Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Lessons learned”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Tutorial: Lessons Learned for Behavior Analysts from Data Scientists

Big data is a computing term used to refer to large and complex data sets, typically consisting of terabytes or more of diverse data that is produced rapidly. The analysis of such complex data sets requires advanced analysis techniques with the capacity to identify patterns and abstract meanings from the vast data. The field of data science combines computer science with mathematics/statistics and leverages artificial intelligence, in particular machine learning, to analyze big data. This field holds great promise for behavior analysis, where both clinical and research studies produce large volumes of diverse data at a rapid pace (i.e., big data). This article presents basic lessons for the behavior analytic researchers and clinicians regarding integration of data science into the field of behavior analysis. We provide guidance on how to collect, protect, and process the data, while highlighting the importance of collaborating with data scientists to select a proper machine learning model that aligns with the project goals and develop models with input from human experts. Here, we hope this serves as a guide to support the behavior analysts interested in the field of data science to advance their practice or research, and helps them avoid some common pitfalls.

42 ENGINEERING↗
DOE OSTI2025

A Practical guide to Parsing MCNP Inputs: Lessons Learned from Implementing Context-Free Parsing in MontePy

Monte Carlo N-Particle (MCNP) is a widely used Monte Carlo transport solver that began development in the 1960’s. Due to this MCNP input files uses a custom input syntax, for which there are no off-the-shelf parsing libraries available. For MontePy to create an effective Object-Oriented interface for MCNP input files, an context-free parser was implemented to be able to fully parse the files. MontePy uses a number of shortcuts and optimizations to avoid creating a single universal input file parser. . These lessons can be applied to working with the many other custom input syntax languages persistent throughout the nuclear industry.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Environmental Permitting and Compliance Cost Reduction Strategies for the MHK Industry: Lessons Learned from Other Industries

The marine and hydrokinetic (MHK) industry plays a vital role in the U.S. clean energy strategy by providing a renewable, domestic energy source that may offset the need for traditional energy sources. The first MHK deployments in the U.S. have incurred very high permitting costs and long timelines for deploying projects, which increases project risk and discourages investment. A key challenge to advancing an economically competitive U.S. MHK industry is reducing the time and cost required for environmental permitting and compliance with government regulations. Other industries such as offshore oil and gas, offshore wind energy, subsea power and data cables, onshore wind energy, and solar energy facilities have all developed more robust permitting and compliance pathways that provide lessons for the MHK industry in the U.S. and may help inform the global consenting process. Based on in-depth review and research into each of the other industries, we describe the environmental permitting pathways, the main environmental concerns and types of monitoring typically associated with them, and factors that appear to have eased environmental permitting and compliance issues.

other industries↗
DOE OSTI2022

Modeling Air Handling Units to Create a Diverse Fault Dataset for FDD Innovation: Lessons Learned and Recommendations

As energy management and information systems (e.g., automated fault detection and diagnostics [AFDD] tools) become more prevalent in the commercial building stock, it is important to determine the effectiveness of these technologies by benchmarking their performance. The authors have been working to develop the largest publicly available dataset of HVAC fault datasets for performance benchmarking applications, covering the most common HVAC systems and designs including chiller plants, rooftop packaged units, dual duct air handling unit and single duct air handling units. This study covers the development, modeling, and validation of a synthetic fault dataset for the air handling unit (AHU), one of the most common HVAC configurations found in the commercial building stock. Despite this being a common system, real-world time series data are scarce and usually do not span a wide range of weather conditions. Due to this limitation, two detailed AHU models, which included the single duct AHU and dual duct AHU developed in the Modelica language and HVACSIM+ were employed to carry out annual simulations of numerous common sensor faults, mechanical faults, and control sequence faults. The fault inclusive data were then validated by comparing fault effects on system performance to expected symptoms. We summarize the nature of each fault and their impacts under different weather and operation conditions. We report some lessons learnt during the efforts of validating the high volumes of the FDD data sets. Finally, we highlight considerations for FDD developers that may want to use this dataset to assess their algorithms’ performance and their improvement over time.

