. In order to meet the operational and safety goals of the program, the Space Shuttle software had to be extremely high quality, reliable, robust, reconfigurable and maintainable. To achieve this, the software development team evolved a software process focused on continuous process improvement and defect elimination that consistently produced highly predictable and top quality results, providing software managers the confidence needed to sign each Certificate of Flight Readiness (COFR). This process, which has been appraised at Capability Maturity Model (CMM)/Capability Maturity Model Integration (CMMI) Level 5, has resulted in one of the lowest software defect rates in the industry. This paper will present an overview of the evolution of the Primary Avionics Software System (PASS) project and processes over thirty years, an argument for strong statistical control of software processes with examples, an overview of the success story for identifying and driving out errors before flight, a case study of the few significant software issues and how they were either identified before flight or slipped through the process onto a flight vehicle, and identification of the valuable lessons learned over the life of the project.

Hickey, Christopher J.↗
NASA NTRS2004

NASA Tech Briefs, July 2004

Topics: Optoelectronic Sensor System for Guidance in Docking; Hybrid Piezoelectric/Fiber-Optic Sensor Sheets; Multisensor Arrays for Greater Reliability and Accuracy; Integrated-Optic Oxygen Sensors; Ka-Band Autonomous Formation Flying Sensor; CMOS VLSI Active-Pixel Sensor for Tracking; Lightweight, Self-Deploying Foam Antenna Structures; Electrically Small Microstrip Quarter-Wave Monopole Antennas; A 2-to-28-MHz Phase-Locked Loop; Portable Electromyograph; Open-Source Software for Modeling of Nanoelectronic Devices; Software for Generating Strip Maps from SAR Data; Calibration Software for use with Jurassicprok; Software for Probabilistic Risk Reduction; Software Processes SAR Motion-Measurement Data; Improved Method of Purifying Carbon Nanotubes; Patterned Growth of Carbon Nanotubes or Nanofibers; Lightweight, Rack-Mountable Composite Cold Plate/Shelves; SiC-Based Miniature High-Temperature Cantilever Anemometer; Inlet Housing for a Partial-Admission Turbine; Lightweight Thermoformed Structural Components and Optics; Growing High-Quality InAs Quantum Dots for Infrared Lasers; Selected Papers on Protoplanetary Disks; Module for Oxygenating Water without Generating Bubbles; Coastal Research Imaging Spectrometer; Rapid Switching and Modulation by use of Coupled VCSELs; Laser-Induced-Fluorescence Photogrammetry and Videogrammetry; Laboratory Apparatus Generates Dual-Species Cold Atomic Beam; Laser Ablation of Materials for Propulsion of Spacecraft; Small Active Radiation Monitor; Hybrid Image-Plane/Stereo Manipulation; Partitioning a Gridded Rectangle into Smaller Rectangles; Digital Radar-Signal Processors Implemented in FPGAs; Part 1 of a Computational Study of a Drop-Laden Mixing Layer; and Some Improvements in Signal-Conditioning Circuits.

Source record↗
NASA NTRS2013

NASA Tech Briefs, May 2013

Topics include: Test Waveform Applications for JPL STRS Operating Environment; Pneumatic Proboscis Heat-Flow Probe; Method to Measure Total Noise Temperature of a Wireless Receiver During Operation; Cursor Control Device Test Battery; Functional Near-Infrared Spectroscopy Signals Measure Neuronal Activity in the Cortex; ESD Test Apparatus for Soldering Irons; FPGA-Based X-Ray Detection and Measurement for an X-Ray Polarimeter; Sequential Probability Ratio Test for Spacecraft Collision Avoidance Maneuver Decisions; Silicon/Carbon Nanotube Photocathode for Splitting Water; Advanced Materials and Fabrication Techniques for the Orion Attitude Control Motor; Flight Hardware Packaging Design for Stringent EMC Radiated Emission Requirements; RF Reference Switch for Spaceflight Radiometer Calibration; An Offload NIC for NASA, NLR, and Grid Computing; Multi-Scale CNT-Based Reinforcing Polymer Matrix Composites for Lightweight Structures; Ceramic Adhesive and Methods for On-Orbit Repair of Re-Entry Vehicles; Self-Healing Nanocomposites for Reusable Composite Cryotanks; Pt-Ni and Pt-Co Catalyst Synthesis Route for Fuel Cell Applications; Aerogel-Based Multilayer Insulation with Micrometeoroid Protection; Manufacturing of Nanocomposite Carbon Fibers and Composite Cylinders; Optimized Radiator Geometries for Hot Lunar Thermal Environments; A Mission Concept: Re-Entry Hopper-Aero-Space-Craft System on-Mars (REARM-Mars); New Class of Flow Batteries for Terrestrial and Aerospace Energy Storage Applications; Reliability of CCGA 1152 and CCGA 1272 Interconnect Packages for Extreme Thermal Environments; Using a Blender to Assess the Microbial Density of Encapsulated Organisms; Mixed Integer Programming and Heuristic Scheduling for Space Communication; Video Altimeter and Obstruction Detector for an Aircraft; Control Software for Piezo Stepping Actuators; Galactic Cosmic Ray Event-Based Risk Model (GERM) Code; Sasquatch Footprint Tool; and Multi-User Space Link Extension (SLE) System.

