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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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DOE OSTI2023

Multidisciplinary design optimization in Architecture, Engineering, and Construction: a detailed review and call for collaboration

The design of buildings has become a complex and multidisciplinary problem involving multiple conflicting objectives as architects and designers address competing technical, economic, environmental, and societal concerns. This has been driving research in Architecture, Engineering, and Construction (AEC) toward rigorous multidisciplinary decision-making frameworks that generate and evaluate numerous design alternatives using multi-objective optimization in concert with simulation and analysis models of varying fidelity and computational expense. While such frameworks are well known and widely employed in the aerospace and systems engineering domains, efforts by design professionals and researchers in the AEC field are scattered at best. In this paper, we provide a detailed review of recent developments in optimization frameworks in the AEC field and subsequently highlight how such developments are largely compartmentalized into separate domains such as structural, energy, daylighting, and other performance factors. Herein, we further discuss the technical challenges involved in concurrent coupled multidisciplinary design optimization (MDO) in the AEC field such as interoperability issues between Building Information Modeling (BIM) environments, analysis/simulation tools, and optimization frameworks. We conclude by outlining research needed for more unified modeling and simulation-based optimization frameworks to aid in complex and multidisciplinary building design processes. We also highlight the need for the identification and development of multi-fidelity simulation tools for use across multiple design phases. As such, this paper contributes a novel roadmap to leverage aerospace and systems engineering research in MDO into the field of AEC, along with a call for researchers in the MDO community to seek collaborations in AEC field.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2012

Mixture-process variable approach to optimize a microemulsion electrokinetic chromatography method for the quality control of a nutraceutical based on coenzyme Q10

In recent years, multivariate optimization has played an increasing role in analytical method development. ICH guidelines recommend using statistical design of experiments to identify the design space, in which multivariate combinations of composition variables and process variables have been demonstrated to provide quality results. Considering a microemulsion electrokinetic chromatography method (MEEKC), the performance of the electrophoretic run depends on the proportions of mixture components (MCs) of the microemulsion and on the values of process variables (PVs). In the present work, for the first time in the literature, a mixture-process variable (MPV) approach was applied to optimize a MEEKC method for the analysis of coenzyme Q10 (Q10), ascorbic acid (AA), and folic acid (FA) contained in nutraceuticals. The MCs (buffer, surfactant-cosurfactant, oil) and the PVs (voltage, buffer concentration, buffer pH) were simultaneously changed according to a MPV experimental design. A 62-run MPV design was generated using the I-optimality criterion, assuming a 46-term MPV model allowing for special-cubic blending of the MCs, quadratic effects of the PVs, and some MC-PV interactions. The obtained data were used to develop MPV models that express the performance of an electrophoretic run (measured as peak efficiencies of Q10, AA, and FA) in terms of the MCs and PVs. Contour and perturbation plots were drawn for each of the responses. Finally, the MPV models and criteria for the peak efficiencies were used to develop the design space and an optimal subregion (i.e., the settings of the mixture MCs and PVs that satisfy the respective criteria), as well as a unique optimal combination of MCs and PVs.

Coenzyme Q10↗
DOE OSTI2018

Wind load analysis of a new linear Fresnel receiver assembly design

A linear Fresnel collector includes a low-profile reflector array and a receiver assembly with one or more absorber tubes and an optional secondary reflector. This combined optical system concentrates sunlight and converts it into thermal energy. The design of a receiver assembly is critical to the performance of a linear Fresnel collector. A position deviation of a few centimeters for the receiver assembly can result in notably reduced performance, thus leading to a direct loss in revenue associated with thermal power production. Wind load is one of the most significant environmental factors that can alter the optical - and therefore thermal - performance of a solar power system due to displacements after installation. At the same time, an over-designed receiver assembly may add unnecessary construction cost to a typically high-cost-constrained system. Thus, wind load analysis is particularly important when considering optimal engineering design of a receiver assembly and its supporting structure to cost-effectively mitigate the impacts of wind. In this study, a detailed computational fluid dynamics (CFD) model is adopted to derive the wind load of a commercial linear Fresnel receiver assembly. This wind load is then used as a reference to optimize the detailed engineering design. The CFD model is first carefully developed and benchmarked within a critical regime toward turbulence. The drag force, lift force, and vortex-shedding frequencies are derived at both the operating and survival wind-speed limits for target project deployment locations. The wind load analysis results provide a valuable reference for future engineering design and prototyping.

