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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Development of Additively Manufactured Complex Tools for Autoclave Cure Composites

IACMI Project 4.9, Tooling for Composites with Washout Features Produced by Additive Manufacturing, assembled a team including the industry lead, Ability Composites, NREL and Colorado State University (CSU). Ability Composites had originally expressed interest in alternate methods of producing tooling for composite parts. In follow-up discussions, it became clear that one of the bigger tooling challenges revolved around small production volume composite parts that were tooled on washout material due to the complex geometry. To build an understanding of the potential, both from a technology and a cost perspective, for replacing conventional washout tooling with 3D printed thermoplastic tooling, a number of commercially available dissolvable FDM printing materials were evaluated, leading to tooling representative of commercial articles of interest to Ability Composites. Ultimately, Ability Composites was able to directly compare autoclave processed prepreg composite parts produced on conventional washout tooling to composite parts molded on 3D printed dissolvable tooling produced at CSU. Small, laboratory test specimens were developed to investigate the structural performance of the candidate materials under autoclave processing conditions, which were nominally 121 °C (250 °F) and 345 kPa (50 psi). In addition, several internal structural configurations (infills) were evaluated under autoclave conditions using model materials. The results of these tests indicated that two materials, Stratasys ST 130 and Infinite Materials Solutions Aquasys 180 (AQ 180), were the best candidates, given the specified autoclave processing conditions. ST-130 was slightly more robust than AQ-180; however, the AQ-180 was carried forward as it was dissolvable in water, not requiring the basic solution needed to dissolve ST-130. Based on the preliminary material and 3D printed structures evaluations, larger tools with a truncated square pyramid geometry were created to produce prepreg composite test articles for 3D printed dissolvable tool evaluation under standard autoclave fabrication conditions. Two tools were manufactured using ST-130 and one tool using traditional ceramic washout tooling media. The tools were evaluated for geometric fidelity and surface roughness changes before and after carbon fiber/epoxy prepreg composites were manufactured on the tooling. The autoclave processing did not impact the geometry significantly and was completed at 121 °C and 345 kPa, indicating satisfactory tool performance. The results from surface roughness testing of both the resulting composite and the associated tooling indicated that an adequate surface resulted without the need for a surface sealing step, as was required for the conventional washout tooling. Based on results of the truncated pyramid tests as a basis, ST-130, AQ-120 and AQ-180 materials were carried forward to the tool geometry of interest to Ability Composites. These hollow rectangular bent ducts, which were complex in nature and not extractable after cure, were used to understand the impacts of tool material and thickness. One ST-130 tool was produced as a partially solid part, with an enclosed 40% dense infill region to reduce weight and material use. This was the same approach evaluated in the truncated pyramid portion of the study. This tool was to be envelope vacuum bagged and directly compared to a monolithic tool of conventional washout material. The traditional monolithic ceramic tool was manufactured by Ability composites using CNC-based subtractive methods. An additional five dissolvable polymer tools, manufactured from ST-130, AQ-120, and AQ-180, using a hollow design were 3D printed and used to produce carbon fiber/epoxy composite evaluation articles. These hollow dissolvable tools were expected to be less influenced by the autoclave conditions as the wall was solid and vacuum bagging was inside and outside the tool. This alternative geometry was also evaluated as an option in techno-economic modeling. Print times were reduced from in excess of 3 days to under 30 hours, while surface quality and and tool integrity were substantially improved in the transition from the partially solid tool to the hollow tooling concept. Ability Composites produced autoclave-cured prepreg ducts on each of these tools. The autoclave conditions utilized were more severe than those of the initial trials, reaching temperatures of 160 °C and a pressure of 414 kPa. Under these conditions, the partially solid 3D printed tool with skin and 40% dense infill crushed significantly; however, the thicker ST-130 hollow tool showed good promise, deforming only slightly. The thinner hollow tool walls were unsuccessful as were the other materials. Overall, the hollow tool manufacturing process saved significant amounts of time and material in manufacturing as compared to the solid ducts and produced composite surface quality improvements compared to the traditional washout tooling. The TEM was developed to allow direct comparisons between conventional washout tool manufacturing processes and those developed at CSU. It also allowed for two separate 3D printed tool geometries to be analyzed and compared. In this case, the square bent duct tool geometry was determined to be representative of common washout tools. This geometry was compared with a scaled-up version of it to assess differences in the two manufacturing processes based on tool size. The model was developed to make use of user input in the form of geometry details, process steps, manufacturing parameters, bulk material costs, capital equipment costs, and general costs to calculate overall labor, material, capital equipment, and energy costs per manufactured tool for the conventional and additive manufacturing processes for the two representative geometries. It was also able to estimate step-by-step process times for the manufacturing process and geometries. Based on significant input from Ability Composites and CSU from their knowledge gained from hands-on manufacturing of the 3D printed bent duct tool geometry, costs and process times were calculated for the two manufacturing processes. Results showed that the additive manufacturing techniques developed at CSU can substantially reduce the costs of tool manufacturing by reducing labor times and material usage. This is because additive manufacturing is a relatively hands-off process and allows for the tool design to be optimized to reduce material usage. The disadvantage, however, is that process times for additive manufacturing are significantly longer. The three-dimensional (3D) printing process is slow if tight tolerances are required, but the analysis did show that print times could be reduced with the hollow tool geometry. Also, further advances in additive manufacturing could expedite the process. Costs and process times for the tool washout process were calculated separately. They showed that costs are relatively insignificant when compared to the overall tool manufacturing processes, but with increases in tool size, costs for the conventional manufacturing approach are larger than for additive manufacturing. Again, the washout process for conventional tools is very hands-on, whereas for additively manufactured tools the print medium is dissolved in an automated detergent bath at the sacrifice of process time. The analysis showed that optimizing the additively manufactured tools may also reduce washout times. Overall, Project 4.9 demonstrated that commercially available dissolvable 3D printing materials exist that can be used to produce dissolvable tooling capable of surviving prepreg composites fabrication under autoclave conditions of 121 °C (250 °F) and 345 kPa (50 psi). An alternative hollow dissolvable tool design was developed which was structurally superior to the initial concept and was cost and time effective versus conventional washout tooling. The 3D printed sacrificial tool required no added surface sealing steps prior to composite part layup and cure, offering a significant advantage over the porous conventional washout tooling.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Evaluating Operators in Deep Neural Networks for Improving Performance Portability of SYCL

