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At least 973 records · Page 54

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DOE OSTI2024

Axial H-Bonding Solvent Controls Inhomogeneous Spectral Broadening, While Peripheral H-Bonding Solvent Controls Vibronic Broadening: Cresyl Violet in Methanol

The dynamics of the nuclei of both a chromophore and its condensed-phase environment control many spectral features, including the vibronic and inhomogeneous broadening present in spectral line shapes. For the cresyl violet chromophore in methanol, we here analyze and isolate the effect of specific chromophore–solvent interactions on simulated spectral densities, reorganization energies, and linear absorption spectra. Employing both chromophore and its condensed-phase environment control many spectral features, including the vibronic and inhomogeneous broadening present in spectral line shapes. For the cresyl violet chromophore in methanol, we here analyze and isolate the effect of specific chromophore–solvent interactions on simulated spectral densities, reorganization energies, and linear absorption spectra. Employing both force field and ab initio molecular dynamics trajectories along with the inclusion of only certain solvent molecules in the excited-state calculations, we determine that the methanol molecules axial to the chromophore are responsible for the majority of inhomogeneous broadening, with a single methanol molecule that forms an axial hydrogen bond dominating the response. Furthermore, the strong peripheral hydrogen bonds do not contribute to spectral broadening, as they are very stable throughout the dynamics and do not lead to increased energy-gap fluctuations. We also find that treating the strong peripheral hydrogen bonds as molecular mechanical point charges during the molecular dynamics simulation underestimates the vibronic coupling. Including these peripheral hydrogen bonding methanol molecules in the quantum-mechanical region in a geometry optimization increases the vibronic coupling, suggesting that a more advanced treatment of these strongly interacting solvent molecules during the molecular dynamics trajectory may be necessary to capture the full vibronic spectral broadening.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Photon–photon chemical thermodynamics of frequency conversion processes in highly multimode systems

Abstract Frequency generation in highly multimode nonlinear optical systems is inherently a complex process, giving rise to an exceedingly convoluted landscape of evolution dynamics. While predicting and controlling the global conversion efficiencies in such nonlinear environments has long been considered impossible, here, we formally address this challenge even in scenarios involving a very large number of spatial modes. By utilizing fundamental notions from optical statistical mechanics, we develop a universal theoretical framework that effectively treats all frequency components as chemical reactants/products, capable of undergoing optical thermodynamic reactions facilitated by a variety of multi-wave mixing effects. These photon–photon reactions are governed by conservation laws that directly determine the optical temperatures and chemical potentials of the ensued chemical equilibria for each frequency species. In this context, we develop a comprehensive stoichiometric model and formally derive an expression that relates the chemical potentials to the optical stoichiometric coefficients, in a manner akin to atomic/molecular chemical reactions. This advancement unlocks new predictive capabilities that can facilitate the optimization of frequency generation in highly multimode photonic arrangements, surpassing the limitations of conventional schemes that rely exclusively on nonlinear optical dynamics. Notably, we identify a universal regime of Rayleigh–Jeans thermalization where an optical reaction at near-zero optical temperatures can promote the complete and entropically irreversible conversion of light to the fundamental mode at a target frequency. Our theoretical results are corroborated by numerical simulations in settings where second-harmonic generation, sum-frequency generation and four-wave mixing processes can manifest.

Optics↗

DOE OSTI2025

Rapid data acquisition and machine learning-assisted composition design of functionally graded alloys via wire arc additive manufacturing

Abstract The lack of high-quality datasets in materials science hinders artificial intelligence (AI)-driven alloy design. To address this challenge, wire arc additive manufacturing (WAAM) was employed to fabricate graded alloys, generating extensive data for machine learning (ML)-assisted property prediction. ML models were developed using high-throughput experiments, computational models, and genetic algorithm to optimize feature selection, successfully predicting hardness and porosity. The ML model demonstrated its efficacy by designing a gradient alloy with enhanced properties. However, scaling up revealed uncertainties in tensile property and porosity due to differences in size and thermal conditions between the designed alloy build and the gradient print used to construct the ML model. This underscores the need for uncertainty quantification and process optimization in WAAM-driven alloy design. Our work advances AI-integrated additive manufacturing, offering a rapid approach to exploring process–structure–property relationships and accelerating materials development.

