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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Trends in Light and Temperature Sensitivity Recommendations among Licensed Biotechnology Drug Products

Inherent structural and functional properties of biotechnology-derived therapeutic biologics make them susceptible to light- and temperature-induced degradation and consequently can influence their quality. Photosensitivity of therapeutic proteins continues to be examined, but the commonalities and trends of storage conditions and information about light and temperature sensitivity among currently licensed therapeutic proteins has not been previously surveyed. Using a comprehensive and relational database approach, we conducted a scientific survey of all licensed biotechnology-derived drug products with the goal of providing evidence-based information about recommended storage conditions of formulations sorted by light- and temperature-related attributes as described for each product at licensure. We report the prevalence of indications for light and temperature sensitivity in formulations categorized by their presentation type, number of doses, container type, dosage form and active molecule type. We also report the storage temperature range across formulations and diluents for reconstitution and dilution. Formulations with excipients that potentially facilitate light-induced and thermal degradation were also noted. The result of our analysis indicates that light and temperature sensitivity are prevalent across therapeutic protein formulations. However, when a formulation is reconstituted or diluted, both light and temperature sensitivity are less clear. In addition, light and temperature sensitivity are more well defined in liquid formulations than lyophilized powder formulations, and more well defined in products manufactured in autoinjectors, prefilled-syringes, and pens than products in vials. Overall, our report provides a data-driven summary of storage conditions among therapeutic protein formulations to support the development of future biologic drug products.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Mastering HPC Runtime Prediction: From Observing Patterns to a Methodological Approach

The continual expansion of high-performance computing (HPC) brings with it an increasing need for efficiency. Heavy investment in energy, hardware, and software infrastructure to support peta- and exascale computing requires the optimization of existing systems and, wherever possible, the discernment and adoption of best-practices towards these goals. Such is the case for runtime prediction. When a job is submitted to an HPC system, an estimate of its runtime is provided by the user in the form of "requested wallclock". Error in this user-provided estimate can lead to jobs being prematurely killed by the scheduler, increased wait time on the queue, and decreased system utilization. More than fifteen years of research has been directed at mitigating these effects by using data-driven runtime predictions. Codified here is a set of commonalities and insights emerging from this body of work, which we present as recommendations and best practices. These practices are combined into a methodological approach described and evaluated on an 11-million-job dataset from the National Renewable Energy Laboratory's petascale HPC system, Eagle. This dataset and the accompanying codebase have been released to the public domain for the benefit of the wider HPC research community.

high performance computing↗
DOE OSTI2023

Mastering HPC Runtime Prediction: From Observing Patterns to a Methodological Approach: Preprint

The continual expansion of high-performance computing (HPC) brings with it an increasing need for efficiency. Heavy investment in energy, hardware, and software infrastructure to support peta- and exascale computing requires the optimization of existing systems and, wherever possible, the discernment and adoption of best-practices towards these goals. Such is the case for runtime prediction. When a job is submitted to an HPC system, an estimate of its runtime is provided by the user in the form of "requested wallclock''. Error in this user-provided estimate can lead to jobs being prematurely killed by the scheduler, increased wait time on the queue, and decreased system utilization. More than fifteen years of research has been directed at mitigating these effects by using data-driven runtime predictions. Codified here is a set of commonalities and insights emerging from this body of work, which we present as recommendations and best practices. These practices are combined into a methodological approach described and evaluated on an 11-million-job dataset from the National Renewable Energy Laboratory's petascale HPC system, Eagle. This dataset and the accompanying codebase have been released to the public domain for the benefit of the wider HPC research community.

high performance computing↗
DOE OSTI2024

System and method of making a cast steel alloy crankshaft having low porosity

Systems and methods of making a cast steel alloy crankshaft for an internal combustion engine are provided. The method comprises providing a mold of the crankshaft. The mold has cavities to form the crankshaft. The method further comprises melting a first metallic material at between 1400 degrees Celsius (° C.) and 1600° C. to define a molten metallic material. In addition, the method further comprises feeding the molten metallic material at a riser connection angle of between 30° and 75° in the cavities of the negative sand cast mold. The method further comprises cooling the molten metallic material at a solidification time of between 5 seconds (sec) and 20 sec in the negative sand cast mold with at least one chill member to define a solidified metallic material having dimensions of the cast steel alloy crankshaft. Furthermore, the method comprises separating the solidified metallic material from the negative sand cast mold to define the cast steel alloy crankshaft.

