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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Deep Generative Models in Energy System Applications: Review, Challenges, and Future Directions

In recent years, with the advent of mature machine learning products like ChatGPT, Stable Diffusion, and Sora, the world has witnessed tremendous changes driven by the rapid development of generative artificial intelligence (GAI). Beyond applications in text, speech, image, and video creation, deep generative models (DGMs) underpinning these cutting-edge technologies have also been employed by domain researchers to address scientific and engineering challenges. This paper aims to fill a gap in the research community by providing a systematic review of how DGMs have been utilized in energy system applications. After introducing four most popular DGMs, we review and categorize 196 research articles into five focus areas: data generation, forecasting, situational awareness, modeling, and optimal decision-making. Through this classification, we uncover trends in how DGMs are employed for each type of problem, highlighting GAI techniques that contribute to breakthroughs over traditional methods. We discuss limitations in existing literature, engineering challenges, and propose future directions, all tailored to the unique nature of problems in energy system engineering. Our goal is to offer insights for energy system domain researchers, providing a comprehensive view of existing studies and potential future opportunities.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Application of machine learning for modeling brønsted-guggenheim-scatchard specific ion interaction theory (SIT) coefficients

Machine learning methodologies can provide insight into Brønsted-Guggenheim-Scatchard specific ion interaction theory (SIT) parameter values where experimental data availability may be limited. This study develops and executes machine learning frameworks to model the SIT interaction coefficient, ε. Key findings include successful estimations of ε via artificial neural networks using clustering and value prediction approaches. Additionally, applicability to other chemical parameters is also assessed briefly. Models developed here provide support for a use-case of machine learning in geologic nuclear waste disposal research applications, namely in predictions of chemical behaviors of high ionic strength solutions (i.e., subsurface brines).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Multi-stage heat pump with two-phase injection for cold climate applications implementing natural refrigerants

Heat pump adoption in cold climates is limited by various technical challenges, including heat capacity degradation, compressor deterioration, and frost accumulation on the outdoor heat exchanger. Furthermore, wide-spread adoption of heat pumps with high global warming potential presents a possible environmental concern. Thus, we present a heat pump concept termed the “saturation cycle,” which uses numerous compression stages to improve energy efficiency and injects two-phase refrigerant between compression stages to dramatically reduce compressor discharge temperatures. Given the growing interest in heat pumps, there is a need for a straightforward framework for parametric evaluation of natural refrigerants in high-efficiency heat pump cycle architectures, like the saturation cycle, in adverse climates. This work develops a thermodynamic modeling framework and focuses on the application of natural refrigerants to explore the performance trends at extremely cold climates down to −25 °C. This study shows that a two-stage saturation cycle and four-stage saturation cycle can reduce the compressor discharge superheating by up to 51–57 % or 73–80 %, respectively, depending on the refrigerant. Furthermore, at −25 °C, a two-stage R-290 saturation cycle heat pump theoretically can use 25 % less power than a single-stage heat pump or can provide up to 59 % more heating capacity when using the same compressor power as the baseline single-stage heat pump. The exergetic parametric analyses show that two-phase refrigerant injection fundamentally improves the thermodynamic performance of the heat pump, reducing overall exergy destruction by 10 % compared to a vapor-injection heat pump, compared at −25 °C. Thus, the saturation cycle is a promising heat pump cycle architecture for cold climate applications due to its superior energy savings (or capacity improvement) and substantial de-superheating capabilities.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Growth of carbon nanotube forests on flexible metal substrates: Advances, challenges, and applications

Owing to their remarkable properties and ordered architecture, vertically aligned CNT forests (VACNT) hold promises for countless applications ranging from energy storage to multifunctional fiber production. Over the last three decades, studies of carbon nanotube (CNT) growth have provided invaluable insight into the CNT nucleation and growth mechanism and enabled critical advancements in nanotechnology. Nevertheless, the ability to fully harness the exceptional VACNT properties in commercial devices is bottlenecked by compatibility of current CNT synthesis processes with large-scale manufacturing. Metal foil substrates present an economic alternative to traditional insulating substrates, like silicon or quartz, and are compatible with both roll-to-roll and automated, large-scale, batch processes. Compared to CNT growth on Si substrates, metal foils present additional challenges, such as increased roughness and reactivity at high temperatures. Nonetheless, many successful VACNT growths on conducting metal foils have been reported, which demonstrate the feasibility of metal substrate use for large-volume production of high-quality CNTs. Here, in this review, we discuss advancements in the field of VACNT growth on metal foil and specifically examine different choices for metal substrates, barrier and catalyst layers, deposition and growth methods, resulting CNT characteristics, and highlight a number of applications that benefit from CNT growth on metal substrates and transition to large-scale manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2022

