Trajectory Optimization and Mission Design for Interplanetary Transfers to Mars and Ceres

Introduction: South Africa is ranked among the 20 highest greenhouse gas (GHG) emitters worldwide, mainly due to its heavy reliance on coal for energy. The residential building industry can play a significant role towards the minimization of both GHG emissions and energy consumption. This research assesses the beneficial impacts of utilizing wall envelope insulation measures using five insulation materials, for a detached residential building, within the seven energy zones of South Africa in accordance with the South African National code for Building energy efficiency (SANS10400-XA).Methods: Lifecycle analysis (for 50 years), regression methods and sequential search techniques of building energy modelling were used to determine both the energy saving implications of adopting the insulation materials (at various levels of thickness), and their corresponding energy payback periods. The study area consisted of eight selected locations representing all the seven energy zones in South Africa (Welkom, Witbank, Thohoyandou, Cape Town, Pretoria, Ixopo, Sutherland, and Fraserburg).Results: Sutherland (zone 6), Cape Town (zone 4) and Fraserburg (zone 7) benefitted most from energy savings due to application of insulation measures. Witbank (Zone 2), Thohoyandou (zone 3) and Pretoria (zone 5) never benefitted from application of wall envelope insulation (in terms of energy savings). Cellulose and straw yielded the lowest payback periods. Generally, polyurethane yielded the highest net energy savings at lower insulation thickness levels, while cellulose was preferable at higher insulation thickness levels in Welkom (zone1) Cape Town (zone 4), Ixopo (zone 5H), Sutherland (zone 6) and Fraserburg (zone 7).Discussion: Lower optimal wall envelope insulation thicknesses (that maximized energy savings) appeared to correspond to locations with higher annual temperature ranges. Higher temperature ranges (hence, higher degree days) would ensure that more energy is saved in order to keep the indoor temperatures within the acceptable comfort limits, due to application of insulation. However, the selection of insulation material is also influenced by other factors such as resistance to insect and mould attack (which easily affect bio-insulation materials), fire retardancy, durability (which affects cellulose), sound proofing, structural strength, and resistance to water vapour.

Ventilation is vital in various processes where dilution of dust and gases and removal of heat are some of the primary reasons. Millions of Dollars are annually spent on investments, electricity bill, maintenance and repair costs of the fan components. Energy consumption represents one of the biggest components of ownership costs and therefore many steps are taken in order to develop and implement Energy Conservation Measures (ECMs). Very little attention have been paid on critical elements and processes equipments in the case of variable loads when revolutionary ECM types are required to design and maintain energy (efficient) ventilation systems. In fluids movement, variable load is due to variable fluid power. This could appear as a result of air density variation (dust and gas content) but also as a result of impeller pitch variation. Variable impeller pitch mechanisms are used for big fan units (hundreds of kW of installed power) in order to minimize the energy consumption when no air movement is required. Classic methods of estimating energy savings by using VFDs are assuming constant pitch impellers and constant load (air power). These methods are not applicable for big fans yielding variable performances while working at variable load. This research focuses on techniques of obtaining energy savings by controlling the parameters of the power supply as a combination of variable frequency of the power supply with reduction of magnetic loading of the motor. This work present result of two years' activity in reviewing various techniques of obtaining energy savings with fans performing at variable loads by using various ECMs. A mathematical model has been created to estimate and maximize energy savings by using combined techniques (variable frequency and variable magnetic loading). Power/energy savings have been estimated for every ECM, including reliability indicators estimation of the system impeller. Proposed method is suitable for any application yielding variable performances concomitant with variable load factors: conveyors, escalators, saw mills, pumps, variable pitch fans.

The study aims to address advanced approaches to the optimal settings of heating structures and panels to improve thermal comfort and energy efficiency in buildings. The study analysed various materials for thermal insulation, different heating systems and energy-efficient windows, using common formulas to calculate their thermal performance and efficiency. The study analysed various modern methods of optimising heating systems to improve thermal comfort and energy efficiency in buildings. As such, the study determined that the use of intelligent heating control systems, such as smart thermostats and sensors, can significantly reduce energy consumption. The study also determined that heat pumps, especially geothermal and air source heat pumps, are effective solutions for sustainable heating. The study results confirmed that three-chamber windows with a third gap between the glass panes can maximise energy savings by significantly reducing heat loss and providing high thermal insulation. The study also determined that condensing gas boilers and heat pumps are optimal for efficient energy savings. Low-temperature heating systems, such as underfloor heating and efficient radiators, contribute to even heat distribution and energy savings. The use of renewable energy sources, such as solar collectors and wind turbines, was also determined to be an effective means of reducing dependence on traditional energy resources. The study also showed that the integration of various technologies and systems into a single climate control system allows for maximum efficiency and comfort. As a result, the combined use of innovative methods and materials not only significantly reduces energy consumption but also improves living conditions in modern buildings.

