Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Property management”

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.

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

At least 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Abstract for CRADA among NETL, Ohio University, and MetalKraft Technologies, LLC

The electrical grid faces challenges for meeting accelerated electricity demand arising from the electrification of building and transportation sectors. According to the DOE Grid Development Office, 70% of transmission lines are approaching the end of their lifecycle. The aging grid and insufficient transmission capacity necessitate the development of new technologies to improve overall grid performance. The conventional conductors used for electrical transmission lines suffer from substantial energy losses, high costs and low durability. These inefficiencies result in higher operational costs, increased energy waste, and difficulties in managing power flow. Improving the grid will require metals and composites with higher electrical conductivity and strength than the materials currently used. This CRADA will develop technologies to improve the grid by fabricating carbon metal composites with improved electrical, structural, and mechanical properties in comparison to metals that do not contain carbon additives. The composites will be made by incorporating carbon materials (graphite, graphene) into aluminum, copper, and other metals. The work will focus primarily on improving material properties that enhance the performance of these materials in electrical conductor applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Characterizing The Mechanical Properties of Polyurethane Foams

The Department of Energy has many radioactive facilities that are on the path of deactivation and decommissioning (D and D). These facilities can sit cold and dark for many years before final disposition, and must be maintained to ensure no radiological release occurs in the interim. Improvements and additions to D and D tool sets can greatly: Save time and money, Reduce worker risk. Conventional fixatives widely used often take the form of paints or films that are not readily applicable to 3-dimensional void spaces. Foams are one promising platform that may offer solutions to a number of contaminated problem sets such as: Gloveboxes, Pipes, Tanks. SRNL is working to characterize various commercial foams that would encapsulate the interior volume of a given space and are capable of immobilizing any remaining contamination. One key performance metric of these foams is how well they will perform in an accident scenario. To this end, SRNL is researching the mechanical properties of these foams to ensure that the material can withstand the environment of application while maintaining structural integrity. ASTM standard E3191-18 served as a guiding document for this project, outlining the requirements that foaming fixatives used for the mitigation of radioactive contamination need to meet before being implemented. Objective: Quantification of the mechanical properties of 6 commercial polyurethane foams was performed using multiple ASTM standards to record measurements for: Compression testing: Flexible Foams, Force required to produce 50% compression, Rigid Foams, Compressive and apparent modulus, Point of 10% core deformation, The 'Zero Deformation' point, Compressive strength, Yield point. Tensile testing: Tensile strength, Tensile stress, Percent elongation. Experiment 1: An electromechanical compressive tester (MTS Criterion Series 43) was used to evaluate 6 foams (4 flexible, 2 rigid). Per ASTM D1621, each rigid foam was compressed at 10% of the measured thickness per minute until the sample was 13% of it's original thickness. Per ASTM D35/4, each flexible foam was pre-flexed twice to 80% original thickness at a rate of 250 mm/min, then compressed to 80% original thickness at a rate of 50 mm/min. Experiment 2: An electromechanical tensile tester (MTS Criterion Series 43) was used to evaluate 6 foams (4 flexible, 2 rigid). Per ASTM D1623, rigid foams were pulled apart at a rate of 1.27 mm/min until the sample broke. Per ASTM D35/4, flexible foams were pulled apart at a rate of 500 mm/min until the sample broke. The strongest material in both compression and tensile testing scenarios was found to be the rigid intumescent polyurethane Hilti foam. The experiments revealed that the Hilti foam in a tensile scenario had a peak stress value that was larger than the closest competitor by a factor of 2.3 and a compressive yield point that was larger than the closest competitor by a factor of 1.4, indicating that the Hilti foam is the best choice for implementation in mechanically harsh environments. The performance metrics measured can serve as a basis for future mechanical tests that would help set relevant ASTM standards (E3191) for intumescent polyurethane foams in fixating applications. Tests like surface adhesion, impact, and flame tests would serve as a better indicator as to how this material would perform in environmentally harsh scenarios often found in decommissioned nuclear facilities. Further tests of the foams' intumescent properties would also be important should these foams be implemented in environmentally harsh scenarios.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

hydro-hifem v.1.0

SAND2021-3180 O The hydro-hifem 1.0 software is used to simulate transient physical processes in large geologic environments consisting of complex fractures and man-made infrastructures with affordable computational cost and mesh refinement. The Earth models with such complex model features can be designed by considering the reduction of insignificant dimensions of fractures/infrastructures and embracing their corresponding hierarchical material properties, and their computational meshes are generated by using Cubit. The finite element solutions are automatically distributed across nodes of parallel cluster for efficient computation of transient fluid flow or heat conduction in geologic media. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Beskardes, GungorDidem↗
DOE OSTI2021