17 WIND ENERGY↗
DOE OSTI2014

MO-G-BRE-03: Automated Continuous Monitoring of Patient Setup with Second-Check Independent Image Registration

Purpose: To create a non-supervised quality assurance program to monitor image-based patient setup. The system acts a secondary check by independently computing shifts and rotations and interfaces with Varian's database to verify therapist's work and warn against sub-optimal setups. Methods: Temporary digitally-reconstructed radiographs (DRRs) and OBI radiographic image files created by Varian's treatment console during patient setup are intercepted and used as input in an independent registration module customized for accuracy that determines the optimal rotations and shifts. To deal with the poor quality of OBI images, a histogram equalization of the live images to the DDR counterparts is performed as a pre-processing step. A search for the most sensitive metric was performed by plotting search spaces subject to various translations and convergence analysis was applied to ensure the optimizer finds the global minima. Final system configuration uses the NCC metric with 150 histogram bins and a one plus one optimizer running for 2000 iterations with customized scales for translations and rotations in a multi-stage optimization setup that first corrects and translations and subsequently rotations. Results: The system was installed clinically to monitor and provide almost real-time feedback on patient positioning. On a 2 month-basis uncorrected pitch values were of a mean 0.016° with standard deviation of 1.692°, and couch rotations of − 0.090°± 1.547°. The couch shifts were −0.157°±0.466° cm for the vertical, 0.045°±0.286 laterally and 0.084°± 0.501° longitudinally. Uncorrected pitch angles were the most common source of discrepancies. Large variations in the pitch angles were correlated with patient motion inside the mask. Conclusion: A system for automated quality assurance of therapist's registration was designed and tested in clinical practice. The approach complements the clinical software's automated registration in terms of algorithm configuration and performance and constitutes a practical approach to implement safe and cost-effective radiotherapy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Near-Optimal Performance of Stochastic Model Predictive Control

Here, this article presents a regret analysis for stochastic model predictive control (SMPC) in linear systems with quadratic performance index and additive and multiplicative uncertainties. Under a finite support assumption, the problem can be cast as a finite-dimensional quadratic program, but the problem becomes quickly intractable as the problem size grows exponentially in the horizon length. SMPC aims to compute approximate solutions by solving a sequence of problems with truncated prediction horizons and committing the solution in a receding-horizon fashion. Although this approach is widely used in practice, its performance relative to the optimal solution is not well understood. This article reports for the first time a rigorous near-optimal performance guarantee of SMPC: under stabilizability and detectability conditions, the regret of SMPC is exponentially small in the prediction horizon length, allowing SMPC to achieve near-optimal performance at a substantially reduced computational expense.

93E20, 93B45↗
DOE OSTI2024

Analyzing Prospects for Quantum Advantage in Topological Data Analysis

Lloyd [Nat. Commun. , 10138 (2016)] were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that superquadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for superpolynomial advantage. We then introduce and analyze specific problem examples which have parameters in the regime where superpolynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve seemingly classically intractable instances. Published by the American Physical Society 2024