On multidomain multiscale modeling and simulation of a novel partial pressure and temperature swing adsorptive reactor ($\mathrm{PPTSAR}$) with application to the water gas shift reaction

Here in this work, a dynamic, multidomain, multiscale model of a novel Partial Pressure and Temperature Swing Adsorptive Reactor (PPTSAR) Process is developed and simulated for a Water Gas Shift Reaction (WGSR) application. The proposed model couples the reactor wall, catalyst/adsorbent pellet, and fluid mixture domains at their respective scales to comprehensively model mass, momentum, and energy transport phenomena occurring within each domain. The resulting model elucidates the PPTSAR’s start-up, transient, and long-term, periodic, dynamic behavior, during both adsorption/reaction and desorption operating modes. In the WGSR application considered, the use of two PPTSARs is shown to allow continuous operation that delivers CO conversion greater than 95% with simultaneous complete CO 2 capture.

42 ENGINEERING↗
DOE OSTI2021

Multifunctional application of nanoscratch technique to characterize cementitious materials

This study aims to further explore the multifunctional applicability of nanoscratch technique to cementitious materials for comprehensive characterization. Based on the investigation of deformation, applied force, and fracture toughness of hardened cement paste during scratching, the methods of phase identification, fracture toughness of individual phase, and the interfacial transition zone (ITZ) thickness quantification were proposed and discussed. After redefining the vertical loading rate of scratching, the slope of the square scratching depth vs. scratching length is recognized as a reliable parameter to distinguish the individual phases for the first time. A fracture toughness-based method is proposed to quantify the thickness of ITZ between clinker and hydrates. Meanwhile, the applicability of phase identification by the scratch method is discussed for different transverse scratching speeds and the vertical loading rates. The findings will benefit the investigation and mechanism understanding of cementitious materials at the microscale by utilizing nanoscratch technique.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Singly and doubly oxidized carbenes and their applications in catalysis

Over the last three decades, the highly tunable properties of N-heterocyclic carbenes (NHCs) and other stable singlet carbenes have led to a variety of applications. This perspective shows a novel facet of carbenes—i.e., their reductive properties—that allows them to function as catalysts in single-electron transfer (SET) reactions. The isolation and even the spectroscopic characterization of a singly oxidized carbene have yet to be done, but these species readily abstract hydrogen atoms while giving back the carbene conjugate acid, which behaves as the resting state of catalytic cycles. In sharp contrast, a doubly oxidized carbene has been isolated, and there is a strong likelihood that many other carbene dications will be isolated. Their first Lewis acidity is very high, suggesting possible applications in Lewis acid catalysis.

dication↗
DOE OSTI2024

Development of a SnO 2 -based 44 Ti/ 44 Sc generator for medical applications

Towards application of 44 Sc for diagnostic nuclear medicine, a 44 Ti/ 44 Sc generator based on an inorganic resin has been evaluated. Unlike other radionuclide generators used for medical applications, the long-term retention of the parent 44 Ti is vital due to its long half life. In this work, tin dioxide (SnO 2 ), a robust inorganic-based resin, has been synthesized and used as the stationary phase for a 44 Ti/ 44 Sc generator. The sorption behavior of 44 Ti/ 44 Sc was tested on SnO 2 with varying acids, concentrations, and times. Preliminary batch study results showed >88 % 44 Ti retention to the resin at lower acid concentrations (0.05 M HNO 3 and 0.05 M HCl). A pilot generator was evaluated for a year, demonstrating 85.3 ± 2.8 % 44 Sc elution yields and 0.71 ± 0.14 % 44 Ti breakthrough in 5 M HNO 3 . Based on capacity studies, a 7.4 MBq (200 µCi) upscaled generator system was constructed for further evaluation of the SnO 2 resin stability and the efficacy of the eluted 44 Sc for radiolabeling. 44 Sc could be regularly eluted from this generator in 5 M HNO 3 with an overall average radiochemical yield 84.7 ± 9.5 %. Post-elution processing of the 44 Sc with DGA-normal resin removed all 44 Ti present and allowed for high 44 Sc-DOTA labeling yields of 94.2 ± 0.5 %. Overall, SnO 2 has been shown to be a viable material for a 44 Ti/ 44 Sc generator.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2023