SYCL is a portable programming model for heterogeneous computing, so it is important to obtain reasonable performance portability of SYCL. Towards the goal of better understanding and improving performance portability of SYCL for machine learning workloads, we have been developing benchmarks for basic operators in deep neural networks (DNNs). These operators could be offloaded to heterogeneous computing devices such as graphics processing units (GPUs) to speed up computation. In this work, we introduce the benchmarks, evaluate the performance of the operators on GPU-based systems, and describe the causes of the performance gap between the SYCL and Compute Unified Device Architecture (CUDA) kernels. We find that the causes are related to the utilization of the texture cache for read-only data, optimization of the memory accesses with strength reduction, shared local memory accesses, and register usage per thread. We hope that the efforts of developing benchmarks for studying performance portability will stimulate discussion and interactions within the community.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Decarbonization of heat pump dual fuel systems using a practical model predictive control: Field demonstration in a small commercial building

In the transition from fossil fuel to electrified heating, a concerning trend is emerging in certain regions of the US. Owners of buildings with gas-based systems leave them in place after adding heat pumps (HPs). Existing control solutions for these hybrid (dual fuel) systems are rudimentary and fall short of realizing the full carbon reduction potential of these systems. Model predictive control (MPC) is often regarded as the benchmark for achieving optimal control in integrated systems. However, in the case of small-medium commercial buildings (SMCBs), the control and communication infrastructure required to facilitate the implementation of such advanced controls is often lacking. This paper presents a field implementation of easy-to-deploy MPC for a dual fuel heating system consisting of HPs and a gas-fired furnace (GF) for SMCBs. The control system is deployed on an open-source middleware platform and utilizes low-cost sensor devices to be used for real SMCBs without major retrofits. Here, we demonstrated this MPC in a real office building with 5 HPs and 1 GF for 2 months. The test results showed that MPC reduced 27% of cost while completely eliminating GF usage by shifting 23% of the thermal load from occupied-peak time to non-occupied-non-peak times.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Towards reverse mode automatic differentiation of Kokkos-based codes