Networking infrastructure consumes a sizable fraction of the electricity supply. A network design model aimed at maximizing energy savings by aggregating traffic demand at a small set of resources, to put under-utilized resources to sleep, is offset by legacy models aimed at maximizing the network throughput by spreading the load across network resources. Traffic fluctuations and sudden spikes further complicate the problem. In this paper, we show that the problem is NP-hard, and propose a heuristic which targets optimal power usage for common traffic demand, while accommodating traffic fluctuations. The efficacy of our design is tested on two published backbone networks: NLR and NSFNET. Simulation results reveal that the proposed heuristic closely matches the results of the optimal algorithm in both energy savings and network utilization, while enjoying polynomial time complexity.

This study summarizes the results from a simulation study for a typical villa in Muscat, a hot humid city in Oman. A housing model was generated in the DesignBuilder software. The model was compared against real utility readings before it was further taken for the energy calculations. Different thermal characteristics of building envelope including the heat transmission, thermal mass, solar heat gain through windows and air infiltration were evaluated. Many energy-efficient envelope design options were generated. Sensitivity analysis was then performed to narrow down the design parameters to the most significant parameters. From the sensitivity analysis, it was found that no significant energy savings are achieved beyond 15 cm of thermal insulation for both walls and roof. The solar heat gain coefficient (SHGC) was found more significant than the U-values of the glazing. It was also found that the house should be air tight as the air infiltration was significant. External shading was advantageous too in hot climates. A brute force optimization approach was then carried out using unsubsidized and subsidized energy cost scenarios. A maximum energy savings of 26.7% and a minimum energy savings of 18.4% were achieved for both energy cost scenarios. For maximum energy savings, 15 cm insulation for walls and roof, a double low-e selective tinted glazing, 100 cm of overhang shading were found to be the optimal design. The optimal designs for minimum life cycle cost were, however, different for the two energy cost scenarios. For unsubsidized energy cost, 10 cm insulation for walls and roof, a single green tinted glazing, and 100 cm of overhang shading were found to be the optimal design. For subsidized energy cost, a 7.5 cm for wall and roof are the optimal while other parameters are similar to the unsubsidized case. The results from the study will help both the policy makers, designers, and owners to select their optimal designs based on different cost functions.

Motivation: MRI significantly contributes to healthcare-related greenhouse gas emissions; therefore, reducing MRI energy use through accelerated imaging and deep-learning reconstruction (DLR) is essential. Goal(s): To quantify the energy savings and emissions reductions achievable using accelerated MRI combined with DLR, while ensuring minimal compromise in image quality. Approach: Energy consumption was measured on three 3T MRI scanners during standard and accelerated imaging with DLR, validated using ACR phantom tests and human subject imaging. Results: Accelerated MRI with DLR reduced scan times and energy consumption by 3-5 times without compromising diagnostic image quality, yielding potential institutional savings of 74.1 metric tons of CO₂eq emissions annually. Impact: Accelerated MRI with DLR enables significant clinical efficiency and sustainability improvements, facilitating rapid, high-quality imaging. Future studies can explore enhanced DLR algorithms, optimizing diagnostic performance while maximizing energy savings and emissions reductions across healthcare settings.