Wang, Liang↗
DOE OSTI2026

Additive Manufacturing Evaporative Casting

Traditional lost foam casting has been around for decades. The process uses foam forms blown by an injection molding like process or CNC milled into the desired shape, then pouring molten metal over the foam pattern to create a metallic object. Additive Manufacturing Evaporative Casting (AMEC) is a new process that eliminates foam forms by using additive manufacturing to 3D print the desired cast geometry. This not only saves time and money but allows for more advanced and complex designs that can’t be achieved by carving foam. Additionally, AMEC doesn’t require molds and tooling like other casting and foundry options. Because the AMEC process is new, extensive testing is needed to develop a better understanding of the process to minimize defects, quantify material properties, and start computer modeling for the process. This CRADA (collaborative research and development agreement) between ORNL and Skuld seeks to improve the process, develop a computational model, characterize material properties, and explore new applications.

36 MATERIALS SCIENCE↗
DOE OSTI2023

System and method of making an enhanced cast iron crankshaft

A method and system for manufacturing a cast iron crankshaft for a vehicle are provided. The system comprises a molding unit arranged to form a negative sand cast mold of the cast iron crankshaft. The mold comprising at least one molded cavity having a pattern with dimensions of the cast iron crankshaft. The system further comprises a feeding mechanism comprising a riser having a connector through which molten metallic material flows to the cast mold. The feeding mechanism feeds the molten metallic material at a riser connection angle in the at least one mold cavity. The riser connection angle corresponds to a connector modulus. The connector modulus is 20% greater than a cast modulus. The riser geometry corresponds to a riser modulus. The riser modulus is 20% greater than the connector modulus. The system further comprises a furnace, a cooling area, a separation unit, a controller and a power source.

Wang, Liang↗
DOE OSTI2021

Experimental Validation of Subsystem Models for a Novel Variable Displacement Hydraulic Motor

A novel, variable displacement, low-speed high-torque hydraulic motor is being developed that is expected to be highly efficient across a broad operating range. To ensure the final hardware achieves the expected performance, the models used in the development of the motor must be experimentally validated and revised, as necessary. The specific focus in this work is on mechanical energy loss models that were used to guide the design of a single-cylinder motor prototype and on experimental tests used for model validation. Ideally each model, whether friction loss in a piston/cylinder interface or energy loss due to leakage in a valve , would be individually validated by an independent test. This granular approach would remove any question of where the error lies and result in highly accurate models. However, many motor components have multiple forms of energy loss, creating difficulty in validating individual losses. Additionally, it is not physically realizable to divide many components into an individual model equivalent test. Testing the motor as a full assembly is possible, but pinpointing the source of discrepancies between the model and the hardware becomes difficult with dozens of models potentially being partially responsible. A compromise was found by separating the motor into functional component groups that are characterized by the type of loss and ability to test each subcomponent independently. By checking for correlation between test observations and model predictions, revisions could be implemented into the models. This allows future solutions to be more accurately predicted in the design phase to drive the design of better machines.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Characterization of dot-specific and tunable effective g factors in a GaAs/AlGaAs double quantum dot single-hole device

Difference in g factors in multidot structures can form the basis of dot-selective spin manipulation under global microwave irradiation. Here employing electric dipole spin resonance facilitated by strong spin-orbit interaction (SOI), we observe differences in the extracted values of the single-hole effective g factors of the constituent quantum dots of a GaAs/AlGaAs double quantum dot device at the level of ~ 5 %–10%. We examine the continuous change in the hole g factor with electrical detuning over a wide range of interdot tunnel couplings and for different out-of-plane magnetic fields. The observed tendency of the quantum dot effective g factors to steadily increase on decreasing the interdot coupling or on increasing the magnetic field is attributed to the impact on the SOI of changing the dot confinement potential and heavy-hole light-hole mixing.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Ultra compact Bragg grating devices with broadband selectivity

Current silicon waveguide Bragg gratings typically introduce perturbation to the optical mode in the form of modulation of the waveguide width or cladding. However, since such a perturbation approach is limited to weak perturbations to avoid intolerable scattering loss and higher-order modal coupling, it is difficult to produce ultra-wide stopbands. In this Letter, we report an ultra-compact Bragg grating device with strong perturbations by etching nanoholes in the waveguide core to enable an ultra-large stopband with apodization achieved by proper location of the nanoholes. With this approach, a 15 µm long device can generate a stopband as wide as 110 nm that covers the entire C + L band with a 40 dB extinction ratio and over a 10 dB sidelobe suppression ratio (SSR). Similar structures can be further optimized to achieve higher SSR of > <#comment/> 17 d B for a stopband of about 80 nm.

