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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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Outer planet probe missions are part of the on-going Mission and Technology Roadmap effort to define the framework for the future NASA Solar System Exploration program...A baseline mission/system design is described to which the technology needs can be related.
The ISWAT (International Space Weather Action Teams) heliosphere clusters H1 and H2 have a focus on interplanetary space and its characteristics, especially on the large-scale co-rotating and transient structures impacting Earth. Solar wind stream interaction regions, generated by the interaction between high-speed solar wind originating in large-scale open coronal magnetic fields and slower solar wind from closed magnetic fields, are regions of compressed plasma and magnetic field followed by high-speed streams that recur at the ~27 day solar rotation period. Short-term reconfigurations of the lower coronal magnetic field generate flare emissions and provide the energy to accelerate enormous amounts of magnetised plasma and particles in the form of coronal mass ejections into interplanetary space. The dynamic interplay between these phenomena changes the configuration of interplanetary space on various temporal and spatial scales which in turn influences the propagation of individual structures. While considerable efforts have been made to model the solar wind, we outline the limitations arising from the rather large uncertainties in parameters inferred from observations that make reliable predictions of the structures impacting Earth difficult. Moreover, the increased complexity of interplanetary space as solar activity rises in cycle 25 is likely to pose a challenge to these models. Combining observational and modeling expertise will extend our knowledge of the relationship between these different phenomena and the underlying physical processes, leading to improved models and scientific understanding and more-reliable space-weather forecasting. The current paper summarizes the efforts and progress achieved in recent years, identifies open questions, and gives an outlook for the next 5–10 years. It acts as basis for updating the existing COSPAR roadmap by Schrijver et al. (2015), as well as providing a useful and practical guide for peer-users and the next generation of space weather scientists.
A joint workshop between the Air Force Office of Scientific Research, the Office of Naval Research, and the NASA Hypersonic Technology Project was held in June 2023 on the topic of high-speed boundary-layer transition. The purpose of this workshop was to gather subject matter experts in the areas of supersonic and hypersonic boundary-layer transition in the areas of geometries of interest, computational methods, facilities and diagnostics, flight experiments, flow control, and new approaches and to discuss the current state of the art in the field. As gaps and accomplishments were identified, roadmaps were also created to guide future investments in these areas. The top recommendations are (1) to define relevant common geometries of interest that can be worked by multiple research groups; (2) improve modeling of high-enthalpy flows involving chemistry and ablation, studies of receptivity of freestream disturbances and atmospheric particulates into the boundary layer, surface feature characterization and modeling of changes to geometry; (3) design and determine viable flow control methods; and (4) move towards incorporating boundary layer transition prediction into a standard practice for design of high-speed vehicles. To execute this last recommendation successfully, investments beyond fundamental (basic and early applied) research are needed.
Slides used to present roadmap to state agencies, EEI stakeholders, EPA stakeholders, and other conferences
The Accident Tolerant Fuels (ATF) program aims to develop next generation Light Water Reactor (LWR) fuels with improved performance, reliability, and safety. The program has developed a roadmap to support the insertion of lead fuel rods (LFRs) or lead fuel assemblies (LFAs) of an accident tolerant fuel into a commercial LWR by the end of FY 2022. In order to achieve this goal, novel fuel compositions and cladding materials are being assembled as drop-in irradiation experiments to allow for prioritization of concepts to further the ATF mission. Development of fabrication methodology and qualification techniques for small-scale irradiation test samples is reported.
The American Association of Physicists in Medicine began the Medical Physics Leadership Academy Journal Club in the fall of 2020. The initiative was launched to provide a forum for medical physicists to learn about leadership topics using published material, discuss and reflect on the material, and consider incorporating the discussed skills into their professional practice. This report presents the framework for the MPLA Journal Club program, describes the lessons learned over the last 2 years, summarizes the data collected from attendees, and highlights the roadmap for the program moving forward.