Derivative computation is a key component of optimization, sensitivity analysis, uncertainty quantification, and the solving of nonlinear problems. Automatic differentiation (AD) is a powerful technique for evaluating such derivatives, and in recent years, has been integrated into programming environments such as Jax, PyTorch, and TensorFlow to support derivative computations needed for training of machine learning models, facilitating wide-spread use of these technologies. The C++ language has become the de facto standard for scientific computing due to numerous factors, yet language complexity has made the wide-spread adoption of AD technologies for C++ difficult, hampering the incorporation of powerful differentiable programming approaches into C++ scientific simulations. This is exacerbated by the increasing emergence of architectures, such as GPUs, with limited memory capabilities and requiring massive thread-level concurrency. C++ AD tools must effectively use these environments to bring novel scientific simulations to next-generation DOE experimental and observational facilities. In this project, we investigated source transformation-based automatic differentiation using LLVM compiler infrastructure to automatically generate portable and efficient gradient computations of Kokkos-based code. We have demonstrated that our proposed strategy is feasible by investigating the usage of a prototype LLVM-based source transformation tool to generate gradients of simple functions made of sequences of simple Kokkos parallel regions. Speedups of up to 500x compared to Sacado were observed on NVIDIA V100 GPU.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Analysis and design of DC-link capacitor in cascaded H-bridge multilevel active front-end converter

Medium-voltage grid-tied systems often use a cascaded H-bridge multi-level active front-end. In this converter, dc link bus capacitors play an important role in stabilizing the converter and enabling both active and reactive power injections. The present work provides analytical expressions for the capacitor current, which are essential for optimizing system design (especially capacitor size vs. lifetime). Then, the expression is incorporated into the grid connected bidirectional power system model. Consequently, this work contributes to the guiding principles to choose accurate dc link capacitor ratings against grid-side power delivery requirements. DC-link capacitors come with an expiration date which is dependent on the operating power and voltage values. Lack of information about the lifetime of the dc-link capacitor creates uncertainty in the duration of online operation of the converter, thus increasing the probability of a contingency outage. The lifetime of the capacitor can range from only a few seconds of online operation of the station due to under-sizing, to a compromised quality of power flowing through other solid state devices which may not have been designed for the transients that are caused by over-sizing of the dc-link capacitor. A better estimate of the lifetime of these capacitors can help maximize usage and maintain scheduled outages more efficiently, without disruption in service.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Edge ICRF simulations in 3D geometry: From MHD equilibrium to coupling determination

We present in this work a consistent numerical scheme that allows the computation of 3D magnetic fields a nd 3D density profiles and their usage in ion cyclotron range of frequencies (ICRF) coupling simulations. We first utilize the PARVMEC code to compute the 3D free-boundary plasma equilibrium in the ideal magnetohydrodynamic (MHD) approximation. Since the PARVMEC solution is only defined within the last closed flux surface (LCFS), the magnetic field domain is extended to the scrape-off layer (SOL) via the BMW code, which computes a divergence-free magnetic field solution arising from the external conductors’ vacuum field a nd t he PARVMEC flux surface currents. This magnetic reconstruction is then used in the EMC3-EIRENE transport code in order to compute 3D density profiles. In the last step, the RAPLICASOL code is utilized to compute the ICRF antenna S-matrices resulting from the 3D density profiles. We exemplify this scheme for the ASDEX Upgrade tokamak. A new implementation of a curved model for the ASDEX Upgrade ICRF 2-strap antenna in RAPLICASOL allows simulations in realistic geometry, without any coordinate transformations.

