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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Battery Life Prediction Using Reduced-Order Physics Models and Machine Learning (CRADA Final Report)

Phase 1 (Original CRADA, plus no-cost extension modifications #1-3, 6/1/2017 to 3/13/2021): The Australian Department of Defence (AUDoD) is performing accelerated aging tests of Li-ion batteries to benchmark their reliability and degradation characteristics. Using its previously developed battery lifetime predictive model framework, the National Laboratory of the Rockies (NLR) will develop analytical models based the AUDoD data to predict lifetime of the multiple Li-ion battery chemistries under real-world use scenarios of interest to AUDoD. The NLR model is based on physical degradation mechanisms encountered by Li-ion batteries and has been previously validated. Phase 2 (CRADA modification #4, plus no-cost extension modification #5, 2/22/2021 to 3/30/2025): Train and support Australian Department of Defence personnel to use NLR software for model-based estimation of Li-ion battery lifetime using accelerated battery aging data collected by the Australian Department of Defence. Under separate DOE funding from 2019 to 2021, NLR enhanced its battery life-prediction software using machine learning algorithms to automate portions of the model-fitting process, requiring significantly less labor and expert judgment and also adding uncertainty quantification, increasing statistical rigor. Under Phase 2, NLR will customize NLR Software and provide it to AuDoD. NLR will enhance its NLR Model to capture aging modes of AuDoD's multi-cell modules, including cell-balancing effects. NLR will develop example single-cell and multi-cell models based on one AuDoD battery aging dataset. NLR will train AuDoD personnel on NLR Software. By the conclusion of the project, NLR will have provided AuDoD the training materials, a user manual and software needed to perform their own analysis of additional and/or future battery aging datasets.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Experiment Based Superposition Thermal Modeling of Laser Powder Bed Fusion

This paper evaluates experiment-based superposition thermal modeling for Laser Powder Bed Fusion (LPBF) with a pulsed laser. An analytical pulsed laser thermal model establishes the modeling procedure. The framework inverts a powder bed’s single pulse temperature response from experimental spatiotemporal Short-Wave Infrared (SWIR) camera data. Superimposing this response along a scan path simulates multi-pulse LPBF. Results show the experimentally informed superposition model rapidly and accurately predicts a layer’s temperature history. The model has applications in correction of thermally driven LPBF errors and in-situ part qualification.

Lough, Cody↗
DOE OSTI2020

Ester-Linked Crystalline Covalent Organic Frameworks

Ester-linked, crystalline, porous covalent organic frameworks (COFs) have been synthesized and structurally characterized. Transesterification reactions between ditopic 2-pyridinyl aromatic carboxylates and tri- or tetratopic phenols gave the corresponding ester-linked COFs. They crystallize as 2D structures in kgm (COF-119) and hcb (COF-120, 121, 122) topologies with surface areas of up to 2092 m 2 /g. Notably, crystalline COF-122 comprises edges spanning over 10 phenylene units, an aspect that had only been achieved in metal-organic frameworks. Finally, this work expands the scope of reticular chemistry to include, for the first time, crystalline ester-linked COFs related to common polyesters.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Distributed heterogeneous compute infrastructure for the study of additive manufacturing systems

We present the current status of a large-scale computing framework to address the need of the multidisciplinary effort to study chemical dynamics. Specifically, we are enabling scientists to process and store experimental data, run large-scale computationally expensive high-fidelity physical simulation, and analyze these results using the state-of-the-art data analytics tools, machine learning, and uncertainty quantification methods using heterogeneous computing resources, such as CPU and GPU cluster. The framework can integrate or abstract out multiple domains based on roles. In order to develop this framework, we have leveraged an existing framework coupled with in-house heterogeneous computing resources. We present the results of using this framework on a single metadata triggered workflow to accelerate an additive manufacturing use case.

heterogeneous computing, hpc, workflows, reproduci↗
DOE OSTI2021

Data analytics approach to predict high-temperature cyclic oxidation kinetics of NiCr-based Alloys