Source record↗
DOE OSTI2024

Coupled Multiphysics Modeling of Lithium-Ion Batteries for Automotive Crashworthiness Applications

Considerable advances have been made in battery safety models, but achieving predictive accuracy across a wide range of conditions continues to be challenging. Interactions between dynamically evolving mechanical, electrical, and thermal state variables make model prediction difficult during mechanical abuse scenarios. In this study, we develop a physics-based modeling approach that allows for choosing between different mechanical and electrochemical models depending on the required level of analysis. We demonstrate the use of this approach to connect cell-level abuse response to electrode-level and particle-level transport phenomena. A pseudo-two-dimensional model and simplified single-particle models are calibrated to electrical-thermal cycling data and applied to mechanically induced short-circuit scenarios to understand how the choice of electrochemical model affects the model prediction under abuse scenarios. These models are implemented using user-defined subroutines on ls-dyna finite element software and can be coupled with existing automotive crash safety models.

analysis and design of components↗
DOE OSTI2022

Storage Futures Study: Grid Operational Impacts of Widespread Storage Deployment

This report, the fifth in the Storage Futures Study series, uses cost-driven scenarios from the ReEDS model as a starting point to examine the operational impacts of grid-scale storage deployment and relationships between this deployment and the contribution of variable renewable energy. We use commercial production cost modeling software to evaluate hourly operation of five scenarios that reach between 210 gigawatts (GW) and 930 GW of installed storage by 2050. We find that storage plays an important role in these power systems between now and 2050 - by storing the lowest-marginal cost generation (often, overgeneration from solar or wind plants) and generating energy during the highest net load periods of the day and year. Storage helps with the integration of variable renewable energy and by providing an important resource to provide continued reliable power.

25 ENERGY STORAGE↗
DOE OSTI2019

Development of Heat Pipe Reactor Modeling in SAM

System Analysis Module (SAM) is under development at Argonne National Laboratory as a modern system-level modeling and simulation tool for advanced non-light water reactor safety analyses. It utilizes the object-oriented application framework MOOSE to leverage the modern software environment and advanced numerical methods available in PETSc. The capabilities of SAM are being extended to enable the transient modeling, analysis, and design of various advanced nuclear reactor systems. This report presents the development of new capabilities for modeling the heat pipe type reactor systems. The need for power at remote locations away from a reliable electrical grid is an important niche for nuclear energy. Heat pipe-cooled fast-spectrum nuclear reactors are well suited for these applications. The key feature of the heat pipe reactors is the use of heat pipes for heat removal from the reactor core. The heat pipe makes use of the phase change of the working fluid and transports a large amount of heat from the evaporator to the condensation end with very small temperature drops. In contrast to the traditional nuclear reactor system that makes use of pumped loop for extracting the thermal power, the heat pipe reactors make use of hundreds of heat pipes for removing the thermal power (including the decay heat) passively. This could potentially significantly improve the reliability and safety of the reactor systems. The essential part in the analysis of a heat pipe type reactor is the modeling of heat transport inside the heat pipe. The capability of SAM is extended in this work to enable the modeling of the conventional heat pipe. Two alternative modeling options, 2D-RZ Heat conduction and 3D-1D coupling, are developed, which are both based on the assumption that the heat pipe vapor core can be modeled as a superconductor of extremely high thermal conductivity. Both modeling options are verified with a simplified thermal resistance model. The heat pipes are also coupled with the fuel region of a prototype micro reactor under both normal and off-normal conditions. It is confirmed that both modeling options can correctly model the heat transport in the heat-pipe reactors.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials using MALAMUTE

The Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy aims to develop and qualify additively manufactured materials for nuclear applications. One key challenge to this is the microstructural variability observed in the additively manufactured products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-throughput experimental and modeling techniques to accelerate qualification. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the additive manufacturing process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture microstructural variability is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning models to develop a digital twin for additive manufacturing that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation. The melting and subsequent solidification that occurs during the additive process is a complex phenomenon that requires multiscale multiphysics analysis. This work package focuses on understanding the role of process variabilities on the unique microstructural characteristics of additively manufactured materials. Microstructural features at the subgrain level, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. Idaho National Laboratory’s Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for additively manufactured materials in an efficient, reliable, and cost-effective way. This work focuses on capturing the microstructural variabilities at the subgrain level that are often missing in the part-scale models. In fiscal year 2025, we significantly advanced upon our work in the last fiscal year, in terms of the predictive capabilities of the physics-based and ML models, by adding the capabilities to capture subgrain-level micro-segregation during solidification using phase-field model and to predict the time-dependent dynamics of the AM process through the MOGPAR model. The alloy solidification model in MOOSE incorporates the thermodynamic properties and free energy relevant to 316 stainless steel. The model demonstrates the Cr and Ni segregation that occurs during solidification, including that the rate of solidification. The microstructural evolution model is connected to the process conditions via the surrogate model developed in this work. This enables predictions of the final microstructure in conjunctions with the manufacturing process. This work supports AMMT's rapid qualification goals by laying the foundation for an efficient and cost-effective model establishing the PSPP correlation for AM. The generated microstructures and predicted micro-segregation can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work helps to identify the key microstructural features at the subgrain level that are significant in property and performance predictions of additively manufactured components. This work will also provide inputs to the large-scale process variability models to reevaluate and validate assumptions and simplifications made in the part-scale models. Furthermore, through active learning this work can help identify the data need from both modeling and experimental sides for development of a robust digital twin for additive manufacturing and accelerate the AMMT's qualification efforts.

36 - MATERIALS SCIENCE↗
NASA NTRS1991

The process group approach to reliable distributed computing

The difficulty of developing reliable distributed software is an impediment to applying distributed computing technology in many settings. Experience with the ISIS system suggests that a structured approach based on virtually synchronous process groups yields systems which are substantially easier to develop, fault-tolerance, and self-managing. Six years of research on ISIS are reviewed, describing the model, the types of applications to which ISIS was applied, and some of the reasoning that underlies a recent effort to redesign and reimplement ISIS as a much smaller, lightweight system.

Birman, Kenneth P.↗
NASA NTRS1991

COSTMODL: An automated software development cost estimation tool

The cost of developing computer software continues to consume an increasing portion of many organizations' total budgets, both in the public and private sector. As this trend develops, the capability to produce reliable estimates of the effort and schedule required to develop a candidate software product takes on increasing importance. The COSTMODL program was developed to provide an in-house capability to perform development cost estimates for NASA software projects. COSTMODL is an automated software development cost estimation tool which incorporates five cost estimation algorithms including the latest models for the Ada language and incrementally developed products. The principal characteristic which sets COSTMODL apart from other software cost estimation programs is its capacity to be completely customized to a particular environment. The estimation equations can be recalibrated to reflect the programmer productivity characteristics demonstrated by the user's organization, and the set of significant factors which effect software development costs can be customized to reflect any unique properties of the user's development environment. Careful use of a capability such as COSTMODL can significantly reduce the risk of cost overruns and failed projects.

Roush, George B.↗
NASA NTRS2000

The NASA IVHM Technology Experiment for X-37

The NASA IVHM (Integrated Vehicle Health Management) technology experiment for X-37 is presented. The goals and objectives of this program are: to reduce cost and increase reliability of space transportation; to demonstrate benefits of in-flight IVHM to the operation of a Reusable Launch Vehicle; to advance this IVHM technology to Technology Readiness Level approx. 7 within a flight environment; and to operate IVHM software on the Vehicle Management Computer. The following sections are included: Background (X-37 & Livingstone), Livingstone model example from DS-1, Experiment overview, X-37 IVHM scope, Stanley interface to livingstone model, Right ruddervator actuator, Motor state diagram, inferred nominal state, and X-37 informed maintenance experiment.