Wang, Xin↗

DOE OSTI2015

Problem of two-level hierarchical minimax program control the final state of regional social and economic system in the presence of risks

This article discusses a discrete-time dynamical system consisting of a set a controllable objects (region and forming it municipalities). The dynamics each of these is described by the corresponding vector nonlinear discrete-time recurrent vector equations and its control system consist from two levels: basic (control level I) that is dominating and subordinate level (control level II). Both levels have different criterions of functioning and united a priori by determined informational and control connections defined in advance. In this paper we study the problem of optimization of guaranteed result for program control by the final state of regional social and economic system in the presence of risks. For this problem we proposed in this work an economical and mathematical model of two-level hierarchical minimax program control the final state of regional social and economic system in the presence of risks and the general scheme for its solving.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

Coolant Channel Design for Additively Manufactured Reactor Cores

Additive manufacturing (AM) methods are currently being explored for applications in nuclear reactors to make advanced reactors more efficient, safe, and reliable. The Transformational Challenge Reactor (TCR) program has explored AM for nuclear by designing a high-temperature gas reactor (HTGR) using an AM silicon carbide fuel form with uranium nitride–tristructural isotropic fuel. In this study, we detail the design process for the TCR fuel form’s coolant channels using computational fluid dynamics models with conjugate heat transfer. Additionally, this work discusses how these models were interfaced with other design teams, project milestones, and the agile design method used to mature the reactor design. The methodology deployed was able to create a channel design with lower maximum fuel temperatures and thermal stresses in the fuel form over traditional channel designs that can be manufactured subtractively. These results were achieved with only small manufacturing penalties. Results are discussed and presented on lessons learned for designing AM components for nuclear reactors. Finally, areas of opportunity are discussed for advanced design tools to further automate design activities and optimize reactors with fewer built-in assumptions.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2024

Review of solar-enabled desalination and implications for zero-liquid-discharge applications

Abstract The production of freshwater from desalinating abundant saline water on the planet is increasingly considered a climate change adaptation measure. Yet, there are challenges associated with the high cost, intensive energy demand, and environmental implications of desalination. Effective integration of solar energy generation and freshwater production can address both issues. This review article highlights recent key advances in such integration achieved in a joint-research university-national laboratory partnership under the auspices of the United States Department of Energy and parallel efforts worldwide. First, an overview of current and emerging desalination technologies and associated pretreatment, brine treatment, and valorization technologies that together can result in zero-liquid-discharge systems is presented, and their technological readiness levels are evaluated. Then, advanced modeling techniques and new software platforms that enable optimization of solar-desalination applications with the dual objective of cost and environmental impact minimization are discussed.

14 SOLAR ENERGY↗

DOE OSTI2018

Robust Decentralized Secondary Frequency Control in Power Systems: Merits and Trade-Offs

Frequency restoration in power systems is conven- tionally performed by broadcasting a centralized signal to local controllers. As a result of the energy transition, technological advances, and the scientific interest in distributed control and optimization methods, a plethora of distributed frequency control strategies have been proposed recently that rely on communication amongst local controllers. In this paper we propose a fully decentralized leaky integral controller for frequency restoration that is derived from a classic lag element. We study steady-state, asymptotic optimality, nominal stability, input-to-state stability, noise rejection, transient performance, and robustness properties of this controller in closed loop with a nonlinear and multivariable power system model. Here, we demonstrate that the leaky integral controller can strike an acceptable trade-off between performance and robustness as well as between asymptotic disturbance rejection and transient convergence rate by tuning its DC gain and time constant. We compare our findings to conventional decentralized integral control and distributed- averaging-based integral control in theory and simulations.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2018

Analysis of a Length Extensional Piezoelectric Transformer for Compact and Efficient Particle Acceleration

To produce a compact and efficient particle accelerator, several different piezoelectric transformer configurations are evaluated for their applicability in this demanding application area. Using a derived 1-D model for three different electroding and mounting schemes, accelerator efficiency and gradient are optimized. In conclusion, it is shown that with some advancements in material yield strength and advanced bonding techniques, reaching 4 MeV/m with over 40% efficiency is possible.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

The Manufacturing Data and Machine Learning Platform: Enabling Real-time Monitoring and Control of Scientific Experiments via IoT

IoT devices and sensor networks present new opportunities for measuring, monitoring, and guiding scientific experiments. Sensors, cameras, and instruments can be combined to provide previously unachievable insights into the state of ongoing experiments. However, IoT devices can vary greatly in the type, volume, and velocity of data they generate, making it challenging to fully realize this potential. Indeed, synergizing diverse IoT data streams in near-real time can require the use of machine learning (ML). In addition, new tools and technologies are required to facilitate the collection, aggregation, and manipulation of sensor data in order to simplify the application of ML models and in turn, fully realize the utility of IoT devices in laboratories. Here we will demonstrate how the use of the Argonne-developed Manufacturing Data and Machine Learning (MDML) platform can analyze and use IoT devices in a manufacturing experiment. MDML is designed to standardize the research and operational environment for advanced data analytics and AI-enabled automated process optimization by providing the infrastructure to integrate AI in cyber-physical systems for in situ analysis. We will show that MDML is capable of processing diverse IoT data streams, using multiple computing resources, and integrating ML models to guide an experiment.