Lopez, Graham↗
DOE OSTI2020

US Department of Energy, Office of Science High Performance Computing Facility Operational Assessment 2019 Oak Ridge Leadership Computing Facility

Oak Ridge National Laboratory's (ORNL's) Leadership Computing Facility (OLCF) continues to surpass its operational target goals: supporting users; delivering fast, reliable computational ecosystems; creating innovative solutions for high performance computing (HPC) needs; and managing risks, safety, and security associated with operating some of the most powerful computers in the world. The results can be seen in the cutting-edge science conducted by users and the praise from the research community. Calendar year (CY) 2019 was a big year as OLCF staff ran five world-class resources (the leadershipclass computers Titan and Summit, the large analysis cluster called Eos, and the massive parallel filesystems called Atlas and Alpine)) and also began power and cooling upgrades for a 2021 exascale system called Frontier. While continuing exceptional operation of Titan, Eos, and Rhea, the OLCF released the Summit supercomputer for production on January 1, 2019. Summit debuted as the most capable and efficient system in its class and has been recognized as the most powerful system in the world for its performance on both the high performance linpack (HPL) and conjugate gradient (HPCG) benchmark applications since June 2018 according to TOP500. Summit represents the culmination of a multiyear effort between the OLCF, IBM, NVIDIA, and Mellanox to deliver a system that is unmatched for modeling, simulation, data analysis, and learning. To hit the ground running with science-ready applications on day one, application teams worked closely with the OLCF through the Center for Accelerated Application Readiness (CAAR) program for years in advance of the Summit deployment. CY 2019 was filled with outstanding results and accomplishments: a very high rating from users on overall satisfaction for the sixth year in a row; a tremendous amount of core-hours delivered to researchers from two leadership-class systems; and success in delivering on the allocation split of roughly 60%, 30%, and 10% of core-hours offered for the Innovative and Novel Computational Impact on Theory and Experiment (INCITE), Advanced Scientific Computing Research Leadership Computing Challenge (ALCC), and Director's Discretionary (DD) programs, respectively (see Operational Performance section). These accomplishments, coupled with the high utilization rates (overall and capability usage), represent the fulfillment of the promise of both leadership-class machines: efficient facilitation of leadership-class computational applications. Table ES.1 presents a summary of the 2019 OLCF metric targets and the associated results. More information can be found in the Operational Performance section for each OLCF resource. The scientific accomplishments of OLCF users are a strong indication of long-term operational success, with publications this year in such notable journals and publications as Nature, Nature Physics, Nature Plants, Physical Review X, Journal of the American Physical Society, Cell, Nano Letters, and Trends in Biotechnology. Crucial domain-specific discoveries facilitated by resources at the OLCF are described in the High Performance Computing Facility Operational Assessment 2019 Oak Ridge Leadership Computing Facility (OAR) Strategic Results section. For example, researchers used Summit to pinpoint and understand the production of proteins from genetic information, including mutations and the functional expression of disease (Section 8.2).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Privacy-preserving federated learning: Application to behind-the-meter solar photovoltaic generation forecasting

Here, the growing usage of decentralized renewable energy sources has made accurate estimation of their aggregated generation crucial for maintaining grid flexibility and reliability. However, the majority of distributed photovoltaic (PV) systems are behind-the-meter (BTM) and invisible to utilities, leading to three challenges in obtaining an accurate forecast of their aggregated output. Firstly, traditional centralized prediction algorithms used in previous studies may not be appropriate due to privacy concerns. There is therefore a need for decentralized forecasting methods, such as federated learning (FL), to protect privacy. Secondly, there has been no comparison between localized, centralized, and decentralized forecasting methods for BTM PV production, and the trade-off between prediction accuracy and privacy has not been explored. Lastly, the computational time of data-driven prediction algorithms has not been examined. This article presents a FL power forecasting method for PVs, which uses federated learning as a decentralized collaborative modeling approach to train a single model on data from multiple BTM sites. The machine learning network used to design this FL-based BTM PV forecasting model is a multi-layered perceptron, which ensures privacy and security of the data. Comparing the suggested FL forecasting model to non-private centralized and entirely private localized models revealed that it has a high level of accuracy, with an RMSE that is 18.17% lower than localized models and 9.9% higher than centralized models.