U.S.-China Clean Energy Research Center Building Energy Efficiency (CERC-BEE) Open-Source Retrofit Targeting Tool (CRADA FP00007338 Final Report)

To increase the cost-saving energy and carbon dioxide (CO 2 ) emissions reductions in buildings and portfolios at the scale and speed necessary to limit climate change, researchers at LBNL and Johnson Controls (JCI) developed the Building Efficiency Targeting Tool for Energy Retrofits (BETTER). BETTER is a software tool that consists of three components: (1) the BETTER analytical engine source code (which was developed with intellectual property provided by JCI under CRADA FP00007338); (2) the BETTER web application, developed by LBNL and McQuillen Interactive Pty. Ltd; and (3) the BETTER application programming interface (API), also developed by LBNL and McQuillen Interactive Pty. Ltd. BETTER enables building and portfolio owners, managers, and service providers worldwide to quickly, easily identify cost-saving energy efficiency retrofits in existing buildings and portfolios without expensive site visits or complex modeling. With minimal data input, the tool benchmarks a building’s electric and fossil energy usage against peers; quantifies energy, cost and greenhouse gas (GHG) emission reduction potentials at the building and portfolio levels; and recommends energy efficiency measures to decarbonize and electrify buildings and portfolios, targeting specific energy savings levels. No other tool so comprehensively analyzes buildings and portfolios with such ease. If fully implemented, it is estimated that BETTER could help reduce emissions equivalent to planting 1.3 billion trees globally by 2030. Moreover, an additional 50-75% of embodied GHG emissions could be avoided in each case where BETTER results in a building being retrofitted instead of demolished and replaced, providing substantial additional decarbonization benefits for the buildings sector. BETTER has garnered multiple awards and avid interest from investors. In 2020, it earned a R&D 100 Award for innovation and a LBNL Director’s Award for Technology Transfer. In 2021, BETTER was named an EarthX E-Capital Summit Climate Tech Prize semi-finalist

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

National Renewable Energy Laboratory Wildland Fire Management Plan (August 2022-August 2025): February-August 2022

The National Renewable Energy Laboratory (NREL) is located in a moderate wildfire risk region. The region has a history of damaging wildland fire activity and is also susceptible to changing environmental conditions (drought, elevated temperatures) that may lead to an increase in wildfire frequency and consequence over time. An integrated, site-wide wildland fire management plan is required by the Department of Energy and is to be consistent with the Federal Wildland Fire Management Policy. The NREL Wildland Fire Management Plan (WFMP) provides an integrated approach to reducing risk to wildland fire at NREL. The goals of the WFMP are to protect human health and safety, protect NREL facilities and research, enhance community protection, diminish risk and consequences of wildland fires, and maintain the health of the ecosystem. The WFMP accomplishes these goals through the use of tools such as fire behavior modeling, fire growth modeling, a structure ignition risk assessment, and through the application of robust fire prevention, emergency management, and environmental programs. The NREL WFMP was developed in a collaborative manner with input from regional stakeholders including cities, counties, local fire departments, and other governmental agencies. The WFMP provides for recommendations to reduce overall wildland fire risk to life safety and property at NREL.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The Building Science Advisor: A Web-Based Tool to Assess the Durability of Building Envelope Components

The Department of Energy (DOE) and Oak Ridge National Laboratory (ORNL) have developed an innovative new design tool that will put building science expert advice in the hands of every building designer who cares to manage moisture risk in high-R envelope components. As modern buildings become increasingly more airtight, constructed with modern labor-saving materials, and equipped with air-conditioning, they have become less forgiving to moisture intrusion. The Building Science Advisor (BSA) provides building science knowledge and advice based on expert experience, field measurements, laboratory tests, and computer simulations.BSA users are prompted to enter relevant information about the building location, design, and material selection options like cladding, structural system, and insulation. Influential factors such as climate, building air tightness, material properties, and internal moisture loads are also considered to estimate and compare the moisture durability performance of several design options. If the BSA deems the assembly’s performance unsatisfactory, it will provide the reasons why, and suggest necessary changes to wall design to ensure more robust performance.Using the BSA tool enables building designers to confidently select assembly design characteristics that achieve their design goals with the least moisture durability risk. Links to design-specific guidance are also be provided to help users manage any remaining risk. This tool will further enable DOE’s Building Technologies Office (BTO) to meet its long-term energy goal of a 50% reduction in building energy consumption by reducing builder concern about using highly energy efficient wall systems.