Machine Learning↗

DOE OSTI2021

Data Science in Chemical Engineering: Applications to Molecular Science

Chemical engineering is being rapidly transformed by the tools of data science. On the horizon, artificial intelligence (AI) applications will impact a huge swath of our work, ranging from the discovery and design of new molecules to operations and manufacturing and many areas in between. Early adoption of data science, machine learning, and early examples of AI in chemical engineering has been rich with examples of molecular data science—the application tools for molecular discovery and property optimization at the atomic scale. Here, we summarize key advances in this nascent subfield while introducing molecular data science for a broad chemical engineering readership. We introduce the field through the concept of a molecular data science life cycle and discuss relevant aspects of five distinct phases of this process: creation of curated data sets, molecular representations, data-driven property prediction, generation of new molecules, and feasibility and synthesizability considerations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

ENSIGN

ENSIGN is a data analytics software package offering a modern unsupervised machine learning solution for scalable discovery in Big Data. The analytics in ENSIGN are based on an advanced mathematical tool called tensor decomposition and they are optimized to run efficiently on a range of computing platforms (from small multicore Desktop platforms to large Supercomputing clusters and novel high-end memory-driven computing platforms such as HPE Superdome Flex). ENSIGN enables the user to extract deep insights from the entirety of massive-scale (100s of Gigabytes or Terabytes scale) multidimensional data. ENSIGN uncovers latent patterns in data without the user having to specify or describe what the patterns are; the user, in the first place, may not even know such patterns existed and that they have to look for such patterns. The insights gained from ENSIGN could be trailheads that can be used as starting points for deeper forensic investigation.

Baskaran, Muthu↗

DOE OSTI2022

ENSIGN

Baskaran, Muthu↗

DOE OSTI2014

Development of Kinetics for Soot Oxidation at High Pressures Under Fuel-Lean Conditions

The focus of the proposed research was to develop kinetic models for soot oxidation with the hope of developing a validated, predictive, multi-scale, combustion model to optimize the design and operation of evolving fuels in advanced engines for transportation applications. The work focused on the relatively unstudied area of the fundamental mechanism for soot oxidation. The objectives include understanding of the kinetics of soot oxidation by O2 under high pressure which require: 1) development of intrinsic kinetics for the surface oxidation, which takes into account the dependence of reactivity upon nanostructure and 2) evolution of nanostructure and its impact upon oxidation rate and 3) inclusion of internal surface area development and possible fragmentation resulting from pore development and /or surface oxidation. These objectives were explored for a variety of pure fuel components and surrogate fuels. This project was a joint effort between the University of Utah (UU) and Pennsylvania State University (Penn State). The work at the UU focuses on experimental studies using a two-stage burner and a high- pressure thermogravimetric analyzer (TGA). Penn State provided HRTEM images and guidance in the fringe analysis algorithms and parameter quantification for the images. This report focuses on completion done under supplemental funding.

02 PETROLEUM↗

DOE OSTI2016

Biological and Biomimetic Low-Temperature Routes to Materials for Energy Applications

New materials are needed to significantly improve the efficiencies of energy harnessing, transduction and storage, yet the synthesis of advanced composites and multi-metallic semiconductors with nanostructures optimized for these functions remains poorly understood and even less well controlled. To help address this need, we proposed three goals: (1) to further investigate the hierarchical structure of the biologically synthesized silica comprising the skeletal spicules of sponges that we discovered, to better resolve the role and mechanism of templating by the hierarchically assembled silicatein protein filament; (2) to extend our molecular and genetic analyses and engineering of silicatein, the self-assembling, structure-directing, silica-synthesizing enzyme we discovered and characterized, to better understand and manipulate the catalysis and templating of semiconductor synthesis,; and (3) to further investigate, scale up and harness the biologically inspired, low-temperature, kinetically controlled catalytic synthesis method we developed (based on the mechanism we discovered in silicatein) to investigate the kinetic control of the structure-function relationships in magnetic materials, and develop new materials for energy applications. The bio-inspired catalytic synthesis method we have developed is low-cost, low temperature, and operates without the use of polluting chemicals. In addition to direct applications for improvement of batteries and fuel cells, the broader impact of this research includes a deeper fundamental understanding of the factors governing kinetically controlled synthesis and its control of the emergent nanostructure and performance of a wide range of nanomaterials for energy applications.

