Search NASA⌕ Search

SEARCH · Search NASA

Results for “User Needs”

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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

The disCO2ver Platform: Curating Data and Tools for Geologic Carbon Sequestration and Deep Subsurface Research Systems

The U.S. DOE National Energy Technology Laboratory has invested 12+ years of development into the data repository and digital laboratory, the Energy Data eXchange (EDX, edx.netl.doe.gov). Supporting a variety of research areas across the DOE Office of Fossil Energy and Carbon Management, the platform has successfully curated and preserved thousands of data products from DOE research. The Carbon Storage Program has successfully supported data curation, upload, and publishing of data products on EDX for many years, demonstrating a success story of how resources like EDX can effectively help with long term preservation and publishing of DOE data products. EDX continues to shift towards cloud-supported infrastructure, taking a hybrid approach combining on-premises compute and storage integrated with cloud-hosted services. The integration of cloud compute and hybrid architecture enables the development of EDX-hosted platforms that tailor the data and tools hosted on them to a specific community, enables implementation of machine learning tools for data discovery and filtering, and enables the hosting of virtual (online user interface) tools. Geologic carbon sequestration (GCS) research continues to scale up in response to the current administration goals to reduce greenhouse gas emissions and transition the energy economy. Over the last year, EDX’s disCO2ver platform has been developed in response to the need for access to data products and tools to support the scaling up of GCS research. disCO2ver provides access to data resources and tools, produced by DOE and outside authoritative external resources. The platform also provides a user-access control component for the virtualization and cloud hosting of tools. Tools that need to be virtualized, to eliminate the need for users to download the tool and use local compute resources, is essential to supporting big-data analysis and machine learning that is becoming common place in carbon storage modeling, risk analysis, and data publishing practices. This talk will review the EDX’s disCO2ver platform and the current work ongoing to curate data and tools to support GCS and deep subsurface systems research.

Morkner, Paige↗
DOE OSTI2024

SOFAST CAD Layout Tool Overview and User Guide (Document Version 1.0)

Concentrating solar mirrors need to have correct geometry to accurately focus sunlight onto a receiver. SOFAST is a tool created at Sandia National Laboratories which measures a mirror’s surface normal at a dense sampling of points, enabling evaluation of a given mirror’s expected performance. SOFAST supports general mirrors, including concentrating solar heliostats, troughs, and dishes. The examples presented in this report predominantly focus on heliostats.

14 SOLAR ENERGY↗
DOE OSTI2025

Understanding and Modeling Pooled Rideshare Acceptance: Influential Factors, Preferred User Experiences, and Implications

Ridesharing allows people to share a vehicle with others traveling in the same direction, which can reduce costs and traffic congestion. Pooled rideshare (PR) services, such as UberX Share and Lyft Shared, offer an economical and environmentally friendly alternative by matching passengers traveling similar routes. However, despite these benefits, PR adoption remains low due to concerns about safety, privacy, and convenience. This research explores the factors influencing PR adoption and provides recommendations to improve user acceptance. A nationwide survey of 5,385 participants across the U.S. was conducted to understand why people choose or avoid PR. The study identified five key factors influencing PR consideration: safety, service experience, privacy, traffic/environment, and time/cost. Additional research examined ways to optimize PR experiences by identifying four critical factors: comfort/ease of use, convenience, vehicle technology/accessibility, and passenger safety. To measure the impact of these factors, a statistical model called the Pooled Rideshare Acceptance Model (PRAM) was developed, providing insights into how each element influences PR adoption. Further analysis using the Pooled Rideshare Acceptance Model Multigroup Analyses (PRAMMA) revealed how demographic characteristics such as age, gender, income, and past rideshare experience shape PR perceptions. Some key findings from the multigroup analyses showed that younger users valued technological features and environmental benefits, while older users prioritized reliability and service transparency. Additionally, privacy concerns were more significant for female users, while convenience was critical for higher-income groups. These results emphasize that a 'onesize-fits-all' approach to PR service design is not effective, highlighting the need for tailored strategies to address different user segments. Further, workshops were conducted with researchers and students to translate the findings into real-world solutions. These workshops and 3 all the statistical analyses led to the development of 95 actionable recommendations. The recommendations focus on key areas such as safety, service reliability, user education, and accessibility, offering tangible improvements to PR services. The insights from this study provide valuable guidance for policymakers, transportation network companies (TNCs), and researchers aiming to make PR services safer, more accessible, and widely accepted. By addressing user concerns, PR can become a more viable transportation option, supporting sustainable urban mobility and reducing reliance on private vehicles. Additionally, these findings emphasize the importance of user-centric service design in encouraging broader PR adoption. Future research should explore evolving trends in PR preferences, technological advancements, and policy changes to ensure continued improvements. By implementing these recommendations, PR services can better align with user expectations, enhance trust in shared mobility, and contribute to a more efficient transportation ecosystem.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Mooring Load Monitoring of a Wave Energy Converter Using a Self-Synchronizing Underwater Acoustic Network