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

X-Ray Fluorescence Quantitative and Qualitative Analysis Using Micro Glass Beads

The fused glass bead method is the accepted 'best practice' method of preparation of samples for X-ray fluorescence (XRF) analysis. It minimizes the effects of sample heterogeneity and simplifies calibration by dissolving the sample into a uniform glass matrix. Glass fusion XRF samples are typically prepared by grinding sample powder with Lithium Tetraborate / Lithium Bromide and fusing the mixture at 1000 deg. C. In most cases sample-to-flux ratios of between 1:1 to 1:10 are used (1:1 for determination of trace elements and 1:10 for major elements). Typical sample mass consumed is in the range of 1000 to 2500 mg. It would be advantageous to reduce this sample mass in cases where the sample material is rare, valuable or highly radioactive. This study examines the effects of using lower sample-to-flux ratios and smaller fusion bead size on the detection and quantification of major and trace elements by XRF analysis. We have employed a micro fused glass bead process, using sample-to-flux ratios of 1:10, 1:15 and 1:25, to determine the effects on measurement precision, accuracy and detection limit. Objectives: - Created micro fused glass beads of a series of standards and andesite sample with sample-to-flux ratios of 1:10, 1:15 and 1:25. - Perform an qualitative analysis of the different sample-to-flux ratio micro beads and compare the results with the 1:5.3 beads routinely used at SRNL. - Quantitate the major components in the different sample-to-flux ratio micro beads using a wavelength dispersive x-ray fluorescence instrument. Advantages: - Reduce sample amount needed. - Speed up waste tank characterization. - Reduce personnel exposure. - Potential sealed source. Fused micro glass beads with sample-to-flux ratios of 1:10, 1:15 and 1:25 were created using sample amounts of 123 mg, 82 mg and 52 mg respectively. It was possible to detect most of the major oxides and some trace elements in the fused micro glass beads with different sample-to-flux ratios. The quantitative analysis showed good linear correlation between the intensity of the analysis and the concentration of the elements. Good precision and accuracy results were obtained for the majority of the major oxides in the three fused micro glass beads with different sample-to-flux ratios. Path Forward: Enhance the precision and accuracy of the method by optimizing different sample preparation steps, such as weighing and sample transfer. Continue to increase the sample-to-flux ratio to determine the minimum amount of sample needed to perform a multielement analysis. Consider the use of synthetic standards to lower the detection limit of the analysis. Used the micro bead sample preparation method to analyze radioactive samples.

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

Predicting Dynamic-to-Static Correction Factor from Petrophysical Data and Chemostratigraphy using Unsupervised Machine Learning

Estimating static mechanical properties of stratigraphic layers is critical for optimizing subsurface engineering applications. To estimate dynamic-to-static correction factor F ds (static-to-dynamic Young’s modulus ratio) across the Caney shale interval in Oklahoma, USA, we integrated triaxial test measurements and petrophysical data, including well logs and X-ray fluorescence (XRF) using unsupervised machine learning (ML). We used a novel workflow that includes principal component analysis (PCA) to reduce data set dimensionality of well logs and XRF data sets—both separately and combined—creating three scenarios, and later applied inverse distance weighting (IDW) to derive F ds profiles for these scenarios. Furthermore, we applied K-means clustering on each scenario to predict depositional facies, and built a stiffness zonation profile through chemostratigraphic analysis of the terrigenous elements to validate the predicted F ds . The predicted F ds profile from each scenario using the PCA-IDW method was compared with the constant F ds approach from our previous study by calculating the root mean square error (RMSE). The combined data sets scenario yielded the lowest RMSE value of 0.113, while the RMSE values for the well logs and XRF scenarios were 0.131 and 0.129, respectively. In addition, the predicted F ds from the XRF scenario well-matched the stiffness zonation from the chemostratigraphic analysis that was built using the optimized K-means clustering of nine clusters for that scenario. These methods and findings offer a valuable tool for refining lithological classification and improving the F ds profile, potentially enhancing drilling and stimulation strategies for subsurface energy engineering applications.

clastic rock↗
DOE OSTI2017

RISMC Industry Application no. 1 (ECCS/LOCA) Core characterization automation: Reference PWR designs for IA no. 1