Li, Ang (ORCID:0000000232251280)↗
DOE OSTI2022

Improved Polypropylene Thermoformability through Polyethylene Layering

Due to its low cost, stiffness, and recyclability, isotactic polypropylene (iPP) is an excellent candidate for packaging applications. However, iPP is notoriously difficult to thermoform due to its low melt strength. The addition of just 10 thin layers of high-molecular-weight, linear low-density polyethylene (LLDPE) into iPP sheets by coextrusion significantly increased extensional viscosity and reduced sag. Both LLDPE and iPP were metallocene-catalyzed with excellent adhesion as measured in our previous work. Here, we performed a series of hot tensile tests and sheet sag measurements to determine the properties of the iPP sheet and the multilayer sheet between 130 and 180 °C. To evaluate the thermoformability of these multilayer sheets, truncated conical cups were positive vacuum formed at different temperatures and heating times, and the crush strength was measured. Cups that released easily from the mold with good shape retention and a crush strength within 80% of the maximum value were used to define a temperature–time thermoformability window. We estimated the maximum stress that occurred during the thermoforming process to be 5 MPa. Layer thicknesses before and after thermoforming were used to estimate an average strain of 0.78. The thin LLDPE layers decreased the yield stress below 5 MPa. This enabled thermoforming at sheet temperatures as low as 150 °C. The immiscible LLDPE interfaces increased extensional viscosity, which decreased sag in the multilayer sheets compared to iPP. This broadened the thermoforming range to temperatures as high as 180 °C and allowed longer heating times. These highly thermoformable, layered sheets may be recycled as iPP since they contain only 8% of LLDPE.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Data Centers and Digital Assurance Workshop 3 – Mitigations for Digital Assurance Risks

The third session of the TADA (Technical Assistance for Digital Assurance) Data Centers Cohort, held on November 18, 2025, focused on developing mitigation strategies for digital assurance risks identified in previous workshops. Hosted by Idaho National Laboratory (INL) and ScottMadden, the session emphasized the application of Cyber-Informed Engineering (CIE) to data center infrastructure, particularly at the utility–data center interface. Participants revisited and ranked key digital assurance risks, including architecture and interface weaknesses, governance gaps, and AI-enabled threats. The workshop introduced the 12 principles of CIE, advocating for consequence-focused design, engineered controls, and secure information architecture to proactively reduce cyber-physical vulnerabilities. These principles were applied to critical data center systems such as power distribution, UPS, cooling, SCADA/BMS, and grid-forming batteries. The session also addressed governance challenges at the interconnection boundary, highlighting the need for clear roles in telemetry sharing, firmware management, and trip settings. Special attention was given to emerging risks from behind-the-meter (BTM) generation, including reverse-power flow and the integration of small modular reactors (SMRs), which shift data centers from large loads to complex generation nodes. Participants explored how interconnection agreements can serve as enforceable instruments for digital assurance, and reviewed gaps in current standards such as NERC CIP, IEC 62443, and IEEE 1547. The workshop concluded with pathways to standardization, including model agreement language, state-level programs, and expanded NERC guidance. INL also presented tools and frameworks for secure procurement and supplier risk management, reinforcing the need for integrated engineering and policy solutions to secure the evolving data center–grid ecosystem. Session 3 of 3.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

The relative influences of hydrologic information and dams’ hydropower scheduling decisions on electricity price forecasts

Price dynamics in wholesale electricity markets are driven by supply and demand. In markets with hydroelectric dams, the timing and amount of hydropower offered can influence prices in similar ways to wind and solar power. Unlike variable renewable energy, however, the supply of hydropower in wholesale markets is a function of both water availability and operational decisions at dams. Dam operators maximize revenues in wholesale markets by aligning generation with the periods of highest expected prices, and these scheduling decisions may in turn influence prices. Here, we examine the relative importance of two types of information in predicting forward electricity prices: a) water availability at dams, in the form of short-to-medium-range hydrological forecasts; and b) hourly scheduling decisions at dams. Using softly coupled hydrologic, hydropower scheduling, and power systems models spanning the U.S. Western Interconnection, we quantify the importance of hydrologic forecast accuracy in correctly predicting wholesale electricity prices and compare this with the influence of dam operators’ own hourly scheduling decisions on realized market prices. We find that aligning hydropower generation schedules with the periods of high forecasted prices causes larger, inadvertent price forecast errors than imperfect hydrologic forecasts. This suggests that knowledge of how water is managed by dam operators within the week is more important than weekly inflow forecast errors when predicting forward electricity prices. Our findings have implications for optimal hydropower scheduling by region. Specifically, accounting for price effects is critical in markets dominated by hydropower capacity.