Desjarlais, Andre Omer↗
DOE OSTI2021

Vibrational spectroscopy analysis of silica and silicate glass networks

Vibrational spectroscopy has been widely used to investigate various structural aspects of the glass network, and there are a plethora of papers reporting subtle but consistent changes in infrared and Raman spectral features of glass upon alterations of glass compositions, thermal histories, mechanical stresses, or surface treatments. However, interpretations of such spectral features are still obscured due to the lack of well-established physical principles accurately describing vibrational modes of the non-crystalline glass network. Due to the non-equilibrium nature of the glass network, three-dimensionally connected without any long-range orders, vibrational spectral features of glass cannot be interpreted using the analogy with those of isolated molecular moieties or crystalline counterparts. Here, this feature article explains why such comparisons are outdated and describes the recent advances made from theoretical calculations of vibrational spectral features of amorphous networks or comparisons of computational results with experimental data. For the interpretation of vibrational spectral features of silica and silicate glasses, the following empirical relationships are suggested: (i) the intensity-weighted peak position of the Si-O-Si stretch mode negatively correlates with the weighted average of the Si-O bond length distribution, and (ii) the broad band of the Si-O-Si bending mode negatively correlates with the Si-O-Si bond angle distribution. Selected examples of vibrational spectroscopic imaging of surface defects are discussed to deliberate the implication of these findings in the structure-property relationship of silica and silicate glass materials. Unanswered questions and continuing research challenges are identified.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Sensitivity Analysis and Effective Parametrization of PEM Fuel Cell Models

The cost of proton-exchange-membrane fuel cells (PEMFCs) remains a major hurdle in large-scale commercialization of this technology. To improve their performance and reduce cost, novel materials and electrode designs are continuously envisioned, e.g., non-PGM catalyst layers, ultra-thin Pt/Pt-Ni based catalyst layers, structured ionomer arrays or NSTF catalyst layers.1 Understanding the impact of these improvement strategies can be extremely time and cost intensive due to complex physical phenomena and large design space. We have previously developed a PEMFC modeling framework2 which has been a time and cost effective tool for understanding and optimizing the complex multi-physics phenomena within PEMFCs; however, several of the cell parameters used in the modeling have large spread in measured data.3 Furthermore, several transport parameters such as water adsorption kinetics have not been accurately measured and the approximations are spread over several orders of magnitude. These uncertainties cause problems in ascertaining accuracy of the modeling approach and they reduce the predictive power of the numerical models. The aim of this work is to identify the sensitivity of PEFC numerical model outputs to various input parameters. The previously in-house developed MEA modeling framework2 is used for PEMFC modeling. The sensitivity of the model outputs with respect to inputs parameters is obtained by analyzing the condition numbers for different output-input pairs at varying operating conditions. An example of the sensitivity analysis is shown in Figure 1. The condition numbers are obtained for the entire possible range of input parameters at varying operating conditions to identify the most crucial parameters of the PEMFC model. Based on our preliminary analysis, parameters related to kinetics (exchange current density and ECSA) and heat/water management in electrodes and ionomer (ionomer fraction, thermal conductivity) are most crucial. One of the major goals of this work is to identify the most crucial set of parameters towards which the model shows maximum sensitivity. This will guide future experimentalists to measure these properties with higher accuracy. Furthermore, the sensitivity analysis will also enable us to optimize the PEMFC performance by selectively targeting the most sensitive parameters and thereby making the largest impact. Acknowledgements The work is funded under the Fuel Cell Performance and Durability Consortium (FC-PAD), by the Fuel Cell Technologies Office (FCTO), Office of Energy Efficiency and Renewable Energy (EERE), of the U.S. Department of Energy under contract number DE-AC02-05CH11231. The authors would like to thank Nathan Craig at Robert Bosch LLC for his valuable input in designing the sensitivity analysis. The authors would also like to thank Giovanna Bucci and Matthias Hanauer at Robert Bosch for their valuable inputs and discussion. References P. K. Sinha, W. Gu, A. Kongkanand and E. Thompson, J. Electrochem. Soc., 158, B831 (2011). L. M. Pant, M. R. Gerhardt, N. Macauley, R. Mukundan, R. L. Borup and A. Z. Weber, Electrochim. Acta, 326, 134963 (2019). R. Vetter and J. O. Schumacher, ArXiv181110091 Phys. (2018). Figure 1