An electronic-structure-centered perspective is presented on permanent-magnet (PM) design, highlighting two key levers, that is, saturation magnetization (M s ), governed by 3d-band filling and exchange physics, and magnetocrystalline anisotropy energy (MAE), arising from spin-orbit coupling (SOC) on anisotropic orbital populations. Reviewing current practices, including DFT-based MAE/J ij extraction, atomistic-spin and micromagnetic modeling, and high-throughput machine learning (ML) pipelines, three bottlenecks limiting predictive discovery is identified that is i) electronic-structure accuracy for small MAE (sensitive to functional choice, Hubbard U, and many-body effects), ii) finite-temperature and kinetic realism (phonon/magnon renormalization, ordering kinetics), and iii) descriptor and multiscale decoupling (lack of SOC-weighted and orbital-resolved fingerprints). Deep dives into the electronic-structure of Nd─Fe─B and Fe─N show how these fingerprints govern magnetic performance, motivating DFT- and quantum-mechanics-based descriptors for discovery. Unbiased, structure-driven exploration, coupled with high-throughput simulations, ML, generative AI, and reasoning models, accelerates candidate identification and propagates insights across scales. Addressing supply-chain risks, on future needs of designing “critical-element-free” magnets with tailored microstructure and high energy products is emphasized. By integrating electronic fingerprints, AI reasoning, and multiscale modeling, a practical roadmap is provided for rare-earth-lean or rare-earth free, high-performance, sustainable PMs.
Hexagonal boron nitride (h-BN) is a layered inorganic synthetic crystal exhibiting high temperature stability and high thermal conductivity. As a ceramic material it has been widely used for thermal management, heat shielding, lubrication, and as a filler material for structural composites. Recent scientific advances in isolating atomically thin monolayers from layered van der Waals crystals to study their unique properties has propelled research interest in mono/few layered h-BN as a wide bandgap insulating support for nanoscale electronics, tunnel barriers, communications, neutron detectors, optics, sensing, novel separations, quantum emission from defects, among others. Realizing these futuristic applications hinges on scalable cost-effective high-quality h-BN synthesis. Here, in this work, the authors review scalable approaches of high-quality mono/multilayer h-BN synthesis, discuss the challenges and opportunities for each method, and contextualize their relevance to emerging applications. Maintaining a stoichiometric balance B:N = 1 as the atoms incorporate into the growing layered crystal and maintaining stacking order between layers during multi-layer synthesis emerge as some of the main challenges for h-BN synthesis and the development of processes to address these aspects can inform and guide the synthesis of other layered materials with more than one constituent element. Finally, the authors contextualize h-BN synthesis efforts along with quality requirements for emerging applications via a technological roadmap.
Computational chemistry is an indispensable tool for understanding molecules and predicting chemical properties. However, traditional computational methods face significant challenges due to the difficulty of solving the Schrödinger equations and the increasing computational cost with the size of the molecular system. In response, there has been a surge of interest in leveraging artificial intelligence (AI) and machine learning (ML) techniques to in silico experiments. Integrating AI and ML into computational chemistry increases the scalability and speed of the exploration of chemical space. However, challenges remain, particularly regarding the reproducibility and transferability of ML models. This review highlights the evolution of ML in learning from, complementing, or replacing traditional computational chemistry for energy and property predictions. Starting from models trained entirely on numerical data, a journey set forth toward the ideal model incorporating or learning the physical laws of quantum mechanics. This paper also reviews existing computational methods and ML models and their intertwining, outlines a roadmap for future research, and identifies areas for improvement and innovation. Ultimately, the goal is to develop AI architectures capable of predicting accurate and transferable solutions to the Schrödinger equation, thereby revolutionizing in silico experiments within chemistry and materials science.
Nanocomposites made from alginate and nanoclay are extensively applied for diverse biomedical applications. However, the lack of a clear understanding of the interactions between alginate and nanoclay makes it difficult to rationally design the nanocomposites for different material extrusion-based 3D bioprinting strategies. Here, a combined analytical model is proposed to accurately predict the interaction mechanisms between alginate and nanoclay through small-angle neutron scattering. These mechanisms are summarized into a phase diagram that can guide the design of alginate-nanoclay nanocomposites for different bioprinting applications. The rheological properties of various nanocomposites are measured to validate the proposed interaction mechanisms at the macroscale. Accordingly, three representative extrusion-based bioprinting strategies are linked with the nanocomposite design and applied to freeform fabricate complex structures. In conclusion, a roadmap is summarized to bridge the gap between biomaterial design and bioprinting processes, enabling the rapid and rational selection of biomaterial formula based on available 3D printing methods, and vice versa.
