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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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DOE OSTI2022

Carbon Dioxide Capture at Nucleophilic Hydroxide Sites in Oxidation–Resistant Cyclodextrin–Based Metal–Organic Frameworks

Carbon capture and sequestration (CCS) from industrial point sources and direct air capture are necessary to combat global climate change. A particular challenge faced by amine-based sorbents—the current leading technology—is poor stability towards O 2 . Here, we demonstrate that CO 2 chemisorption in γ-cylodextrin-based metal-organic frameworks (CD-MOFs) occurs via HCO 3 – formation at nucleophilic OH – sites within the framework pores, rather than via previously proposed pathways. Here, the new framework KHCO 3 CD-MOF possesses rapid and high-capacity CO 2 uptake, good thermal, oxidative, and cycling stabilities, and selective CO 2 capture under mixed gas conditions. Because of its low cost and performance under realistic conditions, KHCO 3 CD-MOF is a promising new platform for CCS. More broadly, our work demonstrates that the encapsulation of reactive OH – sites within a porous framework represents a potentially general strategy for the design of oxidation-resistant adsorbents for CO 2 capture.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A2Cloud‐RF : A random forest based statistical framework to guide resource selection for high‐performance scientific computing on the cloud

Summary This article proposes a random‐forest based A2Cloud framework to match scientific applications with Cloud providers and their instances for high performance. The framework leverages four engines for this task: PERF engine, Cloud trace engine, A2Cloud‐ext engine, and the random forest classifier (RFC) engine. The PERF engine profiles the application to obtain performance characteristics, including the number of single‐precision (SP) floating‐point operations (FLOPs), double‐precision (DP) FLOPs, x87 operations, memory accesses, and disk accesses. The Cloud trace engine obtains the corresponding performance characteristics of the selected Cloud instances including: SP floating point operations per second (FLOPS), DP FLOPS, x87 operations per second, memory bandwidth, and disk bandwidth. The A2Cloud‐ext engine uses the application and Cloud instance characteristics to generate objective scores that represent the application‐to‐Cloud match. The RFC engine uses these objective scores to generate two types of random forests to assist users with rapid analysis: application‐specific random forests (ARF) and application‐class based random forests. The ARF consider only the input application's characteristics to generate a random forest and provide numerical ratings to the selected Cloud instances. To generate the application‐class based random forests, the RFC engine downloads the application profiles and scores of previously tested applications that perform similar to the input application. Using these data, the RFC engine creates a random forest for instance recommendation. We exhaustively test this framework using eight real‐world applications across 12 instances from different Cloud providers. Our tests show significant statistical agreement between the instance ratings given by the framework and the ratings obtained via actual Cloud executions.

Samuel, David↗
DOE OSTI2023

Exploring young adult texts within the Historically Responsive Literacy Framework with preservice teachers

Abstract While culturally responsive texts have become more common in teacher education, too often, preservice teachers (PTs) are not asked to examine how to use these books pedagogically. To address this issue, in a young adult (YA) literature course, the Framework for Culturally and Historically Responsive Literacy (HRL) (Muhammad, 2000) Cultivating genius: An equity framework for culturally and historically responsive literac y, 2020; Scholastic) was used in conjunction with representative and inclusive YA trade books. Data analysis was conducted on PT's course work, including HRL unit plans, to determine whether PTs were able to implement Muhammad's (2020) framework using YA texts. The findings of this study show that PTs' understanding of the HRL tenets was at varying levels. While there were unit plans with learning goals that matched Muhammad's purpose and definition for each tenet and YA connections for every tenet, PTs showed a better understanding of the tenets of identity and intellect than skills and Criticality. Implications of these findings including exposing PTs to learning frameworks “written by people of color and designed for children of color” (Muhammad, 2020, p. 11) to create instruction that meets their future students' needs and does not just skim the surface of culturally responsive pedagogies.