14 SOLAR ENERGY↗
DOE OSTI2022

Keyhole-mode Microscopy Dataset for Laser Powder-bed Fusion Modeling

Laser powder-bed fusion (LPBF) is an additive manufacturing (AM) technology that uses high-power sintering lasers to precisely construct metal designs. Material is accumulated by selectively sintering regions of a metal powder layer to a growing structure underneath forming a 3D geometry. Certain conditions make fusion in this process to operate in "keyhole-mode," characterized by targeted materials evaporating as plasma. While keyhole-mode operations can produce deeper molten pools than that observed in "conduction-mode", this mode of operation is often undesirable, as its molten regions can collapse on themselves, encapsulating metal vapors and forming cavities. The formation of such cavities negatively affects strength and consistency of the fused materials. To identify and avoid these detrimental effects of keyhole-mode operation, considerable data collection and analysis regarding this mode of operation is needed. Therefore, under the support of the "Open Data Initiative," Lawrence Livermore National Lab is releasing a dataset for analysis of this keyhole effect, and the conditions which transition operation from conduction-mode to keyhole-mode melting. This dataset consists of 600+ micrographs from laser powder-bed single track runs, with differing cross-section angles and input parameters, operating under both normal and keyhole mode operations. This journal documents the collection, organization, and usage of this dataset.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Optimizing entanglement and Bell inequality violation in top antitop events

A top quark and an antitop quark produced together at colliders have correlated spins. These spins constitute a quantum state that can exhibit entanglement and violate Bell’s inequality. In realistic collider experiments, most analyses allow the axes, as well the Lorentz frame, to vary event by event, thus introducing a dependence on the choice of event-dependent basis leading us to adopt “fictitious states,” rather than genuine quantum states. The basis dependence of fictitious states allows for an optimization procedure, which makes the usage of fictitious states advantageous in measuring entanglement and Bell inequality violation. In this work, we show analytically that the basis that diagonalizes the spin-spin correlations is optimal for maximizing spin correlations, entanglement, and Bell inequality violation. We show that the optimal basis is approximately the same as the fixed beam basis (or the rotated beam basis) near the t t ¯ production threshold, while it approaches the helicity basis far above threshold. Using this basis, we present the sensitivity for entanglement and Bell inequality violation in t t ¯ events at the Large Hadron Collider (LHC) and a future e + e − collider. Since observing Bell inequality violation appears to be quite challenging experimentally, and requires a large dataset in collider experiments, choosing the optimal basis is crucially important to observe Bell inequality violation. Our method and general approach are equally applicable to other systems beyond t t ¯ , including interactions beyond the Standard Model. Published by the American Physical Society 2025

Cheng, Kun (ORCID:0000000249592997)↗
DOE OSTI2024

4-Clique network minor embedding for quantum annealers

Quantum annealing is a quantum algorithm for computing solutions to combinatorial optimization problems. This study proposes a method for minor embedding optimization problems onto sparse quantum annealing hardware graphs called 4-clique network minor embedding. This method is in contrast to the standard minor embedding technique of using a path of linearly connected qubits in order to represent a logical variable state. The 4-clique minor embedding is possible on Pegasus graph connectivity, which is the native hardware graph for some of the current D-Wave quantum annealers. The Pegasus hardware graph contains many cliques of size 4, making it possible to form a graph composed entirely of paths of connected 4-cliques on which a problem can be minor-embedded. The 4-clique chains come at the cost of additional qubit usage on the hardware graph, but they allow for stronger coupling within each chain, thereby increasing chain integrity, reducing chain breaks, and allow for greater usage of the available energy scale for programming logical problem coefficients on current quantum annealers. The 4-clique minor embedding technique is compared with the standard linear path minor embedding with experiments on two D-Wave quantum annealing processors with Pegasus hardware graphs. We show proof-of-concept experiments where the 4-clique minor embeddings can use weak chain strengths while successfully carrying out the computation of minimizing random all-to-all spin glass problem instances. Published by the American Physical Society 2024

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Long‐Term Impacts of Global Solid Biofuel Emissions on Ambient Air Quality and Human Health for 2000–2019