Perovskite solar cells (PSCs) have emerged as a transformative photovoltaic technology, offering high power conversion efficiency (PCE) and the potential for cost-effective manufacturing. However, stability and large-scale manufacturing remain critical challenges that must be addressed for widespread adoption. This review provides a roadmap from single-junction perovskite solar cells to tandem deployment in space. First, material-level innovations are discussed, including mixed-cation and low-dimensional perovskites, transport materials, and additives that improve thermal and structural stability while enhancing efficiency. Then, we examine both established industrial standards and emerging scientific protocols aimed at stabilizing PSCs under operational conditions, including tandem cell integration strategies and encapsulation techniques to mitigate performance degradation. Manufacturing scalability is a focal point, where deposition methods and green solvents are explored to improve large-area film uniformity and reduce environmental impact. Additionally, the increasing viability of PSCs in extraterrestrial environments is assessed, with emphasis on their performance in space applications, radiation resistance, and flexible lamination methods for deployment in extreme conditions. Progress across materials innovation, device architectures, stability testing protocols, and both terrestrial and extraterrestrial applications collectively drives perovskite photovoltaics toward higher efficiency, stability, and cost-effectiveness.
Abstract Cement manufacturing is energy‐intensive (5Gj/t) and comprises a significant portion of the energy footprint of concrete systems. Incorporating modern monitoring, simulation and control systems will allow lower energy use, lower environmental impact, and lower costs of this widely used construction material. One of the goals of the CESMII roadmap project on the Smart Manufacturing of Cement included developing an analytical process model for clinker quality that includes the chemistry of the kiln feed and accounts for critical process variables. This predictive model will be used in nonlinear model predictive control system designed to significantly reduce process energy use while maintaining or improving product quality. In the cement manufacturing plant used in this study, the kiln feed (meal) is tested every 12 h and used to estimate the mineral composition of the cement kiln output (clinker) using the stoichiometry‐based Bogue's model and the expertise of the plant operators. During kiln operation, kiln output (clinker) is sampled and tested every 2 h to measure its chemical and mineral composition. The predicted and measured values of the clinker composition are used by the plant operators to adjust the kiln input stream and the production process characteristics to maintain stable operation and uniform product quality. However, the time delay between prediction and testing, along with inaccuracies inherent in the Bogue's model have made any process changes designed to minimize energy use problematic, especially in‐light of potential clinker quality issues that process changes often pose. A new analytical model that integrates quality information and process operation information has been developed from data collected from 2 years of production from an operating cement facility. To make the model fuel‐type‐independent, consumed heat energy was computed in the model instead of fuel type and amount. A Feedforward Network was trained and tailored from collected data. Many data‐based simulations were conducted to quantitatively evaluate the proposed model and the 5‐fold cross‐validation procedure was used to test the models. The resulting predictive model was shown to have a low root mean square error (MSE) with respect to the estimated clinker mineral composition compared to that using the industry standard “Bogue’ model”. The end goal of this work was to develop a single machine learning tool that allows the use of quality control data and process control variables to improve energy efficiency of the process in a continuous fashion. The proposed nonlinear model predictive control system (NMPC) can generate predicted kiln production characteristics based on manipulated variables in manner that accurately follows the target product quality values. Simulation results also show that the proposed model produced accurate predictions of kiln outputs that fell within the required constraints, while manipulating control variables within typical operational ranges.
Intrinsic structural and oxidic defects activate graphitic carbon electrodes towards electrochemical reactions underpinning energy conversion and storage technologies. Yet, these defects can also disrupt the long-range and periodic arrangement of carbon atoms, thus, the characterization of graphitic carbon electrodes necessitates in-situ atomistic differentiation of graphitic regions from mesoscopic bulk disorder. Here, we leverage the combined techniques of in-situ attenuated total reflectance infrared spectroscopy and first-principles calculations to reveal that graphitic carbon electrodes exhibit electric-field dependent infrared activity that is sensitive to the bulk mesoscopic intrinsic disorder. With this platform, we identify graphitic regions from amorphous domains by discovering that they demonstrate opposing electric-field-dependent infrared activity under electrochemical conditions. Our work provides a roadmap for identifying mesoscopic disorder in bulk carbon materials under potential bias.