Abstract Although of practical importance, there is no established modeling framework to accurately predict high-temperature cyclic oxidation kinetics of multi-component alloys due to the inherent complexity. We present a data analytics approach to predict the oxidation rate constant of NiCr-based alloys as a function of composition and temperature with a highly consistent and well-curated experimental dataset. Two characteristic oxidation models, i.e., a simple parabolic law and a statistical cyclic oxidation model, have been chosen to numerically represent the high-temperature oxidation kinetics of commercial and model NiCr-based alloys. We have successfully trained machine learning (ML) models using highly ranked key input features identified by correlation analysis to accurately predict experimental parabolic rate constants ( k p ). This study demonstrates the potential of ML approaches to predict oxidation kinetics of alloys over wide composition and temperature ranges. This approach can also serve as a basis for introducing more physically meaningful ML input features to predict the comprehensive cyclic oxidation behavior of multi-component high-temperature alloys with proper constraints based on the known underlying mechanisms.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Hybrid Data-Driven Physics-Based Model Framework Implementation: Towards a Secure Cyber-Physical Operation of the Smart Grid

False data injection cyber-attack detection models on smart grid operation have been much explored recently, considering analytical physics-based and data-driven solutions. Recently, a hybrid data-driven physics-based model framework for monitoring the smart grid is developed. However, the framework has not been implemented in real-time environment yet. In this paper, the framework of the hybrid model is developed within a real-time simulation environment. OPAL-RT real-time simulator is used to enable Hardware-in-the-Loop testing of the framework. IEEE 9-bus system is considered as a testing grid for gaining insight. The process of building the framework and the challenges faced during development are presented. The performance of the framework is investigated under various false data injection attacks.

false data injection attack, machine learning, sta↗
DOE OSTI2020

An Automated Disruption-Tolerant Key Management Framework for Critical Systems

Key management is critical to secure operation. Distributed control systems, such as Supervisory Control and Data Acquisition (SCADA) systems, have unique operational requirements that make conventional key management solutions less effective and burdensome. This paper presents a novel Kerberos-based framework for automated, disruption-tolerant key management for control system environments. Here, experimental tests and their results are presented to quantify the expected performance overhead of this approach. Additionally, Zeek sensor analytics are presented to aid in monitoring the health and security of the key management framework operation.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2020

Research Needs for Trusted Analytics in National Security Settings

As artificial intelligence, machine learning, and statistical modeling methods become commonplace in national security applications, the drive to create trusted analytics becomes increasingly important. The goal of this report is to identify areas of research that can provide the foundational understanding and technical prerequisites for the development and deployment of trusted analytics in national security settings. Our review of the literature covered several disjoint research communities, including computer science, statistics, human factors, and several branches of psychology and cognitive science, which tend not to interact with one another or cite each other's literatures. As a result, there exists no agreed-upon theoretical framework for understanding how various factors influence trust and no well-established empirical paradigm for studying these effects. This report therefore takes three steps. First, we define several key terms in an effort to provide a unifying language for trusted analytics and to manage the scope of the problem. Second, we outline an empirical perspective that identifies key independent, moderating, and dependent variables in assessing trusted analytics. Though not a substitute for a theoretical framework, the empirical perspective does support research and development of trusted analytics in the national security domain. Finally, we discuss several research gaps relevant to developing trusted analytics for the national security mission space.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Investigating the mechanical stability of flexible metal–organic frameworks

As we continue to develop metal–organic frameworks (MOFs) for potential industrial applications, it becomes increasingly imperative to understand their mechanical stability. Notably, amongst flexible MOFs, structure-property relationships regarding their compressibility under pressure remain unclear. In this work, we conducted in situ variable pressure powder X-ray diffraction (PXRD) measurements up to moderate pressures (<1 GPa) using a synchrotron source on two families of flexible MOFs: (i) NU-1400 and NU-1401, and (ii) MIL-88B, MIL-88B-(CH 3 ) 2 , and MIL-88B-(CH 3 ) 4 . In this project scope, we found a positive correlation between bulk moduli and degree of flexibility, where increased rigidity (e.g., smaller swelling or breathing amplitude) arising from steric hindrance was deleterious, and observed reversibility in the unit cell compression of these MOFs. This study serves as a primer for the community to begin to untangle the factors that engender flexible frameworks with mechanical resilience.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Selective sorption of oxygen and nitrous oxide by an electron donor-incorporated flexible coordination network