The U.S. NRC is currently proposing rulemaking '10 CFR 50.46c' to revise the loss-of-coolant-accident (LOCA) and emergency core cooling system (ECCS) acceptance criteria to include the effects of higher burn-up on cladding performance. The key implications of this proposition are that both cladding and ECCS performance need to be considered in a coupled manner. This implies that LOCA methodologies will need to be updated for regulatory purposes. The Risk-Informed Safety Margin Characterization (RISMC) Pathway, as part of the DOE Light Water Reactor Sustainability (LWRS) research and development program initialed several programs at the Idaho National Laboratory (INL) to support the industry by offering potential solutions to this new LOCA/ECCS analysis. One of these programs is the Industry Application no. 1 (IA1). It's goal is to develop an integrated evaluation model called LOCA Toolkit for the US (LOTUS) to revisit how risks are evaluated in a LOCA under 10 CFR 50.46c. The focus of LOTUS is to establish the automation interfaces among the physics disciplines involved, namely the core design automation, the fuels/clad performance, the system analysis, the risk assessment and the core design optimization. The risk-informed margins management approach for IA1 will provide a means of quantifying the impact on the key LOCA analysis figures-of-merit like peak cladding temperature (PCT), equivalent cladding reacted (ECR), etc. of a change in LOCA analysis inputs. The information obtained can then be used for decision-making and margin management. The modular approach for LOTUS will enable plant owners/vendors to further customize the LOTUS framework for use within their established codes and methods. One of the LOTUS modules deals with the core design automation (CD-A). The purpose of this module is to supply the LOCA analysis with initial conditions, i.e. assembly/pin power histories, power shapes, etc. to be employed by the Fuels/Clad Performance (EP) and System Analysis (SA) modules. In the CD-A module, the plant owner/operator will characterize his core design with the tools he has. For the current LOTUS demonstration, the tools used are the already coupled codes PHISICS and RELAP5-3D2,3. Cross section generation will be done using the Studsvik lattice code HELIOS-2.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2016

SU-F-T-245: The Investigation of Failure Mode and Effects Analysis and PDCA for the Radiotherapy Risk Reduction

Purpose: To optimize the clinical processes of radiotherapy and to reduce the radiotherapy risks by implementing the powerful risk management tools of failure mode and effects analysis(FMEA) and PDCA(plan-do-check-act). Methods: A multidiciplinary QA(Quality Assurance) team from our department consisting of oncologists, physicists, dosimetrists, therapists and administrator was established and an entire workflow QA process management using FMEA and PDCA tools was implemented for the whole treatment process. After the primary process tree was created, the failure modes and Risk priority numbers(RPNs) were determined by each member, and then the RPNs were averaged after team discussion. Results: 3 of 9 failure modes with RPN above 100 in the practice were identified in the first PDCA cycle, which were further analyzed to investigate the RPNs: including of patient registration error, prescription error and treating wrong patient. New process controls reduced the occurrence, or detectability scores from the top 3 failure modes. Two important corrective actions reduced the highest RPNs from 300 to 50, and the error rate of radiotherapy decreased remarkably. Conclusion: FMEA and PDCA are helpful in identifying potential problems in the radiotherapy process, which was proven to improve the safety, quality and efficiency of radiation therapy in our department. The implementation of the FMEA approach may improve the understanding of the overall process of radiotherapy while may identify potential flaws in the whole process. Further more, repeating the PDCA cycle can bring us closer to the goal: higher safety and accuracy radiotherapy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Enabling Production of Algal Biofuels by Techno-Economic Optimization of Co-Product Suites

Recent techno-economic analysis (TEA) has underscored that for algal biofuels to be cost competitive with petroleum fuels, co-products are necessary to offset the cost of fuel production. The co-product suite must scale with fuel production while also maximizing value from the non-fuel precursor components. The co-product suite also depends on algal biomass composition, which is highly dynamic and depends on environmental conditions during cultivation. Intentional shifts in composition during cultivation are often associated with reduced biomass productivity, which can increase feedstock production costs for the algae-based biorefinery. The optimal algae-based biorefinery configuration is thus a function of many factors. We have found that comprehensive TEA, which requires the construction of process models with detailed mass and energy balances, along with a complete accounting of capital and operating expenditures for a commercial-scale production facility, provides invaluable insight into the viability of a proposed biorefinery configuration. This insight is reflected in improved viability for one biorefining approach that we have developed over the last 10 years, namely, the Combined Algal Processing (CAP) approach. This approach fractionates algal biomass into carbohydrate-, lipid-, and protein-rich fractions, and tailors upgrading chemistry to the composition of each fraction. In particular, transitioning from valorization of only the lipids to a co-product suite from multiple components of high-carbohydrate algal biomass can reduce the minimum fuel selling price (MFSP) from more than $8/gallon of gasoline equivalent (GGE) to $2.50/GGE. This paper summarizes that progress and discusses several surprising implications in this optimization approach.