Pule, Heather↗
DOE OSTI2015

Evaluation of a performance appraisal framework for radiation therapists in planning and simulation

Constantly evolving technology and techniques within radiation therapy require practitioners to maintain a continuous approach to professional development and training. Systems of performance appraisal and adoption of regular feedback mechanisms are vital to support this development yet frequently lack structure and rely on informal peer support. A Radiation Therapy Performance Appraisal Framework (RT-PAF) for radiation therapists in planning and simulation was developed to define expectations of practice and promote a supportive and objective culture of performance and skills appraisal. Evaluation of the framework was conducted via an anonymous online survey tool. Nine peer reviewers and fourteen recipients provided feedback on its effectiveness and the challenges and limitations of the approach. Findings from the evaluation were positive and suggested that both groups gained benefit from and expressed a strong interest in embedding the approach more routinely. Respondents identified common challenges related to the limited ability to implement suggested development strategies; this was strongly associated with time and rostering issues. This framework successfully defined expectations for practice and provided a fair and objective feedback process that focussed on skills development. It empowered staff to maintain their skills and reach their professional potential. Management support, particularly in regard to provision of protected time was highlighted as critical to the framework's ongoing success. The demonstrated benefits arising in terms of staff satisfaction and development highlight the importance of this commitment to the modern radiation therapy workforce.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2023

Interface flux recovery framework for constructing partitioned heterogeneous time-integration methods

A common approach for the development of partitioned schemes employing different time integrators on different subdomains is to lag the coupling terms in time. This can lead to accuracy issues, especially in multistage methods. Here, in this article, we present a novel framework for partitioned heterogeneous time-integration methods, which allows the coupling of arbitrary multistage and multistep methods without reducing their order of accuracy. At the core of our approach are accurate estimates of the interface flux obtained from the Schur complement of an auxiliary monolithic system. We use these estimates to construct a polynomial-in-time approximation of the interface flux over the current time coupling window. This approximation provides the interface boundary conditions necessary to decouple the subdomain problems at any point within the coupling window. In so doing our framework enables a flexible choice of time-integrators for the individual subproblems without compromising the time-accuracy at the coupled problem level. This feature is the main distinction between our framework and other approaches. To demonstrate the framework, we construct a family of partitioned heterogeneous time-integration methods, combining multistage and multistep methods, for a simplified tracer transport component of the coupled air-sea system in Earth system models. We report numerical tests evaluating accuracy and flux conservation for different pairs of time-integrators from the explicit Runge-Kutta and Adams-Moulton families.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

A Corrected Score Function Framework for Modelling Circadian Gene Expression

Many biological processes display oscillatory behaviour based on an approximately 24 h internal timing system specific to each individual. One process of particular interest is gene expression, for which several circadian transcriptomic studies have identified associations between gene expression during a 24 h period and an individual's health. A challenge with analysing data from these studies is that each individual's internal timing system is offset relative to the 24 h day-night cycle, where day–night cycle time is recorded for each collected sample. Laboratory procedures can accurately determine each individual's offset and determine the internal time of sample collection. However, these laboratory procedures are labour-intensive and expensive. Here, in this paper, we propose a corrected score function framework to obtain a regression model of gene expression given internal time when the offset of each individual is too burdensome to determine. A feature of this framework is that it does not require the probability distribution generating offsets to be symmetric with a mean of zero. Simulation studies validate the use of this corrected score function framework for cosinor regression, which is prevalent in circadian transcriptomic studies. Illustrations with data from three circadian transcriptomic studies further demonstrate that the proposed framework consistently mitigates bias relative to using a score function that does not account for this offset.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