Pant, Lalit↗
DOE OSTI2021

Geant4 based positron beam source (GPos) v1.0

GPos is a software that was created to determine the properties of positron beams resulting from the interaction of the LBNL BELLA center PetaWatt laser-driven plasma-capillary accelerated electron beam and the atoms of a thin solid target. GPos is written in C++, easily compiled with cmake and the spack package manager, which allows for multi-thread and MPI parallel computing. Its functions expand on the Geant4 toolkit library and allow for propagation of the modelled particles through vacuum drift distances with a focusing element (thin lens approximation). Users can change beam-foil-drift-lens parameters - to adapt GPos to other particle sources and infrastructures - in a simple input file. The code particle data output format, openPMD, which is compatible, for example, with the input of the ECP WarpX project code used to explore the physics of particle acceleration in plasmas. Using GPos in conjunction to WarpX allowed us to test various configurations for designing a high-quality and high-energy positron source at BELLA -required for us to address positron acceleration challenges in the development of future linear colliders. GPos can also be advantageous when tackling the physics of muon sources for future muon colliders as well as for the investigation of positron sources in lower energy regimes for applications like annihilation spectroscopy and astrophysical gamma-ray-bursts.

Pinto de Almeida Amorim, Ligia↗
DOE OSTI2025

Optimizing Alabama’s CO 2 Storage in Shelby County (Project OASIS) - Deliverable Task 7: Risk Assessment

The “Optimizing Alabama’s CO 2 Storage in Shelby County: Project OASIS” CarbonSAFE Phase II Project seeks to build on regional data sets that demonstrate that the subsurface within Shelby County, Alabama has the potential to store commercial volumes of CO 2 safely, permanently, and economically. The primary target CO 2 Storage Complex is the deep Ketona Dolomite located within a 140 square mile area of the Valley and Ridge Region of Alabama. This deep saline reservoir is beneath a confining system encompassing at least 6,500 ft of shales and other low permeability sediments. Project OASIS drilled a deep stratigraphic test well to confirm the geological properties of the confining system and storage reservoir(s) within the Storage Complex. The geological data was incorporated into numerical models to establish the areal extent of the CO 2 plume and help design the storage site and its monitoring system. The project is managed by the Southern States Energy Board (SSEB), an interstate compact organization consisting of governors and state legislative leaders from sixteen southern states, Puerto Rico, and the U.S. Virgin Islands, as well as an appointee by the President of the United States. The organizational compact provides it with access to state government organizations and legislatures. The Board also maintains an Associate Members program comprised of energy resource companies, utilities, trade groups, academic R&D science and technology experts and energy consultants. Further, the SSEB staff is experienced in managing and coordinating complex energy and environmental programs, from research programs to full-scale design and demonstrations of new and innovative technologies. Project OASIS is a public-private partnership of six entities with multiple principal investigators (PIs). SSEB’s Lead PI and Co-PI are responsible for all aspects of project performance in accordance with the DOE-NETL Cooperative Agreement. SSEB has issued subgrants to Advanced Resources International, Inc., Alabama A&M University, Auburn University, Crescent Resource Innovation, and Oklahoma State University. Advanced Resources International, Inc., issued subgrants to Baker Hughes and Loudon Technical Services for field services

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2023

Influence of soil depth, irrigation, and plant genotype on the soil microbiome, metaphenome, and carbon chemistry