Casillas, Armando↗
DOE OSTI2026

Bioremediation Retrospective: DOE Experiences and Lessons Learned – 26131

The U.S. Department of Energy (DOE) has implemented bioremediation strategies for chlorinated volatile organic compounds (cVOCs) for more than three decades across multiple contaminated groundwater sites. A retrospective analysis conducted by the Savannah River National Laboratory (SRNL) and collaborating field sites evaluated the design, performance, and outcomes of bioremediation projects at DOE’s Savannah River, Hanford, Idaho, Mound, and Pinellas sites. This retrospective was based on a series of case studies, leveraging historical documentation and site interviews. Technical approaches employed at the various sites were explored, including active bioremediation, enhanced attenuation (EA), and monitored natural attenuation, with specific sub-sections discussing anaerobic, aerobic, and combined anaerobic-aerobic strategies. Each case study includes detailed descriptions of the site-specific conditions, deployment strategies, regulatory considerations, metrics, and performance. Key findings include general cost savings compared to traditional remedies such as pump and treat (P&T), significant success in applying enhanced attenuation to transition remedies toward a passive site management strategy, and documentation of robust subsurface microbial communities at most sites limiting the need for bioaugmentation. Challenges highlighted in the study include underperformance due to poor amendment delivery in low-permeability zones and unfavorable biogeochemical conditions, emphasizing the importance of site-specific designs. Cost analysis across DOE sites suggests bioremediation may offer long-term economic benefits. The retrospective study underscores valuable lessons that can help inform current and future bioremediation efforts.

Newby, Deborah T. [Savannah River National Laborat↗
DOE OSTI2025

Conceptualizing Coproduced Climate Research as Care: Practical Lessons Learned With Women Farmland‐Owners in the Central Midwest United States

ABSTRACT This article's team of interdisciplinary researchers and conservation educator‐practitioners learned with, and from, a group of women farmland‐owners regarding how to conceptualize coproduced climate research by putting “care” at the center—care for the soil, for relationships, for data. We outline the creation and evolution of a storytelling‐based conservation program that allowed our diverse group to discover how the language of care could integrate climate analysis, conservation, and relationship‐building to foster tangible solutions. As a result of the project, the women landowners took actions that supported social‐environmental resilience—from planting cover crops to fostering watershed/neighborhood relationships. Our diverse group of women landowners and researchers had very different experiences with conservation and often very different views on climate change itself, but, through storytelling and the language of care, we not only coproduced knowledge but also created relationships and action. This article outlines specific practices for how to inflect a coproduced process for climate resilience with practices that promote care and yield action projects.

Shenk, Linda [Department of English Iowa State Uni↗
DOE OSTI2023

Blind photovoltaic modeling intercomparison: A multidimensional data analysis and lessons learned

The Photovoltaic (PV) Performance Modeling Collaborative (PVPMC) organized a blind PV performance modeling intercomparison to allow PV modelers to blindly test their models and modeling ability against real system data. Measured weather and irradiance data were provided along with detailed descriptions of PV systems from two locations (Albuquerque, New Mexico, USA, and Roskilde, Denmark). Participants were asked to simulate the plane-of-array irradiance, module temperature, and DC power output from six systems and submit their results to Sandia for processing. The results showed overall median mean bias (i.e., the average error per participant) of 0.6% in annual irradiation and –3.3% in annual energy yield. While most PV performance modeling results seem to exhibit higher precision and accuracy as compared to an earlier blind PV modeling study in 2010, human errors, modeling skills, and derates were found to still cause significant errors in the estimates.

14 SOLAR ENERGY↗
DOE OSTI2021

Lessons learned from studying Histoplasma capsulatum extracellular vesicles

Histoplasma capsulatum is a major endemic mycosis. Our laboratories have demonstrated that H. capsulatum produces extracellular vesicles (EV) that are loaded with diverse compounds that influence virulence. We have further shown that H. capsulatum dynamically regulates the loading and release of fungal EV in response to stimuli and growth conditions. This chapter details the current knowledge of EV biology in H. capsulatum and the impact of this information on our understanding of this important process that is closely linked to pathogenesis.

Zamith-Miranda, Daniel↗
DOE OSTI2022

Lessons Learned on the Interface Between Quantum and Conventional Networking

The future Quantum Internet is expected to be based on a hybrid architecture with core quantum transport capabilities complemented by conventional networking. Practical and foundational considerations indicate the need for conventional control and data planes that (i) utilize extensive existing telecommunications fiber infrastructure, and (ii) provide parallel conventional data channels needed for quantum networking protocols. We propose a quantum-conventional network (QCN) harness to implement a new architecture to meet these requirements. The QCN control plane carries the control and management traffic, whereas its data plane handles the conventional and quantum data communications. We established a local area QCN connecting three quantum laboratories over dedicated fiber and conventional network connections. We describe considerations and tradeoffs for layering QCN functionalities, informed by our recent quantum entanglement distribution experiments conducted over this network.