09 BIOMASS FUELS↗
DOE OSTI2019

Optimal design of phase change material storage for steam production using annual simulation

A direct steam generation (DSG) solar collector can be an efficient process-heat application compared to electricity generation, and it can significantly reduce dependence on non-renewable energy sources for steam generation. Due to intermittency of the sun, thermal energy storage is required to increase the capacity factor or the yearly utilization of the system. For this study, phase change material (PCM) is used to store the heat from the steam produced in the solar collector. Sodium formate is selected as the PCM material on a cost basis. A detailed off-design analysis is carried to predict the annual performance of the system. Fins are used to enhance the thermal performance of the PCM, and an simplified method is derived to predict the required fin mass. With an increase in the number of fins, the thermal performance of the system increases significantly at design point. Furthermore the fins are expensive, so an annual simulation and techno-economic analysis is carried to calculate the optimal number of fins and other operating parameters.

14 SOLAR ENERGY↗
DOE OSTI2026

Solar Thermal Energy Planner (STEP 1): A New Decision Support Tool for Solar Industrial Process Heat Applications

Solar thermal technologies are a promising technology to supply low-cost thermal energy to industrial processes, but there are often significant barriers to entry to industrial owners considering these technologies for their energy demands. To overcome this barrier and convey economic value to customers, NREL and Sandia National Laboratories developed Solar Thermal Energy Planner (STEP 1), a new web-based decision support tool for solar industrial process heat systems. At SolarPACES 2024, the STEP 1 tool was still under development; progress, methodologies, and a preliminary case study was presented. With the STEP 1 tool launch in May 2025, in this work, the initial version of the full public tool will be presented with demonstrations of its capabilities using a few case studies. First, the user's process heat needs such as location, process media (e.g., steam, air), process temperature, land availability, electricity and fuel costs, among other parameters. STEP 1 features a mapping interface that allows users to draw land and roof boundaries. The process media and temperature inform technology selection criteria modules that determine the appropriate solar thermal collection technologies, as well as congruent heat transfer media (e.g., hot water, oil, salt). Once the solar thermal technology selected, its nominal thermal production for the given site is characterized using NREL's System Advisor Model (SAM). Then, a modified version of NREL's REopt optimal sizing and dispatch optimization tool determines cost-optimal sizing. Within minutes, the user receives the results of the technoeconomics analysis, including the size and performance of the cost-optimal solar-plus-storage system. The cost of the system is compared to business-as-usual (e.g., an existing, standalone natural gas boiler). Users can download key results to store for sensitivity analyses. Examples of flat plate collector, parabolic trough, and molten salt tower applications with and without PV hybridization for different industrial facility types are presented in this work. The STEP 1 tool aims to reduce barriers to the adoption of solar heating solutions stemming from a lack of familiarity and technical background with solar system design options and costs among industry stakeholders.

14 SOLAR ENERGY↗
DOE OSTI2018

Dakota Graphical User Interface

The Dakota GUI is an interactive graphical analysis environment for creating, running, and interpreting Dakota optimization and uncertainty quantification studies. It includes problem (Dakota study) set-up, option specification, simulation interfacing, analysis execution, and results visualization. Through the use of wizards, templates, and views, Dakota GUI helps uses navigate Dakota’s complex capability landscape.