The emergence of power efficiency as a primary constraint in processor and system design poses new challenges concerning power and energy awareness for numerical libraries and scientific applications. Power consumption also plays a major role in the design of data centers, which may house petascale or exascale-level computing systems. At these extreme scales, understanding and improving the energy efficiency of numerical libraries and their related applications becomes a crucial part of the successful implementation and operation of the computing system. In this paper, we study and investigate the practice of controlling a compute system's power usage, and we explore how different power caps affect the performance of numerical algorithms with different computational intensities. Further, we determine the impact, in terms of performance and energy usage, that these caps have on a system running scientific applications. This analysis will enable us to characterize the types of algorithms that benefit most from these power management schemes. Our experiments are performed using a set of representative kernels and several popular scientific benchmarks. Lastly, we quantify a number of power and performance measurements and draw observations and conclusions that can be viewed as a roadmap to achieving energy efficiency in the design and execution of scientific algorithms.
We present that growing energy demand and the need to reduce environmental impact, increase energy security, and rural economic development has encouraged the development of sustainable renewable fuels. Non-food lignocellulosic biomass is a suitable source for sustainable energy because the biomass feedstocks are low cost, abundant, and carbon neutral. Recent thermochemical conversion studies are frequently directed at converting biomass to high quality liquid fuel precursors or chemicals in a single step. Supercritical ethanol has been selected as a promising solvent medium to deconstruct lignocellulosic biomass since the ethanol has extraordinary solubility towards lignocellulosic biomass and can be resourced from cellulosic ethanol facilities. This review provides critical insight into both catalytic and non-catalytic strategies of lignocellulose deconstruction. In this context, the supercritical ethanol deconstruction pathways are thoroughly reviewed; gas chromatography coupled with mass spectrometry (GC-MS), one-dimensional and two-dimensional nuclear magnetic resonance spectrometry (NMR), and elemental analysis strategies towards liquid biomass deconstruction products are critically presented. Lastly, this review aims to provide readers a broad and accurate roadmap of this novel biomass to biofuel conversion techniques.
Coastal ecosystems are at the nexus of many high priority challenges in environmental sciences, including predicting the influences of compounding disturbances exacerbated by climate change on biogeochemical cycling. Extreme events such as hurricanes, flooding, landslides, and wildfires influence biogeochemical cycling in these systems. However, while research in coastal science is fundamentally transdisciplinary – as drivers of biogeochemical and ecological processes often span scientific and environmental domains – traditional place-based approaches are still often employed to understand coastal ecosystems. In this perspective, we argue that integration among distributed research sites from a macrosystem perspective is crucial to understand how compounding disturbances affect coastal ecosystems. We identify a roadmap for the implementation of an integrated network-of-networks framework that leverages existing research network sites in coastal ecosystems to advance continental-scale process understanding for studying extreme events and global change. We also identify specific ways that existing research efforts can maximize mutual benefit, and where additional infrastructure investments might increase return-on-investment along the coast, using the coastal continental US as a case study.
Security engineering approaches can often focus on a particular domain—physical security, cyber security, or personnel security, for example. Yet, security systems engineering consistently faces challenges requiring socio-technical solutions to address evolving and dynamic complexity. While some drivers of this complexity stem from complex risk environments, innovative adversaries, and disruptive technologies, other drivers are endogenous and emerge from the interactions across security engineering approaches. In response, INCOSE's Systems Security Working Group identified the need to better coordinate “disparate security solutions [that] operate independently” as one of eleven key concepts in their IS21 FuSE Security Roadmap. From this perspective, this need for “security orchestration” aligns with the perspective that security is a property that emerges from interactions within complex systems. Current efforts at Sandia National Laboratories are developing a systems security engineering approach that describes high consequence facility (HCF) security as a multidomain set of interacting layers. The result is a multilayered network (MLN)-based approach that captures the interactions between infrastructure, physical components, digital components, and humans in nuclear security systems. This article will summarize the MLN-based approach to HCF security and describe two preliminary results demonstrating potential benefits from incorporating interactions across disparate security solutions. Here, leveraging the logical structure of networks, this MLN model-based approach provides an example of how security orchestration provides enhanced systems security engineering solutions.
The chemistry of plasma–liquid interactions involves a complex interplay of physical and chemical processes at the plasma–liquid interface. These interactions give rise to the generation, transport, and transformation of various reactive species. Since the publication of the Lorenz Roadmap in 2016, significant progress has been made in understanding the interfacial transport and coupled reactions of plasma-generated species with inorganic and organic compounds. However, critical aspects of plasma–liquid chemistry and mass transfer still require further investigation. Here, this review summarizes recent work on processes at the plasma–liquid interface and the coupled reactions in the liquid phase. We highlight key findings related to the involvement of O atoms, H radicals, solvated electrons, photons, and nitrogen-derived species at the interface and within the bulk liquid.