ABSTRACT Climate change is causing an increase in drought in many soil ecosystems and a loss of soil organic carbon. Calcareous soils may partially mitigate these losses via carbon capture and storage. Here, we aimed to determine how irrigation-supplied soil moisture and perennial plants impact biotic and abiotic soil properties that underpin deep soil carbon chemistry in an unfertilized calcareous soil. Soil was sampled up to 1 m in depth from irrigated and planted field treatments and was analyzed using a suite of omics and chemical analyses. The soil microbial community composition was impacted more by irrigation and plant cover treatments than by soil depth. By contrast, metabolomes, lipidomes, and proteomes differed more with soil depth than treatments. Deep soil (>50 cm) had higher soil pH and calcium concentrations and higher levels of organic acids, bicarbonate, and triacylglycerides. By contrast, surface soil (0–5 cm) had higher concentrations of soil organic matter, organic carbon, oxidizable carbon, and total nitrogen. Surface soils also had higher amounts of sugars, sugar alcohols, phosphocholines, and proteins that reflect osmotic and oxidative stress responses. The lipidome was more responsive to perennial tall wheatgrass treatments compared to the metabolome or proteome, with a striking change in diacylglyceride composition. Permanganate oxidizable carbon was more consistently correlated to metabolites and proteins than soil organic and inorganic carbon and soil organic matter. This study reveals specific compounds that reflect differences in organic, inorganic, and oxidizable soil carbon fractions that are impacted by interactions between irrigation-supplied moisture and plant cover in calcareous soil profiles. IMPORTANCE Carbon is cycled through the air, plants, and belowground environment. Understanding soil carbon cycling in deep soil profiles will be important to mitigate climate change. Soil carbon cycling is impacted by water, plants, and soil microorganisms, in addition to soil mineralogy. Measuring biotic and abiotic soil properties provides a perspective of how soil microorganisms interact with the surrounding chemical environment. This study emphasizes the importance of considering biotic interactions with inorganic and oxidizable soil carbon in addition to total organic carbon in carbonate-containing soils for better informing soil carbon management decisions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

or-topas: Operations Research Toolkit for Pyomo Alternative Solutions

SAND2026-16702O OR-TOPAS: Operations Research Toolkit for Pyomo Alternative Solutions is a tool that enhances optimization applications defined by the Pyomo modeling library. It offers functions to generate optimal or near-optimal solutions, operating independently of Pyomo’s solver interface. Users can configure these functions with specific solver names and options, resulting in a custom solution object that returns a list of solutions. The OR-TOPAS library includes methods tailored to the properties of the model, such as binary integer programs versus linear programs, and specific solver interfaces like Gurobi. It does not provide models for specific applications, but it is applicable to a wide range of Pyomo optimization models. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Siirola, John [Sandia National Lab. (SNL-CA), Live↗
DOE OSTI2021

Machine learning-enabled prediction of chemical durability of A 2 B 2 O 7 pyrochlore and fluorite

Pyrochlore-structure type and its derivative in a general formula A 2 B 2 O 7 (A = rare earth elements and actinides; B = Ti, Sn, Zr, Hf, Pb, Si, etc.) display excellent structural flexibility and rich crystal chemistry as promising nuclear waste form materials capable of immobilizing actinides and fission products. It is essential to understand these materials’ chemical durability and element release of radionuclides in order to evaluate their performance in near-field environment. However, it is a formidable grand technological challenge to experimentally perform durability testing across hundreds of thousands of possibilities resulting from their extreme compositional complexities due to cation substitutions at both A and B-sites. In this work, we demonstrate a machine learning approach to determine the key materials parameters and structural characteristics governing the leaching behaviors from a small set of selected compositions as model systems, enabling a science-based prediction of their chemical durability that can be extended to a wide range of chemical compositions. The combination of four key structural characteristics and materials parameters, including ionic radius size difference , ionic potential difference , electronegativity difference , and lattice parameter , creates features an optimized prediction of the chemical durability. Two machine learning models, linear regression and Kernel ridge regression models, are trained on the randomly-split training dataset derived from the experimentally-determined elemental release rates, and subsequently tested on the testing dataset. The predicted leaching rates from both machine learning models show an excellent agreement with the experimental data, demonstrating the feasibility of rapidly evaluating the material properties of new compositions. These results highlight the immense potential of synergizing informatics through machine learning-based models and well-controlled experiments of selected model systems to accelerate materials design and discovery with optimized compositions and performance of promising materials for effective nuclear waste management.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Fracture properties of high-entropy alloys

Abstract Since the concept of high-entropy alloys (HEAs) as materials with at least four or five principal elements in (near)-equiatomic composition was introduced in 2004, this new class of materials has penetrated essentially all materials science-related fields. The main reason for this is that some face-centered-cubic alloy compositions have been shown to exhibit truly outstanding mechanical properties with extraordinary combinations of strength, ductility, and fracture toughness, particularly at cryogenic temperatures, whereas certain body-centered-cubic refractory compositions display remarkable high-temperature strength. While significant efforts have been put into rapid screening and narrowing the compositional space of HEAs to a manageable scope, there are still only a few metallic alloys that push the limits of mechanical performance. Here, we review work on some of the most damage-tolerant HEAs discovered to date and discuss the fundamental reasons why their resistance to fracture and subsequent stable crack growth is so exceptional. Graphical abstract