A self-synchronizing underwater acoustic network, designed for remote monitoring of mooring loads in Wave Energy Converters (WEC), has been developed and tested. This network uses Time Division Multiple Access and operates self-contained with the ability for users to remotely transmit commands to the network as needed. Each node is a self-contained unit, consisting of a protocol adaptor board, an underwater acoustic modem and a battery pack. A node can be connected to a load cell, to a topside user or to the WEC. Every node is swapable. The protocol adaptor board, named Protocol Adaptor for Digital LOad Cell (PADLOC) supports a variety of digital load cell message formats (CAN, MODBUS, custom ASCII) and underwater acoustic modem serial formats. PADLOC enables topside users to connect to separate load cells through a user-specific command.

acoustic↗
DOE OSTI2025

The Natural Products Magnetic Resonance Database (NP-MRD) for 2025

The Natural Products Magnetic Resonance Database or NP-MRD (https://np-mrd.org) is a comprehensive, freely accessible, web-based resource for the deposition, distribution, extraction and retrieval of nuclear magnetic resonance (NMR) data on natural products. The NP-MRD was initially established to support compound de-replication and data dissemination for the natural products community. However, that community has now grown to include many users from the metabolomics, microbiomics, foodomics and nutrition science fields. Indeed, since its launch in 2021, the NP-MRD has expanded enormously in size, scope and popularity. The current version of NP-MRD now contains nearly 7X more compounds (281,859 vs. 40,908) and 7X more NMR spectra (5.1 million vs. 817,000) than the first release. More specifically, an additional 4.6 million predicted spectra and another 11,000 spectra simulated from experimental chemical shifts were deposited into the database. Likewise, the number of NMR raw spectral data depositions has grown from a 165 spectra per year to more than 10,000 per year. As a result of this expansion, the number of monthly webpage views has grown from 55 to 20,000 and the number of monthly visitors has increased from 7 to 2500. To address this growth and to better support the expanding needs of its diverse community of users, many additional improvements to the NP-MRD have been made. These include significant enhancements to the data submission process, important improvements to the visualization and display of NMR spectra, notable updates to the database’s spectral search utilities and useful additions to support better NMR spectral analysis/prediction. Significant efforts have also been undertaken to remediate and update many of NP-MRD’s database entries. This manuscript describes these database improvements and expansion efforts, along with how they have been implemented and what future upgrades to the NP-MRD are planned.

Artifical Intelligence↗
DOE OSTI2025

Creating Apptainer Workflows with Docker-Compose-like Utilities

Creating Apptainer Workflows with Docker-Compose-like Utilities In this presentation, I will explore the utilization of a tool called process-compose, inspired by docker-compose, to create Apptainer-based services. This approach allows for easy deployment and management of fully containerized applications on High Performance Computing (HPC) systems without requiring elevated privileges. Benefits to the Ecosystem: By incorporating process-compose and Apptainer, I aim to address several key challenges in the HPC ecosystem: Simplified Workflow Management: Process-compose provides a user-friendly interface for defining and managing complex containerized application services, reducing the setup time and lowering the barrier to entry for new users. Enhanced Portability: Apptainer ensures that containerized applications can run consistently across different HPC environments, promoting greater portability and reducing compatibility issues. Process-compose is also a single binary that does not need to be installed by admin level users. Community Driven Solutions: This approach aligns with the goals of the High Performance Software Foundation (HPSF) to advance community-driven solutions. By sharing our experiences and insights, I hope to foster collaboration and innovation within the HPC community. Increased Productivity: The combination of process-compose and Apptainer streamlines the serve deployment process, allowing researchers and developers to focus more on their scientific work rather than the intricacies of system or service administration. Through this presentation, attendees will gain valuable insights into the practical implementation of containerized workflows on HPC systems, learn about the benefits of using process-compose and Apptainer, and understand how these tools can contribute to a more efficient HPC ecosystem.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Accuracy of HVAC Load Predictions: Validation of EnergyPlus and DOE-2 using FLEXLAB Measurements