8D likelihood effective Higgs couplings extraction framework in h → 4ℓ

We present an overview of a comprehensive analysis framework aimed at performing direct extraction of all possible effective Higgs couplings to neutral electroweak gauge bosons in the decay to electrons and muons, the so called ‘golden channel’. Our framework is based primarily on a maximum likelihood method constructed from analytic expressions of the fully differential cross sections for h → 4l and for the dominant irreducible $ q\overline{q} $ → 4l background, where 4l = 2e2μ, 4e, 4μ. Detector effects are included by an explicit convolution of these analytic expressions with the appropriate transfer function over all center of mass variables. Utilizing the full set of observables, we construct an unbinned detector-level likelihood which is continuous in the effective couplings. We consider possible ZZ, Zγ, and γγ couplings simultaneously, allowing for general CP odd/even admixtures. A broad overview is given of how the convolution is performed and we discuss the principles and theoretical basis of the framework. This framework can be used in a variety of ways to study Higgs couplings in the golden channel using data obtained at the LHC and other future colliders.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Keeping it simple: simplified frameworks for long-lived particles at neutrino facilities

Modern-day accelerator neutrino facilities are excellent venues for searches for new-physics particles. Many distinct new-physics models predict overlapping signatures and phenomenology in these experiments. In this work, we advocate for the adoption of simplified frameworks when studying these types of new-physics signatures, which are characterized by a small number of primary variables, including particle masses, lifetimes, and production and decay modes/rates that most directly control signal event rates and kinematics. In particular, taking the example of long-lived particles that decay inside a neutrino detector as a test case, we study formulate and study simplified frameworks in the context of light scalars/fermions produced in kaon decays which then decay into final states containing an electron-positron pair. We show that using these simplified frameworks can allow for individual experimental analyses to be applicable to a wide variety of specific model scenarios. As a side benefit, we demonstrate that using this approach can allow for the T2K collaboration, by reinterpreting its search for Heavy Neutral Leptons, to be capable of setting world-leading limits on the Higgs-Portal Scalar model. Furthermore, we argue the simplified framework interpretation can serve as a bridge to model identification in the hopeful detection of a new-physics signal. As an illustration, we perform a first determination of the likelihood that, in the presence of a new-physics signal in a detector like the DUNE ND-GAr, multiple different new-physics hypotheses (such as the Higgs-Portal Scalar and Heavy Neutral Lepton ones) can be disentangled. We demonstrate that this model discrimination is favorable for some portions of detectable new-physics parameter space but for others, it is more challenging.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

Organizing environmental flow frameworks to meet hydropower mitigation needs

The global recognition of the importance of natural flow regimes to sustain the ecological integrity of river systems has led to increased societal pressure on the hydropower industry to change plant operations to improve downstream aquatic ecosystems. However, a complete reinstatement of natural flow regimes is often unrealistic when balancing water needs for ecosystems, energy production, and other human uses. Thus, stakeholders must identify a prioritized subset of flow prescriptions that meet ecological objectives in light of realistic constraints. Yet, isolating aspects of flow regimes to restore downstream of hydropower facilities is among the greatest challenges of environmental flow science due, in part, to the sheer volume of available environmental flow tools in conjunction with complex negotiation-based regulatory procedures. Here, we propose an organizational framework that structures information and existing flow paradigms into a staged process that assists stakeholders in implementing environmental flows for hydropower facilities. The framework identifies areas where regulations fall short of the needed scientific process, and provide suggestions for stakeholders to ameliorate those situations through advanced preparation. We highlight the strengths of existing flow paradigms in their application to hydropower settings and suggest when and where tools are most applicable. In conclusion, our suggested framework increases the effectiveness and efficiency of the e-flow implementation process by rapidly establishing a knowledge base and decreasing uncertainty so more time can be devoted to filling knowledge gaps. As a result, the framework provides the structure for a coordinated research agenda to further the science of environmental flows related to hydropower environments.