As transistor geometries get smaller, static leakage power dominates the power consumption in modern processors. This phenomenon diminishes the ability of frequency scaling-based techniques to save energy. Modern processors also provide sleep states which minimize leakage power by gating portions of the processor and/or the system clock. This paper presents partitioned fixed-priority scheduling solutions for utilizing these sleep states to efficiently schedule periodic real-time tasks, and maximize energy savings on multi-core processors. The techniques presented rely on an Enhanced Version of Energy-Saving Rate-Harmonized Scheduling (ES-RHS), and our newly proposed Energy-Saving Rate-Monotonic Scheduling (ES-RMS) policy to maximize the time the processor spends in the lowest-power deep sleep state. In some modern multi-core processors, all cores need to transition synchronously into deep sleep. For this class of processors, we present a partitioning technique called Max-SyncSleep which utilizes a priori task information, to maximize the synchronous deep sleep duration across all processing cores. The performance of Max-SyncSleep is compared to the classical Worst-Fit Decreasing load balancing heuristic. We also illustrate the benefits of using ES-RMS over ES-RHS for this class of processors. For processors which allow cores to individually transition into deep sleep, we prove that, while utilizing ES-RHS on each core, any feasible partition can optimally utilize all of the processor's idle durations to put it into deep sleep. Experimental evaluations indicate that the proposed techniques can provide significant energy savings.

One of the most important challenges for every machining shopfloor, especially a small one, is to achieve the required productivity with minimal energy consumption and engaged power. The paper presents a way to determine the optimal combination of values of cutting parameters such as depth of cut, feed rate and cutting speed from the range of their recommended values, which are usually additionally limited by the real conditions of available machines and tools. The optimal combination is the one which ensures targeted productivity and maximum energy savings at the same time. As an example, a real practice case of external rough turning of an AISI 1045 steel workpiece is presented. The selection of the optimal combination of cutting parameter values is based on a model which is developed using in situ measurements of energy consumption and engaged power in an experiment that emulates the critical operation in terms of energy consumption. The results show that optimization of cutting parameter values that enables the minimum of total energy consumption while achieving target productivity, does not necessarily enable the maximum of energy savings for a given operation. In the example from real practice shown in the paper, this optimization approach can cause higher total energy consumption by as much as 15.9% compared to the combination of parameters that ensure maximum productivity.

The largest amount of energy consumption in an automotive assembly plant is in paint shop. Optimizing the energy usage in paint shops will result in maximum energy savings. Instead of inventing new chemicals, new painting processes or new control systems in painting booths and ovens, our research focuses on developing an optimal scheduling procedure of vehicle sequence to achieve the goal of energy reduction. Specifically, by selecting appropriate batch and sequence policies, the paint quality can be improved and repaints can be reduced so that less material and energy will be consumed. It is shown that such scheduling and control method can lead to significant energy savings with no extra investment, nor changes to existing painting processes.

A Multi-objective Optimization Model for Building Envelope Retrofit Planning

Municipalities face decisions about maintaining and upgrading their streetlighting. Do they continue to replace bulbs in their existing lighting systems, or move to energy-efficient systems piecemeal or all at once? This research paper argues that the financial and environmental benefits of a complete shift to efficient Light Emitting Diode (LED) fixtures make the investment worthwhile. Analysis of data from Tredyffrin Township in southeastern Pennsylvania, that is currently making the switch to LED lighting systems, supports the conclusion that committing to one-time full replacement can maximize energy savings and that the issuance of green bonds, over standard municipal bonds, for financing is both viable and advantageous as it can lead to additional savings and budget surplus. Despite a higher initial investment, the extended lifespan of LED lights, compared with Mercury Vapor (MV) lamps, drastically lowers maintenance and replacement costs, leading to considerable long-term savings. By using green bonds, a financing instrument made available for environmentally beneficial projects, a municipality's financing costs can be kept low and large projects can be undertaken, for the fullest financial and environmental benefit.

The transportation sector is seeing the flourishing of one of the most interesting technologies, autonomous driving (AD). In particular, Cooperative Adaptive Cruise Control (CACC) systems ensure higher levels both of safety and comfort, enhancing at the same time the reduction of energy consumption. In this framework a real-time velocity planner for a Battery Electric Vehicle, based on a Deep Reinforcement Learning algorithm called Deep Deterministic Policy Gradient (DDPG), has been developed, aiming at maximizing energy savings, and improving comfort, thanks to the exchange of information on distance, speed and acceleration through the exploitation of vehicle-to-vehicle technology (V2V). The aforementioned DDPG algorithm relies on a multi-objective reward function that is adaptive to different driving cycles. The simulation results show how the agent can obtain good results on standard cycles, such as WLTP, UDDS and AUDC, and on real-world driving cycles. Moreover, it displays great adaptability to driving cycles different from the training one.