Large-scale metal strip for power storage and energy conversion applications by machining-based deformation processing

Machining-based deformation processing is used to produce metal foil and flat wire (strip) with suitable properties and quality for electrical power and renewable energy applications. In contrast to conventional multistage rolling, the strip is produced in a single-step and with much less process energy. Examples are presented from metal systems of varied workability, and strip product scale in terms of size and production rate. By utilizing the large-strain deformation intrinsic to cutting, bulk strip with ultrafine-grained microstructure, and crystallographic shear-texture favourable for formability, are achieved. In conclusion, implications for production of commercial strip for electric motor applications and battery electrodes are discussed.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

On a fully-implicit VMS-stabilized FE formulation for low Mach number compressible resistive MHD with application to MCF

This study presents the development and evaluation of a fully-implicit variational multiscale (VMS) stabilized unstructured finite element (FE) formulation for compressible magnetohydrodynamics (MHD) model, at low Mach number regime. The model describes the dynamics of a compressible conducting fluid in the low Mach number limit in the presence of electromagnetic fields and can be used to study aspects of astrophysical phenomena, important science and technology applications, and basic plasma physics phenomena. The specific applications that motivate this study are macroscopic simulations of the longer time-scale stability and disruptions of magnetic confinement fusion (MCF) devices, specifically the ITER tokamak. The discussion considers the development of the VMS FE representation, the structure of the stabilizing terms that deal with significant convective flows, the stabilization of the nearly incompressible response of the fluid flow, and the stabilization of the constraint that enforces the solenoidal involution on the magnetic field. The nonlinear discretized system is solved with scalable preconditioned Newton–Krylov iterative methods, which employs a multiphysics block preconditioning method based on approximate block factorizations and Schur complements. The study presents an evaluation of the VMS method on a 2D cartesian tearing mode instability, and illustrates the scalability of the solvers on MCF relevant problems. A set of results are also presented for longer time-scale stability and disruptions for the ITER tokamak. These include a vertical displacement event (VDE), and a (1,1) internal kink mode. Here, the formulation is demonstrated to be scalable and also reasonably robust with respect to the Lundquist number scaling.

42 ENGINEERING↗
DOE OSTI2024

A conservative discontinuous-Galerkin-in-time (DGiT) multirate time integration framework for interface-coupled problems with applications to solid–solid interaction and air–sea models

In this paper we extend the DGiT multirate framework, developed in Connors and Sockwell (2022) for scalar transmission problems, to a solid–solid interaction (SSI) problem involving two coupled elastic solids and a coupled air–sea model with the rotating, thermal shallow water equations. In so doing we aim to demonstrate the broad applicability of the mathematical theory and governing principles established in Connors and Sockwell (2022) to coupled problems characterized by subproblems evolving at different temporal scales. Further, multirate time integration algorithms employing different time steps, optimized for the dynamics of each subproblem, can significantly improve simulation efficiency for such coupled problems. However, development of multirate algorithms is a highly non-trivial task due to the coupling, which can impact accuracy, stability or other desired properties such as preservation of system invariants. DGiT provides a general template for multirate time integration that can achieve these properties. To elucidate the manner in which DGiT accomplishes this task, we fully detail each step in the application of the framework to the SSI and air–sea coupled problems. Numerical examples illustrate key properties of the resulting multirate schemes for both problems.

42 ENGINEERING↗
DOE OSTI2021

Investigation of grid-based vorticity-velocity large eddy simulation off-body solvers for application to overset CFD

Accurately predicting unsteady wakes and vortex-dominated flows is essential to a wide range of engineering applications, including aircraft, rotorcraft, shipboard operations, bio-inspired unsteady flight and propulsion, wind turbines, and urban flows. While current CFD software can model the complete flow field and wake system, the computational costs incurred in high Reynolds number unsteady turbulent flow simulations often remain prohibitive for routine engineering use, particularly for applications involving moving components. Prior work has demonstrated that by adopting a vorticity-velocity formulation in a grid-based off-body flow solver (VorTran-M and VorTran-M2) one can lower these costs by several orders of magnitude when compared to conventional approaches. This paper describes the extensions made to VorTran-M2 to support turbulent flows, and associated benchmarking activity to assess its performance for problems involving strong stretching and diffusion processes, whose competing contributions to the vorticity field are core drivers of turbulent flow evolution. Predictions are presented for: (i) the Kida-Pelz problem whose inviscid form is of mathematical interest due to its apparent formation of singular flow in finite time; and (ii) the Taylor Green vortex arrangement, which has been extensively studied as a fundamental simulation challenge in the turbulent modeling community. Here, the results are used to evaluate the overall predictive ability and performance of two sub-grid scale models incorporated into VorTran-M2. Results indicate that the computational cost savings seen previously for inviscid and convection dominated problems extend to turbulent flow simulations supporting the viability of VorTran-M2 as a low cost means for accurately modeling the far-field and background flow, particularly when long duration vorticity evolution is of interest.