13 HYDRO ENERGY↗
DOE OSTI2021

An infeasible-start framework for convex quadratic optimization, with application to constraint-reduced interior-point and other methods

A framework is proposed for solving general convex quadratic programs (CQPs) from an infeasible starting point by invoking an existing feasible-start algorithm tailored for inequality-constrained CQPs. The central tool is an exact penalty function scheme equipped with a penalty-parameter updating rule. The feasible-start algorithm merely has to satisfy certain general requirements, and so is the updating rule. Under mild assumptions, the framework is proved to converge on CQPs with both inequality and equality constraints and, at a negligible additional cost per iteration, produces an infeasibility certificate, together with a feasible point for an (approximately) ℓ 1 -least relaxed feasible problem, when the given problem does not have a feasible solution. The framework is applied to a feasible-start constraint-reduced interior-point algorithm previously proved to be highly performant on problems with many more inequality constraints than variables (“imbalanced”). Numerical comparison with popular codes (OSQP, qpOASES, MOSEK) is reported on both randomly generated problems and support-vector machine classifier training problems. The results show that the former typically outperforms the latter on imbalanced problems. Finally, application of the proposed infeasible-start framework to other feasible-start algorithms is briefly considered, and is tested on a simplex iteration.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Integrated Simulation Framework for Additively Manufactured Ti-6Al-4V: Melt Pool Dynamics, Microstructure, Solid-State Phase Transformation, and Microelastic Response

In this work, to accelerate the establishment of fundamental understanding of the additive manufacturing (AM) process and its influence on microstructural evolution and related properties, we develop a multiphysics and multiscale modeling framework that integrates: (1) a high-fidelity powder-scale three-dimensional simulation of transient heat transfer and melt flow dynamics, (2) cellular automaton simulation of solidification grain structure and texture, (3) phase-field modeling of precipitation and dissolution of second-phase precipitate during repeated thermal cycles, and (4) microstructure-based micro- and mesoscopic elastic response calculation. Using Ti-6Al-4V as a model system, we demonstrate the application of the integrated framework to simulate complex microstructure evolution during a single-track laser powder bed fusion process and the associated mechanical response. Our modeling framework successfully captures the solidification β grain structure as a function of laser power and scanning speed, α precipitation upon subsequent cooling with different rates, and elastic response of the resulting (α + β) two-phase microstructure. The key features of solidification and second-phase precipitate microstructures, and their dependence on processing parameters, agree well with existing experimental observations. Finally, the established modeling framework is generally applicable to other metallic materials fabricated by AM.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A general spatial-temporal framework for short-term building temperature forecasting at arbitrary locations with crowdsourcing weather data

Weather forecasting has been a critical component to predict and control building energy consumption for better building energy management. Without accessibility to other data sources, the onsite observed temperatures or the airport temperatures are used in forecast models. In this paper, we present a novel approach by utilizing the crowdsourcing weather data from neighboring personal weather stations (PWS) to improve the weather forecast accuracy around buildings using a general spatial-temporal modeling framework. The final forecast is based on the ensemble of local forecasts for the target location using neighboring PWSs. Our approach is distinguished from existing literature in various aspects. First, we leverage the crowdsourcing weather data from PWS in addition to public data sources. In this way, the data is at much finer time resolution (e.g., at 5-minute frequency) and spatial resolution (e.g., arbitrary location vs grid). Second, our proposed model incorporates spatial-temporal correlation information of weather variables between the target building and a set of neighboring PWSs so that underlying correlations can be effectively captured to improve forecasting performance. Here, we demonstrate the performance of the proposed framework by comparing to the benchmark models on temperature forecasting for a building located at an arbitrary location at San Antonio, Texas, USA. In general, the proposed model framework equipped with machine learning technique such as Random Forest can improve forecasting by 50% compares with persistent model and has 90% chance to outperform airport forecast in short-term forecasting. In a real-time setting, the proposed model framework can provide more accurate temperature forecasting results compared with using airport temperature forecast for most forecast horizon. Moreover, we analyze the sensitivity of model parameters to gain insights on how crowdsourcing data from the neighboring personal weather stations impacts forecasting performance. Finally, we implement our model in other cities such as Syracuse and Chicago to test the model's performance in different landforms and climate types.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

An active learning high-throughput microstructure calibration framework for solving inverse structure–process problems in materials informatics