Glickman, Matthew↗
DOE OSTI2013

A GENERALIZED STOCHASTIC COLLOCATION APPROACH TO CONSTRAINED OPTIMIZATION FOR RANDOM DATA IDENTIFICATION PROBLEMS

We present a scalable, parallel mechanism for stochastic identification/control for problems constrained by partial differential equations with random input data. Several identification objectives will be discussed that either minimize the expectation of a tracking cost functional or minimize the difference of desired statistical quantities in the appropriate $L^p$ norm, and the distributed parameters/control can both deterministic or stochastic. Given an objective we prove the existence of an optimal solution, establish the validity of the Lagrange multiplier rule and obtain a stochastic optimality system of equations. The modeling process may describe the solution in terms of high dimensional spaces, particularly in the case when the input data (coefficients, forcing terms, boundary conditions, geometry, etc) are affected by a large amount of uncertainty. For higher accuracy, the computer simulation must increase the number of random variables (dimensions), and expend more effort approximating the quantity of interest in each individual dimension. Hence, we introduce a novel stochastic parameter identification algorithm that integrates an adjoint-based deterministic algorithm with the sparse grid stochastic collocation FEM approach. This allows for decoupled, moderately high dimensional, parameterized computations of the stochastic optimality system, where at each collocation point, deterministic analysis and techniques can be utilized. The advantage of our approach is that it allows for the optimal identification of statistical moments (mean value, variance, covariance, etc.) or even the whole probability distribution of the input random fields, given the probability distribution of some responses of the system (quantities of physical interest). Our rigorously derived error estimates, for the fully discrete problems, will be described and used to compare the efficiency of the method with several other techniques. Numerical examples illustrate the theoretical results and demonstrate the distinctions between the various stochastic identification objectives.

Webster, Clayton G↗
DOE OSTI2022

Resilient El Rito, Microgrif System Laboratory (Village of El Rito) (Final CTAP Report)

Sandia provided technical assistance to Kit Carson Electric Cooperative (KCEC) to assess the technical merits of a proposed community resilience microgrid project in the Village of El Rito, New Mexico (NM). The project includes a proposed community resilience microgrid in the Village of El Rito, NM, around the campus of Northern New Mexico College (NNMC). A conceptual microgrid analysis plan was performed, considering a campus and community-wide approach. The analysis results provided conceptual microgrid configurations, optimized according to the performance metrics defined. The campus microgrid was studied independently and many conceptual microgrid solutions were provided that met the performance requirements. Considering the existing 1.5 MW PV system on campus far exceeds the simulated campus load peak and energy demand, a small battery installation was deemed sufficient to support the campus microgrid goals. Following the analysis and consultation, it was determined that the core Resilient El Rito team will need to further investigate the results for additional economic and environmental considerations to continue toward the best approach for their goals and needs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Autonomous Flow Electrochemistry for Accelerated Catalyst Discovery

Our objective is to develop an Autonomous Chemical Experimentation (ACE) platform that accelerates discovery of new catalytic transformations and other energy-relevant chemical reactions and processes. We intentionally designed ACE to be highly modular, both with respect to its rapid deployment to different chemistries and experimental workflows as well as incorporation of a wide range of different AI algorithms. In addition to the development of the core software architecture, initial efforts were made to incorporate Large Language Models to provide human-interpretable reasoning of the optimizer’s actions, and to develop a user-friendly graphical interface for experimental researchers. ACE was demonstrated using a flow electrocatalysis platform containing an inline FTIR spectrometer for real-time analysis and quantification of the reaction outcome. Human-in-the-loop experiments were performed in which a human researcher conducted an experiment using electrode potentials suggested by ACE, then fed the spectral data back to ACE for decision making. After confirming the successful function of the optimizer, efforts were next directed to automation of the hardware and performed full autonomy tests using three reactions: catalytic oxidation of formate, catalytic oxidation of cyclohexanol, and oxidation of hydroquinone. These studies confirm that ACE can close the loop between reaction execution, analysis, and optimization. They also reveal that more improved product detection methods will be essential for ACE to make well-informed decisions for reactions with low conversions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