36 MATERIALS SCIENCE↗

DOE OSTI2017

Developing a predictive model for the chemical composition of soot nanoparticles

In order to provide the scientific foundation to enable technology breakthroughs in transportation fuel, it is important to develop a combustion modeling capability to optimize the operation and design of evolving fuels in advanced engines for transportation applications. The goal of this proposal is to develop a validated predictive model to describe the chemical composition of soot nanoparticles in premixed and diffusion flames. Atomistic studies in conjunction with state-of-the-art experiments are the distinguishing characteristics of this unique interdisciplinary effort. The modeling effort has been conducted at the University of Michigan by Prof. A. Violi. The experimental work has entailed a series of studies using different techniques to analyze gas-phase soot precursor chemistry and soot particle production in premixed and diffusion flames. Measurements have provided spatial distributions of polycyclic aromatic hydrocarbons and other gas-phase species and size and composition of incipient soot nanoparticles for comparison with model results. The experimental team includes Dr. N. Hansen and H. Michelsen at Sandia National Labs' Combustion Research Facility, and Dr. K. Wilson as collaborator at Lawrence Berkeley National Lab's Advanced Light Source. Our results show that the chemical and physical properties of nanoparticles affect the coagulation behavior in soot formation, and our results on an experimentally validated, predictive model for the chemical composition of soot nanoparticles will not only enhance our understanding of soot formation since but will also allow the prediction of particle size distributions under combustion conditions. These results provide a novel description of soot formation based on physical and chemical properties of the particles for use in the next generation of soot models and an enhanced capability for facilitating the design of alternative fuels and the engines they will power.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2017

Combustion energy frontier research center (CEFRC) final report (August 1, 2009 – July 31, 2016)

The Combustion Energy Frontier Research Center (CEFRC) was established to tackle the single overarching grand challenge of energy sustainability, energy security and global warming: to develop a “validated, predictive, multi-scale, combustion modeling capability to optimize the design and operation of evolving fuels in advanced engines for transportation applications,” as identified in the DOE report on “Basic Energy Needs for Clean and Efficient Combustion of 21st Century Transportation Fuels”. The challenge is particularly daunting since energy conversion efficiencies and exhaust emissions are governed by coupled chemical and transport processes at multiple length scales ranging from electron excitation to molecular rearrangements to nanoscale particulate formation to turbulent fuel/air mixing. To tackle this challenge, the CEFRC assembled a world-class team of 15 principal investigators, with the objectives to: 1) develop and test theoretical models to predict elementary reaction rates, molecule thermalization rates, chemical bond dissociation energies, and nonequilibrium transport properties using quantum chemistry calculations that account for strong electron correlation and multiple electronic potential energy surfaces; 2) develop automated kinetic mechanism generation, reduction, and error control methods for predicting alternative fuel including biofuel oxidation, fuel droplet decomposition, and NOx and particulate formation; 3) validate and improve the predictions of these models by measuring ignition delay times, species profiles, flame structures, burning limits, turbulence-kinetic coupling, and NOx and soot emissions at high-pressures and near-limit conditions, by using advanced experimental diagnostic techniques including multiple laser techniques, molecular beam sampling and synchrotron photoionization, and by conducting the measurements in high-pressure shock tubes, jet-stirred and flow reactors, flame bombs, counterflow flames, and advanced-design rapid compression ignition instruments; and 4) develop a suite of validated petascale high-fidelity simulation and modeling capabilities to understand and predict chemistry-turbulence-radiation coupling for new fuels in new regimes, including the high pressure, low-temperature combustion in advanced engine and turbine designs, and 5) establish a knowledge highway between researchers and engineers in academia, national laboratories, and industry to facilitate the dissemination and exchange of knowledge on national and international levels, and enrich the talent pool and capabilities of the next generation of combustion scientists and engineers. The technical activities of the CEFRC were conducted through three Disciplinary Working Groups – Chemistry Theory, Experiment and Mechanism, and Reacting Flows, which coordinated the Center’s research on the development of combustion chemistry of Foundation Fuels (C0–C4 hydrocarbons), Alcohols, and Biodiesel through three corresponding Mechanism Thrust Groups. Such a two-dimensional coordinated and tightly interwoven research structure has been proven to be highly effective in assuring the interplay between the developments of the fundamentals of combustion science and the utilization of the various categories of fuels. The Center has accomplished the above goals over the five year period (August 1, 2009 – July 31, 2014) with appropriated funding, followed by two additional no-cost-extension (NCE) years. The research results are documented in 230 journal publications, with six legacy review papers on the study of combustion chemistry using shock tubes, flow reactors, rapid compression machines, and flames, on uncertainty quantification, and on theoretical reaction dynamics and chemical modeling of combustion. A robust outreach program complemented these PI-led research activities, consisting of: 1) a roving post-doc program comprised of a corps of Center-appointed, co- or multi-sponsored post-doctoral fellows with rotating assignments to conduct seed projects initiated by at least two PIs, in residence with these sponsoring PIs, to rapidly pursue new and high-risk, high-payoff interdisciplinary ideas; 2) an annual summer school on combustion heavily attended (~200) by senior graduate students and practicing researchers covering advanced topics on chemical kinetics, fluid mechanics, turbulent combustion, engine combustion, new technologies, etc.; 3) a robust open web-site providing Center and community information as well as the lecture videos and notes of the summer school; and 4) widely distributed biannual newsletters.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2017