42 ENGINEERING↗
DOE OSTI2025

Sandwich-structured paper composite with water and oil resistance for food packaging and tableware applications

The growing environmental challenges posed by plastic waste from disposable tableware highlight the urgent need for sustainable alternatives. Traditional plastics decompose over centuries, generating microplastics that threaten ecosystems and human health. While lignin has emerged as a promising material for plastic replacement, its inherent dark brown color and processing challenges in paper-based products have limited its application, particularly in food-contact materials. Here, to address these limitations, we have developed biodegradable sandwich-structured paper composites comprising parchment paper as surface layers and a lignin-polymer core with polyvinyl alcohol (PVA) and polylactic acid (PLA). This innovative structure eliminates the need for binders, minimizing potential food contamination, and enables direct application as packaging or molded tableware. Lignin micro- and nano-particles (LMNP) enhances durability and provides natural water and oil resistance without harmful additives, such as per- and polyfluoroalkyl substances (PFAS), while PVA and PLA improve the composite's tenacity. The resulting material, composed of 65 wt% lignin, demonstrates excellent water and oil resistance, with no penetration exceeding 1 h, and exhibits enhanced tensile strength (45 MPa), making it a viable and eco-friendly alternative for disposable tableware.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Polylactic acid (PLA)-based multifunctional and biodegradable nanocomposites and their applications

Polylactic acid (PLA)-based nanocomposites are emerging as multifunctional, biodegradable materials, offering sustainable alternatives to petroleum-based plastics. This review examines recent advancements in PLA nanocomposites, focusing on enhanced mechanical strength, thermal stability, and biodegradability achieved through nanofillers like metallic particles, carbon-based materials, and ceramics. Techniques such as in situ polymerization, melt mixing, and electrospinning enable application-specific improvements. PLA's limitations, including brittleness and low barrier properties, are addressed to support diverse applications: in packaging (e.g., extended shelf life), biomedicine (e.g., degradable implants), and electronics (e.g., flexible devices). Despite challenges with filler dispersion and thermal resistance, continued innovations expand PLA's potential across multiple industries, contributing to a sustainable, circular economy.

Biodegradable polymers↗
DOE OSTI2024

Multi-level Monte Carlo methods in chemical applications with Lennard-Jones potentials and other landscapes with isolated singularities

We describe and compare outcomes of various Multi-Level Monte Carlo (MLMC) method variants, motivated by the potential of improved computational efficiency over rejection based Monte Carlo, which scales poorly with problem dimension. With an eye toward its application to computational chemical physics, we test MLMC's ability to sample trajectories on two problems — a familiar double-well potential, with known stationary distributions, and a Lennard-Jones solid potential (a Galton Board). By sampling Brownian motion trajectories, we are able to compute expectations of observable averages. These multi-basin potential energy problems capture the essence of the challenges with using MLMC, namely, maintaining correspondence of sample paths as time-resolution is varied. Addressing this challenge properly can lead to MLMC significantly outperforming standard Monte Carlo path sampling. We describe the essence of this problem and suggest strategies that circumvent diverging multilevel sample paths for an important class of problems. In the tests we also compare the computational cost of several, “adaptive,” variants of MLMC. Our results demonstrate that MLMC overcomes the collision, time scale limitation of the more familiar Brownian path MC samplers, and our implementation provides tunable error thresholds, making MLMC a promising candidate for application to larger and more complex molecular systems.