Gludovatz, Bernd (ORCID:0000000224203879)↗
DOE OSTI2021

Mapdfn.py

SAND2021-4824 O mapdfn.py takes the output of dfnWorks describing a discrete fracture network (DFN) and converts the DFN to an equivalent porous medium representation. This is done by looping through cells in a 3-D model domain, tagging those intersected by fractures, and calculating effective properties. Coordinate transformations are imported from an open source script (transformations.py) developed at the University of California. An associated driver script, mapdfn2pflotran.py, calls the functions in mapdfn.py and writes the mapdfn.py output to hdf5 files for use with PFLOTRAN. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. SAND2021-4824 O

Stein, Emily↗
DOE OSTI2024

A nanoscale photonic thermal transistor for sub-second heat flow switching

Abstract Control of heat flow is critical for thermal logic devices and thermal management and has been explored theoretically. However, experimental progress on active control of heat flow has been limited. Here, we describe a nanoscale radiative thermal transistor that comprises of a hot source and a cold drain (both are ~250 nm-thick silicon nitride membranes), which are analogous to the source and drain electrodes of a transistor. The source and drain are in close proximity to a vanadium oxide (VO x )-based planar gate electrode, whose dielectric properties can be adjusted by changing its temperature. We demonstrate that when the gate is located close ( < ~1 µm) to the source-drain device and undergoes a metal-insulator transition, the radiative heat transfer between the source and drain can be changed by a factor of three. More importantly, our nanomembrane-based thermal transistor features fast switching times ( ~ 500 ms as opposed to minutes for past three-terminal thermal transistors) due to its small thermal mass. Our experiments are supported by detailed calculations that highlight the mechanism of thermal modulation. We anticipate that the advances reported here will open new opportunities for designing thermal circuits or thermal logic devices for advanced thermal management.

42 ENGINEERING↗
DOE OSTI2026

Lawrence Livermore National Laboratory – Innovation and Partnerships Office (IPO): FY24 Technology Transfer Report (2025 Report V3)

Lawrence Livermore National Laboratory’s (LLNL) Innovation and Partnerships Office (IPO) is the focal point for Laboratory engagement with industry. Organizations partner with LLNL to unlock the commercial viability of breakthrough technologies invented at the Laboratory and collaborate to mature them in order to solve complex industry challenges. LLNL technology transfer connects cutting-edge science with real-world applications, empowering businesses to thrive while advancing the nation’s technological edge. We do this by identifying new economic opportunities, protecting LLNL intellectual property (IP) and transferring it to the private sector through licensing and partnerships

42 ENGINEERING↗
DOE OSTI2022

A gradient-based deep neural network model for simulating multiphase flow in porous media

We report simulation of multiphase flow in porous media is crucial for the effective management of subsurface energy and environment-related activities. The numerical simulators used for modeling such processes rely on spatial and temporal discretization of the governing mass and energy balance partial-differential equations (PDEs) into algebraic systems via finite-difference/volume/element methods. These simulators usually require dedicated software development and maintenance, and suffer low efficiency from a runtime and memory standpoint for problems with multi-scale heterogeneity, coupled-physics processes or fluids with complex phase behavior. Therefore, developing cost-effective, data-driven models can become a practical choice, and in this work, we choose deep learning approaches as they can handle high dimensional data and accurately predict state variables with strong nonlinearity. In this paper, we describe a gradient-based deep neural network (GDNN) constrained by the physics related to multiphase flow in porous media. We tackle the nonlinearity of flow in porous media induced by rock heterogeneity, fluid properties, and fluid-rock interactions by decomposing the nonlinear PDEs into a dictionary of elementary differential operators. We use a combination of operators to handle rock spatial heterogeneity and fluid flow by advection. Since the augmented differential operators are inherently related to the physics of fluid flow, we treat them as first principles prior knowledge to regularize the GDNN training. We use the example of pressure management at geologic CO 2 storage sites, where CO 2 is injected in saline aquifers and brine is produced, and apply GDNN to construct a predictive model that is trained with physics-based simulation data and emulates the physics process. We demonstrate that GDNN can effectively predict the nonlinear patterns of subsurface responses, including the temporal and spatial evolution of the pressure and saturation plumes. We also successfully extend the GDNN to convolutional neural network (CNN), namely gradient-based CNN (GCNN), and validate its capability to improve the prediction accuracy. GDNN has great potential to tackle challenging problems that are governed by highly nonlinear physics and enable the development of data-driven models with higher fidelity.

42 ENGINEERING↗