Determining a process–structure–property relationship is the holy grail of materials science, where both computational prediction in the forward direction and materials design in the inverse direction are essential. Problems in materials design are often considered in the context of process–property linkage by bypassing the materials structure, or in the context of structure–property linkage as in microstructure-sensitive design problems. However, there is a lack of research effort in studying materials design problems in the context of process–structure linkage, which has a great implication in reverse engineering. In this paper, given a target microstructure, we propose an active learning high-throughput microstructure calibration framework to derive a set of processing parameters, which can produce an optimal microstructure that is statistically equivalent to the target microstructure. The proposed framework is formulated as a noisy multi-objective optimization problem, where each objective function measures a deterministic or statistical difference of the same microstructure descriptor between a candidate microstructure and a target microstructure. Furthermore, to significantly reduce the physical waiting wall-time, we enable the high-throughput feature of the microstructure calibration framework by adopting an asynchronously parallel Bayesian optimization to exploit high-performance computing resources. Case studies in additive manufacturing and grain growth are used to demonstrate the applicability of the proposed framework, where kinetic Monte Carlo (kMC) simulation is used as a forward predictive model, such that for a given target microstructure, the target processing parameters that produced this microstructure are successfully recovered.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A semantics-driven framework to enable demand flexibility control applications in real buildings

Decarbonising and digitalising the energy sector requires scalable and interoperable Demand Flexibility (DF) applications. Semantic models are promising technologies for achieving these goals, but existing studies focused on DF applications exhibit limitations. These include dependence on bespoke ontologies, lack of computational methods to generate semantic models, ineffective temporal data management and absence of platforms that use these models to easily develop, configure and deploy controls in real buildings. This paper introduces a semantics-driven framework to enable DF control applications in real buildings. The framework supports the generation of semantic models that adhere to Brick and SAREF while using metadata from Building Information Models (BIM) and Building Automation Systems (BAS). The work also introduces a web platform that leverages these models and an actor and microservices architecture to streamline the development, configuration and deployment of DF controls. The paper demonstrates the framework through a case study, illustrating its ability to integrate diverse data sources, execute DF actuation in a real building, and promote modularity for easy reuse, extension, and customisation of applications. The paper also discusses the alignment between Brick and SAREF, the value of leveraging BIM data sources, and the framework's benefits over existing approaches, demonstrating a 75% reduction in effort for developing, configuring, and deploying building controls.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Extension of TOUGH-FLAC to the finite strain framework

Here, the TOUGH-FLAC simulator for coupled thermal-hydraulic-mechanical processes modeling has been extended to the finite strain framework. In the approach selected, this extension has required modifications to the flow simulator (TOUGH2) and to the coupling scheme between the geomechanics and the flow sub-problems. In TOUGH2, the mass and energy balance equations have been extended to account for volume changes. Additionally, as large deformations are computed by FLAC 3D , the geometry is updated in the flow sub-problem. The Voronoi partition needed in TOUGH2 is computed using an external open source library (Voro++) that uses the centroids of the deformed geomechanics mesh as generators of the Voronoi diagram. TOUGH-FLAC in infinitesimal and finite strain frameworks is verified against analytical solutions and other approaches to couple flow and geomechanics. Within the finite strain framework, TOUGH-FLAC is also successfully applied to a large-scale case. The extension of TOUGH-FLAC to the finite strain framework has little impact to the user as only one additional executable is needed (for Voro++), and the input files and the workflow of a simulation are the same as in standard TOUGH-FLAC. With this new provision for finite strains, TOUGH-FLAC can be used in the analysis of a wider range of engineering problems, and the areas of application of this simulator are therefore broadened.