The aim of the project reported here was to better understand the level of accuracy of three building energy simulation (BES) engines (‘engines’) — EnergyPlus™, DOE-2.1e, and DOE-2.2 — by identifying and investigating significant deviations between the performance predicted by these engines and actual performance as measured in the FLEXLAB® test facility at Lawrence Berkeley National Laboratory (LBNL). The specific test conditions included some of those prescribed in ANSI/ASHRAE Standard 140 - Standard Method of Test for the Evaluation of Building Energy Analysis Computer Programs. Detailed measurements of FLEXLAB performance, including indoor temperatures and heat fluxes and air-flow and water flow rates and temperatures in the Heating, Ventilating and Air Conditioning (HVAC) system, together with hourly weather data, were recorded and used in analyzing the simulation results from EnergyPlus v8.8, DOE-2.2 v3.65 and DOE-2.1e v127. These engines are commonly used in the United States for building energy code compliance, federal, state, and utility incentives programs, as well as energy efficient design of new buildings and energy retrofit of existing buildings. Seven conventional overhead mixing ventilation scenarios were tested and each engine was found to have a similar level of agreement with the measurements of space-level heating and sensible cooling loads. These results provide useful information regarding the accuracy of these engines in predicting the cooling and heating load elements of whole building energy performance. This information is intended for practitioners who are concerned about transitioning between simulation tools with different engines and for managers of utility programs leveraging these tools for evaluating and/or projecting measure savings to be incentivized under their programs. The results of the comparisons of simulated and measured performance indicate that the predictions from all three engines are not significantly different. The 24-hour average value of the absolute mean bias indicates the likely magnitude of the error in any particular case. The average mean bias is reduced by cancelation of overprediction in one case by underprediction in another. The daytime absolute mean biases, which may be more important for both energy performance and occupant comfort, are ~6%, presumably because of the greater complexity involved in simulating in the presence of solar radiation. EnergyPlus typically overpredicts the cooling load and/or underpredicts the heating load by ~1.5% and the DOE-2 engines typically underpredict the cooling load by approximately the same amount. The Root Mean Square Error is relatively more sensitive to shorter term variations in the difference between predicted and measured loads; the three engines have similar values, ~10%, suggesting that the uncertainties in their predictions of peak loads may also be similar in magnitude. The implication of these results is that users, both designers and program analysts, can use EnergyPlus, DOE-2.1e, or DOE-2.2 to model conventional commercial buildings equipped with overhead mixing ventilation with a similar level of confidence. Further work is required to better understand the variability in the level of agreement between the engine predictions and FLEXLAB measurements, where a particular engine will agree well with FLEXLAB in some cases and not so well in others and another engine will agree or disagree in different cases. As the sources of this variability are identified and eliminated or reduced significantly, it is recommended that the experimental capabilities and methods developed in the study reported here should be applied to validating heating and cooling load calculations for spaces with different types of furniture and miscellaneous loads. These methods should then be applied to low energy space conditioning systems in EnergyPlus including, in particular, radiant slab and radiant ceiling panel cooling and heating systems and ‘mixed mode’ systems that combine mechanical cooling and natural ventilation systems, focusing on controls, including control of thermal mass. The work reported here addresses the conventional method of heating and cooling occupied spaces; other methods, such as the use of radiant heating and cooling systems have the potential to provide equivalent occupant comfort, or better, with lower energy consumption. These systems are addressed more explicitly in EnergyPlus but there is a need for empirical validation to give users the same level of confidence in modeling these systems that they have, or should have, in modeling conventional systems, based on the results presented here.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

