SEARCH · Engineering Papers

Results for “technical 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.

SourceResearch connectionFrom yearThrough year

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

At least 1,351 records · Page 75

All sources NASA NTRS DOE OSTI

DOE OSTI2026

Community-Informed Urban Flood Modeling for Impact Mitigation

Climate change is intensifying the hydrologic cycle, leading to more frequent and severe rainfall-driven (pluvial) flooding in urban areas. In the mid-Atlantic US cities, aging and under-designed stormwater infrastructure is increasingly strained by these events, resulting in recurring damage to property and disruptions to transportation networks. In this study, we combine community engagement with hydrologic modeling to develop and evaluate potential urban flood adaptation strategies. Over a three-year period, local technical experts and community representatives met regularly to discuss flooding concerns, identify priorities, and co-develop adaptation strategies. These discussions informed the development of an urban flooding model (EPA Storm Water Management Model) for the Baltimore Harbor watershed, the focus location of this study. The flooding model integrates complex surface and subsurface stormwater infrastructure data, local expert knowledge, and community insights. We simulate stakeholder-prioritized adaptations, such as green and gray infrastructure strategies. Model results demonstrate that enhanced infrastructure maintenance is the most effective adaptation for reducing flood depths, but has varied effects across the watershed, and can increase flooding in some locations. Spatially concentrated greening provides limited benefit to the watershed as a whole, but moderate benefit in community priority areas. Together, these adaptations have the potential to reduce flood depths by as much as 58% in some locations, greatly reducing property damage and mobility impacts, primary concerns of stakeholders. Future work will implement robust optimization tools to search for adaptations which meet stakeholder objectives and perform highly under varied future climate conditions. This work contributes to the expanding literature on collaborative modeling and demonstrates that community-engaged approaches can enhance model credibility and generate more actionable insights for communities seeking to strengthen climate resilience.

Baltimore MD↗

DOE OSTI2017

Final Technical Report for GO17004 Regulatory Logic: Codes and Standards for the Hydrogen Economy

The objectives of this project are to: develop a robust supporting research and development program to provide critical hydrogen behavior data and a detailed understanding of hydrogen combustion and safety across a range of scenarios, needed to establish setback distances in building codes and minimize the overall data gaps in code development; support and facilitate the completion of technical specifications by the International Organization for Standardization (ISO) for gaseous hydrogen refueling (TS 20012) and standards for on-board liquid (ISO 13985) and gaseous or gaseous blend (ISO 15869) hydrogen storage by 2007; support and facilitate the effort, led by the NFPA, to complete the draft Hydrogen Technologies Code (NFPA 2) by 2008; with experimental data and input from Technology Validation Program element activities, support and facilitate the completion of standards for bulk hydrogen storage (e.g., NFPA 55) by 2008; facilitate the adoption of the most recently available model codes (e.g., from the International Code Council [ICC]) in key regions; complete preliminary research and development on hydrogen release scenarios to support the establishment of setback distances in building codes and provide a sound basis for model code development and adoption; support and facilitate the development of Global Technical Regulations (GTRs) by 2010 for hydrogen vehicle systems under the United Nations Economic Commission for Europe, World Forum for Harmonization of Vehicle Regulations and Working Party on Pollution and Energy Program (ECE-WP29/GRPE); and to Support and facilitate the completion by 2012 of necessary codes and standards needed for the early commercialization and market entry of hydrogen energy technologies.

08 HYDROGEN↗

DOE OSTI2017

SME Acceptability Determination For DWPF Process Control (U)

The statistical system described in this document is called the Product Composition Control System (PCCS). K. G. Brown and R. L. Postles were the originators and developers of this system as well as the authors of the first three versions of this technical basis document for PCCS. PCCS has guided acceptability decisions for the processing at the Defense Waste Processing Facility (DWPF) at the Savannah River Site (SRS) since the start of radioactive operations in 1996. The author of this revision to the document gratefully acknowledges the firm technical foundation that Brown and Postles established to support the ongoing successful operation at the DWPF. Their integration of the glass propertycomposition models, developed under the direction of C. M. Jantzen, into a coherent and robust control system, has served the DWPF well over the last 20+ years, even as new challenges, such as the introduction into the DWPF flowsheet of auxiliary streams from the Actinide Removal Process (ARP) and other processes, were met. The purpose of this revision is to provide a technical basis for modifications to PCCS required to support the introduction of waste streams from the Salt Waste Processing Facility (SWPF) into the DWPF flowsheet. An expanded glass composition region is anticipated by the introduction of waste streams from SWPF, and property-composition studies of that glass region have been conducted. Jantzen, once again, directed the development of glass property-composition models applicable for this expanded composition region. The author gratefully acknowledges the technical contributions of C.M. Jantzen leading to the development of these glass property-composition models. The integration of these models into the PCCS constraints necessary to administer future acceptability decisions for the processing at DWPF is provided by this sixth revision of this document.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