Alshowkan, Muneer↗
DOE OSTI2023

The Kokkos OpenMPTarget Backend: Implementation and Lessons Learned

As the supercomputing landscape diversifies, solutions such as Kokkos to write vendor agnostic applications and libraries have risen in popularity. Kokkos provides a programming model designed for performance portability, which allows developers to write a single source implementation that can run efficiently on various architectures. At its heart, Kokkos maps parallel algorithms to architecture and vendor specific backends written in lower level programming models such as CUDA and HIP. Another approach to writing vendor agnostic parallel code is using OpenMP’s directives based approach, which lets developers annotate code to express parallelism. It is implemented at the compiler level and is supported by all major high performance computing vendors, as well as the primary Open Source toolchains GNU and LLVM. Since its inception, Kokkos has used OpenMP to parallelize on CPU architectures. In this paper, we explore leveraging OpenMP for a GPU backend and discuss the challenges we encountered when mapping the Kokkos APIs and semantics to OpenMP target constructs. As an exemplar workload we chose a simple conjugate gradient solver for sparse matrices. We find that performance on NVIDIA and AMD GPUs varies widely based on details of the implementation strategy and the chosen compiler. Furthermore, the performance of the OpenMP implementations decreases with increasing complexity of the investigated algorithms.

Gayatri, Rahulkumar↗
DOE OSTI2021

Lessons Learned in Employing Data Analytics to Predict Oxidation Kinetics and Spallation Behavior of High-Temperature NiCr-Based Alloys

Machine learning (ML) can offer many advantages in predicting material properties over traditional materials development methods based solely on limited experimental investigations or physical-based simulations with the capability to reduce development cost, risk, and time. However, so far, limited efforts have been made to predict alloy oxidation kinetics and spallation behavior via ML due to the lack of consistently measured and sufficient experimental data and the inherent complexity in oxidation behavior of multicomponent high-temperature alloys. A previous study reported the ability of ML to predict oxidation kinetics of NiCr-based alloys as a function of alloy composition and operating conditions. Here, the performance of a ML model in predicting rate constants and spallation probability was evaluated in light of the roles of the data distribution of the experimental dataset (data analytics), the alloy composition, the exposure environment and the chosen oxidation approach to extracting kinetic values from the measured mass changes (but using either a simple parabolic law or a statistical cyclic oxidation model). Potential strategies to improve the predictions and enhance the extrapolative capability of the previously trained model will be discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2024

GPS-supported smartphone app-based integrated travel diary and time-use data collection: challenges and lessons learned

Travel behaviour and time-use data are two vital data sources for travel demand modelling. Travel behaviour is traditionally collected through household travel surveys, enhanced by using GPS-supported smartphone apps for passive location data collection. However, recruiting individuals willing to install these apps with sustained motivation to continue participation has been a critical challenge. This paper shares insights from a travel and time-use data collection procedure in Chicago and Sydney using the Fourstep app. Social media platforms were utilised as a solution to recruit participants in Chicago, where an international market research company failed to accomplish the task. This paper also discusses the challenges we faced and suggests ways to overcome them, offering valuable guidance to researchers in recruiting participants for smartphone application-based data collection. It also offers an analysis of travel, time-use, and travel-based multitasking behaviours based on the data collected from the Chicago and Sydney samples.

GPS-supported smartphone apps↗
DOE OSTI2025

Lessons learned from the design and operation of a small-scale cross-flow tidal turbine

In 2023, a first-generation prototype of a small-scale marine current turbine was operated in Sequim Bay, Washington (USA) for 141 days. The system, referred to as the Turbine Lander, was the product of a laboratory-to-field effort to develop a system that enables enhanced ocean sensing or vehicle recharge in remote, energetic settings. The turbine consists of a vertical-axis, cantilevered rotor (1.19 m x 0.85 m) with four foils installed on a gravity foundation. A broader range of constraints including the deployment strategy, site characteristics, and estimated loads, drove the system’s design. This work presents the design, characterization, operation, and post-recovery engineering assessment of the Turbine Lander. Pre-deployment characterization efforts yielded a peak power coefficient of approximately 0.3 for the rotor, although system losses resulted in much lower water-to-wire efficiencies under most operating conditions. The results demonstrate the importance of co-design among key components of the powertrain and control systems to achieve acceptable system efficiency across operating conditions.