Electricity markets↗
DOE OSTI2023

Optimization with Neural Network Feasibility Surrogates: Formulations and Application to Security-Constrained Optimal Power Flow

In many areas of constrained optimization, representing all possible constraints that give rise to an accurate feasible region can be difficult and computationally prohibitive for online use. Satisfying feasibility constraints becomes more challenging in high-dimensional, non-convex regimes which are common in engineering applications. A prominent example that is explored in the manuscript is the security-constrained optimal power flow (SCOPF) problem, which minimizes power generation costs, while enforcing system feasibility under contingency failures in the transmission network. In its full form, this problem has been modeled as a nonlinear two-stage stochastic programming problem. In this work, we propose a hybrid structure that incorporates and takes advantage of both a high-fidelity physical model and fast machine learning surrogates. Neural network (NN) models have been shown to classify highly non-linear functions and can be trained offline but require large training sets. In this work, we present how model-guided sampling can efficiently create datasets that are highly informative to a NN classifier for non-convex functions. We show how the resultant NN surrogates can be integrated into a non-linear program as smooth, continuous functions to simultaneously optimize the objective function and enforce feasibility using existing non-linear solvers. Overall, this allows us to optimize instances of the SCOPF problem with an order of magnitude CPU improvement over existing methods.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Photovoltaics module reliability for the terawatt age

Photovoltaics (PV), or solar electricity generation, has become the cheapest form of energy in many locations worldwide and, combined with energy storage, has the potential to satisfy most of our electricity needs. PV has grown at an annual compounded growth rate of approximately 30% in the last three decades. Solar energy systems will continue their impressive growth in distributed energy, microgrids, and utility scale, as efforts are made for dependable electricity in an age with increasing extreme weather. However, within this remarkable success lies a new challenge. The growth curve, combined with rapid product innovation and scale up, means that the majority of PV systems are new, without the three years of performance data that have been required in the past to estimate product lifetime. PV reliability has to address this challenge. In this review we present a brief summary of PV reliability starting with brief historical synopsis, detailing some of the technological challenges and present a framework required for long lifetime.

14 SOLAR ENERGY↗
DOE OSTI2021

National Alliance for Water Innovation (NAWI) Master Technology Roadmap

The National Alliance for Water Innovation (NAWI) is a research consortium formed to accelerate transformative research in desalination and treatment to lower the cost and energy required to produce clean water from nontraditional water sources and realize a circular water economy. Prior to developing this master roadmap, a team of NAWI researchers and water professionals engaged in a detailed process of evaluating water uses, state-of-the-art technologies, emerging technologies and existing uses of desalination and advanced water technologies within five major water user categories in the United States. The resulting Water User Sector Roadmaps were complemented by a Baseline Analysis of a suite of representative treatment systems conducted by NAWI researchers working in collaboration with the hub’s Industrial Partners. This Master Roadmap, which synthesizes the findings from these different efforts as well as feedback from members of NAWI’s Research Advisory Council, serves as the basis for identifying NAWI’s research Areas of Interest (AOIs).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Isolation of a Terminal Cobalt Nitride in a Metal–Organic Framework

Transition metal nitrides are reactive intermediates in biological and industrial processes. Chemists have synthesized molecular model complexes of such reactive species to understand their function and electronic requirements for new applications. However, molecular chemistry can suffer from intra- and intermolecular decomposition pathways, which preclude further discovery of unknown reactive species. Metal-organic frameworks offer an opportunity for creating long-lived forms of such species with the vacuum of the pore suppressing degradation while simultaneously enabling substrate access for controlled reactivity studies. Here, in this study, we report the characterization of an elusive terminal cobalt nitride species generated through photolysis or thermolysis of a site-isolated cobalt azide within the evacuated metal-organic framework CoN 3 -MFU-4l. The first crystal structure of such a species is presented, with vibrational, X-ray absorption, and electron paramagnetic resonance spectroscopies providing further direct evidence for its formation while elucidating its electronic structure. The system additionally enables subsequent reactivity studies with selected substrates, revealing a unique ambiphilic behavior for a metal nitride species.

Börgel, Jonas [University of California, Berkeley,↗
DOE OSTI2025

Texture Analysis of Inconel 718 with Different Modes During Single-Track Laser Surface Re-Melting

An in-depth understanding of the texture formation in melt pools allows for the modification of the surface layer microstructure and corresponding material properties, providing an opportunity to integrate laser surface re-melting into metal additive manufacturing. This study investigates crystallographic texture formation at different cooling rates in single melting tracks on the Inconel 718 (IN718) plate produced by laser surface re-melting. The cooling rate varies from 2.31 × 10 5 °C/s to 9.56 × 10 5 °C/s with the increase in scanning rates from 400 mm/s to 1200 mm/s, measured by recently developed real-time temperature monitoring of melt pools. Columnar grains are dominant, with distinct crystallographic textures forming in the melt pools. At a slower scanning speed, the keyhole mode shows three different textures forming at different depths (crystallographically layered structure), while, at a faster scanning speed, the conduction mode shows only random grain orientation. There are no pores/voids detected, and the columnar grain morphology and columnar grain width (8.6 μm to 12.4 μm) follow the analysis framework in terms of thermal gradient and solidification rate analysis. This implies that laser surface re-melting provides the potential to modify the surface structure from a random grain orientation to a crystallographically layered structure.

36 MATERIALS SCIENCE↗