Globally, solid biofuels (SB) have been widely used for household cooking and energy production for decades due to electricity shortages and socio-economic barriers to adopting renewable energy alternatives. This has detrimental effects on air quality, human health, and climate through trace gas and aerosol emissions. Despite numerous studies, the long-term consequences of SB emissions remain poorly understood. Here, we use the Community Earth System Model and the Community Emissions Data System emission inventory to investigate the SB emission impacts on air quality and human health for 2000–2019. Global SB emission increased the ambient PM 2.5 (particulate matter with aerodynamic diameters ≤2.5 μm) and ozone (O 3 ) concentrations up to 23.61 μg/m 3 and 13.69 ppbv, with significant effects found in India, China, and the Rest of Asia (ROA). Our study estimates total annual premature deaths (APDs) associated with global SB-attributable PM 2.5 and O 3 exposure as 1.11 million [95% confidence interval (95% CI): 1.00–1.22 million] in 2000 up to 1.43 million (95% CI: 1.30–1.56 million) in 2019. China's SB emissions and associated APDs have reduced substantially, whereas India and ROA had a major leap in both estimates in 2019 compared to 2000. China's progress in cutting residential SB emissions accounts for its improvements. Our study urges the reduction of SB usage and emissions to potentially improve overall air quality and human health conditions, especially in highly populated, low- and middle-income countries, where the poor air quality and associated health burden attributable to SB emissions are estimated to be higher.

O 3↗
DOE OSTI2021

Tutorial: Langevin Dynamics methods for aerosol particle trajectory simulations and collision rate constant modeling

The Langevin Dynamics (LD) method (also known in the literature as Brownian Dynamics) is routinely used to simulate aerosol particle trajectories for transport rate constant calculations as well as to understand aerosol particle transport in internal and external fluid flows. This tutorial intends to explain the methodological details of setting up a LD simulation of a population of aerosol particles and to deduce rate constants from an ensemble of classical trajectories. We discuss the applicability and limitations of the translational Langevin equation to model the combined stochastic and deterministic motion of particles in fields of force or fluid flow. The drag force and stochastic “diffusion” force terms that appear in the Langevin equation are discussed elaborately, along with a summary of common forces relevant to aerosol systems (electrostatic, gravity, van der Waals, …); a commonly used first order and a fourth order Runge-Kutta time stepping schemes for linear stochastic ordinary differential equations are presented. A MATLAB® implementation of a LD code for simulating particle settling under gravity using the first order scheme is included for illustration. Scaling analysis of aerosol transport processes and the selection of timestep and domain size for trajectory simulations are demonstrated through two specific aerosol processes: particle diffusion charging and coagulation. Fortran® implementations of the first order and fourth order time-stepping schemes are included for simulating the 3D motion of a particle in a periodic domain. Lastly, potential applications and caveats to the usage of LD are included as a summary.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Stochastic Home Energy Management Systems with Varying Controllable Resources

This paper studies the performance of a model predictive control (MPC) algorithm in a home energy management system (HEMS) as the set of controllable resources varies and under both a constant and a time-of-use (TOU) electricity price structure. The set of controllable resources includes residentially-owned photovoltaic (PV) panels, a home battery system (HBS), an electric vehicle (EV), and a home heating, ventilation, and air conditioning (HVAC) system. The HEMS optimally schedules the set of controllable resources given user preferences such as indoor thermal comfort and electricity cost sensitivity. The home energy management system is built on a chance constrained, MPC-based algorithm, where the chance constraint ensures the indoor thermal comfort is satisfied with a high probability given uncertainty in the outdoor temperature and solar irradiance forecasts. Simulation results for varying sets of controllable resources under two different electricity price structures demonstrate the variation in the HEMS control with respect to HBS operation, electricity cost, and grid power usage.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