58 GEOSCIENCES↗
DOE OSTI2024

In situ visualisation of zeolite anisotropic framework flexibility during catalysis

Zeolites exhibit framework flexibility driving their chemical and catalytic properties. Since the zeolitic pores are extremely small, a slight strain generated in the crystal induces compelling changes in shape, connectivity, accessibility, and the framework chemical properties. These modifications affected the adsorption and desorption of reactants/products and the diffusion within the channels during reaction. Using in situ 3D Bragg coherent X-ray diffraction imaging, we unveil the dynamics of the zeolite structure during catalysis, contraction and/or expansion of its framework also known as zeolite framework flexibility. Here, we imaged three-dimensionally a single faujasite zeolite crystal during the ethanol dehydration reaction revealing anisotropic lattice dynamics simultaneously to guest molecules formation. Understanding zeolite flexibility could permit to tune zeolites properties towards potentially higher adsorption and selectivity.

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

An encoder–decoder LSTM-based EMPC framework applied to a building HVAC system

Numerous studies have demonstrated the benefit of economic model predictive control (EMPC) applied to building heating, ventilation, and air conditioning (HVAC) systems. However, the construction and training of predictive models for building HVAC systems are widely recognized as a key technological barrier preventing large-scale adoption of EMPC for buildings. In this work, an encoder–decoder long short-term memory-based EMPC framework is developed. The key advantage of the approach is that a model may be automatically generated from a list of inputs and outputs. From the definition of inputs and outputs, the constructed model may be trained and automatically embedded into the EMPC framework for real-time estimation and control. The overall end-to-end EMPC framework from model training to on-line estimation and control are described. To this end, the encoder–decoder model provides a natural framework for state estimation (encoder), which is required to provide an initial condition for the predictive model of EMPC (decoder). Closed-loop simulations using EnergyPlus are performed to demonstrate the approach. The simulated closed-loop system consists of a building zone from a multi-zone building, which is served by an air handling unit-variable air volume HVAC system. For the HVAC example considered, the trained encoder–decoder model can predict the indoor air temperature and HVAC sensible cooling rate of a building zone over a two-day horizon with high accuracy. Overall, we find that considering a time-of-use electric rate structure, the EMPC, which manipulates the zone temperature setpoint, can reduce the HVAC power consumption cost relative to keeping the zone temperature setpoint at its maximum value (i.e., minimum energy approach).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

NeuroSEM: A hybrid framework for simulating multiphysics problems by coupling PINNs and spectral elements

Multiphysics problems that are characterized by complex interactions among fluid dynamics, heat transfer, structural mechanics, and electromagnetics, are inherently challenging due to their coupled nature. While experimental data on certain state variables may be available, integrating these data with numerical solvers remains a significant challenge. Physics-informed neural networks (PINNs) have shown promising results in various engineering disciplines, particularly in handling noisy data and solving inverse problems in partial differential equations (PDEs). However, their effectiveness in forecasting nonlinear phenomena in multiphysics regimes, particularly involving turbulence, is yet to be fully established. Here, this study introduces NeuroSEM, a hybrid framework integrating PINNs with the highfidelity Spectral Element Method (SEM) solver, Nektar++. NeuroSEM leverages the strengths of both PINNs and SEM, providing robust solutions for multiphysics problems. PINNs are trained to assimilate data and model physical phenomena in specific subdomains, which are then integrated into the Nektar++ solver. We demonstrate the efficiency and accuracy of NeuroSEM for thermal convection in cavity flow and flow past a cylinder. The framework effectively handles data assimilation by addressing those subdomains and state variables where the data is available. We applied NeuroSEM to the Rayleigh-B´enard convection system, including cases with missing thermal boundary conditions and noisy datasets. Finally, we applied the proposed NeuroSEM framework to real particle image velocimetry (PIV) data to capture flow patterns characterized by horseshoe vortical structures. Our results indicate that NeuroSEM accurately models the physical phenomena and assimilates the data within the specified subdomains. The framework’s plug-and-play nature facilitates its extension to other multiphysics or multiscale problems. Furthermore, NeuroSEM is optimized for efficient execution on emerging integrated GPU-CPU architectures. This hybrid approach enhances the accuracy and efficiency of simulations, making it a powerful tool for tackling complex engineering challenges in various scientific domains.

42 ENGINEERING↗