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At least 1,405 records · Page 78

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DOE OSTI2025

Heating Ventilation Air-Conditioning (HVAC) System Analysis and Optimization for Revit with the OpenStudio Analytical Framework (CRADA Final Report)

This project proposes to integrate the NREL-developed OpenStudio (OS) and its accompanying Analytical Framework (OSAF) into Autodesk’s (ADSK’s) market leading suite of building information modeling (BIM) tools. As a result, BIM users will gain access to advanced (but user-friendly) building energy modeling (BEM), calibration, and design optimization capabilities. Following integration, the BIM software will automatically generate a consistent OS-based BEM model, allowing users to develop projects in an integrated manner, moving back and forth between architectural and engineering perspectives without redundancy or data loss, saving time and effort.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Co-optimization of fuel properties, combustion system geometry, and injection strategy for conventional diesel fuel

Here, studies have shown that fuel properties can impact an engine’s operation in several ways, including ignition delay, sooting tendency, mixture formation, and combustion temperature. In mixing-controlled compression ignition (MCCI) engines, the fuel system design and piston bowl geometry significantly affect combustion performance and emissions. Based on current information, it is difficult to draw conclusions about fuel property effects and sensitivities. The central fuel hypothesis approach used in the US Department of Energy Co-Optima program has worked well for spark ignition fuels: identifying critical fuel property ranges is sufficient to screen fuel blends that are expected to maximize efficiency and reduce pollutant emissions. However, for MCCI-relevant fuels, the information gained from past studies is not sufficient to build such a merit function or to allow for performing a similar screening of fuel blends. It is hypothesized that a co-optimization of a fuel’s physical and chemical properties, combustion system geometry, and injection strategy could leverage synergies between the effects of the fuel properties and geometries, resulting in improved performance over state-of-the-art. A machine learning–assisted unconstrained global optimization algorithm was used to explore a design space comprising 23 independent variables. The results show that physical property effects were minimal even for large variations in fuel properties, and the only interaction effect that was observed was the effect of varied fuel density parameters on fuel/air mixture formation. Nevertheless, these interactions were not sufficient in magnitude to significantly affect optimization results. Therefore, analysis of the results suggests that fuel physical properties cannot be leveraged in a co-optimization context to increase engine efficiency.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2025

Non-dimensional performance and safety parameters for heat pipes

The use of heat pipes in safety-critical systems such as nuclear microreactors dictates the development of generalized, practical, scalable performance and safety parameters. Traditional dimensional metrics, while informative, lack the universality required for comparative analysis across varying designs and operating regimes. Here, this work introduces a comprehensive set of non-dimensional parameters to characterize heat pipe performance and safety, including capillary performance, effective thermal conductivity, response time, exergetic efficiency, allowable temperature gradients, allowable rate of temperature change, priming coefficients, and factor of safety. A reference heat pipe design representative of microreactor applications was analyzed via the developed parameters using both traditional analytical models and Sockeye simulations under transient and steady-state conditions. Sodium, potassium, and water were evaluated as working fluids to demonstrate the applicability of the framework across a broad temperature range. The proposed non-dimensional parameters effectively captured key thermal-hydraulic behaviors and safety concerns, as was demonstrated via Sockeye simulations. This framework supports the development of design optimization strategies, operational protocols, and safety assurance practices for advanced reactor systems and other high-reliability applications.

42 - ENGINEERING↗

DOE OSTI2026

Temperature dependent interfacial microstructure in HIP-bonded Al6061/Zr joints

Pressing (HIP) at 450 °C and 560 °C under 103 MPa for 90 minutes to evaluate interfacial diffusion and microstructural evolution. Sound metallurgical bonding was achieved at both temperatures, with Zr serving as an effective diffusion barrier. Microstructural analysis revealed the the formation of a (Al,Si)3Zr intermetallic layer at the Al/Zr interface, whose thickness increased from ~100 nm at 450 °C to ~2100 nm at 560 °C, consistent with temperature-driven diffusion kinetics. The thin and continuous reaction layer observed at 450 °C indicates controlled interdiffusion and minimal reaction zone growth, favorable for maintaining interface stability and ductility. At 560 °C, enhanced atomic mobility led to pronounced intermetallic development, confirming diffusion-controlled growth behavior. These results provide valuable insights for optimizing Al/Zr interface design and diffusion barrier performance in advanced structural and energy systems.

Al/Zr interface↗

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↗