rcsb-api : Python Toolkit for Streamlining Access to RCSB Protein Data Bank APIs

The Protein Data Bank (PDB) was founded in 1971 as the first open-access digital data resource in biology to serve as the single global archive for three-dimensional (3D) macromolecular structure data. Current PDB holdings exceed 230,000 experimentally determined structures of proteins, nucleic acids, viruses, and macromolecular machines. The RCSB Protein Data Bank RCSB.org research-focused web portal facilitates search, analyses, and visualization of every PDB structure along with more than one million Computed Structure Models from AlphaFold DB and the ModelArchive. It is powered by a set of publicly available Application Programming Interfaces (APIs) that both support RCSB.org users and provide programmatic access to PDB data. Given the breadth and levels of granularity encompassed in this rich data collection, efficiently accessing the information programmatically may be challenging for new users. RCSB PDB has developed a Python software package, rcsb-api , that facilitates easy and efficient use of RCSB PDB APIs within a Python environment. This software tool is designed to streamline access to the extensive corpus of data housed within the PDB, enabling researchers to search, retrieve, and analyze 3D biostructure data seamlessly. Its use will accelerate research in structural biology, molecular biology and biochemistry, drug discovery, and bioinformatics by providing more efficient tools for data integration and analysis. The new toolkit is available on GitHub (github.com/rcsb/py-rcsb-api) and published to the public Python package repository (PyPI) to foster wider usage and support basic and applied research in fundamental biology, biomedicine, and the energy sciences.

FAIR principles↗
DOE OSTI2022

Consumer Purchasing Behavior and Usage of Lighting in the Residential Sector

We present the results of a 2019 online survey of 1,800 adults in the United States who recently purchased a light bulb to understand how consumers make decisions about purchasing light bulbs and how light bulbs are used in the residential sector. From our survey, we found that purchases of LED bulbs made up the largest percentage of sales by lighting technology and respondents generally had positive impressions of LEDs. We estimated the average daily hours of use for bulbs being purchased depending on bulb shape. We also provide an estimate for the lumen distribution of purchased light bulbs and the distribution of installation locations by bulb shape. We found that incandescent bulb purchasers were primarily replacing a failed incandescent bulb and these purchasers were less likely to have favorable impressions of LEDs. However, we found that the majority of participants replacing an incandescent bulb purchased either a CFL or LED. Using an adaptive conjoint analysis technique, we found that the five most important factors to consumers when choosing a light bulb are purchase price, bulb lifetime, energy cost savings relative to an incandescent bulb, light appearance, and energy savings relative to an incandescent bulb. The results of this study provide an in-depth look at the lighting market in 2019 and may provide a basis for developing models of the residential lighting market.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Optimal reference genes for RNA tissue analysis in small animal models of hemorrhagic fever viruses

Abstract Reverse-transcription quantitative polymerase chain reaction assays are frequently used to evaluate gene expression in animal model studies. Data analyses depend on normalization using a suitable reference gene (RG) to minimize effects of variation due to sample collection, sample processing, or experimental set-up. Here, we investigated the suitability of nine potential RGs in laboratory animals commonly used to study viral hemorrhagic fever infection. Using tissues (liver, spleen, gonad [ovary or testis], kidney, heart, lung, eye, brain, and blood) collected from naïve animals and those infected with Crimean–Congo hemorrhagic fever (mice), Nipah (hamsters), or Lassa (guinea pigs) viruses, optimal species-specific RGs were identified based on five web-based algorithms to assess RG stability. Notably, the Ppia RG demonstrated stability across all rodent tissues tested. Optimal RG pairs that include Ppia were determined for each rodent species ( Ppia and Gusb for mice; Ppia and Hrpt for hamsters; and Ppia and Gapdh for guinea pigs). These RG pair assays were multiplexed with viral targets to improve assay turnaround time and economize sample usage. Finally, a pan-rodent Ppia assay capable of detecting Ppia across multiple rodent species was developed and successfully used in ecological investigations of field-caught rodents, further supporting its pan-species utility.

Davies, Katherine A.↗
DOE OSTI2026

Energy Cost Estimate Tool v2.0: Methodology and Usage

Information about home energy costs is essential for appraisers, lenders, and buyers, but reliable data are often unavailable in real estate transactions. To fill this gap, the National Laboratory of the Rockies developed the Energy Cost Estimate (ECE) tool. The tool provides flexible, data-driven estimates of annual household energy use and costs across the contiguous United States, using only a small number of inputs typically found in mortgage appraisals. This report presents the methodology behind version 2.0 of the ECE tool, demonstrates how users can generate estimates through its interface, and discusses its applications and limitations.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