The SENSEI Generic In Situ Interface: Tool and Processing Portability at Scale [Book Chapter]

One key challenge when doing in situ processing is the investment required to add code to numerical simulations needed to take advantage of in situ processing. Such instrumentation code is often specialized, and tailored to a specific in situ method or infrastructure. Then, if a simulation wants to use other in situ tools, each of which has its own bespoke API [4], then the simulation code team will quickly become overwhelmed with having a different set of instrumentation APIs, one per in situ tool or method. In an ideal situation, such instrumentation need happen only once, and then the instrumentation API provides access to a large diversity of tools. In this way, a data producer’s instrumentation need not be modified if the user desires to take advantage of a different set of in situ tools. The SENSEI generic in situ interface addresses this challenge, which means that SENSEI-instrumented codes enjoy the benefit of being able to use a diversity of tools at scale, tools that include Libsim, Catalyst, Ascent, as well as user-defined methods written in C++ or Python. SENSEI has been shown to scale to greater than 1M-way concurrency on HPC platforms, and provides support for a rich and diverse collection of common scientific data models. Furthermore, this chapter presents the key design challenges that enable tool and processing portability at scale, some performance analysis, and example science applications of the methods.

Bethel, E. Wes↗
DOE OSTI2023

All polymer microfluidic chips—A fixed target sample delivery workhorse for serial crystallography

The development of x-ray free electron laser (XFEL) light sources and serial crystallography methodologies has led to a revolution in protein crystallography, enabling the determination of previously unobtainable protein structures and near-atomic resolution of otherwise poorly diffracting protein crystals. However, to utilize XFEL sources efficiently demands the continuous, rapid delivery of a large number of difficult-to-handle microcrystals to the x-ray beam. A recently developed fixed-target system, in which crystals of interest are enclosed within a sample holder, which is rastered through the x-ray beam, is discussed in detail in this Perspective. The fixed target is easy to use, maintains sample hydration, and can be readily modified to allow a broad range of sample types and different beamline requirements. Recent innovations demonstrate the potential of such microfluidic-based fixed targets to be an all-around “workhorse” for serial crystallography measurements. In conclusion, this Perspective will summarize recent advancements in microfluidic fixed targets for serial crystallography, examine needs for future development, and guide users in designing, choosing, and utilizing a fixed-target sample delivery device for their system.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Multisource Mobile Transfer Learning Algorithm Based on Dynamic Model Compression

With the development of the Internet of Things, the application of computer vision on mobile phones is becoming more and more extensive and people have higher and higher requirements for the timeliness of the recognition results returned and the processing capabilities of the mobile phone for image recognition. However, the processing capability and storage capability of the user terminal equipment cannot meet the needs of identifying and storing a large number of pictures, and the data transmission process will cause high energy consumption of the terminal equipment. At the same time, multisource deep transfer learning has outstanding performance in computer vision and image classification. However, due to the huge amount of calculation of the deep network model, it is impossible to use the existing excellent network model to realize image recognition and classification on the mobile terminal. In order to solve the abovementioned problems, we propose a multisource mobile transfer learning algorithm based on dynamic model compression, this algorithm considers the realization of multisource transfer learning computing in the case of multiple mobile device computing source domains, and the method also guarantees data privacy and security for each device (origin domain). Meanwhile, extensive experiments show that our method can achieve remarkable results in popular image classification datasets.

Gao, Peng↗
DOE OSTI2020

hectorui: A web-based interactive scenario builder and visualization application for the Hector climate model

In a world of increasingly online, distributed, and diverse interdisciplinary research, there is a need to provide accessible and user-friendly interactive visualization tools that elucidate complex models and their output products. Hector UI is an R Shiny web interface built to extend the simple global climate model Hector (Hartin et al. 2015), originally designed in C++ with an accompanying R interface. Traditionally, Hector has only had a command line interface available which requires fluency in C++ or R and a full understanding of Hector’s parameter space to run the model and create output. Hector UI provides a fast, efficient solution that makes the model more accessible to a broader user base. This implementation allows users that may not be fluent in R or C++ to interactively explore model scenarios and outputs in an easy to use, guided point and click interface.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

HelioScope Energy Performance Modeling Validation: Cooperative Research and Development Final Report, CRADA Number CRD-17-00685