Advanced Fluid Reduced Order Models for Compressible Flow.

This report summarizes fiscal year (FY) 2017 progress towards developing and implementing within the SPARC in-house finite volume flow solver advanced fluid reduced order models (ROMs) for compressible captive-carriage flow problems of interest to Sandia National Laboratories for the design and qualification of nuclear weapons components. The proposed projection-based model order reduction (MOR) approach, known as the Proper Orthogonal Decomposition (POD)/Least- Squares Petrov-Galerkin (LSPG) method, can substantially reduce the CPU-time requirement for these simulations, thereby enabling advanced analyses such as uncertainty quantification and de- sign optimization. Following a description of the project objectives and FY17 targets, we overview briefly the POD/LSPG approach to model reduction implemented within SPARC . We then study the viability of these ROMs for long-time predictive simulations in the context of a two-dimensional viscous laminar cavity problem, and describe some FY17 enhancements to the proposed model reduction methodology that led to ROMs with improved predictive capabilities. Also described in this report are some FY17 efforts pursued in parallel to the primary objective of determining whether the ROMs in SPARC are viable for the targeted application. These include the implemen- tation and verification of some higher-order finite volume discretization methods within SPARC (towards using the code to study the viability of ROMs on three-dimensional cavity problems) and a novel structure-preserving constrained POD/LSPG formulation that can improve the accuracy of projection-based reduced order models. We conclude the report by summarizing the key takeaways from our FY17 findings, and providing some perspectives for future work.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2016

ADMS State of the Industry and Gap Analysis

An Advanced distribution management system (ADMS) is a platform for optimized distribution system operational management. This platform comprises of distribution management system (DMS) applications, supervisory control and data acquisition (SCADA), outage management system (OMS), and distributed energy resource management system (DERMS). One of the primary objectives of this work is to study and analyze several ADMS component and auxiliary systems. All the important component and auxiliary systems, SCADA, GISs, DMSs, AMRs/AMIs, OMSs, and DERMS, are discussed in this report. Their current generation technologies are analyzed, and their integration (or evolution) with an ADMS technology is discussed. An ADMS technology state of the art and gap analysis is also presented. There are two technical gaps observed. The integration challenge between the component operational systems is the single largest challenge for ADMS design and deployment. Another significant challenge noted is concerning essential ADMS applications, for instance, fault location, isolation, and service restoration (FLISR), volt-var optimization (VVO), etc. There are a relatively small number of ADMS application developers as ADMS software platform is not open source. There is another critical gap and while not being technical in nature (when compared the two above) is still important to consider. The data models currently residing in utility GIS systems are either incomplete or inaccurate or both. This data is essential for planning and operations because it is typically one of the primary sources from which power system model are created. To achieve the full potential of ADMS, the ability to execute acute Power Flow solution is an important pre-requisite. These critical gaps are hindering wider Utility adoption of an ADMS technology. The development of an open architecture platform can eliminate many of these barriers and also aid seamless integration of distribution Utility legacy systems with an ADMS.

24 POWER TRANSMISSION AND DISTRIBUTION↗