Source record↗
NASA NTRS2004

Overview of Intelligent Systems and Operations Development

To achieve NASA's ambitious mission objectives for the future, aircraft and spacecraft will need intelligence to take the correct action in a variety of circumstances. Vehicle intelligence can be defined as the ability to "do the right thing" when faced with a complex decision-making situation. It will be necessary to implement integrated autonomous operations and low-level adaptive flight control technologies to direct actions that enhance the safety and success of complex missions despite component failures, degraded performance, operator errors, and environment uncertainty. This paper will describe the array of technologies required to meet these complex objectives. This includes the integration of high-level reasoning and autonomous capabilities with multiple subsystem controllers for robust performance. Future intelligent systems will use models of the system, its environment, and other intelligent agents with which it interacts. They will also require planners, reasoning engines, and adaptive controllers that can recommend or execute commands enabling the system to respond intelligently. The presentation will also address the development of highly dependable software, which is a key component to ensure the reliability of intelligent systems.

Pallix, Joan↗
NASA NTRS2020

Development of a Supervisory Tool for Fault Detection and Diagnosis of DC Electric Power Systems with the Application of Deep Space Vehicles

This dissertation formulates the problem of fault detection and diagnosis of DC electric power systems for the application of autonomous spacecraft. The ability to accurately identify and isolate failures in the electrical power system is critical to ensure the reliability of a spacecraft. This problem becomes more pronounced during deep space missions that lack the ability to monitor from ground control. The current state of electrical power system fault supervision is insufficient to guarantee highly reliable and robust operation. To solve this issue, a combination of model-based and rules-based techniques are used in a hierarchical framework to improve the diagnostic performance of the spacecraft electrical power system. Noise, disturbances, and modeling errors are considered in the design of the method. Practical considerations related to the hardware and software are discussed for the flight application. A wide array of failure types are simulated in a series of experiments to assess the functionality of the design. The experiments showed that the methods used improved the diagnostic capability of the autonomous system while taking into account the limitations attributed to flight software requirements. The significance of this study is to provide a framework capable of advanced diagnostics of an electrical power system with little to no interaction from a human operator.

Fault Detection and Diagnosis↗
DOE OSTI2023

Analyzing Hardware and Software Common Cause Failures in Digital Instrumentation and Control Systems using Dual Error Propagation Method

This paper develops a methodology for quantifying software common cause failures (CCFs) in digital instrumentation and control (I&C) systems of nuclear power plants. To support the transition of analog I&C systems to digital in nuclear power plants, probabilistic risk assessment (PRA) techniques are used. The hardware components of the I&C systems have reliability databases that can be used in the PRA studies. However, the failure data for redundant software components of the systems is sparse. Failure of components constitutes a CCF, wherein two or more components or systems fail due to a single shared cause and coupling mechanism. This paper proposes a quantification approach that can simultaneously model hardware and software components, incorporate the CCFs of software systems in the models, and bridge the gap between the failure quantification of models and the development of CCF parametric databases. We demonstrate the dual error propagation method (DEPM) by developing I&C systems failure models for a representative digital reactor trip system. The DEPM models are built to simulate the control and data flows within the systems and can accommodate failure states. By expanding DEPM to software CCFs, we generated alpha factor parameter estimates for each of the modeled error propagation mechanisms.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1986

Modeling, simulation and control for a cryogenic fluid management facility, preliminary report

The synthesis of a control system for a cryogenic fluid management facility was studied. The severe demand for reliability as well as instrumentation and control unique to the Space Station environment are prime considerations. Realizing that the effective control system depends heavily on quantitative description of the facility dynamics, a methodology for process identification and parameter estimation is postulated. A block diagram of the associated control system is also produced. Finally, an on-line adaptive control strategy is developed utilizing optimization of the velocity form control parameters (proportional gains, integration and derivative time constants) in appropriate difference equations for direct digital control. Of special concern are the communications, software and hardware supporting interaction between the ground and orbital systems. It is visualized that specialist in the OSI/ISO utilizing the Ada programming language will influence further development, testing and validation of the simplistic models presented here for adaptation to the actual flight environment.

Turner, Max A.↗