HelioScope is a unique solar design and energy performance modeling tool that bridges the worlds of research and industry, bringing the most rigorous methods from the performance modeling community to thousands of solar developers of all backgrounds. However, many financial institutions and municipalities are hesitant to accept the energy production modeling results of HelioScope (including shading losses) in place of costly, time-intensive, and/or redundant methods due to lack of vetting from a respected institution like NREL. We expect that the proposed validation exercises will give municipalities and financial institutions the comfort needed to incorporate HelioScope into their operations, thereby significantly reducing the needs of existing and potential HelioScope users' to otherwise obtain information that HelioScope produces automatically. Folsom Labs was selected for a Small Business Voucher from the U.S. Department of Energy for the National Renewable Energy Laboratory to validate the performance of HelioScope’s simulation engine against measured PV system performance.

14 SOLAR ENERGY↗
DOE OSTI2020

Herbaceous Feedstock 2020 (State of Technology Report)

The Energy Independence and Security Act (EISA) of 2007 required a minimum supply of 36 million gallons of renewable fuels per year by 2022. In order to achieve these goals, the Bioenergy Technologies Office (BETO) has set cost and technology targets for producing advanced and cellulosic biofuels. One of the targets is to validate feedstock supply infrastructures and systems with 90% overall operating effectiveness and field-to-reactor throat delivered cost less than $85.51/dry ton (2016). As stated by the 2017 Multi-Year Program Plan (DOE 2017), the research and development focus of the Feedstock Technologies (FT) platform is reducing the cost, improving the supply chain logistic efficiency, improving biomass quality, and increasing the supply volume. In addition, BETO oversees annual State of Technology (SOT) report that assesses current technologies that are relevant to BETO’s targets based on actual data and experimental results. Feedstocks are essential to achieving BETO goals because the cost, quality, and quantity of feedstock available and accessible at any given time limit the maximum volume of biofuels that can be produced. In accordance with the 2016 Multi-Year Program Plan (DOE 2016a), FT focuses on (1) reducing the delivered cost of sustainably produced biomass, (2) preserving and improving the physical and chemical quality parameters of harvested biomass to meet the individual needs of biorefineries and other biomass users, and (3) expanding the quantity of feedstock materials accessible to the bioenergy industry. This is done by identifying, developing, demonstrating, and validating efficient and economical integrated systems for harvest and collection, storage, handling, transport, and preprocessing raw biomass from a variety of crops to reliably deliver the required supplies of high-quality, affordable feedstocks to biorefineries as the industry expands. The elements of cost, quality, and quantity are key considerations when developing advanced feedstock supply concepts and systems (DOE 2016a).

09 BIOMASS FUELS↗
DOE OSTI2022

Camp Lejeune Federal Fleet Tiger Team EVSE Site Assessment

The National Renewable Energy Laboratory (NREL) was contracted by the Department of Defense (DOD) Environmental Security Technology Certification Program (ESTCP) and the Department of Energy (DOE) Federal Energy Management Program (FEMP) to develop a web tool and complete pilot assessments for the installation of Electric Vehicle Supply Equipment (EVSE). NREL has deployed Tiger Teams comprising engineers and fleet experts to support DOD and DOE on several EVSE site assessments since 2015. However, the advancements developed in support of this report, including a cost-estimation tool and geospatial mapping tool, will enable a user-driven path to assess EVSE needs. This report details the site design recommendations for Camp Lejeune, North Carolina.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2022

Alternative Technologies Adopters Outreach Survey Report

Brookhaven National Laboratory (BNL), in collaboration with Ioanna Iliopulos and Christopher Boyd (independent contractors), conducted quantitative and qualitative surveys to document views and opinions of scientists and researchers with firsthand experience operating x-ray devices for a wide range of research applications. The goal of the study was to fill critical information gaps and identify misconceptions widely held among the research community regarding the transition from Cesium-137 irradiators to x-ray devices. The survey instrument focused on three distinct yet interrelated areas that impact users' experience: operational impacts, including infrastructure needs, workflow, personnel management, and training, and regulatory compliance; research design and investigation impacts, including calibration, dosimetry, continuity of previous studies, development of new research agendas, and the outcomes for both; and funding impacts, specifically how grant sources and opportunities have responded to the transition.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