Co-design↗
DOE OSTI2020

Metabolic flux analysis reaching genome wide coverage: lessons learned and future perspectives

13 C-MFA is currently the only technique capable of elucidating intracellular metabolic fluxes. Generally, in 13 C-MFA studies the reactions that can carry flux are mostly pre-specified by only considering canonical pathways and ignoring alternate ones. This may bias flux elucidation and cause labeling data to erroneously confirm implied assumptions. Here, by expanding the scope of the metabolic mapping models to match known genome-scale metabolism such estimation biases can be eliminated. However, this model expansion to genome-scale requires the construction of expanded atom mapping models, more efficient flux estimation algorithms, and formal estimation of confidence levels. Even though significant progress has been made in this direction, a number of challenges remain before widespread adoption by the community.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Electrocoagulation of high-salinity produced water: lessons learned from its early applications in unconventional reservoir plays

Ample research has demonstrated that electrocoagulation is a versatile technology capable of facilitating the removal of a wide range of physical, biological, organic, and inorganic constituents such as suspended solids, turbidity, bacteria, viruses, oil and grease, organic carbon (including chemical and biological oxygen demand), silica, fluoride, and so on. Despite its purported advantages and extensive lab-scale evaluations, it has not yet been widely adopted for water and wastewater treatment and reuse at commercial scale. This is symptomatic of a chasm between its technological capabilities on one hand and field performance and reputation on the other. Herein, we opine as to why this might be the case using our collective experiences with the oil and gas industry as an exemplar. In this study, we highlight scientific, technological, engineering, and business issues that need to be addressed to realize the full potential of this promising technology.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

The first field application of a low-cost MPC for grid-interactive K-12 schools: Lessons-learned and savings assessment

K-12 schools are the largest energy consumers in the public sector, with their HVAC energy consumption representing the largest portion of their total energy use. While transitioning these schools to grid-interactive HVAC system operation through advanced controls offers significant financial and environmental benefits, and model predictive control (MPC) has been identified as a promising solution to achieve that, very few MPCs are affordable and have been deployed in K-12 schools. This situation raises concerns about the unclear real-world benefits of MPC technology among facility managers and industries. To address this gap, this paper presents a low-cost MPC solution that requires minimal control infrastructure costs and a unique field demonstration at a K-12 school, conducted for both cooling and heating seasons. This work adopted a previously developed MPC and extended it for use in the school application. The MPC aims to coordinate multiple packaged units to eliminate unnecessary peaks and shift cooling or heating loads in response to grid signals based on load conditions, while maintaining thermostat temperatures within school-defined bounds. Throughout the field tests, the MPC achieved a 24% reduction in peak demand during the cooling season and shifted cooling or heating loads by up to 16% in response to the school's utility tariff, considering load conditions, while also allowing end-users to override thermostat setpoints. Further, the paper also discusses the limitations of this study and future research directions for better performance of the MPC at K-12 schools.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Deploying advanced supervisory control strategies to small- and medium-sized commercial buildings: Case study and lessons learned

Deploying advanced supervisory control strategies (ASCSs) in small- and medium-sized commercial buildings (SMBs) is vital but faces two issues: (1) a lack of building control and communication infrastructure (BCCI) in SMBs and (2) the significant engineering efforts required to implement and configurate ASCSs. Despite hindering the large-scale adoption of ASCSs in SMBs, these issues have not been adequately explored in the literature, which tends to focus more on feasibility than scalability. This paper provides a comprehensive evaluation of these two issues through a case study of an occupied SMB in eastern Tennessee of the United States. Specifically, we design and implement a BCCI for the studied building with commercial off-the-shelf products to accommodate the needs for deploying ASCSs. Here, we then deploy two ASCSs—a rule-based setback control and a model predictive control (MPC)—with the BCCI and evaluate their performance throughout the summer of 2024. This study reveals that the main bottlenecks in establishing BCCIs for SMBs are the high initial cost (∼$56/m 2 ) and communication delays (up to 9 min). Additionally, the assessment of the two ASCSs indicates that the majority (∼75 %) of the engineering effort required for implementation and configuration is spent on model identification, debugging, and tuning.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