Incorporating strong electron donor functionality into flexible coordination networks is intriguing for sorption applications due to a built-in mechanism for electron-withdrawing guests. Here we report a 2D flexible porous coordination network, [Ni 2 (4,4'-bipyridine)(VTTF) 2 ]n(1) (where H 2 VTTF = 2,2;-[1,2-bis(4-benzoic acid)-1,2ethanediylidene]bis-1,3-benzodithiole), which exhibits large structural deformation from the as-synthesized or open phase (1α) into the closed phase (1β) after guest removal, as demonstrated by X-ray and electron diffraction. Interestingly, upon exposure to electron-withdrawing species, 1β reversibly undergoes guest accommodation transitions; 1α$\supset$O 2 (90 K) and 1α$\supset$N 2 O (185 K). Moreover, the 1β phase showed exclusive O 2 sorption over other gases (N 2 , Ar, and CO) at 120 K. The phase transformations between the 1α and 1β phases under these gases were carefully investigated by in-situ X-ray diffraction, in-situ spectroscopic studies, and DFT calculations, validating that the unusual sorption was attributed to the combination of flexible frameworks and VTTF (electron-donor) that induces strong interactions with electron-withdrawing species.

36 MATERIALS SCIENCE↗
DOE OSTI2022

FlameNEST: explicit profile likelihoods with the Noble Element Simulation Technique

We present FlameNEST, a framework providing explicit likelihood evaluations in noble element particle detectors using data-driven models from the Noble Element Simulation Technique. FlameNEST provides a way to perform statistical analyses on real data with no dependence on large, computationally expensive Monte Carlo simulations by evaluating the likelihood on an event-by-event basis using analytic probability elements convolved together in a single TensorFlow multiplication. Furthermore, this robust framework creates opportunities for simple inter-collaboration analyses which will be fundamental for the future of experimental dark matter physics.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Probing ligand conformation and net dimensionality in a series of tetraphenylethene-based metal–organic frameworks

Tetraphenylethene-based ligands with lowered symmetry are promising building blocks for the construction of novel luminescent metal–organic frameworks (MOFs). However, few examples have been reported, and predicting the ligand conformation and the dimensionality of the resulting MOF remains challenging. In order to uncover how synthetic conditions and accessible ligand conformations may affect the resulting MOF structure, four new MOF structures were synthesized under solvothermal conditions using the meta-coordinated tetraphenylethene-based ligand m-ETTC and paddlewheel SBUs composed of Co(II), Cu(II), and Zn(II). WSU-10 (WSU = Washington State University) is formed with either Zn or Cu comprising stacked psuedo-2D layers. The dimensionality of WSU-10 can be intentionally increased through the addition of pyrazine as a pillar ligand into the synthesis, forming the 3D structure WSU-11. The third structure, WSU-20, is formed by the combination of Zn or Co with m-ETTC and is intrinsically 3D without the use of a pillar ligand; interestingly, this is the result of a distortion in the paddlewheel SBU. Finally, Cu was also found to form a new structure (WSU-12), which displays an m-ETTC conformation unique from that found in the other isolated MOFs. Structural features are compared across the series and a mechanistic relationship between WSU-10 and -20 is proposed, providing insight into the factors that can encourage the generation of frameworks with increased dimensionality.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Cybersecurity Assessment Tools for Distributed Energy Resources

This growing number of smart devices that support DERs can increase the number of access points outside a utility’s administrative domain, which can increase the potential for cyberattack. With the integration of DERs at federal sites, the cybersecurity vulnerabilities of DER systems must be understood and addressed. This presentation covers two NREL tools available. The Distributed Energy Resource Cybersecurity Framework (DER-CF) is a web-based holistic tool for evaluating cybersecurity posture including governance, physical security and technical management. The Distributed Energy Resource Risk Manager (DER-RM) extends the DER-CF by applying it to the NIST risk management framework process. It will be downloadable application that runs locally and documents all the major requirements for achieving Authority to Operate the DER.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