Load Composition Analysis in Support of the NERC Load Modeling Task Force. 2019-2020 Field Test of the Composite Load Model

In 2015, NERC’s reliability standards were revised to require the use of dynamic load models in transmission planning studies. To comply with the standards, planners must use load models that explicitly represent the dynamic behavior of the different constituents of load at each load bus within their transmission planning models. The most important of these constituents are motor-driven and power electronics-based loads. Collectively, these representations are known as composite load models. In anticipation of the compliance date for the new standards, NERC’s Load Modeling Task Force (LMTF), in 2019, initiated a field test of composite load models involving the regional reliability planning entities. In support of the field test, DOE and BPA researchers developed region-specific composite load models that could be assigned to each non-industrial load bus in the planning models for each of the North American interconnections. Separate models were developed for each hour of a summer peak day, a winter peak day, and a spring light-load day. This report is the technical documentation for the load composition analysis that was conducted to develop these non-industrial composite load models.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Model Guidance to Address Barriers to Combined Heat and Power and Waste Heat to Power

Congress directed that the US Department of Energy (DOE) initiate a review of interconnection rules to identify barriers to interconnection and ways to better integrate combined heat and power (CHP) and waste heat to power (WHP) in the electric grid. Oak Ridge National Laboratory (ORNL) supports DOE by providing technical assistance for CHP. DOE tasked ORNL with the interconnection review and development of model guidance to enable developers and CHP owners to interconnect more CHP. This report summarizes the work that DOE initiated in response to the congressional request.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS2019

Active Flow Control on Vertical Tail Models

Active flow control (AFC) subscale experiments were conducted at the Lucas Wind Tunnel of the California Institute of Technology. Tests were performed on a generic vertical tail model at low speeds. Fluidic oscillators were used at the trailing edge of the main element (vertical stabilizer) to redirect the flow over the rudder and delay or prevent flow separation. Side force increases in excess of 50% were achieved with a 2% momentum coefficient (C(sub μ)) input. The results indicated that a collective C(sub μ) of about 1% could increase the side force by 30–50%. This result is achieved by reducing the spanwise flow on the swept back wings that contributes to early flow separation near their tips. These experiments provided the technical backdrop to test the full-scale Boeing 757 vertical tail model equipped with a fluidic oscillator system at the National Full-scale Aerodynamics Complex 40-by 80-foot Wind Tunnel, NASA Ames Research Center. The C(sub μ) is shown to be an important parameter for scaling a fluidic oscillator AFC system from subscale to full-scale wind tunnel tests. The results of these tests provided the required rationale to use a fluidic oscillator AFC configuration for a follow-on flight test on the Boeing 757 ecoDemonstrator.

Andino, Marlyn Y.↗

NASA NTRSDate not supplied

Expressing Functionality Using Design Patterns, A Real-World Example

Digital Engineering is a transformative strategy that leverages an integrated model-based approach to improve communication, decision making, design understanding, and acquisition efficiency of system development. As modern systems are derived from pre-existing systems, harvesting tacit knowledge from existing systems in a useful, model-based way will reduce the experiential learning and cognition required for new system development, contributing to a Digital Engineering transformation. This study enables such a technique by demonstrating the use of architectural theory in expressing tacit knowledge from existing systems and presenting that knowledge according to the model-based view offered by the Functional Architecture of Systems methodology. By validating the functional information expressed by the design pattern, this work solidifies design patterns as a critical component of a technical architecture and proposes a functional architecture viewpoint for describing mined knowledge in a usable, model-based, way suitable for archival in a modern knowledge management system.

Systems Engineering↗

NASA NTRS2003

A Product Development Decision Model for Cockpit Weather Information System

There is a significant market demand for advanced cockpit weather information products. However, it is unclear how to identify the most promising technological options that provide the desired mix of consumer requirements by employing feasible technical systems at a price that achieves market success. This study develops a unique product development decision model that employs Quality Function Deployment (QFD) and Kano's model of consumer choice. This model is specifically designed for exploration and resolution of this and similar information technology related product development problems.