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

Clinically applicable 53-Gene prognostic assay predicts chemotherapy benefit in gastric cancer: A multicenter study

BACKGROUND:We previously established a 53-gene prognostic signature for overall survival (OS) of gastric cancer patients. This retrospective multi-center study aimed to develop a clinically applicable gene expression detection assay and to investigate the prognostic value of this signature. METHODS:A TCGA gastric adenocarcinoma cohort (TCGA-STAD) was used for comparing 53-gene signature with other gene signatures. A high-throughput mRNA hybridization gene expression assay was developed to quantify the expression of 53-genes in formalin-fixed paraffin-embedded tissues of 540 patients enrolled from three hospitals. 180 patents were randomly selected from two hospitals to build a prognostic prediction model based on the 53-gene signature using leave-p-out (one-third out) cross-validation method together with Cox regression and Kaplan-Meier analysis, and the model was assessed on three validation cohorts. FINDINGS:In the evaluation phase, studies based on TCGA-STAD showed that the 53-gene signature was significantly superior to other three prognostic signatures and was independent of TCGA molecular subtypes and clinical factors. For clinical validation and utility, the prognostic scores were generated using the newly developed assay, which was reliable and sensitive, in 100 sampling training sets and were significantly associated with OS in 100 sampling validation sets. The scores were significantly associated with OS in three independent and combined validation cohorts, and in patients with stages II and III/IV. The multivariate Cox regression demonstrated that the prognostic power of the score was independent of clinical factors, consistent with those findings in the TCGA dataset. Finally, patients with good prognostic scores exhibited significantly a better 5-year OS rate from adjuvant FOLFOX chemotherapy after surgery than from other chemotherapies. INTERPRETATION:The 53-gene prognostic score system is clinically applicable for predicting the OS of patients independent of clinical factors in gastric cancers, which could also be a promising predictive biomarker for FOLFOX regimen. FUNDING:Chinese National Science and Technology, National Natural Science Foundation and Natural Science Foundation of Jiangsu Province.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Data efficiency assessment of generative adversarial networks in energy applications

This study investigates the data requirements of generative artificial intelligence (AI), particularly generative adversarial networks (GANs), for reliable data augmentation in energy applications. Generative AI, though seen as a solution to data limitations, requires substantial data to learn meaningful distributions—a challenge often overlooked. This study addresses the challenge through synthetic data generation for critical heat flux (CHF) and power grid demand, focusing on renewable and nuclear energy. Two variants of GAN employed are conditional GAN (cGAN) and Wasserstein GAN (wGAN). Our findings include the strong dependency of GAN on data size, with performance declining on smaller datasets and varying performance when generalizing to unseen experiments. Mass flux and heated length significantly influence CHF predictions. wGAN is more robust to feature exclusion, making it suitable for constrained synthetic data generation. In energy demand forecasting, wGAN performed well for solar, wind, and load predictions. Longer lookback hours and larger datasets improved predictions, especially for load power. Seasonal variations posed challenges, with wGAN achieving a relatively high error of Root Mean Squared Error (RMSE) of 0.32 for load power prediction, compared to RMSE of 0.07 under same-season conditions. Feature exclusions impacted cGAN the most, while wGAN showed greater robustness. This study concludes that, while generative AI is effective for data augmentation, it requires substantial data and careful training to generate realistic synthetic data and generalize to new experiments in engineering applications.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Simulation-aided occupant-centric building design: A critical review of tools, methods, and applications

Occupants are active participants in their built environment, affecting its performance while simultaneously being affected by its design and indoor environmental conditions. With recent advances in computer modeling, simulation tools, and analysis techniques, topics such as human-building interactions and occupant behavior have gained significant interest in the literature given their premise of improving building design processes and operating strategies. In practice, the focus of occupant-centric literature has been mostly geared towards the latter (i.e., operation), leaving the implications on building design practices underexplored. This paper fills the gap by providing a critical review of existing studies applying computer-based modeling and simulation to guide occupant-centric building design. The reviewed papers are organized along four main themes, namely occupant-centric: (i) metrics of building performance, (ii) modeling and simulation approaches, (iii) design methods and applications, and (iv) supporting practices and mechanisms. Important barriers are identified for a more effective application of occupant-centric building design practices, including the limited consideration of metrics beyond energy efficiency (e.g., occupant well-being and space planning), the limited implementation and validation of the proposed methods, and the lack of integration of occupant behavior modeling in existing building performance simulation tools. Finally, future research directions are discussed, covering large-scale international data collection efforts to move from generic assumptions about occupant behavior to specific/localized knowledge, improved metrics of measuring building performance, and improved industry practices, such as building codes, to promote an occupant-in-the-loop approach to the building design process.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