Life-cycle costing (LCC) is increasingly important when making decisions about sustainable buildings such as LEED-certified buildings. For educational buildings, the LCC can be used to identify the cost-optimal solution out of different alternatives such as renewable energy strategies to improve energy efficiency. The application of photovoltaic (PV) panels and the ability to accurately predict power delivery over the course of time play a key role in the life cycle of such sustainable building facilities. Two cost drivers in the performance of this type of renewable energy systems involve the efficiency with which sunlight is converted into power and how this relationship changes over time. An accurate quantification of power decline over time, also known as degradation rate, is essential to all the project stakeholders. In this study, an investment framework is used to compare scenarios for improving the performance of educational buildings, maximize energy savings, and meet the NZEB (net zero energy building) criteria. LCC is used to compare a building ‘base case’ to alternative energy efficiency strategies involving the application of PV panels. Cost and PV panel efficiency over time in are evaluated. The energy payback period (EPBP), simple payback period (SPBP), and return on investment (ROI) are used to measure the economic performance of proposed cost-optimal alternatives. This approach is used to determine the cost-effectiveness of the energy efficiency improvements, considering future operational savings and higher initial investments.

Efficient management of heating, ventilation, and air conditioning (HVAC) systems is paramount for mitigating energy consumption and enhancing climate resilience in modern infrastructure. This review explores the significance of leveraging project management tools to optimize energy efficiency within HVAC operations, thus fostering a pathway towards climate resilience. The integration of project management methodologies offers a systematic approach to address challenges associated with energy consumption in HVAC systems, thereby facilitating informed decision-making processes. Firstly, the review delves into the pressing need for energy conservation in HVAC operations amidst escalating concerns over climate change and its adverse impacts. The imperative to minimize energy consumption in buildings, particularly through HVAC systems, is highlighted as a pivotal step towards achieving climate resilience and sustainability goals. Subsequently, the review outlines the role of project management tools in orchestrating effective strategies to enhance energy efficiency within HVAC operations. By employing techniques such as project scheduling, risk management, and resource allocation, project managers can streamline the implementation of energy-efficient measures, thereby optimizing HVAC system performance and reducing carbon emissions. Moreover, the review underscores the importance of data analytics and technological advancements in augmenting the efficacy of project management tools for energy efficiency in HVAC operations. Leveraging real-time data monitoring, predictive analytics, and IoT-enabled devices enables proactive maintenance and continuous optimization of HVAC systems, thereby maximizing energy savings and bolstering climate resilience. Furthermore, the review elucidates the potential benefits and challenges associated with the adoption of project management tools for energy efficiency in HVAC operations. While improved cost-effectiveness, environmental sustainability, and operational performance emerge as primary benefits, challenges such as initial investment costs, technological complexities, and organizational inertia necessitate careful consideration. This review advocates for the integration of project management tools as a viable approach to foster energy efficiency in HVAC operations, thereby paving the way towards enhanced climate resilience and sustainability in built environments. By embracing innovative methodologies and technological solutions, stakeholders can mitigate energy consumption, reduce greenhouse gas emissions, and fortify infrastructure against the impacts of climate change. Keywords: HVAC, Climate Resilience, Project Management, Energy, Review.

ABSTRACTMedium‐voltage back‐to‐back (MVB2B) converters can connect two distribution systems and quantifiably transfer power between them. This function can enable the MVB2B converter to exchange distributed energy resource (DER)‐generated power between two systems and bring significant value to enhancing distribution system DER adoption. Our previous work analysed and demonstrated the value MVB2B converter can bring to DER integration. As continuous work, this paper presents a methodology that helps address the MVB2B converter sizing and location selection problem in distribution systems with high DER penetrations. The proposed methodology aims to address three critical problems for MVB2B converter implementation in the real world: (1) which distribution systems are better to be connected, (2) what converter size is appropriate for connecting the distribution systems, and (3) where the optimal connection points are in the systems for connecting the MVB2B converter. The proposed methodology has been demonstrated by case studies that include various scenarios involving distribution systems with different dominated load types and high photovoltaic penetrations. The results demonstrate that selecting the optimal converter size based on net revenue and time of return considerations leads to a balance between maximizing energy savings and minimizing financial payback periods. Furthermore, feeder pair selection based on load profile standard deviation effectively identifies systems that derive the greatest value from MVB2B integration. Finally, an optimized connection point selection approach using a voltage load sensitivity matrix ensures minimal system impact while facilitating efficient power exchange. These findings provide practical insights for the real‐world deployment of MVB2B converters to enhance DER hosting capacity and improve grid resilience.