Sireli, Yesim↗

NASA NTRS2003

A Product Development Decision Model for Cockpit Weather Information Systems

Sireli, Yesim↗

DOE OSTI2023

Modeling the 4D discharge of lithium-ion batteries with a multiscale time-dependent deep learning framework

The lithium-ion battery (LIB) field is moving towards the direction of investigating spatially resolved physical phenomena in the 3D porous microstructure of electrodes. These pore-scale simulations give new insights into the local dynamics of lithiation/de-lithiation and charge transport, Nevertheless, the computational time of these simulations limits the integration of these models in optimization workflows of cycling conditions or electrode manufacturing processes. Machine learning models present a way of assessing in real-time the performance of materials. While several successful techniques for replicating simulations with machine learning have been proposed, this case study presents a more demanding problem, due to the necessity of understanding the behavior of heterogeneous 3D local data, as it evolves in time: this poses both a scientific and a technical challenge. To this end, we propose an autoregressive multiscale convolutional neural network model to predict relevant quantities at the pore-scale in the solid phase: the lithium concentration (in the active material) and potential (in the active material and carbon binder). Here, these are ultimately used to reconstruct the battery discharge curve. 3D images of the electrode microstructures are the input to the network, trained with a dataset of finite element method simulations to predict the discharge behavior of the cathode side in lithium ion batteries. We propose this machine learning model as a proof-of-concept of the applicability of multiscale networks for time-dependent physics problems. The trained model exhibits very high accuracy (with errors lower than 2 %) in forecasting the discharge behavior of new unseen cathodes.

25 ENERGY STORAGE↗

DOE OSTI2022

Framework For Optimization Of Resources And Economics Ecosystem

The Framework for Optimization of ResourCes and Economics is a collection of software tools, models, and datasets acquired and developed under the Integrated Energy Systems (IES) program to enable analysis of technical and economic viability of myriad IES configurations. FORCE is the consolidating interface and data repository for all the IES toolsets ranging from macro technoeconomic analysis to transient process modeling and experimental validation for integrated energy systems.

Frick, KonorL↗

DOE OSTI2019

Estimation of Behind-the-Meter Solar Generation by Integrating Physical with Statistical Models

Accurate estimation of solar photovoltaic (PV) generation is crucial for distribution grid control and optimization. Unfortunately, most of the residential solar PV installations are behind-the-meter. Thus, utilities only have access to the net load readings. This paper presents an unsupervised framework for estimating solar PV generation by disaggregating the net load readings. The proposed framework synergistically combines a physical PV system performance model with a statistical model for load estimation. Specifically, our algorithm iteratively estimates solar PV generation with a physical model and electric load with the Hidden Markov model regression. The proposed algorithm is also capable of estimating the key technical parameters of the solar PV systems. Our proposed method is validated against net load and solar PV generation data gathered from residential customers located in Austin, Texas. The validation results show that our method reduces mean squared error by 44% compared to the state-of-the-art disaggregation algorithm.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS2008

Mentoring SFRM: A New Approach to International Space Station Flight Controller Training

The Mission Operations Directorate (MOD) of the Johnson Space Center is responsible for providing continuous operations support for the International Space Station (ISS). Operations support requires flight controllers who are skilled in team performance as well as the technical operations of the ISS. Space Flight Resource Management (SFRM), a NASA adapted variant of Crew Resource Management (CRM), is the competency model used in the MOD. ISS flight controller certification has evolved to include a balanced focus on development of SFRM and technical expertise. The latest challenge the MOD faces is how to certify an ISS flight controller (operator) to a basic level of effectiveness in 1 year. SFRM training uses a two-pronged approach to expediting operator certification: 1) imbed SFRM skills training into all operator technical training and 2) use senior flight controllers as mentors. This paper focuses on how the MOD uses senior flight controllers as mentors to train SFRM skills. Methods: A mentor works with an operator throughout the training flow. Inserted into the training flow are guided-discussion sessions and on-the-job observation opportunities focusing on specific SFRM skills, including: situational leadership, conflict management, stress management, cross-cultural awareness, self care and team care while on-console, communication, workload management, and situation awareness. The mentor and operator discuss the science and art behind the skills, cultural effects on skills applications, recognition of good and bad skills applications, recognition of how skills application changes subtly in different situations, and individual goals and techniques for improving skills. Discussion: This mentoring program provides an additional means of transferring SFRM knowledge compared to traditional CRM training programs. Our future endeavors in training SFRM skills (as well as other organization s) may benefit from adding team performance skills mentoring. This paper explains our mentoring approach and discusses its effectiveness and future applicability in promoting SFRM/CRM skills.