A Cybersecurity Threat Profile for a Connected Lighting System

In anticipation of improved energy performance and cost savings, cities and building owners are increasingly considering “smart lighting initiatives” that aim to convert their collection of simple luminaires (i.e., lighting fixtures) into an intelligent connected lighting system (CLS) capable of remotely monitoring energy consumption and fault conditions, and possibly implementing adaptive lighting schemes. The U.S. Department of Energy (DOE) has set an national goal of tripling the energy efficiency and demand flexibility of the buildings sector by 2030, relative to 2020 levels 1. It is forecast that connected lighting systems can contribute to that goal by delivering 125 TWh of annual energy savings by 2035 2, equivalent to the annual output of 50 typical (500 MW) power plants. However, these energy savings and the DOE goal are put at significant risk if connected technologies are not adopted due to real or perceived cybersecurity concerns. Connected IoT devices such as these have historically been rife with vulnerabilities which sometimes put security considerations secondary to functionality and operability. What are the cybersecurity threats that will impact these systems, as formerly banal luminaires transition into intelligent connected devices that collect information about themselves, their surrounding environment, and possibly us? In this paper we analyze a threat profile performed on a fault-detection use case for streetlights. A threat profile establishes security requirements, justifies security measures, yields actionable controls, and effectively communicates risk to stakeholders. This effort provides critical information for making threat-based decisions to increase security at a reasonable cost, and can effectively be used by development teams, software architects, and managers to make cybersecurity a part of their ongoing culture of awareness, training, and prevention. This leads to more secure systems and better-understood security. On-premise, cloud, and hybrid architectures with different authentication mechanisms were modeled and later categorized using the Microsoft STRIDE framework. An analysis of the recommended controls for each threat was performed to determine which controls could and should be put in place by manufacturers or third-party suppliers, and which controls need to be left up the end-user to implement. Fifty-seven threats were identified. Among our key findings: (1) 65% (37/57) of the threats did not involve the luminaires, but rather the other components needed to communicate with and manage them; (2) 63% (36/57) of the threats could have been mitigated through manufacturer-implemented defensive techniques or “controls”; and (3) 23% (13/57) of the threats were dependent on the network configuration. Recommendations based on the results of this work are made to key stakeholder groups. Notably, lighting technology developers are advised to address all threats that can be reasonably controlled with baked-in technology solutions (e.g., encryption or authentication controls), and employ some form of secure supply chain management and tracking where other parts (e.g., sensors, microprocessors) of a luminaire must also be built and manufactured with the proper security controls in place. Developers should also review threats involving assets not developed in-house to understand how connectivity with other devices will affect their product during system operation and determine if a compensating control for a defense-in-depth strategy will be needed. Finally, those interested in deploying CLS should compare the differences between cloud and on-premise models to determine which is more suitable for their needs and the abilities of their security team.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

2020 Medical Device Sterilization Workshop: Continuing the Conversation, Summary Report

Technical advancements are making accelerator-based sources of radiation viable candidates for sterilization of medical devices. Electron beams and x-rays are becoming more cost-competitive and new facilities are being built to provide needed capacity and redundancy. As the industry becomes more receptive to these modalities, there is a need for information and data to enable prospective users to evaluate these options for their products. The workshops offered in 20191 and now in 2020 strive to foster conversation between device manufacturers, equipment and service providers on needs, capabilities, and knowledge.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Coupling of Laminar-Turbulent Transition with RANS Computational Fluid Dynamics

This project combines several new concepts to create a boundary layer transition prediction capability that is suitable for analyzing modern hypersonic flight vehicles. The first new concept is the use of ''optimization'' methods to detect the hydrodynamic instabilities that cause boundary layer transition; the use of this method removes the need for many limiting assumptions of other methods and enables quantification of the interactions between boundary layer instabilities and the flow field imperfections that generate them. The second new concept is the execution of transition analysis within a conventional hypersonics CFD code, using the same mesh and numerical schemes for the transition analysis and the laminar flow simulation. This feature enables rapid execution of transition analysis with less user oversight required and no interpolation steps needed.

42 ENGINEERING↗