Huning, Therese↗

NASA NTRS2009

Mentoring SFRM: A New Approach to International Space Station Flight Control Training

Huning, Therese↗

DOE OSTI2015

Evolving Obs4MIPs to Support Phase 6 of the Coupled Model Intercomparison Project (CMIP6)

Over the past four years, an initiative known as Observations for Model Intercomparison Projects (Obs4MIPs) has successfully completed its pilot phase by adopting a set of technical protocols (dataset format, metadata standards, and documentation requirements) for dataset contributions, producing datasets that conform to these standards and archiving them for distribution on the Earth System Grid Federation (ESGF) alongside the fifth phase of the Coupled Model Intercomparison Project (CMIP5) model output. This pilot phase of Obs4MIPs, initiated by the National Aeronautics and Space Administration’s (NASA) Jet Propulsion Laboratory (JPL) and the Department of Energy’s (DOE) Program for Climate Model Diagnosis and Intercomparison (PCMDI) at Lawrence Livermore National Laboratory, supported CMIP5 and provided a path to improve the coordination between observational communities and major climate modeling intercomparison projects such as CMIP. Obs4MIPs is now being embraced by the international community, with the World Climate Research Programme (WCRP) Data Advisory Council (WDAC) empaneling a task team to provide guidance and governance for Obs4MIPs at an international level, in conjunction with the existing NASA Science Working Group that is more tightly focused on NASA satellite data products. Here, following the example of the first DOE–NASA Obs4MIPs meeting, and with an initial design of CMIP6 being published, a meeting of over 50 experts in both climate modeling and satellite data from the United States, Europe, Japan, and Australia convened at NASA headquarters in Washington, D.C., for the purpose of planning the evolution of Obs4MIPs and its connection to the CMIP6 experiments.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

A Deep Generative Model for Non-Intrusive Identification of EV Charging Profiles

The proliferation of electric vehicles (EVs) brings environmental benefits and technical challenges to power grids. An identification algorithm which can accurately extract individual EV charging profiles out of widely available smart meter measurements has attracted great interests. This paper proposes a non-intrusive identification framework for EV charging profile extraction, which is driven by deep generative models (DGM). First, the proposed DGM is designed as a representation layer embedded into the Markov process and used to model the joint probability distribution of available time-series data. A novel contribution is to approximate posterior distributions by neural networks whose parameters are obtained by variational inference and supervised learning. Second, the EV charging status is inferred from the DGM via dynamic programming. Lastly, the desired EV charging profile can be reconstructed by the rated power of EV models and inferred status. Compared with the benchmark Hidden Markov Models, the proposed framework can better handle noise in data with less computational complexity and better overall accuracy performances with smaller recall. The proposed framework is validated by numerical experiments on the Pecan Street dataset.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2025

LinkML: an open data modeling framework

Background Scientific research relies on well-structured, standardized data; however, much of it is stored in formats such as free-text lab notebooks, nonstandardized spreadsheets, or data repositories. This lack of structure challenges interoperability, making data integration, validation, and reuse difficult. Findings LinkML (Linked Data Modeling Language) is an open framework that simplifies the process of authoring, validating, and sharing data. LinkML can describe a range of data structures, from flat, list-based models to complex, interrelated, and normalized models that utilize polymorphism and compound inheritance. It offers an approachable syntax that is not tied to any one technical architecture and can be integrated seamlessly with many existing frameworks. The LinkML syntax provides a standard way to describe schemas, classes, and relationships, allowing modelers to build well-defined, stable, and optionally ontology-aligned data structures. Once defined, LinkML schemas may be imported into other LinkML schemas. These key features make LinkML an accessible platform for interdisciplinary collaboration and a reliable way to define and share data semantics. Conclusions LinkML helps reduce heterogeneity, complexity, and the proliferation of single-use data models while simultaneously enabling compliance with FAIR (Findable, Accessible, Interoperable, and Reusable) data standards. LinkML has seen increasing adoption in various fields, including biology, chemistry, biomedicine, microbiome research, finance, electrical engineering, transportation, and commercial software development. In short, LinkML makes implicit models explicitly computable and allows data to be standardized at their origin. LinkML documentation and code are available at https://linkml.io/.

AI-ready data↗

DOE OSTI2026

A Guide for Public Utility Commissions: Building Internal Technical Capacity and Recruiting Talent for Grid Resilience

This guide offers insight into how PUCs can strategically expand their technical workforce to meet evolving gird resilience demands. It outlines critical skill sets needed to support informed regulatory decision-making around resilience, such as modeling and weather forecasting, electric power systems analysis, and advanced data interpretation. In addition, it provides strategies for developing technical talent both internally and through additional recruitment efforts. Two appendices provide a list of grid resilience training resources and a compendium of sample job descriptions that reflect grid resilience technical expertise for PUC consideration.

24 POWER TRANSMISSION AND DISTRIBUTION↗